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SYSTEMS AND METHODS FOR CONTROLLING RIGHTS RELATED
TO DIGITAL KNOWLEDGE
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority to the following U.S.
Provisional Patent
Applications: Serial No. 63/052,475 (Attorney Docket No. SFTX-0018-P01), filed
July 16,
2020, entitled "METHODS AND SYSTEMS FOR MANAGEMENT OF DIGITAL
KNOWLEDGE", Serial No. 63/054,603 (Attorney Docket No. SFTX-0017-P02), filed
July
21, 2020, entitled "DIGITAL TWIN SYSTEMS AND METHODS FOR FINANCIAL
SYSTEMS"; and Serial No. 63/127,980 Attorney Docket No. SFTX-0016-P01), filed
December 18, 2020, entitled "MARKET ORCHESTRATION SYSTEM FOR
FACILITATING ELECTRONIC MARKETPLACE TRANSACTIONS.".
[0002] Each of the foregoing applications is incorporated herein by reference
in its entirety.
BACKGROUND
[0003] An incredible amount of information is digitally exchanged on a regular
basis, and the
amount is increasing each day. This information can include valuable and
sensitive
information, such as trade secrets, know how, patented material, and works of
authorship.
Some of the information is subject to access and control restrictions, such as
restrictions on
who can view, edit, change, use, transmit, sell, buy, rent, review, license,
and source the
digital information (e.g., vis-a-vis patent licenses, trademark licenses,
contract agreements,
copyright licenses, and the like). Setting and enforcing access and control
restrictions is
difficult, as any computer-based system for doing so has potential flaws, such
as risks of
impropriety or unreliability of an owner or maintainer of the system, or risks
of other parties
gaining unauthorized access and illegitimately accessing, copying, editing, or
otherwise
tampering with the digital knowledge.
[0004] Lending transactions provide financing for a wide variety of needs,
ranging from
housing and education to corporate and government projects, among many others,
while
enabling lenders to earn financial returns. However, lending transactions are
plagued by a
number of problems, including opacity and asymmetry of information, moral
hazard induced
by shifting of the consequences of risky or inappropriate behavior, complexity
of application
and negotiation processes, burdensome regulatory and policy regimes,
difficulty in
determining the value of property that is used as collateral or backing for
obligations,
difficulty in determining the reliability or financial health of entities, and
others.
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[0005] Machines and automated agents are increasingly involved in market
activities,
including for data collection, forecasting, planning, transaction execution,
and other
activities. This includes increasingly high-performance systems, such as used
in high-speed
trading. A need exists for methods and systems that improve the machines that
enable
markets, including for increased efficiency, speed, reliability, and the like
for participants in
such markets.
[0006] Many markets are increasingly distributed, rather than centralized,
with distributed
ledgers like Blockchain, peer-to-peer interaction models, and micro-
transactions replacing or
complementing traditional models that involve centralized authorities or
intermediaries. A
need exists for improved machines that enable distributed transactions to
occur at scale
among large numbers of participants, including human participants and
automated agents.
[0007] Operations on blockchains, such as ones using cryptocurrency,
increasingly require
energy-intensive computing operations, such as calculating very large hash
functions on
growing chains of blocks. Systems using proof-of-work, proof-of-stake, and the
like have led
to "mining" operations by which computer processing power is applied at a
large scale in
order to perform calculations that support collective trust in transactions
that are recorded in
blockchains.
[0008] Many applications of artificial intelligence also require energy-
intensive computing
operations, such as where very large neural networks, with very large numbers
of
interconnections, perform operations on large numbers of inputs to produce one
or more
outputs, such as a prediction, classification, optimization, control output,
or the like.
[0009] The growth of the Internet of Things and cloud computing platforms have
also led to
the proliferation of devices, applications, and connections among them, such
that data
centers, housing servers and other IT components, consume a significant
fraction of the
energy consumption of the United States and other developed countries.
[0010] As a result of these and other trends, energy consumption has become a
major factor
in utilization of computing resources, such that energy resources and
computing resources (or
simply "energy and compute") have begun to converge from various standpoints,
such as
requisitioning, purchasing, provisioning, configuration, and management of
inputs, activities,
outputs and the like. Projects have been undertaken, for example, to place
large scale
computing resource facilities, such as BitcoinTM or other cryptocurrency
mining operations,
in close proximity to large-scale hydropower sources, such as Niagara Falls.
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[0011] A major challenge for facility owners and operators is the uncertainty
involved in
optimizing a facility, such as resulting from volatility in the cost and
availability of inputs (in
particular where less stable renewable resources are involved), variability in
the cost and
availability of computing and networking resources (such as where network
performance
varies), and volatility and uncertainty in various end markets to which energy
and compute
resources can be applied (such as volatility in cryptocurrencies, volatility
in energy markets,
volatility in pricing in various other markets, and uncertainty in the utility
of artificial
intelligence in a wide range of applications), among other factors.
SUMMARY
[0012] Example embodiments herein disclose systems, procedures, and aspects
that provide
cryptographically secure blockchains for knowledge systems capable of storing
digital
knowledge for providing convenient and secure control of the same. Example
methods and
systems herein provide for improvements in determining property valuation,
reliability of
financial health of entities, transparency, symmetry of information, and
application and
negotiation processes in the lending environment. Example methods and systems
herein
provide for improvements to the machines that enable markets, providing for
increased
efficiency, speed, and/or reliability for participants in such markets.
Example methods and
systems herein provide for improvements to data collection, storage and
processing,
automated configuration of inputs, resource, and outputs, and means for
facility optimization
for an energy and compute facility.
[0013] In one or more example embodiments, a knowledge distribution system for
controlling rights related to digital knowledge is disclosed. The knowledge
distribution
system may be a blockchain for knowledge system that allows for storage of
digital
knowledge, buying and selling of digital knowledge, tokenization of digital
knowledge,
and/or reviewing/auditing of the digital knowledge via a cryptographically
secure distributed
ledger. Smart contracts may be implemented on the distributed ledger and
controlling of
rights to digital knowledge, transferring digital knowledge, and adherence of
parties to
agreements related to the digital knowledge. The blockchain for knowledge
system can also
facilitate third parties reviewing, auditing, or verifying information related
to digital
knowledge.
[0014] There can be a number of practical obstacles to the sharing of
knowledge such as the
absence of trust between parties that could potentially benefit from sharing
of the knowledge.
A platform exists for a digital knowledge distribution system that facilitates
orchestration of
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the sharing of knowledge by providing a high degree of control over the extent
to which
counterparties can access shared knowledge. Even where knowledge is secure and
well-
controlled, some types of knowledge are so sensitive that an owner may be
unwilling to share
the entire set of knowledge with a single counterparty. In embodiments, a
platform is
disclosed for a digital knowledge distribution system that facilitates
handling and control of
subsets of knowledge, including automated handling of aggregation of
knowledge, or related
outputs, that result from division of knowledge subsets.
[0015] The knowledge distribution system may include a ledger management
system
configured to create and manage a distributed ledger where the distributed
ledger may be
distributed over nodes of a network and may include blocks linked via
cryptography. A smart
contract system may be communication with the distributed ledger and may be
configured to
implement and manage a smart contract via the distributed ledger. The smart
contract may be
stored in the distributed ledger and may include a triggering event. The smart
contract may be
configured to perform a smart contract action with respect to the digital
knowledge in
response to an occurrence of the triggering event. The knowledge distribution
system may be
configured to receive from a user an instance of the digital knowledge. The
digital knowledge
may be tokenized such that the instance of the digital knowledge can be
manipulated as a
token on the distributed ledger. The tokenized digital knowledge may be stored
via the
distributed ledger. Commitments of parties to the smart contract may be
processed. The
knowledge distribution system may be configured to manage rights of control of
and access
to the tokenized digital knowledge according to the smart contract and manage
the smart
contract action in response to the triggering event.
[0016] One or more of the following example features may be included. The
digital
knowledge may include intellectual property where the smart contract embeds
intellectual
property licensing terms for intellectual property embedded in the distributed
ledger, and
where executing an operation on the distributed ledger may provide access to
the intellectual
property and may process a commitment of a party to the smart contract to the
intellectual
property licensing terms. A smart contract wrapper on the distributed ledger
may allow an
operation on the ledger to add intellectual property to an aggregate stack of
intellectual
property, may allow an operation on the ledger to add intellectual property to
agree to an
apportionment of royalties among the parties in the ledger, may allow an
operation on the
ledger to add intellectual property to an aggregate stack of intellectual
property, and/or may
allow an operation on the ledger to process a commitment of a party to a
contract term. The
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tokenized digital knowledge may include an instruction set. The distributed
ledger may be
configured to provide provable access to the instruction set and execute the
instruction set on
a system resulting in recording a transaction in the distributed ledger. The
tokenized digital
knowledge may include executable algorithmic logic, a three-dimensional (3D)
printer
instruction set, an instruction set for a coating process, an instruction set
for a semiconductor
fabrication process, a firmware program, an instruction set for a field-
programmable gate
array, serverless code logic, an instruction set for a crystal fabrication
system, an instruction
set for a food preparation process, an instruction set for a polymer
production process, an
instruction set for a chemical synthesis process, an instruction set for a
biological production
process, a data set for a digital twin, and/or a trade secret with an expert
wrapper. The system
may be configured to aggregate views of a trade secret into a chain that
proves which
knowledge recipients of the parties have viewed the trade secret. The
knowledge distribution
system may include a reporting system configured to report an analytic result
based on
operations performed on the distributed ledger or the digital knowledge. The
distributed
ledger may be configured to aggregate a set of instructions where an operation
on the
distributed ledger may add at least one instruction to a pre-existing set of
instructions to
provide a modified set of instructions. The smart contract may be configured
to manage
allocation of instruction sub-sets to the distributed ledger and access to the
instruction sub-
sets. The distributed ledger may be configured to log parties who have
contributed to an
instance of the digital knowledge by storing data related to the parties in at
least one of the
blocks. The knowledge distribution system may be configured to log a source of
an instance
of the digital knowledge by storing data related to the source in at least one
of the blocks. The
distributed ledger may be configured such that a private network of authorized
participants
may establish cryptography-based consensus required for verification of new
blocks to be
added to the blocks. The ledger management system may be configured to
facilitate
crowdsourcing of information added to a block of the blocks of the distributed
ledger. The
distributed ledger may be configured such to store a review of an instance of
the digital
knowledge by a crowdsourcer in a block of the blocks. The distributed ledger
may be
configured such to store a signature of an instance of the digital knowledge
by a
crowdsourcer in a block of the blocks. The distributed ledger may be
configured such to store
a verification of an instance of the digital knowledge by a crowdsourcer in a
block of the
blocks. The ledger management system may be configured to establish
cryptographic
currency tokens that may be tradeable among users of the distributed ledger.
The knowledge
distribution system may include an account management system in communication
with the
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distributed ledger that may be configured to facilitate creation and
management of user
accounts related to users of the knowledge distribution system. The knowledge
distribution
system may include a user interface system in communication with the
distributed ledger and
may be configured to present a user interface to a user of the knowledge
distribution system
where the user interface allows the user to view data related to an instance
of the digital
knowledge. The knowledge distribution system may include a marketplace system
in
communication with the distributed ledger and may be configured to establish
and maintain a
digital marketplace that may be configured to visually present data related to
an instance of
the digital knowledge to a user of the knowledge distribution system. The
knowledge
distribution system may include a knowledge datastore in communication with
the distributed
ledger and may be configured to store data related to the digital knowledge.
The knowledge
distribution system may include a client datastore in communication with the
distributed
ledger and may be configured to store data related to users of the knowledge
distribution
system. The knowledge distribution system may include a smart contract
datastore in
communication with the distributed ledger and may be configured to store data
related to the
smart contract. The knowledge distribution system may include a reporting
system in
communication with the distributed ledger and may be configured to analyze
said tokenized
digital knowledge and report an analytic result based on the analysis of the
tokenized digital
knowledge. The smart contract may be generated using a parameterizable smart
contract
template. The smart contract may include parameters based on type of digital
knowledge to
be tokenized. The parameters may include financial parameters, royalty
parameters, usage
parameters, output produced parameters, allocation of consideration
parameters, identity
parameters, and/or access condition parameters.
[0017] In other example embodiments, a knowledge distribution system may use a
distributed ledger and smart contracts to facilitate management and exchange
of access,
licensing, and ownership rights of digital knowledge.
[0018] In other example embodiments, a computer-implemented method for
controlling
rights related to digital knowledge is disclosed. The method may include
creating and
managing a distributed ledger that is distributed over nodes of a network and
includes blocks
linked via cryptography. A smart contract may be implemented and managed via
the
distributed ledger where the smart contract may be stored in the distributed
ledger and may
include a triggering event. A smart contract action may be performed with
respect to the
digital knowledge in response to an occurrence of the triggering event. An
instance of the
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digital knowledge may be received. The digital knowledge may be tokenized such
that the
instance of the digital knowledge can be manipulated as a token on the
distributed ledger. The
tokenized digital knowledge may be stored via the distributed ledger.
Commitments of parties
to the smart contract may be processed. The method may include management of
rights over
control of and access to the tokenized digital knowledge according to the
smart contract and
management of the smart contract action in response to the triggering event.
[0019] One or more of the following example features may be included. A
knowledge
exchange for the exchange of the tokenized digital knowledge based on the
smart contract
may be orchestrated. The knowledge exchange of the tokenized digital knowledge
may be
integrated with another exchange where the knowledge exchange facilitates
exchange of
valuable and/or sensitive knowledge related to a subject matter of the other
exchange.
[0020] In other example embodiments, a knowledge distribution system for
controlling rights
related to digital knowledge is disclosed. The knowledge distribution system
may include a
ledger management system configured to create and manage a distributed ledger.
The
distributed ledger may be distributed over nodes of a network and may include
blocks linked
via cryptography. A smart contract system may be in communication with the
distributed
ledger and may be configured to implement and manage a smart contract via the
distributed
ledger. The smart contract may be stored in the distributed ledger and may
include a
triggering event. The smart contract may be configured to perform a smart
contract action
with respect to the digital knowledge in response to an occurrence of the
triggering event.
The knowledge distribution system may be configured to receive from a
knowledge provider
device an instance of the digital knowledge including a three-dimensional (3D)
printer
instruction set for 3D printing an object. The digital knowledge may be
tokenized such that
the instance of the digital knowledge may be manipulated as a token on the
distributed ledger.
The tokenized digital knowledge may be stored via the distributed ledger.
Commitments of
the knowledge provider and a knowledge recipient of the 3D printer instruction
set to the
smart contract may be processed. The knowledge distribution system may be
configured to
manage rights of control of and access to the tokenized digital knowledge
according to the
smart contract and may manage the smart contract action according to a
condition and the
triggering event.
[0021] One or more of the following example features may be included. The 3D
printer
instruction set may include a 3D printing schematic. The object may be at
least one of a
custom part, a custom product, a manufacturing part, a replacement part, a
toy, a medical
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device, and a tool. The knowledge recipient may use a knowledge recipient
device to
download and use the 3D printer instruction set. The knowledge recipient
device may be at
least one of a computing device, a server, a 3D printer, and a manufacturing
device. The
knowledge recipient may use a knowledge recipient device to purchase the
tokenized digital
knowledge corresponding to the 3D printer instruction set. The knowledge
distribution
system may include an event listener configured to listen to an application
programming
interface (API) that may provide a connection between the knowledge
distribution system
and a knowledge recipient device of the knowledge recipient. The smart
contract may be
configured to trigger the condition of the knowledge recipient to make a
payment when the
3D printer instruction set may be transferred or used based on the rights of
control of and
access to the tokenized digital knowledge. The rights of control of and access
to the tokenized
digital knowledge may include a permission for a user to 3D print using
multiple instances of
the 3D printer instruction set. The rights of control of and access to the
tokenized digital
knowledge may include at least one of 3D printer requirements, a time period
during which
the object can be 3D printed, whether the tokenized digital knowledge is
transferred to a
downstream knowledge recipient, warranties, disclaimers, indemnifications, and
certifications with respect to the object. Information related to the 3D
printer instruction set
of the tokenized digital knowledge may be modified on the distributed ledger
when the 3D
printer instruction set is at least one of purchased, downloaded, and used. In
examples,
information related to the 3D printer instruction set may include at least one
of origin, date of
creation, names of one or more contributing individuals, groups, and/or
companies, pricing,
market trends for related schematics, serial numbers, and part identifiers.
The smart contract
action may be one of an assignment of a serial number to the object that is 3D
printed,
monitoring for the triggering event, verifying fulfillment of an obligation
based on the
condition, verifying payment and/or transfer of the tokenized digital
knowledge, transferring
the tokenized digital knowledge, logging one or more transactions in the
distributed ledger,
performing one or more operations with respect to the distributed ledger, and
creating one or
more new blocks in the distributed ledger. The smart contract action may
include verifying
that the condition is met as defined in the smart contract where the condition
may be one of
printer requirements, payment received or currency transferred from a
knowledge recipient
device of the knowledge recipient, and transfer of the tokenized digital
knowledge to the
knowledge recipient device. When the tokenized digital knowledge may be
transferred to a
knowledge recipient device of a knowledge recipient, a 3D printer may be
configured to print
the object according to the 3D printer instruction set. The knowledge
distribution system may
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include a smart contract generator that may be configured to parametrize a
smart contract
template based on at least one of information provided by the knowledge
provider, the
condition, and the triggering event.
[0022] In other example embodiments, a computer-implemented method for
controlling
rights related to digital knowledge is disclosed. The method may include
creating and
managing a distributed ledger that is distributed over nodes of a network and
includes blocks
linked via cryptography. A smart contract may be implemented and managed via
the
distributed ledger where the smart contract may be stored in the distributed
ledger and may
include a triggering event. A smart contract action may be performed with
respect to the
digital knowledge in response to an occurrence of the triggering event. The
method may
include receiving from a knowledge provider device an instance of the digital
knowledge that
includes a three-dimensional (3D) printer instruction set for 3D printing an
object. The digital
knowledge may be tokenized such that the instance of the digital knowledge can
be
manipulated as a token on the distributed ledger. The tokenized digital
knowledge may be
stored via the distributed ledger. Commitments of the knowledge provider and a
knowledge
recipient of the 3D printer instruction set to the smart contract may be
processed. The method
may include management of rights of control of and access to the tokenized
digital
knowledge according to the smart contract, and management of the smart
contract action
according to a condition and the triggering event.
[0023] One or more of the following example features may be included. An
element of the
instance of the digital knowledge via the smart contract may be crowdsourced.
The element
of the instance of the digital knowledge may be managed by a smart contract
system
according to the smart contract.
[0024] Provided herein is a lending transaction enablement platform having a
set of data-
integrated microservices including data collection and monitoring services,
blockchain
services, and smart contract services for handling lending entities and
transactions. The
platform is capable of enabling a wide range of dedicated solutions, which may
share data
collection and storage infrastructure, and which may share or exchange inputs,
events,
activities, and outputs, such as to reinforce learning, enable automation, and
enable adaptive
intelligence across the various solutions.
[0025] Aspects of the present disclosure relate to a method for electronically
facilitating
licensing of one or more personality rights of a licensor. The method may
include receiving
an access request from a licensee to obtain approval to license personality
rights from a set of
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available licensors. The method may include selectively granting access to the
licensee based
on the access request. The method may include receiving confirmation of a
deposit of an
amount of funds from the licensee. The method may include issuing an amount of
cryptocurrency corresponding to the amount of funds deposited by the licensee
to an account
of the licensee. The method may include receiving a smart contract request to
create a smart
contract governing the licensing of the one or more personality rights of the
licensor by the
licensee. The smart contract request may indicate one or more terms including
a
consideration amount of cryptocurrency to be paid to the licensor in exchange
for one or
more obligations on the licensor. The method may include generating the smart
contract
based on the smart contract request. The method may include escrowing the
consideration
amount of cryptocurrency from the account of the licensee. The method may
include
deploying the smart contract to a distributed ledger. The method may include
verifying, by
the smart contract, that the licensor has performed the one or more
obligations. The method
may include, in response to receiving verification that the licensor has
performed the one or
more obligations, releasing at least a portion of the consideration amount of
cryptocurrency
into a licensor account of the licensor. The method may include outputting a
record indicating
a completion of a licensing transaction defined by the smart contract to the
distributed ledger.
[0026] Other aspects of the present disclosure relate to a system configured
for electronically
facilitating licensing of one or more personality rights of a licensor. The
system may include
one or more hardware processors configured by machine-readable instructions.
The
processor(s) may be configured to receive an access request from a licensee to
obtain
approval to license personality rights from a set of available licensors. The
processor(s) may
be configured to selectively grant access to the licensee based on the access
request. The
processor(s) may be configured to receive confirmation of a deposit of an
amount of funds
from the licensee. The processor(s) may be configured to issue an amount of
cryptocurrency
corresponding to the amount of funds deposited by the licensee to an account
of the licensee.
The processor(s) may be configured to receive a smart contract request to
create a smart
contract governing the licensing of the one or more personality rights of the
licensor by the
licensee. The smart contract request may indicate one or more terms including
a
consideration amount of cryptocurrency to be paid to the licensor in exchange
for one or
more obligations on the licensor. The processor(s) may be configured to
generate the smart
contract based on the smart contract request. The processor(s) may be
configured to escrow
the consideration amount of cryptocurrency from the account of the licensee.
The
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processor(s) may be configured to deploy the smart contract to a distributed
ledger. The
processor(s) may be configured to verify, by the smart contract, that the
licensor has
performed the one or more obligations. The processor(s) may be configured to,
in response to
receiving verification that the licensor has performed the one or more
obligations, release at
least a portion of the consideration amount of cryptocurrency into a licensor
account of the
licensor. The processor(s) may be configured to output a record indicating a
completion of a
licensing transaction defined by the smart contract to the distributed ledger.
[0027] Brief Description of the Figures
[0028] The disclosure and the following detailed description of certain
embodiments thereof
may be understood by reference to the following figures:
[0029] Fig. 1 is a schematic diagram of components of a platform for enabling
intelligent
transactions in accordance with embodiments of the present disclosure.
[0030] Figs. 2A and 2B are schematic diagrams of additional components of a
platform for
enabling intelligent transactions in accordance with embodiments of the
present disclosure.
[0031] Fig. 3 is a schematic diagram of additional components of a platform
for enabling
intelligent transactions in accordance with embodiments of the present
disclosure.
[0032] Figs. 4 to Fig. 31 are schematic diagrams of embodiments of neural net
systems that
may connect to, be integrated in, and be accessible by the platform for
enabling intelligent
transactions including ones involving expert systems, self-organization,
machine learning,
artificial intelligence and including neural net systems trained for pattern
recognition, for
classification of one or more parameters, characteristics, or phenomena, for
support of
autonomous control, and other purposes in accordance with embodiments of the
present
disclosure.
[0033] Fig. 32 is a schematic diagram of components of an environment
including an
intelligent energy and compute facility, a host intelligent energy and compute
facility
resource management platform, a set of data sources, a set of expert systems,
interfaces to a
set of market platforms and external resources, and a set of user or client
systems and devices
in accordance with embodiments of the present disclosure.
[0034] Fig. 33 depicts components and interactions of a transactional,
financial and
marketplace enablement system.
[0035] Fig. 34 depicts components and interactions of a set of data handling
layers of a
transactional, financial and marketplace enablement system.
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[0036] Fig. 35 depicts adaptive intelligence and robotic process automation
capabilities of a
transactional, financial and marketplace enablement system.
[0037] Fig. 36 depicts opportunity mining capabilities of a transactional,
financial and
marketplace enablement system.
[0038] Fig. 37 depicts adaptive edge computation management and edge
intelligence
capabilities of a transactional, financial and marketplace enablement system.
[0039] Fig. 38 depicts protocol adaptation and adaptive data storage
capabilities of a
transactional, financial and marketplace enablement system.
[0040] Fig. 39 depicts robotic operational analytic capabilities of a
transactional, financial
and marketplace enablement system.
[0041] Fig. 40 depicts a blockchain and smart contract platform for a forward
market for
access rights to events.
[0042] Fig. 41 depicts an algorithm and a dashboard of a blockchain and smart
contract
platform for a forward market for access rights to events.
[0043] Fig. 42 depicts a blockchain and smart contract platform for forward
market demand
aggregation.
[0044] Fig. 43 depicts an algorithm and a dashboard of a blockchain and smart
contract
platform for forward market demand aggregation.
[0045] Fig. 44 depicts a blockchain and smart contract platform for
crowdsourcing for
innovation.
[0046] Fig. 45 depicts an algorithm and a dashboard of a blockchain and smart
contract
platform for crowdsourcing for innovation.
[0047] Fig. 46 depicts a blockchain and smart contract platform for
crowdsourcing for
evidence.
[0048] Fig. 47 depicts an algorithm and a dashboard of a blockchain and smart
contract
platform for crowdsourcing for evidence.
[0049] Fig. 48 depicts components and interactions of an embodiment of a
lending platform
having a set of data-integrated microservices including data collection and
monitoring
services for handling lending entities and transactions.
[0050] Fig. 49 depicts components and interactions of an embodiment of a
lending platform
in which a set of lending solutions are supported by a data-integrated set of
data collection
and monitoring services, adaptive intelligent systems, and data storage
systems.
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[0051] Fig. 50 depicts components and interactions of an embodiment of a
lending platform
having a set of data integrated blockchain services, smart contract services,
social network
analytic services, crowdsourcing services and Internet of Things data
collection and
monitoring services for collecting, monitoring and processing information
about entities
involved in or related to a lending transaction.
[0052] Fig. 51 depicts components and interactions of a lending platform
having an Internet
of Things and sensor platform for monitoring at least one of a set of assets,
a set of collateral,
and a guarantee for a loan, a bond, or a debt transaction.
[0053] Fig. 52 depicts components and interactions of a lending platform
having a
crowdsourcing system for collecting information related to entities involved
in a lending
transaction.
[0054] Fig. 53 depicts an embodiment of a crowdsourcing workflow enabled by a
lending
platform.
[0055] Fig. 54 depicts components and interactions of an embodiment of a
lending platform
having a smart contract system that automatically adjusts an interest rate for
a loan based on
information collected via at least one of an Internet of Things system, a
crowdsourcing
system, a set of social network analytic services and a set of data collection
and monitoring
services.
[0056] Fig. 55 depicts components and interactions of an embodiment of a
lending platform
having a smart contract that automatically restructures debt based on a
monitored condition.
[0057] Fig. 56 depicts components and interactions of a lending platform
having a set of data
collection and monitoring systems for validating the reliability of a
guarantee for a loan,
including an Internet of Things system and a social network analytics system.
[0058] Fig. 57 depicts components and interactions of a lending platform
having a robotic
process automation system for negotiation of a set of terms and conditions for
a loan.
[0059] Fig. 58 depicts components and interactions of a lending platform
having a robotic
process automation system for loan collection.
[0060] Fig. 59 depicts components and interactions of a lending platform
having a robotic
process automation system for consolidating a set of loans.
[0061] Fig. 60 depicts components and interactions of a lending platform
having a robotic
process automation system for managing a factoring loan.
[0062] Fig. 61 depicts components and interactions of a lending platform
having a robotic
process automation system for brokering a mortgage loan.
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[0063] Fig. 62 depicts components and interactions of a lending platform
having a
crowdsourcing and automated classification system for validating condition of
an issuer for a
bond, a social network monitoring system with artificial intelligence for
classifying a
condition about a bond, and an Internet of Things data collection and
monitoring system with
artificial intelligence for classifying a condition about a bond.
[0064] Fig. 63 depicts components and interactions of a lending platform
having a system
that manages the terms and conditions of a loan based on a parameter monitored
by the IoT,
by a parameter determined by a social network analytic system, or a parameter
determined by
a crowdsourcing system.
[0065] Fig. 64 depicts components and interactions of a lending platform
having an
automated blockchain custody service for managing a set of custodial assets.
[0066] Fig. 65 depicts components and interactions of a lending platform
having an
underwriting system for a loan with a set of data-integrated microservices
including data
collection and monitoring services, blockchain services, artificial
intelligence services, and
smart contract services for underwriting lending entities and transactions.
[0067] Fig. 66 depicts components and interactions of a lending platform
having a loan
marketing system with a set of data-integrated microservices including data
collection and
monitoring services, blockchain services, artificial intelligence services and
smart contract
services for marketing a loan to a set of prospective parties.
[0068] Fig. 67 depicts components and interactions of a lending platform
having a rating
system with a set of data-integrated microservices including data collection
and monitoring
services, blockchain services, artificial intelligence services, and smart
contract services for
rating a set of loan-related entities.
[0069] Fig. 68 depicts components and interactions of a lending platform
having a regulatory
and/or compliance system with a set of data-integrated microservices including
data
collection and monitoring services, blockchain services, artificial
intelligence services, and
smart contract services for automatically facilitating compliance with at
least one of a law, a
regulation and a policy that applies to a lending transaction.
[0070] Fig. 69, depicts a system for automated loan management.
[0071] Fig. 70 depicts a system.
[0072] Fig. 71 depicts a method for handling a loan.
[0073] Fig. 72 depicts a system for adaptive intelligence and robotic process
automation
capabilities of a transactional, financial and marketplace enablement.
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[0074] Fig. 73 depicts a method for automated smart contract creation and
collateral
assignment.
[0075] Fig. 74 depicts a system for handling a loan.
[0076] Fig. 75 depicts a method for handling a loan.
[0077] Fig. 76 depicts a system for adaptive intelligence and robotic process
automation.
[0078] Fig. 77 depicts a method for loan creation and management.
[0079] Fig. 78 depicts a system for adaptive intelligence and robotic process
automation
capabilities of a transactional, financial and marketplace enablement.
[0080] Fig. 79 depicts a method for robotic process automation of
transactional, financial and
marketplace activities.
[0081] Fig. 80 depicts a system for adaptive intelligence and robotic process
automation.
[0082] Fig. 81 depicts a method for automated transactional, financial and
marketplace
activities.
[0083] Fig. 82 depicts a system for adaptive intelligence and robotic process.
[0084] Fig. 83 depicts a method for performing loan related actions.
[0085] Fig. 84 depicts a system for adaptive intelligence and robotic process.
[0086] Fig. 85 depicts a method for performing loan related actions.
[0087] Fig. 86 depicts a system for adaptive intelligence and robotic process.
[0088] Fig. 87 depicts a method for performing loan related actions.
[0089] Fig. 88 depicts a smart contract system for managing collateral for a
loan.
[0090] Fig. 89 depicts a smart contract method for managing collateral for a
loan.
[0091] Fig. 90 depicts a system for validating conditions of collateral or a
guarantor for a
loan.
[0092] Fig. 91 depicts a crowdsourcing method for validating conditions of
collateral or a
guarantor for a loan.
[0093] Fig. 92 depicts a smart contract system for modifying a loan.
[0094] Fig. 93 depicts a smart contract method for modifying a loan.
[0095] Fig. 94 depicts a smart contract system for modifying a loan.
[0096] Fig. 95 depicts a smart contract method for modifying a loan.
[0097] Fig. 96 depicts a smart contract system for modifying a loan.
[0098] Fig. 97 depicts a smart contract method for modifying a loan.
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[0099] Fig. 98 depicts a monitoring system for validating conditions of a
guarantee for a
loan.
[00100] Fig. 99 depicts a monitoring method for validating conditions of a
guarantee for a
loan.
[00101] Fig. 100 depicts a robotic process automation system for negotiating a
loan.
[00102] Fig. 101 depicts a robotic process automation method for negotiating a
loan.
[00103] Fig. 102 depicts a system for adaptive intelligence and robotic
process automation.
[00104] Fig. 103 depicts a loan collection method.
[00105] Fig. 104 depicts a system for adaptive intelligence and robotic
process automation.
[00106] Fig. 105 depicts a loan refinancing method.
[00107] Fig. 106 depicts a system for adaptive intelligence and robotic
process automation.
[00108] Fig. 107 depicts a for loan consolidation method.
[00109] Fig. 108 depicts a system for adaptive intelligence and robotic
process automation.
[00110] Fig. 109 depicts a loan factoring method.
[00111] Fig. 110 depicts a system for adaptive intelligence and robotic
process automation.
[00112] Fig. 111 depicts a mortgage brokering method.
[00113] Fig. 112 depicts a system for adaptive intelligence and robotic
process automation.
[00114] Fig. 113 depicts a method for debt management.
[00115] Fig. 114 depicts a system for adaptive intelligence and robotic
process automation.
[00116] Fig. 115 depicts a method for bond management.
[00117] Fig. 116 depicts a system for monitoring a condition of an issuer for
a bond.
[00118] Fig. 117 depicts a method for monitoring a condition of an issuer for
a bond
[00119] Fig. 118 depicts a system for monitoring a condition of an issuer for
a bond.
[00120] Fig. 119 depicts a method for monitoring a condition of an issuer for
a bond.
[00121] Fig. 120 depicts a system for automatic subsidized loan management.
[00122] Fig. 121 depicts a method for automatically modifying subsidized loan
terms and
conditions.
[00123] Fig. 122 depicts a system to automatically modify terms and conditions
of a loan.
[00124] Fig. 123 depicts a method for collecting social network information
about an entity
involved in a subsidized loan transaction.
[00125] Fig. 124 depicts a system for automating handling of a subsidized loan
using
crowdsourcing.
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[00126] Fig. 125 depicts a method for automating handling of a subsidized
loan.
[00127] Fig. 126 depicts a system for asset access control.
[00128] Fig. 127 depicts a method for asset access control.
[00129] Fig. 128 depicts a system automated handling of loan foreclosure.
[00130] Fig. 129 depicts a method for facilitating foreclosure on collateral.
[00131] Fig. 130 depicts an example energy and computing resource platform.
[00132] Fig. 131 depicts an example facility data record.
[00133] Fig. 132 depicts an example schema of a person data record.
[00134] Fig. 133 depicts a cognitive processing system.
[00135] Fig. 134 depicts a process for a lead generation system to generate a
lead list.
[00136] Fig. 135 depicts a process for a lead generation system to determine
facility outputs
for identified leads.
[00137] Fig. 136 depicts a process to generate and output personalized
content.
[00138] Fig. 137 depicts a schematic illustrating an example of a portion of
an information
technology system for transaction artificial intelligence leveraging digital
twins according to
some embodiments of the present disclosure.
[00139] Fig. 138 depicts a schematic illustrating a compliance system that
facilitates the
licensing of personality rights according to some embodiments of the present
disclosure.
[00140] Fig. 139 depicts a schematic illustrating an example set of components
of a
compliance system according to some embodiments of the present disclosure.
[00141] Fig. 140 depicts a set of operations of a method for vetting a
potential licensee for
purposes of licensing personality rights of a licensor according to some
embodiments of the
present disclosure.
[00142] Fig. 141 depicts a set of operations of a method for facilitating the
licensing of
personality rights of a licensor by a licensee according to some embodiments
of the present
disclosure.
[00143] Fig. 142 depicts a set of operations of a method for detecting
potential
circumvention of rules or regulations by a licensor and/or licensee according
to some
embodiments of the present disclosure.
[00144] Fig. 143 depicts a method for selecting an Al solution.
[00145] Fig. 144 depicts a method for selecting an Al solution.
[00146] Fig. 145 depicts an example of an assembled Al solution.
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[00147] Fig. 146 depicts a method for selecting an Al solution.
[00148] Fig. 147 depicts a method for selecting an Al solution.
[00149] Fig. 148 depicts an Al solution selection and configuration system.
[00150] Fig. 149 depicts an Al solution selection and configuration system.
[00151] Fig. 150 depicts an Al solution selection and configuration system.
[00152] Fig. 151 depicts a component configuration circuit.
[00153] Fig. 152 depicts an Al solution selection and configuration system.
[00154] Fig. 153 depicts a system for selecting and configuring an artificial
intelligence
model.
[00155] Fig. 154 depicts a method of selecting and configuring an artificial
intelligence
model.
[00156] Fig. 155 is a schematic illustrating examples of architecture of a
digital twin system
according to embodiments of the present disclosure.
[00157] Fig. 156 is a schematic illustrating exemplary components of a digital
twin
management system according to embodiments of the present disclosure.
[00158] Fig. 157 is a schematic illustrating examples of a digital twin I/O
system that
interfaces with an environment, the digital twin system, and/or components
thereof to provide
bi-directional transfer of data between coupled components according to
embodiments of the
present disclosure.
[00159] Fig. 158 is a schematic illustrating an example set of identified
states related to
industrial environments that the digital twin system may identify and/or store
for access by
intelligent systems (e.g., a cognitive intelligence system) or users of the
digital twin system
according to embodiments of the present disclosure.
[00160] Fig. 159 is a schematic illustrating example embodiments of methods
for updating a
set of properties of a digital twin of the present disclosure on behalf of a
client application
and/or one or more embedded digital twins.
[00161] Fig. 160 illustrates example embodiments of a display interface of the
present
disclosure that renders a digital twin of a dryer centrifuge with information
relating to the
dryer centrifuge.
[00162] Fig. 161 is a schematic illustrating an example embodiment of a method
for
updating a set of vibration fault level states of machine components such as
bearings in the
digital twin of an industrial machine, on behalf of a client application.
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[00163] Fig. 162 is a schematic illustrating an example embodiment of a method
for
updating a set of vibration severity unit values of machine components such as
bearings in
the digital twin of a machine on behalf of a client application.
[00164] Fig. 163 is a schematic illustrating an example embodiment of a method
for
updating a set of probability of failure values in the digital twins of
machine components on
behalf of a client application.
[00165] Fig. 164 is a schematic illustrating an example embodiment of a method
for
updating a set of probability of downtime values of machines in the digital
twin of a
manufacturing facility on behalf of a client application.
[00166] Fig. 165 is a schematic illustrating an example embodiment of a method
for
updating a set of probability of shutdown values of manufacturing facilities
in the digital twin
of an enterprise on behalf of a client application.
[00167] Fig. 166 is a schematic illustrating an example embodiment of a method
for
updating a set of cost of downtime values of machines in the digital twin of a
manufacturing
facility.
[00168] Fig. 167 is a schematic illustrating an example embodiment of a method
for
updating one or more manufacturing KPI values in a digital twin of a
manufacturing facility,
on behalf of a client application.
[00169] Fig. 168 is a schematic diagram of components of a knowledge
distribution system
and a communication network for facilitating management of digital knowledge
in
accordance with embodiments of the present disclosure.
[00170] Fig. 169 is a schematic diagram of a ledger network of the knowledge
distribution
system in accordance with embodiments of the present disclosure.
[00171] Fig. 170 is a schematic diagram of the knowledge distribution system
of FIG. 168
including details of a smart contract and a smart contract system of the
knowledge
distribution system in accordance with embodiments of the present disclosure.
[00172] Fig. 171 is a schematic diagram of a plurality of datastores of the
knowledge
distribution system in accordance with embodiments of the present disclosure.
[00173] Fig. 172 illustrates a method of deploying a knowledge token and
related smart
contract via the knowledge distribution system in accordance with embodiments
of the
present disclosure.
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[00174] Fig. 173 illustrates a method of performing high level process flow of
a smart
contract that distributes digital knowledge via the knowledge distribution
system in
accordance with embodiments of the present disclosure.
[00175] Fig. 174 is a schematic diagram of another embodiment of components of
the
knowledge distribution system and a communication network for facilitating
management of
digital knowledge in accordance with embodiments of the present disclosure.
[00176] Fig. 175 depicts a knowledge distribution system for controlling
rights related to
digital knowledge.
[00177] Fig. 176 depicts a computer-implemented method for controlling rights
related to
digital knowledge.
[00178] Fig. 177 depicts a computer-implemented method for controlling rights
related to
digital knowledge.
[00179] Fig. 178 depicts a knowledge distribution system for controlling
rights related to
digital knowledge.
[00180] Fig. 179 depicts possible components of a 3D printer instruction set.
[00181] Fig. 180 depicts possible content of tokenized digital knowledge.
[00182] Fig. 181 depicts possible smart contract actions.
[00183] Fig. 182 depicts possible conditions relating to triggering events.
[00184] Fig. 183 depicts possible control and access rights.
[00185] Fig. 184 depicts possible triggering events.
[00186] Fig. 185 depicts a computer-implemented method for controlling rights
related to
digital knowledge.
[00187] Fig. 186 depicts a computer-implemented method for controlling rights
related to
digital knowledge.
[00188] Fig. 187 depicts possible crowdsourced information.
[00189] Fig. 188 depicts possible contents of a distributed ledger.
[00190] Fig. 189 depicts possible parameters.
[00191] Fig. 190 depicts an embodiment of a knowledge distribution system for
controlling
rights related to digital knowledge.
[00192] Figs. 191-196 depict embodiments of operations for controlling rights
related to
digital knowledge.
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DETAILED DESCRIPTION
[00193] The term services/microservices (and similar terms) as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, a service/microservice includes any system (or platform)
configured to
functionally perform the operations of the service, where the system may be
data-integrated,
including data collection circuits, blockchain circuits, artificial
intelligence circuits, and/or
smart contract circuits for handling lending entities and transactions.
Services/microservices
may facilitate data handling and may include facilities for data extraction,
transformation and
loading; data cleansing and deduplication facilities; data normalization
facilities; data
synchronization facilities; data security facilities; computational facilities
(e.g., for
performing pre-defined calculation operations on data streams and providing an
output
stream); compression and de-compression facilities; analytic facilities (such
as providing
automated production of data visualizations), data processing facilities,
and/or data storage
facilities (including storage retention, formatting, compression, migration,
etc.), and others.
[00194] Services/microservices may include controllers, processors, network
infrastructure,
input/output devices, servers, client devices (e.g., laptops, desktops,
terminals, mobile
devices, and/or dedicated devices), sensors (e.g., IoT sensors associated with
one or more
entities, equipment, and/or collateral), actuators (e.g., automated locks,
notification devices,
lights, camera controls, etc.), virtualized versions of any one or more of the
foregoing (e.g.,
outsourced computing resources such as a cloud storage, computing operations;
virtual
sensors; subscribed data to be gathered such as stock or commodity prices,
recordal logs,
etc.), and/or include components configured as computer readable instructions
that, when
performed by a processor, cause the processor to perform one or more functions
of the
service, etc. Services may be distributed across a number of devices, and/or
functions of a
service may be performed by one or more devices cooperating to perform the
given function
of the service.
[00195] Services/ microservices may include application programming interfaces
that
facilitate connection among the components of the system performing the
service (e.g.,
microservices) and between the system to entities (e.g., programs, web sites,
user devices,
etc.) that are external to the system. Without limitation to any other aspect
of the present
disclosure, example microservices that may be present in certain embodiments
include (a) a
multi-modal set of data collection circuits that collect information about and
monitor entities
related to a lending transaction; (b) blockchain circuits for maintaining a
secure historical
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ledger of events related to a loan, the blockchain circuits having access
control features that
govern access by a set of parties involved in a loan; (c) a set of application
programming
interfaces, data integration services, data processing workflows and user
interfaces for
handling loan-related events and loan-related activities; and (d) smart
contract circuits for
specifying terms and conditions of smart contracts that govern at least one of
loan terms and
conditions, loan-related events and loan-related activities. Any of the
services/microservices
may be controlled by or have control over a controller. Certain systems may
not be
considered to be a service/microservice. For example, a point of sale device
that simply
charges a set cost for a good or service may not be a service. In another
example, a service
that tracks the cost of a good or service and triggers notifications when the
value changes
may not be a valuation service itself, but may rely on valuation services,
and/or may form a
portion of a valuation service in certain embodiments. It can be seen that a
given circuit,
controller, or device may be a service or a part of a service in certain
embodiments, such as
when the functions or capabilities of the circuit, controller, or device are
configured to
support a service or microservice as described herein, but may not be a
service or part of a
service for other embodiments (e.g., where the functions or capabilities of
the circuit,
controller, or device are not relevant to a service or microservice as
described herein). In
another example, a mobile device being operated by a user may form a portion
of a service as
described herein at a first point in time (e.g., when the user accesses a
feature of the service
through an application or other communication from the mobile device, and/or
when a
monitoring function is being performed via the mobile device), but may not
form a portion of
the service at a second point in time (e.g., after a transaction is completed,
after the user un-
installs an application, and/or when a monitoring function is stopped and/or
passed to another
device). Accordingly, the benefits of the present disclosure may be applied in
a wide variety
of processes or systems, and any such processes or systems may be considered a
service (or a
part of a service) herein.
[00196] One of skill in the art, having the benefit of the disclosure herein
and knowledge
about a contemplated system ordinarily available to that person, can readily
determine which
aspects of the present disclosure will benefit a particular system, how to
combine processes
and systems from the present disclosure to construct, provide performance
characteristics
(e.g., bandwidth, computing power, time response, etc.), and/or provide
operational
capabilities (e.g., time between checks, up-time requirements including
longitudinal (e.g.,
continuous operating time) and/or sequential (e.g., time-of-day, calendar
time, etc.),
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resolution and/or accuracy of sensing, data determinations (e.g., accuracy,
timing, amount of
data), and/or actuator confirmation capability) of components of the service
that are sufficient
to provide a given embodiment of a service, platform, and/or microservice as
described
herein. Certain considerations for the person of skill in the art, in
determining the
configuration of components, circuits, controllers, and/or devices to
implement a service,
platform, and/or microservice ("service" in the listing following) as
described herein include,
without limitation: the balance of capital costs versus operating costs in
implementing and
operating the service; the availability, speed, and/or bandwidth of network
services available
for system components, service users, and/or other entities that interact with
the service; the
response time of considerations for the service (e.g., how quickly decisions
within the service
must be implemented to support the commercial function of the service, the
operating time
for various artificial intelligence or other high computation operations)
and/or the capital or
operating cost to support a given response time; the location of interacting
components of the
service, and the effects of such locations on operations of the service (e.g.,
data storage
locations and relevant regulatory schemes, network communication limitations
and/or costs,
power costs as a function of the location, support availability for time zones
relevant to the
service, etc.); the availability of certain sensor types, the related support
for those sensors,
and the availability of sufficient substitutes (e.g., a camera may require
supportive lighting,
and/or high network bandwidth or local storage) for the sensing purpose; an
aspect of the
underlying value of an aspect of the service (e.g., a principal amount of a
loan, a value of
collateral, a volatility of the collateral value, a net worth or relative net
worth of a lender,
guarantor, and/or borrower, etc.) including the time sensitivity of the
underlying value (e.g.,
if it changes quickly or slowly relative to the operations of the service or
the term of the
loan); a trust indicator between parties of a transaction (e.g., history of
performance between
the parties, a credit rating, social rating, or other external indicator,
conformance of activity
related to the transaction to an industry standard or other normalized
transaction type, etc.);
and/or the availability of cost recovery options (e.g., subscriptions, fees,
payment for
services, etc.) for given configurations and/or capabilities of the service,
platform, and/or
microservice. Without limitation to any other aspect of the present
disclosure, certain
operations performed by services herein include: performing real-time
alterations to a loan
based on tracked data; utilizing data to execute a collateral-backed smart
contract; re-
evaluating debt transactions in response to a tracked condition or data, and
the like. While
specific examples of services/microservices and considerations are described
herein for
purposes of illustration, any system benefitting from the disclosures herein,
and any
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considerations understood to one of skill in the art having the benefit of the
disclosures
herein, are specifically contemplated within the scope of the present
disclosure.
[00197] Without limitation, services include a financial service (e.g., a loan
transaction
service), a data collection service (e.g., a data collection service for
collecting and monitoring
data), a blockchain service (e.g., a blockchain service to maintain secure
data), data
integration services (e.g., a data integration service to aggregate data),
smart contract services
(e.g., a smart contract service to determine aspects of smart contracts),
software services (e.g.,
a software service to extract data related to the entities from publicly
available information
sites), crowdsourcing services (e.g., a crowdsourcing service to solicit and
report
information), Internet of Things services (e.g., an Internet of Things service
to monitor an
environment), publishing services (e.g., a publishing services to publish
data), microservices
(e.g., having a set of application programming interfaces that facilitate
connection among the
microservices), valuation services (e.g., that use a valuation model to set a
value for collateral
based on information), artificial intelligence services, market value data
collection services
(e.g., that monitor and report on marketplace information), clustering
services (e.g., for
grouping the collateral items based on similarity of attributes), social
networking services
(e.g., that enables configuration with respect to parameters of a social
network), asset
identification services (e.g., for identifying a set of assets for which a
financial institution is
responsible for taking custody), identity management services (e.g., by which
a financial
institution verifies identities and credentials), and the like, and/or similar
functional
terminology. Example services to perform one or more functions herein include
computing
devices; servers; networked devices; user interfaces; inter-device interfaces
such as
communication protocols, shared information and/or information storage, and/or
application
programming interfaces (APIs); sensors (e.g., IoT sensors operationally
coupled to monitored
components, equipment, locations, or the like); distributed ledgers; circuits;
and/or computer
readable code configured to cause a processor to execute one or more functions
of the
service. One or more aspects or components of services herein may be
distributed across a
number of devices, and/or may consolidated, in whole or part, on a given
device. In
embodiments, aspects or components of services herein may be implemented at
least in part
through circuits, such as, in non-limiting examples, a data collection service
implemented at
least in part as a data collection circuit structed to collect and monitor
data, a blockchain
service implemented at least in part as a blockchain circuit structured to
maintain secure data,
data integration services implemented at least in part as a data integration
circuit structured to
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aggregate data, smart contract services implemented at least in part as a
smart contract circuit
structed to determine aspects of smart contracts, software services
implemented at least in
part as a software service circuit structured to extract data related to the
entities from publicly
available information sites, crowdsourcing services implemented at least in
part as a
crowdsourcing circuit structured to solicit and report information, Internet
of Things services
implemented at least in part as an Internet of Things circuit structured to
monitor an
environment, publishing services implemented at least in part as a publishing
services circuit
structured to publish data, microservice service implemented at least in part
as a microservice
circuit structured to interconnect a plurality of service circuits, valuation
service implemented
at least in part as valuation services circuit structured to access a
valuation model to set a
value for collateral based on data, artificial intelligence service
implemented at least in part as
an artificial intelligence services circuit, market value data collection
service implemented at
least in part as market value data collection service circuit structured to
monitor and report on
marketplace information, clustering service implemented at least in part as a
clustering
services circuit structured to group collateral items based on similarity of
attributes, a social
networking service implemented at least in part as a social networking
analytic services
circuit structured to configure parameters with respect to a social network,
asset identification
services implemented at least in part as an asset identification service
circuit for identifying a
set of assets for which a financial institution is responsible for taking
custody, identity
management services implemented at least in part as an identity management
service circuit
enabling a financial institution to verify identities and credentials, and the
like. Accordingly,
the benefits of the present disclosure may be applied in a wide variety of
systems, and any
such systems may be considered with respect to items and services herein,
while in certain
embodiments a given system may not be considered with respect to items and
services herein.
One of skill in the art, having the benefit of the disclosure herein and
knowledge about a
contemplated system ordinarily available to that person, can readily determine
which aspects
of the present disclosure will benefit a particular system, and/or how to
combine processes
and systems from the present disclosure to enhance operations of the
contemplated system.
Among the considerations that one of skill in the art may contemplate to
determine a
configuration for a particular service include: the distribution and access
devices available to
one or more parties to a particular transaction; jurisdictional limitations on
the storage, type,
and communication of certain types of information; requirements or desired
aspects of
security and verification of information communication for the service; the
response time of
information gathering, inter-party communications, and determinations to be
made by
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algorithms, machine learning components, and/or artificial intelligence
components of the
service; cost considerations of the service, including capital expenses and
operating costs, as
well as which party or entity will bear the costs and availability to recover
costs such as
through subscriptions, service fees, or the like; the amount of information to
be stored and/or
communicated to support the service; and/or the processing or computing power
to be
utilized to support the service.
[00198] The terms items and services (and similar terms) as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, items and service include any items and service, including,
without limitation,
items and services used as a reward, used as collateral, become the subject of
a negotiation,
and the like, such as, without limitation, an application for a warranty or
guarantee with
respect to an item that is the subject of a loan, collateral for a loan, or
the like, such as a
product, a service, an offering, a solution, a physical product, software, a
level of service,
quality of service, a financial instrument, a debt, an item of collateral,
performance of a
service, or other items. Without limitation to any other aspect or description
of the present
disclosure, items and service include any items and service, including,
without limitation,
items and services as applied to physical items (e.g., a vehicle, a ship, a
plane, a building, a
home, real estate property, undeveloped land, a farm, a crop, a municipal
facility, a
warehouse, a set of inventory, an antique, a fixture, an item of furniture, an
item of
equipment, a tool, an item of machinery, and an item of personal property), a
financial item
(e.g., a commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency), a
consumable item (e.g., an edible item, a beverage), a highly valued item
(e.g., a precious
metal, an item of jewelry, a gemstone), an intellectual item (e.g., an item of
intellectual
property, an intellectual property right, a contractual right), and the like.
Accordingly, the
benefits of the present disclosure may be applied in a wide variety of
systems, and any such
systems may be considered with respect to items and services herein, while in
certain
embodiments a given system may not be considered with respect to items and
services herein.
One of skill in the art, having the benefit of the disclosure herein and
knowledge about a
contemplated system ordinarily available to that person, can readily determine
which aspects
of the present disclosure will benefit a particular system, and/or how to
combine processes
and systems from the present disclosure to enhance operations of the
contemplated system.
[00199] The terms agent, automated agent, and similar terms as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
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disclosure, an agent or automated agent may process events relevant to at
least one of the
value, the condition, and the ownership of items of collateral or assets. The
agent or
automated agent may also undertake an action related to a loan, debt
transaction, bond
transaction, subsidized loan, or the like to which the collateral or asset is
subject, such as in
response to the processed events. The agent or automated agent may interact
with a
marketplace for purposes of collecting data, testing spot market transactions,
executing
transactions, and the like, where dynamic system behavior involves complex
interactions that
a user may desire to understand, predict, control, and/or optimize. Certain
systems may not be
considered an agent or an automated agent. For example, if events are merely
collected but
not processed, the system may not be an agent or automated agent. In some
embodiments, if a
loan-related action is undertaken not in response to a processed event, it may
not have been
undertaken by an agent or automated agent. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure include
and/or benefit
from agents or automated agent. Certain considerations for the person of skill
in the art, or
embodiments of the present disclosure with respect to an agent or automated
agent include,
without limitation: rules that determine when there is a change in a value,
condition or
ownership of an asset or collateral, and/or rules to determine if a change
warrants a further
action on a loan or other transaction, and other considerations. While
specific examples of
market values and marketplace information are described herein for purposes of
illustration,
any embodiment benefitting from the disclosures herein, and any considerations
understood
to one of skill in the art having the benefit of the disclosures herein are
specifically
contemplated within the scope of the present disclosure.
[00200] The term marketplace information, market value and similar terms as
utilized
herein should be understood broadly. Without limitation to any other aspect or
description of
the present disclosure, marketplace information and market value describe a
status or value of
an asset, collateral, food, or service at a defined point or period in time.
Market value may
refer to the expected value placed on an item in a marketplace or auction
setting, or pricing or
financial data for items that are similar to the item, asset, or collateral in
at least one public
marketplace. For a company, market value may be the number of its outstanding
shares
multiplied by the current share price. Valuation services may include market
value data
collection services that monitor and report on marketplace information
relevant to the value
(e.g., market value) of collateral, the issuer, a set of bonds, and a set of
assets. a set of
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subsidized loans, a party, and the like. Market values may be dynamic in
nature because they
depend on an assortment of factors, from physical operating conditions to
economic climate
to the dynamics of demand and supply. Market value may be affected by, and
marketplace
information may include, proximity to other assets, inventory or supply of
assets, demand for
assets, origin of items, history of items, underlying current value of item
components, a
bankruptcy condition of an entity, a foreclosure status of an entity, a
contractual default status
of an entity, a regulatory violation status of an entity, a criminal status of
an entity, an export
controls status of an entity, an embargo status of an entity, a tariff status
of an entity, a tax
status of an entity, a credit report of an entity, a credit rating of an
entity, a website rating of
an entity, a set of customer reviews for a product of an entity, a social
network rating of an
entity, a set of credentials of an entity, a set of referrals of an entity, a
set of testimonials for
an entity, a set of behavior of an entity, a location of an entity, and a
geolocation of an entity.
In certain embodiments, a market value may include information such as a
volatility of a
value, a sensitivity of a value (e.g., relative to other parameters having an
uncertainty
associated therewith), and/or a specific value of the valuated object to a
particular party (e.g.,
an object may have more value as possessed by a first party than as possessed
by a second
party).
[00201] Certain information may not be marketplace information or a market
value. For
example, where variables related to a value are not market-derived, they may
be a value-in-
use or an investment value. In certain embodiments, an investment value may be
considered a
market value (e.g., when the valuating party intends to utilize the asset as
an investment if
acquired), and not a market value in other embodiments (e.g., when the
valuating party
intends to immediately liquidate the investment if acquired). One of skill in
the art, having
the benefit of the disclosure herein and knowledge about a contemplated system
ordinarily
available to that person, can readily determine which aspects of the present
disclosure will
benefit from marketplace information or a market value. Certain considerations
for the person
of skill in the art, in determining whether the term market value is referring
to an asset, item,
collateral, good, or service include: the presence of other similar assets in
a marketplace, the
change in value depending on location, an opening bid of an item exceeding a
list price, and
other considerations. While specific examples of market values and marketplace
information
are described herein for purposes of illustration, any embodiment benefitting
from the
disclosures herein, and any considerations understood to one of skill in the
art having the
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benefit of the disclosures herein are specifically contemplated within the
scope of the present
disclosure.
[00202] The term apportion value or apportioned value and similar terms as
utilized herein
should be understood broadly. Without limitation to any other aspect or
description of the
present disclosure, apportion value describes a proportional distribution or
allocation of value
proportionally, or a process to divide and assign value according to a rule of
proportional
distribution. Apportionment of the value may be to several parties (e.g., each
of the several
parties is a beneficiary of a portion of the value), to several transactions
(e.g., each of the
transactions utilizes a portion of the value), and/or in a many-to-many
relationship (e.g., a
group of objects has an aggregate value that is apportioned between a number
of parties
and/or transactions). In some embodiments, the value may be a net loss and the
apportioned
value is the allocation of a liability to each entity. In other embodiments,
apportioned value
may refer to the distribution or allocation of an economic benefit, real
estate, collateral or the
like. In certain embodiments, apportionment may include a consideration of the
value relative
to the parties - for example, a S10 million asset apportioned 50/50 between
two parties, where
the parties have distinct value considerations for the asset, may result in
one party crediting
the apportionment differing resulting values from the apportionment. In
certain embodiments,
apportionment may include a consideration of the value relative to given
transactions - for
example a first type of transaction (e.g., a long-term loan) may have a
different valuation of a
given asset than a second type of transaction (e.g., a short-term line of
credit).
[00203] Certain conditions or processes may not relate to apportioned value.
For example,
the total value of an item may provide its inherent worth, but not how much of
the value is
held by each identified entity. One of skill in the art, having the benefit of
the disclosure
herein and knowledge about apportioned value, can readily determine which
aspects of the
present disclosure will benefit a particular application for apportioned
value. Certain
considerations for the person of skill in the art, or embodiments of the
present disclosure with
respect to an apportioned value include, without limitation: the currency of
the principal sum,
the anticipated transaction type (loan, bond or debt), the specific type of
collateral, the ratio
of the loan to value, the ratio of the collateral to the loan, the gross
transaction/loan amount,
the amount of the principal sum, the number of entities owed, the value of the
collateral, and
the like. While specific examples of apportioned values are described herein
for purposes of
illustration, any embodiment benefitting from the disclosures herein, and any
considerations
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understood to one of skill in the art having the benefit of the disclosures
herein are
specifically contemplated within the scope of the present disclosure.
[00204] The term financial condition and similar terms as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, financial condition describes a current status of an entity's
assets, liabilities, and
equity positions at a defined point or period in time. The financial condition
may be
memorialized in financial statement. The financial condition may further
include an
assessment of the ability of the entity to survive future risk scenarios or
meet future or
maturing obligations. Financial condition may be based on a set of attributes
of the entity
selected from among a publicly stated valuation of the entity, a set of
property owned by the
entity as indicated by public records, a valuation of a set of property owned
by the entity, a
bankruptcy condition of an entity, a foreclosure status of an entity, a
contractual default status
of an entity, a regulatory violation status of an entity, a criminal status of
an entity, an export
controls status of an entity, an embargo status of an entity, a tariff status
of an entity, a tax
status of an entity, a credit report of an entity, a credit rating of an
entity, a website rating of
an entity, a set of customer reviews for a product of an entity, a social
network rating of an
entity, a set of credentials of an entity, a set of referrals of an entity, a
set of testimonials for
an entity, a set of behavior of an entity, a location of an entity, and a
geolocation of an entity.
A financial condition may also describe a requirement or threshold for an
agreement or loan.
For example, conditions for allowing a developer to proceed may be various
certifications
and their agreement to a financial payout. That is, the developer's ability to
proceed is
conditioned upon a financial element, among others. Certain conditions may not
be a
financial condition. For example, a credit card balance alone may be a clue as
to the financial
condition, but may not be the financial condition on its own. In another
example, a payment
schedule may determine how long a debt may be on an entity's balance sheet,
but in a silo
may not accurately provide a financial condition. One of skill in the art,
having the benefit of
the disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure include
and/or will
benefit from a financial condition. Certain considerations for the person of
skill in the art, in
determining whether the term financial condition is referring to a current
status of an entity's
assets, liabilities, and equity positions at a defined point or period in time
and/or for a given
purpose include: the reporting of more than one financial data point, the
ratio of a loan to
value of collateral, the ratio of the collateral to the loan, the gross
transaction/loan amount,
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the credit scores of the borrower and the lender, and other considerations.
While specific
examples of financial conditions are described herein for purposes of
illustration, any
embodiment benefitting from the disclosures herein, and any considerations
understood to
one of skill in the art having the benefit of the disclosures herein are
specifically
contemplated within the scope of the present disclosure.
[00205] The term interest rate and similar terms, as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure,
interest rate includes an amount of interest due per period, as a proportion
of an amount lent,
deposited or borrowed. The total interest on an amount lent or borrowed may
depend on the
principal sum, the interest rate, the compounding frequency, and the length of
time over
which it is lent, deposited or borrowed. Typically, interest rate is expressed
as an annual
percentage but can be defined for any time period. The interest rate relates
to the amount a
bank or other lender charges to borrow its money, or the rate a bank or other
entity pays its
savers for keeping money in an account. Interest rate may be variable or
fixed. For example,
an interest rate may vary in accordance with a government or other stakeholder
directive, the
currency of the principal sum lent or borrowed, the term to maturity of the
investment, the
perceived default probability of the borrower, supply and demand in the
market, the amount
of collateral, the status of an economy, or special features like call
provisions. In certain
embodiments, an interest rate may be a relative rate (e.g., relative to a
prime rate, an inflation
index, etc.). In certain embodiments, an interest rate may further consider
costs or fees
applied (e.g., "points") to adjust the interest rate. A nominal interest rate
may not be adjusted
for inflation while a real interest rate takes inflation into account. Certain
examples may not
be an interest rate for purposes of particular embodiments. For example, a
bank account
growing by a fixed dollar amount each year, and/or a fixed fee amount, may not
be an
example of an interest rate for certain embodiments. One of skill in the art,
having the benefit
of the disclosure herein and knowledge about interest rates, can readily
determine the
characteristics of an interest rate for a particular embodiment. Certain
considerations for the
person of skill in the art, or embodiments of the present disclosure with
respect to an interest
rate include, without limitation: the currency of the principal sum, variables
for setting an
interest rate, criteria for modifying an interest rate, the anticipated
transaction type (loan,
bond or debt), the specific type of collateral, the ratio of the loan to
value, the ratio of the
collateral to the loan, the gross transaction/loan amount, the amount of the
principal sum, the
appropriate lifespans of transactions and/or collateral for a particular
industry, the likelihood
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that a lender will sell and/or consolidate a loan before the term, and the
like. While specific
examples of interest rates are described herein for purposes of illustration,
any embodiment
benefitting from the disclosures herein, and any considerations understood to
one of skill in
the art having the benefit of the disclosures herein are specifically
contemplated within the
scope of the present disclosure.
[00206] The term valuation services (and similar terms) as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, a valuation service includes any service that sets a value for a
good or service.
Valuation services may use a valuation model to set a value for collateral
based on
information from data collection and monitoring services. Smart contract
services may
process output from the set of valuation services and assign items of
collateral sufficient to
provide security for a loan and/or apportion value for an item of collateral
among a set of
lenders and/or transactions. Valuation services may include artificial
intelligence services that
may iteratively improve the valuation model based on outcome data relating to
transactions in
collateral. Valuation services may include market value data collection
services that may
monitor and report on marketplace information relevant to the value of
collateral. Certain
processes may not be considered to be a valuation service. For example, a
point of sale device
that simply charges a set cost for a good or service may not be a valuation
service. In another
example, a service that tracks the cost of a good or service and triggers
notifications when the
value changes may not be a valuation service itself, but may rely on valuation
services and/or
form a part of a valuation service. Accordingly, the benefits of the present
disclosure may be
applied in a wide variety of processes systems, and any such processes or
systems may be
considered a valuation service herein, while in certain embodiments a given
service may not
be considered a valuation service herein. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system and how to combine processes and systems from the present disclosure to
enhance
operations of the contemplated system and/or to provide a valuation service.
Certain
considerations for the person of skill in the art, in determining whether a
contemplated
system is a valuation service and/or whether aspects of the present disclosure
can benefit or
enhance the contemplated system include, without limitation: perform real-time
alterations to
a loan based on a value of a collateral; utilize marketplace data to execute a
collateral-backed
smart contract; re-evaluate collateral based on a storage condition or
geolocation; the
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tendency of the collateral to have a volatile value, be utilized, and/or be
moved; and the like.
While specific examples of valuation services and considerations are described
herein for
purposes of illustration, any system benefitting from the disclosures herein,
and any
considerations understood to one of skill in the art having the benefit of the
disclosures
herein, are specifically contemplated within the scope of the present
disclosure.
[00207] The term collateral attributes (and similar terms) as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, collateral attributes include any identification of the durability
(ability of the
collateral to withstand wear or the useful life of the collateral), value,
identification (does the
collateral have definite characteristics that make it easy to identify or
market), stability of
value (does the collateral maintain value over time), standardization, grade,
quality,
marketability, liquidity, transferability, desirability, trackability,
deliverability (ability of the
collateral be delivered or transfer without a deterioration in value), market
transparency (is
the collateral value easily verifiable or widely agreed upon), physical or
virtual. Collateral
attributes may be measured in absolute or relative terms, and/or may include
qualitative (e.g.,
categorical descriptions) or quantitative descriptions. Collateral attributes
may be different
for different industries, products, elements, uses, and the like. Collateral
attributes may be
assigned quantitative or qualitative values. Values associated with collateral
attributes may be
based on a scale (such as 1-10) or a relative designation (high, low, better,
etc.). Collateral
may include various components; each component may have collateral attributes.
Collateral
may, therefore, have multiple values for the same collateral attribute. In
some embodiments,
multiple values of collateral attributes may be combined to generate one value
for each
attribute. Some collateral attributes may apply only to specific portions of
collateral. Some
collateral attributes, even for a given component of the collateral, may have
distinct values
depending upon the party of interest (e.g., a party that values an aspect of
the collateral more
highly than another party) and/or depending upon the type of transaction
(e.g., the collateral
may be more valuable or appropriate for a first type of loan than for a second
type of loan).
Certain attributes associated with collateral may not be collateral attributes
as described
herein depending upon the purpose of the collateral attributes herein. For
example, a product
may be rated as durable relative to similar products; however, if the life of
the product is
much lower than the term of a particular loan in consideration, the durability
of the product
may be rated differently (e.g., not durable) or irrelevant (e.g., where the
current inventory of
the product is attached as the collateral, and is expected to change out
during the term of the
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loan). Accordingly, the benefits of the present disclosure may be applied to a
variety of
attributes, and any such attributes may be considered collateral attributes
herein, while in
certain embodiments a given attribute may not be considered a collateral
attribute herein. One
of skill in the art, having the benefit of the disclosure herein and knowledge
about
contemplated collateral attributes ordinarily available to that person, can
readily determine
which aspects of the present disclosure will benefit a particular collateral
attribute. Certain
considerations for the person of skill in the art, in determining whether a
contemplated
attribute is a collateral attribute and/or whether aspects of the present
disclosure can benefit
or enhance the contemplated system include, without limitation: the source of
the attribute
and the source of the value of the attribute (e.g. does the attribute and
attribute value comes
from a reputable source), the volatility of the attribute (e.g. does the
attribute values for the
collateral fluctuate, is the attribute a new attribute for the collateral),
relative differences in
attribute values for similar collateral, exceptional values for attributes
(e.g., some attribute
values may be high, such as, in the 98th percentile or very low, such as in
the 2nd percentile,
compared to similar class of collateral), the fungibility of the collateral,
the type of
transaction related to the collateral, and/or the purpose of the utilization
of collateral for a
particular party or transaction. While specific examples of collateral
attributes and
considerations are described herein for purposes of illustration, any system
benefitting from
the disclosures herein, and any considerations understood to one of skill in
the art having the
benefit of the disclosures herein, are specifically contemplated within the
scope of the present
disclosure.
[00208] The term blockchain services (and similar terms) as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, blockchain services include any service related to the processing,
recordation,
and/or updating of a blockchain, and may include services for processing
blocks, computing
hash values, generating new blocks in a blockchain, appending a block to the
blockchain,
creating a fork in the blockchain, merging of forks in the blockchain,
verifying previous
computations, updating a shared ledger, updating a distributed ledger,
generating
cryptographic keys, verifying transactions, maintaining a blockchain, updating
a blockchain,
verifying a blockchain, generating random numbers. The services may be
performed by
execution of computer readable instructions on local computers and/or by
remote servers and
computers. Certain services may not be considered blockchain services
individually but may
be considered blockchain services based on the final use of the service and/or
in a particular
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embodiment - for example, a computing a hash value may be performed in a
context outside
of a blockchain such in the context of secure communication. Some initial
services may be
invoked without first being applied to blockchains, but further actions or
services in
conjunction with the initial services may associate the initial service with
aspects of
blockchains. For example, a random number may be periodically generated and
stored in
memory; the random numbers may initially not be generated for blockchain
purposes but
may be utilized for blockchains. Accordingly, the benefits of the present
disclosure may be
applied in a wide variety of services, and any such services may be considered
blockchain
services herein, while in certain embodiments a given service may not be
considered a
blockchain service herein. One of skill in the art, having the benefit of the
disclosure herein
and knowledge about a contemplated blockchain service ordinarily available to
that person,
can readily determine which aspects of the present disclosure can be
configured to
implement, and/or will benefit, a particular blockchain service. Certain
considerations for the
person of skill in the art, in determining whether a contemplated service is a
blockchain
service and/or whether aspects of the present disclosure can benefit or
enhance the
contemplated system include, without limitation: the application of the
service, the source of
the service (e.g., if the service is associated with a known or verifiable
blockchain service
provider), responsiveness of the service (e.g., some blockchain services may
have an
expected completion time, and/or may be determined through utilization), cost
of the service,
the amount of data requested for the service, and/or the amount of data
generated by the
service (blocks of blockchain or keys associated with blockchains may be a
specific size or a
specific range of sizes). While specific examples of blockchain services and
considerations
are described herein for purposes of illustration, any system benefitting from
the disclosures
herein, and any considerations understood to one of skill in the art having
the benefit of the
disclosures herein, are specifically contemplated within the scope of the
present disclosure.
[00209] The term blockchain (and variations such as cryptocurrency ledger, and
the like) as
utilized herein may be understood broadly to describe a cryptocurrency ledger
that records,
administrates or otherwise processes online transactions. A blockchain may be
public,
private, or a combination thereof, without limitation. A blockchain may also
be used to
represent a set of digital transactions, agreement, terms or other digital
value. Without
limitation to any other aspect or description of the present disclosure, in
the former case, a
blockchain may also be used in conjunction with investment applications, token-
trading
applications, and/or digital/cryptocurrency based marketplaces. A blockchain
can also be
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associated with rendering consideration, such as providing goods, services,
items, fees, access
to a restricted area or event, data or other valuable benefit. Blockchains in
various forms may
be included where discussing a unit of consideration, collateral, currency,
cryptocurrency or
any other form of value. One of skill in the art, having the benefit of the
disclosure herein and
knowledge ordinarily available about a contemplated system, can readily
determine the value
symbolized or represented by a blockchain. While specific examples of
blockchains are
described herein for purposes of illustration, any embodiment benefitting from
the disclosures
herein, and any considerations understood to one of skill in the art having
the benefit of the
disclosures herein, are specifically contemplated within the scope of the
present disclosure.
[00210] The terms ledger and distributed ledger (and similar terms) as
utilized herein should
be understood broadly. Without limitation to any other aspect or description
of the present
disclosure, a ledger may be a document, file, computer file, database, book,
and the like
which maintains a record of transactions. Ledgers may be physical or digital.
Ledgers may
include records related to sales, accounts, purchases, transactions, assets,
liabilities, incomes,
expenses, capital, and the like. Ledgers may provide a history of transactions
that may be
associated with time. Ledgers may be centralized or decentralized/distributed.
A centralized
ledger may be a document that is controlled, updated, or viewable by one or
more selected
entities or a clearinghouse and wherein changes or updates to the ledger are
governed or
controlled by the entity or clearinghouse. A distributed ledger may be a
ledger that is
distributed across a plurality of entities, participants or regions which may
independently,
concurrently, or consensually, update, or modify their copies of the ledger.
Ledgers and
distributed ledgers may include security measures and cryptographic functions
for signing,
concealing, or verifying content. In the case of distributed ledgers,
blockchain technology
may be used. In the case of distributed ledgers implemented using blockchain,
the ledger may
be Merkle trees comprising a linked list of nodes in which each node contains
hashed or
encrypted transactional data of the previous nodes. Certain records of
transactions may not be
considered ledgers. A file, computer file, database, or book may or may not be
a ledger
depending on what data it stores, how the data is organized, maintained, or
secured. For
example, a list of transactions may not be considered a ledger if it cannot be
trusted or
verified, and/or if it is based on inconsistent, fraudulent, or incomplete
data. Data in ledgers
may be organized in any format such as tables, lists, binary streams of data,
or the like which
may depend on convenience, source of data, type of data, environment,
applications, and the
like. A ledger that is shared among various entities may not be a distributed
ledger, but the
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distinction of distributed may be based on which entities are authorized to
make changes to
the ledger and/or how the changes are shared and processed among the different
entities.
Accordingly, the benefits of the present disclosure may be applied in a wide
variety of data,
and any such data may be considered ledgers herein, while in certain
embodiments a given
data may not be considered a ledger herein. One of skill in the art, having
the benefit of the
disclosure herein and knowledge about contemplated ledgers and distributed
ledger ordinarily
available to that person, can readily determine which aspects of the present
disclosure can be
utilized to implement, and/or will benefit a particular ledger. Certain
considerations for the
person of skill in the art, in determining whether a contemplated data is a
ledger and/or
whether aspects of the present disclosure can benefit or enhance the
contemplated ledger
include, without limitation: the security of the data in the ledger (can the
data be tampered or
modified), the time associated with making changes to the data in the ledger,
cost of making
changes (computationally and monetarily), detail of data, organization of data
(does the data
need to be processed for use in an application), who controls the ledger (can
the party be
trusted or relied to manage the ledger), confidentiality of the data (who can
see or track the
data in the ledger), size of the infrastructure, communication requirements
(distributed
ledgers may require a communication interface or specific infrastructure),
resiliency. While
specific examples of blockchain services and considerations are described
herein for purposes
of illustration, any system benefitting from the disclosures herein, and any
considerations
understood to one of skill in the art having the benefit of the disclosures
herein, are
specifically contemplated within the scope of the present disclosure.
[00211] The term loan (and similar terms) as utilized herein should be
understood broadly.
Without limitation to any other aspect or description of the present
disclosure, a loan may be
an agreement related to an asset that is borrowed, and that is expected to be
returned in kind
(e.g., money borrowed and money returned) or as an agreed transaction (e.g., a
first good or
service is borrowed, and money, a second good or service, or a combination, is
returned).
Assets may be money, property, time, physical objects, virtual objects,
services, a right (e.g.,
a ticket, a license, or other rights), a depreciation amount, a credit (e.g.,
a tax credit, an
emissions credit, etc.), an agreed assumption of a risk or liability, and/or
any combination
thereof. A loan may be based on a formal or informal agreement between a
borrower and a
lender wherein a lender may provide an asset to the borrower for a predefined
amount of
time, a variable period of time, or indefinitely. Lenders and borrowers may be
individuals,
entities, corporations, governments, groups of people, organizations, and the
like. Loan types
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may include mortgage loans, personal loans, secured loans, unsecured loans,
concessional
loans, commercial loans, microloans, and the like. The agreement between the
borrower and
the lender may specify terms of the loan. The borrower may be required to
return an asset or
repay with a different asset than was borrowed. In some cases, a loan may
require interest to
be repaid on the borrowed asset. Borrowers and lenders may be intermediaries
between other
entities and may never possess or use the asset. In some embodiments, a loan
may not be
associated with direct transfer of goods but may be associated with usage
rights or shared
usage rights. In certain embodiments, the agreement between the borrower and
the lender
may be executed between the borrower and the lender, and/or executed between
an
intermediary (e.g., a beneficiary of a loan right such as through a sale of
the loan). In certain
embodiment, the agreement between the borrower and the lender may be executed
through
services herein, such as through a smart contract service that determines at
least a portion of
the terms and conditions of the loans, and in certain embodiments may commit
the borrower
and/or the lender to the terms of the agreement, which may be a smart
contract. In certain
embodiments, the smart contract service may populate the terms of the
agreement, and
present them to the borrower and/or lender for execution. In certain
embodiments, the smart
contract service may automatically commit one of the borrower or the lender to
the terms (at
least as an offer) and may present the offer to the other one of the borrower
or the lender for
execution. In certain embodiments, a loan agreement may include multiple
borrowers and/or
multiple lenders, for example where a set of loans includes a number of
beneficiaries of
payment on the set of loans, and/or a number of borrowers on the set of loans.
In certain
embodiments, the risks and/or obligations of the set of loans may be
individualized (e.g., each
borrower and/or lender is related to specific loans of the set of loans),
apportioned (e.g., a
default on a particular loan has an associated loss apportioned between the
lenders), and/or
combinations of these (e.g., one or more subsets of the set of loans is
treated individually
and/or apportioned).
[00212] Certain agreements may not be considered a loan. An agreement to
transfer or
borrow assets may not be a loan depending on what assets are transferred, how
the assets
were transferred, or the parties involved. For example, in some cases, the
transfer of assets
may be for an indefinite time and may be considered a sale of the asset or a
permanent
transfer. Likewise, if an asset is borrowed or transferred without clear or
definite terms or
lack of consensus between the lender and the borrower it may, in some cases,
not be
considered a loan. An agreement may be considered a loan even if a formal
agreement is not
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directly codified in a written agreement as long as the parties willingly and
knowingly agreed
to the arrangement, and/or ordinary practices (e.g., in a particular industry)
may treat the
transaction as a loan. Accordingly, the benefits of the present disclosure may
be applied in a
wide variety of agreements, and any such agreement may be considered a loan
herein, while
in certain embodiments a given agreement may not be considered a loan herein.
One of skill
in the art, having the benefit of the disclosure herein and knowledge about
contemplated
loans ordinarily available to that person, can readily determine which aspects
of the present
disclosure implement a loan, utilize a loan, or benefit a particular loan
transaction. Certain
considerations for the person of skill in the art, in determining whether a
contemplated data is
a loan and/or whether aspects of the present disclosure can benefit or enhance
the
contemplated loan include, without limitation: the value of the assets
involved, the ability of
the borrower to return or repay the loan, the types of assets involved (e.g.,
whether the asset
is consumed through utilization), the repayment time frame associated with the
loan, the
interest on the loan, how the agreement of the loan was arranged, formality of
the agreement,
detail of the agreement, the detail of the agreements of the loan, the
collateral attributes
associated with the loan, and/or the ordinary business expectations of any of
the foregoing in
a particular context. While specific examples of loans and considerations are
described herein
for purposes of illustration, any system benefitting from the disclosures
herein, and any
considerations understood to one of skill in the art having the benefit of the
disclosures
herein, are specifically contemplated within the scope of the present
disclosure.
[00213] The term loan related event(s) (and similar terms, including loan-
related events) as
utilized herein should be understood broadly. Without limitation to any other
aspect or
description of the present disclosure, a loan related events may include any
event related to
terms of the loan or events triggered by the agreement associated with the
loan. Loan-related
events may include default on loan, breach of contract, fulfillment,
repayment, payment,
change in interest, late fee assessment, refund assessment, distribution, and
the like. Loan-
related events may be triggered by explicit agreement terms; for example - an
agreement may
specify a rise in interest rate after a time period has elapsed from the
beginning of the loan;
the rise in interest rate triggered by the agreement may be a loan related
event. Loan-related
events may be triggered implicitly by related loan agreement terms. In certain
embodiments,
any occurrence that may be considered relevant to assumptions of the loan
agreement, and/or
expectations of the parties to the loan agreement, may be considered an
occurrence of an
event. For example, if collateral for a loan is expected to be replaceable
(e.g., an inventory as
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collateral), then a change in inventory levels may be considered an occurrence
of a loan
related event. In another example, if review and/or confirmation of the
collateral is expected,
then a lack of access to the collateral, the disablement or failure of a
monitoring sensor, etc.
may be considered an occurrence of a loan related event. In certain
embodiments, circuits,
controllers, or other devices described herein may automatically trigger the
determination of
a loan-related events. In some embodiments, loan-related events may be
triggered by entities
that manage loans or loan-related contracts. Loan-related events may be
conditionally
triggered based on one or more conditions in the loan agreement. Loan related
events may be
related to tasks or requirements that need to be completed by the lender,
borrower, or a third
party. Certain events may be considered loan-related events in certain
embodiments and/or in
certain contexts, but may not be considered a loan-related event in another
embodiment or
context. Many events may be associated with loans but may be caused by
external triggers
not associated with a loan. However, in certain embodiments, an externally
triggered event
(e.g., a commodity price change related to a collateral item) may be loan-
related events. For
example, renegotiation of loan terms initiated by a lender may not be
considered a loan
related event if the terms and/or performance of the existing loan agreement
did not trigger
the renegotiation. Accordingly, the benefits of the present disclosure may be
applied in a
wide variety of events, and any such event may be considered a loan related
event herein,
while in certain embodiments given events may not be considered a loan related
event herein.
One of skill in the art, having the benefit of the disclosure herein and
knowledge about a
contemplated system ordinarily available to that person, can readily determine
which aspects
of the present disclosure may be considered a loan-related event for the
contemplated system
and/or for particular transactions supported by the system. Certain
considerations for the
person of skill in the art, in determining whether a contemplated data is a
loan related event
and/or whether aspects of the present disclosure can benefit or enhance the
contemplated
transaction system include, without limitation: the impact of the related
event on the loan
(events that cause default or termination of the loan may have higher impact),
the cost
(capital and/or operating) associated with the event, the cost (capital and/or
operating)
associated with monitoring for an occurrence of the event, the entities
responsible for
responding to the event, a time period and/or response time associated with
the event (e.g.,
time required to complete the event and time that is allotted from the time
the event is
triggered to when processing or detection of the event is desired to occur),
the entity
responsible for the event, the data required for processing the event (e.g.,
confidential
information may have different safeguards or restrictions), the availability
of mitigating
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actions if an undetected event occurs, and/or the remedies available to an at-
risk party if the
event occurs without detection. While specific examples of loan-related events
and
considerations are described herein for purposes of illustration, any system
benefitting from
the disclosures herein, and any considerations understood to one of skill in
the art having the
benefit of the disclosures herein, are specifically contemplated within the
scope of the present
disclosure.
[00214] The term loan-related activities (and similar terms) as utilized
herein should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, a loan related activity may include activities related to the
generation,
maintenance, termination, collection, enforcement, servicing, billing,
marketing, ability to
perform, or negotiation of a loan. Loan-related activity may include
activities related to the
signing of a loan agreement or a promissory note, review of loan documents,
processing of
payments, evaluation of collateral, evaluation of compliance of the borrower
or lender to the
loan terms, renegotiation of terms, perfection of security or collateral for
the loan, and/or a
negation of terms. Loan-related activities may relate to events associated
with a loan before
formal agreement on the terms, such as activities associated with initial
negotiations. Loan-
related activities may relate to events during the life of the loan and after
the termination of a
loan. Loan-related activities may be performed by a lender, borrower, or a
third party. Certain
activities may not be considered loan related activities services individually
but may be
considered loan related activities based on the specificity of the activity to
the loan lifecycle-
for example, billing or invoicing related to outstanding loans may be
considered a loan
related activity, however when the invoicing or billing of loans is combined
with billing or
invoicing for non loan-related elements the invoicing may not be considered a
loan related
activity. Some activities may be performed in relation to an asset regardless
if a loan is
associated with the asset; in these cases, the activity may not be considered
a loan related
activity. For example, regular audits related to an asset may occur regardless
if the asset is
associated with a loan and may not be considered a loan related activity. In
another example,
a regular audit related to an asset may be required by a loan agreement and
would not
typically occur but for the association with a loan, in this case, the
activity may be considered
a loan related activity. In some embodiments, activities may be considered
loan-related
activities if the activity would otherwise not occur if the loan is not active
or present, but may
still be considered a loan-related activity in some instances (e.g., if
auditing occurs normally,
but the lender does not have the ability to enforce or review the audit, then
the audit may be
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considered a loan-related activity even though it already occurs otherwise).
Accordingly, the
benefits of the present disclosure may be applied in a wide variety of events,
and any such
event may be considered a loan related event herein, while in certain
embodiments given
events may not be considered a loan related events herein. One of skill in the
art, having the
benefit of the disclosure herein and knowledge about a contemplated system
ordinarily
available to that person, can readily determine a loan related activity for
the purposes of the
contemplated system. Certain considerations for the person of skill in the
art, in determining
whether a contemplated data is a loan related activity and/or whether aspects
of the present
disclosure can benefit or enhance the contemplated loan include, without
limitation: the
necessity of the activity for the loan (can the loan agreement or terms be
satisfied without the
activity), the cost of the activity, the specificity of the activity to the
loan (is the activity
similar or identical to other industries), time involved in the activity, the
impact of the
activity on a loan life cycle, entity performing the activity, amount of data
required for the
activity (does the activity require confidential information related to the
loan, or personal
information related to the entities), and/or the ability of parties to enforce
and/or review the
activity. While specific examples of loan-related events and considerations
are described
herein for purposes of illustration, any system benefitting from the
disclosures herein, and
any considerations understood to one of skill in the art having the benefit of
the disclosures
herein, are specifically contemplated within the scope of the present
disclosure.
[00215] The terms loan-terms, loan terms, terms for a loan, terms and
conditions, and the
like as utilized herein should be understood broadly ("loan terms"). Without
limitation to any
other aspect or description of the present disclosure, loan terms may relate
to conditions,
rules, limitations, contract obligations, and the like related to the timing,
repayment,
origination, and other enforceable conditions agreed to by the borrower and
the lender of the
loan. Loan terms may be specified in a formal contract between a borrower and
the lender.
Loan terms may specify aspects of an interest rate, collateral, foreclose
conditions,
consequence of debt, payment options, payment schedule, a covenant, and the
like. Loan
terms may be negotiable or may change during the life of a loan. Loan terms
may be change
or be affected by outside parameters such as market prices, bond prices,
conditions associated
with a lender or borrower, and the like. Certain aspects of a loan may not be
considered loan
terms. In certain embodiments, aspects of loan that have not been formally
agreed upon
between a lender and a borrower, and/or that are not ordinarily understood in
the course of
business (and/or the particular industry) may not be considered loan terms.
Certain aspects of
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a loan may be preliminary or informal until they have been formally agreed or
confirmed in a
contract or a formal agreement. Certain aspects of a loan may not be
considered loan terms
individually but may not be considered loan terms based on the specificity of
the aspect to a
specific loan. Certain aspects of a loan may not be considered loan terms at a
particular time
during the loan, but may be considered loan terms at another time during the
loan (e.g.,
obligations and/or waivers that may occur through the performance of the
parties, and/or
expiration of a loan term). For example, an interest rate may generally not be
considered a
loan term until it is defined in relation of a loan and defined as to how the
interest
compounded (annual, monthly), calculated, and the like. An aspect of a loan
may not be
considered a term if it is indefinite or unenforceable. Some aspects may be
manifestations or
related to terms of a loan but may themselves not be the terms. For example, a
loan term may
be the repayment period of a loan, such as one year. The term may not specify
how the loan
is to be repaid in the year. The loan may be repaid with 12 monthly payments
or one annual
payment. A monthly payment plan in this case may not be considered a loan term
as it can be
just one or many options for repayment not directly specified by a loan.
Accordingly, the
benefits of the present disclosure may be applied in a wide variety of loan
aspects, and any
such aspect may be considered a loan term herein, while in certain embodiments
given
aspects may not be considered loan terms herein. One of skill in the art,
having the benefit of
the disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure are loan
terms for the
contemplated system.
[00216] Certain considerations for the person of skill in the art, in
determining whether a
contemplated data is a loan term and/or whether aspects of the present
disclosure can benefit
or enhance the contemplated loan include, without limitation: the
enforceability of the terms
(can the conditions be enforced by the lender or the lender or the borrower),
the cost of
enforcing the terms (amount of time, or effort required ensure the conditions
are being
followed), the complexity of the terms (how easily can they be followed or
understood by the
parties involved, are the terms error prone or easily misunderstood), entities
responsible for
the terms, fairness of the terms, stability of the terms (how often do they
change),
observability of the terms (can the terms be verified by a another party),
favorability of the
terms to one party (do the terms favor the borrower or the lender), risk
associated with the
loan (terms may depend on the probability that the loan may not be repaid),
characteristics of
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the borrower or lender (their ability to meet the terms), and/or ordinary
expectations for the
loan and/or related industry.
[00217] While specific examples of loan terms are described herein for
purposes of
illustration, any system benefitting from the disclosures herein, and any
considerations
understood to one of skill in the art having the benefit of the disclosures
herein, are
specifically contemplated within the scope of the present disclosure.
[00218] The term loan conditions, loan-conditions, conditions for a loan,
terms and
conditions, and the like as utilized herein should be understood broadly
("loan conditions").
Without limitation to any other aspect or description of the present
disclosure, loan conditions
may relate to rules, limits, and/or obligations related to a loan. Loan
conditions may relate to
rules or necessary obligations for obtaining a loan, for maintaining a loan,
for applying for a
loan, for transferring a loan, and the like. Loan conditions may include
principal amount of
debt, a balance of debt, a fixed interest rate, a variable interest rate, a
payment amount, a
payment schedule, a balloon payment schedule, a specification of collateral, a
specification of
substitutability of collateral, treatment of collateral, access to collateral,
a party, a guarantee,
a guarantor, a security, a personal guarantee, a lien, a duration, a covenant,
a foreclose
condition, a default condition, conditions related to other debts of the
borrower, and a
consequence of default.
[00219] Certain aspects of a loan may not be considered loan conditions.
Aspects of loan
that have not been formally agreed upon between a lender and a borrower,
and/or that are not
ordinarily understood in the course of business (and/or the particular
industry), may not be
considered loan conditions. Certain aspects of a loan may be preliminary or
informal until
they have been formally agreed or confirmed in a contract or a formal
agreement. Certain
aspects of a loan may not be considered loan conditions individually but may
be considered
loan conditions based on the specificity of the aspect to a specific loan.
Certain aspects of a
loan may not be considered loan conditions at a particular time during the
loan, but may be
considered loan conditions at another time during the loan (e.g., obligations
and/or waivers
that may occur through the performance of the parties, and/or expiration of a
loan condition).
Accordingly, the benefits of the present disclosure may be applied in a wide
variety of loan
aspects, and any such aspect may be considered loan conditions herein, while
in certain
embodiments given aspects may not be considered loan conditions herein. One of
skill in the
art, having the benefit of the disclosure herein and knowledge about a
contemplated system
ordinarily available to that person, can readily determine which aspects of
the present
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disclosure are loan conditions for the contemplated system. Certain
considerations for the
person of skill in the art, in determining whether a contemplated data is a
loan condition
and/or whether aspects of the present disclosure can benefit or enhance the
contemplated loan
include, without limitation: the enforceability of the condition (can the
conditions be enforced
by the lender or the lender or the borrower), the cost of enforcing the
condition (amount of
time, or effort required ensure the conditions are being followed), the
complexity of the
condition (how easily can they be followed or understood by the parties
involved, are the
conditions error prone or easily misunderstood), entities responsible for the
conditions,
fairness of the conditions, observability of the conditions (can the
conditions be verified by a
another party), favorability of the conditions to one party (do the conditions
favor the
borrower or the lender), risk associated with the loan (conditions may depend
on the
probability that the loan may not be repaid), and/or ordinary expectations for
the loan and/or
related industry.
[00220] While specific examples of loan conditions are described herein for
purposes of
illustration, any system benefitting from the disclosures herein, and any
considerations
understood to one of skill in the art having the benefit of the disclosures
herein, are
specifically contemplated within the scope of the present disclosure.
[00221] The term loan collateral, collateral, item of collateral, collateral
item, and the like as
utilized herein should be understood broadly. Without limitation to any other
aspect or
description of the present disclosure, a loan collateral may relate to any
asset or property that
a borrower promises to a lender as backup in exchange for a loan, and/or as
security for the
loan. Collateral may be any item of value that is accepted as an alternate
form of repayment
in case of default on a loan. Collateral may include any number of physical or
virtual items
such as a vehicle, a ship, a plane, a building, a home, real estate property,
undeveloped land, a
farm, a crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a
currency, a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a
beverage, a precious metal, an item of jewelry, a gemstone, an item of
intellectual property,
an intellectual property right, a contractual right, an antique, a fixture, an
item of furniture, an
item of equipment, a tool, an item of machinery, and an item of personal
property. Collateral
may include more than one item or types of items.
[00222] A collateral item may describe an asset, a property, a value or other
item defined as
a security for a loan or a transaction. A set of collateral items may be
defined, and within that
set substitution, removal or addition of collateral items may be affected. For
example, a
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collateral item may be, without limitation: a vehicle, a ship, a plane, a
building, a home, real
estate property, undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of
inventory, a commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an item of
jewelry, a
gemstone, an item of intellectual property, an intellectual property right, a
contractual right,
an antique, a fixture, an item of furniture, an item of equipment, a tool, an
item of machinery,
or an item of personal property, or the like. If a set or plurality of
collateral items is defined,
substitution, removal or addition of collateral items may be affected, such as
substituting,
removing or adding a collateral item to or from a set of collateral items.
Without limitation to
any other aspect or description of the present disclosure, a collateral item
or set of collateral
items may also be used in conjunction with other terms to an agreement or
loan, such as a
representation, a warranty, an indemnity, a covenant, a balance of debt, a
fixed interest rate, a
variable interest rate, a payment amount, a payment schedule, a balloon
payment schedule, a
specification of collateral, a specification of substitutability of
collateral, a security, a
personal guarantee, a lien, a duration, a foreclose condition, a default
condition, and a
consequence of default. In certain embodiments, a smart contract may calculate
whether a
borrower has satisfied conditions or covenants and in cases where the borrower
has not
satisfied such conditions or covenants, may enable automated action or trigger
another
conditions or terms that may affect the status, ownership or transfer of a
collateral item, or
initiate the substitution, removal or addition of collateral items to a set of
collateral for a loan.
One of skill in the art, having the benefit of the disclosure herein and
knowledge about
collateral items, can readily determine the purposes and use of collateral
items in various
embodiments and contexts disclosed herein, including the substitution, removal
and addition
thereof.
[00223] While specific examples of loan collateral are described herein for
purposes of
illustration, any system benefitting from the disclosures herein, and any
considerations
understood to one of skill in the art having the benefit of the disclosures
herein, are
specifically contemplated within the scope of the present disclosure.
[00224] The term smart contract services (and similar terms) as utilized
herein should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, a smart contract service includes any service or application that
manages a smart
contract or a smart lending contract. For example, the smart contract service
may specify
terms and conditions of a smart contract, such as in a rules database, or
process output from a
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set of valuation services and assign items of collateral sufficient to provide
security for a
loan. Smart contract services may automatically execute a set of rules or
conditions that
embody the smart contract, wherein the execution may be based on or take
advantage of
collected data. Smart contract services may automatically initiate a demand
for payment of a
loan, automatically initiate a foreclosure process, automatically initiate an
action to claim
substitute or backup collateral or transfer ownership of collateral,
automatically initiate an
inspection process, automatically change a payment or interest rate term that
is based on the
collateral, and may also configure smart contracts to automatically undertake
a loan-related
action. Smart contracts may govern at least one of loan terms and conditions,
loan-related
events and loan-related activities. Smart contracts may be agreements that are
encoded as
computer protocols and may facilitate, verify, or enforce the negotiation or
performance of a
smart contract. Smart contracts may or may not be one or more of partially or
fully self-
executing, or partially or fully self-enforcing.
[00225] Certain processes may not be considered to be smart-contract related
individually,
but may be considered smart-contract related in an aggregated system - for
example
automatically undertaking a loan-related action may not be smart contract-
related in one
instance, but in another instance, may be governed by terms of a smart
contract. Accordingly,
the benefits of the present disclosure may be applied in a wide variety of
processes systems,
and any such processes or systems may be considered a smart contract or smart
contract
service herein, while in certain embodiments a given service may not be
considered a smart
contract service herein.
[00226] One of skill in the art, having the benefit of the disclosure herein
and knowledge
about a contemplated system ordinarily available to that person, can readily
determine which
aspects of the present disclosure will benefit a particular system and how to
combine
processes and systems from the present disclosure to implement a smart
contract service
and/or enhance operations of the contemplated system. Certain considerations
for the person
of skill in the art, in determining whether a contemplated system includes a
smart contract
service or smart contract and/or whether aspects of the present disclosure can
benefit or
enhance the contemplated system include, without limitation: ability to
transfer ownership of
collateral automatically in response to an event; automated actions available
upon a finding
of covenant compliance (or lack of compliance); the amenity of the collateral
to clustering,
re-balancing, distribution, addition, substitution, and removal of items from
collateral; the
modification parameters of an aspect of a loan in response to an event (e.g.,
timing,
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complexity, suitability for the loan type, etc.); the complexity of terms and
conditions of
loans for the system, including benefits from rapid determination and/or
predictions of
changes to entities (e.g., in the collateral, a financial condition of a
party, offset collateral,
and/or in an industry related to a party) related to the loan; the suitability
of automated
generation of terms and conditions and/or execution of terms and conditions
for the types of
loans, parties, and/or industries contemplated for the system; and the like.
While specific
examples of smart contract services and considerations are described herein
for purposes of
illustration, any system benefitting from the disclosures herein, and any
considerations
understood to one of skill in the art having the benefit of the disclosures
herein, are
specifically contemplated within the scope of the present disclosure.
[00227] The term IoT system (and similar terms) as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure, an
IoT system includes any system of uniquely identified and interrelated
computing devices,
mechanical and digital machines, sensors and objects that are able to transfer
data over a
network without intervention. Certain components may not be considered an IoT
system
individually, but may be considered an IoT system in an aggregated system -
for example a
single networked.
[00228] The sensor, smart speaker, and/or medical device may be not an IoT
system, but
may be a part of a larger system and/or be accumulated with a number of other
similar
components to be considered an IoT system and/or a part of an IoT system. In
certain
embodiments, a system may be considered an IoT system for some purposes but
not for other
purposes - for example a smart speaker may be considered part of an IoT system
for certain
operations, such as for providing surround sound, or the like, but not part of
an IoT system
for other operations such as directly streaming content from a single, locally
networked
source. Additionally, in certain embodiments, otherwise similar looking
systems may be
differentiated in determining whether such systems are IoT systems, and/or
which type of IoT
system. For example, one group of medical devices may not, at a given time, be
sharing to an
aggregated HER database, while another group of medical devices may be sharing
data to an
aggregate HER for the purposes of a clinical study, and accordingly one group
of medical
devices may be an IoT system, while the other is not. Accordingly, the
benefits of the present
disclosure may be applied in a wide variety of systems, and any such systems
may be
considered an IoT system herein, while in certain embodiments a given system
may not be
considered an IoT system herein. One of skill in the art, having the benefit
of the disclosure
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herein and knowledge about a contemplated system ordinarily available to that
person, can
readily determine which aspects of the present disclosure will benefit a
particular system,
how to combine processes and systems from the present disclosure to enhance
operations of
the contemplated system, and which circuits, controllers, and/or devices
include an IoT
system for the contemplated system. Certain considerations for the person of
skill in the art,
in determining whether a contemplated system is an IoT system and/or whether
aspects of the
present disclosure can benefit or enhance the contemplated system include,
without
limitation: the transmission environment of the system (e.g., availability of
low power, inter-
device networking); the shared data storage of a group of devices;
establishment of a
geofence by a group of devices; service as blockchain nodes; the performance
of asset,
collateral, or entity monitoring; the relay of data between devices; ability
to aggregate data
from a plurality of sensors or monitoring devices, and the like. While
specific examples of
IoT systems and considerations are described herein for purposes of
illustration, any system
benefitting from the disclosures herein, and any considerations understood to
one of skill in
the art having the benefit of the disclosures herein, are specifically
contemplated within the
scope of the present disclosure.
[00229] The term data collection services (and similar terms) as utilized
herein should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, a data collection service includes any service that collects data
or information,
including any circuit, controller, device, or application that may store,
transmit, transfer,
share, process, organize, compare, report on and/or aggregate data. The data
collection
service may include data collection devices (e.g., sensors) and/or may be in
communication
with data collection devices. The data collection service may monitor
entities, such as to
identify data or information for collection. The data collection service may
be event-driven,
run on a periodic basis, or retrieve data from an application at particular
points in the
application's execution. Certain processes may not be considered to be a data
collection
service individually, but may be considered a data collection service in an
aggregated system
- for example a networked storage device may be a component of a data
collection service in
one instance, but in another instance, may have stand-alone functionality.
Accordingly, the
benefits of the present disclosure may be applied in a wide variety of
processes systems, and
any such processes or systems may be considered a data collection service
herein, while in
certain embodiments a given service may not be considered a data collection
service herein.
One of skill in the art, having the benefit of the disclosure herein and
knowledge about a
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contemplated system ordinarily available to that person, can readily determine
which aspects
of the present disclosure will benefit a particular system and how to combine
processes and
systems from the present disclosure implement a data collection service and/or
to enhance
operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is a data collection service
and/or whether
aspects of the present disclosure can benefit or enhance the contemplated
system include,
without limitation: ability to modify a business rule on the fly and alter a
data collection
protocol; perform real-time monitoring of events; connection of a device for
data collection
to a monitoring infrastructure, execution of computer readable instructions
that cause a
processor to log or track events; use of an automated inspection system;
occurrence of sales
at a networked point-of-sale; need for data from one or more distributed
sensors or cameras;
and the like. While specific examples of data collection services and
considerations are
described herein for purposes of illustration, any system benefitting from the
disclosures
herein, and any considerations understood to one of skill in the art having
the benefit of the
disclosures herein, are specifically contemplated within the scope of the
present disclosure.
[00230] The term data integration services (and similar terms) as utilized
herein should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, a data integration service includes any service that integrates
data or information,
including any device or application that may extract, transform, load,
normalize, compress,
decompress, encode, decode, and otherwise process data packets, signals, and
other
information. The data integration service may monitor entities, such as to
identify data or
information for integration. The data integration service may integrate data
regardless of
required frequency, communication protocol, or business rules needed for
intricate
integration patterns. Accordingly, the benefits of the present disclosure may
be applied in a
wide variety of processes systems, and any such processes or systems may be
considered a
data integration service herein, while in certain embodiments a given service
may not be
considered a data integration service herein. One of skill in the art, having
the benefit of the
disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system and how to combine processes and systems from the present disclosure to
implement
a data integration service and/or enhance operations of the contemplated
system. Certain
considerations for the person of skill in the art, in determining whether a
contemplated
system is a data integration service and/or whether aspects of the present
disclosure can
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benefit or enhance the contemplated system include, without limitation:
ability to modify a
business rule on the fly and alter a data integration protocol; communication
with third party
databases to pull in data to integrate with; synchronization of data across
disparate platforms;
connection to a central data warehouse; data storage capacity, processing
capacity, and/or
communication capacity distributed throughout the system; the connection of
separate,
automated workflows; and the like. While specific examples of data integration
services and
considerations are described herein for purposes of illustration, any system
benefitting from
the disclosures herein, and any considerations understood to one of skill in
the art having the
benefit of the disclosures herein, are specifically contemplated within the
scope of the present
disclosure.
[00231] The term computational services (and similar terms) as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, computational services may be included as a part of one or more
services,
platforms, or microservices, such as blockchain services, data collection
services, data
integration services, valuation services, smart contract services, data
monitoring services,
data mining, and/or any service that facilitates collection, access,
processing, transformation,
analysis, storage, visualization, or sharing of data. Certain processes may
not be considered to
be a computational service. For example, a process may not be considered a
computational
service depending on the sorts of rules governing the service, an end product
of the service,
or the intent of the service. Accordingly, the benefits of the present
disclosure may be applied
in a wide variety of processes systems, and any such processes or systems may
be considered
a computational service herein, while in certain embodiments a given service
may not be
considered a computational service herein. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system and how to combine processes and systems from the present disclosure to
implement
one or more computational service, and/or to enhance operations of the
contemplated system.
Certain considerations for the person of skill in the art, in determining
whether a
contemplated system is a computational service and/or whether aspects of the
present
disclosure can benefit or enhance the contemplated system include, without
limitation:
agreement-based access to the service; mediate an exchange between different
services;
provides on demand computational power to a web service; accomplishes one or
more of
monitoring, collection, access, processing, transformation, analysis, storage,
integration,
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visualization, mining, or sharing of data. While specific examples of
computational services
and considerations are described herein for purposes of illustration, any
system benefitting
from the disclosures herein, and any considerations understood to one of skill
in the art
having the benefit of the disclosures herein, are specifically contemplated
within the scope of
the present disclosure.
[00232] The term sensor as utilized herein should be understood broadly.
Without limitation
to any other aspect or description of the present disclosure, a sensor may be
a device, module,
machine, or subsystem that detects or measures a physical quality, event or
change. In
embodiments, may record, indicate, transmit, or otherwise respond to the
detection or
measurement. Examples of sensors may be sensors for sensing movement of
entities, for
sensing temperatures, pressures or other attributes about entities or their
environments,
cameras that capture still or video images of entities, sensors that collect
data about collateral
or assets, such as, for example, regarding the location, condition (health,
physical, or
otherwise), quality, security, possession, or the like. In embodiments,
sensors may be
sensitive to, but not influential on, the property to be measured but
insensitive to other
properties. Sensors may be analog or digital. Sensors may include processors,
transmitters,
transceivers, memory, power, sensing circuit, electrochemical fluid
reservoirs, light sources,
and the like. Further examples of sensors contemplated for use in the system
include
biosensors, chemical sensors, black silicon sensor, IR sensor, acoustic
sensor, induction
sensor, motion sensor, optical sensor, opacity sensor, proximity sensor,
inductive sensor,
Eddy-current sensor, passive infrared proximity sensor, radar, capacitance
sensor, capacitive
displacement sensor, hall-effect sensor, magnetic sensor, GPS sensor, thermal
imaging
sensor, thermocouple, thermistor, photoelectric sensor, ultrasonic sensor,
infrared laser
sensor, inertial motion sensor, MEMS internal motion sensor, ultrasonic 3D
motion sensor,
accelerometer, inclinometer, force sensor, piezoelectric sensor, rotary
encoders, linear
encoders, ozone sensor, smoke sensor, heat sensor, magnetometer, carbon
dioxide detector,
carbon monoxide detector, oxygen sensor, glucose sensor, smoke detector, metal
detector,
rain sensor, altimeter, GPS, detection of being outside, detection of context,
detection of
activity, object detector (e.g. collateral), marker detector (e.g. geo-
location marker), laser
rangefinder, sonar, capacitance, optical response, heart rate sensor, or an
RF/micropower
impulse radio (MIR) sensor. In certain embodiments, a sensor may be a virtual
sensor - for
example determining a parameter of interest as a calculation based on other
sensed
parameters in the system. In certain embodiments, a sensor may be a smart
sensor - for
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example reporting a sensed value as an abstracted communication (e.g., as a
network
communication) of the sensed value. In certain embodiments, a sensor may
provide a sensed
value directly (e.g., as a voltage level, frequency parameter, etc.) to a
circuit, controller, or
other device in the system. One of skill in the art, having the benefit of the
disclosure herein
and knowledge about a contemplated system ordinarily available to that person,
can readily
determine which aspects of the present disclosure will benefit from a sensor.
Certain
considerations for the person of skill in the art, in determining whether a
contemplated device
is a sensor and/or whether aspects of the present disclosure can benefit from
or be enhanced
by the contemplated sensor include, without limitation: the conditioning of an
activation/deactivation of a system to an environmental quality; the
conversion of electrical
output into measured quantities; the ability to enforce a geofence; the
automatic modification
of a loan in response to change in collateral; and the like. While specific
examples of sensors
and considerations are described herein for purposes of illustration, any
system benefitting
from the disclosures herein, and any considerations understood to one of skill
in the art
having the benefit of the disclosures herein, are specifically contemplated
within the scope of
the present disclosure.
[00233] The term storage condition and similar terms, as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, storage condition includes an environment, physical location,
environmental
quality, level of exposure, security measures, maintenance description,
accessibility
description, and the like related to the storage of an asset, collateral, or
an entity specified and
monitored in a contract, loan, or agreement or backing the contract, loan or
other agreement,
and the like. Based on a storage condition of a collateral, an asset, or
entity, actions may be
taken to, maintain, improve, and/or confirm a condition of the asset or the
use of that asset as
collateral. Based on a storage condition, actions may be taken to alter the
terms or conditions
of a loan or bond. Storage condition may be classified in accordance with
various rules,
thresholds, conditional procedures, workflows, model parameters, and the like
and may be
based on self-reporting or on data from Internet of Things devices, data from
a set of
environmental condition sensors, data from a set of social network analytic
services and a set
of algorithms for querying network domains, social media data, crowdsourced
data, and the
like. The storage condition may be tied to a geographic location relating to
the collateral, the
issuer, the borrower, the distribution of the funds or other geographic
locations. Examples of
IoT data may include images, sensor data, location data, and the like.
Examples of social
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media data or crowdsourced data may include behavior of parties to the loan,
financial
condition of parties, adherence to a party's a term or condition of the loan,
or bond, or the
like. Parties to the loan may include issuers of a bond, related entities,
lender, borrower, 3rd
parties with an interest in the debt. Storage condition may relate to an asset
or type of
collateral such as a municipal asset, a vehicle, a ship, a plane, a building,
a home, real estate
property, undeveloped land, a farm, a crop, a municipal facility, a warehouse,
a set of
inventory, a commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an item of
jewelry, a
gemstone, an item of intellectual property, an intellectual property right, a
contractual right,
an antique, a fixture, an item of furniture, an item of equipment, a tool, an
item of machinery,
and an item of personal property. The storage condition may include an
environment where
environment may include an environment selected from among a municipal
environment, a
corporate environment, a securities trading environment, a real property
environment, a
commercial facility, a warehousing facility, a transportation environment, a
manufacturing
environment, a storage environment, a home, and a vehicle. Actions based on
the storage
condition of a collateral, an asset or an entity may include managing,
reporting on, altering,
syndicating, consolidating, terminating, maintaining, modifying terms and/or
conditions,
foreclosing an asset, or otherwise handling a loan, contract, or agreement.
One of skill in the
art, having the benefit of the disclosure herein and knowledge about a
contemplated storage
condition, can readily determine which aspects of the present disclosure will
benefit a
particular application for a storage condition. Certain considerations for the
person of skill in
the art, or embodiments of the present disclosure in choosing an appropriate
storage condition
to manage and/or monitor, include, without limitation: the legality of the
condition given the
jurisdiction of the transaction, the data available for a given collateral,
the anticipated
transaction type (loan, bond or debt), the specific type of collateral, the
ratio of the loan to
value, the ratio of the collateral to the loan, the gross transaction/loan
amount, the credit
scores of the borrower and the lender, ordinary practices in the industry, and
other
considerations. While specific examples of storage conditions are described
herein for
purposes of illustration, any embodiment benefitting from the disclosures
herein, and any
considerations understood to one of skill in the art having the benefit of the
disclosures herein
are specifically contemplated within the scope of the present disclosure.
[00234] The term geolocation and similar terms, as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure,
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geolocation includes the identification or estimation of the real-world
geographic location of
an object, including the generation of a set of geographic coordinates (e.g.
latitude and
longitude) and/or street address. Based on a geolocation of a collateral, an
asset, or entity,
actions may be taken to maintain or improve a condition of the asset or the
use of that asset as
collateral. Based on a geolocation, actions may be taken to alter the terms or
conditions of a
loan or bond. Based on a geolocation, determinations or predictions related to
a transaction
may be performed - for example based upon the weather, civil unrest in a
particular area,
and/or local disasters (e.g., an earthquake, flood, tornado, hurricane,
industrial accident, etc.).
Geolocations may be determined in accordance with various rules, thresholds,
conditional
procedures, workflows, model parameters, and the like and may be based on self-
reporting or
on data from Internet of Things devices, data from a set of environmental
condition sensors,
data from a set of social network analytic services and a set of algorithms
for querying
network domains, social media data, crowdsourced data, and the like. Examples
of
geolocation data may include GPS coordinates, images, sensor data, street
address, and the
like. Geolocation data may be quantitative (e.g., longitude/latitude, relative
to a plat map,
etc.) and/or qualitative (e.g., categorical such as "coastal", "rural", etc.;
"within New York
City", etc.). Geolocation data may be absolute (e.g., GPS location) or
relative (e.g., within
100 yards of an expected location). Examples of social media data or
crowdsourced data may
include behavior of parties to the loan as inferred by their geolocation,
financial condition of
parties inferred by geolocation, adherence of parties to a term or condition
of the loan, or
bond, or the like. Geolocation may be determined for an asset or type of
collateral such as a
municipal asset, a vehicle, a ship, a plane, a building, a home, real estate
property,
undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of
inventory, a
commodity, a security, a currency, a token of value, a ticket, a consumable
item, an edible
item, a beverage, a precious metal, an item of jewelry, a gemstone, an
antique, a fixture, an
item of furniture, an item of equipment, a tool, an item of machinery, and an
item of personal
property. Geolocation may be determined for an entity such as one of the
parties, a third-
party (e.g., an inspection service, maintenance service, cleaning service,
etc. relevant to a
transaction), or any other entity related to a transaction. The geolocation
may include an
environment selected from among a municipal environment, a corporate
environment, a
securities trading environment, a real property environment, a commercial
facility, a
warehousing facility, a transportation environment, a manufacturing
environment, a storage
environment, a home, and a vehicle. Actions based on the geolocation of a
collateral, an asset
or an entity may include managing, reporting on, altering, syndicating,
consolidating,
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terminating, maintaining, modifying terms and/or conditions, foreclosing an
asset, or
otherwise handling a loan, contract, or agreement. One of skill in the art,
having the benefit of
the disclosure herein and knowledge about a contemplated system, can readily
determine
which aspects of the present disclosure will benefit a particular application
for a geolocation,
and which location aspect of an item is a geolocation for the contemplated
system. Certain
considerations for the person of skill in the art, or embodiments of the
present disclosure in
choosing an appropriate geolocation to manage, include, without limitation:
the legality of the
geolocation given the jurisdiction of the transaction, the data available for
a given collateral,
the anticipated transaction type (loan, bond or debt), the specific type of
collateral, the ratio
of the loan to value, the ratio of the collateral to the loan, the gross
transaction/loan amount,
the frequency of travel of the borrower to certain jurisdictions and other
considerations, the
mobility of the collateral, and/or a likelihood of location-specific event
occurrence relevant to
the transaction (e.g., weather, location of a relevant industrial facility,
availability of relevant
services, etc.). While specific examples of geolocation are described herein
for purposes of
illustration, any embodiment benefitting from the disclosures herein, and any
considerations
understood to one of skill in the art having the benefit of the disclosures
herein are
specifically contemplated within the scope of the present disclosure.
[00235] The term jurisdictional location and similar terms, as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, jurisdictional location refers to the laws and legal authority
governing a loan
entity. The jurisdictional location may be based on a geolocation of an
entity, a registration
location of an entity (e.g. a ship's flag state, a state of incorporation for
a business, and the
like), a granting state for certain rights such as intellectual priority, and
the like. In certain
embodiments, a jurisdictional location may be one or more of the geolocations
for an entity in
the system. In certain embodiments, a jurisdictional location may not be the
same as the
geolocation of any entity in the system (e.g., where an agreement specifies
some other
jurisdiction). In certain embodiments, a jurisdictional location may vary for
entities in the
system (e.g., borrower at A, lender at B, collateral positioned at C,
agreement enforced at D,
etc.). In certain embodiments, a jurisdictional location for a given entity
may vary during the
operations of the system (e.g., due to movement of collateral, related data,
changes in terms
and conditions, etc.). In certain embodiments, a given entity of the system
may have more
than one jurisdictional location (e.g., due to operations of the relevant law,
and/or options
available to one or more parties), and/or may have distinct jurisdictional
locations for
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different purposes. A jurisdictional location of an item of collateral, an
asset, or entity,
actions may dictate certain terms or conditions of a loan or bond, and/or may
indicate
different obligations for notices to parties, foreclosure and/or default
execution, treatment of
collateral and/or debt security, and/or treatment of various data within the
system. While
specific examples of jurisdictional location are described herein for purposes
of illustration,
any embodiment benefitting from the disclosures herein, and any considerations
understood
to one of skill in the art having the benefit of the disclosures herein are
specifically
contemplated within the scope of the present disclosure.
[00236] The terms token of value, token, and variations such as cryptocurrency
token, and
the like, as utilized herein, in the context of increments of value, may be
understood broadly
to describe either: (a) a unit of currency or cryptocurrency (e.g. a
cryptocurrency token), and
(b) may also be used to represent a credential that can be exchanged for a
good, service, data
or other valuable consideration (e.g. a token of value). Without limitation to
any other aspect
or description of the present disclosure, in the former case, a token may also
be used in
conjunction with investment applications, token-trading applications, and
token-based
marketplaces. In the latter case, a token can also be associated with
rendering consideration,
such as providing goods, services, fees, access to a restricted area or event,
data or other
valuable benefit. Tokens can be contingent (e.g. contingent access token) or
not contingent.
For example, a token of value may be exchanged for accommodations, (e.g. hotel
rooms),
dining/food goods and services, space (e.g. shared space, workspace,
convention space, etc.),
fitness/wellness goods or services, event tickets or event admissions, travel,
flights or other
transportation, digital content, virtual goods, license keys, or other
valuable goods, services,
data or consideration. Tokens in various forms may be included where
discussing a unit of
consideration, collateral, or value, whether currency, cryptocurrency or any
other form of
value such as goods, services, data or other benefits. One of skill in the
art, having the benefit
of the disclosure herein and knowledge about a token, can readily determine
the value
symbolized or represented by a token, whether currency, cryptocurrency, good,
service, data
or other value. While specific examples of tokens are described herein for
purposes of
illustration, any embodiment benefitting from the disclosures herein, and any
considerations
understood to one of skill in the art having the benefit of the disclosures
herein, are
specifically contemplated within the scope of the present disclosure.
[00237] The term pricing data as utilized herein may be understood broadly to
describe a
quantity of information such as a price or cost, of one or more items in a
marketplace.
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Without limitation to any other aspect or description of the present
disclosure, pricing data
may also be used in conjunction with spot market pricing, forward market
pricing, pricing
discount information, promotional pricing, and other information relating to
the cost or price
of items. Pricing data may satisfy one or more conditions, or may trigger
application of one
or more rules of a smart contract. Pricing data may be used in conjunction
with other forms of
data such as market value data, accounting data, access data, asset and
facility data, worker
data, event data, underwriting data, claims data or other forms of data.
Pricing data may be
adjusted for the context of the valued item (e.g., condition, liquidity,
location, etc.) and/or for
the context of a particular party. One of skill in the art, having the benefit
of the disclosure
herein and knowledge about pricing data, can readily determine the purposes
and use of
pricing data in various embodiments and contexts disclosed herein.
[00238] Without limitation to any other aspect or description of the present
disclosure, a
token includes any token including, without limitation, a token of value, such
as collateral, an
asset, a reward, such as in a token serving as representation of value, such
as a value holding
voucher that can be exchanged for goods or services. Certain components may
not be
considered tokens individually, but may be considered tokens in an aggregated
system - for
example, a value placed on an asset may not be in itself be a token, but the
value of an asset
may be placed in a token of value, such as to be stored, exchanged, traded,
and the like. For
instance, in a non-limiting example, a blockchain circuit may be structured to
provide lenders
a mechanism to store the value of assets, where the value attributed to the
token is stored in a
distributed ledger of the blockchain circuit, but the token itself, assigned
the value, may be
exchanged or traded such as through a token marketplace. In certain
embodiments, a token
may be considered a token for some purposes but not for other purposes - for
example a
token may be used as an indication of ownership of an asset, but this use of a
token would not
be traded as a value where a token including the value of the asset might.
Accordingly, the
benefits of the present disclosure may be applied in a wide variety of
systems, and any such
systems may be considered a token herein, while in certain embodiments a given
system may
not be considered a token herein. One of skill in the art, having the benefit
of the disclosure
herein and knowledge about a contemplated system ordinarily available to that
person, can
readily determine which aspects of the present disclosure will benefit a
particular system,
and/or how to combine processes and systems from the present disclosure to
enhance
operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is a token and/or whether
aspects of the
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present disclosure can benefit or enhance the contemplated system include,
without
limitation, access data such as relating to rights of access, tickets, and
tokens; use in an
investment application such as for investment in shares, interests, and
tokens; a token-trading
application; a token-based marketplace; forms of consideration such as
monetary rewards and
tokens; translating the value of a resources in tokens; a cryptocurrency
token; indications of
ownership such as identity information, event information, and token
information; a
blockchain-based access token traded in a marketplace application; pricing
application such
as for setting and monitoring pricing for contingent access rights, underlying
access rights,
tokens, and fees; trading applications such as for trading or exchanging
contingent access
rights or underlying access rights or tokens; tokens created and stored on a
blockchain for
contingent access rights resulting in an ownership (e.g., a ticket); and the
like.
[00239] The term financial data as utilized herein may be understood broadly
to describe a
collection of financial information about an asset, collateral or other item
or items. Financial
data may include revenues, expenses, assets, liabilities, equity, bond
ratings, default, return
on assets (ROA), return on investment (ROI), past performance, expected future
performance, earnings per share (EPS), internal rate of return (IRR), earnings
announcements, ratios, statistical analysis of any of the foregoing (e.g.
moving averages), and
the like. Without limitation to any other aspect or description of the present
disclosure,
financial data may also be used in conjunction with pricing data and market
value data.
Financial data may satisfy one or more conditions, or may trigger application
of one or more
rules of a smart contract. Financial data may be used in conjunction with
other forms of data
such as market value data, pricing data, accounting data, access data, asset
and facility data,
worker data, event data, underwriting data, claims data or other forms of
data. One of skill in
the art, having the benefit of the disclosure herein and knowledge about
financial data, can
readily determine the purposes and use of pricing data in various embodiments
and contexts
disclosed herein.
[00240] The term covenant as utilized herein may be understood broadly to
describe a term,
agreement or promise, such as performance of some action or inaction. For
example, a
covenant may relate to behavior of a party or legal status of a party. Without
limitation to any
other aspect or description of the present disclosure, a covenant may also be
used in
conjunction with other related terms to an agreement or loan, such as a
representation, a
warranty, an indemnity, a balance of debt, a fixed interest rate, a variable
interest rate, a
payment amount, a payment schedule, a balloon payment schedule, a
specification of
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collateral, a specification of substitutability of collateral, a party, a
guarantee, a guarantor, a
security, a personal guarantee, a lien, a duration, a foreclose condition, a
default condition,
and a consequence of default. A covenant or lack of performance of a covenant
may satisfy
one or more conditions, or may trigger collection, breach or other terms and
conditions. In
certain embodiments, a smart contract may calculate whether a covenant is
satisfied and in
cases where the covenant is not satisfied, may enable automated action or
trigger other
conditions or terms. One of skill in the art, having the benefit of the
disclosure herein and
knowledge about covenants, can readily determine the purposes and use of
covenants in
various embodiments and contexts disclosed herein.
[00241] The term entity as utilized herein may be understood broadly to
describe a party, a
third-party (e.g., an auditor, regulator, service provider, etc.), and/or an
identifiable related
object such as an item of collateral related to a transaction. Example
entities include an
individual, partnership, corporation, limited liability company or other legal
organization.
Other example entities include an identifiable item of collateral, offset
collateral, potential
collateral, or the like. For example, an entity may be a given party, such as
an individual, to
an agreement or loan. Data or other terms herein may be characterized as
having a context
relating to an entity, such as entity-oriented data. An entity may be
characterized with a
specific context or application, such as a human entity, physical entity,
transactional entity or
a financial entity, without limitation. An entity may have representatives
that represent or act
on its behalf. Without limitation to any other aspect or description of the
present disclosure,
an entity may also be used in conjunction with other related entities or terms
to an agreement
or loan, such as a representation, a warranty, an indemnity, a covenant, a
balance of debt, a
fixed interest rate, a variable interest rate, a payment amount, a payment
schedule, a balloon
payment schedule, a specification of collateral, a specification of
substitutability of collateral,
a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a
foreclose condition, a default condition, and a consequence of default. An
entity may have a
set of attributes such as: a publicly stated valuation, a set of property
owned by the entity as
indicated by public records, a valuation of a set of property owned by the
entity, a bankruptcy
condition, a foreclosure status, a contractual default status, a regulatory
violation status, a
criminal status, an export controls status, an embargo status, a tariff
status, a tax status, a
credit report, a credit rating, a website rating, a set of customer reviews
for a product of an
entity, a social network rating, a set of credentials, a set of referrals, a
set of testimonials, a
set of behavior, a location, and a geolocation, without limitation. In certain
embodiments, a
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smart contract may calculate whether an entity has satisfied conditions or
covenants and in
cases where the entity has not satisfied such conditions or covenants, may
enable automated
action or trigger other conditions or terms. One of skill in the art, having
the benefit of the
disclosure herein and knowledge about entities, can readily determine the
purposes and use of
entities in various embodiments and contexts disclosed herein.
[00242] The term party as utilized herein may be understood broadly to
describe a member
of an agreement, such as an individual, partnership, corporation, limited
liability company or
other legal organization. For example, a party may be a primary lender, a
secondary lender, a
lending syndicate, a corporate lender, a government lender, a bank lender, a
secured lender, a
bond issuer, a bond purchaser, an unsecured lender, a guarantor, a provider of
security, a
borrower, a debtor, an underwriter, an inspector, an assessor, an auditor, a
valuation
professional, a government official, an accountant or other entities having
rights or
obligations to an agreement, transaction or loan. A party may characterize a
different term,
such as transaction as in the term multi-party transaction, where multiple
parties are involved
in a transaction, or the like, without limitation. A party may have
representatives that
represent or act on its behalf. In certain embodiments, the term party may
reference a
potential party or a prospective party - for example an intended lender or
borrower interacting
with a system, that may not yet be committed to an actual agreement during the
interactions
with the system. Without limitation to any other aspect or description of the
present
disclosure, an party may also be used in conjunction with other related
parties or terms to an
agreement or loan, such as a representation, a warranty, an indemnity, a
covenant, a balance
of debt, a fixed interest rate, a variable interest rate, a payment amount, a
payment schedule, a
balloon payment schedule, a specification of collateral, a specification of
substitutability of
collateral, an entity, a guarantee, a guarantor, a security, a personal
guarantee, a lien, a
duration, a foreclose condition, a default condition, and a consequence of
default. A party
may have a set of attributes such as: an identity, a creditworthiness, an
activity, a behavior, a
business practice, a status of performance of a contract, information about
accounts
receivable, information about accounts payable, information about the value of
collateral, and
other types of information, without limitation. In certain embodiments, a
smart contract may
calculate whether a party has satisfied conditions or covenants and in cases
where the party
has not satisfied such conditions or covenants, may enable automated action or
trigger other
conditions or terms. One of skill in the art, having the benefit of the
disclosure herein and
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knowledge about parties, can readily determine the purposes and use of parties
in various
embodiments and contexts disclosed herein.
[00243] The term party attribute, entity attribute, or party/entity attribute
as utilized herein
may be understood broadly to describe a value, characteristic, or status of a
party or entity.
For example, attributes of a party or entity may be, without limitation:
value, quality,
location, net worth, price, physical condition, health condition, security,
safety, ownership,
identity, creditworthiness, activity, behavior, business practice, status of
performance of a
contract, information about accounts receivable, information about accounts
payable,
information about the value of collateral, and other types of information, and
the like. In
certain embodiments, a smart contract may calculate values, status or
conditions associated
with attributes of a party or entity, and in cases where the party or entity
has not satisfied
such conditions or covenants, may enable automated action or trigger other
conditions or
terms. One of skill in the art, having the benefit of the disclosure herein
and knowledge about
attributes of a party or entity, can readily determine the purposes and use of
these attributes in
various embodiments and contexts disclosed herein.
[00244] The term lender as utilized herein may be understood broadly to
describe a party to
an agreement offering an asset for lending, proceeds of a loan, and may
include an individual,
partnership, corporation, limited liability company, or other legal
organization. For example,
a lender may be a primary lender, a secondary lender, a lending syndicate, a
corporate lender,
a government lender, a bank lender, a secured lender, an unsecured lender, or
other party
having rights or obligations to an agreement, transaction or loan offering a
loan to a
borrower, without limitation. A lender may have representatives that represent
or act on its
behalf. Without limitation to any other aspect or description of the present
disclosure, an
party may also be used in conjunction with other related parties or terms to
an agreement or
loan, such as a borrower, a guarantor, a representation, a warranty, an
indemnity, a covenant,
a balance of debt, a fixed interest rate, a variable interest rate, a payment
amount, a payment
schedule, a balloon payment schedule, a specification of collateral, a
specification of
substitutability of collateral, a security, a personal guarantee, a lien, a
duration, a foreclose
condition, a default condition, and a consequence of default. In certain
embodiments, a smart
contract may calculate whether a lender has satisfied conditions or covenants
and in cases
where the lender has not satisfied such conditions or covenants, may enable
automated
action, a notification or alert, or trigger other conditions or terms. One of
skill in the art,
having the benefit of the disclosure herein and knowledge about a lender, can
readily
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determine the purposes and use of a lender in various embodiments and contexts
disclosed
herein.
[00245] The term crowdsourcing services as utilized herein may be understood
broadly to
describe services offered or rendered in conjunction with a crowdsourcing
model or
transaction, wherein a large group of people or entities supply contributions
to fulfill a need,
such as a loan, for the transaction. Crowdsourcing services may be provided by
a platform or
system, without limitation. A crowdsourcing request may be communicated to a
group of
information suppliers and by which responses to the request may be collected
and processed
to provide a reward to at least one successful information supplier. The
request and
parameters may be configured to obtain information related to the condition of
a set of
collateral for a loan. The crowdsourcing request may be published. In certain
embodiments,
without limitation, crowdsourcing services may be performed by a smart
contract, wherein
the reward is managed by a smart contract that processes responses to the
crowdsourcing
request and automatically allocates a reward to information that satisfies a
set of parameter
configured for the crowdsourcing request. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about crowdsourcing services, can readily
determine the
purposes and use of crowdsourcing services in various embodiments and contexts
disclosed
herein.
[00246] The term publishing services as utilized herein may be understood to
describe a set
of services to publish a crowdsourcing request. Publishing services may be
provided by a
platform or system, without limitation. In certain embodiments, without
limitation, publishing
services may be performed by a smart contract, wherein the crowdsourcing
request is
published or publication is initiated by the smart contract. One of skill in
the art, having the
benefit of the disclosure herein and knowledge about publishing services, can
readily
determine the purposes and use of publishing services in various embodiments
and contexts
disclosed herein.
[00247] The term interface as utilized herein may be understood broadly to
describe a
component by which interaction or communication is achieved, such as a
component of a
computer, which may be embodied in software, hardware or a combination
thereof. For
example, an interface may serve a number of different purposes or be
configured for different
applications or contexts, such as, without limitation: an application
programming interface, a
graphic user interface, user interface, software interface, marketplace
interface, demand
aggregation interface, crowdsourcing interface, secure access control
interface, network
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interface, data integration interface or a cloud computing interface, or
combinations thereof.
An interface may serve to act as a way to enter, receive or display data,
within the scope of
lending, refinancing, collection, consolidation, factoring, brokering or
foreclosure, without
limitation. An interface may serve as an interface for another interface.
Without limitation to
any other aspect or description of the present disclosure, an interface may be
used in
conjunction with applications, processes, modules, services, layers, devices,
components,
machines, products, sub-systems, interfaces, connections, or as part of a
system. In certain
embodiments, an interface may be embodied in software, hardware or a
combination thereof,
as well as stored on a medium or in memory. One of skill in the art, having
the benefit of the
disclosure herein and knowledge about an interface, can readily determine the
purposes and
use of an interface in various embodiments and contexts disclosed herein.
[00248] The term graphical user interface as utilized herein may be understood
as a type of
interface to allow a user to interact with a system, computer or other
interfaces, in which
interaction or communication is achieved through graphical devices or
representations. A
graphical user interface may be a component of a computer, which may be
embodied in
computer readable instructions, hardware, or a combination thereof. A
graphical user
interface may serve a number of different purposes or be configured for
different applications
or contexts. Such an interface may serve to act as a way to receive or display
data using
visual representation, stimulus or interactive data, without limitation. A
graphical user
interface may serve as an interface for another graphical user interface or
other interfaces.
Without limitation to any other aspect or description of the present
disclosure, a graphical
user interface may be used in conjunction with applications, processes,
modules, services,
layers, devices, components, machines, products, sub-systems, interfaces,
connections, or as
part of a system. In certain embodiments, a graphical user interface may be
embodied in
computer readable instructions, hardware or a combination thereof, as well as
stored on a
medium or in memory. Graphical user interfaces may be configured for any input
types,
including keyboards, a mouse, a touch screen, and the like. Graphical user
interfaces may be
configured for any desired user interaction environments, including for
example a dedicated
application, a web page interface, or combinations of these. One of skill in
the art, having the
benefit of the disclosure herein and knowledge about a graphical user
interface, can readily
determine the purposes and use of a graphical user interface in various
embodiments and
contexts disclosed herein.
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[00249] The term user interface as utilized herein may be understood as a type
of interface to
allow a user to interact with a system, computer or other apparatus, in which
interaction or
communication is achieved through graphical devices or representations. A user
interface
may be a component of a computer, which may be embodied in software, hardware
or a
combination thereof. The user interface may be stored on a medium or in
memory. User
interfaces may include drop-down menus, tables, forms, or the like with
default, templated,
recommended, or pre-configured conditions. In certain embodiments, a user
interface may
include voice interaction. Without limitation to any other aspect or
description of the present
disclosure, a user interface may be used in conjunction with applications,
circuits, controllers,
processes, modules, services, layers, devices, components, machines, products,
sub-systems,
interfaces, connections, or as part of a system. User interfaces may serve a
number of
different purposes or be configured for different applications or contexts.
For example, a
lender-side user interface may include features to view a plurality of
customer profiles, but
may be restricted from making certain changes. A debtor-side user interface
may include
features to view details and make changes to a user account. A 3rd party
neutral-side
interface (e.g. a 3rd party not having an interest in an underlying
transaction, such as a
regulator, auditor, etc.) may have features that enable a view of company
oversight and
anonymized user data without the ability to manipulate any data, and may have
scheduled
access depending upon the 3rd party and the purpose for the access. A 3rd
party interested-
side interface (e.g. a 3rd party that may have an interest in an underlying
transaction, such as
a collector, debtor advocate, investigator, partial owner, etc.) may include
features enabling a
view of particular user data with restrictions on making changes. Many more
features of these
user interfaces may be available to implements embodiments of the systems
and/or
procedures described throughout the present disclosure. Accordingly, the
benefits of the
present disclosure may be applied in a wide variety of processes and systems,
and any such
processes or systems may be considered a service herein. One of skill in the
art, having the
benefit of the disclosure herein and knowledge about a user interface, can
readily determine
the purposes and use of a user interface in various embodiments and contexts
disclosed
herein. Certain considerations for the person of skill in the art, in
determining whether a
contemplated interface is a user interface and/or whether aspects of the
present disclosure can
benefit or enhance the contemplated system include, without limitation:
configurable views,
ability to restrict manipulation or views, report functions, ability to
manipulate user profile
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and data, implement regulatory requirements, provide the desired user features
for borrowers,
lenders, and 3rd parties, and the like.
[00250] Interfaces and dashboards as utilized herein may further be understood
broadly to
describe a component by which interaction or communication is achieved, such
as a
component of a computer, which may be embodied in software, hardware or a
combination
thereof. Interfaces and dashboards may acquire, receive, present or otherwise
administrate an
item, service, offering or other aspects of a transaction or loan. For
example, interfaces and
dashboards may serve a number of different purposes or be configured for
different
applications or contexts, such as, without limitation: an application
programming interface, a
graphic user interface, user interface, software interface, marketplace
interface, demand
aggregation interface, crowdsourcing interface, secure access control
interface, network
interface, data integration interface or a cloud computing interface, or
combinations thereof.
An interface or dashboard may serve to act as a way to receive or display
data, within the
context of lending, refinancing, collection, consolidation, factoring,
brokering or foreclosure,
without limitation. An interface or dashboard may serve as an interface or
dashboard for
another interface or dashboard. Without limitation to any other aspect or
description of the
present disclosure, an interface may be used in conjunction with applications,
circuits,
controllers, processes, modules, services, layers, devices, components,
machines, products,
sub-systems, interfaces, connections, or as part of a system. In certain
embodiments, an
interface or dashboard may be embodied in computer readable instructions,
hardware or a
combination thereof, as well as stored on a medium or in memory. One of skill
in the art,
having the benefit of the disclosure herein and knowledge ordinarily available
about a
contemplated system, can readily determine the purposes and use of interfaces
and/or
dashboards in various embodiments and contexts disclosed herein.
[00251] The term domain as utilized herein may be understood broadly to
describe a scope
or context of a transaction and/or communications related to a transaction.
For example, a
domain may serve a number of different purposes or be configured for different
applications
or contexts, such as, without limitation: a domain for execution, a domain for
a digital asset,
domains to which a request will be published, domains to which social network
data
collection and monitoring services will be applied, domains to which Internet
of Things data
collection and monitoring services will be applied, network domains,
geolocation domains,
jurisdictional location domains, and time domains. Without limitation to any
other aspect or
description of the present disclosure, one or more domains may be utilized
relative to any
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applications, circuits, controllers, processes, modules, services, layers,
devices, components,
machines, products, sub-systems, interfaces, connections, or as part of a
system. In certain
embodiments, a domain may be embodied in computer readable instructions,
hardware, or a
combination thereof, as well as stored on a medium or in memory. One of skill
in the art,
having the benefit of the disclosure herein and knowledge about a domain, can
readily
determine the purposes and use of a domain in various embodiments and contexts
disclosed
herein.
[00252] The term request (and variations) as utilized herein may be understood
broadly to
describe the action or instance of initiating or asking for a thing (e.g.
information, a response,
an object, and the like) to be provided. A specific type of request may also
serve a number of
different purposes or be configured for different applications or contexts,
such as, without
limitation: a formal legal request (e.g. a subpoena), a request to refinance
(e.g. a loan), or a
crowdsourcing request. Systems may be utilized to perform requests as well as
fulfill
requests. Requests in various forms may be included where discussing a legal
action, a
refinancing of a loan, or a crowdsourcing service, without limitation. One of
skill in the art,
having the benefit of the disclosure herein and knowledge about a contemplated
system, can
readily determine the value of a request implemented in an embodiment. While
specific
examples of requests are described herein for purposes of illustration, any
embodiment
benefitting from the disclosures herein, and any considerations understood to
one of skill in
the art having the benefit of the disclosures herein, are specifically
contemplated within the
scope of the present disclosure.
[00253] The term reward (and variations) as utilized herein may be understood
broadly to
describe a thing or consideration received or provided in response to an
action or stimulus.
Rewards can be of a financial type, or non-financial type, without limitation.
A specific type
of reward may also serve a number of different purposes or be configured for
different
applications or contexts, such as, without limitation: a reward event, claims
for rewards,
monetary rewards, rewards captured as a data set, rewards points, and other
forms of rewards.
Rewards may be triggered, allocated, generated for innovation, provided for
the submission
of evidence, requested, offered, selected, administrated, managed, configured,
allocated,
conveyed, identified, without limitation, as well as other actions. Systems
may be utilized to
perform the aforementioned actions. Rewards in various forms may be included
where
discussing a particular behavior, or encouragement of a particular behavior,
without
limitation. In certain embodiments herein, a reward may be utilized as a
specific incentive
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(e.g., rewarding a particular person that responds to a crowdsourcing request)
or as a general
incentive (e.g., providing a reward responsive to a successful crowdsourcing
request, in
addition to or alternatively to a reward to the particular person that
responded). One of skill in
the art, having the benefit of the disclosure herein and knowledge about a
reward, can readily
determine the value of a reward implemented in an embodiment. While specific
examples of
rewards are described herein for purposes of illustration, any embodiment
benefitting from
the disclosures herein, and any considerations understood to one of skill in
the art having the
benefit of the disclosures herein, are specifically contemplated within the
scope of the present
disclosure.
[00254] The term robotic process automation system as utilized herein may be
understood
broadly to describe a system capable of performing tasks or providing needs
for a system of
the present disclosure. For example, a robotic process automation system,
without limitation,
can be configured for: negotiation of a set of terms and conditions for a
loan, negotiation of
refinancing of a loan, loan collection, consolidating a set of loans, managing
a factoring loan,
brokering a mortgage loan, training for foreclosure negotiations, configuring
a crowdsourcing
request based on a set of attributes for a loan, setting a reward, determining
a set of domains
to which a request will be published, configuring the content of a request,
configuring a data
collection and monitoring action based on a set of attributes of a loan,
determining a set of
domains to which the Internet of Things data collection and monitoring
services will be
applied, and iteratively training and improving based on a set of outcomes. A
robotic process
automation system may include: a set of data collection and monitoring
services, an artificial
intelligence system, and another robotic process automation system which is a
component of
the higher level robotic process automation system. The robotic process
automation system
may include: at least one of the set of mortgage loan activities and the set
of mortgage loan
interactions includes activities among marketing activity, identification of a
set of prospective
borrowers, identification of property, identification of collateral,
qualification of borrower,
title search, title verification, property assessment, property inspection,
property valuation,
income verification, borrower demographic analysis, identification of capital
providers,
determination of available interest rates, determination of available payment
terms and
conditions, analysis of existing mortgage, comparative analysis of existing
and new mortgage
terms, completion of application workflow, population of fields of
application, preparation of
mortgage agreement, completion of schedule to mortgage agreement, negotiation
of mortgage
terms and conditions with capital provider, negotiation of mortgage terms and
conditions
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with borrower, transfer of title, placement of lien and closing of mortgage
agreement.
Example and non-limiting robotic process automation systems may include one or
more user
interfaces, interfaces with circuits and/or controllers throughout the system
to provide,
request, and/or share data, and/or one or more artificial intelligence
circuits configured to
iteratively improve one or more operations of the robotic process automation
system. One of
skill in the art, having the benefit of the disclosure herein and knowledge
ordinarily available
about a contemplated robotic process automation system, can readily determine
the circuits,
controllers, and/or devices to include to implement a robotic process
automation system
performing the selected functions for the contemplated system. While specific
examples of
robotic process automation systems are described herein for purposes of
illustration, any
embodiment benefitting from the disclosures herein, and any considerations
understood.
[00255] The term loan-related action (and other related terms such as loan-
related event and
loan-related activity) are utilized herein and may be understood broadly to
describe one or
multiple actions, events or activities relating to a transaction that includes
a loan within the
transaction. The action, event or activity may occur in many different
contexts of loans, such
as lending, refinancing, consolidation, factoring, brokering, foreclosure,
administration,
negotiating, collecting, procuring, enforcing and data processing (e.g. data
collection), or
combinations thereof, without limitation. A loan-related action may be used in
the form of a
noun (e.g. a notice of default has been communicated to the borrower with
formal notice,
which could be considered a loan-related action). A loan-related action,
event, or activity may
refer to a single instance, or may characterize a group of actions, events or
activities. For
example, a single action such as providing a specific notice to a borrower of
an overdue
payment may be considered a loan-related action. Similarly, a group of actions
from start to
finish relating to a default may also be considered a single loan-related
action. Appraisal,
inspection, funding and recording, without limitation, may all also be
considered loan-related
actions that have occurred, as well as events relating to the loan, and may
also be loan-related
events. Similarly, these activities of completing these actions may also be
considered loan-
related activities (e.g. appraising, inspecting, funding, recording, etc.),
without limitation. In
certain embodiments, a smart contract or robotic process automation system may
perform
loan-related actions, loan-related events, or loan-related activities for one
or more of the
parties, and process appropriate tasks for completion of the same. In some
cases the smart
contract or robotic process automation system may not complete a loan-related
action, and
depending upon such outcome this may enable an automated action or may trigger
other
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conditions or terms. One of skill in the art, having the benefit of the
disclosure herein and
knowledge about loan-related actions, events, and activities can readily
determine the
purposes and use of this term in various forms and embodiments as described
throughout the
present disclosure.
[00256] The term loan-related action, events, and activities, as noted herein,
may also more
specifically be utilized to describe a context for calling of a loan. A
calling of a loan is an
action wherein the lender can demand the loan be repaid, usually triggered by
some other
condition or term, such as delinquent payment(s). For example, a loan-related
action for
calling of the loan may occur when a borrower misses three payments in a row,
such that
there is a severe delinquency in the loan payment schedule, and the loan goes
into default. In
such a scenario, a lender may be initiating loan-related actions for calling
of the loan to
protect its rights. In such a scenario, perhaps the borrower pays a sum to
cure the delinquency
and penalties, which may also be considered as a loan-related action for
calling of the loan. In
some circumstances, a smart contract or robotic process automation system may
initiate,
administrate or process loan-related actions for calling of the loan, which
without limitation,
may including providing notice, researching and collecting payment history, or
other tasks
performed as a part of the calling of the loan. One of skill in the art,
having the benefit of the
disclosure herein and knowledge about loan-related actions for calling of the
loan, or other
forms of the term and its various forms, can readily determine the purposes
and use of this
term in the context of an event or other various embodiments and contexts
disclosed herein.
[00257] The term loan-related action, events, and activities, as noted herein,
may also more
specifically be utilized to describe a context for payment of a loan.
Typically in transactions
involving loans, without limitation, a loan is repaid on a payment schedule.
Various actions
may be taken to provide a borrower with information to pay back the loan, as
well as actions
for a lender to receive payment for the loan. For example, if a borrower makes
a payment on
the loan, a loan-related action for payment of the loan may occur. Without
limitation, such a
payment may comprise several actions that may occur with respect to the
payment on the
loan, such as: the payment being tendered to the lender, the loan ledger or
accounting
reflecting that a payment has been made, a receipt provided to the borrower of
the payment
made, and the next payment being requested of the borrower. In some
circumstances, a smart
contract or robotic process automation system may initiate, administrate or
process such loan-
related actions for payment of the loan, which without limitation, may
including providing
notice to the lender, researching and collecting payment history, providing a
receipt to the
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borrower, providing notice of the next payment due to the borrower, or other
actions
associated with payment of the loan. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about loan-related actions for payment of a
loan, or other
forms of the term and its various forms, can readily determine the purposes
and use of this
term in the context of an event or other various embodiments and contexts
disclosed herein.
[00258] The term loan-related action, events, and activities, as noted herein,
may also more
specifically be utilized to describe a context for a payment schedule or
alternative payment
schedule. Typically in transactions involving loans, without limitation, a
loan is repaid on a
payment schedule, which may be modified over time. Or, such a payment schedule
may be
developed and agreed in the alternative, with an alternative payment schedule.
Various
actions may be taken in the context of a payment schedule or alternate payment
schedule for
the lender or the borrower, such as: the amount of such payments, when such
payments are
due, what penalties or fees may attach to late payments, or other terms. For
example, if a
borrower makes an early payment on the loan, a loan-related action for payment
schedule and
alternative payment schedule of the loan may occur; in such case, perhaps the
payment is
applied as principal, with the regular payment still being due. Without
limitation, loan-related
actions for a payment schedule and alternative payment schedule may comprise
several
actions that may occur with respect to the payment on the loan, such as: the
payment being
tendered to the lender, the loan ledger or accounting reflecting that a
payment has been made,
a receipt provided to the borrower of the payment made, a calculation if any
fees are attached
or due, and the next payment being requested of the borrower. In certain
embodiments, an
activity to determine a payment schedule or alternative payment schedule may
be a loan-
related action, event, or activity. In certain embodiments, an activity to
communicate the
payment schedule or alternative payment schedule (e.g., to the borrower, the
lender, or a 3rd
party) may be a loan-related action, event, or activity. In some
circumstances, a smart
contract circuit or robotic process automation system may initiate,
administrate, or process
such loan-related actions for payment schedule and alternative payment
schedule, which
without limitation, may include providing notice to the lender, researching
and collecting
payment history, providing a receipt to the borrower, calculating the next due
date,
calculating the final payment amount and date, providing notice of the next
payment due to
the borrower, determining the payment schedule or an alternate payment
schedule,
communicating the payment scheduler or an alternate payment schedule, or other
actions
associated with payment of the loan. One of skill in the art, having the
benefit of the
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disclosure herein and knowledge about loan-related actions for payment
schedule and
alternative payment schedule, or other forms of the term and its various
forms, can readily
determine the purposes and use of this term in the context of an event or
other various
embodiments and contexts disclosed herein.
[00259] The term regulatory notice requirement (and any derivatives) as
utilized herein may
be understood broadly to describe an obligation or condition to communicate a
notification or
message to another party or entity. The regulatory notice requirement may be
required under
one or more conditions that are triggered, or generally required. For example,
a lender may
have a regulatory notice requirement to provide notice to a borrower of a
default of a loan, or
change of an interest rate of a loan, or other notifications relating to a
transaction or loan. The
regulatory aspect of the term may be attributed to jurisdiction-specific laws,
rules, or codes
that require certain obligations of communication. In certain embodiments, a
policy directive
may be treated as a regulatory notice requirement - for example where a lender
has an
internal notice policy that may exceed the regulatory requirements of one or
more of the
jurisdictional locations related to a transaction. The notice aspect generally
relates to formal
communications, which may take many different forms, but may specifically be
specified as
a particular form of notice, such as a certified mail, facsimile, email
transmission, or other
physical or electronic form, a content for the notice, and/or a timing
requirement related to
the notice. The requirement aspect relates to the necessity of a party to
complete its obligation
to be in compliance with laws, rules, codes, policies, standard practices, or
terms of an
agreement or loan. In certain embodiments, a smart contract may process or
trigger
regulatory notice requirements and provide appropriate notice to a borrower.
This may be
based on location of at least one of: the lender, the borrower, the funds
provided via the loan,
the repayment of the loan, and the collateral of the loan, or other locations
as designated by
the terms of the loan, transaction, or agreement. In cases where a party or
entity has not
satisfied such regulatory notice requirements, certain changes in the rights
or obligations
between the parties may be triggered - for example where a lender provides a
non-compliant
notice to the borrower, an automated action or trigger based on the terms and
conditions of
the loan, and/or based on external information (e.g., a regulatory
prescription, internal policy
of the lender, etc.) may be affected by a smart contract circuit and/or
robotic process
automation system may be implemented. One of skill in the art, having the
benefit of the
disclosure herein and knowledge ordinarily available about a contemplated
system, can
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readily determine the purposes and use of regulatory notice requirements in
various
embodiments and contexts disclosed herein.
[00260] The term regulatory notice requirement may also be utilized herein to
describe an
obligation or condition to communicate a notification or message to another
party or entity
based upon a general or specific policy, rather than based on a particular
jurisdiction, or laws,
rules, or codes of a particular location (as in regulatory notice requirement
that may be
jurisdiction-specific). The regulatory notice requirement may be prudent or
suggested, rather
than obligatory or required, under one or more conditions that are triggered,
or generally
required. For example, a lender may have a regulatory notice requirement that
is policy based
to provide notice to a borrower of a new informational website, or will
experience a change
of an interest rate of a loan in the future, or other notifications relating
to a transaction or loan
that are advisory or helpful, rather than mandatory (although mandatory
notices may also fall
under a policy basis). Thus, in policy based uses of the regulatory notice
requirement term, a
smart contract circuit may process or trigger regulatory notice requirements
and provide
appropriate notice to a borrower which may or may not necessarily be required
by a law, rule
or code. The basis of the notice or communication may be out of prudence,
courtesy, custom,
or obligation.
[00261] The term regulatory notice may also be utilized herein to describe an
obligation or
condition to communicate a notification or message to another party or entity
specifically,
such as a lender or borrower. The regulatory notice may be specifically
directed toward any
party or entity, or a group of parties or entities. For example, a particular
notice or
communication may be advisable or required to be provided to a borrower, such
as on
circumstances of a borrower's failure to provide scheduled payments on a loan
resulting in a
default. As such, such a regulatory notice directed to a particular user, such
as a lender or
borrower, may be as a result of a regulatory notice requirement that is
jurisdiction-specific or
policy-based, or otherwise. Thus, in some circumstances a smart contract may
process or
trigger a regulatory notice and provide appropriate notice to a specific party
such as a
borrower, which may or may not necessarily be required by a law, rule or code,
but may
otherwise be provided out of prudence, courtesy or custom. In cases where a
party or entity
has not satisfied such regulatory notice requirements to a specific party or
parties, it may
create circumstances where certain rights may be forgiven by one or more
parties or entities,
or may enable automated action or trigger other conditions or terms. One of
skill in the art,
having the benefit of the disclosure herein and knowledge ordinarily available
about a
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contemplated system, can readily determine the purposes and use of regulatory
notice
requirements based in various embodiments and contexts disclosed herein.
[00262] The term regulatory foreclosure requirement (and any derivatives) as
utilized herein
may be understood broadly to describe an obligation or condition in order to
trigger, process
or complete default of a loan, foreclosure or recapture of collateral, or
other related
foreclosure actions. The regulatory foreclosure requirement may be required
under one or
more conditions that are triggered, or generally required. For example, a
lender may have a
regulatory foreclosure requirement to provide notice to a borrower of a
default of a loan, or
other notifications relating to the default of a loan prior to foreclosure.
The regulatory aspect
of the term may be attributed to jurisdiction-specific laws, rules, or codes
that require certain
obligations of communication. The foreclosure aspect generally relates to the
specific remedy
of foreclosure, or a recapture of collateral property and default of a loan,
which may take
many different forms, but may be specified in the terms of the loan. The
requirement aspect
relates to the necessity of a party to complete its obligation in order to be
in compliance or
performance of laws, rules, codes or terms of an agreement or loan. In certain
embodiments,
a smart contract circuit may process or trigger regulatory foreclosure
requirements and
process appropriate tasks relating to such a foreclosure action. This may be
based on a
jurisdictional location of at least one of the lender, the borrower, the fund
provided via the
loan, the repayment of the loan, and the collateral of the loan, or other
locations as designated
by the terms of the loan, transaction, or agreement. In cases where a party or
entity has not
satisfied such regulatory foreclosure requirements, certain rights may be
forgiven by the party
or entity (e.g. a lender), or such a failure to comply with the regulatory
notice requirement
may enable automated action or trigger other conditions or terms. One of skill
in the art,
having the benefit of the disclosure herein and knowledge ordinarily available
about a
contemplated system, can readily determine the purposes and use of regulatory
foreclosure
requirements in various embodiments and contexts disclosed herein.
[00263] The term regulatory foreclosure requirement may also be utilized
herein to describe
an obligation or in order to trigger, process or complete default of a loan,
foreclosure or
recapture of collateral, or other related foreclosure actions. based upon a
general or specific
policy rather than based on a particular jurisdiction, or laws, rules, or
codes of a particular
location (as in regulatory foreclosure requirement that may be jurisdiction-
specific). The
regulatory foreclosure requirement may be prudent or suggested, rather than
obligatory or
required, under one or more conditions that are triggered, or generally
required. For example,
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a lender may have a regulatory foreclosure requirement that is policy based to
provide notice
to a borrower of a default of a loan, or other notifications relating to a
transaction or loan that
are advisory or helpful, rather than mandatory (although mandatory notices may
also fall
under a policy basis). Thus, in policy based uses of the regulatory
foreclosure requirement
term, a smart contract may process or trigger regulatory foreclosure
requirements and provide
appropriate notice to a borrower which may or may not necessarily be required
by a law, rule
or code. The basis of the notice or communication may be out of prudence,
courtesy, custom,
industry practice, or obligation.
[00264] The term regulatory foreclosure requirements may also be utilized
herein to
describe an obligation or condition that is to be performed with regard to a
specific user, such
as a lender or a borrower. The regulatory notice may be specifically directed
toward any party
or entity, or a group of parties or entities. For example, a particular notice
or communication
may be advisable or required to be provided to a borrower, such as on
circumstances of a
borrower's failure to provide scheduled payments on a loan resulting in a
default. As such,
such a regulatory foreclosure requirement is directed to a particular user,
such as a lender or
borrower, and may be a result of a regulatory foreclosure requirement that is
jurisdiction-
specific or policy-based, or otherwise. For example, the foreclosure
requirement may be
related to a specific entity involved with a transaction (e.g., the current
borrower has been a
customer for 30 years, so s/he receives unique treatment), or to a class of
entities (e.g.,
"preferred" borrowers, or "first time default" borrowers). Thus, in some
circumstances a
smart contract circuit may process or trigger an obligation or action that
must be taken
pursuant to a foreclosure, where the action is directed or from a specific
party such as a
lender or a borrower, which may or may not necessarily be required by a law,
rule or code,
but may otherwise be provided out of prudence, courtesy, or custom. In certain
embodiments,
the obligation or condition that is to be performed with regard to the
specific user may form a
part of the terms and conditions or otherwise be known to the specific user to
which it applies
(e.g., an insurance company or bank that advertises a specific practice with
regard to a
specific class of customers, such as first-time default customers, first-time
accident
customers, etc.), and in certain embodiments the obligation or condition that
is to be
performed with regard to the specific user may be unknown to the specific user
to which it
applies (e.g., a bank has a policy relating to a class of users to which the
specific user
belongs, but the specific user is not aware of the classification).
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[00265] The terms value, valuation, valuation model (and similar terms) as
utilized herein
should be understood broadly to describe an approach to evaluate and determine
the
estimated value for collateral. Without limitation to any other aspect or
description of the
present disclosure, a valuation model may be used in conjunction with:
collateral (e.g. a
secured property), artificial intelligence services (e.g. to improve a
valuation model), data
collection and monitoring services (e.g. to set a valuation amount), valuation
services (e.g.
the process of informing, using, and/or improving a valuation model), and/or
outcomes
relating to transactions in collateral (e.g. as a basis of improving the
valuation model).
"Jurisdiction-specific valuation model" is also used as a valuation model used
in a specific
geographic/jurisdictional area or region; wherein, the jurisdiction can be
specific to
jurisdiction of the lender, the borrower, the delivery of funds, the payment
of the loan or the
collateral of the loan, or combinations thereof. In certain embodiments, a
jurisdiction-specific
valuation model considers jurisdictional effects on a valuation of collateral,
including at least:
rights and obligations for borrowers and lenders in the relevant
jurisdiction(s); jurisdictional
effects on the ability to move, import, export, substitute, and/or liquidate
the collateral;
jurisdictional effects on the timing between default and foreclosure or
collection of collateral;
and/or jurisdictional effects on the volatility and/or sensitivity of
collateral value
determinations. In certain embodiments, a geolocation-specific valuation model
considers
geolocation effects on a valuation of the collateral, which may include a
similar list of
considerations relative jurisdictional effects (although the jurisdictional
location(s) may be
distinct from the geolocation(s)), but may also include additional effects,
such as: weather-
related effects; distance of the collateral from monitoring, maintenance, or
seizure services;
and/or proximity of risk phenomenon (e.g., fault lines, industrial locations,
a nuclear plant,
etc.). A valuation model may utilize a valuation of offset collateral (e.g., a
similar item of
collateral, a generic value such as a market value of similar or fungible
collateral, and/or a
value of an item that correlates with a value of the collateral) as a part of
the valuation of the
collateral. In certain embodiments, an artificial intelligence circuit
includes one or more
machine learning and/or artificial intelligence algorithms, to improve a
valuation model,
including, for example, utilizing information over time between multiple
transactions
involving similar or offset collateral, and/or utilizing outcome information
(e.g., where loan
transactions are completed successfully or unsuccessfully, and/or in response
to collateral
seizure or liquidation events that demonstrate real-world collateral valuation
determinations)
from the same or other transactions to iteratively improve the valuation
model. In certain
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embodiments, an artificial intelligence circuit is trained on a collateral
valuation data set, for
example previously determined valuations and/or through interactions with a
trainer (e.g., a
human, accounting valuations, and/or other valuation data). In certain
embodiments, the
valuation model and/or parameters of the valuation model (e.g., assumptions,
calibration
values, etc.) may be determined and/or negotiated as a part of the terms and
conditions of the
transaction (e.g., a loan, a set of loans, and/or a subset of the set of
loans). One of skill in the
art, having the benefit of the disclosure herein and knowledge ordinarily
available about a
contemplated system, can readily determine which aspects of the present
disclosure will
benefit a particular application for a valuation model, and how to choose or
combine
valuation models to implement an embodiment of a valuation model. Certain
considerations
for the person of skill in the art, or embodiments of the present disclosure
in choosing an
appropriate valuation model, include, without limitation: the legal
considerations of a
valuation model given the jurisdiction of the collateral; the data available
for a given
collateral; the anticipated transaction/loan type(s); the specific type of
collateral; the ratio of
the loan to value; the ratio of the collateral to the loan; the gross
transaction/loan amount; the
credit scores of the borrower; accounting practices for the loan type and/or
related industry;
uncertainties related to any of the foregoing; and/or sensitivities related to
any of the
foregoing. While specific examples of valuation models and considerations are
described
herein for purposes of illustration, any embodiment benefitting from the
disclosures herein,
and any considerations understood to one of skill in the art having the
benefit of the
disclosures herein, are specifically contemplated within the scope of the
present disclosure
[00266] The term market value data, or marketplace information, (and other
forms or
variations) as utilized herein may be understood broadly to describe data or
information
relating to the valuation of a property, asset, collateral or other valuable
items which may be
used as the subject of a loan, collateral or transaction. Market value data or
marketplace
information may change from time to time, and may be estimated, calculated, or
objectively
or subjectively determined from various sources of information. Market value
data or
marketplace information may be related directly to an item of collateral or to
an off-set item
of collateral. Market value data or marketplace information may include
financial data,
market ratings, product ratings, customer data, market research to understand
customer needs
or preferences, competitive intelligence re. competitors, suppliers, and the
like, entities sales,
transactions, customer acquisition cost, customer lifetime value, brand
awareness, churn rate,
and the like. The term may occur in many different contexts of contracts or
loans, such as
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lending, refinancing, consolidation, factoring, brokering, foreclosure, and
data processing
(e.g. data collection), or combinations thereof, without limitation. Market
value data or
marketplace information may be used as a noun to identify a single figure or a
plurality of
figures or data. For example, market value data or marketplace information may
be utilized
by a lender to determine if a property or asset will serve as collateral for a
secured loan, or
may alternatively be utilized in the determination of foreclosure if a loan is
in default, without
limitation to these circumstances in use of the term. Marketplace value data
or marketplace
information may also be used to determine loan-to-value figures or
calculations. In certain
embodiments, a collection service, smart contract circuit, and/or robotic
process automation
system may estimate or calculate market value data or marketplace information
from one or
more sources of data or information. In some cases market data value or
marketplace
information, depending upon the data/information contained therein, may enable
automated
action or trigger other conditions or terms. One of skill in the art, having
the benefit of the
disclosure herein and knowledge ordinarily available about a contemplated
system and
available relevant marketplace information, can readily determine the purposes
and use of
this term in various forms, embodiments and contexts disclosed herein.
[00267] The terms similar collateral, similar to collateral, off-set
collateral, and other forms
or variations as utilized herein may be understood broadly to describe a
property, asset or
valuable item that may be like in nature to a collateral (e.g. an article of
value held in
security) regarding a loan or other transaction. Similar collateral may refer
to a property,
asset, collateral or other valuable item which may be aggregated, substituted,
or otherwise
referred to in conjunction with other collateral, whether the similarity comes
in the form of a
common attribute such as type of item of collateral, category of the item of
collateral, an age
of the item of collateral, a condition of the item of collateral, a history of
the item of
collateral, an ownership of the item of collateral, a caretaker of the item of
collateral, a
security of the item of collateral, a condition of an owner of the item of
collateral, a lien on
the item of collateral, a storage condition of the item of collateral, a
geolocation of the item of
collateral, and a jurisdictional location of the item of collateral, and the
like. In certain
embodiments, an offset collateral references an item that has a value
correlation with an item
of collateral - for example an offset collateral may exhibit similar price
movements,
volatility, storage requirements, or the like for an item of collateral. In
certain embodiments,
similar collateral may be aggregated to form a larger security interest or
collateral for an
additional loan or distribution, or transaction. In certain embodiments,
offset collateral may
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be utilized to inform a valuation of the collateral. In certain embodiments, a
smart contract
circuit or robotic process automation system may estimate or calculate
figures, data or
information relating to similar collateral, or may perform a function with
respect to
aggregating similar collateral. One of skill in the art, having the benefit of
the disclosure
herein and knowledge ordinarily available about a contemplated system can
readily
determine the purposes and use of similar collateral, offset collateral, or
related terms as they
relate to collateral in various forms, embodiments, and contexts disclosed
herein.
[00268] The term restructure (and other forms such as restructuring) as
utilized herein may
be understood broadly to describe a modification of terms or conditions,
properties,
collateral, or other considerations affecting a loan or transaction.
Restructuring may result in
a successful outcome where amended terms or conditions are adopted between
parties, or an
unsuccessful outcome where no modification or restructure occurs, without
limitation.
Restructuring can occur in many contexts of contracts or loans, such as
application, lending,
refinancing, collection, consolidation, factoring, brokering, foreclosure, and
combinations
thereof, without limitation. Debt may also be restructured, which may indicate
that debts
owed to a party are modified as to timing, amounts, collateral, or other
terms. For example, a
borrower may restructure debt of a loan to accommodate a change of financial
conditions, or
a lender may offer to a borrower the restructuring of a debt for its own needs
or prudence. In
certain embodiments, a smart contract circuit or robotic process automation
system may
automatically or manually restructure debt based on a monitored condition, or
create options
for restructuring a debt, administrate the process of negotiating or effecting
the restructuring
of a debt, or other actions in connection with restructuring or modifying
terms of a loan or
transaction. One of skill in the art, having the benefit of the disclosure
herein and knowledge
ordinarily available about a contemplated system, can readily determine the
purposes and use
of this term, whether in the context of debt or otherwise, in various
embodiments and
contexts disclosed herein.
[00269] The term social network data collection, social network monitoring
services, and
social network data collection and monitoring services (and its various forms
or derivatives)
as utilized herein may be understood broadly to describe services relating to
the acquisition,
organizing, observing, or otherwise acting upon data or information derived
from one or
more social networks. The social network data collection and monitoring
services may be a
part of a related system of services or a standalone set of services. Social
network data
collection and monitoring services may be provided by a platform or system,
without
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limitation. Social network data collection and monitoring services may be used
in a variety of
contexts such as lending, refinancing, negotiation, collection, consolidation,
factoring,
brokering, foreclosure, and combinations thereof, without limitation. Requests
of social
network data collection and monitoring, with configuration parameters, may be
requested by
other services, automatically initiated or triggered to occur based on
conditions or
circumstances that occur. An interface may be provided to configure, initiate,
display or
otherwise interact with social network data collection and monitoring
services. Social
networks, as utilized herein, reference any mass platform where data and
communications
occur between individuals and/or entities, where the data and communications
are at least
partially accessible to an embodiment system. In certain embodiments, the
social network
data includes publicly available (e.g., accessible without any authorization)
information. In
certain embodiments, the social network data includes information that is
properly accessible
to an embodiment system, but may include subscription access or other access
to information
that is not freely available to the public, but may be accessible (e.g.,
consistent with a privacy
policy of the social network with its users). A social network may be
primarily social in
nature, but may additionally or alternatively include professional networks,
alumni networks,
industry related networks, academically oriented networks, or the like. In
certain
embodiments, a social network may be a crowdsourcing platform, such as a
platform
configured to accept queries or requests directed to users (and/or a subset of
users, potentially
meeting specified criteria), where users may be aware that certain
communications will be
shared and accessible to requestors, at least a portion of users of the
platform, and/or publicly
available. In certain embodiments, without limitation, social network data
collection and
monitoring services may be performed by a smart contract circuit or a robotic
process
automation system. One of skill in the art, having the benefit of the
disclosure herein and
knowledge ordinarily available about a contemplated system, can readily
determine the
purposes and use of social network data collection and monitoring services in
various
embodiments and contexts disclosed herein.
[00270] The term crowdsource and social network information as utilized herein
may further
be understood broadly to describe information acquired or provided in
conjunction with a
crowdsourcing model or transaction, or information acquired or provided on or
in
conjunction with a social network. Crowdsource and social network information
may be
provided by a platform or system, without limitation. Crowdsource and social
network
information may be acquired, provided or communicated to or from a group of
information
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suppliers and by which responses to the request may be collected and
processed.
Crowdsource and social network information may provide information, conditions
or factors
relating to a loan or agreement. Crowdsource and social network information
may be private
or published, or combinations thereof, without limitation. In certain
embodiments, without
limitation, crowdsource and social network information may be acquired,
provided,
organized or processed, without limitation, by a smart contract circuit,
wherein the
crowdsource and social network information may be managed by a smart contract
circuit that
processes the information to satisfy a set of configured parameters. One of
skill in the art,
having the benefit of the disclosure herein and knowledge ordinarily available
about a
contemplated system can readily determine the purposes and use of this term in
various
embodiments and contexts disclosed herein.
[00271] The term negotiate (and other forms such as negotiating or
negotiation) as utilized
herein may be understood broadly to describe discussions or communications to
bring about
or obtain a compromise, outcome, or agreement between parties or entities.
Negotiation may
result in a successful outcome where terms are agreed between parties, or an
unsuccessful
outcome where the parties do not agree to specific terms, or combinations
thereof, without
limitation. A negotiation may be successful in one aspect or for a particular
purpose, and
unsuccessful in another aspect or for another purpose. Negotiation can occur
in many
contexts of contracts or loans, such as lending, refinancing, collection,
consolidation,
factoring, brokering, foreclosure, and combinations thereof, without
limitation. For example,
a borrower may negotiate an interest rate or loan terms with a lender. In
another example, a
borrower in default may negotiate an alternative resolution to avoid
foreclosure with a lender.
In certain embodiments, a smart contract circuit or robotic process automation
system may
negotiate for one or more of the parties, and process appropriate tasks for
completing or
attempting to complete a negotiation of terms. In some cases negotiation by
the smart
contract or robotic process automation system may not complete or be
successful. Successful
negotiation may enable automated action or trigger other conditions or terms
to be
implemented by the smart contract circuit or robotic process automation
system. One of skill
in the art, having the benefit of the disclosure herein and knowledge
ordinarily available
about a contemplated system, can readily determine the purposes and use of
negotiation in
various embodiments and contexts disclosed herein.
[00272] The term negotiate in various forms may more specifically be utilized
herein in
verb form (e.g. to negotiate) or in noun forms (e.g. a negotiation), or other
forms to describe a
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context of mutual discussion leading to an outcome. For example, a robotic
process
automation system may negotiate terms and conditions on behalf of a party,
which would be
a use as a verb clause. In another example, a robotic process automation
system may be
negotiating terms and conditions for modification of a loan, or negotiating a
consolidation
offer, or other terms. As a noun clause, a negotiation (e.g. an event) may be
performed by a
robotic process automation system. Thus, in some circumstances a smart
contract circuit or
robotic process automation system may negotiate (e.g. as a verb clause) terms
and conditions,
or the description of doing so may be considered a negotiation (e.g. as a noun
clause). One of
skill in the art, having the benefit of the disclosure herein and knowledge
about negotiating
and negotiation, or other forms of the word negotiate, can readily determine
the purposes and
use of this term in various embodiments and contexts disclosed herein.
[00273] The term negotiate in various forms may also specifically be utilized
to describe an
outcome, such as a mutual compromise or completion of negotiation leading to
an outcome.
For example, a loan may, by robotic process automation system or otherwise, be
considered
negotiated as a successful outcome that has resulted in an agreement between
parties, where
the negotiation has reached completion. Thus, in some circumstances a smart
contract circuit
or robotic process automation system may have negotiated to completion a set
of terms and
conditions, or a negotiated loan. One of skill in the art, having the benefit
of the disclosure
herein and knowledge ordinarily available for a contemplated system, can
readily determine
the purposes and use of this term as it relates to a mutually agreed outcome
through
completion of negotiation in various embodiments and contexts disclosed
herein.
[00274] The term negotiate in various forms may also specifically be utilized
to characterize
an event such as a negotiating event, or an event negotiation, including
reaching a set of
agreeable terms between parties. An event requiring mutual agreement or
compromise
between parties may be considered a negotiating event, without limitation. For
example,
during the procurement of a loan, the process of reaching a mutually
acceptable set of terms
and conditions between parties could be considered a negotiating event. Thus,
in some
circumstances a smart contract circuit or robotic process automation system
may
accommodate the communications, actions, or behaviors of the parties for a
negotiated event.
[00275] The term collection (and other forms such as collect or collecting) as
utilized herein
may be understood broadly to describe the acquisition of a tangible (e.g.
physical item),
intangible (e.g. data, a license, or a right), or monetary (e.g. payment)
item, or other
obligation or asset from a source. The term generally may relate to the entire
prospective
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acquisition of such an item from related tasks in early stages to related
tasks in late stages or
full completion of the acquisition of the item. Collection may result in a
successful outcome
where the item is tendered to a party, or may or an unsuccessful outcome where
the item is
not tendered or acquired to a party, or combinations thereof (e.g., a late or
otherwise deficient
tender of the item), without limitation. Collection may occur in many
different contexts of
contracts or loans, such as lending, refinancing, consolidation, factoring,
brokering,
foreclosure, and data processing (e.g. data collection), or combinations
thereof, without
limitation. Collection may be used in the form of a noun (e.g. data collection
or the collection
of an overdue payment where it refers to an event or characterizes an event),
may refer as a
noun to an assortment of items (e.g. a collection of collateral for a loan
where it refers to a
number of items in a transaction), or may be used in the form of a verb (e.g.
collecting a
payment from the borrower). For example, a lender may collect an overdue
payment from a
borrower through an online payment, or may have a successful collection of
overdue
payments acquired through a customer service telephone call. In certain
embodiments, a
smart contract circuit or robotic process automation system may perform
collection for one or
more of the parties, and process appropriate tasks for completing or
attempting collection for
one or more items (e.g., an overdue payment). In some cases negotiation by the
smart
contract or robotic process automation system may not complete or be
successful, and
depending upon such outcomes this may enable automated action or trigger other
conditions
or terms. One of skill in the art, having the benefit of the disclosure herein
and knowledge
ordinarily available about a contemplated system, can readily determine the
purposes and use
of collection in various forms, embodiments, and contexts disclosed herein.
[00276] The term collection in various forms may also more specifically be
utilized herein in
noun form to describe a context for an event or thing, such as a collection
event, or a
collection payment. For example, a collection event may refer to a
communication to a party
or other activity that relates to acquisition of an item in such an activity,
without limitation. A
collection payment, for example, may relate to a payment made by a borrower
that has been
acquired through the process of collection, or through a collection department
with a lender.
Although not limited to an overdue, delinquent or defaulted loan, collection
may characterize
an event, payment or department, or other noun associated with a transaction
or loan, as being
a remedy for something that has become overdue. Thus, in some circumstances a
smart
contract circuit or robotic process automation system may collect a payment or
installment
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from a borrower, and the activity of doing so may be considered a collection
event, without
limitation.
[00277] The term collection in various forms may also more specifically be
utilized herein
as an adjective or other forms to describe a context relating to litigation,
such as the outcome
of a collection litigation (e.g. litigation regarding overdue or default
payments on a loan). For
example, the outcome of a collection litigation may be related to delinquent
payments which
are owed by a borrower or other party, and collection efforts relating to
those delinquent
payments may be litigated by parties. Thus, in some circumstances a smart
contract circuit or
robotic process automation system may receive, determine or otherwise
administrate the
outcome of collection litigation.
[00278] The term collection in various forms may also more specifically be
utilized herein
as an adjective or other forms to describe a context relating to an action of
acquisition, such
as a collection action (e.g. actions to induce tendering or acquisition of
overdue or default
payments on a loan or other obligation). The terms collection yield, financial
yield of
collection, and/or collection financial yield may be used. The result of such
a collection
action may or may not have a financial yield. For example, a collection action
may result in
the payment of one or more outstanding payments on a loan, which may render a
financial
yield to another party such as the lender. Thus, in some circumstances a smart
contract circuit
or robotic process automation system may render a financial yield from a
collection action, or
otherwise administrate or in some manner assist in a financial yield of a
collection action. In
embodiments, a collection action may include the need for collection
litigation.
[00279] The term collection in various forms (collection ROI, ROI on
collection, ROI on
collection activity, collection activity ROI, and the like) may also more
specifically be
utilized herein to describe a context relating to an action of receiving
value, such as a
collection action (e.g. actions to induce tendering or acquisition of overdue
or default
payments on a loan or other obligation), wherein there is a return on
investment (ROI). The
result of such a collection action may or may not have an ROI, either with
respect to the
collection action itself (as an ROI on the collection action) or as an ROI on
the broader loan
or transaction that is the subject of the collection action. For example, an
ROI on a collection
action may be prudent or not with respect to a default loan, without
limitation, depending
upon whether the ROI will be provided to a party such as the lender. A
projected ROI on
collection may be estimated, or may also be calculated given real events that
transpire. In
some circumstances, a smart contract circuit or robotic process automation
system may
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render an estimated ROI for a collection action or collection event, or may
calculate an ROI
for actual events transpiring in a collection action or collection event,
without limitation. In
embodiments, such a ROI may be a positive or negative figure, whether
estimated or actual.
[00280] The term reputation, measure of reputation, lender reputation,
borrower reputation,
entity reputation, and the like may include general, widely held beliefs,
opinions, and/or
perceptions that are generally held about an individual, entity, collateral,
and the like. A
measure for reputation may be determined based on social data including
likes/dislikes,
review of entity or products and services provided by the entity, rankings of
the company or
product, current and historic market and financial data include price,
forecast, buy/sell
recommendations, financial news regarding entity, competitors, and partners.
Reputations
may be cumulative in that a product reputation and the reputation of a company
leader or lead
scientist may influence the overall reputation of the entity. Reputation of an
institute
associated with an entity (e.g. a school being attended by a student) may
influence the
reputation of the entity. In some circumstances, a smart contract circuit or
robotic process
automation system may collect or initiate collection of data related to the
above and
determine a measure or ranking of reputation. A measure or ranking of an
entity's reputation
may be used by a smart contract circuit or robotic process automation system
in determining
whether to enter into an agreement with the entity, determination of terms and
conditions of a
loan, interest rates, and the like. In certain embodiments, indicia of a
reputation determination
may be related to outcomes of one or more transactions (e.g., a comparison of
"likes" on a
particular social media data set to an outcome index, such as successful
payments, successful
negotiation outcomes, ability to liquidate a particular type of collateral,
etc.) to determine the
measure or ranking of an entity's reputation. One of skill in the art, having
the benefit of the
disclosure herein and knowledge ordinarily available about a contemplated
system, can
readily determine the purposes and use of the reputation, a measure or ranking
of the
reputation, and/or utilization of the reputation in negotiations,
determination of terms and
conditions, determination of whether to proceed with a transaction, and other
various
embodiments and contexts disclosed herein.
[00281] The term collection in various forms (e.g. collector) may also more
specifically be
utilized herein to describe a party or entity that induces, administrates, or
facilitates a
collection action, collection event, or other collection related context. The
measure of
reputation of a party involved, such as a collector, or during the context of
a collection, may
be estimated or calculated using objective, subjective, or historical metrics
or data. For
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example, a collector may be involved in a collection action, and the
reputation of that
collector may be used to determine decisions, actions or conditions.
Similarly, a collection
may be also used to describe objective, subjective or historical metrics or
data to measure the
reputation of a party involved, such as a lender, borrower or debtor. In some
circumstances, a
smart contract circuit or robotic process automation system may render a
collection or
measures, or implement a collector, within the context of a transaction or
loan.
[00282] The term collection and data collection in various forms, including
data collection
systems, may also more specifically be utilized herein to describe a context
relating to the
acquisition, organization, or processing of data, or combinations thereof,
without limitation.
The result of such a data collection may be related or wholly unrelated to a
collection of
items (e.g., grouping of the items, either physically or logically), or
actions taken for
delinquent payments (e.g., collection of collateral, a debt, or the like),
without limitation. For
example, a data collection may be performed by a data collection system,
wherein data is
acquired, organized or processed for decision-making, monitoring, or other
purposes of
prospective or actual transaction or loan. In some circumstances, a smart
contract or robotic
process automation system may incorporate data collection or a data collection
system, to
perform portions or entire tasks of data collection, without limitation. One
of skill in the art,
having the benefit of the disclosure herein and knowledge ordinarily available
for a
contemplated system, can readily determine and distinguish the purposes and
use of
collection in the context of data or information as used herein.
[00283] The terms refinance, refinancing activity(ies), refinancing
interactions, refinancing
outcomes, and similar terms, as utilized herein should be understood broadly.
Without
limitation to any other aspect or description of the present disclosure
refinance and
refinancing activities include replacing an existing mortgage, loan, bond,
debt transaction, or
the like with a new mortgage, loan, bond, or debt transaction that pays off or
ends the
previous financial arrangement. In certain embodiments, any change to terms
and conditions
of a loan, and/or any material change to terms and conditions of a loan, may
be considered a
refinancing activity. In certain embodiments, a refinancing activity is
considered only those
changes to a loan agreement that result in a different financial outcome for
the loan
agreement. Typically, the new loan should be advantageous to the borrower or
issuer, and/or
mutually agreeable (e.g., improving a raw financial outcome of one, and a
security or other
outcome for the other). Refinancing may be done to reduce interest rates,
lower regular
payments, change the loan term, change the collateral associated with the
loan, consolidate
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debt into a single loan, restructure debt, change a type of loan (e.g.
variable rate to fixed rate),
pay off a loan that is due, in response to an improved credit score, to
enlarge the loan, and/or
in response to a change in market conditions (e.g. interest rates, value of
collateral, and the
like).
[00284] Refinancing activity may include initiating an offer to refinance,
initiating a request
to refinance, configuring a refinancing interest rate, configuring a
refinancing payment
schedule, configuring a refinancing balance in a response to the amount or
terms of the
refinanced loan, configuring collateral for a refinancing including changes in
collateral used,
changes in terms and conditions for the collateral, a change in the amount of
collateral and
the like, managing use of proceeds of a refinancing, removing or placing a
lien on different
items of collateral as appropriate given changes in terms and conditions as
part of a
refinancing, verifying title for a new or existing item of collateral to be
used to secure the
refinanced loan, managing an inspection process title for a new or existing
item of collateral
to be used to secure the refinanced loan, populating an application to
refinance a loan,
negotiating terms and conditions for a refinanced loan and closing a
refinancing. Refinance
and refinancing activities may be disclosed in the context of data collection
and monitoring
services that collect a training set of interactions between entities for a
set of loan refinancing
activities. Refinance and refinancing activities may be disclosed in the
context of an artificial
intelligence system that is trained using the collected training set of
interactions that includes
both refinancing activities and outcomes. The trained artificial intelligence
may then be used
to recommend a refinance activity, evaluate a refinance activity, make a
prediction around an
expected outcome of refinancing activity, and the like. Refinance and
refinancing activities
may be disclosed in the context of smart contract systems which may automate a
subset of the
interactions and activities of refinancing. In an example, a smart contract
system may
automatically adjust an interest rate for a loan based on information
collected via at least one
of an Internet of Things system, a crowdsourcing system, a set of social
network analytic
services and a set of data collection and monitoring services. The interest
rate may be
adjusted based on rules, thresholds, model parameters that determine, or
recommend, an
interest rate for refinancing a loan based on interest rates available to the
lender from
secondary lenders, risk factors of the borrower (including predicted risk
based on one or more
predictive models using artificial intelligence), marketing factors (such as
competing interest
rates offered by other lenders), and the like. Outcomes and events of a
refinancing activity
may be recorded in a distributed ledger. Based on the outcome of a refinance
activity, a smart
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contract for the refinance loan may be automatically reconfigured to define
the terms and
conditions for the new loan such as a principal amount of debt, a balance of
debt, a fixed
interest rate, a variable interest rate, a payment amount, a payment schedule,
a balloon
payment schedule, a specification of collateral, a specification of
substitutability of collateral,
a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a
duration, a
covenant, a foreclose condition, a default condition, and a consequence of
default.
[00285] One of skill in the art, having the benefit of the disclosure herein
and knowledge
ordinarily available about a contemplated system can readily determine which
aspects of the
present disclosure will benefit from a particular application of a refinance
activity, how to
choose or combine refinance activities, how to implement systems, services, or
circuits to
automatically perform of one or more (or all) aspects of a refinance activity,
and the like.
Certain considerations for the person of skill in the art, or embodiments of
the present
disclosure in choosing an appropriate training sets of interactions with which
to train an
artificial intelligence to take action, recommend or predict the outcome of
certain refinance
activities. While specific examples of refinance and refinancing activities
are described
herein for purposes of illustration, any embodiment benefitting from the
disclosures herein,
and any considerations understood to one of skill in the art having the
benefit of the
disclosures herein, are specifically contemplated within the scope of the
present disclosure.
[00286] The terms consolidate, consolidation activity(ies), loan
consolidation, debt
consolidation, consolidation plan, and similar terms, as utilized herein
should be understood
broadly. Without limitation to any other aspect or description of the present
disclosure
consolidate, consolidation activity(ies), loan consolidation, debt
consolidation, or
consolidation plan are related to the use of a single large loan to pay off
several smaller loans,
and/or the use of one or more of a set of loans to pay off at least a portion
of one or more of a
second set of loans. In embodiments, loan consolidation may be secured (i.e.,
backed by
collateral) or unsecured. Loans may be consolidated to obtain a lower interest
rate than one or
more of the current loans, to reduce total monthly loan payments, and/or to
bring a debtor
into compliance on the consolidated loans or other debt obligations of the
debtor. Loans that
may be classified as candidates for consolidation may be determined based on a
model that
processes attributes of entities involved in the set of loans including
identity of a party,
interest rate, payment balance, payment terms, payment schedule, type of loan,
type of
collateral, financial condition of party, payment status, condition of
collateral, and value of
collateral. Consolidation activities may include managing at least one of
identification of
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loans from a set of candidate loans, preparation of a consolidation offer,
preparation of a
consolidation plan, preparation of content communicating a consolidation
offer, scheduling a
consolidation offer, communicating a consolidation offer, negotiating a
modification of a
consolidation offer, preparing a consolidation agreement, executing a
consolidation
agreement, modifying collateral for a set of loans, handling an application
workflow for
consolidation, managing an inspection, managing an assessment, setting an
interest rate,
deferring a payment requirement, setting a payment schedule, and closing a
consolidation
agreement. In embodiments, there may be systems, circuits, and/or services
configured to
create, configure (such as using one or more templates or libraries), modify,
set, or otherwise
handle (such as in a user interface) various rules, thresholds, conditional
procedures,
workflows, model parameters, and the like to determine, or recommend, a
consolidation
action or plan for a lending transaction or a set of loans based on one or
more events,
conditions, states, actions, or the like. In embodiments, a consolidation plan
may be based on
various factors, such as the status of payments, interest rates of the set of
loans, prevailing
interest rates in a platform marketplace or external marketplace, the status
of the borrowers of
a set of loans, the status of collateral or assets, risk factors of the
borrower, the lender, one or
more guarantors, market risk factors and the like. Consolidation and
consolidation activities
may be disclosed in the context of data collection and monitoring services
that collect a
training set of interactions between entities for a set of loan consolidation
activities.
consolidation and consolidation activities may be disclosed in the context of
an artificial
intelligence system that is trained using the collected training set of
interactions that includes
both consolidation activities and outcomes associated with those activities.
The trained
artificial intelligence may then be used to recommend a consolidation
activity, evaluate a
consolidation activity, make a prediction around an expected outcome of
consolidation
activity, and the like based models including status of debt, condition of
collateral or assets
used to secure or back a set of loans, the state of a business or business
operation (e.g.,
receivables, payables, or the like), conditions of parties (such as net worth,
wealth, debt,
location, and other conditions), behaviors of parties (such as behaviors
indicating preferences,
behaviors indicating debt preferences), and others. Debt consolidation, loan
consolidation and
associated consolidation activities may be disclosed in the context of smart
contract systems
which may automate a subset of the interactions and activities of
consolidation. In
embodiments, consolidation may include consolidation with respect to terms and
conditions
of sets of loans, selection of appropriate loans, configuration of payment
terms for
consolidated loans, configuration of payoff plans for pre-existing loans,
communications to
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encourage consolidation, and the like. In embodiments, the artificial
intelligence of a smart
contract may automatically recommend or set rules, thresholds, actions,
parameters and the
like (optionally by learning to do so based on a training set of outcomes over
time), resulting
in a recommended consolidation plan, which may specify a series of actions
required to
accomplish a recommended or desired outcome of consolidation (such as within a
range of
acceptable outcomes), which may be automated and may involve conditional
execution of
steps based on monitored conditions and/or smart contract terms, which may be
created,
configured, and/or accounted for by the consolidation plan. Consolidation
plans may be
determined and executed based at least one part on market factors (such as
competing interest
rates offered by other lenders, values of collateral, and the like) as well as
regulatory and/or
compliance factors. Consolidation plans may be generated and/or executed for
creation of
new consolidated loans, for secondary loans related to consolidated loans, for
modifications
of existing loans related to consolidation, for refinancing terms of a
consolidated loan, for
foreclosure situations (e.g., changing from secured loan rates to unsecured
loan rates), for
bankruptcy or insolvency situations, for situations involving market changes
(e.g., changes in
prevailing interest rates) and others. consolidation.
[00287] Certain of the activities related to loans, collateral, entities and
the like may apply
to a wide variety of loans and may not apply explicitly to consolidation
activities. The
categorization of the activities as consolidation activities may be based on
the context of the
loan for which the activities are taking place. However, one of skill in the
art, having the
benefit of the disclosure herein and knowledge ordinarily available about a
contemplated
system can readily determine which aspects of the present disclosure will
benefit from a
particular application of a consolidation activity, how to choose or combine
consolidation
activities, how to implement selected services, circuits, and/or systems
described herein to
perform certain loan consolidation operations, and the like. While specific
examples of
consolidation and consolidation activities are described herein for purposes
of illustration,
any embodiment benefitting from the disclosures herein, and any considerations
understood
to one of skill in the art having the benefit of the disclosures herein, are
specifically
contemplated within the scope of the present disclosure.
[00288] The terms factoring a loan, factoring a loan transaction, factors,
factoring a loan
interaction, factoring assets or sets of assets used for factoring and similar
terms, as utilized
herein should be understood broadly. Without limitation to any other aspect or
description of
the present disclosure factoring may be applied to factoring assets such as
invoices,
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inventory, accounts receivable, and the like, where the realized value of the
item is in the
future. For example, the accounts receivable is worth more when it has been
paid and there is
less risk of default. Inventory and Work in Progress (WIP) may be worth more
as final
product rather than components. References to accounts receivable should be
understood to
encompass these terms and not be limiting. Factoring may include a sale of
accounts
receivable at a discounted rate for value in the present (often cash).
Factoring may also
include the use of accounts receivable as collateral for a short term loan. In
both cases the
value of the accounts receivable or invoices may be discounted for multiple
reasons including
the future value of money, a term of the accounts receivable (e.g., 30 day net
payment vs. 90
day net payment), a degree of default risk on the accounts receivable, a
status of receivables,
a status of work-in-progress (WIP), a status of inventory, a status of
delivery and/or
shipment, financial condition(s) of parties owing against the accounts
receivable, a status of
shipped and/or billed, a status of payments, a status of the borrower, a
status of inventory, a
risk factor of a borrower, a lender, one or more guarantors, market risk
factors, a status of
debt (are there other liens present on the accounts receivable or payment owed
on the
inventory, a condition of collateral assets (e.g. the condition of the
inventory- is it current or
out of date, are invoices in arrears), a state of a business or business
operation, a condition of
a party to the transaction (such as net worth, wealth, debt, location, and
other conditions), a
behavior of a party to the transaction (such as behaviors indicating
preferences, behaviors
indicating negotiation styles, and the like), current interest rates, any
current regulatory and
compliance issues associated with the inventory or accounts receivable (e.g.
if inventory is
being factored, has the intended product received appropriate approvals), and
there legal
actions against the borrower, and many others, including predicted risk based
on one or more
predictive models using artificial intelligence). A factor is an individual,
business, entity, or
groups thereof which agree to provide value in exchange for either the
outright acquisition of
the invoices in a sale or the use of the invoices as collateral for a loan for
the value. Factoring
a loan may include the identification of candidates (both lenders and
borrowers) for factoring,
a plan for factoring specifying the proposed receivables (e.g. all, some, only
those meeting
certain criteria), and a proposed discount factor, communication of the plan
to potential
parties, proffering an offer and receiving an offer, verification of quality
of receivables,
conditions regarding treatment of the receivables for the term of the loan.
While specific
examples of factoring and factoring activities are described herein for
purposes of illustration,
any embodiment benefitting from the disclosures herein, and any considerations
understood
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to one of skill in the art having the benefit of the disclosures herein, are
specifically
contemplated within the scope of the present disclosure.
[00289] The terms mortgage, brokering a mortgage, mortgage collateral,
mortgage loan
activities, and/or mortgage related activities as utilized herein should be
understood broadly.
Without limitation to any other aspect or description of the present
disclosure, a mortgage is
an interaction where a borrower provides the title or a lien on the title of
an item of value,
typically property, to a lender as security in exchange for money or another
item of value, to
be repaid, typically with interest, to the lender. The exchange includes the
condition that,
upon repayment of the loan, the title reverts to the borrower and/or the lien
on the property is
removed. The brokering of a mortgage may include the identification of
potential properties,
lenders, and other parties to the loan, and arranging or negotiating the terms
of the mortgage.
Certain components or activities may not be considered mortgage related
individually, but
may be considered mortgage related when used in conjunction with a mortgage,
act upon a
mortgage, are related to an entity or party to a mortgage, and the like. For
example, brokering
may apply to the offering of a variety of loans including unsecured loans,
outright sale of
property and the like. Mortgage activities and mortgage interactions may
include mortgage
marketing activity, identification of a set of prospective borrowers,
identification of property
to mortgage, identification of collateral property to mortgage, qualification
of borrower, title
search and/or title verification for prospective mortgage property, property
assessment,
property inspection, or property valuation for prospective mortgage property,
income
verification, borrower demographic analysis, identification of capital
providers,
determination of available interest rates, determination of available payment
terms and
conditions, analysis of existing mortgage(s), comparative analysis of existing
and new
mortgage terms, completion of application workflow (e.g. keep the application
moving
forward by initiating next steps in the process as appropriate), population of
fields of
application, preparation of mortgage agreement, completion of schedule for
mortgage
agreement, negotiation of mortgage terms and conditions with capital provider,
negotiation of
mortgage terms and conditions with borrower, transfer of title, placement of
lien on
mortgaged property and closing of mortgage agreement, and similar terms, as
utilized herein
should be understood broadly. While specific examples of mortgages and
mortgage brokering
are described herein for purposes of illustration, any embodiment benefitting
from the
disclosures herein, and any considerations understood to one of skill in the
art having the
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benefit of the disclosures herein, are specifically contemplated within the
scope of the present
disclosure.
[00290] The terms debt management, debt transactions, debt actions, debt terms
and
conditions, syndicating debt, consolidating debt, and/or debt portfolios, as
utilized herein
should be understood broadly. Without limitation to any other aspect or
description of the
present disclosure a debt includes something of monetary value that is owed to
another. A
loan typically results in the borrower holding the debt (e.g. the money that
must be paid back
according to the terms of the loan, which may include interest). Consolidation
of debt
includes the use of a new, single loan to pay back multiple loans (or various
other
configurations of debt structuring as described herein, and as understood to
one of skill in the
art). Often the new loan may have better terms or lower interest rates. Debt
portfolios include
a number of pieces or groups of debt, often having different characteristics
including term,
risk, and the like. Debt portfolio management may involve decisions regarding
the quantity
and quality of the debt being held and how best to balance the various debts
to achieve a
desired risk/reward position based on: investment policy, return on risk
determinations for
individual pieces of debt, or groups of debt. Debt may be syndicated where
multiple lenders
fund a single loan (or set of loans) to a borrower. Debt portfolios may be
sold to a third party
(e.g., at a discounted rate). Debt compliance includes the various measures
taken to ensure
that debt is repaid. Demonstrating compliance may include documentation of the
actions
taken to repay the debt.
[00291] Transactions related to a debt (debt transactions) and actions related
to the debt
(debt actions) may include offering a debt transaction, underwriting a debt
transaction, setting
an interest rate, deferring a payment requirement, modifying an interest rate,
validating title,
managing inspection, recording a change in title, assessing the value of an
asset, calling a
loan, closing a transaction, setting terms and conditions for a transaction,
providing notices
required to be provided, foreclosing on a set of assets, modifying terms and
conditions,
setting a rating for an entity, syndicating debt, and/or consolidating debt.
Debt terms and
conditions may include a balance of debt, a principal amount of debt, a fixed
interest rate, a
variable interest rate, a payment amount, a payment schedule, a balloon
payment schedule, a
specification of assets that back the bond, a specification of
substitutability of assets, a party,
an issuer, a purchaser, a guarantee, a guarantor, a security, a personal
guarantee, a lien, a
duration, a covenant, a foreclose condition, a default condition, and a
consequence of default.
While specific examples of debt management and debt management activities are
described
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herein for purposes of illustration, any embodiment benefitting from the
disclosures herein,
and any considerations understood to one of skill in the art having the
benefit of the
disclosures herein, are specifically contemplated within the scope of the
present disclosure.
[00292] The terms condition, condition classification, classification models,
condition
management, and similar terms, as utilized herein should be understood
broadly. Without
limitation to any other aspect or description of the present disclosure
condition, condition
classification, classification models, condition management, include
classifying or
determining a condition of an asset, issuer, borrower, loan, debt, bond,
regulatory status, term
or condition for a bond, loan or debt transaction that is specified and
monitored in the
contract, and the like. Based on a classified condition of an asset, condition
management may
include actions to maintain or improve a condition of the asset or the use of
that asset as
collateral. Based on a classified condition of an issuer, borrower, party
regulatory status, and
the like, condition management may include actions to alter the terms or
conditions of a loan
or bond. Condition classification may include various rules, thresholds,
conditional
procedures, workflows, model parameters, and the like to classify a condition
of an asset,
issuer, borrower, loan, debt, bond, regulatory status, term or condition for a
bond, loan or
debt transaction, and the like based on data from Internet of Things devices,
data from a set
of environmental condition sensors, data from a set of social network analytic
services and a
set of algorithms for querying network domains, social media data,
crowdsourced data, and
the like. Condition classification may include grouping or labeling entities,
or clustering the
entities, as similarly positioned with regard to some aspect of the classified
condition (e.g., a
risk, quality, ROI, likelihood for recovery, likelihood to default, or some
other aspect of the
related debt).
[00293] Various classification models are disclosed where the classification
and
classification model may be tied to a geographic location relating to the
collateral, the issuer,
the borrower, the distribution of the funds or other geographic locations.
Classification and
classification models are disclosed where artificial intelligence is used to
improve a
classification model (e.g. refine a model by making refinements using
artificial intelligence
data). Thus artificial intelligence may be considered, in some instances, as a
part of a
classification model and vice versa. Classification and classification models
are disclosed
where social media data, crowdsourced data, or IoT data is used as input for
refining a model,
or as input to a classification model. Examples of IoT data may include
images, sensor data,
location data, and the like. Examples of social media data or crowdsourced
data may include
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behavior of parties to the loan, financial condition of parties, adherence to
a parties to a term
or condition of the loan, or bond, or the like. Parties to the loan may
include issuers of a
bond, related entities, lender, borrower, 3rd parties with an interest in the
debt. Condition
management may be discussed in connection with smart contract services which
may include
condition classification, data collection and monitoring, and bond, loan and
debt transaction
management. Data collection and monitoring services are also discussed in
conjunction with
classification and classification models which are related when classifying an
issuer of a bond
issuer, an asset or collateral asset related to the bond, collateral assets
backing the bond,
parties to the bond, and sets of the same. In some embodiments a
classification model may be
included when discussing bond types. Specific steps, factors or refinements
may be
considered a part of a classification model. In various embodiments, the
classification model
may change both in an embodiment, or in the same embodiment which is tied to a
specific
jurisdiction. Different classification models may use different data sets
(e.g. based on the
issuer, the borrower, the collateral assets, the bond type, the loan type, and
the like) and
multiple classification models may be used in a single classification. For
example, one type
of bond, such as a municipal bond, may allow a classification model that is
based on bond
data from municipalities of similar size and economic prosperity, whereas
another
classification model may emphasize data from IoT sensors associated with a
collateral asset.
Accordingly, different classification models will offer benefits or risks over
other
classification models, depending upon the embodiment and the specifics of the
bond, loan or
debt transaction. A classification model includes an approach or concept for
classification.
Conditions classified for a bond, loan, or debt transaction may include a
principal amount of
debt, a balance of debt, a fixed interest rate, a variable interest rate, a
payment amount, a
payment schedule, a balloon payment schedule, a specification of assets that
back the bond,
loan or debt transaction, a specification of substitutability of assets, a
party, an issuer, a
purchaser, a guarantee, a guarantor, a security, a personal guarantee, a lien,
a duration, a
covenant, a foreclose condition, a default condition, and/or a consequence of
default.
Conditions classified may include type of bond issuer such as a municipality,
a corporation, a
contractor, a government entity, a non-governmental entity, and a non-profit
entity. Entities
may include a set of issuers, a set of bonds, a set of parties, and/or a set
of assets. Conditions
classified may include an entity condition such as net worth, wealth, debt,
location, and other
conditions), behaviors of parties (such as behaviors indicating preferences,
behaviors
indicating debt preferences), and the like. Conditions classified may include
an asset or type
of collateral such as a municipal asset, a vehicle, a ship, a plane, a
building, a home, real
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estate property, undeveloped land, a farm, a crop, a municipal facility, a
warehouse, a set of
inventory, a commodity, a security, a currency, a token of value, a ticket, a
cryptocurrency, a
consumable item, an edible item, a beverage, a precious metal, an item of
jewelry, a
gemstone, an item of intellectual property, an intellectual property right, a
contractual right,
an antique, a fixture, an item of furniture, an item of equipment, a tool, an
item of machinery,
and an item of personal property. Conditions classified may include a bond
type where bond
type may include a municipal bond, a government bond, a treasury bond, an
asset-backed
bond, and a corporate bond. Conditions classified may include a default
condition, a
foreclosure condition, a condition indicating violation of a covenant, a
financial risk
condition, a behavioral risk condition, a policy risk condition, a financial
health condition, a
physical defect condition, a physical health condition, an entity risk
condition and an entity
health condition. Conditions classified may include an environment where
environment may
include an environment selected from among a municipal environment, a
corporate
environment, a securities trading environment, a real property environment, a
commercial
facility, a warehousing facility, a transportation environment, a
manufacturing environment, a
storage environment, a home, and a vehicle. Actions based on the condition of
an asset,
issuer, borrower, loan, debt, bond, regulatory status and the like, may
include managing,
reporting on, syndicating, consolidating, or otherwise handling a set of bonds
(such as
municipal bonds, corporate bonds, performance bonds, and others), a set of
loans (subsidized
and unsubsidized, debt transactions and the like, monitoring, classifying,
predicting, or
otherwise handling the reliability, quality, status, health condition,
financial condition,
physical condition or other information about a guarantee, a guarantor, a set
of collateral
supporting a guarantee, a set of assets backing a guarantee, or the like. Bond
transaction
activities in response to a condition of the bond may include offering a debt
transaction,
underwriting a debt transaction, setting an interest rate, deferring a payment
requirement,
modifying an interest rate, validating title, managing inspection, recording a
change in title,
assessing the value of an asset, calling a loan, closing a transaction,
setting terms and
conditions for a transaction, providing notices required to be provided,
foreclosing on a set of
assets, modifying terms and conditions, setting a rating for an entity,
syndicating debt, and/or
consolidating debt.
[00294] One of skill in the art, having the benefit of the disclosure herein
and knowledge
ordinarily available about a contemplated system, can readily determine which
aspects of the
present disclosure will benefit a particular application for a classification
model, how to
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choose or combine classification models to arrive at a condition, and/or
calculate a value of
collateral given the required data. Certain considerations for the person of
skill in the art, or
embodiments of the present disclosure in choosing an appropriate condition to
manage,
include, without limitation: the legality of the condition given the
jurisdiction of the
transaction, the data available for a given collateral, the anticipated
transaction type (loan,
bond or debt), the specific type of collateral, the ratio of the loan to
value, the ratio of the
collateral to the loan, the gross transaction/loan amount, the credit scores
of the borrower and
the lender, and other considerations. While specific examples of conditions,
condition
classification, classification models, and condition management are described
herein for
purposes of illustration, any embodiment benefitting from the disclosures
herein, and any
considerations understood to one of skill in the art having the benefit of the
disclosures
herein, are specifically contemplated within the scope of the present
disclosure.
[00295] The terms classify, classifying, classification, categorization,
categorizing,
categorize (and similar terms) as utilized herein should be understood
broadly. Without
limitation to any other aspect or description of the present disclosure,
classifying a condition
or item may include actions to sort the condition or item into a group or
category based on
some aspect, attribute, or characteristic of the condition or item where the
condition or item is
common or similar for all the items placed in that classification, despite
divergent
classifications or categories based on other aspects or conditions at the
time. Classification
may include recognition of one or more parameters, features, characteristics,
or phenomena
associated with a condition or parameter of an item, entity, person, process,
item, financial
construct, or the like. Conditions classified by a condition classifying
system may include a
default condition, a foreclosure condition, a condition indicating violation
of a covenant, a
financial risk condition, a behavioral risk condition, a contractual
performance condition, a
policy risk condition, a financial health condition, a physical defect
condition, a physical
health condition, an entity risk condition, and/or an entity health condition.
A classification
model may automatically classify or categorize items, entities, process,
items, financial
constructs or the like based on data received from a variety of sources. The
classification
model may classify items based on a single attribute or a combination of
attributes, and/or
may utilize data regarding the items to be classified and a model. The
classification model
may classify individual items, entities, financial constructs or groups of the
same. A bond
may be classified based on the type of bond ((e.g. municipal bonds, corporate
bonds,
performance bonds, and the like), rate of return, bond rating (3rd party
indicator of bond
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quality with respect to bond issuer's financial strength, and/or ability to
bap bond's principal
and interest, and the like. Lenders or bond issuers may be classified based on
the type of
lender or issuer, permitted attributes (e.g. based on income, wealth, location
(domestic or
foreign), various risk factors, status of issuers, and the like. Borrowers may
be classified
based on permitted attributes (e.g. income, wealth, total assets, location,
credit history), risk
factors, current status (e.g. employed, a student), behaviors of parties (such
as behaviors
indicating preferences, reliability, and the like), and the like. A condition
classifying system
may classify a student recipient of a loan based on progress of the student
toward a degree,
the student's grades or standing in their classes, students' status at the
school (matriculated,
on probation and the like), the participation of a student in a non-profit
activity, a deferment
status of the student, and the participation of the student in a public
interest activity.
Conditions classified by a condition classifying system may include a state of
a set of
collateral for a loan or a state of an entity relevant to a guarantee for a
loan. Conditions
classified by a condition classifying system may include a medical condition
of a borrower,
guarantor, subsidizer or the like. Conditions classified by a condition
classifying system may
include compliance with at least one of a law, a regulation, or a policy
related to a lending
transaction or lending institute. Conditions classified by a condition
classifying system may
include a condition of an issuer for a bond, a condition of a bond, a rating
of a loan-related
entity, and the like. Conditions classified by a condition classifying system
may include an
identify of a machine, a component, or an operational mode. Conditions
classified by a
condition classifying system may include a state or context (such as a state
of a machine, a
process, a workflow, a marketplace, a storage system, a network, a data
collector, or the like).
A condition classifying system may classify a process involving a state or
context (e.g., a data
storage process, a network coding process, a network selection process, a data
marketplace
process, a power generation process, a manufacturing process, a refining
process, a digging
process, a boring process, and/or other process described herein. A condition
classifying
system may classify a set of loan refinancing actions based on a predicted
outcome of the set
of loan refinancing actions. A condition classifying system may classify a set
of loans as
candidates for consolidation based on attributes such as identity of a party,
an interest rate, a
payment balance, payment terms, payment schedule, a type of loan, a type of
collateral, a
financial condition of party, a payment status, a condition of collateral, a
value of collateral,
and the like. A condition classifying system may classify the entities
involved in a set of
factoring loans, bond issuance activities, mortgage loans, and the like. A
condition classifying
system may classify a set of entities based on projected outcomes from various
loan
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management activities. A condition classifying system may classify a condition
of a set of
issuers based on information from Internet of Things data collection and
monitoring services,
a set of parameters associated with an issuer, a set of social network
monitoring and analytic
services, and the like. A condition classifying system may classify a set of
loan collection
actions, loan consolidation actions, loan negotiation actions, loan
refinancing actions and the
like based on a set of projected outcomes for those activities and entities.
[00296] The term subsidized loan, subsidizing a loan, (and similar terms) as
utilized herein
should be understood broadly. Without limitation to any other aspect or
description of the
present disclosure, a subsidized loan is the loan of money or an item of value
wherein
payment of interest on the value of the loan may be deferred, postponed or
delayed, with or
without accrual, such as while the borrower is in school, is unemployed, is
ill, and the like. In
embodiments, a loan may be subsidized when the payment of interest on a
portion or subset
of the loan is borne or guaranteed by someone other than the borrower.
Examples of
subsidized loans may include a municipal subsidized loan, a government
subsidized loan, a
student loan, an asset-backed subsidized loan, and a corporate subsidized
loan. An example
of a subsidized student loan may include student loans which may be subsidized
by the
government and on which interest may be deferred or not accrue based on
progress of the
student toward a degree, the participation of a student in a non-profit
activity, a deferment
status of the student, and the participation of the student in a public
interest activity. An
example of a government subsidized housing loan may include governmental
subsidies which
may exempt the borrower from paying closing costs, first mortgage payment and
the like.
Conditions for such subsidized loans may include location of the property
(rural or urban),
income of the borrower, military status of the borrower, ability of the
purchased home to
meet health and safety standards, a limit on the profits you can earn on the
sale of your home,
and the like. Certain usages of the word loan may not apply to a subsidized
loan but rather to
a regular loan. One of skill in the art, having the benefit of the disclosure
herein and
knowledge about a contemplated system ordinarily available to that person, can
readily
determine which aspects of the present disclosure will benefit from
consideration of a
subsidized loan (e.g., in determining the value of the loan, negotiations
related to the loan,
terms and conditions related to the loan, etc.) wherein the borrower may be
relieved of some
of the loan obligations common for non-subsidized loans, where the subsidy may
include
forgiveness, delay or deferment of interest on a loan, or the payment of the
interest by a third
party. The subsidy may include the payment of closing costs including points,
first payment
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and the like by a person or entity other than the borrower, and/or how to
combine processes
and systems from the present disclosure to enhance or benefit from title
validation.
[00297] The term subsidized loan management (and similar terms) as utilized
herein should
be understood broadly. Without limitation to any other aspect or description
of the present
disclosure, subsidized loan management may include a plurality of activities
and solutions for
managing or responding to one or more events related to a subsidized loan
wherein such
events may include requests for a subsidized loan, offering a subsidized loan,
accepting a
subsidized loan, providing underwriting information for a subsidized loan,
providing a credit
report on a borrower seeking a subsidized loan, deferring a required payment
as part of the
loan subsidy, setting an interest rate for a subsidized loan where a lower
interest rate may be
part of the subsidy, deferring a payment requirement as part of the loan
subsidy, identifying
collateral for a loan, validating title for collateral or security for a loan,
recording a change in
title of property, assessing the value of collateral or security for a loan,
inspecting property
that is involved in a loan, identifying a change in condition of an entity
relevant to a loan, a
change in value of an entity that is relevant to a loan, a change in job
status of a borrower, a
change in financial rating of a lender, a change in financial value of an item
offered as a
security, providing insurance for a loan, providing evidence of insurance for
property related
to a loan, providing evidence of eligibility for a loan, identifying security
for a loan,
underwriting a loan, making a payment on a loan, defaulting on a loan, calling
a loan, closing
a loan, setting terms and conditions for a loan, foreclosing on property
subject to a loan,
modifying terms and conditions for a loan, for setting terms and conditions
for a loan (such as
a principal amount of debt, a balance of debt, a fixed interest rate, a
variable interest rate, a
payment amount, a payment schedule, a balloon payment schedule, a
specification of
collateral, a specification of substitutability of collateral, a party, a
guarantee, a guarantor, a
security, a personal guarantee, a lien, a duration, a covenant, a foreclose
condition, a default
condition, and a consequence of default), or managing loan-related activities
(such as,
without limitation, finding parties interested in participating in a loan
transaction, handling an
application for a loan, underwriting a loan, forming a legal contract for a
loan, monitoring
performance of a loan, making payments on a loan, restructuring or amending a
loan, settling
a loan, monitoring collateral for a loan, forming a syndicate for a loan,
foreclosing on a loan,
collecting on a loan, consolidating a set of loans, analyzing performance of a
loan, handling a
default of a loan, transferring title of assets or collateral, and closing a
loan transaction), and
the like. In embodiments, a system for handling a subsidized loan may include
classifying a
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set of parameters of a set of subsidized loans on the basis of data relating
to those parameters
obtained from an Internet of Things data collection and monitoring service.
Classifying the
set of parameters of the set of subsidized loans may also be on the bases of
data obtained
from one or more configurable data collection and monitoring services that
leverage social
network analytic services, crowd sourcing services, and the like for obtaining
parameter data
(e.g., determination that a person or entity is qualified for the subsidized
loan, determining a
social value of providing the subsidized loan or removing a subsidization from
a loan,
determining that a subsidizing entity is legitimate, determining appropriate
subsidization
terms based on characteristics of the buyer and/or subsidizer, etc.).
[00298] The term foreclose, foreclosure, foreclose or foreclosure condition,
default
foreclosure collateral, default collateral, (and similar terms) as utilized
herein should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, foreclose condition, default and the like describe the failure of
a borrower to meet
the terms of a loan. Without limitation to any other aspect or description of
the present
disclosure foreclose and foreclosure include processes by which a lender
attempts to recover,
from a borrower in a foreclose or default condition, the balance of a loan or
take away in lieu,
the right of a borrower to redeem a mortgage held in security for the loan.
Failure to meet the
terms of the loan may include failure to make specified payments, failure to
adhere to a
payment schedule, failure to make a balloon payment, failure to appropriately
secure the
collateral, failure to sustain collateral in a specified condition (e.g. in
good repair), acquisition
of a second loan, and the like. Foreclosure may include a notification to the
borrower, the
public, jurisdictional authorities of the forced sale of an item collateral
such as through a
foreclosure auction. Upon foreclosure, an item of collateral may be placed on
a public auction
site (such as eBay, &C or an auction site appropriate for a particular type of
property. The
minimum opening bid for the item of collateral may be set by the lender and
may cover the
balance of the loan, interest on the loan, fees associated with the
foreclosure and the like.
Attempts to recover the balance of the loan may include the transfer of the
deed for an item of
collateral in lieu of foreclosure (e.g. a real-estate mortgage where the
borrower holds the deed
for a property which acts as collateral for the mortgage loan). Foreclosure
may include taking
possession of or repossessing the collateral (e.g. a car, a sports vehicle
such as a boat, ATV,
ski-mobile, jewelry). Foreclosure may include securing an item of collateral
associated with
the loan (such as by locking a connected device, such as a smart lock, smart
container, or the
like that contains or secures collateral). Foreclosure may include arranging
for the shipping of
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an item of collateral by a carrier, freight forwarder of the like. Foreclosure
may include
arranging for the transport of an item of collateral by a drone, a robot, or
the like for
transporting collateral. In embodiments, a loan may allow for the substitution
of collateral or
the shifting of the lien from an item of collateral initially used to secure
the loan to a
substitute collateral where the substitute collateral is of higher value (to
the lender) than the
initial collateral or is an item in which the borrower has a greater equity.
The result of the
substitution of collateral is that when the loan goes into foreclosure, it is
the substitute
collateral that may be the subject of a forced sale or seizure. Certain usages
of the word
default may not apply to such as to foreclose but rather to a regular or
default condition of an
item. One of skill in the art, having the benefit of the disclosure herein and
knowledge about
a contemplated system ordinarily available to that person, can readily
determine which
aspects of the present disclosure will benefit from foreclosure, and/or how to
combine
processes and systems from the present disclosure to enhance or benefit from
foreclosure.
Certain considerations for the person of skill in the art, in determining
whether the term
foreclosure, foreclose condition, default and the like is referring to failure
of a borrower to
meet the terms of a loan and the related attempts by the lender to recover the
balance of the
loan or obtain ownership of the collateral.
[00299] The terms validation of tile, title validation, validating title, and
similar terms, as
utilized herein should be understood broadly. Without limitation to any other
aspect or
description of the present disclosure validation of title and title validation
include any efforts
to verify or confirm the ownership or interest by an individual or entity in
an item of property
such as a vehicle, a ship, a plane, a building, a home, real estate property,
undeveloped land, a
farm, a crop, a municipal facility, a warehouse, a set of inventory, a
commodity, a security, a
currency, a token of value, a ticket, a cryptocurrency, a consumable item, an
edible item, a
beverage, a precious metal, an item of jewelry, a gemstone, an item of
intellectual property,
an intellectual property right, a contractual right, an antique, a fixture, an
item of furniture, an
item of equipment, a tool, an item of machinery, and an item of personal
property. Efforts to
verify ownership may include reference to bills of sale, government
documentation of
transfer of ownership, a legal will transferring ownership, documentation of
retirement of
liens on the item of property, verification of assignment of Intellectual
Property to the
proposed borrower in the appropriate jurisdiction, and the like. For real-
estate property
validation may include a review of deeds and records at a courthouse of a
country, a state, a
county or a district in which a building, a home, real estate property,
undeveloped land, a
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farm, a crop, a municipal facility, a vehicle, a ship, a plane, or a warehouse
is located or
registered. Certain usages of the word validation may not apply to validation
of a title or title
validation but rather to confirmation that a process is operating correctly,
that an individual
has been correctly identified using biometric data, that intellectual property
rights are in
effect, that data is correct and meaningful, and the like. One of skill in the
art, having the
benefit of the disclosure herein and knowledge about a contemplated system
ordinarily
available to that person, can readily determine which aspects of the present
disclosure will
benefit from title validation, and/or how to combine processes and systems
from the present
disclosure to enhance or benefit from title validation. Certain considerations
for the person of
skill in the art, in determining whether the term validation is referring to
title validation, are
specifically contemplated within the scope of the present disclosure.
[00300] Without limitation to any other aspect or description of the present
disclosure,
validation includes any validating system including, without limitation,
validating title for
collateral or security for a loan, validating conditions of collateral for
security or a loan,
validating conditions of a guarantee for a loan, and the like. For instance, a
validation service
may provide lenders a mechanism to deliver loans with more certainty, such as
through
validating loan or security information components (e.g., income, employment,
title,
conditions for a loan, conditions of collateral, and conditions of an asset).
In a non-limiting
example, a validation service circuit may be structured to validate a
plurality of loan
information components with respect to a financial entity configured to
determine a loan
condition for an asset. Certain components may not be considered a validating
system
individually, but may be considered validating in an aggregated system - for
example, an
Internet of Things component may not be considered a validating component on
its own,
however an Internet of Things component utilized for asset data collection and
monitoring
may be considered a validating component when applied to validating a
reliability parameter
of a personal guarantee for a load when the Internet of Things component is
associated with a
collateralized asset. In certain embodiments, otherwise similar looking
systems may be
differentiated in determining whether such systems are for validation. For
example, a
blockchain-based ledger may be used to validate identities in one instance and
to maintain
confidential information in another instance. Accordingly, the benefits of the
present
disclosure may be applied in a wide variety of systems, and any such systems
may be
considered a system for validation herein, while in certain embodiments a
given system may
not be considered a validating system herein. One of skill in the art, having
the benefit of the
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disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system, and/or how to combine processes and systems from the present
disclosure to enhance
operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is a validating system
and/or whether
aspects of the present disclosure can benefit or enhance the contemplated
system include,
without limitation: a lending platform having a social network monitoring
system for
validating the reliability of a guarantee for a loan; a lending platform
having an Internet of
Things data collection and monitoring system for validating reliability of a
guarantee for a
loan; a lending platform having a crowdsourcing and automated classification
system for
validating conditions of an issuer for a bond; a crowdsourcing system for
validating quality,
title, or other conditions of collateral for a loan; a biometric identify
validation application
such as utilizing DNA or fingerprints; IoT devices utilized to collectively
validate location
and identity of a fixed asset that is tagged by a virtual asset tag;
validation systems utilizing
voting or consensus protocols; artificial intelligence systems trained to
recognize and validate
events; validating information such as title records, video footage,
photographs, or witnessed
statements; validation representations related to behavior, such as to
validate occurrence of
conditions of compliance, to validate occurrence of conditions of default, to
deter improper
behavior or misrepresentations, to reduce uncertainty, or to reduce
asymmetries of
information; and the like.
[00301] The term underwriting (and similar terms) as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure,
underwriting includes any underwriting, including, without limitation,
relating to
underwriters, providing underwriting information for a loan, underwriting a
debt transaction,
underwriting a bond transaction, underwriting a subsidized loan transaction,
underwriting a
securities transaction, and the like. Underwriting services may be provided by
financial
entities, such as banks, insurance or investment houses, and the like, whereby
the financial
entity guarantees payment in case of a determination of a loss condition
(e.g., damage or
financial loss) and accept the financial risk for liability arising from the
guarantee. For
instance, a bank may underwrite a loan through a mechanism to perform a credit
analysis that
may lead to a determination of a loan to be granted, such as through analysis
of personal
information components related to an individual borrower requesting a consumer
loan (e.g.,
employment history, salary and financial statements publicly available
information such as
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the borrower's credit history), analysis of business financial information
components from a
company requesting a commercial load (e.g., tangible net worth, ratio of debt
to worth
(leverage), and available liquidity (current ratio)), and the like. In a non-
limiting example, an
underwriting services circuit may be structured to underwrite a financial
transaction including
a plurality of financial information components with respect to a financial
entity configured
to determine a financial condition for an asset. In certain embodiments,
underwriting
components may be considered underwriting for some purposes but not for other
purposes -
for example, an artificial intelligence system to collect and analyze
transaction data may be
utilized in conjunction with a smart contract platform to monitor loan
transactions, but
alternately used to collect and analyze underwriting data, such as utilizing a
model trained by
human expert underwriters. Accordingly, the benefits of the present disclosure
may be
applied in a wide variety of systems, and any such systems may be considered
underwriting
herein, while in certain embodiments a given system may not be considered
underwriting
herein. One of skill in the art, having the benefit of the disclosure herein
and knowledge
about a contemplated system ordinarily available to that person, can readily
determine which
aspects of the present disclosure will benefit a particular system, and/or how
to combine
processes and systems from the present disclosure to enhance operations of the
contemplated
system. Certain considerations for the person of skill in the art, in
determining whether a
contemplated system is underwriting and/or whether aspects of the present
disclosure can
benefit or enhance the contemplated system include, without limitation: a
lending platform
having an underwriting system for a loan with a set of data-integrated
microservices such as
including data collection and monitoring services, blockchain services,
artificial intelligence
services, and smart contract services for underwriting lending entities and
transactions;
underwriting processes, operations, and services; underwriting data, such as
data relating to
identities of prospective and actual parties involved insurance and other
transactions,
actuarial data, data relating to probability of occurrence and/or extent of
risk associated with
activities, data relating to observed activities and other data used to
underwrite or estimate
risk; an underwriting application, such as, without limitation, for
underwriting any insurance
offering, any loan, or any other transaction, including any application for
detecting,
characterizing or predicting the likelihood and/or scope of a risk, an
underwriting or
inspection flow about an entity serving a lending solution, an analytics
solution, or an asset
management solution; underwriting of insurance policies, loans, warranties, or
guarantees; a
blockchain and smart contract platform for aggregating identity and behavior
information for
insurance underwriting, such as with an optional distributed ledger to record
a set of events,
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transactions, activities, identities, facts, and other information associated
with an
underwriting process; a crowdsourcing platform such as for underwriting of
various types of
loans, and guarantees; an underwriting system for a loan with a set of data-
integrated
microservices including data collection and monitoring services, blockchain
services,
artificial intelligence services, and smart contract services for underwriting
lending entities
and transactions; an underwriting solution to create, configure, modify, set
or otherwise
handle various rules, thresholds, conditional procedures, workflows, or model
parameters; an
underwriting action or plan for management a set of loans of a given type or
types based on
one or more events, conditions, states, actions, secondary loans or
transactions to back loans,
for collection, consolidation, foreclosure, situations of bankruptcy of
insolvency,
modifications of existing loans, situations involving market changes,
foreclosure activities;
adaptive intelligent systems including artificial intelligent models trained
on a training set of
underwriting activities by experts and/or on outcomes of underwriting actions
to generate a
set of predictions, classifications, control instructions, plans, models;
underwriting system for
a loan with a set of data-integrated microservices including data collection
and monitoring
services, blockchain services, artificial intelligence services, and smart
contract services for
underwriting lending entities and transactions; and the like.
[00302] The term insuring (and similar terms) as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure,
insuring includes any insuring, including, without limitation, providing
insurance for a loan,
providing evidence of insurance for an asset related to a loan, a first entity
accepting a risk or
liability for another entity, and the like. Insuring, or insurance, may be a
mechanism through
which a holder of the insurance is provided protection from a financial loss,
such as in a form
of risk management against the risk of a contingent or uncertain loss. The
insuring
mechanism may provide for an insurance, determine the need for an insurance,
determine
evidence of insurance, and the like, such as related to an asset, transaction
for an asset, loan
for an asset, security, and the like. An entity which provides insurance may
be known as an
insurer, insurance company, insurance carrier, underwriter, and the like. For
instance, a
mechanism for insuring may provide a financial entity with a mechanism to
determine
evidence of insurance for an asset related to a loan. In a non-limiting
example, an insurance
service circuit may be structured to determine an evidence condition of
insurance for an asset
based on a plurality of insurance information components with respect to a
financial entity
configured to determine a loan condition for an asset. In certain embodiments,
components
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may be considered insuring for some purposes but not for other purposes - for
example a
blockchain and smart contract platform may be utilized to manage aspects of a
loan
transaction such as for identity and confidentiality, but may alternately be
utilized to
aggregate identity and behavior information for insurance underwriting.
Accordingly, the
benefits of the present disclosure may be applied in a wide variety of
systems, and any such
systems may be considered insuring herein, while in certain embodiments a
given system
may not be considered insuring herein. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system, and/or how to combine processes and systems from the present
disclosure to enhance
operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is insuring and/or whether
aspects of the
present disclosure can benefit or enhance the contemplated system include,
without
limitation: insurance facilities such as branches, offices, storage
facilities, data centers,
underwriting operations and others; insurance claims, such as for business
interruption
insurance, product liability insurance, insurance on goods, facilities, or
equipment, flood
insurance, insurance for contract-related risks, and many others, as well as
claims data
relating to product liability, general liability, workers compensation, injury
and other liability
claims and claims data relating to contracts, such as supply contract
performance claims,
product delivery requirements, contract claims, claims for damages, claims to
redeem points
or rewards, claims of access rights, warranty claims, indemnification claims,
energy
production requirements, delivery requirements, timing requirements,
milestones, key
performance indicators and others; insurance-related lending; an insurance
service, an
insurance brokerage service, a life insurance service, a health insurance
service, a retirement
insurance service, a property insurance service, a casualty insurance service,
a finance and
insurance service, a reinsurance service; a blockchain and smart contract
platform for
aggregating identity and behavior information for insurance underwriting;
identities of
applicants for insurance, identities of parties that may be willing to offer
insurance,
information regarding risks that may be insured (of any type, without
limitation, such as
property, life, travel, infringement, health, home, commercial liability,
product liability, auto,
fire, flood, casualty, retirement, unemployment; distributed ledger may be
utilized to
facilitate offering and underwriting of microinsurance, such as for defined
risks related to
defined activities for defined time periods that are narrower than for typical
insurance
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policies; providing insurance for a loan, providing evidence of insurance for
property related
to a loan; and the like.
[00303] The term aggregation (and similar terms) as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure, an
aggregation or to aggregate includes any aggregation including, without
limitation,
aggregating items together, such as aggregating or linking similar items
together (e.g.,
collateral to provide collateral for a set of loans, collateral items for a
set of loans is
aggregated in real time based on a similarity in status of the set of items,
and the like),
collecting data together (e.g., for storage, for communication, for analysis,
as training data for
a model, and the like), summarizing aggregated items or data into a simpler
description, or
any other method for creating a whole formed by combining several (e.g.,
disparate)
elements. Further, an aggregator may be any system or platform for
aggregating, such as
described. Certain components may not be considered aggregation individually
but may be
considered aggregation in an aggregated system - for example a collection of
loans may not
be considered an aggregation of loans of itself but may be an aggregation if
collected as such.
In a non-limiting example, an aggregation circuit may be structured to provide
lenders a
mechanism to aggregate loans together from a plurality of loans, such as based
on a loan
attribute, parameter, term or condition, financial entity, and the like, to
become an
aggregation of loans. In certain embodiments, an aggregation may be considered
an
aggregation for some purposes but not for other purposes - for example for
example, an
aggregation of asset collateral conditions may be collected for the purpose of
aggregating
loans together in one instance and for the purpose of determining a default
action in another
instance. Additionally, in certain embodiments, otherwise similar looking
systems may be
differentiated in determining whether such systems are aggregators, and/or
which type of
aggregating systems. For example, a first and second aggregator may both
aggregate financial
entity data, where the first aggregator aggregates for the sake of building a
training set for an
analysis model circuit and where the second aggregator aggregates financial
entity data for
storage in a blockchain-based distributed ledger. Accordingly, the benefits of
the present
disclosure may be applied in a wide variety of systems, and any such systems
may be
considered as aggregation herein, while in certain embodiments a given system
may not be
considered aggregation herein. One of skill in the art, having the benefit of
the disclosure
herein and knowledge about a contemplated system ordinarily available to that
person, can
readily determine which aspects of the present disclosure will benefit a
particular system,
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and/or how to combine processes and systems from the present disclosure to
enhance
operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is aggregation and/or
whether aspects of
the present disclosure can benefit or enhance the contemplated system include,
without
limitation forward market demand aggregation (e.g., blockchain and smart
contract platform
for forward market demand aggregation, interest expressed or committed in a
demand
aggregation interface, blockchain used to aggregate future demand in a forward
market with
respect to a variety of products and services, process a set of potential
configurations having
different parameters for a subset of configurations that are consistent with
each other and the
subset of configurations used to aggregate committed future demand for the
offering that
satisfies a sufficiently large subset at a profitable price, and the like);
correlated aggregated
data (including trend information) on worker ages, credentials, experience
(including by
process type) with data on the processes in which those workers are involved;
demand for
accommodations aggregated in advance and conveniently fulfilled by automatic
recognition
of conditions that satisfy pre-configured commitments represented on a
blockchain (e.g.,
distributed ledger); transportation offerings aggregated and fulfilled (e.g.,
with a wide range
of pre-defined contingencies); aggregation of goods and services on the
blockchain (e.g., a
distributed ledger used for demand planning); with respect to a demand
aggregation interface
(e.g., presented to one or more consumers); aggregation of multiple
submissions; aggregating
identity and behavior information (e.g., insurance underwriting); accumulation
and
aggregation of multiple parties; aggregation of data for a set of collateral;
aggregated value of
collateral or assets (e.g., based on real time condition monitoring, rea-time
market data
collection and integration, and the like); aggregated tranches of loans;
collateral for smart
contract aggregated with other similar collateral; and the like.
[00304] The term linking (and similar terms) as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure,
linking includes any linking, including, without limitation, linking as a
relationship between
two things or situations (e.g., where one thing affects the other). For
instance, linking a subset
of similar items such as collateral to provide collateral for a set of loans.
Certain components
may not be considered linked individually, but may be considered in a process
of linking in
an aggregated system - for example, a smart contracts circuit may be
structured to operate in
conjunction with a blockchain circuit as part of a loan processing platform
but where the
smart contracts circuit processes contracts without storing information
through the blockchain
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circuit, however the two circuits could be linked through the smart contracts
circuit linking
financial entity information through a distributed ledger on the blockchain
circuit. In certain
embodiments, linking may be considered linking for some purposes but not for
other
purposes - for example, linking goods and services for users and radio
frequency linking
between access points are different forms of linking, where the linking of
goods and services
for users links thinks together while an RF link is a communications link
between
transceivers. Additionally, in certain embodiments, otherwise similar looking
systems may be
differentiated in determining whether such system are linking, and/or which
type of linking.
For example, linking similar data together for analysis is different from
linking similar data
together for graphing. Accordingly, the benefits of the present disclosure may
be applied in a
wide variety of systems, and any such systems may be considered linking
herein, while in
certain embodiments a given system may not be considered a linking herein. One
of skill in
the art, having the benefit of the disclosure herein and knowledge about a
contemplated
system ordinarily available to that person, can readily determine which
aspects of the present
disclosure will benefit a particular system, and/or how to combine processes
and systems
from the present disclosure to enhance operations of the contemplated system.
Certain
considerations for the person of skill in the art, in determining whether a
contemplated
system is linking and/or whether aspects of the present disclosure can benefit
or enhance the
contemplated system include, without limitation linking marketplaces or
external
marketplaces with a system or platform; linking data (e.g., data cluster
including links and
nodes); storage and retrieval of data linked to local processes; links (e.g.
with respect to
nodes) in a common knowledge graph; data linked to proximity or location
(e.g., of the
asset); linking to an environment (e.g., goods, services, assets, and the
like); linking events
(e.g., for storage such as in a blockchain, for communication or analysis);
linking ownership
or access rights; linking to access tokens (e.g., travel offerings linked to
access tokens); links
to one or more resources (e.g., secured by cryptographic or other techniques);
linking a
message to a smart contract; and the like.
[00305] The term indicator of interest (and similar terms) as utilized herein
should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, an indicator of interest includes any indicator of interest
including, without
limitation, an indicator of interest from a user or plurality of users or
parties related to a
transaction and the like (e.g., parties interested in participating in a loan
transaction), the
recording or storing of such an interest (e.g., a circuit for recording an
interest input from a
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user, entity, circuit, system, and the like), a circuit analyzing interest
related data and setting
an indicator of interest (e.g., a circuit setting or communicating an
indicator based on inputs
to the circuit, such as from users, parties, entities, systems, circuits, and
the like), a model
trained to determine an indicator of interest from input data related to an
interest by one of a
plurality of inputs from users, parties, or financial entities, and the like.
Certain components
may not be considered indicators of interest individually, but may be
considered an indicator
of interest in an aggregated system - for example, a party may seek
information relating to a
transaction such as though a translation marketplace where the party is
interested in seeking
information, but that may not be considered an indicator of interest in a
transaction. However,
when the party asserts a specific interest (e.g., through a user interface
with control inputs for
indicating interest) the party's interest may be recorded (e.g., in a storage
circuit, in a
blockchain circuit), analyzed (e.g., through an analysis circuit, a data
collection circuit),
monitored (e.g., through a monitoring circuit), and the like. In a non-
limiting example,
indicators of interest may be recorded (e.g., in a blockchain through a
distributed ledger)
from a set of parties with respect to the product, service, or the like, such
as ones that define
parameters under which a party is willing to commit to purchase a product or
service. In
certain embodiments, an indicator of interest may be considered an indicator
of interest for
some purposes but not for other purposes - for example, a user may indicate an
interest for a
loan transaction but that does not necessarily mean the user is indicating an
interest in
providing a type of collateral related to the loan transaction. For instance,
a data collection
circuit may record an indicator of interest for the transaction but may have a
separate circuit
structure for determining an indication of interest for collateral.
Additionally, in certain
embodiments, otherwise similar looking systems may be differentiated in
determining
whether such system are determining an indication of interest, and/or which
type of indicator
of interest exists. For example, one circuit or system may collect data from a
plurality of
parties to determine an indicator of interest in securing a loan and a second
circuit or system
may collect data from a plurality of parties to determine an indicator of
interest in
determining ownership rights related to a loan. Accordingly, the benefits of
the present
disclosure may be applied in a wide variety of systems, and any such systems
may be
considered an indicator of interest herein, while in certain embodiments a
given system may
not be considered an indicator of interest herein. One of skill in the art,
having the benefit of
the disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system, and/or how to combine processes and systems from the present
disclosure to enhance
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operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is an indicator of interest
and/or whether
aspects of the present disclosure can benefit or enhance the contemplated
system include,
without limitation parties indicating an interest in participating in a
transaction (e.g., a loan
transaction), parties indicating an interest in securing in a product or
service, recording or
storing an indication of interest (e.g., through a storage circuit or
blockchain circuit),
analyzing an indication of interest (e.g., through a data collection and/or
monitoring circuit),
and the like.
[00306] The term accommodations (and similar terms) as utilized herein should
be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, an accommodation includes any service, activity, event, and the
like such as
including, without limitation, a room, group of rooms, table, seating,
building, event, shared
spaces offered by individuals (e.g., Airbnb spaces), bed-and-breakfasts,
workspaces,
conference rooms, convention spaces, fitness accommodations, health and
wellness
accommodations, dining accommodations, and the like, in which someone may
live, stay, sit,
reside, participate, and the like. As such, an accommodation may be purchased
(e.g., a ticket
through a sports ticketing application), reserved or booked (e.g., a
reservation through a hotel
reservation application), provided as a reward or gift, traded or exchanged
(e.g., through a
marketplace), provided as an access right (e.g., offering by way of an
aggregation demand),
provided based on a contingency (e.g., a reservation for a room being
contingent on the
availability of a nearby event), and the like. Certain components may not be
considered an
accommodation individually but may be considered an accommodation in an
aggregated
system - for example, a resource such as a room in a hotel may not in itself
be considered an
accommodation but a reservation for the room may be. For instance, a
blockchain and smart
contract platform for forward market rights for accommodations may provide a
mechanism to
provide access rights with respect to accommodations. In a non-limiting
example, a
blockchain circuit may be structured to store access rights in a forward
demand market,
where the access rights may be stored in a distributed ledger with related
shared access to a
plurality of actionable entities. In certain embodiments, an accommodation may
be
considered an accommodation for some purposes but not for other purposes - for
example, a
reservation for a room may be an accommodation on its own, but may not be
accommodation
that is satisfied if a related contingency is not met as agreed upon at the
time of the e.g.
reservation. Additionally, in certain embodiments, otherwise similar looking
systems may be
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differentiated in determining whether such systems are related to an
accommodation, and/or
which type of accommodation. For example, an accommodation offering may be
made based
on different systems, such as one where the accommodation offering is
determined by a
system collecting data related to forward demand and a second one where the
accommodation offering is provided as a reward based on a system processing a
performance
parameter. Accordingly, the benefits of the present disclosure may be applied
in a wide
variety of systems, and any such systems may be considered as related to an
accommodation
herein, while in certain embodiments a given system may not be considered
related to an
accommodation herein. One of skill in the art, having the benefit of the
disclosure herein and
knowledge about a contemplated system ordinarily available to that person, can
readily
determine which aspects of the present disclosure will benefit a particular
system, and/or how
to combine processes and systems from the present disclosure to enhance
operations of the
contemplated system. Certain considerations for the person of skill in the
art, in determining
whether a contemplated system is related to accommodation and/or whether
aspects of the
present disclosure can benefit or enhance the contemplated system include,
without limitation
accommodations provided as determined through a service circuit, trading or
exchanging
services (e.g., through an application and/or user interface), as an
accommodation offering
such as with respect to a combination of products, services, and access
rights, processed (e.g.,
aggregation demand for the offering in a forward market), accommodation
through booking
in advance, accommodation through booking in advance upon meeting a certain
condition
(e.g., relating to a price within a given time window), and the like.
[00307] The term contingencies (and similar terms) as utilized herein should
be understood
broadly. Without limitation to any other aspect or description of the present
disclosure, a
contingency includes any contingency including, without limitation, any action
that is
dependent upon a second action. For instance, a service may be provided as
contingent on a
certain parameter value, such as collecting data as condition upon an asset
tag indication from
an Internet of Things circuit. In another instance, an accommodation such as a
hotel
reservation may be contingent upon a concert (local to the hotel and at the
same time as the
reservation) proceeding as scheduled. Certain components may not be considered
as relating
to a contingency individually, but may be considered related to a contingency
in an
aggregated system - for example, a data input collected from a data collection
service circuit
may be stored, analyzed, processed, and the like, and not be considered with
respect to a
contingency, however a smart contracts service circuit may apply a contract
term as being
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contingent upon the collected data. For instance, the data may indicate a
collateral status with
respect to a loan transaction, and the smart contracts service circuit may
apply that data to a
term of contract that depends upon the collateral. In certain embodiments, a
contingency may
be considered contingency for some purposes but not for other purposes - for
example, a
delivery of contingent access rights for a future event may be contingent upon
a loan
condition being satisfied, but the loan condition on its own may not be
considered a
contingency in the absence of the contingency linkage between the condition
and the access
rights. Additionally, in certain embodiments, otherwise similar looking
systems may be
differentiated in determining whether such systems are related to a
contingency, and/or which
type of contingency. For example, two algorithms may both create a forward
market event
access right token, but where the first algorithm creates the token free of
contingencies and
the second algorithm creates a token with a contingency for delivery of the
token.
Accordingly, the benefits of the present disclosure may be applied in a wide
variety of
systems, and any such systems may be considered a contingency herein, while in
certain
embodiments a given system may not be considered a contingency herein. One of
skill in the
art, having the benefit of the disclosure herein and knowledge about a
contemplated system
ordinarily available to that person, can readily determine which aspects of
the present
disclosure will benefit a particular system, and/or how to combine processes
and systems
from the present disclosure to enhance operations of the contemplated system.
Certain
considerations for the person of skill in the art, in determining whether a
contemplated
system is a contingency and/or whether aspects of the present disclosure can
benefit or
enhance the contemplated system include, without limitation a forward market
operated
within or by the platform may be a contingent forward market, such as one
where a future
right is vested, is triggered, or emerges based on the occurrence of an event,
satisfaction of a
condition, or the like; a blockchain used to make a contingent market in any
form of event or
access token by securely storing access rights on a distributed ledger;
setting and monitoring
pricing for contingent access rights, underlying access rights, tokens, fees
and the like;
optimizing offerings, timing, pricing, or the like, to recognize and predict
patterns, to
establish rules and contingencies; exchanging contingent access rights or
underlying access
rights or tokens access tokens and/or contingent access tokens; creating a
contingent forward
market event access right token where a token may be created and stored on a
blockchain for
contingent access right that could result in the ownership of a ticket;
discovery and delivery
of contingent access rights to future events; contingencies that influence or
represent future
demand for an offering, such as including a set of products, services, or the
like; pre-defined
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contingencies; optimized offerings, timing, pricing, or the like, to recognize
and predict
patterns, to establish rules and contingencies; creation of a contingent
future offering within
the dashboard; contingent access rights that may result in the ownership of
the virtual good or
each smart contract to purchase the virtual good if and when it becomes
available under
defined conditions; and the like.
[00308] The term level of service (and similar terms) as utilized herein
should be understood
broadly. Without limitation to any other aspect or description of the present
disclosure, a
level of service includes any level of service including, without limitation,
any qualitative or
quantitative measure of the extent to which a service is provided, such as,
and without
limitation, a first class vs. business class service (e.g., travel reservation
or postal delivery),
the degree to which a resource is available (e.g., service level A indicating
that the resource is
highly available vs. service level C indicating that the resource is
constrained, such as in
terms of traffic flow restrictions on a roadway), the degree to which an
operational parameter
is performing (e.g., a system is operating at a high state of service vs a low
state of service,
and the like. In embodiments, level of service may be multi-modal such that
the level of
service is variable where a system or circuit provides a service rating (e.g.,
where the service
rating is used as an input to an analytical circuit for determining an outcome
based on the
service rating). Certain components may not be considered relative to a level
of service
individually, but may be considered relative to a level of service in an
aggregated system - for
example a system for monitoring a traffic flow rate may provide data on a
current rate but not
indicate a level of service, but when the determined traffic flow rate is
provided to a
monitoring circuit the monitoring circuit may compare the determined traffic
flow rate to past
traffic flow rates and determine a level of service based on the comparison.
In certain
embodiments, a level of service may be considered a level of service for some
purposes but
not for other purposes - for example, the availability of first class travel
accommodation may
be considered a level of service for determining whether a ticket will be
purchased but not to
project a future demand for the flight. Additionally, in certain embodiments,
otherwise
similar looking systems may be differentiated in determining whether such
system utilizes a
level of service, and/or which type of level of service. For example, an
artificial intelligence
circuit may be trained on past level of service with respect to traffic flow
patterns on a certain
freeway and used to predict future traffic flow patterns based on current flow
rates, but a
similar artificial intelligence circuit may predict future traffic flow
patterns based on the time
of day. Accordingly, the benefits of the present disclosure may be applied in
a wide variety of
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systems, and any such systems may be considered with respect to levels of
service herein,
while in certain embodiments a given system may not be considered with respect
to levels of
service herein. One of skill in the art, having the benefit of the disclosure
herein and
knowledge about a contemplated system ordinarily available to that person, can
readily
determine which aspects of the present disclosure will benefit a particular
system, and/or how
to combine processes and systems from the present disclosure to enhance
operations of the
contemplated system. Certain considerations for the person of skill in the
art, in determining
whether a contemplated system is a level of service and/or whether aspects of
the present
disclosure can benefit or enhance the contemplated system include, without
limitation
transportation or accommodation offerings with predefined contingencies and
parameters
such as with respect to price, mode of service, and level of service; warranty
or guarantee
application, transportation marketplace, and the like.
[00309] The term payment (and similar terms) as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure, a
payment includes any payment including, without limitation, an action or
process of paying
(e.g., a payment to a loan) or of being paid (e.g., a payment from insurance),
an amount paid
or payable (e.g., a payment of S1000 being made), a repayment (e.g., to pay
back a loan), a
mode of payment (e.g., use of loyalty programs, rewards points, or particular
currencies,
including cryptocurrencies) and the like. Certain components may not be
considered
payments individually, but may be considered payments in an aggregated system -
for
example, submitting an amount of money may not be considered a payment as
such, but
when applied to a payment to satisfy the requirement of a loan may be
considered a payment
(or repayment). For instance, a data collection circuit may provide lenders a
mechanism to
monitor repayments of a loan. In a non-limiting example, the data collection
circuit may be
structured to monitor the payments of a plurality of loan components with
respect to a
financial loan contract configured to determine a loan condition for an asset.
In certain
embodiments, a payment may be considered a payment for some purposes but not
for other
purposes - for example a payment to a financial entity may be for a repayment
amount to pay
back the loan, or it may be to satisfy a collateral obligation in a loan
default condition.
Additionally, in certain embodiments, otherwise similar looking systems may be
differentiated in determining whether such system are related to a payment,
and/or which
type of payment. For example, funds may be applied to reserve an accommodation
or to
satisfy the delivery of services after the accommodation has been satisfied.
Accordingly, the
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benefits of the present disclosure may be applied in a wide variety of
systems, and any such
systems may be considered a payment herein, while in certain embodiments a
given system
may not be considered a payment herein. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system, and/or how to combine processes and systems from the present
disclosure to enhance
operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is a payment and/or whether
aspects of the
present disclosure can benefit or enhance the contemplated system include,
without
limitation, deferring a required payment; deferring a payment requirement;
payment of a
loan; a payment amount; a payment schedule; a balloon payment schedule;
payment
performance and satisfaction; modes of payment; and the like.
[00310] The term location (and similar terms) as utilized herein should be
understood
broadly. Without limitation to any other aspect or description of the present
disclosure, a
location includes any location including, without limitation, a particular
place or position of a
person, place, or item, or location information regarding the position of a
person, place, or
item, such as a geolocation (e.g., geolocation of a collateral), a storage
location (e.g., the
storage location of an asset), a location of a person (e.g., lender, borrower,
worker), location
information with respect to the same, and the like. Certain components may not
be considered
with respect to location individually, but may be considered with respect to
location in an
aggregated system - for example, a smart contract circuit may be structured to
specify a
requirement for a collateral to be stored at a fixed location but not specify
the specific
location for a specific collateral. In certain embodiments, a location may be
considered a
location for some purposes but not for other purposes - for example, the
address location of a
borrower may be required for processing a loan in one instance, and a specific
location for
processing a default condition in another instance. Additionally, in certain
embodiments,
otherwise similar looking systems may be differentiated in determining whether
such system
are a location, and/or which type of location. For example, the location of a
music concert
may be required to be in a concert hall seating 10,000 people in one instance
but specify the
location of an actual concert hall in another. Accordingly, the benefits of
the present
disclosure may be applied in a wide variety of systems, and any such systems
may be
considered with respect to a location herein, while in certain embodiments a
given system
may not be considered with respect to a location herein. One of skill in the
art, having the
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benefit of the disclosure herein and knowledge about a contemplated system
ordinarily
available to that person, can readily determine which aspects of the present
disclosure will
benefit a particular system, and/or how to combine processes and systems from
the present
disclosure to enhance operations of the contemplated system. Certain
considerations for the
person of skill in the art, in determining whether a contemplated system is
considered with
respect to a location and/or whether aspects of the present disclosure can
benefit or enhance
the contemplated system include, without limitation a geolocation of an item
or collateral; a
storage location of item or asset; location information; location of a lender
or a borrower;
location-based product or service targeting application; a location-based
fraud detection
application; indoor location monitoring systems (e.g., cameras, IR systems,
motion-detection
systems); locations of workers (including routes taken through a location);
location
parameters; event location; specific location of an event; and the like.
[00311] The term route (and similar terms) as utilized herein should be
understood broadly.
Without limitation to any other aspect or description of the present
disclosure, a route
includes any route including, without limitation, a way or course taken in
getting from a
starting point to a destination, to send or direct along a specified course,
and the like. Certain
components may not be considered with respect to a route individually, but may
be
considered a route in an aggregated system - for example a mobile data
collector may specify
a requirement for a route for collecting data based on an input from a
monitoring circuit, but
only in receiving that input does the mobile data collector determine what
route to take and
begin traveling along the route. In certain embodiments, a route may be
considered a route
for some purposes but not for other purposes - for example possible routes
through a road
system may be considered differently than specific routes taken through from
one location to
another location. Additionally, in certain embodiments, otherwise similar
looking systems
may be differentiated in determining whether such systems are specified with
respect to a
location, and/or which types of locations. For example, routes depicted on a
map may
indicate possible routes or actual routes taken by individuals. Accordingly,
the benefits of the
present disclosure may be applied in a wide variety of systems, and any such
systems may be
considered with respect to a route herein, while in certain embodiments a
given system may
not be considered with respect to a route herein. One of skill in the art,
having the benefit of
the disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system, and/or how to combine processes and systems from the present
disclosure to enhance
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operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is utilizing a route and/or
whether aspects
of the present disclosure can benefit or enhance the contemplated system
include, without
limitation delivery routes; routes taken through a location; heat map showing
routes traveled
by customers or workers within an environment; determining what resources are
deployed to
what routes or types of travel; direct route or multi-stop route, such as from
the destination of
the consumer to a specific location or to wherever an event takes place; a
route for a mobile
data collector; and the like.
[00312] The term future offering (and similar terms) as utilized herein should
be understood
broadly. Without limitation to any other aspect or description of the present
disclosure, a
future offing includes any offer of an item or service in the future
including, without
limitation, a future offer to provide an item or service, a future offer with
respect to a
proposed purchase, a future offering made through a forward market platform, a
future
offering determined by a smart contract circuit, and the like. Further, a
future offering may be
a contingent future offer or an offer based on conditions resulting on the
offer being a future
offering, such as where the future offer is contingent upon or with the
conditions imposed by
a predetermined condition (e.g., a security may be purchased for S1000 at a
set future date
contingent upon a predetermined state of a market indicator). Certain
components may not be
considered a future offering individually, but may be considered a future
offering in an
aggregated system - for example, an offer for a loan may not be considered a
future offering
if the offer is not authorized through a collective agreement amongst a
plurality of parties
related to the offer, but may be considered a future offer once a vote has
been collected and
stored through a distributed ledger, such as through a blockchain circuit. In
certain
embodiments, a future offering may be considered a future offering for some
purposes but
not for other purposes - for example, a future offering may be contingent upon
a condition
being met in the future, and so the future offering may not be considered a
future offer until
the condition is met. Additionally, in certain embodiments, otherwise similar
looking systems
may be differentiated in determining whether such systems are future
offerings, and/or which
type of future offerings. For example, two security offerings may be
determined to be
offerings to be made at a future time, however, one may have immediate
contingences to be
met and thus may not be considered to be a future offering but rather an
immediate offering
with future declarations. Accordingly, the benefits of the present disclosure
may be applied in
a wide variety of systems, and any such systems may be considered in
association with a
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future offering herein, while in certain embodiments a given system may not be
considered in
association with a future offering herein. One of skill in the art, having the
benefit of the
disclosure herein and knowledge about a contemplated system ordinarily
available to that
person, can readily determine which aspects of the present disclosure will
benefit a particular
system, and/or how to combine processes and systems from the present
disclosure to enhance
operations of the contemplated system. Certain considerations for the person
of skill in the
art, in determining whether a contemplated system is in association with a
future offering
and/or whether aspects of the present disclosure can benefit or enhance the
contemplated
system include, without limitation a forward offering, a contingent forward
offering, a
forward offing in a forward market platform (e.g., for creating a future
offering or contingent
future offering associated with identifying offering data from a platform-
operated
marketplace or external marketplace); a future offering with respect to
entering into a smart
contract (e.g., by executing an indication of a commitment to purchase,
attend, or otherwise
consume a future offering), and the like.
[00313] The term access right (and derivatives or variations) as utilized
herein may be
understood broadly to describe an entitlement to acquire or possess a
property, article, or
other thing of value. A contingent access right may be conditioned upon a
trigger or condition
being met before such an access right becomes entitled, vested or otherwise
defensible. An
access right or contingent access right may also serve specific purposes or be
configured for
different applications or contexts, such as, without limitation, loan-related
actions or any
service or offering. Without limitation, notices may be required to be
provided to the owner
of a property, article or item of value before such access rights or
contingent access rights are
exercised. Access rights and contingent access rights in various forms may be
included where
discussing a legal action, a delinquent or defaulted loan or agreement, or
other circumstances
where a lender may be seeking remedy, without limitation. One of skill in the
art, having the
benefit of the disclosure herein and knowledge ordinarily available about a
contemplated
system, can readily determine the value of such rights implemented in an
embodiment. While
specific examples of access rights and contingent access rights are described
herein for
purposes of illustration, any embodiment benefitting from the disclosures
herein, and any
considerations understood to one of skill in the art having the benefit of the
disclosures
herein, are specifically contemplated within the scope of the present
disclosure.
[00314] The term smart contract (and other forms or variations) as utilized
herein may be
understood broadly to describe a method, system, connected resource or wide
area network
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offering one or more resources useful to assist or perform actions, tasks or
things by
embodiments disclosed herein. A smart contract may be a set of steps or a
process to
negotiate, administrate, restructure or implement an agreement or loan between
parties. A
smart contract may also be implemented as an application, website, FTP site,
server,
appliance or other connected component or Internet related system that renders
resources to
negotiate, administrate, restructure or implement an agreement or loan between
parties. A
smart contract may be a self-contained system, or may be part of a larger
system or
component that may also be a smart contract. For example, a smart contract may
refer to a
loan or an agreement itself, conditions or terms, or may refer to a system to
implement such a
loan or agreement. In certain embodiments, a smart contract circuit or robotic
process
automation system may incorporate or be incorporated into automatic robotic
process
automation system to perform one or more purposes or tasks, whether part of a
loan or
transaction process, or otherwise. One of skill in the art, having the benefit
of the disclosure
herein and knowledge ordinarily available about a contemplated system can
readily
determine the purposes and use of this term as it relates to a smart contract
in various forms,
embodiments and contexts disclosed herein.
[00315] The term allocation of reward (and variations) as utilized herein may
be understood
broadly to describe a thing or consideration allocated or provided as
consideration, or
provided for a purpose. The allocation of rewards can be of a financial type,
or non-financial
type, without limitation. A specific type of allocation of reward may also
serve a number of
different purposes or be configured for different applications or contexts,
such as, without
limitation: a reward event, claims for rewards, monetary rewards, rewards
captured as a data
set, rewards points, and other forms of rewards. Thus an allocation of rewards
may be
provided as a consideration within the context of a loan or agreement. Systems
may be
utilized to allocate rewards. The allocation of rewards in various forms may
be included
where discussing a particular behavior, or encouragement of a particular
behavior, without
limitation. An allocation of a reward may include an actual dispensation of
the award, and/or
a recordation of the reward. The allocation of rewards may be performed by a
smart contract
circuit or a robotic processing automation system. One of skill in the art,
having the benefit of
the disclosure herein and knowledge ordinarily available about a contemplated
system, can
readily determine the value of the allocation of rewards in an embodiment.
While specific
examples of the allocation of rewards are described herein for purposes of
illustration, any
embodiment benefitting from the disclosures herein, and any considerations
understood to
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one of skill in the art having the benefit of the disclosures herein, are
specifically
contemplated within the scope of the present disclosure.
[00316] The term satisfaction of parameters or conditions (and other
derivatives, forms or
variations) as utilized herein may be understood broadly to describe
completion, presence or
proof of parameters or conditions that have been met. The term generally may
relate to a
process of determining such satisfaction of parameters or conditions, or may
relate to the
completion of such a process with a result, without limitation. Satisfaction
may result in a
successful outcome of other triggers or conditions or terms that may come into
execution,
without limitation. Satisfaction of parameters or conditions may occur in many
different
contexts of contracts or loans, such as lending, refinancing, consolidation,
factoring,
brokering, foreclosure, and data processing (e.g. data collection), or
combinations thereof,
without limitation. Satisfaction of parameters or conditions may be used in
the form of a noun
(e.g. the satisfaction of the debt repayment), or may be used in a verb form
to describe the
process of determining a result to parameters or conditions. For example, a
borrower may
have satisfaction of parameters by making a certain number of payments on
time, or may
cause satisfaction of a condition allowing access rights to an owner if a loan
defaults, without
limitation. In certain embodiments, a smart contract or robotic process
automation system
may perform or determine satisfaction of parameters or conditions for one or
more of the
parties and process appropriate tasks for satisfaction of parameters or
conditions. In some
cases satisfaction of parameters or conditions by the smart contract or
robotic process
automation system may not complete or be successful, and depending upon such
outcomes,
this may enable automated action or trigger other conditions or terms. One of
skill in the art,
having the benefit of the disclosure herein and knowledge ordinarily available
about a
contemplated system can readily determine the purposes and use of this term in
various
forms, embodiments and contexts disclosed herein.
[00317] The term information (and other forms such as info or informational,
without
limitation) as utilized herein may be understood broadly in a variety of
contexts with respect
to an agreement or a loan. The term generally may relate to a large context,
such as
information regarding an agreement or loan, or may specifically relate to a
finite piece of
information (e.g. a specific detail of an event that happened on a specific
date). Thus,
information may occur in many different contexts of contracts or loans, and
may be used in
the contexts, without limitation of evidence, transactions, access, and the
like. Or, without
limitation, information may be used in conjunction with stages of an agreement
or
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transaction, such as lending, refinancing, consolidation, factoring,
brokering, foreclosure, and
information processing (e.g. data or information collection), or combinations
thereof. For
example, information as evidence, transaction, access, etc. may be used in the
form of a noun
(e.g. the information was acquired from the borrower), or may refer as a noun
to an
assortment of informational items (e.g. the information about the loan may be
found in the
smart contract), or may be used in the form of characterizing as an adjective
(e.g. the
borrower was providing an information submission). For example, a lender may
collect an
overdue payment from a borrower through an online payment, or may have a
successful
collection of overdue payments acquired through a customer service telephone
call. In certain
embodiments, a smart contract circuit or robotic process automation system may
perform
collection, administration, calculating, providing, or other tasks for one or
more of the parties
and process appropriate tasks relating to information (e.g. providing notice
of an overdue
payment). In some cases information by the smart contract circuit or robotic
process
automation system may be incomplete, and depending upon such outcomes this may
enable
automated action or trigger other conditions or terms. One of skill in the
art, having the
benefit of the disclosure herein and knowledge ordinarily available about a
contemplated
system can readily determine the purposes and use of information as evidence,
transaction,
access, etc. in various forms, embodiments and contexts disclosed herein.
[00318] Information may be linked to external information (e.g. external
sources). The term
more specifically may relate to the acquisition, parsing, receiving, or other
relation to an
external origin or source, without limitation. Thus, information linked to
external information
or sources may be used in conjunction with stages of an agreement or
transaction, such as
lending, refinancing, consolidation, factoring, brokering, foreclosure, and
information
processing (e.g. data or information collection), or combinations thereof. For
example,
information linked to external information may change as the external
information changes,
such as a borrower's credit score, which is based on an external source. In
certain
embodiments, a smart contract circuit or robotic process automation system may
perform
acquisition, administration, calculating, receiving, updating, providing or
other tasks for one
or more of the parties and process appropriate tasks relating to information
that is linked to
external information. In some cases information that is linked to external
information by the
smart contract or robotic process automation system may be incomplete, and
depending upon
such outcomes this may enable automated action or trigger other conditions or
terms. One of
skill in the art, having the benefit of the disclosure herein and knowledge
ordinarily available
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about a contemplated system can readily determine the purposes and use of this
term in
various forms, embodiments and contexts disclosed herein.
[00319] Information that is a part of a loan or agreement may be separated
from information
presented in an access location. The term more specifically may relate to the
characterization
that information can be apportioned, split, restricted, or otherwise separated
from other
information within the context of a loan or agreement. Thus, information
presented or
received on an access location may not necessarily be the whole information
available for a
given context. For example, information provided to a borrower may be
different information
received by a lender from an external source, and may be different than
information received
or presented from an access location. In certain embodiments, a smart contract
circuit or
robotic process automation system may perform separation of information or
other tasks for
one or more of the parties and process appropriate tasks. One of skill in the
art, having the
benefit of the disclosure herein and knowledge ordinarily available about a
contemplated
system, can readily determine the purposes and use of this term in various
forms,
embodiments and contexts disclosed herein.
[00320] The term encryption of information and control of access (and other
related terms)
as utilized herein may be understood broadly to describe generally whether a
party or parties
may observe or possess certain information, actions, events or activities
relating to a
transaction or loan. Encryption of information may be utilized to prevent a
party from
accessing, observing or receiving information, or may alternatively be used to
prevent parties
outside the transaction or loan from being able to access, observe or receive
confidential (or
other) information. Control of access to information relates to the
determination of whether a
party is entitled to such access of information. Encryption of information or
control of access
may occur in many different contexts of loans, such as lending, refinancing,
consolidation,
factoring, brokering, foreclosure, administration, negotiating, collecting,
procuring,
enforcing, and data processing (e.g., data collection), or combinations
thereof, without
limitation. An encryption of information or control of access to information
may refer to a
single instance, or may characterize a larger amount of information, actions,
events or
activities, without limitation. For example, a borrower or lender may have
access to
information about a loan, but other parties outside the loan or agreement may
not be able to
access the loan information due to encryption of the information, or a control
of access to the
loan details. In certain embodiments, a smart contract circuit or robotic
process automation
system may perform encryption of information or control of access to
information for one or
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more of the parties and process appropriate tasks for encryption or control of
access of
information. One of skill in the art, having the benefit of the disclosure
herein and knowledge
ordinarily available about a contemplated system can readily determine the
purposes and use
of this term in various forms, embodiments and contexts disclosed herein.
[00321] The term potential access party list (and other related terms) as
utilized herein may
be understood broadly to describe generally whether a party or parties may
observe or
possess certain information, actions, events or activities relating to a
transaction or loan. A
potential access party list may be utilized to authorize one or more parties
to access, observe
or receive information, or may alternatively be used to prevent parties from
being able to do
so. A potential access party list information relates to the determination of
whether a party
(either on the potential access party list or not on the list) is entitled to
such access of
information. A potential access party list may occur in many different
contexts of loans, such
as lending, refinancing, consolidation, factoring, brokering, foreclosure,
administration,
negotiating, collecting, procuring, enforcing and data processing (e.g. data
collection), or
combinations thereof, without limitation. A potential access party list may
refer to a single
instance, or may characterize a larger amount of parties or information,
actions, events or
activities, without limitation. For example, a potential access party list may
grant (or deny)
access to information about a loan, but other parties outside potential access
party list may
not be able to (or may be granted) access the loan information. In certain
embodiments, a
smart contract circuit or robotic process automation system may perform
administration or
enforcement of a potential access party list for one or more of the parties
and process
appropriate tasks for encryption or control of access of information. One of
skill in the art,
having the benefit of the disclosure herein and knowledge ordinarily available
about a
contemplated system can readily determine the purposes and use of this term in
various
forms, embodiments and contexts disclosed herein.
[00322] The term offering, making an offer, and similar terms as utilized
herein should be
understood broadly. Without limitation to any other aspect or description of
the present
disclosure, an offering includes any offer of an item or service including,
without limitation,
an insurance offering, a security offering, an offer to provide an item or
service, an offer with
respect to a proposed purchase, an offering made through a forward market
platform, a future
offering, a contingent offering, offers related to lending (e.g. lending,
refinancing, collection,
consolidation, factoring, brokering, foreclosure), an offering determined by a
smart contract
circuit, an offer directed to a customer/debtor, an offering directed to a
provider/lender, a 3rd
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party offer (e.g. regulator, auditor, partial owner, tiered provider) and the
like. Offerings may
include physical goods, virtual goods, software, physical services, access
rights,
entertainment content, accommodations, or many other items, services,
solutions, or
considerations. In an example, a third party offer may be to schedule a band
instead of just an
offer of tickets for sale. Further, an offer may be based on pre-determined
conditions or
contingencies. Certain components may not be considered an offering
individually, but may
be considered an offering in an aggregated system - for example, an offer for
insurance may
not be considered an offering if the offer is not approved by one or more
parties related to the
offer, however once approval has been granted, it may be considered an offer.
Accordingly,
the benefits of the present disclosure may be applied in a wide variety of
systems, and any
such systems may be considered in association with an offering herein, while
in certain
embodiments a given system may not be considered in association with an
offering herein.
One of skill in the art, having the benefit of the disclosure herein and
knowledge about a
contemplated system ordinarily available to that person, can readily determine
which aspects
of the present disclosure will benefit a particular system, and/or how to
combine processes
and systems from the present disclosure to enhance operations of the
contemplated system.
Certain considerations for the person of skill in the art, in determining
whether a
contemplated system is in association with an offering and/or whether aspects
of the present
disclosure can benefit or enhance the contemplated system include, without
limitation the
item or service being offered, a contingency related to the offer, a way of
tracking if a
contingency or condition has been met, an approval of the offering, an
execution of an
exchange of consideration for the offering, and the like.
[00323] The term artificial intelligence (Al) solution should be understood
broadly. Without
limitation to any other aspect of the present disclosure, an Al solution
includes a coordinated
group of Al related aspects to perform one or more tasks or operations as set
forth throughout
the present disclosure. An example Al solution includes one or more Al
components,
including any Al components set forth herein, including at least a neural
network, an expert
system, and/or a machine learning component. The example Al solution may
include as an
aspect the types of components of the solution, such as a heuristic Al
component, a model
based Al component, a neural network of a selected type (e.g., recursive,
convolutional,
perceptron, etc.), and/or an Al component of any type having a selected
processing capability
(e.g., signal processing, frequency component analysis, auditory processing,
visual
processing, speech processing, text recognition, etc.). Without limitation to
any other aspect
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of the present disclosure, a given Al solution may be formed from the number
and type of Al
components of the Al solution, the connectivity of the Al components (e.g., to
each other, to
inputs from a system including or interacting with the Al solution, and/or to
outputs to the
system including or interacting with the Al solution). The given Al solution
may additionally
be formed from the connection of the Al components to each other within the Al
solution,
and to boundary elements (e.g., inputs, outputs, stored intermediary data,
etc.) in
communication with the Al solution. The given Al solution may additionally be
formed from
a configuration of each of the Al components of the Al solution, where the
configuration may
include aspects such as: model calibrations for an Al component; connectivity
and/or flow
between Al components (e.g., serial and/or parallel coupling, feedback loops,
logic junctions,
etc.); the number, selected input data, and/or input data processing of inputs
to an Al
component; a depth and/or complexity of a neural network or other components;
a training
data description of an Al component (e.g., training data parameters such as
content, amount
of training data, statistical description of valid training data, etc.);
and/or a selection and/or
hybrid description of a type for an Al component. An Al solution includes a
selection of Al
elements, flow connectivity of those Al elements, and/or configuration of
those Al elements.
[00324] One of skill in the art, having the benefit of the present disclosure,
can readily
determine an Al solution for a given system, and/or configure operations to
perform a
selection and/or configuration operation for an Al solution for a given
system. Certain
considerations to determining an Al solution, and/or configuring operations to
perform a
selection and/or configuration operation for an Al solution include, without
limitation: an
availability of Al components and/or component types for a given
implementation; an
availability of supporting infrastructure to implement given Al components
(e.g., data input
values available, including data quality, level of service, resolution,
sampling rate, etc.;
availability of suitable training data for a given Al solution; availability
of expert inputs, such
as for an expert system and/or to develop a model training data set;
regulatory and/or policy
based considerations including permitted action by the Al solution,
requirements for
acquisition and/or retention of sensitive data, difficult to obtain data,
and/or expensive data);
operational considerations for a system including or interacting with the Al
solution,
including response time specifications, safety considerations, liability
considerations, etc.;
available computing resources such as processing capability, network
communication
capability, and/or memory storage capability (e.g., to support initial data,
training data, input
data such as cached, buffered, or stored input data, iterative improvement
state data, output
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data such as cached, buffered, or stored output data, and/or intermediate data
storage, such as
data to support ongoing calculations, historical data, and/or accumulation
data); the types of
tasks to be performed by the Al solution, and the suitability of Al components
for those tasks,
sensitivity of Al components performing the tasks (e.g., variability of the
output space
relative to a disturbance size of the input space); the interactions of Al
components within the
entire Al solution (e.g., a low capability rationality Al component may be
coupled with a
higher capability Al component that may provide high sensitivity and/or
unbounded response
to inputs); and/or model implementation considerations (e.g., requirements to
re-calibrate,
aging constraints of a model, etc.).
[00325] A selected and/or configured Al solution may be utilized with any of
the systems,
procedures, and/or aspects of embodiments as set forth throughout the present
disclosure. For
example, a system utilizing an expert system may include the expert system as
all or a part of
a selected, configured Al solution. In another example, a system utilizing a
neural network,
and/or a combination of neural networks, may include the neural network(s) as
all or a part of
a selected, configured Al solution. The described aspects of an Al solution,
including the
selection and configuration of the Al solution, are non-limiting
illustrations.
[00326] Referring to Figure 1, a set of systems, methods, components, modules,
machines,
articles, blocks, circuits, services, programs, applications, hardware,
software and other
elements are provided, collectively referred to herein interchangeably as the
system 100 or
the platform 100, The platform 100 enables a wide range of improvements of and
for various
machines, systems, and other components that enable transactions involving the
exchange of
value (such as using currency, cryptocurrency, tokens, rewards or the like, as
well as a wide
range of in-kind and other resources) in various markets, including current or
spot markets
170, forward markets 130 and the like, for various goods, services, and
resources. As used
herein, "currency" should be understood to encompass fiat currency issued or
regulated by
governments, cryptocurrencies, tokens of value, tickets, loyalty points,
rewards points,
coupons, and other elements that represent or may be exchanged for value.
Resources, such
as ones that may be exchanged for value in a marketplace, should be understood
to
encompass goods, services, natural resources, energy resources, computing
resources, energy
storage resources, data storage resources, network bandwidth resources,
processing resources
and the like, including resources for which value is exchanged and resources
that enable a
transaction to occur (such as necessary computing and processing resources,
storage
resources, network resources, and energy resources that enable a transaction).
The platform
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100 may include a set of forward purchase and sale machines 110, each of which
may be
configured as an expert system or automated intelligent agent for interaction
with one or
more of the set of spot markets 170 and forward markets 130. Enabling the set
of forward
purchase and sale machines 110 are an intelligent resource purchasing system
164 having a
set of intelligent agents for purchasing resources in spot and forward
markets; an intelligent
resource allocation and coordination system 168 for the intelligent sale of
allocated or
coordinated resources, such as compute resources, energy resources, and other
resources
involved in or enabling a transaction; an intelligent sale engine 172 for
intelligent
coordination of a sale of allocated resources in spot and futures markets; and
an automated
spot market testing and arbitrage transaction execution engine 194 for
performing spot testing
of spot and forward markets, such as with micro-transactions and, where
conditions indicate
favorable arbitrage conditions, automatically executing transactions in
resources that take
advantage of the favorable conditions. Each of the engines may use model-based
or rule-
based expert systems, such as based on rules or heuristics, as well as deep
learning systems
by which rules or heuristics may be learned over trials involving a large set
of inputs. The
engines may use any of the expert systems and artificial intelligence
capabilities described
throughout this disclosure. Interactions within the platform 100, including of
all platform
components, and of interactions among them and with various markets, may be
tracked and
collected, such as by a data aggregation system 144, such as for aggregating
data on
purchases and sales in various marketplaces by the set of machines described
herein.
Aggregated data may include tracking and outcome data that may be fed to
artificial
intelligence and machine learning systems, such as to train or supervise the
same. The various
engines may operate on a range of data sources, including aggregated data from
marketplace
transactions, tracking data regarding the behavior of each of the engines, and
a set of external
data sources 182, which may include social media data sources 180 (such as
social
networking sites like FacebookTM and TwitterTm), Internet of Things (IoT) data
sources
(including from sensors, cameras, data collectors, and instrumented machines
and systems),
such as IoT sources that provide information about machines and systems that
enable
transactions and machines and systems that are involved in production and
consumption of
resources. External data sources 182 may include behavioral data sources, such
as automated
agent behavioral data sources 188 (such as tracking and reporting on behavior
of automated
agents that are used for conversation and dialog management, agents used for
control
functions for machines and systems, agents used for purchasing and sales,
agents used for
data collection, agents used for advertising, and others), human behavioral
data sources (such
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as data sources tracking online behavior, mobility behavior, energy
consumption behavior,
energy production behavior, network utilization behavior, compute and
processing behavior,
resource consumption behavior, resource production behavior, purchasing
behavior, attention
behavior, social behavior, and others), and entity behavioral data sources 190
(such as
behavior of business organizations and other entities, such as purchasing
behavior,
consumption behavior, production behavior, market activity, merger and
acquisition
behavior, transaction behavior, location behavior, and others). The IoT,
social and behavioral
data from and about sensors, machines, humans, entities, and automated agents
may
collectively be used to populate expert systems, machine learning systems, and
other
intelligent systems and engines described throughout this disclosure, such as
being provided
as inputs to deep learning systems and being provided as feedback or outcomes
for purposes
of training, supervision, and iterative improvement of systems for prediction,
forecasting,
classification, automation and control. The data may be organized as a stream
of events. The
data may be stored in a distributed ledger or other distributed system. The
data may be stored
in a knowledge graph where nodes represent entities and links represent
relationships. The
external data sources may be queried via various database query functions. The
data sources
182 may be accessed via APIs, brokers, connectors, protocols like REST and
SOAP, and
other data ingestion and extraction techniques. Data may be enriched with
metadata and may
be subject to transformation and loading into suitable forms for consumption
by the engines,
such as by cleansing, normalization, de-duplication and the like.
[00327] The platform 100 may include a set of intelligent forecasting engines
192 for
forecasting events, activities, variables, and parameters of spot markets 170,
forward markets
130, resources that are traded in such markets, resources that enable such
markets, behaviors
(such as any of those tracked in the external data sources 182), transactions,
and the like. The
forecasting engines 192 may operate on data from the data aggregation system
144 about
elements of the platform 100 and on data from the external data sources 182.
The platform
may include a set of intelligent transaction engines 136 for automatically
executing
transactions in spot markets 170 and forward markets 130. This may include
executing
intelligent cryptocurrency transactions with an intelligent cryptocurrency
execution engine
183 as described in more detail below. The platform 100 may make use of asset
of improved
distributed ledgers 113 and improved smart contracts 103, including ones that
embed and
operate on proprietary information, instruction sets and the like that enable
complex
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transactions to occur among individuals with reduced (or without) reliance on
intermediaries.
These and other components are described in more detail throughout this
disclosure.
[00328] Referring to the block diagram of Figure 2, further details and
additional
components of the platform 100 and interactions among them are depicted. The
set of
forward purchase and sale machines 110 may include a regeneration capacity
allocation
engine 102 (such as for allocating energy generation or regeneration capacity,
such as within
a hybrid vehicle or system that includes energy generation or regeneration
capacity, a
renewable energy system that has energy storage, or other energy storage
system, where
energy is allocated for one or more of sale on a forward market 130, sale in a
spot market
170, use in completing a transaction (e.g., mining for cryptocurrency), or
other purposes. For
example, the regeneration capacity allocation engine 102 may explore available
options for
use of stored energy, such as sale in current and forward energy markets that
accept energy
from producers, keeping the energy in storage for future use, or using the
energy for work
(which may include processing work, such as processing activities of the
platform like data
collection or processing, or processing work for executing transactions,
including mining
activities for cryptocurrencies).
[00329] The set of forward purchase and sale machines 110 may include an
energy purchase
and sale machine 104 for purchasing or selling energy, such as in an energy
spot market 148
or an energy forward market 122. The energy purchase and sale machine 104 may
use an
expert system, neural network or other intelligence to determine timing of
purchases, such as
based on current and anticipated state information with respect to pricing and
availability of
energy and based on current and anticipated state information with respect to
needs for
energy, including needs for energy to perform computing tasks, cryptocurrency
mining, data
collection actions, and other work, such as work done by automated agents and
systems and
work required for humans or entities based on their behavior. For example, the
energy
purchase machine may recognize, by machine learning, that a business is likely
to require a
block of energy in order to perform an increased level of manufacturing based
on an increase
in orders or market demand and may purchase the energy at a favorable price on
a futures
market, based on a combination of energy market data and entity behavioral
data. Continuing
the example, market demand may be understood by machine learning, such as by
processing
human behavioral data sources 184, such as social media posts, e-commerce data
and the like
that indicate increasing demand. The energy purchase and sale machine 104 may
sell energy
in the energy spot market 148 or the energy forward market 122. Sale may also
be conducted
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by an expert system operating on the various data sources described herein,
including with
training on outcomes and human supervision.
[00330] The set of forward purchase and sale machines 110 may include a
renewable energy
credit (REC) purchase and sale machine 108, which may purchase renewable
energy credits,
pollution credits, and other environmental or regulatory credits in a spot
market 150 or
forward market 124 for such credits. Purchasing may be configured and managed
by an
expert system operating on any of the external data sources 182 or on data
aggregated by the
set of data aggregations systems 144 for the platform. Renewable energy
credits and other
credits may be purchased by an automated system using an expert system,
including machine
learning or other artificial intelligence, such as where credits are purchased
with favorable
timing based on an understanding of supply and demand that is determined by
processing
inputs from the data sources. The expert system may be trained on a data set
of outcomes
from purchases under historical input conditions. The expert system may be
trained on a data
set of human purchase decisions and/or may be supervised by one or more human
operators.
The renewable energy credit (REC) purchase and sale machine 108 may also sell
renewable
energy credits, pollution credits, and other environmental or regulatory
credits in a spot
market 150 or forward market 124 for such credits. Sale may also be conducted
by an expert
system operating on the various data sources described herein, including with
training on
outcomes and human supervision.
[00331] The set of forward purchase and sale machines 110 may include an
attention
purchase and sale machine 112, which may purchase one or more attention-
related resources,
such as advertising space, search listing, keyword listing, banner
advertisements,
participation in a panel or survey activity, participation in a trial or
pilot, or the like in a spot
market for attention 152 or a forward market for attention 128. Attention
resources may
include the attention of automated agents, such as bots, crawlers, dialog
managers, and the
like that are used for searching, shopping and purchasing. Purchasing of
attention resources
may be configured and managed by an expert system operating on any of the
external data
sources 182 or on data aggregated by the set of data aggregations systems 144
for the
platform. Attention resources may be purchased by an automated system using an
expert
system, including machine learning or other artificial intelligence, such as
where resources
are purchased with favorable timing, such as based on an understanding of
supply and
demand, that is determined by processing inputs from the various data sources.
For example,
the attention purchase machine 112 may purchase advertising space in a forward
market for
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advertising based on learning from a wide range of inputs about market
conditions, behavior
data, and data regarding activities of agent and systems within the platform
100. The expert
system may be trained on a data set of outcomes from purchases under
historical input
conditions. The expert system may be trained on a data set of human purchase
decisions
and/or may be supervised by one or more human operators. The attention
purchase and sale
machine 112 may also sell one or more attention-related resources, such as
advertising space,
search listing, keyword listing, banner advertisements, participation in a
panel or survey
activity, participation in a trial or pilot, or the like in a spot market for
attention 152 or a
forward market for attention 128, which may include offering or selling access
to, or attention
or, one or more automated agents of the platform 100. Sale may also be
conducted by an
expert system operating on the various data sources described herein,
including with training
on outcomes and human supervision.
[00332] The set of forward purchase and sale machines 110 may include a
compute purchase
and sale machine 114, which may purchase one or more computation-related
resources, such
as processing resources, database resources, computation resources, server
resources, disk
resources, input/output resources, temporary storage resources, memory
resources, virtual
machine resources, container resources, and others in a spot market for
compute 154 or a
forward market for compute 132. Purchasing of compute resources may be
configured and
managed by an expert system operating on any of the external data sources 182
or on data
aggregated by the set of data aggregations systems 144 for the platform.
Compute resources
may be purchased by an automated system using an expert system, including
machine
learning or other artificial intelligence, such as where resources are
purchased with favorable
timing, such as based on an understanding of supply and demand, that is
determined by
processing inputs from the various data sources. For example, the compute
purchase machine
114 may purchase or reserve compute resources on a cloud platform in a forward
market for
compute resources based on learning from a wide range of inputs about market
conditions,
behavior data, and data regarding activities of agent and systems within the
platform 100,
such as to obtain such resources at favorable prices during surge periods of
demand for
computing. The expert system may be trained on a data set of outcomes from
purchases under
historical input conditions. The expert system may be trained on a data set of
human purchase
decisions and/or may be supervised by one or more human operators. The compute
purchase
and sale machine 114 may also sell one or more computation-related resources
that are
connected to, part of, or managed by the platform 100, such as processing
resources, database
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resources, computation resources, server resources, disk resources,
input/output resources,
temporary storage resources, memory resources, virtual machine resources,
container
resources, and others in a spot market for compute 154 or a forward market for
compute 132.
Sale may also be conducted by an expert system operating on the various data
sources
described herein, including with training on outcomes and human supervision.
[00333] The set of forward purchase and sale machines 110 may include a data
storage
purchase and sale machine 118, which may purchase one or more data-related
resources, such
as database resources, disk resources, server resources, memory resources, RAM
resources,
network attached storage resources, storage attached network (SAN) resources,
tape
resources, time-based data access resources, virtual machine resources,
container resources,
and others in a spot market for storage 158 or a forward market for data
storage 134.
Purchasing of data storage resources may be configured and managed by an
expert system
operating on any of the external data sources 182 or on data aggregated by the
set of data
aggregations systems 144 for the platform. Data storage resources may be
purchased by an
automated system using an expert system, including machine learning or other
artificial
intelligence, such as where resources are purchased with favorable timing,
such as based on
an understanding of supply and demand, that is determined by processing inputs
from the
various data sources. For example, the compute purchase machine 114 may
purchase or
reserve compute resources on a cloud platform in a forward market for compute
resources
based on learning from a wide range of inputs about market conditions,
behavior data, and
data regarding activities of agent and systems within the platform 100, such
as to obtain such
resources at favorable prices during surge periods of demand for storage. The
expert system
may be trained on a data set of outcomes from purchases under historical input
conditions.
The expert system may be trained on a data set of human purchase decisions
and/or may be
supervised by one or more human operators. The data storage purchase and sale
machine 118
may also sell one or more data storage-related resources that are connected
to, part of, or
managed by the platform 100 in a spot market for storage resources 158 or a
forward market
for storage 134. Sale may also be conducted by an expert system operating on
the various
data sources described herein, including with training on outcomes and human
supervision.
[00334] The set of forward purchase and sale machines 110 may include a
bandwidth
purchase and sale machine 120, which may purchase one or more bandwidth-
related
resources, such as cellular bandwidth, Wi-Fi bandwidth, radio bandwidth,
access point
bandwidth, beacon bandwidth, local area network bandwidth, wide area network
bandwidth,
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enterprise network bandwidth, server bandwidth, storage input/output
bandwidth, advertising
network bandwidth, market bandwidth, or other bandwidth, in a spot market for
bandwidth
160 or a forward market for bandwidth 138. Purchasing of bandwidth resources
may be
configured and managed by an expert system operating on any of the external
data sources
182 or on data aggregated by the set of data aggregations systems 144 for the
platform.
Bandwidth resources may be purchased by an automated system using an expert
system,
including machine learning or other artificial intelligence, such as where
resources are
purchased with favorable timing, such as based on an understanding of supply
and demand,
that is determined by processing inputs from the various data sources. For
example, the
bandwidth purchase and sale machine 120 may purchase or reserve bandwidth on a
network
resource for a future networking activity managed by the platform based on
learning from a
wide range of inputs about market conditions, behavior data, and data
regarding activities of
agent and systems within the platform 100, such as to obtain such resources at
favorable
prices during surge periods of demand for bandwidth. The expert system may be
trained on a
data set of outcomes from purchases under historical input conditions. The
expert system may
be trained on a data set of human purchase decisions and/or may be supervised
by one or
more human operators. The bandwidth purchase and sale machine 120 may also
sell one or
more bandwidth-related resources that are connected to, part of, or managed by
the platform
100 in a spot market for bandwidth resources 160 or a forward market for
bandwidth 138.
Sale may also be conducted by an expert system operating on the various data
sources
described herein, including with training on outcomes and human supervision.
[00335] The set of forward purchase and sale machines 110 may include a
spectrum
purchase and sale machine 142, which may purchase one or more spectrum-related
resources,
such as cellular spectrum, 3G spectrum, 4G spectrum, LTE spectrum, 5G
spectrum, cognitive
radio spectrum, peer-to-peer network spectrum, emergency responder spectrum
and the like
in a spot market for spectrum 162 or a forward market for spectrum 140.
Purchasing
of spectrum resources may be configured and managed by an expert system
operating on any
of the external data sources 182 or on data aggregated by the set of data
aggregations systems
144 for the platform. Spectrum resources may be purchased by an automated
system using an
expert system, including machine learning or other artificial intelligence,
such as where
resources are purchased with favorable timing, such as based on an
understanding of supply
and demand, that is determined by processing inputs from the various data
sources. For
example, the spectrum purchase and sale machine 142 may purchase or reserve
spectrum on a
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network resource for a future networking activity managed by the platform
based on learning
from a wide range of inputs about market conditions, behavior data, and data
regarding
activities of agent and systems within the platform 100, such as to obtain
such resources at
favorable prices during surge periods of demand for spectrum. The expert
system may be
trained on a data set of outcomes from purchases under historical input
conditions. The expert
system may be trained on a data set of human purchase decisions and/or may be
supervised
by one or more human operators. The spectrum purchase and sale machine 142 may
also sell
one or more spectrum-related resources that are connected to, part of, or
managed by the
platform 100 in a spot market for spectrum resources 162 or a forward market
for bandwidth
140. Sale may also be conducted by an expert system operating on the various
data sources
described herein, including with training on outcomes and human supervision.
[00336] In embodiments, the intelligent resource coordination and allocation
engine 168,
including the resource purchasing engine 164, the sale engine 172 and the
testing and
arbitrate engine 194, may provide coordinated and automated allocation of
resources and
coordinated execution of transactions across the various forward markets 130
and spot
markets 170 by coordinating the various purchase and sale machines, such as by
an expert
system, such as a machine learning system (which may model-based or a deep
learning
system, and which may be trained on outcomes and/or supervised by humans). For
example,
the coordination and allocation engine 168 may coordinate purchasing of
resources for a set
of assets and coordinated sale of resources available from a set of assets,
such as a fleet of
vehicles, a data center of processing and data storage resources, an
information technology
network (on premises, cloud, or hybrids), a fleet of energy production systems
(renewable or
non-renewable), a smart home or building (including appliances, machines,
infrastructure
components and systems, and the like thereof that consume or produce
resources), and the
like. The platform 100 may optimize allocation of resource purchasing, sale
and utilization
based on data aggregated in the platform, such as by tracking activities of
various engines and
agents, as well as by taking inputs from external data sources 182. In
embodiments, outcomes
may be provided as feedback for training the intelligent resource coordination
and allocation
engine 168, such as outcomes based on yield, profitability, optimization of
resources,
optimization of business objectives, satisfaction of goals, satisfaction of
users or operators, or
the like. For example, as the energy for computational tasks becomes a
significant fraction of
an enterprise's energy usage, the platform 100 may learn to optimize how a set
of machines
that have energy storage capacity allocate that capacity among computing tasks
(such as for
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cryptocurrency mining, application of neural networks, computation on data and
the like),
other useful tasks (that may yield profits or other benefits), storage for
future use, or sale to
the provider of an energy grid. The platform 100 may be used by fleet
operators, enterprises,
governments, municipalities, military units, first responder units,
manufacturers, energy
producers, cloud platform providers, and other enterprises and operators that
own or operate
resources that consume or provide energy, computation, data storage,
bandwidth, or
spectrum. The platform 100 may also be used in connection with markets for
attention, such
as to use available capacity of resources to support attention-based exchanges
of value, such
as in advertising markets, micro-transaction markets, and others.
[00337] Referring still to Figure 2, the platform 100 may include a set of
intelligent
forecasting engines 192 that forecast one or more attributes, parameters,
variables, or other
factors, such as for use as inputs by the set of forward purchase and sale
machines, the
intelligent transaction engines 126 (such as for intelligent cryptocurrency
execution) or for
other purposes. Each of the set of intelligent forecasting engines 192 may use
data that is
tracked, aggregated, processed, or handled within the platform 100, such as by
the data
aggregation system 144, as well as input data from external data sources 182,
such as social
media data sources 180, automated agent behavioral data sources 188, human
behavioral data
sources 184, entity behavioral data sources 190 and IoT data sources 198.
These collective
inputs may be used to forecast attributes, such as using a model (e.g.,
Bayesian, regression, or
other statistical model), a rule, or an expert system, such as a machine
learning system that
has one or more classifiers, pattern recognizers, and predictors, such as any
of the expert
systems described throughout this disclosure. In embodiments, the set of
intelligent
forecasting engines 192 may include one or more specialized engines that
forecast market
attributes, such as capacity, demand, supply, and prices, using particular
data sources for
particular markets. These may include an energy price forecasting engine 215
that bases its
forecast on behavior of an automated agent, a network spectrum price
forecasting engine 217
that bases its forecast on behavior of an automated agent, a REC price
forecasting engine 219
that bases its forecast on behavior of an automated agent, a compute price
forecasting engine
221 that bases its forecast on behavior of an automated agent, a network
spectrum price
forecasting engine 223 that bases its forecast on behavior of an automated
agent. In each
case, observations regarding the behavior of automated agents, such as ones
used for
conversation, for dialog management, for managing electronic commerce, for
managing
advertising and others may be provided as inputs for forecasting to the
engines. The
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intelligent forecasting engines 192 may also include a range of engines that
provide forecasts
at least in part based on entity behavior, such as behavior of business and
other organizations,
such as marketing behavior, sales behavior, product offering behavior,
advertising behavior,
purchasing behavior, transactional behavior, merger and acquisition behavior,
and other
entity behavior. These may include an energy price forecasting engine 225
using entity
behavior, a network spectrum price forecasting engine 227 using entity
behavior, a REC price
forecasting engine 229 using entity behavior, a compute price forecasting
engine 231 using
entity behavior, and a network spectrum price forecasting engine 233 using
entity behavior.
[00338] The intelligent forecasting engines 192 may also include a range of
engines that
provide forecasts at least in part based on human behavior, such as behavior
of consumers
and users, such as purchasing behavior, shopping behavior, sales behavior,
product
interaction behavior, energy utilization behavior, mobility behavior, activity
level behavior,
activity type behavior, transactional behavior, and other human behavior.
These may include
an energy price forecasting engine 235 using human behavior, a network
spectrum price
forecasting engine 237 using human behavior, a REC price forecasting engine
239 using
human behavior, a compute price forecasting engine 241 using human behavior,
and a
network spectrum price forecasting engine 243 using human behavior.
[00339] Referring still to Figure 2, the platform 100 may include a set of
intelligent
transaction engines 136 that automate execution of transactions in forward
markets 130
and/or spot markets 170 based on determination that favorable conditions
exist, such as by
the intelligent resource allocation and coordination engine 168 and/or with
use of forecasts
form the intelligent forecasting engines 192. The intelligent transaction
engines 136 may be
configured to automatically execute transactions, using available market
interfaces, such as
APIs, connectors, ports, network interfaces, and the like, in each of the
markets noted above.
In embodiments, the intelligent transaction engines may execute transactions
based on event
streams that come from external data sources, such as IoT data sources 198 and
social media
data sources 180. The engines may include, for example, an IoT forward energy
transaction
engine 195 and/or an IoT compute market transaction engine 106, either or both
of which
may use data from the Internet of Things to determine timing and other
attributes for market
transaction in a market for one or more of the resources described herein,
such as an energy
market transaction, a compute resource transaction or other resource
transaction. IoT data
may include instrumentation and controls data for one or more machines
(optionally
coordinated as a fleet) that use or produce energy or that use or have compute
resources,
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weather data that influences energy prices or consumption (such as wind data
influencing
production of wind energy), sensor data from energy production environments,
sensor data
from points of use for energy or compute resources (such as vehicle traffic
data, network
traffic data, IT network utilization data, Internet utilization and traffic
data, camera data from
work sites, smart building data, smart home data, and the like), and other
data collected by or
transferred within the Internet of Things, including data stored in IoT
platforms and of cloud
services providers like Amazon, IBM, and others. The engines 136 may include
engines that
use social data to determine timing of other attributes for a market
transaction in one or more
of the resources described herein, such as a social data forward energy
transaction engine 199
and/or a social data compute market transaction engine 116. Social data may
include data
from social networking sites (e.g., FacebookTM, YouTubeTm, TwitterTm,
SnapchatTM,
InstagramTM, and others, data from websites, data from e-commerce sites, and
data from other
sites that contain information that may be relevant to determining or
forecasting behavior of
users or entities, such as data indicating interest or attention to particular
topics, goods or
services, data indicating activity types and levels (such as may be observed
by machine
processing of image data showing individuals engaged in activities, including
travel, work
activities, leisure activities, and the like. Social data may be supplied to
machine learning,
such as for learning user behavior or entity behavior, and/or as an input to
an expert system, a
model, or the like, such as one for determining, based on the social data, the
parameters for a
transaction. For example, an event or set of events in a social data stream
may indicate the
likelihood of a surge of interest in an online resource, a product, or a
service, and compute
resources, bandwidth, storage, or like may be purchased in advance (avoiding
surge pricing)
to accommodate the increased interest reflected by the social data stream.
[00340] Referring to Figure 3, the platform 100 may include capabilities for
transaction
execution that involve one or more distributed ledgers 113 and one or more
smart contracts
103, where the distributed ledgers 113 and smart contracts 103 are configured
to enable
specialized transaction features for specific transaction domains. One such
domain is
intellectual property, which transactions are highly complex, involving
licensing terms and
conditions that are somewhat difficult to manage, as compared to more
straightforward sales
of goods or services. In embodiments, a smart contract wrapper 105, such as
wrapper
aggregating intellectual property, is provided, using a distributed ledger,
wherein the smart
contract embeds IP licensing terms for intellectual property that is embedded
in the
distributed ledger and wherein executing an operation on the distributed
ledger provides
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access to the intellectual property and commits the executing party to the IP
licensing terms.
Licensing terms for a wide range of goods and services, including digital
goods like video,
audio, video game, video game element, music, electronic book and other
digital goods may
be managed by tracking transactions involving them on a distributed ledger,
whereby
publishers may verify a chain of licensing and sublicensing. The distributed
ledger may be
configured to add each licensee to the ledger, and the ledger may be retrieved
at the point of
use of a digital item, such as in a streaming platform, to validate that
licensing has occurred.
[00341] In embodiments, an improved distributed ledger is provided with the
smart contract
wrapper 105, such as an IP wrapper, container, smart contract or similar
mechanism for
aggregating intellectual property licensing terms, wherein a smart contract
wrapper on the
distributed ledger allows an operation on the ledger to add intellectual
property to an
aggregate stack of intellectual property. In many cases, intellectual property
builds on other
intellectual property, such as where software code is derived from other code,
where trade
secrets or know-how for elements of a process are combined to enable a larger
process, where
patents covering sub-components of a system or steps in a process are pooled,
where
elements of a video game include sub-component assets from different creators,
where a book
contains contributions from multiple authors, and the like. In embodiments, a
smart IP
wrapper aggregates licensing terms for different intellectual property items
(including digital
goods, including ones embodying different types of intellectual property
rights, and
transaction data involving the item, as well as optionally one or more
portions of the item
corresponding to the transaction data, are stored in a distributed ledger that
is configured to
enable validation of agreement to the licensing terms (such as at appoint of
use) and/or access
control to the item. In embodiments, a royalty apportionment wrapper 115 may
be provided
in a system having a distributed ledger for aggregating intellectual property
licensing terms,
wherein a smart contract wrapper on the distributed ledger allows an operation
on the ledger
to add intellectual property and to agree to an apportionment of royalties
among the parties in
the ledger. Thus, a ledger may accumulate contributions to the ledger along
with evidence of
agreement to the apportionment of any royalties among the contributors of the
IP that is
embedded in and/or controlled by the ledger. The ledger may record licensing
terms and
automatically vary them as new contributions are made, such as by one or more
rules. For
example, contributors may be given a share of a royalty stack according to a
rule, such as
based on a fractional contribution, such as based on lines of code
contributed, lines of
authorship, contribution to components of a system, and the like. In
embodiments, a
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distributed ledger may be forked into versions that represent varying
combinations of sub-
components of IP, such as to allow users to select combinations that are of
most use, thereby
allowing contributors who have contributed the most value to be rewarded.
Variation and
outcome tracking may be iteratively improved, such as by machine learning.
[00342] In embodiments, a distributed ledger is provided for aggregating
intellectual
property licensing terms, wherein a smart contract wrapper on the distributed
ledger allows
an operation on the ledger to add intellectual property to an aggregate stack
of intellectual
property.
[00343] In embodiments, the platform 100 may have an improved distributed
ledger for
aggregating intellectual property licensing terms, wherein a smart contract
wrapper on the
distributed ledger allows an operation on the ledger to commit a party to a
contract term via
an IP transaction wrapper 119 of the ledger. This may include operations
involving
cryptocurrencies, tokens, or other operations, as well as conventional
payments and in-kind
transfers, such as of various resources described herein. The ledger may
accumulate evidence
of commitments to IP transactions by parties, such as entering into royalty
terms, revenue
sharing terms, IP ownership terms, warranty and liability terms, license
permissions and
restrictions, field of use terms, and many others.
[00344] In embodiments, improved distributed ledgers may include ones having a
tokenized
instruction set, such that operation on the distributed ledger provides
provable access to the
instruction set. A party wishing to share permission to know how, a trade
secret or other
valuable instructions may thus share the instruction set via a distributed
ledger that captures
and stores evidence of an action on the ledger by a third party, thereby
evidencing access and
agreement to terms and conditions of access. In embodiments, the platform 100
may have a
distributed ledger that tokenizes executable algorithmic logic 121, such that
operation on the
distributed ledger provides provable access to the executable algorithmic
logic. A variety of
instruction sets may be stored by a distributed ledger, such as to verify
access and verify
agreement to terms (such as smart contract terms). In embodiments, instruction
sets that
embody trade secrets may be separated into sub-components, so that operations
must occur
on multiple ledgers to get (provable) access to a trade secret. This may
permit parties wishing
to share secrets, such as with multiple sub-contractors or vendors, to main
provable access
control, while separating components among different vendors to avoid sharing
an entire set
with a single party. Various kinds of executable instruction sets may be
stored on specialized
distributed ledgers that may include smart wrappers for specific types of
instruction sets, such
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that provable access control, validation of terms, and tracking of utilization
may be
performed by operations on the distributed ledger (which may include
triggering access
controls within a content management system or other systems upon validation
of actions
taken in a smart contract on the ledger. In embodiments, the platform 100 may
have a
distributed ledger that tokenizes a 3D printer instruction set 123, such that
operation on the
distributed ledger provides provable access to the instruction set.
[00345] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for a coating process 125, such that operation on the
distributed ledger
provides provable access to the instruction set.
[00346] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for a semiconductor fabrication process 129, such that
operation on the
distributed ledger provides provable access to the fabrication process.
[00347] In embodiments, the platform 100 may have a distributed ledger that
tokenizes a
firmware program 131, such that operation on the distributed ledger provides
provable access
to the firmware program.
[00348] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for an FPGA 133, such that operation on the distributed ledger
provides
provable access to the FPGA.
[00349] In embodiments, the platform 100 may have a distributed ledger that
tokenizes
serverless code logic 135, such that operation on the distributed ledger
provides provable
access to the serverless code logic.
[00350] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for a crystal fabrication system 139, such that operation on
the distributed
ledger provides provable access to the instruction set.
[00351] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for a food preparation process 141, such that operation on the
distributed
ledger provides provable access to the instruction set.
[00352] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for a polymer production process 143, such that operation on
the distributed
ledger provides provable access to the instruction set.
[00353] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for chemical synthesis process 145, such that operation on the
distributed
ledger provides provable access to the instruction set.
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[00354] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set for a biological production process 149, such that operation
on the distributed
ledger provides provable access to the instruction set.
[00355] In embodiments, the platform 100 may have a distributed ledger that
tokenizes a
trade secret with an expert wrapper 151, such that operation on the
distributed ledger
provides provable access to the trade secret and the wrapper provides
validation of the trade
secret by the expert. An interface may be provided by which an expert accesses
the trade
secret on the ledger and verifies that the information is accurate and
sufficient to allow a third
party to use the secret.
[00356] In embodiments, the platform 100 may have a distributed ledger that
aggregates
views of a trade secret into a chain that proves which and how many parties
have viewed the
trade secret. Views may be used to allocate value to creators of the trade
secret, to operators
of the platform 100, or the like.
[00357] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
instruction set 111, such that operation on the distributed ledger provides
provable access 155
to the instruction set and execution of the instruction set on a system
results in recording a
transaction in the distributed ledger.
[00358] In embodiments, the platform 100 may have a distributed ledger that
tokenizes an
item of intellectual property and a reporting system that reports an analytic
result based on
the operations performed on the distributed ledger or the intellectual
property.
[00359] In embodiments, the platform 100 may have a distributed ledger that
aggregates a
set of instructions, where an operation on the distributed ledger adds at
least one instruction
to a pre-existing set of instructions 161 to provide a modified set of
instructions.
[00360] Referring still to Figure 3, an intelligent cryptocurrency execution
engine 183 may
provide intelligence for the timing, location and other attributes of a
cryptocurrency
transaction, such as a mining transaction, an exchange transaction, a storage
transaction, a
retrieval transaction, or the like. Cryptocurrencies like BitcoinTM are
increasingly widespread,
with specialized coins having emerged for a wide variety of purposes, such as
exchanging
value in various specialized market domains. Initial offerings of such coins,
or IC0s, are
increasingly subject to regulations, such as securities regulations, and in
some cases to
taxation. Thus, while cryptocurrency transactions typically occur within
computer networks,
jurisdictional factors may be important in determining where, when and how to
execute a
transaction, store a cryptocurrency, exchange it for value. In embodiments,
intelligent
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cryptocurrency execution engine 183 may use features embedded in or wrapped
around the
digital object representing a coin, such as features that cause the execution
of transactions in
the coin to be undertaken with awareness of various conditions, including
geographic
conditions, regulatory conditions, tax conditions, market conditions, and the
like.
[00361] In embodiments, the platform 100 may include a tax aware coin 165 or
smart
wrapper for a cryptocurrency coin that directs execution of a transaction
involving the coin to
a geographic location based on tax treatment of at least one of the coin and
the transaction in
the geographic location.
[00362] In embodiments, the platform 100 may include a location-aware coin 169
or smart
wrapper that enables a self-executing cryptocurrency coin that commits a
transaction upon
recognizing a location-based parameter that provides favorable tax treatment.
[00363] In embodiments, the platform 100 may include an expert system or Al
agent 171
that uses machine learning to optimize the execution of cryptocurrency
transactions based on
tax status. Machine learning may use one or more models or heuristics, such as
populated
with relevant jurisdictional tax data, may be trained on a training set of
human trading
operations, may be supervised by human supervisors, and/or may use a deep
learning
technique based on outcomes over time, such as when operating on a wide range
of internal
system data and external data sources 182 as described throughout this
disclosure.
[00364] In embodiments, the platform 100 may include regulation aware coin 173
having a
coin, a smart wrapper, and/or an expert system that aggregates regulatory
information
covering cryptocurrency transactions and automatically selects a jurisdiction
for an operation
based on the regulatory information. Machine learning may use one or more
models or
heuristics, such as populated with relevant jurisdictional regulatory data,
may be trained on a
training set of human trading operations, may be supervised by human
supervisors, and/or
may use a deep learning technique based on outcomes over time, such as when
operating on a
wide range of internal system data and external data sources 182 as described
throughout this
disclosure.
[00365] In embodiments, the platform 100 may include an energy price-aware
coin 175,
wrapper, or expert system that uses machine learning to optimize the execution
of a
cryptocurrency transaction based on real time energy price information for an
available
energy source. Cryptocurrency transactions, such as coin mining and blockchain
operations,
may be highly energy intensive. An energy price-aware coin may be configured
to time such
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operations based on energy price forecasts, such as with one or more of the
forecasting
engines 192 described throughout this disclosure.
[00366] In embodiments, the platform 100 may include an energy source aware
coin 179,
wrapper, or expert system that uses machine learning to optimize the execution
of a
cryptocurrency transaction based on an understanding of available energy
sources to power
computing resources to execute the transaction. For example, coin mining may
be performed
only when renewable energy sources are available. Machine learning for
optimization of a
transaction may use one or more models or heuristics, such as populated with
relevant energy
source data (such as may be captured in a knowledge graph, which may contain
energy
source information by type, location and operating parameters), may be trained
on a training
set of input-output data for human-initiated transactions, may be supervised
by human
supervisors, and/or may use a deep learning technique based on outcomes over
time, such as
when operating on a wide range of internal system data and external data
sources 182 as
described throughout this disclosure.
[00367] In embodiments, the platform 100 may include a charging cycle aware
coin 181,
wrapper, or an expert system that uses machine learning to optimize charging
and recharging
cycle of a rechargeable battery system to provide energy for execution of a
cryptocurrency
transaction. For example, a battery may be discharged for a cryptocurrency
transaction only if
a minimum threshold of battery charge is maintained for other operational use,
if re-charging
resources are known to be readily available, or the like. Machine learning for
optimization of
charging and recharging may use one or more models or heuristics, such as
populated with
relevant battery data (such as may be captured in a knowledge graph, which may
contain
energy source information by type, location and operating parameters), may be
trained on a
training set of human operations, may be supervised by human supervisors,
and/or may use a
deep learning technique based on outcomes over time, such as when operating on
a wide
range of internal system data and external data sources 182 as described
throughout this
disclosure.
[00368] Optimization of various intelligent coin operations may occur with
machine
learning that is trained on outcomes, such as financial profitability. Any of
the machine
learning systems described throughout this disclosure may be used for
optimization of
intelligent cryptocurrency transaction management.
[00369] In embodiments, compute resources, such as those mentioned throughout
this
disclosure, may be allocated to perform a range of computing tasks, both for
operations that
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occur within the platform 100, ones that are managed by the platform, and ones
that involve
the activities, workflows and processes of various assets that may be owned,
operated or
managed in conjunction with the platform, such as sets or fleets of assets
that have or use
computing resources. Examples of compute tasks include, without limitation,
cryptocurrency
mining, distributed ledger calculations and storage, forecasting tasks,
transaction execution
tasks, spot market testing tasks, internal data collection tasks, external
data collection,
machine learning tasks, and others. As noted above, energy, compute resources,
bandwidth,
spectrum, and other resources may be coordinated, such as by machine learning,
for these
tasks. Outcome and feedback information may be provided for the machine
learning, such as
outcomes for any of the individual tasks and overall outcomes, such as yield
and profitability
for business or other operations involving the tasks.
[00370] In embodiments, networking resources, such as those mentioned
throughout this
disclosure, may be allocated to perform a range of networking tasks, both for
operations that
occur within the platform 100, ones that are managed by the platform, and ones
that involve
the activities, workflows and processes of various assets that may be owned,
operated or
managed in conjunction with the platform, such as sets or fleets of assets
that have or use
networking resources. Examples of networking tasks include cognitive network
coordination,
network coding, peer bandwidth sharing (including, for example cost-based
routing, value-
based routing, outcome-based routing and the like), distributed transaction
execution, spot
market testing, randomization (e.g., using genetic programming with outcome
feedback to
vary network configurations and transmission paths), internal data collection
and external
data collection. As noted above, energy, compute resources, bandwidth,
spectrum, and other
resources may be coordinated, such as by machine learning, for these
networking tasks.
Outcome and feedback information may be provided for the machine learning,
such as
outcomes for any of the individual tasks and overall outcomes, such as yield
and profitability
for business or other operations involving the tasks.
[00371] In embodiments, data storage resources, such as those mentioned
throughout this
disclosure, may be allocated to perform a range of data storage tasks, both
for operations that
occur within the platform 100, ones that are managed by the platform, and ones
that involve
the activities, workflows and processes of various assets that may be owned,
operated or
managed in conjunction with the platform, such as sets or fleets of assets
that have or use
networking resources. Examples of data storage tasks include distributed
ledger storage,
storage of internal data (such as operational data with the platform),
cryptocurrency storage,
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smart wrapper storage, storage of external data, storage of feedback and
outcome data, and
others. As noted above, data storage, energy, compute resources, bandwidth,
spectrum, and
other resources may be coordinated, such as by machine learning, for these
data storage tasks.
Outcome and feedback information may be provided for the machine learning,
such as
outcomes for any of the individual tasks and overall outcomes, such as yield
and profitability
for business or other operations involving the tasks.
[00372] In embodiments, smart contracts, such as ones embodying terms relating
to
intellectual property, trade secrets, know how, instruction sets, algorithmic
logic, and the like
may embody or include contract terms, which may include terms and conditions
for options,
royalty stacking terms, field exclusivity, partial exclusivity, pooling of
intellectual property,
standards terms (such as relating to essential and non-essential patent
usage), technology
transfer terms, consulting service terms, update terms, support terms,
maintenance terms,
derivative works terms, copying terms, and performance-related rights or
metrics, among
many others.
[00373] In embodiments where an instruction set is embodied in digital form,
such as
contained in or managed by a distributed ledger transactions system, various
systems may be
configured with interfaces that allow them to access and use the instruction
sets. In
embodiments, such systems may include access control features that validate
proper licensing
by inspection of a distributed ledger, a key, a token, or the like that
indicates the presence of
access rights to an instruction set. Such systems that execute distributed
instruction sets may
include systems for 3D printing, crystal fabrication, semiconductor
fabrication, coating items,
producing polymers, chemical synthesis and biological production, among
others.
[00374] Networking capabilities and network resources should be understood to
include a
wide range of networking systems, components and capabilities, including
infrastructure
elements for 3G, 4G, LTE, 5G and other cellular network types, access points,
routers, and
other Wi-Fi elements, cognitive networking systems and components, mobile
networking
systems and components, physical layer, MAC layer and application layer
systems and
components, cognitive networking components and capabilities, peer-to-peer
networking
components and capabilities, optical networking components and capabilities,
and others.
[00375] Building blocks on expert systems and Al
[00376] Neural Net Systems
[00377] Referring to Figure 4 through Figure 31, embodiments of the present
disclosure,
including ones involving expert systems, self-organization, machine learning,
artificial
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intelligence, and the like, may benefit from the use of a neural net, such as
a neural net
trained for pattern recognition, for classification of one or more parameters,
characteristics, or
phenomena, for support of autonomous control, and other purposes. References
to a neural
net throughout this disclosure should be understood to encompass a wide range
of different
types of neural networks, machine learning systems, artificial intelligence
systems, and the
like, such as feed forward neural networks, radial basis function neural
networks, self-
organizing neural networks (e.g., Kohonen self-organizing neural networks),
recurrent neural
networks, modular neural networks, artificial neural networks, physical neural
networks,
multi-layered neural networks, convolutional neural networks, hybrids of
neural networks
with other expert systems (e.g., hybrid fuzzy logic ¨ neural network systems),
Autoencoder
neural networks, probabilistic neural networks, time delay neural networks,
convolutional
neural networks, regulatory feedback neural networks, radial basis function
neural networks,
recurrent neural networks, Hopfield neural networks, Boltzmann machine neural
networks,
self-organizing map (SOM) neural networks, learning vector quantization (LVQ)
neural
networks, fully recurrent neural networks, simple recurrent neural networks,
echo state neural
networks, long short-term memory neural networks, bi-directional neural
networks,
hierarchical neural networks, stochastic neural networks, genetic scale RNN
neural networks,
committee of machines neural networks, associative neural networks, physical
neural
networks, instantaneously trained neural networks, spiking neural networks,
neocognitron
neural networks, dynamic neural networks, cascading neural networks, neuro-
fuzzy neural
networks, compositional pattern-producing neural networks, memory neural
networks,
hierarchical temporal memory neural networks, deep feed forward neural
networks, gated
recurrent unit (GCU) neural networks, auto encoder neural networks,
variational auto encoder
neural networks, de-noising auto encoder neural networks, sparse auto-encoder
neural
networks, Markov chain neural networks, restricted Boltzmann machine neural
networks,
deep belief neural networks, deep convolutional neural networks, de-
convolutional neural
networks, deep convolutional inverse graphics neural networks, generative
adversarial neural
networks, liquid state machine neural networks, extreme learning machine
neural networks,
echo state neural networks, deep residual neural networks, support vector
machine neural
networks, neural Turing machine neural networks, and/or holographic
associative memory
neural networks, or hybrids or combinations of the foregoing, or combinations
with other
expert systems, such as rule-based systems, model-based systems (including
ones based on
physical models, statistical models, flow-based models, biological models,
biomimetic
models, and the like).
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[00378] In embodiments, Figures 5 through 31 depict exemplary neural networks
and Figure
4 depicts a legend showing the various components of the neural networks
depicted
throughout Figures 5 to 31. Figure 4 depicts various neural net components
depicted in cells
that are assigned functions and requirements. In embodiments, the various
neural net
examples may include back fed data/sensor cells, data/sensor cells, noisy
input cells, and
hidden cells. The neural net components also include probabilistic hidden
cells, spiking
hidden cells, output cells, match input/output cells, recurrent cells, memory
cells, different
memory cells, kernels, and convolution or pool cells.
[00379] In embodiments, Figure 5 depicts an exemplary perceptron neural
network that may
connect to, integrate with, or interface with the platform 100. The platform
may also be
associated with further neural net systems such as a feed forward neural
network (Figure 6), a
radial basis neural network (Figure 7), a deep feed forward neural network
(Figure 8), a
recurrent neural network (Figure 9), a long/short term neural network (Figure
10), and a gated
recurrent neural network (Figure 11). The platform may also be associated with
further neural
net systems such as an auto encoder neural network (Figure 12), a variational
neural network
(Figure 13), a denoising neural network (Figure 14), a sparse neural network
(Figure 15), a
Markov chain neural network (Figure 16), and a Hopfield network neural network
(Figure
17). The platform may further be associated with additional neural net systems
such as a
Boltzmann machine neural network (Figure 18), a restricted BM neural network
(Figure 19),
a deep belief neural network (Figure 20), a deep convolutional neural network
(Figure 21), a
deconvolutional neural network (Figure 22), and a deep convolutional inverse
graphics neural
network (Figure 23). The platform may also be associated with further neural
net systems
such as a generative adversarial neural network (Figure 24), a liquid state
machine neural
network (Figure 25), an extreme learning machine neural network (Figure 26),
an echo state
neural network (Figure 27), a deep residual neural network (Figure 28), a
Kohonen neural
network (Figure 29), a support vector machine neural network (Figure 30), and
a neural
Turing machine neural network (Figure 31).
[00380] The foregoing neural networks may have a variety of nodes or neurons,
which may
perform a variety of functions on inputs, such as inputs received from sensors
or other data
sources, including other nodes. Functions may involve weights, features,
feature vectors, and
the like. Neurons may include perceptrons, neurons that mimic biological
functions (such as
of the human senses of touch, vision, taste, hearing, and smell), and the
like. Continuous
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neurons, such as with sigmoidal activation, may be used in the context of
various forms of
neural net, such as where back propagation is involved.
[00381] In many embodiments, an expert system or neural network may be
trained, such as
by a human operator or supervisor, or based on a data set, model, or the like.
Training may
include presenting the neural network with one or more training data sets that
represent
values, such as sensor data, event data, parameter data, and other types of
data (including the
many types described throughout this disclosure), as well as one or more
indicators of an
outcome, such as an outcome of a process, an outcome of a calculation, an
outcome of an
event, an outcome of an activity, or the like. Training may include training
in optimization,
such as training a neural network to optimize one or more systems based on one
or more
optimization approaches, such as Bayesian approaches, parametric Bayes
classifier
approaches, k-nearest-neighbor classifier approaches, iterative approaches,
interpolation
approaches, Pareto optimization approaches, algorithmic approaches, and the
like. Feedback
may be provided in a process of variation and selection, such as with a
genetic algorithm that
evolves one or more solutions based on feedback through a series of rounds.
[00382] In embodiments, a plurality of neural networks may be deployed in a
cloud platform
that receives data streams and other inputs collected (such as by mobile data
collectors) in
one or more transactional environments and transmitted to the cloud platform
over one or
more networks, including using network coding to provide efficient
transmission. In the
cloud platform, optionally using massively parallel computational capability,
a plurality of
different neural networks of various types (including modular forms, structure-
adaptive
forms, hybrids, and the like) may be used to undertake prediction,
classification, control
functions, and provide other outputs as described in connection with expert
systems disclosed
throughout this disclosure. The different neural networks may be structured to
compete with
each other (optionally including use evolutionary algorithms, genetic
algorithms, or the like),
such that an appropriate type of neural network, with appropriate input sets,
weights, node
types and functions, and the like, may be selected, such as by an expert
system, for a specific
task involved in a given context, workflow, environment process, system, or
the like.
[00383] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a feed forward neural network,
which moves
information in one direction, such as from a data input, like a data source
related to at least
one resource or parameter related to a transactional environment, such as any
of the data
sources mentioned throughout this disclosure, through a series of neurons or
nodes, to an
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output. Data may move from the input nodes to the output nodes, optionally
passing through
one or more hidden nodes, without loops. In embodiments, feed forward neural
networks may
be constructed with various types of units, such as binary McCulloch-Pitts
neurons, the
simplest of which is a perceptron.
[00384] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a capsule neural network, such
as for
prediction, classification, or control functions with respect to a
transactional environment,
such as relating to one or more of the machines and automated systems
described throughout
this disclosure.
[00385] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a radial basis function (RBF)
neural network,
which may be preferred in some situations involving interpolation in a multi-
dimensional
space (such as where interpolation is helpful in optimizing a multi-
dimensional function, such
as for optimizing a data marketplace as described here, optimizing the
efficiency or output of
a power generation system, a factory system, or the like, or other situation
involving multiple
dimensions. In embodiments, each neuron in the RBF neural network stores an
example from
a training set as a "prototype." Linearity involved in the functioning of this
neural network
offers RBF the advantage of not typically suffering from problems with local
minima or
maxima.
[00386] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a radial basis function (RBF)
neural network,
such as one that employs a distance criterion with respect to a center (e.g.,
a Gaussian
function). A radial basis function may be applied as a replacement for a
hidden layer, such as
a sigmoidal hidden layer transfer, in a multi-layer perceptron. An RBF network
may have two
layers, such as where an input is mapped onto each RBF in a hidden layer. In
embodiments,
an output layer may comprise a linear combination of hidden layer values
representing, for
example, a mean predicted output. The output layer value may provide an output
that is the
same as or similar to that of a regression model in statistics. In
classification problems, the
output layer may be a sigmoid function of a linear combination of hidden layer
values,
representing a posterior probability. Performance in both cases is often
improved by
shrinkage techniques, such as ridge regression in classical statistics. This
corresponds to a
prior belief in small parameter values (and therefore smooth output functions)
in
a Bayesian framework. RBF networks may avoid local minima, because the only
parameters
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that are adjusted in the learning process are the linear mapping from hidden
layer to output
layer. Linearity ensures that the error surface is quadratic and therefore has
a single
minimum. In regression problems, this may be found in one matrix operation. In
classification problems, the fixed non-linearity introduced by the sigmoid
output function
may be handled using an iteratively re-weighted least squares function or the
like. RBF
networks may use kernel methods such as support vector machines (SVM) and
Gaussian
processes (where the RBF is the kernel function). A non-linear kernel function
may be used
to project the input data into a space where the learning problem may be
solved using a linear
model.
[00387] In embodiments, an RBF neural network may include an input layer, a
hidden layer,
and a summation layer. In the input layer, one neuron appears in the input
layer for each
predictor variable. In the case of categorical variables, N-1 neurons are
used, where N is the
number of categories. The input neurons may, in embodiments, standardize the
value ranges
by subtracting the median and dividing by the interquartile range. The input
neurons may
then feed the values to each of the neurons in the hidden layer. In the hidden
layer, a variable
number of neurons may be used (determined by the training process). Each
neuron may
consist of a radial basis function that is centered on a point with as many
dimensions as a
number of predictor variables. The spread (e.g., radius) of the RBF function
may be different
for each dimension. The centers and spreads may be determined by training.
When presented
with the vector of input values from the input layer, a hidden neuron may
compute a
Euclidean distance of the test case from the neuron's center point and then
apply the RBF
kernel function to this distance, such as using the spread values. The
resulting value may then
be passed to the summation layer. In the summation layer, the value coming out
of a neuron
in the hidden layer may be multiplied by a weight associated with the neuron
and may add to
the weighted values of other neurons. This sum becomes the output. For
classification
problems, one output is produced (with a separate set of weights and summation
units) for
each target category. The value output for a category is the probability that
the case being
evaluated has that category. In training of an RBF, various parameters may be
determined,
such as the number of neurons in a hidden layer, the coordinates of the center
of each hidden-
layer function, the spread of each function in each dimension, and the weights
applied to
outputs as they pass to the summation layer. Training may be used by
clustering algorithms
(such as k-means clustering), by evolutionary approaches, and the like.
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[00388] In embodiments, a recurrent neural network may have a time-varying,
real-valued
(more than just zero or one) activation (output). Each connection may have a
modifiable real-
valued weight. Some of the nodes are called labeled nodes, some output nodes,
and others
hidden nodes. For supervised learning in discrete time settings, training
sequences of real-
valued input vectors may become sequences of activations of the input nodes,
one input
vector at a time. At each time step, each non-input unit may compute its
current activation as
a nonlinear function of the weighted sum of the activations of all units from
which it receives
connections. The system may explicitly activate (independent of incoming
signals) some
output units at certain time steps.
[00389] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a self-organizing neural
network, such as a
Kohonen self-organizing neural network, such as for visualization of views of
data, such as
low-dimensional views of high-dimensional data. The self-organizing neural
network may
apply competitive learning to a set of input data, such as from one or more
sensors or other
data inputs from or associated with a transactional environment, including any
machine or
component that relates to the transactional environment. In embodiments, the
self-organizing
neural network may be used to identify structures in data, such as unlabeled
data, such as in
data sensed from a range of data sources about or sensors in or about in a
transactional
environment, where sources of the data are unknown (such as where events may
be coming
from any of a range of unknown sources). The self-organizing neural network
may organize
structures or patterns in the data, such that they may be recognized,
analyzed, and labeled,
such as identifying market behavior structures as corresponding to other
events and signals.
[00390] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a recurrent neural network,
which may allow
for a bi-directional flow of data, such as where connected units (e.g.,
neurons or nodes) form
a directed cycle. Such a network may be used to model or exhibit dynamic
temporal
behavior, such as involved in dynamic systems, such as a wide variety of the
automation
systems, machines and devices described throughout this disclosure, such as an
automated
agent interacting with a marketplace for purposes of collecting data, testing
spot market
transactions, execution transactions, and the like, where dynamic system
behavior involves
complex interactions that a user may desire to understand, predict, control
and/or optimize.
For example, the recurrent neural network may be used to anticipate the state
of a market,
such as one involving a dynamic process or action, such as a change in state
of a resource that
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is traded in or that enables a marketplace of transactional environment. In
embodiments, the
recurrent neural network may use internal memory to process a sequence of
inputs, such as
from other nodes and/or from sensors and other data inputs from or about the
transactional
environment, of the various types described herein. In embodiments, the
recurrent neural
network may also be used for pattern recognition, such as for recognizing a
machine,
component, agent, or other item based on a behavioral signature, a profile, a
set of feature
vectors (such as in an audio file or image), or the like. In a non-limiting
example, a recurrent
neural network may recognize a shift in an operational mode of a marketplace
or machine by
learning to classify the shift from a training data set consisting of a stream
of data from one or
more data sources of sensors applied to or about one or more resources.
[00391] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a modular neural network, which
may
comprise a series of independent neural networks (such as ones of various
types described
herein) that are moderated by an intermediary. Each of the independent neural
networks in
the modular neural network may work with separate inputs, accomplishing
subtasks that
make up the task the modular network as whole is intended to perform. For
example, a
modular neural network may comprise a recurrent neural network for pattern
recognition,
such as to recognize what type of machine or system is being sensed by one or
more sensors
that are provided as input channels to the modular network and an RBF neural
network for
optimizing the behavior of the machine or system once understood. The
intermediary may
accept inputs of each of the individual neural networks, process them, and
create output for
the modular neural network, such an appropriate control parameter, a
prediction of state, or
the like.
[00392] Combinations among any of the pairs, triplets, or larger combinations,
of the various
neural network types described herein, are encompassed by the present
disclosure. This may
include combinations where an expert system uses one neural network for
recognizing a
pattern (e.g., a pattern indicating a problem or fault condition) and a
different neural network
for self-organizing an activity or work flow based on the recognized pattern
(such as
providing an output governing autonomous control of a system in response to
the recognized
condition or pattern). This may also include combinations where an expert
system uses one
neural network for classifying an item (e.g., identifying a machine, a
component, or an
operational mode) and a different neural network for predicting a state of the
item (e.g., a
fault state, an operational state, an anticipated state, a maintenance state,
or the like). Modular
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neural networks may also include situations where an expert system uses one
neural network
for determining a state or context (such as a state of a machine, a process, a
work flow, a
marketplace, a storage system, a network, a data collector, or the like) and a
different neural
network for self-organizing a process involving the state or context (e.g., a
data storage
process, a network coding process, a network selection process, a data
marketplace process, a
power generation process, a manufacturing process, a refining process, a
digging process, a
boring process, or other process described herein).
[00393] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a physical neural network where
one or more
hardware elements is used to perform or simulate neural behavior. In
embodiments, one or
more hardware neurons may be configured to stream voltage values, current
values, or the
like that represent sensor data, such as to calculate information from analog
sensor inputs
representing energy consumption, energy production, or the like, such as by
one or more
machines providing energy or consuming energy for one or more transactions.
One or more
hardware nodes may be configured to stream output data resulting from the
activity of the
neural net. Hardware nodes, which may comprise one or more chips,
microprocessors,
integrated circuits, programmable logic controllers, application-specific
integrated circuits,
field-programmable gate arrays, or the like, may be provided to optimize the
machine that is
producing or consuming energy, or to optimize another parameter of some part
of a neural net
of any of the types described herein. Hardware nodes may include hardware for
acceleration
of calculations (such as dedicated processors for performing basic or more
sophisticated
calculations on input data to provide outputs, dedicated processors for
filtering or
compressing data, dedicated processors for de-compressing data, dedicated
processors for
compression of specific file or data types (e.g., for handling image data,
video streams,
acoustic signals, thermal images, heat maps, or the like), and the like. A
physical neural
network may be embodied in a data collector, including one that may be
reconfigured by
switching or routing inputs in varying configurations, such as to provide
different neural net
configurations within the data collector for handling different types of
inputs (with the
switching and configuration optionally under control of an expert system,
which may include
a software-based neural net located on the data collector or remotely). A
physical, or at least
partially physical, neural network may include physical hardware nodes located
in a storage
system, such as for storing data within a machine, a data storage system, a
distributed ledger,
a mobile device, a server, a cloud resource, or in a transactional
environment, such as for
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accelerating input/output functions to one or more storage elements that
supply data to or take
data from the neural net. A physical, or at least partially physical, neural
network may include
physical hardware nodes located in a network, such as for transmitting data
within, to or from
an industrial environment, such as for accelerating input/output functions to
one or more
network nodes in the net, accelerating relay functions, or the like. In
embodiments, of a
physical neural network, an electrically adjustable resistance material may be
used for
emulating the function of a neural synapse. In embodiments, the physical
hardware emulates
the neurons, and software emulates the neural network between the neurons. In
embodiments,
neural networks complement conventional algorithmic computers. They are
versatile and
may be trained to perform appropriate functions without the need for any
instructions, such as
classification functions, optimization functions, pattern recognition
functions, control
functions, selection functions, evolution functions, and others.
[00394] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a multilayered feed forward
neural network,
such as for complex pattern classification of one or more items, phenomena,
modes, states, or
the like. In embodiments, a multilayered feed forward neural network may be
trained by an
optimization technique, such as a genetic algorithm, such as to explore a
large and complex
space of options to find an optimum, or near-optimum, global solution. For
example, one or
more genetic algorithms may be used to train a multilayered feed forward
neural network to
classify complex phenomena, such as to recognize complex operational modes of
machines,
such as modes involving complex interactions among machines (including
interference
effects, resonance effects, and the like), modes involving non-linear
phenomena, modes
involving critical faults, such as where multiple, simultaneous faults occur,
making root cause
analysis difficult, and others. In embodiments, a multilayered feed forward
neural network
may be used to classify results from monitoring of a marketplace, such as
monitoring
systems, such as automated agents, that operate within the marketplace, as
well as monitoring
resources that enable the marketplace, such as computing, networking, energy,
data storage,
energy storage, and other resources.
[00395] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a feed-forward, back-
propagation multi-layer
perceptron (MLP) neural network, such as for handling one or more remote
sensing
applications, such as for taking inputs from sensors distributed throughout
various
transactional environments. In embodiments, the MLP neural network may be used
for
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classification of transactional environments and resource environments, such
as spot markets,
forward markets, energy markets, renewable energy credit (REC) markets,
networking
markets, advertising markets, spectrum markets, ticketing markets, rewards
markets, compute
markets, and others mentioned throughout this disclosure, as well as physical
resources and
environments that produce them, such as energy resources (including renewable
energy
environments, mining environments, exploration environments, drilling
environments, and
the like, including classification of geological structures (including
underground features and
above ground features), classification of materials (including fluids,
minerals, metals, and the
like), and other problems. This may include fuzzy classification.
[00396] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a structure-adaptive neural
network, where the
structure of a neural network is adapted, such as based on a rule, a sensed
condition, a
contextual parameter, or the like. For example, if a neural network does not
converge on a
solution, such as classifying an item or arriving at a prediction, when acting
on a set of inputs
after some amount of training, the neural network may be modified, such as
from a feed
forward neural network to a recurrent neural network, such as by switching
data paths
between some subset of nodes from unidirectional to bi-directional data paths.
The structure
adaptation may occur under control of an expert system, such as to trigger
adaptation upon
occurrence of a trigger, rule or event, such as recognizing occurrence of a
threshold (such as
an absence of a convergence to a solution within a given amount of time) or
recognizing a
phenomenon as requiring different or additional structure (such as recognizing
that a system
is varying dynamically or in a non-linear fashion). In one non-limiting
example, an expert
system may switch from a simple neural network structure like a feed forward
neural network
to a more complex neural network structure like a recurrent neural network, a
convolutional
neural network, or the like upon receiving an indication that a continuously
variable
transmission is being used to drive a generator, turbine, or the like in a
system being
analyzed.
[00397] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use an autoencoder, autoassociator
or Diabolo
neural network, which may be similar to a multilayer perceptron (MLP) neural
network, such
as where there may be an input layer, an output layer and one or more hidden
layers
connecting them. However, the output layer in the auto-encoder may have the
same number
of units as the input layer, where the purpose of the MLP neural network is to
reconstruct its
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own inputs (rather than just emitting a target value). Therefore, the auto
encoders may
operate as an unsupervised learning model. An auto encoder may be used, for
example,
for unsupervised learning of efficient codings, such as for dimensionality
reduction, for
learning generative models of data, and the like. In embodiments, an auto-
encoding neural
network may be used to self-learn an efficient network coding for transmission
of analog
sensor data from a machine over one or more networks or of digital data from
one or more
data sources. In embodiments, an auto-encoding neural network may be used to
self-learn an
efficient storage approach for storage of streams of data.
[00398] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a probabilistic neural network
(PNN), which,
in embodiments, may comprise a multi-layer (e.g., four-layer) feed forward
neural network,
where layers may include input layers, hidden layers, pattern/summation layers
and an output
layer. In an embodiment of a PNN algorithm, a parent probability distribution
function (PDF)
of each class may be approximated, such as by a Parzen window and/or a non-
parametric
function. Then, using the PDF of each class, the class probability of a new
input is estimated,
and Bayes' rule may be employed, such as to allocate it to the class with the
highest posterior
probability. A PNN may embody a Bayesian network and may use a statistical
algorithm or
analytic technique, such as Kernel Fisher discriminant analysis technique. The
PNN may be
used for classification and pattern recognition in any of a wide range of
embodiments
disclosed herein. In one non-limiting example, a probabilistic neural network
may be used to
predict a fault condition of an engine based on collection of data inputs from
sensors and
instruments for the engine.
[00399] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a time delay neural network
(TDNN), which
may comprise a feed forward architecture for sequential data that recognizes
features
independent of sequence position. In embodiments, to account for time shifts
in data, delays
are added to one or more inputs, or between one or more nodes, so that
multiple data points
(from distinct points in time) are analyzed together. A time delay neural
network may form
part of a larger pattern recognition system, such as using a perceptron
network. In
embodiments, a TDNN may be trained with supervised learning, such as where
connection
weights are trained with back propagation or under feedback. In embodiments, a
TDNN may
be used to process sensor data from distinct streams, such as a stream of
velocity data, a
stream of acceleration data, a stream of temperature data, a stream of
pressure data, and the
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like, where time delays are used to align the data streams in time, such as to
help understand
patterns that involve understanding of the various streams (e.g., changes in
price patterns in
spot or forward markets).
[00400] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a convolutional neural network
(referred to in
some cases as a CNN, a ConyNet, a shift invariant neural network, or a space
invariant neural
network), wherein the units are connected in a pattern similar to the visual
cortex of the
human brain. Neurons may respond to stimuli in a restricted region of space,
referred to as a
receptive field. Receptive fields may partially overlap, such that they
collectively cover the
entire (e.g., visual) field. Node responses may be calculated mathematically,
such as by
a convolution operation, such as using multilayer perceptrons that use minimal
preprocessing.
A convolutional neural network may be used for recognition within images and
video
streams, such as for recognizing a type of machine in a large environment
using a camera
system disposed on a mobile data collector, such as on a drone or mobile
robot. In
embodiments, a convolutional neural network may be used to provide a
recommendation
based on data inputs, including sensor inputs and other contextual
information, such as
recommending a route for a mobile data collector. In embodiments, a
convolutional neural
network may be used for processing inputs, such as for natural language
processing of
instructions provided by one or more parties involved in a workflow in an
environment. In
embodiments, a convolutional neural network may be deployed with a large
number of
neurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4, 5, 6 or
more) layers, and
with many (e.g., millions) of parameters. A convolutional neural net may use
one or more
convolutional nets.
[00401] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a regulatory feedback network,
such as for
recognizing emergent phenomena (such as new types of behavior not previously
understood
in a transactional environment).
[00402] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a self-organizing map (SOM),
involving
unsupervised learning. A set of neurons may learn to map points in an input
space to
coordinates in an output space. The input space may have different dimensions
and topology
from the output space, and the SOM may preserve these while mapping phenomena
into
groups.
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[00403] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a learning vector quantization
neural
net (LVQ). Prototypical representatives of the classes may parameterize,
together with an
appropriate distance measure, in a distance-based classification scheme.
[00404] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use an echo state network (ESN),
which may
comprise a recurrent neural network with a sparsely connected, random hidden
layer. The
weights of output neurons may be changed (e.g., the weights may be trained
based on
feedback). In embodiments, an ESN may be used to handle time series patterns,
such as, in an
example, recognizing a pattern of events associated with a market, such as the
pattern of price
changes in response to stimuli.
[00405] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a Bi-directional, recurrent
neural network
(BRNN), such as using a finite sequence of values (e.g., voltage values from a
sensor) to
predict or label each element of the sequence based on both the past and the
future context of
the element. This may be done by adding the outputs of two RNNs, such as one
processing
the sequence from left to right, the other one from right to left. The
combined outputs are the
predictions of target signals, such as ones provided by a teacher or
supervisor. A bi-
directional RNN may be combined with a long short-term memory RNN.
[00406] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a hierarchical RNN that
connects elements in
various ways to decompose hierarchical behavior, such as into useful
subprograms. In
embodiments, a hierarchical RNN may be used to manage one or more hierarchical
templates
for data collection in a transactional environment.
[00407] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a stochastic neural network,
which may
introduce random variations into the network. Such random variations may be
viewed as a
form of statistical sampling, such as Monte Carlo sampling.
[00408] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a genetic scale recurrent
neural network. In
such embodiments, an RNN (often an LSTM) is used where a series is decomposed
into a
number of scales where every scale informs the primary length between two
consecutive
points. A first order scale consists of a normal RNN, a second order consists
of all points
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separated by two indices and so on. The Nth order RNN connects the first and
last node. The
outputs from all the various scales may be treated as a committee of members,
and the
associated scores may be used genetically for the next iteration.
[00409] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a committee of machines (CoM),
comprising a
collection of different neural networks that together "vote" on a given
example. Because
neural networks may suffer from local minima, starting with the same
architecture and
training, but using randomly different initial weights often gives different
results. A CoM
tends to stabilize the result.
[00410] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use an associative neural network
(ASNN), such
as involving an extension of a committee of machines that combines multiple
feed forward
neural networks and a k-nearest neighbor technique. It may use the correlation
between
ensemble responses as a measure of distance amid the analyzed cases for the
kNN. This
corrects the bias of the neural network ensemble. An associative neural
network may have a
memory that may coincide with a training set. If new data become available,
the network
instantly improves its predictive ability and provides data approximation
(self-learns) without
retraining. Another important feature of ASNN is the possibility to interpret
neural network
results by analysis of correlations between data cases in the space of models.
[00411] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use an instantaneously trained
neural network
(ITNN), where the weights of the hidden and the output layers are mapped
directly from
training vector data.
[00412] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a spiking neural network, which
may
explicitly consider the timing of inputs. The network input and output may be
represented as
a series of spikes (such as a delta function or more complex shapes). SNNs may
process
information in the time domain (e.g., signals that vary over time, such as
signals involving
dynamic behavior of markets or transactional environments). They are often
implemented as
recurrent networks.
[00413] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a dynamic neural network that
addresses
nonlinear multivariate behavior and includes learning of time-dependent
behavior, such as
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transient phenomena and delay effects. Transients may include behavior of
shifting market
variables, such as prices, available quantities, available counterparties, and
the like.
[00414] In embodiments, cascade correlation may be used as an architecture and
supervised
learning algorithm, supplementing adjustment of the weights in a network of
fixed topology.
Cascade-correlation may begin with a minimal network, then automatically
trains and add
new hidden units one by one, creating a multi-layer structure. Once a new
hidden unit has
been added to the network, its input-side weights may be frozen. This unit
then becomes a
permanent feature-detector in the network, available for producing outputs or
for creating
other, more complex feature detectors. The cascade-correlation architecture
may learn
quickly, determine its own size and topology, and retain the structures it has
built even if the
training set changes and requires no back-propagation.
[00415] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a neuro-fuzzy network, such as
involving
a fuzzy inference system in the body of an artificial neural network.
Depending on the type,
several layers may simulate the processes involved in a fuzzy inference, such
as fuzzification,
inference, aggregation and defuzzification. Embedding a fuzzy system in a
general structure
of a neural net as the benefit of using available training methods to find the
parameters of a
fuzzy system.
[00416] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a compositional pattern-
producing network
(CPPN), such as a variation of an associative neural network (ANN) that
differs the set
of activation functions and how they are applied. While typical ANNs often
contain
only sigmoid functions (and sometimes Gaussian functions), CPPNs may include
both types
of functions and many others. Furthermore, CPPNs may be applied across the
entire space of
possible inputs, so that they may represent a complete image. Since they are
compositions of
functions, CPPNs in effect encode images at infinite resolution and may be
sampled for a
particular display at whatever resolution is optimal.
[00417] This type of network may add new patterns without re-training. In
embodiments,
methods and systems described herein that involve an expert system or self-
organization
capability may use a one-shot associative memory network, such as by creating
a specific
memory structure, which assigns each new pattern to an orthogonal plane using
adjacently
connected hierarchical arrays.
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[00418] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a hierarchical temporal memory
(HTM) neural
network, such as involving the structural and algorithmic properties of the
neocortex. HTM
may use a biomimetic model based on memory-prediction theory. HTM may be used
to
discover and infer the high-level causes of observed input patterns and
sequences.
[00419] Holographic Associative Memory
[00420] In embodiments, methods and systems described herein that involve an
expert
system or self-organization capability may use a holographic associative
memory (HAM)
neural network, which may comprise an analog, correlation-based, associative,
stimulus-
response system. Information may be mapped onto the phase orientation of
complex
numbers. The memory is effective for associative memory tasks, generalization
and pattern
recognition with changeable attention.
[00421] In embodiments, various embodiments involving network coding may be
used to
code transmission data among network nodes in a neural net, such as where
nodes are located
in one or more data collectors or machines in a transactional environment.
[00422] Integrated Circuit Building Blocks
[00423] In embodiments, one or more of the controllers, circuits, systems,
data collectors,
storage systems, network elements, or the like as described throughout this
disclosure may be
embodied in or on an integrated circuit, such as an analog, digital, or mixed
signal circuit,
such as a microprocessor, a programmable logic controller, an application-
specific integrated
circuit, a field programmable gate array, or other circuits, such as embodied
on one or more
chips disposed on one or more circuit boards, such as to provide in hardware
(with potentially
accelerated speed, energy performance, input-output performance, or the like)
one or more of
the functions described herein. This may include setting up circuits with up
to billions of
logic gates, flip-flops, multiplexers, and other circuits in a small space,
facilitating high speed
processing, low power dissipation, and reduced manufacturing cost compared
with board-
level integration. In embodiments, a digital IC, typically a microprocessor,
digital signal
processor, microcontroller, or the like may use Boolean algebra to process
digital signals to
embody complex logic, such as involved in the circuits, controllers, and other
systems
described herein. In embodiments, a data collector, an expert system, a
storage system, or the
like may be embodied as a digital integrated circuit, such as a logic IC,
memory chip,
interface IC (e.g., a level shifter, a serializer, a deserializer, and the
like), a power
management IC and/or a programmable device; an analog integrated circuit, such
as a linear
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IC, RF IC, or the like, or a mixed signal IC, such as a data acquisition IC
(including AID
converters, D/A converter, digital potentiometers) and/or a clock/timing IC.
[00424] With reference to Figure 32, the environment includes an intelligent
energy and
compute facility (such as a large scale facility hosting many compute
resources and having
access to a large energy source, such as a hydropower source), as well as a
host intelligent
energy and compute facility resource management platform, referred to in some
cases for
convenience as the energy and information technology platform (with
networking, data
storage, data processing and other resources as described herein), a set of
data sources, a set
of expert systems, interfaces to a set of market platforms and external
resources, and a set of
user (or client) systems and devices.
[00425] Intelligent Energy and Compute Facility
[00426] A facility may be configured to access an inexpensive (at least during
some time
periods) power source (such as a hydropower dam, a wind farm, a solar array, a
nuclear
power plant, or a grid), to contain a large set of networked information
technology resources,
including processing units, servers, and the like that are capable of flexible
utilization (such
as by switching inputs, switching configurations, switching programming and
the like), and
to provide a range of outputs that can also be flexibly configured (such as
passing through
power to a smart grid, providing computational results (such as for
cryptocurrency mining,
artificial intelligence, or analytics). A facility may include a power storage
system, such as
for large scale storage of available power.
[00427] Intelligent Energy and Compute Facility Resource Management Platform
[00428] In operation, a user can access the energy and information technology
platform to
initiate and manage a set of activities that involve optimizing energy and
computing
resources among a diverse set of available tasks. Energy resources may include
hydropower,
nuclear power, wind power, solar power, grid power and the like, as well as
energy storage
resources, such as batteries, gravity power, and storage using thermal
materials, such as
molten salts. Computing resources may include GPUs, FPGAs, servers, chips,
Asics,
processors, data storage media, networking resources, and many others.
Available tasks may
include cryptocurrency hash processing, expert system processing, computer
vision
processing, NLP, path optimization, applications of models such as for
analytics, etc.
[00429] In embodiments, the platform may include various subsystems that may
be
implemented as micro services, such that other subsystems of the system access
the
functionality of a subsystem providing a micro service via application
programming interface
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API. In some embodiments, the various services that are provided by the
subsystems may be
deployed in bundles that are integrated, such as by a set of APIs. Each of the
subsystems is
described in greater detail with respect to Figure 130.
[00430] The External Data Sources can include any system or device that can
provide data
to the platform. Examples of data sources can include market data sources
(e.g., for financial
markets, commercial markets (including e-commerce), advertising markets,
energy markets,
telecommunication markets, and many others). The energy and computing resource
platform
accesses external data sources via a network (e.g., the Internet) in any
suitable manner (e.g.,
crawlers, extract-transform-load (ETL) systems, gateways, brokers, application
programming
interfaces (APIs), spiders, distributed database queries, and the like).
[00431] A facility is a facility that has an energy resource (e.g., a hydro
power resource) and
a set of compute resource (e.g., a set of flexible computing resources that
can be provisioned
and managed to perform computing tasks, such as GPUs, FPGAs and many others, a
set of
flexible networking resources that can similarly be provisioned and managed,
such as by
adjusting network coding protocols and parameters), and the like.
[00432] User and client systems and devices can include any system or device
that may
consume one or more computing or energy resource made available by the energy
and
computing resource platform. Examples include cryptocurrency systems (e.g.,
for Bitcoin and
other cryptocurrency mining operations), expert and artificial intelligence
systems (such as
neural networks and other systems, such as for computer vision, natural
language processing,
path determination and optimization, pattern recognition, deep learning,
supervised learning,
decision support, and many others), energy management systems (such as smart
grid
systems), and many others. User and client systems may include user devices,
such as
smartphones, tablet computer devices, laptop computing devices, personal
computing
devices, smart televisions, gaming consoles, and the like.
[00433] Energy and computing resource platform Components in Figure 130.
[00434] Figure 130 illustrates an example energy and computing resource
platform
according to some embodiments of the present disclosure. In embodiments, the
energy and
computing resource platform may include a processing system 13002, a storage
system
13004, and a communication system 13006.
[00435] The processing device 13002 may include one or more processors and
memory. The
processors may operate in an individual or distributed manner. The processors
may be in the
same physical device or in separate devices, which may or may not be located
in the same
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facility. The memory may store computer-executable instructions that are
executed by the
one or more processors. In embodiments, the processing device 13002 may
execute the
facility management system 13008, the data acquisition system 13010, the
cognitive
processes system 13012, the lead generation system 13014, the content
generation system
13016, and the workflow system 13018.
[00436] The storage device 13004 may include one or more computer-readable
storage
mediums. The computer-readable storage mediums may be located in the same
physical
device or in separate devices, which may or may not be located in the same
facility, which
may or may not be located in the same facility. The computer-readable storage
mediums may
include flash devices, solid-state memory devices, hard disk drives, and the
like. In
embodiments, the storage device 13004 stores one or more of a facility data
store 13020, a
person data store 13022, and an external data store 13024.
[00437] The communication system 13006 may include one or more transceivers
that are
configured to effectuate wireless or wired communication with one or more
external devices,
including user devices and/or servers, via a network (e.g., the Internet
and/or a cellular
network). The communication system 13006 may implement any suitable
communication
protocol. For example, the communication system xxx may implement an IEEE
801.11
wireless communication protocol and/or any suitable cellular communication
protocol to
effectuate wireless communication with external devices and external data
13024 via a
wireless network.
[00438] Energy and Computing Resource Management Platform
[00439] Discovers, provisions, manages and optimizes energy and compute
resources using
artificial intelligence and expert systems with sensitivity to market and
other conditions by
learning on a set of outcomes. Discovers and facilitates cataloging of
resources, optionally by
user entry and/or automated detection (including peer detection). May
implement a graphical
user interface to receive relevant information regarding the energy and
compute resources
that are available. This may include a "digital twin" of an energy and compute
facility that
allows modeling, prediction and the like. May generate a set of data record
that define the
facility or a set of facilities under common ownership or operation by a host.
The data record
may have any suitable schema. In some embodiments (e.g., Fig. 131), the
facility data records
may include a facility identifier (e.g., a unique identifier that corresponds
to the facility), a
facility type (e.g., energy system and capabilities, compute systems and
capabilities,
networking systems and capabilities), facility attributes (e.g., name of the
facility, name of
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the facility initiator, description of the facility, keywords of the facility,
goals of the facility,
timing elements, schedules, and the like), participants/potential participants
in the facility
(e.g., identifiers of owners, operators, hosts, service providers, consumers,
clients, users,
workers, and others), and any suitable metadata (e.g., creation date, launch
date, scheduled
requirements and the like). May generate content, such as a document, message,
alert, report,
webpage and/or application page based on the contents of the data record. For
example, may
obtain the data record of the facility and may populate a webpage template
with the data
contained therein. In addition, there can be management of existing
facilities, updates the data
record of a facility, determinations of outcomes (e.g., energy produced,
compute tasks
completed, processing outcomes achieved, financial outcomes achieved, service
levels met
and many others), and sending of information (e.g., updates, alerts, requests,
instructions, and
the like) to individuals and systems.
[00440] Data Acquisition Systems can acquire various types of data from
different data
sources and organizes that data into one or more data structures. In
embodiments, the data
acquisition system receives data from users via a user interface (e.g., user
types in profile
information). In embodiments, the data acquisition system can retrieve data
from passive
electronic sources. In embodiments, the data acquisition system can implement
crawlers to
crawl different websites or applications. In embodiments, the data acquisition
system can
implement an API to retrieve data from external data sources or user devices
(e.g., various
contact lists from user's phone or email account). In embodiments, the data
acquisition
system can structure the obtained data into appropriate data structures. In
embodiments, the
data acquisition system generates and maintains person records based on data
collected
regarding individuals. In embodiments, a person datastore stores person
records. In some of
these embodiments, the person datastore may include one or more databases,
indexes, tables,
and the like. Each person record may correspond to a respective individual and
may be
organized according to any suitable schema.
[00441] Figure 132 illustrates an example schema of a person record. In the
example, each
person record may include a unique person identifier (e.g., username or
value), and may
define all data relating to a person, including a person's name, facilities
they are a part of or
associated with (e.g., a list of facility identifiers), attributes of the
person (age, location, job,
company, role, skills, competencies, capabilities, education history, job
history, and the like),
a list of contacts or relationships of the person (e.g., in a role hierarchy
or graph), and any
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suitable metadata (e.g., date joined, dates actions were taken, dates input
was received, and
the like).
[00442] In embodiments, the data acquisition system generates and maintains
one or more
graphs based on the retrieved data. In some embodiments, a graph datastore may
store the
one or more graphs. The graph may be specific to a facility or may be a global
graph. The
graph may be used in many different applications (e.g., identifying a set of
roles, such as for
authentication, for approvals, and the like for persons, or identifying system
configurations,
capabilities, or the like, such as hierarchies of energy producing, computing,
networking, or
other systems, subsystems and/or resources).
[00443] In embodiments, a graph may be stored in a graph database, where data
is stored in
a collection of nodes and edges. In some embodiments, a graph has nodes
representing
entities and edges representing relationships, each node may have a node type
(also referred
to as an entity type) and an entity value, each edge may have a relationship
type and may
define a relationship between two entities. For example, a person node may
include a person
ID that identifies the individual represented by the node and a company node
may include a
company identifier that identifies a company. A "works for" edge that is
directed from a
person node to a company node may denote that the person represented by the
edge node
works for the company represented by the company node. In another example, a
person node
may include a person ID that identifies the individual represented by the node
and a facility
node may include a facility identifier that identifies a facility. A "manages"
edge that is
directed from a person node to a facility node may denote that the person
represented by the
person node is a manager of the facility represented by the facility node.
Furthermore in
embodiments, an edge or node may contain or reference additional data. For
example, a
"manages" edge may include a function that indicates a specific function
within a facility that
is managed by a person. The graph(s) can be used in a number of different
applications,
which are discussed with respect to the cognitive processing system.
[00444] In embodiments, validated Identity information may be imported from
one or more
identity information providers, as well as data from LinkedlnTM and other
social network
sources regarding data acquisition and structuring data. In embodiments, the
data acquisition
system may include an identity management system (not shown in Figs) of the
platform may
manage identity stitching, identity resolution, identity normalization, and
the like, such as
determining where an individual represented across different social networking
sites and
email contacts is in fact the same person. In embodiments, the data
acquisition system may
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include a profile aggregation system (not shown in Figs) that finds and
aggregates disparate
pieces of information to generate a comprehensive profile for a person. The
profile
aggregation system may also deduplicate individuals.
[00445] Cognitive Processing Systems
[00446] The cognitive processing system 13312 may implement one or more of
machine
learning processes, artificial intelligence processes, analytics processes,
natural language
processing processes, and natural language generation processes. FIG. 133
illustrates an
example cognitive processing system according to some embodiments of the
present
disclosure. In this example, the cognitive processing system may include a
machine learning
system 13302, an artificial intelligence (Al) system 13304, an analytics
system 13306, a
natural language processing system 13308, and a natural language generation
system 13310.
[00447] Machine Learning System
[00448] In embodiments, the machine learning system may train models, such as
predictive
models (e.g., various types of neural networks, regression based models, and
other machine-
learned models). In embodiments, training can be supervised, semi-supervised,
or
unsupervised. In embodiments, training can be done using training data, which
may be
collected or generated for training purposes.
[00449] A facility output model (or prediction model) may be a model that
receive facility
attributes and outputs one or more predictions regarding the production or
other output of a
facility. Examples of predictions may be the amount of energy a facility will
produce, the
amount of processing the facility will undertake, the amount of data a network
will be able to
transfer, the amount of data that can be stored, the price of a component,
service or the like
(such as supplied to or provided by a facility), a profit generated by
accomplishing a given
tasks, the cost entailed in performing an action, and the like. In each case,
the machine
learning system optionally trains a model based on training data. In
embodiments, the
machine learning system may receive vectors containing facility attributes
(e.g., facility type,
facility capability, objectives sought, constraints or rules that apply to
utilization of resources
or the facility, or the like), person attributes (e.g., role, components
managed, and the like),
and outcomes (e.g., energy produced, computing tasks completed, and financial
results,
among many others). Each vector corresponds to a respective outcome and the
attributes of
the respective facility and respective actions that led to the outcome. The
machine learning
system takes in the vectors and generates predictive model based thereon. In
embodiments,
the machine learning system may store the predictive models in the model
datastore.
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[00450] In embodiments, training can also be done based on feedback received
by the
system, which is also referred to as "reinforcement learning." In embodiments,
the machine
learning system may receive a set of circumstances that led to a prediction
(e.g., attributes of
facility, attributes of a model, and the like) and an outcome related to the
facility and may
update the model according to the feedback.
[00451] In embodiments, training may be provided from a training data set that
is created by
observing actions of a set of humans, such as facility managers managing
facilities that have
various capabilities and that are involved in various contexts and situations.
This may include
use of robotic process automation to learn on a training data set of
interactions of humans
with interfaces, such as graphical user interfaces, of one or more computer
programs, such as
dashboards, control systems, and other systems that are used to manage an
energy and
compute management facility.
[00452] Artificial Intelligence (Al) Systems
[00453] In embodiments, the Al system leverages the predictive models to make
predictions
regarding facilities. Examples of predictions include ones related to inputs
to a facility (e.g.,
available energy, cost of energy, cost of compute resources, networking
capacity and the like,
as well as various market information, such as pricing information for end use
markets), ones
related to components or systems of a facility (including performance
predictions,
maintenance predictions, uptime/downtime predictions, capacity predictions and
the like),
ones related to functions or workflows of the facility (such as ones that
involved conditions
or states that may result in following one or more distinct possible paths
within a workflow, a
process, or the like), ones related to outputs of the facility, and others. In
embodiments, the
Al system receives a facility identifier. In response to the facility
identifier, the Al system
may retrieve attributes corresponding to the facility. In some embodiments,
the Al system
may obtain the facility attributes from a graph. Additionally or
alternatively, the Al system
may obtain the facility attributes from a facility record corresponding to the
facility identifier,
and the person attributes from a person record corresponding to the person
identifier.
[00454] Examples of additional attributes that can be used to make predictions
about a
facility or a related process of system include: related facility information;
owner goals
(including financial goals); client goals; and many more additional or
alternative attributes. In
embodiments, the Al system may output scores for each possible prediction,
where each
prediction corresponds to a possible outcome. For example, in using a
prediction model used
to determine a likelihood that a hydroelectric source for a facility will
produce 5 MW of
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power, the prediction model can output a score for a "will produce" outcome
and a score for a
"will not produce" outcome. The Al system may then select the outcome with the
highest
score as the prediction. Alternatively, the Al system may output the
respective scores to a
requesting system.
[00455] Clustering Systems
[00456] In embodiments, a clustering system clusters records or entities based
on attributes
contained herein. For example, similar facilities, resources, people, clients,
or the like may be
clustered. The clustering system may implement any suitable clustering
algorithm. For
example, when clustering people records to identify a list of customer leads
corresponding to
resources that can be sold by a facility, the clustering system may implement
k-nearest
neighbors clustering, whereby the clustering system identifies k people
records that most
closely relate to the attributes defined for the facility. In another example,
the clustering
system may implement k-means clustering, such that the clustering system
identifies k
different clusters of people records, whereby the clustering system or another
system selects
items from the cluster.
[00457] Analytics System
[00458] In embodiments, an analytics system may perform analytics relating to
various
aspects of the energy and computing resource platform. The analytics system
may analyze
certain communications to determine which configurations of a facility produce
the greatest
yield, what conditions tend to indicate potential faults or problems, and the
like.
[00459] Lead Generation System
[00460] Figure 134 shows the manner by which the lead generation system
generates a lead
list. Lead generation system receives a list of potential leads 13402 (such as
for consumers of
available products or resources). The lead generation system may provide the
list of leads to
the clustering system 13404. The clustering system clusters the profile of the
lead with the
clusters of facility attributes 13406 to identify one or more clusters. In
embodiments, the
clustering system returns a list of leads 13410. In other embodiments, the
clustering system
returns the clusters 13408, and the lead generation system selects the list of
leads 13410 from
the cluster to which a prospect belongs.
[00461] Figure 135 illustrates the manner by which the lead generation system
determines
facility outputs for leads identified in the list of leads. In embodiments,
the lead generation
system provides a lead identifier of a respective lead to the Al system (step
13502). The Al
system may then obtain the lead attributes of the lead and facility attributes
of the facility and
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may feed the respective attributes into a prediction model (step 13504). The
prediction model
outputs a prediction, which may be scores associated with each possible
outcome, or a single
predicted outcome that was selected based on its respective score (e.g., the
outcome having
the highest score) (step 13506). The lead generation system may iterate in
this manner for
each lead in the lead list. For example, the lead generation system may
generate leads that are
consumers of compute capabilities, energy capabilities, predictions and
forecasts,
optimization results, and others.
[00462] In embodiments, the lead generation system categorizes the lead (step
13508) and
generates a lead list (step 13512) which it provides to the facility operator
or host of the
systems, including an indicator of the reason why a lead may be willing to
engage the facility,
such as, for example, that the lead is an intensive user of computing
resources, such as to
forecast behavior of a complex, multi-variable market, or to mine for
cryptocurrency. In
embodiments, where more leads are stored and/or categorized, the lead
generation system
continues checking the lead list (step 13510).
[00463] Content Generation Systems
[00464] In embodiments, a content generation system of the platform generates
content for a
contact event, such as an email, text message, or a post to a network, or a
machine-to-
machine message, such as communicating via an API or a peer-to-peer system. In
embodiments, the content is customized using artificial intelligence based on
the attributes of
the facility, attributes of a recipient (e.g., based on the profile of a
person, the role of a
person, or the like), and/or relating to the project or activity to which the
facility relates. The
content generation system may be seeded with a set of templates, which may be
customized,
such as by training the content generation system on a training set of data
created by human
writers, and which may be further trained by feedback based on outcomes
tracked by the
platform, such as outcomes indicating success of particular forms of
communication in
generating donations to a facility, as well as other indicators as noted
throughout this
disclosure. The content generation system may customize content based on
attributes of the
facility, a project, and/or one or more people, and the like. For example, a
facility manager
may receive short messages about events related to facility operations,
including codes,
acronyms and jargon, while an outside consumer of outputs from the facility
may receive a
more formal report relating to the same event.
[00465] Figure 136 illustrates a manner by which the content generation system
may
generate personalized content. The content generation system receives a
recipient id, a sender
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id (which may be a person or a system, among others), and a facility id (step
13602). The
content generation system may determine the appropriate template (step 13604)
to use based
on the relationships among the recipient, sender and facility and/or based on
other
considerations (e.g., a recipient who is a busy manager is more likely to
respond to less
formal messages or more formal messages). The content generation system may
provide the
template (or an identifier thereof) to the natural language generation system,
along with the
recipient id, the sender id, and the facility id. The natural language
generation system may
obtain facility attributes based on the facility id, and person attributes
corresponding to the
recipient or sender based on their identities (step 13606). The natural
language generation
system may then generate the personalized or customized content (step 13608)
based on the
selected template, the facility parameters, and/or other attributes of the
various types
described herein. The natural language generation system may output the
generated content
(step 13610) to the content generation system.
[00466] In embodiments, a person, such as a facility manager, may approve the
generated
content provided by the content generation system and/or make edits to the
generated content,
then send the content, such as via email and/or other channels. In
embodiments, the platform
tracks the contact event.
[00467] Referring to Fig. 137, an adaptive intelligence system 13704 may
include an
artificial intelligence system 13748, a digital twin system 13720, and an
adaptive device (or
edge) intelligence system 13730. The artificial intelligence system 13748 may
define a
machine learning model 13702 for performing analytics, simulation, decision
making, and
prediction making related to data processing, data analysis, simulation
creation, and
simulation analysis of one or more of the transaction entities. The machine
learning model
13702 is an algorithm and/or statistical model that performs specific tasks
without using
explicit instructions, relying instead on patterns and inference. The machine
learning model
13702 builds one or more mathematical models based on training data to make
predictions
and/or decisions without being explicitly programmed to perform the specific
tasks. The
machine learning model 13702 may receive inputs of sensor data as training
data, including
event data 13724 and state data 13772 related to one or more of the
transaction entities
through data collection systems 13718 and monitoring systems 13706 and
connectivity
facilities 13716. The event data 13724 and state data 13772 may be stored in a
data storage
system 13710 The sensor data input to the machine learning model 13702 may be
used to
train the machine learning model 13702 to perform the analytics, simulation,
decision
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making, and prediction making relating to the data processing, data analysis,
simulation
creation, and simulation analysis of the one or more of the transaction
entities. The machine
learning model 13702 may also use input data from a user or users of the
information
technology system. The machine learning model 13702 may include an artificial
neural
network, a decision tree, a support vector machine, a Bayesian network, a
genetic algorithm,
any other suitable form of machine learning model, or a combination thereof.
The machine
learning model 13702 may be configured to learn through supervised learning,
unsupervised
learning, reinforcement learning, self-learning, feature learning, sparse
dictionary learning,
anomaly detection, association rules, a combination thereof, or any other
suitable algorithm
for learning.
[00468] The artificial intelligence system 13748 may also define the digital
twin system
13720 to create a digital replica of one or more of the transaction entities.
The digital replica
of the one or more of the transaction entities may use substantially real-time
sensor data to
provide for substantially real-time virtual representation of the transaction
entity and provides
for simulation of one or more possible future states of the one or more
transaction entities.
The digital replica exists simultaneously with the one or more transaction
entities being
replicated. The digital replica provides one or more simulations of both
physical elements and
properties of the one or more transaction entities being replicated and the
dynamics thereof,
in embodiments, throughout the lifestyle of the one or more transaction
entities being
replicated. The digital replica may provide a hypothetical simulation of the
one or more
transaction entities, for example during a design phase before the one or more
transaction
entities are constructed or fabricated, or during or after construction or
fabrication of the one
or more transaction entities by allowing for hypothetical extrapolation of
sensor data to
simulate a state of the one or more transaction entities, such as during high
stress, after a
period of time has passed during which component wear may be an issue, during
maximum
throughput operation, after one or more hypothetical or planned improvements
have been
made to the one or more transaction entities, or any other suitable
hypothetical situation. In
some embodiments, the machine learning model 13702 may automatically predict
hypothetical situations for simulation with the digital replica, such as by
predicting possible
improvements to the one or more transaction entities, predicting when one or
more
components of the one or more transaction entities may fail, and/or suggesting
possible
improvements to the one or more transaction entities, such as changes to
timing settings,
arrangement, components, or any other suitable change to the transaction
entities. The digital
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replica allows for simulation of the one or more transaction entities during
both design and
operation phases of the one or more transaction entities, as well as
simulation of hypothetical
operation conditions and configurations of the one or more transaction
entities. The digital
replica allows for invaluable analysis and simulation of the one or more
transaction entities,
by facilitating observation and measurement of nearly any type of metric,
including
temperature, wear, light, vibration, etc. not only in, on, and around each
component of the
one or more transaction entities, but in some embodiments within the one or
more transaction
entities. In some embodiments, the machine learning model 13702 may process
the sensor
data including the event data 13724 and the state data 13772 to define
simulation data for use
by the digital twin system 13720. The machine learning model 13702 may, for
example,
receive state data 13772 and event data 13724 related to a particular
transaction entity of the
plurality of transaction entities and perform a series of operations on the
state data 13772 and
the event data 13724 to format the state data 13772 and the event data 13724
into a format
suitable for use by the digital twin system 13720 in creation of a digital
replica of the
transaction entity. For example, one or more transaction entities may include
a robot
configured to augment products on an adjacent assembly line. The machine
learning model
13702 may collect data from one or more sensors positioned on, near, in,
and/or around the
robot. The machine learning model 13702 may perform operations on the sensor
data to
process the sensor data into simulation data and output the simulation data to
the digital twin
system 13720. The digital twin system 13720 simulation may use the simulation
data to
create one or more digital replicas of the robot, the simulation including for
example metrics
including temperature, wear, speed, rotation, and vibration of the robot and
components
thereof. The simulation may be a substantially real-time simulation, allowing
for a human
user of the information technology to view the simulation of the robot,
metrics related
thereto, and metrics related to components thereof, in substantially real
time. The simulation
may be a predictive or hypothetical situation, allowing for a human user of
the information
technology to view a predictive or hypothetical simulation of the robot,
metrics related
thereto, and metrics related to components thereof.
[00469] In some embodiments, the machine learning model 13702 and the digital
twin
system 13720 may process sensor data and create a digital replica of a set of
transaction
entities of the plurality of transaction entities to facilitate design, real-
time simulation,
predictive simulation, and/or hypothetical simulation of a related group of
transaction
entities. The digital replica of the set of transaction entities may use
substantially real-time
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sensor data to provide for substantially real-time virtual representation of
the set of
transaction entities and provide for simulation of one or more possible future
states of the set
of transaction entities. The digital replica exists simultaneously with the
set of transaction
entities being replicated. The digital replica provides one or more
simulations of both
physical elements and properties of the set of transaction entities being
replicated and the
dynamics thereof, in embodiments throughout the lifestyle of the set of
transaction entities
being replicated. The one or more simulations may include a visual simulation,
such as a
wire-frame virtual representation of the one or more transaction entities that
may be viewable
on a monitor, using an augmented reality (AR) apparatus, or using a virtual
reality (VR)
apparatus. The visual simulation may be able to be manipulated by a human user
of the
information technology system, such as zooming or highlighting components of
the
simulation and/or providing an exploded view of the one or more transaction
entities. The
digital replica may provide a hypothetical simulation of the set of
transaction entities, for
example during a design phase before the one or more transaction entities are
constructed or
fabricated, or during or after construction or fabrication of the one or more
transaction
entities by allowing for hypothetical extrapolation of sensor data to simulate
a state of the set
of transaction entities, such as during high stress, after a period of time
has passed during
which component wear may be an issue, during maximum throughput operation,
after one or
more hypothetical or planned improvements have been made to the set of
transaction entities,
or any other suitable hypothetical situation. In some embodiments, the machine
learning
model 13702 may automatically predict hypothetical situations for simulation
with the digital
replica, such as by predicting possible improvements to the set of transaction
entities,
predicting when one or more components of the set of transaction entities may
fail, and/or
suggesting possible improvements to the set of transaction entities, such as
changes to timing
settings, arrangement, components, or any other suitable change to the
transaction entities.
The digital replica allows for simulation of the set of transaction entities
during both design
and operation phases of the set of transaction entities, as well as simulation
of hypothetical
operation conditions and configurations of the set of transaction entities.
The digital replica
allows for invaluable analysis and simulation of the one or more transaction
entities, by
facilitating observation and measurement of nearly any type of metric,
including temperature,
wear, light, vibration, etc. not only in, on, and around each component of the
set of
transaction entities, but in some embodiments within the set of transaction
entities. In some
embodiments, the machine learning model 13702 may process the sensor data
including the
event data 13724 and the state data 13772 to define simulation data for use by
the digital twin
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system 13720. The machine learning model 13702 may, for example, receive state
data
13772 and event data 13724 related to a particular transaction entity of the
plurality of
transaction entities and perform a series of operations on the state data
13772 and the event
data 13724 to format the state data 13772 and the event data 13724 into a
format suitable for
use by the digital twin system 13720 in the creation of a digital replica of
the set of
transaction entities. For example, a set of transaction entities may include a
die machine
configured to place products on a conveyor belt, the conveyor belt on which
the die machine
is configured to place the products, and a plurality of robots configured to
add parts to the
products as they move along the assembly line. The machine learning model
13702 may
collect data from one or more sensors positioned on, near, in, and/or around
each of the die
machines, the conveyor belt, and the plurality of robots. The machine learning
model 13702
may perform operations on the sensor data to process the sensor data into
simulation data and
output the simulation data to the digital twin system 13720. The digital twin
system 13720
simulation may use the simulation data to create one or more digital replicas
of the die
machine, the conveyor belt, and the plurality of robots, the simulation
including for example
metrics including temperature, wear, speed, rotation, and vibration of the die
machine, the
conveyor belt, and the plurality of robots and components thereof. The
simulation may be a
substantially real-time simulation, allowing for a human user of the
information technology
to view the simulation of the die machine, the conveyor belt, and the
plurality of robots,
metrics related thereto, and metrics related to components thereof, in
substantially real time.
The simulation may be a predictive or hypothetical situation, allowing for a
human user of
the information technology to view a predictive or hypothetical simulation of
the die
machine, the conveyor belt, and the plurality of robots, metrics related
thereto, and metrics
related to components thereof.
[00470] In some embodiments, the machine learning model 13702 may prioritize
collection
of sensor data for use in digital replica simulations of one or more of the
transaction entities.
The machine learning model 13702 may use sensor data and user inputs to train,
thereby
learning which types of sensor data are most effective for creation of digital
replicate
simulations of one or more of the transaction entities. For example, the
machine learning
model 13702 may find that a particular transaction entity has dynamic
properties such as
component wear and throughput affected by temperature, humidity, and load. The
machine
learning model 13702 may, through machine learning, prioritize collection of
sensor data
related to temperature, humidity, and load, and may prioritize processing
sensor data of the
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prioritized type into simulation data for output to the digital twin system
13720. In some
embodiments, the machine learning model 13702 may suggest to a user of the
information
technology system that more and/or different sensors of the prioritized type
be implemented
in the information technology near and around the transaction entity being
simulation such
that more and/or better data of the prioritized type may be used in simulation
of the
transaction entity via the digital replica thereof.
[00471] In some embodiments, the machine learning model 13702 may be
configured to
learn to determine which types of sensor data are to be processed into
simulation data for
transmission to the digital twin system 13720 based on one or both of a
modeling goal and a
quality or type of sensor data. A modeling goal may be an objective set by a
user of the
information technology system or may be predicted or learned by the machine
learning model
13702. Examples of modeling goals include creating a digital replica capable
of showing
dynamics of throughput on an assembly line, which may include collection,
simulation, and
modeling of, e.g., thermal, electrical power, component wear, and other
metrics of a conveyor
belt, an assembly machine, one or more products, and other components of the
transaction
ecosystem. The machine learning model 137102 may be configured to learn to
determine
which types of sensor data are necessary to be processed into simulation data
for transmission
to the digital twin system 13720 to achieve such a model. In some embodiments,
the machine
learning model 13702 may analyze which types of sensor data are being
collected, the quality
and quantity of the sensor data being collected, and what the sensor data
being collected
represents, and may make decisions, predictions, analyses, and/or
determinations related to
which types of sensor data are and/or are not relevant to achieving the
modeling goal and
may make decisions, predictions, analyses, and/or determinations to
prioritize, improve,
and/or achieve the quality and quantity of sensor data being processed into
simulation data
for use by the digital twin system 13720 in achieving the modeling goal.
[00472] In some embodiments, a user of the information technology system may
input a
modeling goal into the machine learning model 13702. The machine learning
model 13702
may learn to analyze training data to output suggestions to the user of the
information
technology system regarding which types of sensor data are most relevant to
achieving the
modeling goal, such as one or more types of sensors positioned in, on, or near
a transaction
entity or a plurality of transaction entities that is relevant to the
achievement of the modeling
goal is and/or are not sufficient for achieving the modeling goal, and how a
different
configuration of the types of sensors, such as by adding, removing, or
repositioning sensors,
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may better facilitate achievement of the modeling goal by the machine learning
model 13702
and the digital twin system 13720. In some embodiments, the machine learning
model 13702
may automatically increase or decrease collection rates, processing, storage,
sampling rates,
bandwidth allocation, bitrates, and other attributes of sensor data collection
to achieve or
better achieve the modeling goal. In some embodiments, the machine learning
model 13702
may make suggestions or predictions to a user of the information technology
system related
to increasing or decreasing collection rates, processing, storage, sampling
rates, bandwidth
allocation, bitrates, and other attributes of sensor data collection to
achieve or better achieve
the modeling goal. In some embodiments, the machine learning model 13702 may
use sensor
data, simulation data, previous, current, and/or future digital replica
simulations of one or
more transaction entities of the plurality of transaction entities to
automatically create and/or
propose modeling goals. In some embodiments, modeling goals automatically
created by the
machine learning model 13702 may be automatically implemented by the machine
learning
model 13702. In some embodiments, modeling goals automatically created by the
machine
learning model 13702 may be proposed to a user of the information technology
system, and
implemented only after acceptance and/or partial acceptance by the user, such
as after
modifications are made to the proposed modeling goal by the user.
[00473] In some embodiments, the user may input the one or more modeling
goals, for
example, by inputting one or more modeling commands to the information
technology
system. The one or more modeling commands may include, for example, a command
for the
machine learning model 13702 and the digital twin system 13720 to create a
digital replica
simulation of one transaction entity or a set of transaction entities, may
include a command
for the digital replica simulation to be one or more of a real-time
simulation, and a
hypothetical simulation. The modeling command may also include, for example,
parameters
for what types of sensor data should be used, sampling rates for the sensor
data, and other
parameters for the sensor data used in the one or more digital replica
simulations. In some
embodiments, the machine learning model 13702 may be configured to predict
modeling
commands, such as by using previous modeling commands as training data. The
machine
learning model 13702 may propose predicted modeling commands to a user of the
information technology system, for example, to facilitate simulation of one or
more of the
transaction entities that may be useful for the management of the transaction
entities and/or to
allow the user to easily identify potential issues with or possible
improvements to the
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transaction entities. The system of Fig. 137 may include a transactions
management platform
and applications.
[00474] In some embodiments, the machine learning model 13702 may be
configured to
evaluate a set of hypothetical simulations of one or more of the transaction
entities. The set of
hypothetical simulations may be created by the machine learning model 13702
and the digital
twin system 13720 as a result of one or more modeling commands, as a result of
one or more
modeling goals, one or more modeling commands, by prediction by the machine
learning
model 13702, or a combination thereof. The machine learning model 13702 may
evaluate the
set of hypothetical simulations based on one or more metrics defined by the
user, one or more
metrics defined by the machine learning model 13702, or a combination thereof.
In some
embodiments, the machine learning model 13702 may evaluate each of the
hypothetical
simulations of the set of hypothetical simulations independently of one
another. In some
embodiments, the machine learning model 13702 may evaluate one or more of the
hypothetical simulations of the set of hypothetical simulations in relation to
one another, for
example by ranking the hypothetical simulations or creating tiers of the
hypothetical
simulations based on one or more metrics.
[00475] In some embodiments, the machine learning model 13702 may include one
or more
model interpretability systems to facilitate human understanding of outputs of
the machine
learning model 13702, as well as information and insight related to cognition
and processes
of the machine learning model 13702, i.e., the one or more model
interpretability systems
allow for human understanding of not only "what" the machine learning model
13702 is
outputting, but also "why" the machine learning model 13702 is outputting the
outputs
thereof, and what process led to the machine learning models 13702 formulating
the outputs.
The one or more model interpretability systems may also be used by a human
user to improve
and guide training of the machine learning model 13702, to help debug the
machine learning
model 13702, to help recognize bias in the machine learning model 13702. The
one or more
model interpretability systems may include one or more of linear regression,
logistic
regression, a generalized linear model (GLM), a generalized additive model
(GAM), a
decision tree, a decision rule, RuleFit, Naive B ayes Classifier, a K-nearest
neighbors
algorithm, a partial dependence plot, individual conditional expectation
(ICE), an
accumulated local effects (ALE) plot, feature interaction, permutation feature
importance, a
global surrogate model, a local surrogate (LIME) model, scoped rules, i.e.,
anchors, Shapley
values, Shapley additive explanations (SHAP), feature visualization, network
dissection, or
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any other suitable machine learning interpretability implementation. In some
embodiments,
the one or more model interpretability systems may include a model dataset
visualization
system. The model dataset visualization system is configured to automatically
provide to a
human user of the information technology system visual analysis related to
distribution of
values of the sensor data, the simulation data, and data nodes of the machine
learning model
13702.
[00476] In some embodiments, the machine learning model 13702 may include
and/or
implement an embedded model interpretability system, such as a Bayesian case
model
(BCM) or glass box. The Bayesian case model uses Bayesian case-based
reasoning, prototype
classification, and clustering to facilitate human understanding of data such
as the sensor
data, the simulation data, and data nodes of the machine learning model 13702.
In some
embodiments, the model interpretability system may include and/or implement a
glass box
interpretability method, such as a Gaussian process, to facilitate human
understanding of data
such as the sensor data, the simulation data, and data nodes of the machine
learning model
13702.
[00477] In some embodiments, the machine learning model 13702 may include
and/or
implement testing with concept activation vectors (TCAV). The TCAV allows the
machine
learning model 13702 to learn human-interpretable concepts, such as "running,"
"not
running," "powered," "not powered," "robot," "human," "truck," or "ship" from
examples by
a process including defining the concept, determining concept activation
vectors, and
calculating directional derivatives. By learning human-interpretable concepts,
objects, states,
etc., TCAV may allow the machine learning model 13702 to output useful
information
related to the transaction entities and data collected therefrom in a format
that is readily
understood by a human user of the information technology system.
[00478] In some embodiments, the machine learning model 13702 may be and/or
include an
artificial neural network, e.g. a connectionist system configured to "learn"
to perform tasks
by considering examples and without being explicitly programmed with task-
specific rules.
The machine learning model 13702 may be based on a collection of connected
units and/or
nodes that may act like artificial neurons that may in some ways emulate
neurons in a
biological brain. The units and/or nodes may each have one or more connections
to other
units and/or nodes. The units and/or nodes may be configured to transmit
information, e.g.
one or more signals, to other units and/or nodes, process signals received
from other units
and/or nodes, and forward processed signals to other units and/or nodes. One
or more of the
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units and/or nodes and connections therebetween may have one or more numerical
"weights"
assigned. The assigned weights may be configured to facilitate learning, i.e.,
training, of the
machine learning model 13702. The weights assigned weights may increase and/or
decrease
one or more signals between one or more units and/or nodes, and in some
embodiments may
have one or more thresholds associated with one or more of the weights. The
one or more
thresholds may be configured such that a signal is only sent between one or
more units and/or
nodes if a signal and/or aggregate signal crosses the threshold. In some
embodiments, the
units and/or nodes may be assigned to a plurality of layers, each of the
layers having one or
both of inputs and outputs. A first layer may be configured to receive
training data, transform
at least a portion of the training data, and transmit signals related to the
training data and
transformation thereof to a second layer. A final layer may be configured to
output an
estimate, conclusion, product, or other consequence of processing of one or
more inputs by
the machine learning model 13702. Each of the layers may perform one or more
types of
transformations, and one or more signals may pass through one or more of the
layers one or
more times. In some embodiments, the machine learning model 13702 may employ
deep
learning and being at least partially modeled and/or configured as a deep
neural network, a
deep belief network, a recurrent neural network, and/or a convolutional neural
network, such
as by being configured to include one or more hidden layers.
[00479] In some embodiments, the machine learning model 13702 may be and/or
include a
decision tree, e.g. a tree-based predictive model configured to identify one
or more
observations and determine one or more conclusions based on an input. The
observations
may be modeled as one or more "branches" of the decision tree, and the
conclusions may be
modeled as one or more "leaves" of the decision tree. In some embodiments, the
decision tree
may be a classification tree. the classification tree may include one or more
leaves
representing one or more class labels, and one or more branches representing
one or more
conjunctions of features configured to lead to the class labels. In some
embodiments, the
decision tree may be a regression tree. The regression tree may be configured
such that one or
more target variables may take continuous values.
[00480] In some embodiments, the machine learning model 13702 may be and/or
include a
support vector machine, e.g. a set of related supervised learning methods
configured for use
in one or both of classification and regression-based modeling of data. The
support vector
machine may be configured to predict whether a new example falls into one or
more
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categories, the one or more categories being configured during training of the
support vector
machine.
[00481] In some embodiments, the machine learning model 13702 may be
configured to
perform regression analysis to determine and/or estimate a relationship
between one or more
inputs and one or more features of the one or more inputs. Regression analysis
may include
linear regression, wherein the machine learning model 13702 may calculate a
single line to
best fit input data according to one or more mathematical criteria.
[00482] In embodiments, inputs to the machine learning model 13702 (such as a
regression
model, Bayesian network, supervised model, or other type of model) may be
tested, such as
by using a set of testing data that is independent from the data set used for
the creation and/or
training of the machine learning model, such as to test the impact of various
inputs to the
accuracy of the model 13702. For example, inputs to the regression model may
be removed,
including single inputs, pairs of inputs, triplets, and the like, to determine
whether the
absence of inputs creates a material degradation of the success of the model
13702. This may
assist with recognition of inputs that are in fact correlated (e.g., are
linear combinations of the
same underlying data), that are overlapping, or the like. Comparison of model
success may
help select among alternative input data sets that provide similar
information, such as to
identify the inputs (among several similar ones) that generate the least
"noise" in the model,
that provide the most impact on model effectiveness for the lowest cost, or
the like. Thus,
input variation and testing of the impact of input variation on model
effectiveness may be
used to prune or enhance model performance for any of the machine learning
systems
described throughout this disclosure.
[00483] In some embodiments, the machine learning model 13702 may be and/or
include a
Bayesian network. The Bayesian network may be a probabilistic graphical model
configured
to represent a set of random variables and conditional independence of the set
of random
variables. The Bayesian network may be configured to represent the random
variables and
conditional independence via a directed acyclic graph. The Bayesian network
may include
one or both of a dynamic Bayesian network and an influence diagram.
[00484] In some embodiments, the machine learning model 13702 may be defined
via
supervised learning, i.e., one or more algorithms configured to build a
mathematical model of
a set of training data containing one or more inputs and desired outputs. The
training data
may consist of a set of training examples, each of the training examples
having one or more
inputs and desired outputs, i.e., a supervisory signal. Each of the training
examples may be
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represented in the machine learning model 13702 by an array and/or a vector,
i.e., a feature
vector. The training data may be represented in the machine learning model
13702 by a
matrix. The machine learning model 13702 may learn one or more functions via
iterative
optimization of an objective function, thereby learning to predict an output
associated with
new inputs. Once optimized, the objective function may provide the machine
learning model
13702 with the ability to accurately determine an output for inputs other than
inputs included
in the training data. In some embodiments, the machine learning model 13702
may be
defined via one or more supervised learning algorithms such as active
learning, statistical
classification, regression analysis, and similarity learning. Active learning
may include
interactively querying, by the machine learning model 13702, a user and/or an
information
source to label new data points with desired outputs. Statistical
classification may include
identifying, by the machine learning model 13702, to which a set of
subcategories, i.e.,
subpopulations, a new observation belongs based on a training set of data
containing
observations having known categories. Regression analysis may include
estimating, by the
machine learning model 13702 relationships between a dependent variable, i.e.,
an outcome
variable, and one or more independent variables, i.e., predictors, covariates,
and/or features.
Similarity learning may include learning, by the machine learning model 13702,
from
examples using a similarity function, the similarity function being designed
to measure how
similar or related two objects are.
[00485] In some embodiments, the machine learning model 13702 may be defined
via
unsupervised learning, i.e., one or more algorithms configured to build a
mathematical model
of a set of data containing only inputs by finding structure in the data such
as grouping or
clustering of data points. In some embodiments, the machine learning model
13702 may learn
from test data, i.e., training data, that has not been labeled, classified, or
categorized. The
unsupervised learning algorithm may include identifying, by the machine
learning model
13702, commonalities in the training data and learning by reacting based on
the presence or
absence of the identified commonalities in new pieces of data. In some
embodiments, the
machine learning model 13702 may generate one or more probability density
functions. In
some embodiments, the machine learning model 13702 may learn by performing
cluster
analysis, such as by assigning a set of observations into subsets, i.e.,
clusters, according to
one or more predesignated criteria, such as according to a similarity metric
of which internal
compactness, separation, estimated density, and/or graph connectivity are
factors.
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[00486] In some embodiments, the machine learning model 13702 may be defined
via semi-
supervised learning, i.e., one or more algorithms using training data wherein
some training
examples may be missing training labels. The semi-supervised learning may be
weakly
supervised learning, wherein the training labels may be noisy, limited, and/or
imprecise. The
noisy, limited, and/or imprecise training labels may be cheaper and/or less
labor intensive to
produce, thus allowing the machine learning model 13702 to train on a larger
set of training
data for less cost and/or labor.
[00487] In some embodiments, the machine learning model 13702 may be defined
via
reinforcement learning, such as one or more algorithms using dynamic
programming
techniques such that the machine learning model 13702 may train by taking
actions in an
environment in order to maximize a cumulative reward. In some embodiments, the
training
data is represented as a Markov Decision Process.
[00488] In some embodiments, the machine learning model 13702 may be defined
via self-
learning, wherein the machine learning model 13702 is configured to train
using training data
with no external rewards and no external teaching, such as by employing a
Crossbar Adaptive
Array (CAA). The CAA may compute decisions about actions and/or emotions about
consequence situations in a crossbar fashion, thereby driving teaching of the
machine
learning model 13702 by interactions between cognition and emotion.
[00489] In some embodiments, the machine learning model 13702 may be defined
via
feature learning, i.e., one or more algorithms designed to discover
increasingly accurate
and/or apt representations of one or more inputs provided during training,
e.g. training data.
Feature learning may include training via principal component analysis and/or
cluster
analysis. Feature learning algorithms may include attempting, by the machine
learning model
13702, to preserve input training data while also transforming the input
training data such
that the transformed input training data is useful. In some embodiments, the
machine learning
model 13702 may be configured to transform the input training data prior to
performing one
or more classifications and/or predictions of the input training data. Thus,
the machine
learning model 13702 may be configured to reconstruct input training data from
one or more
unknown data-generating distributions without necessarily conforming to
implausible
configurations of the input training data according to the distributions. In
some embodiments,
the feature learning algorithm may be performed by the machine learning model
13702 in a
supervised, unsupervised, or semi-supervised manner.
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[00490] In some embodiments, the machine learning model 13702 may be defined
via
anomaly detection, i.e., by identifying rare and/or outlier instances of one
or more items,
events and/or observations. The rare and/or outlier instances may be
identified by the
instances differing significantly from patterns and/or properties of a
majority of the training
data. Unsupervised anomaly detection may include detecting of anomalies, by
the machine
learning model 13702, in an unlabeled training data set under an assumption
that a majority
of the training data is "normal." Supervised anomaly detection may include
training on a data
set wherein at least a portion of the training data has been labeled as
"normal" and/or
"abnormal."
[00491] In some embodiments, the machine learning model 13702 may be defined
via robot
learning. Robot learning may include generation, by the machine learning model
13702, of
one or more curricula, the curricula being sequences of learning experiences,
and
cumulatively acquiring new skills via exploration guided by the machine
learning model
13702 and social interaction with humans by the machine learning model 13702.
Acquisition
of new skills may be facilitated by one or more guidance mechanisms such as
active learning,
maturation, motor synergies, and/or imitation.
[00492] In some embodiments, the machine learning model 13702 can be defined
via
association rule learning. Association rule learning may include discovering
relationships, by
the machine learning model 13702, between variables in databases, in order to
identify strong
rules using some measure of "interestingness." Association rule learning may
include
identifying, learning, and/or evolving rules to store, manipulate and/or apply
knowledge. The
machine learning model 13702 may be configured to learn by identifying and/or
utilizing a
set of relational rules, the relational rules collectively representing
knowledge captured by the
machine learning model 13702. Association rule learning may include one or
more of
learning classifier systems, inductive logic programming, and artificial
immune systems.
Learning classifier systems are algorithms that may combine a discovery
component, such as
one or more genetic algorithms, with a learning component, such as one or more
algorithms
for supervised learning, reinforcement learning, or unsupervised learning.
Inductive logic
programming may include rule-learning, by the machine learning model 13702,
using logic
programming to represent one or more of input examples, background knowledge,
and
hypothesis determined by the machine learning model 13702 during training. The
machine
learning model 13702 may be configured to derive a hypothesized logic program
entailing all
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positive examples given an encoding of known background knowledge and a set of
examples
represented as a logical database of facts.
[00493] Referring to Fig. 138, a compliance system 13800 that facilitates the
licensing of
personality rights using a distributed ledger and cryptocurrency is depicted.
As used herein,
personality rights may refer to an entity's ability to control the use of his,
her, or its identity
for commercial purposes. The term entity, as used herein, may refer to an
individual or an
organization (e.g., a university, a school, a team, a corporation, or the
like) that agrees to
license its personality rights, unless context suggests otherwise. This may
include an entity's
ability to control the use of its name, image, likeness, voice, or the like.
For example, an
individual exercising their personality rights for commercial purposes may
include appearing
in a commercial, television show, or movie, making a sponsored social media
post (e.g.,
Instagram post, Facebook post, Twitter tweet, or the like), having their name
appear on
clothing (e.g., a jersey, t-shirts, sweatshirts, or the like) or other goods,
appearing in a video
game, or the like. In embodiments, individuals may refer to student athletes
or professional
athletes, but may include other classes of individuals as well. While the
current description
makes reference to the NCAA, the system may be used to monitor and facilitate
transactions
relating to other individuals and organizations. For example, the system may
be used in the
context of professional sports, where organizations may use sponsorships and
other licensing
deals to circumvent salary caps or other league rules (e.g., FIFA fair play
rules).
[00494] In embodiments, the compliance system 13800 maintains one or more
digital
ledgers that record transactions relating to the licensing of personality
rights of entities. In
embodiments, a digital ledger may be a distributed ledger that is distributed
amongst a set of
computing devices 13870, 13880, 13890 (also referred to as nodes) and/or may
be encrypted.
Put another way, each participating node may store a copy of the distributed
ledger. An
example of the digital ledger is a Blockchain ledger. In some embodiments, a
distributed
ledger is stored across a set of public nodes. In other embodiments, a
distributed ledger is
stored across a set of whitelisted participant nodes (e.g., on the servers of
participating
universities or teams). In some embodiments, the digital ledger is privately
maintained by the
compliance system 13800. The latter configuration provides a more energy
efficient means of
maintaining a digital ledger; while the former configurations (e.g.,
distributed ledgers)
provide a more secure/verifiable means of maintaining a digital ledger.
[00495] In embodiments, a distributed ledger may store tokens. The tokens may
be
cryptocurrency tokens that are transferrable to licensors and licensees. In
some embodiments,
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a distributed ledger may store the ownership data of each token. A token (or a
portion
thereof) may be owned by the compliance system, the governing organization
(e.g., the
NCAA), a licensor, a licensee, a team, an institution, an individual or the
like. In
embodiments, the distributed ledger may store event records. Event records may
store
information relating to events associated with the entities involved with the
compliance
system. For example, an event record may record an agreement entered into by
two parties,
the completion of an obligation by a licensor, the distribution of funds to a
licensor from a
license, the non-completion of an obligation by a licensor, the distribution
of funds to entities
associated with the licensee (e.g., teammates, institution, team, etc.), and
the like.
[00496] In embodiments, the digital ledger may store smart contracts that
govern agreements
between licensors and licensees. As used herein, a licensee may be an
organization or person
that wishes to enter an agreement to license a licensors personality rights.
Examples of
licensees may include, but are not limited to, a car dealership that wants a
star student athlete
to appear in a print ad, a company that wants the likeness of a licensor
(e.g., an athlete and/or
a team) to appear in a commercial, a video game maker that wants to use team
names, team
apparel, player names and/or numbers in a video game, a shoe maker that wants
an athlete to
endorse a sneaker, a television show producer that wants an athlete to appear
in the television
show, or the like. In embodiments, the compliance system 13800 generates a
smart contract
that memorializes an agreement between the individual and a licensee and
facilitates the
transfer of consideration (e.g., money) when the parties agree that the
individual has
performed his or her requirements as put forth in the agreement. For example,
an athlete may
agree to appear in a commercial on behalf of a local car dealership. The smart
contract in this
example may include an identifier of the athlete (e.g., an individual ID
and/or an individual
account ID), an identifier of the organization (e.g., an organization ID
and/or an organization
account ID), the requirements of the individual (e.g., to appear in a
commercial, to make a
sponsored social media post, to appear at an autograph signing, or the like),
and the
consideration (e.g., a monetary amount). In embodiments, the smart contract
may include
additional terms. In embodiments, the additional terms may include an
allocation rule that
defines a manner by which the consideration is allocated to the athlete and
one or more other
parties (e.g., agent, manager, university, team, teammates, or the like). For
example, in the
context of a student athlete, a smart contract may define a split between the
licensing athlete,
the athletic department of the student athlete's university, and the student
athlete's teammates.
In a specific example, a university may have a policy that requires a player
appearing in any
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advertisement to split the funds according to a 60/20/20 split, whereby 60% of
the funds are
allocated to the student athlete appearing in the commercial, 20% of the funds
are allocated to
the athletic department, and 20% of the funds are allocated to the student
athlete's teammates.
When a smart contract verifies that the athlete has performed his or her
duties with respect to
the smart contract (e.g., appeared for the commercial), the smart contract can
transfer the
agreed upon amount from an account of the licensee to an account of the
athlete and accounts
of any other entities that may be allocated a percentage of the funds in the
smart contract
(e.g., athletic department and teammates).
[00497] In embodiments, the compliance system 13800 utilizes cryptocurrency to
facilitate
the transfer of funds. In embodiments, the cryptocurrency is mined by
participant nodes
and/or generated by the compliance system. The cryptocurrency can be an
established type of
cryptocurrency (e.g., Bitcoin, Ethereum, Litecoin, or the like) or may be a
proprietary
cryptocurrency. In some embodiments, the cryptocurrency is a pegged
cryptocurrency that is
pegged to a particular fiat currency (e.g., pegged to the US dollar. British
Pound, Euro, or the
like). For example, a single unit of cryptocurrency (also referred to as a
"coin") may be
pegged to a single unit of fiat currency (e.g., a US dollar). In embodiments,
a licensee may
exchange fiat currency for a corresponding amount of cryptocurrency. For
example, if the
cryptocurrency is pegged to the dollar, the licensee may exchange an amount of
US dollars
for a corresponding amount of cryptocurrency. In embodiments, the compliance
system
13800 may keep a percentage of the real-world currency as a transaction fee
(e.g., 5%). For
example, in exchanging S10,000, the compliance system 13800 may distribute
S9,500 dollars'
worth of cryptocurrency to an account of the licensee and may keep the S5,000
dollars as a
transaction fee. Once the cryptocurrency is deposited in an account of a
licensee, the licensee
may enter into transactions with individuals.
[00498] In embodiments, the compliance system 13800 may allow organizations to
create
smart contract templates that define one or more conditions/restrictions on
the contract. For
example, an organization may predefine the allocation between the licensee,
the organization,
and any other individuals (e.g., coaches, teammates, representatives).
Additionally or
alternatively, the organization may place minimum and/or maximum amounts of
agreements.
Additionally or alternatively, the organization may place restrictions on when
an agreement
can be entered into and/or performed. For example, players may be restricted
from appearing
in commercials or advertisements during the season and/or during exam periods.
These
details may be stored in an organization datastore 13856A Organizations may
place other
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conditions/restrictions in a smart contract. In these embodiments, an
individual and licensee
wishing to enter to an agreement must use a smart contract template provided
by the
organization to which the individual belongs. In other words, the compliance
system 13800
may only allow an individual that has an active relationship with an
organization (e.g., plays
on a team of a university) to participate in a smart contract if the smart
contract is defined by
or otherwise approved by the organization.
[00499] In embodiments, the compliance system 13800 manages a clearinghouse
process
that approves potential licensees. Before a licensee can participate in
agreements facilitated
by the compliance system 13800, the licensee can provide information relating
to the
licensee. This may include a tax ID number, an entity name, incorporation
information (e.g.,
state and type), a list of key personnel (e.g., directors, executives, board
members, approved
decision makers, and/or the like), and any other suitable information. In
embodiments, the
potential licensee may be required to sign (e.g., eSign or wet ink signature)
a document
indicating that the organization will not willingly use the compliance system
13800 to
circumvent any rules, laws, or regulations (e.g., they will not circumvent
NCAA regulations).
In embodiments, the compliance system 13800 or another entity (e.g., the NCAA)
may verify
the licensee. Once verified, the information is stored in a licensee datastore
13856B and the
licensee may participate in transactions.
[00500] In embodiments, the compliance system 13800 may create accounts for
licensors
once they have joined an organization (e.g., signed an athletic scholarship
with a university).
Once a licensor is verified as being affiliated with the organization, the
compliance system
13800 may create an account for the licensor and may create a relationship
between the
individual and the organization, whereby the licensor may be required to use
smart contracts
that are approved or provided by the organization. Should the licensor join
another
organization (e.g., transfers to another school), the compliance system 13800
may sever the
relationship with the previous organization and may create a new relationship
with the other
organization. Similarly, once a licensor is no longer affiliated with any
organization (e.g., the
player graduates, enters a professional league, retires, or the like), the
compliance system
13800 may prevent the licensor from participating in transactions on the
compliance system
13800.
[00501] In embodiments, the compliance system 13800 may provide a graphical
user
interface that allows users to create smart contracts governing personality
rights licenses. In
these embodiments, the compliance system allows a user (e.g., a licensor) to
select a smart
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contract template. In some embodiments, the compliance system 13800 may
restrict the user
to only select a smart contract template that is associated with an
institution of the licensor. In
embodiments, the graphical user interface allows a user to define certain
terms (e.g., the type
or types of obligations placed on the licensor, an amount of funds to paid, a
date by which the
obligations of the licensor must be completed by, a location at which the
obligation is
completed, and/or other suitable terms). Upon a user providing input for
parameterizing a
smart contract template, the compliance system 13800 may generate a smart
contract by
parameterizing one or more variables in the smart contract with the provided
input. Upon
parameterizing an instance of a smart contract, the compliance system 13800
may deploy the
smart contract. In some embodiments, the compliance system 13800 may deploy
the smart
contract by broadcasting the parameterized smart contract to the participant
nodes, which in
turn may update each respective instance of the distributed ledger with the
new smart
contract. In some embodiments, an institution of the licensor must approve the
parameterized
smart contract before the parameterized smart contract may be deployed to the
distributed
ledger.
[00502] In embodiments, the compliance system 13800 may provide a graphical
user
interface to verify performance of an obligation by a licensor. In some of
these embodiments,
the compliance system 13800 may include an application that is accessed by
licensors, that
allows a licensor to prove that he or she performed an obligation. In some of
these
embodiments, the application may allow a user to record locations that the
licensor went to
(e.g., locations of film or photo shoots), to upload records (e.g., screen
shots of social media
posts) or to provide other corroborating evidence that the licensor has
performed his or her
obligations with respect to a licensing transaction. In this way, the licensor
can prove that he
or she performed the tasks required by the licensing deal. In some
embodiments, the
application may interact with a wearable device or may capture other digital
exhaust, such as
social media posts of the user (e.g., licensor) to collect evidence that
supports or disproves a
licensors claim that he or she performed the obligations under the transaction
agreement. In
embodiments, the corroborating evidence collected by the application may be
recorded by the
application and stored on the distributed ledger as a licensor datastore
13856C.
[00503] In embodiments, the compliance system 13800 (or a smart contract
issued in
connection with the compliance system 13800) may complete transactions
pertaining to a
smart contract governing the licensing of the personality rights of a licensor
upon verification
that licensor has performed his or her obligations defined in the agreement.
As mentioned, the
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licensor may use an application to provide evidence of satisfaction of the
obligations of the
agreement. Additionally or alternatively, the licensee may provide
verification that the
licensor has performed his or her obligations (e.g., using an application). In
embodiments, the
smart contract governing the agreement may receive verification that the
licensor has
performed his or her obligations defined by the agreement. In response the
smart contract
may release (or initiate the release of) the cryptocurrency amount defined in
the smart
contract. The cryptocurrency amount may be distributed to the accounts of the
licensor and
any other parties defined in the agreement (e.g., teammates of the licensor,
the program of the
licensor, the regulating body, or the like).
[00504] In embodiments, the compliance system 13800 is configured to perform
analytics
and provide reports to a regulatory body and/or other entities (e.g., the
other organizations).
In these embodiments, the analytics may be used to identify individuals that
are potentially
circumventing the rules and regulations of the regulatory body. Furthermore,
in some
embodiments, transaction records may be maintained on a distributed ledger,
whereby
different organizations may be able to view agreements entered into by
individuals affiliated
with other organizations such that added levels of transparency and oversight
may
disincentivize individuals, organizations, and/or licensees from circumventing
rules and
regulations.
[00505] In embodiments, the compliance system 13800 may train and/or leverage
machine-
learned models to identify potential instances of circumvention of rules or
regulations. In
these embodiments, the compliance system 13800 may train machine-learned
models using
outcome data. Examples of outcome data may include data relating to a set of
transactions
where an organization (e.g., a team or university), licensee (e.g., a
company), and/or licensor
(e.g., an athlete) were determined to be circumventing rules or regulations
and data relating to
a set of transactions where an organization, licensee, and/or licensor were
found to be in
compliance with the rules and regulations. Examples of machine-learned models
include
neural networks, regression-based models, decisions trees, random forests,
Hidden Markov
Models, Bayesian Models, and the like. In embodiments, the compliance system
13800 may
leverage a machine-learned model by obtaining a set of records relating to
transactions a
licensee, a licensor, and/or an organization (e.g., a team or university) from
the distributed
ledger. The compliance system may extract relevant features, such as the
amount paid to a
particular licensor by a licensee, amounts paid to other licensors on other
teams, affiliations
of the licensor, amounts paid to a licensor by other licensees, and the like,
and may feed the
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features to the machine-learned model. The machine-learned model may issue a
score that
indicates a likelihood that the transaction was legitimate (or illegitimate)
based on the
extracted features. In embodiments, the compliance system 13800 may provide
notifications
to relevant parties (e.g., regulators) when the output of a machine-learned
model indicates
that a transaction was likely illegitimate.
[00506] FIG. 139 illustrates an example system 13900 configured for
electronically
facilitating licensing of one or more personality rights of a licensor, in
accordance with some
embodiments of the present disclosure. In some embodiments, the system 13900
may include
one or more computing platforms 13902. Computing platform(s) 13902 may be
configured to
communicate with one or more remote platforms 13904 according to a
client/server
architecture, a peer-to-peer architecture, and/or other architectures. Remote
platform(s) 13904
may be configured to communicate with other remote platforms via computing
platform(s)
13902 and/or according to a client/server architecture, a peer-to-peer
architecture, and/or
other architectures. Users may access system 13900 via remote platform(s)
13904.
[00507] In embodiments, computing platform(s) 13902 may be configured by
machine-
readable instructions 13906. Machine-readable instructions 13906 may include
one or more
instruction modules. The instruction modules may include computer program
modules. The
instruction modules may include one or more of an access module 13108, a fund
management module 13112, a ledger management module 13116, a verification
module
13118, an analytics module 13120, and/or other instruction modules.
[00508] In embodiments, the access module 13108 may be configured to receive
an access
request from a licensee to obtain approval to license personality rights from
a set of available
licensors. In embodiments, the access module 13108 may be configured to
selectively grant
access to the licensee based on the access request. For example, the access
module 13108
may receive a name of a potential licensee (e.g., corporate name), a list of
principals (e.g.,
executives and/or owners) of the potential licensee, a location of the
licensee, affiliations of
the licensee and the principals thereof, and the like. In embodiments, the
access module
13108 may provide this information to a human that grants access and/or may
feed this
information into an artificial intelligence system that vets potential
licensees. In
embodiments, the access module 13108 is configured to selectively grant access
to a licensor
by verifying that the licensee is permitted to engage with a set of licensors
including the
licensor based on the set of affiliations. Selectively granting access to the
licensor may
include, in response to verifying that the licensee is permitted to engage
with the set of
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licensors, granting the licensee approval to engage with the set of licensees.
The set of
affiliations of the licensee may include organizations to which the licensee
or a principal
associated with the licensee donates to or owns.
[00509] In embodiments, the fund management module 13112 may be configured to
receive
confirmation of a deposit of an amount of funds from the licensee. In some
embodiments, the
fund management module 13112 may be configured to issue an amount of
cryptocurrency
corresponding to the amount of funds deposited by the licensee to an account
of the licensee.
In embodiments, the fund management system 13112 may be configured to escrow
the
consideration amount of cryptocurrency from the account of the licensee until
the funds are
released by a smart contract.
[00510] In embodiments, the ledger management module 13116 may be configured
to
receive a smart contract request to create a smart contract governing the
licensing of the one
or more personality rights of the licensor by the licensee. In embodiments,
the ledger
management module 13116 may be configured to generate the smart contract based
on the
smart contract request. The smart contract may be generated using a smart
contract template
provided by an interested third party (e.g., a university, a governing body,
or the like) and by
one or more parameters provided by a user (e.g., the licensor, the team of the
licensor, an
institution, and/or licensee) By way of non-limiting example, the interested
third party may
be one of a university, a sports team, or a collegiate athletics governance
organization. The
smart contract request may indicate one or more terms including a
consideration amount of
cryptocurrency to be paid to the licensor in exchange for one or more
obligations on the
licensor. In embodiments, the ledger management module 13116 may be configured
to
deploy the smart contract to a distributed ledger. The distributed ledger may
be auditable by a
set of third parties, including the interested third party. The distributed
ledger may be a public
ledger. The distributed ledger may be a private ledger that is only hosted on
computing
devices associated with interested third parties. In embodiments, the
distributed ledger may
be a blockchain.
[00511] In embodiments, the verification module 13118 may be configured to
verify that the
licensor has performed the one or more obligation. In some embodiments,
verifying that a
licensor has performed the one or more obligations may include receiving
location data from
a wearable device associated with the licensor and verifying that the licensor
has performed
the one or more obligations based on the location data, whereby the location
may be used to
show that the licensor was at a particular location at a particular time
(e.g., a photoshoot or a
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filming). In embodiments, verifying that the licensor may have performed the
one or more
obligations includes receiving social media data from a social media website
and verifying
that the licensor has performed the one or more obligations based on the
social media data,
whereby the social media data may be used to show that the licensor has made a
required
social media posting. In embodiments, verifying that the licensor may have
performed the
one or more obligations includes receiving media content from an external data
source and
verifying that the licensor has performed the one or more obligations based on
the media
content, whereby a licensor and/or licensee may upload the media content to
prove that the
licensor has appeared in the media content. By way of non-limiting example,
the media
content may be one of a video, a photograph, or an audio recording. In
embodiments, the
verification module 13118 may generate and output an event record to the
participating nodes
upon verifying that a licensor has performed its obligations. In embodiments,
the verification
module 13118 may generate and output an event record to the participating
nodes that
indicates that the compliance system 13100 has received corroborating evidence
(e.g., social
media data, location data, and/or media contents) that show that the licensor
has performed
his or her obligations. In embodiments, the verification module 13118 may be
configured to
output an event record indicating completion of a licensing transaction
defined by the smart
contract to the distributed ledger.
[00512] In embodiments, the verification module 13118 may be configured to
verify, by the
smart contract, that the licensor has performed the one or more obligations.
In embodiments,
the verification module 13118 and/or a smart contract may be configured to, in
response to
receiving verification that the licensor has performed the one or more
obligations, release at
least a portion of the consideration amount of cryptocurrency into a licensor
account of the
licensor. Releasing the at least a portion of the consideration amount of
cryptocurrency into a
licensee account of the licensee may include identifying an allocation smart
contract
associated with the licensee and distributing the consideration amount of the
cryptocurrency
in accordance with the allocation rules. By way of non-limiting example, the
additional
entities may include one or more of teammates of the licensor, coaches of the
licensor, a team
of the licensor, a university of the licensee, and a governing body (e.g., the
NCAA).
[00513] In embodiments, an analytics module 13120 may be configured to obtain
a set of
records indicating completion of a set of respective transactions from the
distributed ledger.
The set of records may include the record indicating the completion of the
transaction defined
by the smart contract. In embodiments, the analytics module 13120 may be
configured to
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determine whether an organization associated with the licensor is likely in
violation of one or
more regulations based on the set of records and a fraud detection model. The
fraud detection
model may be trained using training data that indicates permissible
transactions and
fraudulent transactions.
[00514] In some implementations, the allocation smart contract may define
allocation rules
governing a manner by which funds resulting from licensing the one or more
personality
rights are to be distributed amongst the licensor and one or more additional
entities.
[00515] In some implementations, by way of non-limiting example, the
regulations may be
provided by one of NCAA, FIFA, NBA, MLB, NFL, MLS, NHL, and the like.
[00516] In some implementations, computing platform(s) 13902, remote
platform(s) 13904,
and/or external resources 13934 may be operatively linked via one or more
electronic
communication links. For example, such electronic communication links may be
established,
at least in part, via a network such as the Internet and/or other networks. It
will be appreciated
that this is not intended to be limiting, and that the scope of this
disclosure includes
implementations in which computing platform(s) 13902, remote platform(s)
13904, and/or
external resources 13934 may be operatively linked via some other
communication media.
[00517] A given remote platform 13904 may include one or more processors
configured to
execute computer program modules. The computer program modules may be
configured to
enable an expert or user associated with the given remote platform 13904 to
interface with
compliance system 13100 and/or external resources 13934, and/or provide other
functionality
attributed herein to remote platform(s). 13904. By way of non-limiting
example, a given
remote platform 13904 and/or a given computing platform 13902 may include one
or more of
a server, a desktop computer, a laptop computer, a handheld computer, a tablet
computing
platform, a Netbook, a Smartphone, a gaming console, and/or other computing
platforms.
[00518] External resources 13934 may include sources of information outside of
compliance
system 13100, external entities participating with compliance system 13100,
and/or other
resources. In some implementations, some or all of the functionality
attributed herein to
external resources 13934 may be provided by resources included in compliance
system
13100.
[00519] Computing platform(s) 202 may include electronic storage 13936, one or
more
processors 13938, and/or other components. Computing platform(s) 1202 may
include
communication lines, or ports to enable the exchange of information with a
network and/or
other computing platforms. Illustration of computing platform(s) 13902 in FIG.
139 is not
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intended to be limiting. Computing platform(s) 13902 may include a plurality
of hardware,
software, and/or firmware components operating together to provide the
functionality
attributed herein to computing platform(s) 13902. For example, computing
platform(s) 13902
may be implemented by a cloud of computing platforms operating together as
computing
platform(s) 13902.
[00520] Electronic storage 13936 may comprise non-transitory storage media
that
electronically stores information. The electronic storage media of electronic
storage 13936
may include one or both of system storage that is provided integrally (i.e.,
substantially non-
removable) with computing platform(s) 13902 and/or removable storage that is
removably
connectable to computing platform(s) 13902 via, for example, a port (e.g., a
USB port, a
firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage
13936 may include
one or more of optically readable storage media (e.g., optical disks, etc.),
magnetically
readable storage media (e.g., magnetic tape, magnetic hard drive, floppy
drive, etc.),
electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state
storage media
(e.g., flash drive, etc.), and/or other electronically readable storage media.
Electronic storage
13936 may include one or more virtual storage resources (e.g., cloud storage,
a virtual private
network, and/or other virtual storage resources). Electronic storage 13936 may
store software
algorithms, information determined by processor(s) 13938, information received
from
computing platform(s) 13902, information received from remote platform(s)
13904, and/or
other information that enables computing platform(s) 13902 to function as
described herein.
[00521] Processor(s) 13938 may be configured to provide information processing
capabilities in computing platform(s) 13902. As such, processor(s) 13938 may
include one or
more of a digital processor, an analog processor, a digital circuit designed
to process
information, an analog circuit designed to process information, a state
machine, and/or other
mechanisms for electronically processing information. Although processor(s)
13938 is shown
in FIG. 139 as a single entity, this is for illustrative purposes only. In
some implementations,
processor(s) 13938 may include a plurality of processing units. These
processing units may
be physically located within the same device, or processor(s) 13938 may
represent processing
functionality of a plurality of devices operating in coordination.
Processor(s) 13938 may be
configured to execute modules 13108, 13112, 13116, 13118, 13120, and/or other
modules.
Processor(s) 13938 may be configured to execute modules 13108, 13112, 13116,
13118,
13120, and/or other modules by software; hardware; firmware; some combination
of
software, hardware, and/or firmware; and/or other mechanisms for configuring
processing
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capabilities on processor(s) 13938. As used herein, the term "module" may
refer to any
component or set of components that perform the functionality attributed to
the module. This
may include one or more physical processors during execution of processor
readable
instructions, the processor readable instructions, circuitry, hardware,
storage media, or any
other components.
[00522] It should be appreciated that although modules 13108, 13112, 13116,
13118, and
13120 are illustrated in FIG. 139 as being implemented within a single
processing unit, in
implementations in which processor(s) 13938 includes multiple processing
units, one or more
of modules 13108, 13112, 13116, 13118, and 13120 may be implemented remotely
from the
other modules. The description of the functionality provided by the different
modules 13108,
13112, 13116, 13118, and 13120 described below is for illustrative purposes,
and is not
intended to be limiting, as any of modules 13108, 13112, 13116, 13118, and/or
13120 may
provide more or less functionality than is described. For example, one or more
of modules
13108, 13112, 13116, 13118, and/or 13120 may be eliminated, and some or all of
its
functionality may be provided by other ones of modules 13108, 13112, 13116,
13118, and/or
13120. As another example, processor(s) 13938 may be configured to execute one
or more
additional modules that may perform some or all of the functionality
attributed below to one
of modules 13108, 13112, 13116, 13118, and/or 13120.
[00523] FIGS. 140 and/or 141 illustrates an example method 14000 for
electronically
facilitating licensing of one or more personality rights of a licensor, in
accordance with some
embodiments of the present disclosure. The operations of method 14000
presented below are
intended to be illustrative. In some embodiments, method 14000 may be
accomplished with
one or more additional operations not described, and/or without one or more of
the operations
discussed. Additionally, the order in which the operations of method 14000 are
illustrated in
FIGS. 140 and/or 141 and described below is not intended to be limiting.
[00524] In some implementations, method 14000 may be implemented in one or
more
processing devices (e.g., a digital processor, an analog processor, a digital
circuit designed to
process information, an analog circuit designed to process information, a
state machine,
and/or other mechanisms for electronically processing information). The one or
more
processing devices may include one or more devices executing some or all of
the operations
of method 14000 in response to instructions stored electronically on an
electronic storage
medium. The one or more processing devices may include one or more devices
configured
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through hardware, firmware, and/or software to be specifically designed for
execution of one
or more of the operations of method 14000.
[00525] FIG. 140 illustrates method 14000, in accordance with one or more
implementations
of the present disclosure.
[00526] At 14002, the method includes receiving an access request from a
licensee to obtain
approval to license personality rights from a set of available licensors.
Operation 14002 may
be performed by one or more hardware processors configured by machine-readable
instructions including a module that is the same as or similar to access
module 13108, in
accordance with one or more implementations.
[00527] At 14004, the method includes selectively granting access to the
licensee based on
the access request. Operation 14004 may be performed by one or more hardware
processors
configured by machine-readable instructions including a module that is the
same as or similar
to access module 13108, in accordance with one or more implementations.
[00528] At 14006, the method includes receiving confirmation of a deposit of
an amount of
funds from the licensee. Operation 14006 may be performed by one or more
hardware
processors configured by machine-readable instructions including a module that
is the same
as or similar to fund management module 13112, in accordance with one or more
implementations.
[00529] At 14008, the method includes issuing an amount of cryptocurrency
corresponding
to the amount of funds deposited by the licensee to an account of the
licensee. Operation
14008 may be performed by one or more hardware processors configured by
machine-
readable instructions including a module that is the same as or similar to
fund management
module 13112, in accordance with one or more implementations.
[00530] FIG. 141 illustrates method 14100, in accordance with one or more
implementations
of the present disclosure.
[00531] At 14122, the method includes receiving a smart contract request to
create a smart
contract governing the licensing of the one or more personality rights of the
licensor by the
licensee. The smart contract request may indicate one or more terms including
a
consideration amount of cryptocurrency to be paid to the licensor in exchange
for one or
more obligations on the licensor. Operation 14122 may be performed by one or
more
hardware processors configured by machine-readable instructions including a
module that is
the same as or similar to the ledger management module 13116, in accordance
with one or
more implementations.
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[00532] At 14124, the method includes generating the smart contract based on
the smart
contract request. Operation 14124 may be performed by one or more hardware
processors
configured by machine-readable instructions including a module that is the
same as or similar
to ledger management module 13116, in accordance with one or more
implementations.
[00533] At 14126, the method includes escrowing the consideration amount of
cryptocurrency from the account of the licensee. Operation 14126 may be
performed by one
or more hardware processors configured by machine-readable instructions
including a
module that is the same as or similar to fund management module 13112, in
accordance with
one or more implementations.
[00534] At 14128, the method includes deploying the smart contract to a
distributed ledger.
Operation 14128 may be performed by one or more hardware processors configured
by
machine-readable instructions including a module that is the same as or
similar to ledger
management module 13116, in accordance with one or more implementations.
[00535] At 14130, the method includes verifying, by the smart contract, that
the licensor has
performed the one or more obligations. Operation 14130 may be performed by one
or more
hardware processors configured by machine-readable instructions including a
module that is
the same as or similar to verification module 13118, in accordance with one or
more
implementations.
[00536] At 14132, the method includes in response to receiving verification
that the licensor
has performed the one or more obligations, releasing at least a portion of the
consideration
amount of cryptocurrency into a licensor account of the licensor. Operation
14132 may be
performed by one or more hardware processors configured by machine-readable
instructions
including a module that is the same as or similar to the verification module
13118, in
accordance with one or more implementations.
[00537] At 14134, the method includes outputting a record indicating a
completion of a
licensing transaction defined by the smart contract to the distributed ledger.
Operation 14134
may be performed by one or more hardware processors configured by machine-
readable
instructions including a module that is the same as or similar to the
verification module
13118 and/or the ledger management module 13116, in accordance with one or
more
implementations.
[00538] FIG. 142 illustrates method 14200, in accordance with one or more
implementations.
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[00539] At 14202, the method includes obtaining a set of records indicating
completion of a
set of respective transactions from the distributed ledger. The set of records
may include the
record indicating the completion of the transaction defined by the smart
contract. Operation
14202 may be performed by one or more hardware processors configured by
machine-
readable instructions including a module that is the same as or similar to the
analytics module
13120, in accordance with one or more implementations.
[00540] At 14204, the method includes determining whether an organization
associated with
the licensor is likely in violation of one or more regulations based on the
set of records and a
fraud detection model. Operation 14204 may be performed by one or more
hardware
processors configured by machine-readable instructions including a module that
is the same
as or similar to the analytics module 13120, in accordance with one or more
implementations.
[00541] Referring to Fig. 143, a computer-implemented method 14300 for
selecting an Al
solution for use in a robotic or automated process is depicted. The computer-
implemented
method may include receiving one or more functional media 14302. The
functional media
may include information indicative of brain activity of a worker engaged in a
task to be
automated. The functional media may be functional imaging, such an MRI, an
FMRI, and the
like from which an area of neocortex activity may be identified. The
functional media may be
an image, a video stream, an audio stream, and the like, from which a type of
brain activity
may be inferred. The functional media may be acquired while the worker is
performing the
work or while performing a simulation of the work, for example in an augmented
reality, a
virtual reality environment, or on a model of the equipment and/or
environment. After being
received, the functional media(s) are analyzed 14304 to identify an activity
level in at least
one brain region 14306. Based on the activity level, a brain region parameter
and/or an
activity parameter are identified 14308. The brain region parameter may
represent a specific
region of the neocortex such as frontal, parietal, occipital, and temporal
lobes of the
neocortex, including primary visual cortex and the primary auditory cortex, or
subdivisions of
the neocortex, including ventrolateral prefrontal cortex (Broca's area), and
orbitofrontal
cortex. The activity parameter may represent functional areas of the brain,
such as visual
processing, inductive reasoning, audio processing, olfactory processing,
muscle control, and
the like. An activity parameter may be representative of a type of activity in
which the worker
is engaged such as visual processing (looking) audio processing (listening),
olfactory
processing (smelling), motion activity, listening to the sound of the
equipment, watching
another negotiator, and the like. An activity level may be representative of a
strength or level
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of activity, such as an extent of the brain region involved, a signal
strength, whether a brain
region is engaged or unengaged, and the like.
[00542] Based on one or more of the brain region parameter, the activity
parameter, or the
activity level, an action parameter may be identified 14310. An action
parameter may provide
additional information regarding the activity parameter. For example, activity
parameter is
indicative of motion, an action parameter may describe a range of motion, a
speed of motion,
a repetition of motion, a use of muscle memory, a smoothness of motion, a flow
of motion, a
timing of motion, and the like. Based on one or more of the brain region
parameter, the
activity parameter, or the activity level, a component to be incorporated in
the final Al
solution may be selected 14312. The component may include one or more of a
model, an
expert system, a neural network, and the like. After the component for the Al
solution has
been selected, configuration parameters may be determined 14314. The
configuration
parameters may be based, in part, on the type of component selected, the brain
region
parameter, the activity parameter, the activity level, or the action
parameter. Configuring and
configuration parameters may include selecting an input for a machine learning
process,
identifying an output to be provided by the machine learning process,
identifying an input for
an operational solution process 14316, identifying an output an operational
solution process,
tuning a learning parameter, identifying a change rates, identifying a
weighting factor,
identifying a parameter for inclusion, identifying a parameter for exclusion
of a parameter,
setting a threshold for input data, setting an output threshold for the
operational robotic
process, or setting a parameter threshold. Additionally, analysis of the
functional media
14304 may include identifying a second brain region parameter or a second
activity
parameter 14318. The component of the Al solution may be revised 14320 based
on the
second brain region parameter or the second activity parameter. A second
component of the
Al solution may be selected 14322 based on the second brain region parameter
or the second
activity parameter. The final Al solution may be assembled from the component
14324 or the
second component 14326. In embodiments, the final Al solution may be assembled
from the
component and the second components, optionally along with any standard or
mandatory
components that enable operation.
[00543] Referring to Fig. 144, a computer-implemented method 14400 for
selecting an Al
solution for use in a robotic or automated process is depicted. The method may
include
receiving a user-related input 14402 comprising a timestamp and analyzing the
user-related
input 14404. The user- related input may include an audio feed, a motion
sensor, a video
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feed, a heartbeat monitor, an eye tracker, a biosensor (e.g., galvanic skin
response), and the
like. The analysis may enable the identification of a series of user actions
and associated
activity parameters 14406. A component for an Al solution may be selected
based on a user
action of the series of user actions 14408. The analysis may enable the
identification of a
second user action of the series of user actions 14410. Based on the second
user action, the
selected component for the Al solution may be revised 14412. A second
component for the
Al solution may be selected 14414 based on the second user action. An action
parameter may
be identified 14416 based on the user action and/or the associated activity
parameters. For
example, if the user action is motion, an action parameter may include a range
of motion, a
speed of motion, a repetition of motion, a use of muscle memory, a smoothness
of motion, a
flow of motion, a timing of motion, and the like. The selected component of
the Al solution
may be configured 14418 based on the action parameter. In embodiments, at
least one device
input performed by the user may be received (14420). The device input may be
synchronized
with the user actions based on the timestamp and a correlation between the
device input and
the user action determined 14419. The component may be revised 14423 based on
the
correlation. The selection of the component of the Al solution may be
partially based on the
correlation between the device input and the user-related input 14421. The Al
solution may
be assembled 14422 from the component. The Al solution may be assembled from
the second
component 14424. In embodiments, the Al may be assembled from both the
component and
the second component, optionally along with any standard or mandatory
components that
enable operation.
[00544] Referring to Fig. 145, an illustrative and non-limiting example of an
assembled Al
solution 14502 is shown. The assembled Al solution 14502 may include the
selected
component 14504 and a second selected component 14506, as well as other
components
14508. Configuration data 14514 for the first selected component and
configuration data
14512 for the second selected component may be provided. Runtime input data
14510 may
be specified as part of the component configuration process. Components may be
structured
to run serially (such as the selected component 14504 and the second selected
component
14506 which received input from the selected component 14504) or in parallel
(such as the
second component 14506 and the other component(s) 14508). Some of the
components may
provide input for other components (such as the selected component 14504
providing input to
the second selected component 14506). Multiple components may provide various
portions
of the overall Al solution output 14518 (such as the second selected component
14506 and
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the other components 14508). This depiction is not meant to be limiting and
the final solution
may include a varying number of components, configuration data and input, as
well as other
components (e.g., sensors, voice modulators, and the like) and may be
interconnected in a
variety of configurations.
[00545] Referring to Figs. 146-147, a computer-implemented method for
selecting an Al
solution for use in a robotic or automated process is depicted. The method may
include
receiving temporal biometric measurement data 14602 of a worker performing a
task and
receiving spatial-temporal environmental data 14604 experienced by the worker
performing
the task. Using the received data, a spatial-temporal activity pattern may be
identified 14606.
Based on the spatial-temporal activity pattern, an active area of the worker's
neocortex may
be identified 14608. A type of reasoning used when performing the task may be
identified
14610 based on the active area of the neocortex and/or the biometric
measurement data, or
the spatial-temporal environmental data. A component may be selected 14612 for
use in the
Al solution to replicate the type of reasoning. The component of the Al
solution may be
configured 14614 based on the spatial-temporal environmental input. A
determination may be
made as to whether a serial or parallel Al solution is optimal 14616. A set of
configuration
inputs to the component may be identified 14618 and an ordered set of inputs
to the
component of the Al solution may be identified 14620. Training the machine may
include
providing various subsets of the spatial-temporal environmental input to
determine
appropriate input weightings and identify efficiencies from combinations of
spatial-temporal
environmental input 14622. Desirable or undesirable combinations of the
spatial-temporal
environmental data may also be identified 14624. Based on the identified
required input,
input environmental data may be processed to reduce input noise 14626 (e.g.
improve signal
to noise for a signal of interest), filtered to provide the appropriate input
signals to the
component, and the like.
[00546] Continuing with reference to Fig. 147, a second temporal biometric
measurement
data of the same worker performing the task may be received 14702 and a
plurality of
performed tasks identified from the biometric measurements 14704. A
performance
parameter may be extracted from the biometric measurements 14706 (e.g. worker
heartrate,
galvanic skin response, and the like). In some embodiments, the component may
be
configured based on the performance parameter 14707. In some embodiments, the
second
temporal biometric measurements may be provided to the configuration module as
a training
set 14709. Results data related to the task may be received 14708 and the
second temporal
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biometric measurement data may be correlated with the received results data
14710. In some
embodiments, the component may be selected based, at least in part, on the
correlation
14711. A series of time intervals between each of the plurality of performed
tasks may be
identified 14712 and the component of the Al solution configured based on at
least one of the
time intervals 14714. For example, if the worker inspects an object for a long
period of time
before moving on to the next action, this may indicate complex visual
processing as well as
mental processing and may indicate that the corresponding component for the
task be
configured for in-depth, fine detail processing and the like.
[00547] Referring to Fig. 141, an Al solution selection and configuration
system 14102 is
depicted. An example selection and configuration system 14102 may include a
media input
module 14104 structured to receive user-related functional media 14114. The
user-related
functional media 14114 may include images of a person engaged in a task to be
automated,
audio recordings, video feeds, biometric data (e.g., heartbeat data, galvanic
skin response
data, and the like), motion data, and the like. A media analysis module 14106
may analyze
the received media and identify an action parameter. The action parameter may
be
representative of a type of activity in which the person appears to be engaged
such as
watching, listening, moving, thinking, and the like. In some embodiments, the
functional
media is indicative of a type of brain activity of a human engaged in the task
to be automated
and the media analysis module 141206 identifies an activity level in at least
one brain region
and provide a brain region parameter corresponding with the activity level in
the identified
brain region. The media analysis module may also identify an activity
parameter indicative of
a level of engagement such as engaged, unengaged, level of activity, type of
activity, and the
like. A solution selection module 14108 may be structured to select at least
one component of
the Al solution for use in the automated process based, at least in part, on
the action
parameter, the brain region parameter, or the activity parameter. The brain
region parameter
or the action parameter may suggest a type of component to select and the
activity parameter
may suggest a level of processing required for that component. For example, an
action
parameter of watching would suggest selecting a component suited to visual
processing. If
the activity parameter was representative of olfactory procession, the input
specification
module may identify at least one chemical sensor as an input. If the activity
parameter is
representative of visual processing the input specification module 13116 may
identify at least
one visual sensor as a robotic input. In some embodiments, the visual sensor
may be selected
to be sensitive to a portion of the visible spectrum with wavelengths between
about 380 to
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700 nanometers. If the activity parameter is representative of auditory
processing, the input
specification module 13116 may identify at least one microphone as a robotic
input. If the
activity parameter was representative of a very high level of concentration,
the solution
selection module 14108 may suggest a level of processing that will be
required, where the
processing might occur, and the like. A component configuration module 14110
may
configure the component 14112. Configuring the component may include:
selecting an input
for a machine learning process for the selected component, identifying an
output to be
provided by the machine learning process, identifying an input for an
operational solution
process, identifying an output an operational solution process, tuning a
learning parameter,
identifying a change rates, identifying a weighting factor, identifying a
parameter for
inclusion, identifying a parameter for exclusion of a parameter, setting a
threshold for input
data, setting an output threshold for the operational robotic process, setting
a parameter
threshold, and the like. A solution assembly module 14118 may assemble the
final Al
solution based on one or more selected components, configuration components,
and required
runtime. An input specification module 14116 may suggest input sources based
on the
selected component, the action parameter, brain region parameter, activity
parameter, or the
like.
[00548] Referring to Fig. 149, an Al solution selection and configuration
system 14902 is
depicted. An example selection system 14902 may include an image input module
14904
structured to receive functional images 14914 of the brain such as, such as
functional MRI or
other magnetic imaging, electroencephalogram (EEG), or other imaging, such as
by
identifying broad brain activity (e.g., wave bands of activity, such as delta,
theta, alpha and
gamma waves), by identifying a set of brain regions that are activated and/or
inactive while
the worker is performing one of the tasks to be automated. The image input
module 14904
may provide a subset of the functional images 14914 to the image analysis
module 14906. In
some embodiments, the image input module 14904 may perform some preprocessing
for the
subset of functional images 14914, such as noise reduction, histogram
adjustment, filtering,
and the like, prior to providing the subset of functional images 14914 to the
image analysis
module 14906. The image analysis module 14906, may identify an activity level
in at least
one brain region and provide a brain region parameter based on the subset of
functional
images. The brain region parameter may represent a specific region of the
neocortex such as
frontal, parietal, occipital, and temporal lobes of the neocortex, including
primary visual
cortex and the primary auditory cortex, or subdivisions of the neocortex,
including
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ventrolateral prefrontal cortex (Broca's area), and orbitofrontal cortex. The
brain region
parameter may represent functional areas of the brain, such as visual
processing, inductive
reasoning, audio processing, olfactory processing, muscle control, and the
like. A solution
selection module 14908 may select a component for use in an Al solution based
on the brain
region parameter, and provide input into a component configuration module
(such as
selecting an input for a machine learning process, identifying an output to be
provided by the
machine learning process, identifying an input for an operational solution
process, identifying
an output an operational solution process, tuning a learning parameter,
identifying a change
rates, identifying a weighting factor, identifying a parameter for inclusion,
identifying a
parameter for exclusion of a parameter, setting a threshold for input data,
setting an output
threshold for the operational robotic process, and setting a parameter
threshold, and the like.
The component configuration module 14910, may use the input to configure the
component
14912. The solution selection module 14908 may also supply data to the input
specification
module 14916. A solution assembly module 14918 may combine the component, and
other
components, to create the Al solution. The Al solution may be set up to
receive inputs as
specified by the input specification module 14916. Although one iteration of
selecting a
component is shown in this figure, it is envisioned, that multiple components
may be
selected, configured and assembled as part of the Al solution
[00549] Referring to Figs. 150-151, an Al solution selection and configuration
system 15002
is depicted. An example Al solution selection and configuration system 15002
may include
an input module 15004 structured to receive a variety of user-related input
such as videos,
audio recording, heartbeat monitors, galvanic skin response data, motion data,
and the like.
There may be temporal data associated with the user-related input. The input
module 15004
may provide a subset of the user-related input data 15014 to the input
analysis module 15006.
The analysis module 15006 may include a temporal analysis module 15018 to
identify timing
of user-related actions. The temporal analysis module 15018 may enable
identification of
timing of user actions. In some embodiments the input module 15004 may perform
some
preprocessing for the subset of the user-related input data 15014, such as
noise reduction,
correlation between types of input data, and the like, prior to providing the
subset of user-
related input data 15014 to the input analysis module 15006. The input
analysis module
15006, may identify a type of brain activity being engaged in (e.g. visual
processing, auditory
processing, olfactory processing, motion control, and the like) and a level of
intensity of
activity based on data such as heartbeat data, galvanic skin response data and
the like. A
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component selection module 15008 may select a component for use in an Al
solution based
on the type of brain activity and provide input into a component configuration
module 15010
which may include an ML input selection module 15102 for selecting an input
for a machine
learning process, an MP output identification module 15104 for identifying an
output to be
provided by the machine learning process, a runtime input selection module
15106 for
identifying an input for an operational solution process, a runtime output
identification
module 15108 for identifying an output of the component, a settings module
15110 for
identifying a change rate, identifying a weighting factor, setting a threshold
for input data,
setting an output threshold for the operational robotic process, and the like,
a parameter
settings module 15112 for tuning a learning parameter, identifying a parameter
for inclusion,
identifying a parameter for exclusion, setting a parameter threshold, and the
like. The
component configuration module 15010 may configure the selected component
15012. The
component selection module 15008 may also supply data to the input
specification module
15016. An Al solution assembly module 15020 may combine the configured
component with
other components, along with any standard or mandatory components, as
necessary, to create
the Al solution. The Al solution may be set up to receive inputs as specified
by the input
specification module 15016. Although one iteration of selecting a component is
shown in this
figure, it is envisioned, that multiple components may be selected, configured
and assembled
as part of the Al solution.
[00550] In embodiments, referring to Fig. 152, an Al solution selection and
configuration
system 15202 is depicted. An example Al solution selection and configuration
system 15202
may include a data input module 15204 to receive an input stream including
temporal user-
related data 15214 which may include video streams, audio streams, equipment
interactions
(e.g. mouse clicks, mouse motion, physical input to a machine) user biometrics
such as
heartbeat, galvanic skin response, eye tracking, and the like. The data input
module 15204
may also receive temporal environmental input data 15220 representative of
environmental
input the user is receiving such as a visual environment, an auditory
environment, olfactory
environment, equipment displays, a device user interface, and the like. The
data input module
15204 may also receive temporal results input data 15203. The data input
module 15204 may
provide a subset of the received data 15214, 15220, 15203 to an input analysis
module 15216.
The data input module 15204 may process the received data 15214, 15220 15203
to reduce
noise, compress the data, correlate some of the data, and the like. The
analysis module 15216
may identify a plurality of user actions to provide to the component selection
module 15208.
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The image analysis module 15216 may include a temporal analysis module 15218
to identify
timing of user actions. The temporal analysis module 15218 may allow for the
correlation
between temporal user-related data 15214, environmental data 15220, and
results data 15203.
Based on the user actions, the component selection module 15208 may select a
component
that would simulate one or more mental processes of the user needed to perform
at least one
of the plurality of user actions. Factors in identifying the selected
component may include the
level of computational intensity needed, time sensitivity, and the like. This
may dictate a type
of component, a location of component (on-board, in the cloud, edge-computing,
and the like.
The input analysis module 15216 may also provide information regarding the
user's actions
and environmental data to the component configuration module 15210. This data
may be
used by the component configuration module as input to a machine learning
algorithm, in
conjunction with the results data to identify which inputs are beneficial and
which are
detrimental to enabling the component to reach desired results, and identify
appropriate
weighting of inputs, parameter settings, and the like. The component
configuration module
15210 configures the component 15212 which is provided to the overall Al
solution 15224
together with configuration information.
[00551] As described elsewhere herein, this disclosure concerns systems and
methods for
the discovery of opportunities for increased automation and intelligence,
including solutions
to domain-specific problems. Further, this disclosure also concerns selection
and
configuration of an artificial intelligence solution (e.g. neural networks,
machine learning
systems, expert systems, etc.) once opportunities are discovered.
[00552] Referring now to Fig. 153, a controller 15308 includes an opportunity
mining
module 153, an artificial intelligence configuration module 15304, and an
artificial
intelligence search engine 15310, optionally having a collaborative filter
15328 and a
clustering engine 15330. The opportunity mining module 153 receives input
15302, such as
attribute input regarding an attribute of a task, a domain, or a domain-
related problem.
[00553] The input 15302 may be processed by the opportunity mining module 153
to
determine whether an artificial intelligence system can be applied to the task
or the domain.
For example, the attribute input 15302 may include an attribute of a task,
domain or problem,
such as a negotiating task, a drafting task, a data entry task, an email
response task, a data
analysis task, a document review task, an equipment operation task, a
forecasting task, an
NLP task, an image recognition task, a pattern recognition task, a motion
detection task, a
route optimization task, and the like. The opportunity mining module 153 may
determine if
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one or more attributes of the task are similar to other tasks that have been
automated or to
which an intelligence has been applied, or based on the attribute of the task,
if the task is
potentially automatable or suitable to have an intelligence applied to it
regardless of whether
it has been done previously. For example, attributes of a drafting task may
include
articulating a first idea, articulating a second idea, articulating a
plurality of ideas, combining
the plurality of ideas in a pairwise fashion, and combining the ideas in a
triplicate fashion.
Articulating ideas may not be suitable for automation, but the task of
combining ideas
pairwise or in triplicate form may be suitable for automation or to have an
intelligence
applied to the task.
[00554] If a determination is made that an artificial intelligence system can
be applied to the
task or the domain, the output 15312 regarding that determination may be used
to trigger an
artificial intelligence search engine 15310 to perform a search of an
artificial intelligence
store 157. The artificial intelligence store 157 may include a plurality of
domain-specific and
general artificial intelligence models 15318, and components of domain-
specific and general
artificial intelligence models 15318. The artificial intelligence store 157
may be organized by
a category. The category may be at least one of an artificial intelligence
model component
type, a domain, an input type, a processing type, an output type, a
computational requirement,
a computational capability, a cost, a training status, or an energy usage. The
artificial
intelligence store may include at least one e-commerce feature. The at least
one e-commerce
feature may include at least one of a rating, a review, a link to relevant
content, a mechanism
for provisioning, a mechanism for licensing, a mechanism for delivery, or a
mechanism for
payment. Models 15318 may be pre-trained, or may be available for training.
Components of
domain-specific and general artificial intelligence models 15318 may include
artificial
intelligence building blocks, such as a component that detects and translates
between
languages, or a component that delivers highly personalized customer
recommendations. One
or more models 15318 and/or components of a model 15318 may be identified in a
search of
the artificial intelligence store 157. Components of a model 15318 may be
identified either as
a stand-alone element to be used in the assembly of a custom Al model 15318 or
as a
component of a complete, optionally pre-trained, model 15318.
[00555] The artificial intelligence store 157 may include metadata 15324 or
other descriptive
material indicating a suitability of an artificial intelligence system for at
least one of solving a
particular type of problem or operating on domain-specific inputs, data, or
other entities. The
metadata 15324, or other descriptive material, category, or e-commerce feature
may be
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searched using the attribute input 15302 and/or other selection criteria
15314. For example,
attributes of a task involving 2D object classification may be searched in the
artificial
intelligence store 157 and its metadata 15324 to reveal that an artificial
intelligence model
15318 suitable for a task involving 2D object classification may be a
convolutional neural
network. Continuing with the example, there may be model diversity even within
the class of
convolutional neural networks (CNN) in the artificial intelligence store 157,
such as a CNN
calibrated to a certain type of 2D object recognition (e.g., straight edges)
and another CNN
calibrated to another kind of 2D object recognition (e.g., combo of curved and
straight
edges). In this example, if the further edge vs. curved attribute of the type
of 2D object is
searched, the artificial intelligence store 157 would present the CNN best
suited to the 2D
object to be classified.
[00556] In embodiments, in addition to the input 15302, at least one selection
criteria 15314
may be used by the artificial intelligence search engine 15310 to search the
artificial
intelligence store 157 for artificial intelligence models 15318 and/or
components thereof.
Selection criteria used in the recommendation of an artificial intelligence
model 15318 or
model component may include at least one of if the model is pre-trained or
not, an
availability of the at least one artificial intelligence model 15318 or
component thereof to
execute in a user environment, an availability of the at least one artificial
intelligence model
15318 or component thereof to a user, a governance principle, a governance
policy, a
computational factor, a network factor, a data availability, a task-specific
factor, a
performance factor, a quality of service factor, a model deployment
consideration, a security
consideration, or a human interface, which may be elsewhere described herein.
For example,
a governance principle, such as a requirement for an anti-bias review of
pedestrian accident-
avoidance systems, may be used to search an artificial intelligence store 157
for artificial
intelligence models to apply to an autonomous driving task. In another
example, a selection
criteria for an artificial intelligence solution to be used with air traffic
control system may be
a requirement for having been trained on adversarial attacks and deceptive
input. In yet
another example, a selection criteria for an artificial intelligence solution
to be used with an
equities trading task may be the requirement for human oversight, and
particularly, human-
based final decisions.
[00557] The artificial intelligence search engine 15310 may rank one or more
results of the
search according to a strength or a weakness of the at least one artificial
intelligence model
15318 or model component relative to the at least one selection criteria
15314. The ranked
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search results may be presented to a user for evaluation and consideration,
and ultimately,
selection. In embodiments, the artificial intelligence search engine 15310 may
further include
a collaborative filter 15328 that receives an indication of an element of the
at least one
artificial intelligence model 15318 or model component from a user that is
used to filter the
search results. In embodiments, the artificial intelligence search engine
15310 may further
include a clustering engine 15330 structured to cluster search results
comprising the at least
one artificial intelligence model 15318 or model component. The clustering
engine 15330
may be at least one of a similarity matrix or a k-means clustering. The
clustering engine
15330 may associate at least one of similar developers, similar domain-
specific problems, or
similar artificial intelligence solutions in the search results.
[00558] Once an artificial intelligence model 15318 or components thereof are
identified by
the artificial intelligence search engine 15310, either by searching with the
input 15302 alone
or with both the input 15302 and a selection criteria 15314, an artificial
intelligence
configuration module 15304 may configure one or more data inputs 15320 to use
with the at
least one artificial intelligence model 15318 or model component. The
artificial intelligence
configuration module 15304 may, in certain embodiments, be operative in
discovering and
selecting what inputs 15320 may enable effective and efficient use of
artificial intelligence
for a given problem. In embodiments, the artificial intelligence configuration
module 15304
may further configure the at least one artificial intelligence model 15318 or
model
component(s) in accordance with at least one configuration criteria 15322. In
embodiments,
individual data inputs and model components may be configured via one or more
configuration criteria, while in other embodiments, a single configuration
criteria governs
configuration of data input, Al component assembly, and the like.
[00559] In embodiments, the at least one configuration criteria 15322 may
include at least
one of an availability of the at least one artificial intelligence model 15318
or model
component to execute in a user environment, an availability of the at least
one artificial
intelligence model 15318 or model component to a user, a governance principle,
a
governance policy, a computational factor, a network factor, a data
availability, a task-
specific factor, a performance factor, a quality of service factor, a model
deployment
consideration, a security consideration, or a human interface. In embodiments,
the at least one
configuration criteria may include at least one of identifying a desired
output, identifying
training data, identifying parameters for exclusion or inclusion in training
or operation of the
model, an input data threshold, an output data threshold, a selection of a
neural network type,
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a selection of an input model type, a setting of initial model weights, a
setting of model size,
a selection of computational deployment environment, a selection of input data
sources for
training, a selection of input data sources for operation, a selection of
feedback
function/outcome measures, a selection of data integration language(s) for
inputs and outputs,
a configuration of APIs for model training, a configuration of APIs 13114 for
model inputs, a
configuration of APIs 13114 for outputs, a configuration of access controls, a
configuration
of security parameters, a configuration of network protocols, a configuration
of storage
parameters, a configuration of economic factors, a configuration of data
flows, a
configuration of high availability, one or more fault tolerance environments,
a price-based
data acquisition strategy, a heuristic method, a decision to make a decision
model, or a
coordination of massively parallel decision making environments. In
embodiments, the at
least one configuration criteria may include parameters for assembly of an Al
solution from a
plurality of identified model components, optionally along with other standard
or mandatory
model components. For example, the model components may be configured to run
in parallel,
to run serially, or in a combination of serial and parallel.
[00560] For example, the artificial intelligence configuration module 15304
may configure
an artificial intelligence model 15318 to weight one data input 15320 more
heavily than
another. For example, in the rain, an autonomous driving solution may weight
input from a
traction control system and a forward radar system more heavily than sensors
targeted to
increasing fuel efficiency, such as sensors measuring road slope and vehicle
speed. After the
rain, the weighting may be reversed.
[00561] In another example, the artificial intelligence configuration module
15304 may
configure an artificial intelligence model 15318 to operate within certain
thresholds of data
input 15320. For example, an artificial intelligence model 15318 may be used
in a
combinatorial drafting task. When only two articulated ideas are provided to
the model
15318, the model 15318 may not be triggered to operate. However, once the
model 15318
receives a third articulated idea, its combinatorial processing of articulated
ideas may
commence.
[00562] The artificial intelligence configuration module 15304 may configure
which sensors
to use as data input 15320, how frequently to sample data, how frequently to
transmit output,
the weighting of various data inputs 15320, thresholds to apply to data from
data inputs
15320, whether an output of one component of the model 15318 is used as input
to another
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component of the model 15318, an order of operation of the components of the
model 15318,
a positioning of a model component within a workflow of a model, and the like.
[00563] The artificial intelligence configuration module 15304 may configure
an artificial
intelligence model 15318 from one or more model components identified by the
artificial
intelligence search engine 15310. For example, if the search result consisted
solely of model
components, the Al configuration module 15304 may configure where to place the
identified
127 components in relation to one another, such as in a workflow or data flow,
as well as in
relation to other components that may be required for the model 15318 to
function.
[00564] In embodiments, an artificial intelligence store 157 may include a set
of interfaces
to artificial intelligence systems, such as enabling the download of relevant
artificial
intelligence applications, establishment of links or other connections to
artificial intelligence
systems (such as links to cloud-deployed artificial intelligence systems via
APIs, ports,
connectors, or other interfaces) and the like.
[00565] Referring now to Fig. 154, a method of artificial intelligence model
identification
and selection may include receiving input regarding an attribute of a task or
a domain 15402,
and processing the input to determine whether an artificial intelligence
system can be applied
to the task or the domain 15404, performing a search of an artificial
intelligence store of a
plurality of domain-specific and general artificial intelligence models and
model components
using the input and/or at least one selection criteria to identify at least
one artificial
intelligence model or model component to apply to the task or the domain
15408, and
configuring one or more data inputs to use with the at least one artificial
intelligence model
15410 or model component. The artificial intelligence store may include
metadata or other
descriptive material indicating a suitability of an artificial intelligence
system for at least one
of solving a particular type of problem or operating on domain-specific
inputs, data, or other
entities.
[00566] The method may further include ranking one or more results of the
search according
to a strength or a weakness of the at least one artificial intelligence model
relative to the at
least one selection criteria 15412. The method may further include configuring
the at least
one artificial intelligence model or model component in accordance with at
least one
configuration criteria 15414. The method may further include collaborative
filtering search
results comprising the at least one artificial intelligence model using an
element of the at least
one artificial intelligence model selected or model component by a user 15416.
The method
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may further include clustering search results comprising the at least one
artificial intelligence
model or model component with a clustering engine 15418.
[00567] FIG. 155 illustrates an example environment of a digital twin system
15500. In
embodiments, the digital twin system 15500 generates a set of digital twins of
a set of
industrial environments 15520 and/or industrial entities within the set of
industrial
environments. In embodiments, the digital twin system 15500 maintains a set of
states of the
respective industrial environments 15520, such as using sensor data obtained
from respective
sensor systems 15530 that monitor the industrial environments 15520. In
embodiments, the
digital twin system 15500 may include a digital twin management system 15502,
a digital
twin I/O system 15504, a digital twin simulation system 15506, a digital twin
dynamic model
system 15508, a cognitive intelligence system 15510, and/or an environment
control module
15512. In embodiments, the digital twin system 15500 may provide a real time
sensor API
that provides a set of capabilities for enabling a set of interfaces for the
sensors of the
respective sensor systems 15530. In embodiments, the digital twin system 15500
may include
and/or employ other suitable APIs, brokers, connectors, bridges, gateways,
hubs, ports,
routers, switches, data integration systems, peer-to-peer systems, and the
like to facilitate the
transferring of data to and from the digital twin system 15500. In these
embodiments, these
connective components may allow an IoT sensor or an intermediary device (e.g.,
a relay, an
edge device, a switch, or the like) within a sensor system 15530 to
communicate data to the
digital twin system 15500 and/or to receive data (e.g., configuration data,
control data, or the
like) from the digital twin system 15500 or another external system. In
embodiments, the
digital twin system 15500 may further include a digital twin datastore 15516
that stores
digital twins 15518 of various industrial environments 15520 and the objects
15522, devices
15524, sensors 15526, and/or humans 15528 in the environment 15520.
[00568] A digital twin may refer to a digital representation of one or more
industrial entities,
such as an industrial environment 15520, a physical object 15522, a device
15524, a sensor
15526, a human 15528, or any combination thereof. Examples of industrial
environments
15520 include, but are not limited to, a factory, a power plant, a food
production facility
(which may include an inspection facility), a commercial kitchen, an indoor
growing facility,
a natural resources excavation site (e.g., a mine, an oil field, etc.), and
the like. Depending on
the type of environment, the types of objects, devices, and sensors that are
found in the
environments will differ. Non-limiting examples of physical objects 15522
include raw
materials, manufactured products, excavated materials, containers (e.g.,
boxes, dumpsters,
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cooling towers, vats, pallets, barrels, palates, bins, and the like),
furniture (e.g., tables,
counters, workstations, shelving, etc.), and the like. Non-limiting examples
of devices 15524
include robots, computers, vehicles (e.g., cars, trucks, tankers, trains,
forklifts, cranes, etc.),
machinery/equipment (e.g., tractors, tillers, drills, presses, assembly lines,
conveyor belts,
etc.), and the like. The sensors 15526 may be any sensor devices and/or sensor
aggregation
devices that are found in a sensor system 15530 within an environment. Non-
limiting
examples of sensors 15526 that may be implemented in a sensor system 15530 may
include
temperature sensors 15532, humidity sensors 15534, vibration sensors 15536,
LIDAR sensors
15538, motion sensors 15540, chemical sensors 15542, audio sensors 15544,
pressure sensors
15546, weight sensors 15548, radiation sensors 15550, video sensors 15552,
wearable
devices 15554, relays 15556, edge devices 15558, crosspoint switches 15560,
and/or any
other suitable sensors. Examples of different types of physical objects 15522,
devices 15524,
sensors 15526, and environments 15520 are referenced throughout the
disclosure.
[00569] In some embodiments, on-device sensor fusion and data storage for
industrial IoT
devices is supported, including on-device sensor fusion and data storage for
an industrial IoT
device, where data from multiple sensors is multiplexed at the device for
storage of a fused
data stream. For example, pressure and temperature data may be multiplexed
into a data
stream that combines pressure and temperature in a time series, such as in a
byte-like
structure (where time, pressure, and temperature are bytes in a data
structure, so that pressure
and temperature remain linked in time, without requiring separate processing
of the streams
by outside systems), or by adding, dividing, multiplying, subtracting, or the
like, such that the
fused data can be stored on the device. Any of the sensor data types described
throughout this
disclosure, including vibration data, can be fused in this manner and stored
in a local data
pool, in storage, or on an IoT device, such as a data collector, a component
of a machine, or
the like.
[00570] In some embodiments, a set of digital twins may represent an entire
organization,
such as energy production organizations, oil and gas organizations, renewable
energy
production organizations, aerospace manufacturers, vehicle manufacturers,
heavy equipment
manufacturers, mining organizations, drilling organizations, offshore platform
organizations,
and the like. In these examples, the digital twins may include digital twins
of one or more
industrial facilities of the organization.
[00571] In embodiments, the digital twin management system 15502 generates
digital twins.
A digital twin may be comprised of (e.g., via reference) other digital twins.
In this way, a
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discrete digital twin may be comprised of a set of other discrete digital
twins. For example, a
digital twin of a machine may include digital twins of sensors on the machine,
digital twins of
components that make up the machine, digital twins of other devices that are
incorporated in
or integrated with the machine (such as systems that provide inputs to the
machine or take
outputs from it), and/or digital twins of products or other items that are
made by the machine.
Taking this example one step further, a digital twin of an industrial facility
(e.g., a factory)
may include a digital twin representing the layout of the industrial facility,
including the
arrangement of physical assets and systems in or around the facility, as well
as digital assets
of the assets within the facility (e.g., the digital twin of the machine), as
well as digital twins
of storage areas in the facility, digital twins of humans collecting vibration
measurements
from machines throughout the facility, and the like. In this second example,
the digital twin
of the industrial facility may reference the embedded digital twins, which may
then reference
other digital twins embedded within those digital twins.
[00572] In some embodiments, a digital twin may represent abstract entities,
such as
workflows and/or processes, including inputs, outputs, sequences of steps,
decision points,
processing loops, and the like that make up such workflows and processes. For
example, a
digital twin may be a digital representation of a manufacturing process, a
logistics workflow,
an agricultural process, a mineral extraction process, or the like. In these
embodiments, the
digital twin may include references to the industrial entities that are
included in the workflow
or process. The digital twin of the manufacturing process may reflect the
various stages of the
process. In some of these embodiments, the digital twin system 15500 receives
real-time data
from the industrial facility (e.g., from a sensor system 15530 of the
environment 15520) in
which the manufacturing process takes place and reflects a current (or
substantially current)
state of the process in real-time.
[00573] In embodiments, the digital representation may include a set of data
structures (e.g.,
classes) that collectively define a set of properties of a represented
physical object 15522,
device 15524, sensor 15526, or environment 15520 and/or possible behaviors
thereof. For
example, the set of properties of a physical object 15522 may include a type
of the physical
object, the dimensions of the object, the mass of the object, the density of
the object, the
material(s) of the object, the physical properties of the material(s), the
surface of the physical
object, the status of the physical object, a location of the physical object,
identifiers of other
digital twins contained within the object, and/or other suitable properties.
Examples of
behavior of a physical object may include a state of the physical object
(e.g., a solid, liquid,
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or gas), a melting point of the physical object, a density of the physical
object when in a
liquid state, a viscosity of the physical object when in a liquid state, a
freezing point of the
physical object, a density of the physical object when in a solid state, a
hardness of the
physical object when in a solid state, the malleability of the physical
object, the buoyancy of
the physical object, the conductivity of the physical object, a burning point
of the physical
object, the manner by which humidity affects the physical object, the manner
by which water
or other liquids affect the physical object, a terminal velocity of the
physical object, and the
like. In another example, the set of properties of a device may include a type
of the device,
the dimensions of the device, the mass of the device, the density of the
density of the device,
the material(s) of the device, the physical properties of the material(s), the
surface of the
device, the output of the device, the status of the device, a location of the
device, a trajectory
of the device, vibration characteristics of the device, identifiers of other
digital twins that the
device is connected to and/or contains, and the like. Examples of the
behaviors of a device
may include a maximum acceleration of a device, a maximum speed of a device,
ranges of
motion of a device, a heating profile of a device, a cooling profile of a
device, processes that
are performed by the device, operations that are performed by the device, and
the like.
Example properties of an environment may include the dimensions of the
environment, the
boundaries of the environment, the temperature of the environment, the
humidity of the
environment, the airflow of the environment, the physical objects in the
environment,
currents of the environment (if a body of water), and the like. Examples of
behaviors of an
environment may include scientific laws that govern the environment, processes
that are
performed in the environment, rules or regulations that must be adhered to in
the
environment, and the like.
[00574] In embodiments, the properties of a digital twin may be adjusted. For
example, the
temperature of a digital twin, a humidity of a digital twin, the shape of a
digital twin, the
material of a digital twin, the dimensions of a digital twin, or any other
suitable parameters
may be adjusted. As the properties of the digital twin are adjusted, other
properties may be
affected as well. For example, if the temperature of an environment 15520 is
increased, the
pressure within the environment may increase as well, such as a pressure of a
gas in
accordance with the ideal gas law. In another example, if a digital twin of a
subzero
environment is increased to above freezing temperatures, the properties of an
embedded twin
of water in a solid state (i.e., ice) may change into a liquid state over
time.
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[00575] Digital twins may be represented in a number of different forms. In
embodiments, a
digital twin may be a visual digital twin that is rendered by a computing
device, such that a
human user can view digital representations of an environment 15520 and/or the
physical
objects 15522, devices 15524, and/or the sensors 15526 within an environment.
In
embodiments, the digital twin may be rendered and output to a display device.
In some of
these embodiments, the digital twin may be rendered in a graphical user
interface, such that a
user may interact with the digital twin. For example, a user may "drill down"
on a particular
element (e.g., a physical object or device) to view additional information
regarding the
element (e.g., a state of a physical object or device, properties of the
physical object or
device, or the like). In some embodiments, the digital twin may be rendered
and output in a
virtual reality display. For example, a user may view a 3D rendering of an
environment (e.g.,
using monitor or a virtual reality headset). While doing so, the user may
view/inspect digital
twins of physical assets or devices in the environment.
[00576] In some embodiments, a data structure of the visual digital twins
(i.e., digital twins
that are configured to be displayed in a 2D or 3D manner) may include surfaces
(e.g., splines,
meshes, polygons meshes, or the like). In some embodiments, the surfaces may
include
texture data, shading information, and/or reflection data. In this way, a
surface may be
displayed in a more realistic manner. In some embodiments, such surfaces may
be rendered
by a visualization engine (not shown) when the digital twin is within a field
of view and/or
when existing in a larger digital twin (e.g., a digital twin of an industrial
environment). In
these embodiments, the digital twin system 15500 may render the surfaces of
digital objects,
whereby a rendered digital twin may be depicted as a set of adjoined surfaces.
[00577] In embodiments, a user may provide input that controls one or more
properties of a
digital twin via a graphical user interface. For example, a user may provide
input that changes
a property of a digital twin. In response, the digital twin system 15500 can
calculate the
effects of the changed property and may update the digital twin and any other
digital twins
affected by the change of the property.
[00578] In embodiments, a user may view processes being performed with respect
to one or
more digital twins (e.g., manufacturing of a product, extracting minerals from
a mine or well,
a livestock inspection line, and the like). In these embodiments, a user may
view the entire
process or specific steps within a process.
[00579] In some embodiments, a digital twin (and any digital twins embedded
therein) may
be represented in a non-visual representation (or "data representation"). In
these
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embodiments, a digital twin and any embedded digital twins exist in a binary
representation
but the relationships between the digital twins are maintained. For example,
in embodiments,
each digital twin and/or the components thereof may be represented by a set of
physical
dimensions that define a shape of the digital twin (or component thereof).
Furthermore, the
data structure embodying the digital twin may include a location of the
digital twin. In some
embodiments, the location of the digital twin may be provided in a set of
coordinates. For
example, a digital twin of an industrial environment may be defined with
respect to a
coordinate space (e.g., a Cartesian coordinate space, a polar coordinate
space, or the like). In
embodiments, embedded digital twins may be represented as a set of one or more
ordered
triples (e.g., [x coordinate, y coordinate, z coordinates] or other vector-
based representations).
In some of these embodiments, each ordered triple may represent a location of
a specific
point (e.g., center point, top point, bottom point, or the like) on the
industrial entity (e.g.,
object, device, or sensor) in relation to the environment in which the
industrial entity resides.
In some embodiments, a data structure of a digital twin may include a vector
that indicates a
motion of the digital twin with respect to the environment. For example,
fluids (e.g., liquids
or gasses) or solids may be represented by a vector that indicates a velocity
(e.g., direction
and magnitude of speed) of the entity represented by the digital twin. In
embodiments, a
vector within a twin may represent a microscopic subcomponent, such as a
particle within a
fluid, and a digital twin may represent physical properties, such as
displacement, velocity,
acceleration, momentum, kinetic energy, vibrational characteristics, thermal
properties,
electromagnetic properties, and the like.
[00580] In some embodiments, a set of two or more digital twins may be
represented by a
graph database that includes nodes and edges that connect the nodes. In some
implementations, an edge may represent a spatial relationship (e.g., "abuts",
"rests upon",
"contains", and the like). In these embodiments, each node in the graph
database represents a
digital twin of an entity (e.g., an industrial entity) and may include the
data structure defining
the digital twin. In these embodiments, each edge in the graph database may
represent a
relationship between two entities represented by connected nodes. In some
implementations,
an edge may represent a spatial relationship (e.g., "abuts", "rests upon",
"interlocks with",
"bears", "contains", and the like). In embodiments, various types of data may
be stored in a
node or an edge. In embodiments, a node may store property data, state data,
and/or metadata
relating to a facility, system, subsystem, and/or component. Types of property
data and state
data will differ based on the entity represented by a node. For example, a
node representing a
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robot may include property data that indicates a material of the robot, the
dimensions of the
robot (or components thereof), a mass of the robot, and the like. In this
example, the state
data of the robot may include a current pose of the robot, a location of the
robot, and the like.
In embodiments, an edge may store relationship data and metadata data relating
to a
relationship between two nodes. Examples of relationship data may include the
nature of the
relationship, whether the relationship is permanent (e.g., a fixed component
would have a
permanent relationship with the structure to which it is attached or resting
on), and the like. In
embodiments, an edge may include metadata concerning the relationship between
two
entities. For example, if a product was produced on an assembly line, one
relationship that
may be documented between a digital twin of the product and the assembly line
may be
"created by". In these embodiments, an example edge representing the "created
by"
relationship may include a timestamp indicating a date and time that the
product was created.
In another example, a sensor may take measurements relating to a state of a
device, whereby
one relationship between the sensor and the device may include "measured" and
may define a
measurement type that is measured by the sensor. In this example, the metadata
stored in an
edge may include a list of N measurements taken and a timestamp of each
respective
measurement. In this way, temporal data relating to the nature of the
relationship between
two entities may be maintained, thereby allowing for an analytics engine,
machine-learning
engine, and/or visualization engine to leverage such temporal relationship
data, such as by
aligning disparate data sets with a series of points in time, such as to
facilitate cause-and-
effect analysis used for prediction systems.
[00581] In some embodiments, a graph database may be implemented in a
hierarchical
manner, such that the graph database relates a set of facilities, systems, and
components. For
example, a digital twin of a manufacturing environment may include a node
representing the
manufacturing environment. The graph database may further include nodes
representing
various systems within the manufacturing environment, such as nodes
representing an HVAC
system, a lighting system, a manufacturing system, and the like, all of which
may connect to
the node representing the manufacturing system. In this example, each of the
systems may
further connect to various subsystems and/or components of the system. For
example, within
the HVAC system, the HVAC system may connect to a subsystem node representing
a
cooling system of the facility, a second subsystem node representing a heating
system of the
facility, a third subsystem node representing the fan system of the facility,
and one or more
nodes representing a thermostat of the facility (or multiple thermostats).
Carrying this
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example further, the subsystem nodes and/or component nodes may connect to
lower level
nodes, which may include subsystem nodes and/or component nodes. For example,
the
subsystem node representing the cooling subsystem may be connected to a
component node
representing an air conditioner unit. Similarly, a component node representing
a thermostat
device may connect to one or more component nodes representing various sensors
(e.g.,
temperature sensors, humidity sensors, and the like).
[00582] In embodiments where a graph database is implemented, a graph database
may
relate to a single environment or may represent a larger enterprise. In the
latter scenario, a
company may have various manufacturing and distribution facilities. In these
embodiments,
an enterprise node representing the enterprise may connect to environment
nodes of each
respective facility. In this way, the digital twin system 15500 may maintain
digital twins for
multiple industrial facilities of an enterprise.
[00583] In embodiments, the digital twin system 15500 may use a graph database
to
generate a digital twin that may be rendered and displayed and/or may be
represented in a
data representation. In the former scenario, the digital twin system 15500 may
receive a
request to render a digital twin, whereby the request includes one or more
parameters that are
indicative of a view that will be depicted. For example, the one or more
parameters may
indicate an industrial environment to be depicted and the type of rendering
(e.g., "real-world
view" that depicts the environment as a human would see it, an "infrared view"
that depicts
objects as a function of their respective temperature, an "airflow view" that
depicts the
airflow in a digital twin, or the like). In response, the digital twin system
15500 may traverse
a graph database and may determine a configuration of the environment to be
depicted based
on the nodes in the graph database that are related (either directly or
through a lower level
node) to the environment node of the environment and the edges that define the
relationships
between the related nodes. Upon determining a configuration, the digital twin
system 15500
may identify the surfaces that are to be depicted and may render those
surfaces. The digital
twin system 15500 may then render the requested digital twin by connecting the
surfaces in
accordance with the configuration. The rendered digital twin may then be
output to a viewing
device (e.g., VR headset, monitor, or the like). In some scenarios, the
digital twin system
15500 may receive real-time sensor data from a sensor system 15530 of an
environment
15520 and may update the visual digital twin based on the sensor data. For
example, the
digital twin system 1550 may receive sensor data (e.g., vibration data from a
vibration sensor
15536) relating to a motor and its set of bearings. Based on the sensor data,
the digital twin
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system 15500 may update the visual digital twin to indicate the approximate
vibrational
characteristics of the set of bearings within a digital twin of the motor.
[00584] In scenarios where the digital twin system 15500 is providing data
representations
of digital twins (e.g., for dynamic modeling, simulations, machine learning),
the digital twin
system 15500 may traverse a graph database and may determine a configuration
of the
environment to be depicted based on the nodes in the graph database that are
related (either
directly or through a lower level node) to the environment node of the
environment and the
edges that define the relationships between the related nodes. In some
scenarios, the digital
twin system 15500 may receive real-time sensor data from a sensor system 15530
of an
environment 15520 and may apply one or more dynamic models to the digital twin
based on
the sensor data. In other scenarios, a data representation of a digital twin
may be used to
perform simulations, as is discussed in greater detail throughout the
specification.
[00585] In some embodiments, the digital twin system 15500 may execute a
digital ghost
that is executed with respect to a digital twin of an industrial environment.
In these
embodiments, the digital ghost may monitor one or more sensors of a sensor
system 15530 of
an industrial environment to detect anomalies that may indicate a malicious
virus or other
security issues.
[00586] As discussed, the digital twin system 15500 may include a digital twin
management
system 15502, a digital twin I/O system 15504, a digital twin simulation
system 15506, a
digital twin dynamic model system 15508, a cognitive intelligence system
15510, and/or an
environment control system 15512.
[00587] In embodiments, the digital twin management system 15502 creates new
digital
twins, maintains/updates existing digital twins, and/or renders digital twins.
The digital twin
management system 15502 may receive user input, uploaded data, and/or sensor
data to
create and maintain existing digital twins. Upon creating a new digital twin,
the digital twin
management system 15502 may store the digital twin in the digital twin
datastore 15516.
Creating, updating, and rendering digital twins are discussed in greater
detail throughout the
disclosure.
[00588] In embodiments, the digital twin I/0 system 15504 receives input from
various
sources and outputs data to various recipients. In embodiments, the digital
twin I/O system
receives sensor data from one or more sensor systems 15530. In these
embodiments, each
sensor system 15530 may include one or more IoT sensors that output respective
sensor data.
Each sensor may be assigned an IP address or may have another suitable
identifier. Each
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sensor may output sensor packets that include an identifier of the sensor and
the sensor data.
In some embodiments, the sensor packets may further include a timestamp
indicating a time
at which the sensor data was collected. In some embodiments, the digital twin
I/O system
15504 may interface with a sensor system 15530 via the real-time sensor API
15514. In these
embodiments, one or more devices (e.g., sensors, aggregators, edge devices) in
the sensor
system 15530 may transmit the sensor packets containing sensor data to the
digital twin I/O
system 15504 via the API. The digital twin I/O system may determine the sensor
system
15530 that transmitted the sensor packets and the contents thereof, and may
provide the
sensor data and any other relevant data (e.g., time stamp, environment
identifier/sensor
system identifier, and the like) to the digital twin management system 15502.
[00589] In embodiments, the digital twin I/O system 15504 may receive imported
data from
one or more sources. For example, the digital twin system 15500 may provide a
portal for
users to create and manage their digital twins. In these embodiments, a user
may upload one
or more files (e.g., image files, LIDAR scans, blueprints, and the like) in
connection with a
new digital twin that is being created. In response, the digital twin I/0
system 15504 may
provide the imported data to the digital twin management system 15502. The
digital twin I/0
system 15504 may receive other suitable types of data without departing from
the scope of
the disclosure.
[00590] In some embodiments, the digital twin simulation system 15506 is
configured to
execute simulations using the digital twin. For example, the digital twin
simulation system
15506 may iteratively adjust one or more parameters of a digital twin and/or
one or more
embedded digital twins. In embodiments, the digital twin simulation system
15506, for each
set of parameters, executes a simulation based on the set of parameters and
may collect the
simulation outcome data resulting from the simulation. Put another way, the
digital twin
simulation system 15506 may collect the properties of the digital twin and the
digital twins
within or containing the digital twin used during the simulation as well as
any outcomes
stemming from the simulation. For example, in running a simulation on a
digital twin of an
indoor agricultural facility, the digital twin simulation system 15506 can
vary the
temperature, humidity, airflow, carbon dioxide and/or other relevant
parameters and can
execute simulations that output outcomes resulting from different combinations
of the
parameters. In another example, the digital twin simulation system 15506 may
simulate the
operation of a specific machine within an industrial facility that produces an
output given a
set of inputs. In some embodiments, the inputs may be varied to determine an
effect of the
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inputs on the machine and the output thereof. In another example, the digital
twin simulation
system 15506 may simulate the vibration of a machine and/or machine
components. In this
example, the digital twin of the machine may include a set of operating
parameters,
interfaces, and capabilities of the machine. In some embodiments, the
operating parameters
may be varied to evaluate the effectiveness of the machine. The digital twin
simulation
system 15506 is discussed in further detail throughout the disclosure.
[00591] In embodiments, the digital twin dynamic model system 15508 is
configured to
model one or more behaviors with respect to a digital twin of an environment.
In
embodiments, the digital twin dynamic model system 15508 may receive a request
to model a
certain type of behavior regarding an environment or a process and may model
that behavior
using a dynamic model, the digital twin of the environment or process, and
sensor data
collected from one or more sensors that are monitoring the environment or
process. For
example, an operator of a machine having bearings may wish to model the
vibration of the
machine and bearings to determine whether the machine and/or bearings can
withstand an
increase in output. In this example, the digital twin dynamic model system
15508 may
execute a dynamic model that is configured to determine whether an increase in
output would
result in adverse consequences (e.g., failures, downtime, or the like). The
digital twin
dynamic model system 15508 is discussed in further detail throughout the
disclosure.
[00592] In embodiments, the cognitive processes system 15510 performs machine
learning
and artificial intelligence related tasks on behalf of the digital twin
system. In embodiments,
the cognitive processes system 15510 may train any suitable type of model,
including but not
limited to various types of neural networks, regression models, random
forests, decision trees,
Hidden Markov models, Bayesian models, and the like. In embodiments, the
cognitive
processes system 15510 trains machine learned models using the output of
simulations
executed by the digital twin simulation system 15506. In some of these
embodiments, the
outcomes of the simulations may be used to supplement training data collected
from real-
world environments and/or processes. In embodiments, the cognitive processes
system 15510
leverages machine learned models to make predictions, identifications,
classifications and
provide decision support relating to the real-world environments and/or
processes represented
by respective digital twins.
[00593] For example, a machine-learned prediction model may be used to predict
the cause
of irregular vibrational patterns (e.g., a suboptimal, critical, or alarm
vibration fault state) for
a bearing of an engine in an industrial facility. In this example, the
cognitive processes
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system 15510 may receive vibration sensor data from one or more vibration
sensors disposed
on or near the engine and may receive maintenance data from the industrial
facility and may
generate a feature vector based on the vibration sensor data and the
maintenance data. The
cognitive processes system 15510 may input the feature vector into a machine-
learned model
trained specifically for the engine (e.g., using a combination simulation data
and real-world
data of causes of irregular vibration patterns) to predict the cause of the
irregular vibration
patterns. In this example, the causes of the irregular vibrational patterns
could be a loose
bearing, a lack of bearing lubrication, a bearing that is out of alignment, a
worn bearing, the
phase of the bearing may be aligned with the phase of the engine, loose
housing, loose bolt,
and the like.
[00594] In another example, a machine-learned model may be used to provide
decision
support to bring a bearing of an engine in an industrial facility operating at
a suboptimal
vibration fault level state to a normal operation vibration fault level state.
In this example, the
cognitive processes system 15510 may receive vibration sensor data from one or
more
vibration sensors disposed on or near the engine and may receive maintenance
data from the
industrial facility and may generate a feature vector based on the vibration
sensor data and
the maintenance data. The cognitive processes system 15510 may input the
feature vector
into a machine-learned model trained specifically for the engine (e.g., using
a combination
simulation data and real-world data of solutions to irregular vibration
patterns) to provide
decision support in achieving a normal operation fault level state of the
bearing. In this
example, the decision support could be a recommendation to tighten the
bearing, lubricate the
bearing, re-align the bearing, order a new bearing, order a new part, collect
additional
vibration measurements, change operating speed of the engine, tighten
housings, tighten
bolts, and the like.
[00595] In another example, a machine-learned model may be used to provide
decision
support relating to vibration measurement collection by a worker. In this
example, the
cognitive processes system 15510 may receive vibration measurement history
data from the
industrial facility and may generate a feature vector based on the vibration
measurement
history data. The cognitive processes system 15510 may input the feature
vector into a
machine-learned model trained specifically for the engine (e.g., using a
combination
simulation data and real-world vibration measurement history data) to provide
decision
support in selecting vibration measurement locations.
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[00596] In yet another example, a machine-learned model may be used to
identify vibration
signatures associated with machine and/or machine component problems. In this
example, the
cognitive processes system 15510 may receive vibration measurement history
data from the
industrial facility and may generate a feature vector based on the vibration
measurement
history data. The cognitive processes system 15510 may input the feature
vector into a
machine-learned model trained specifically for the engine (e.g., using a
combination
simulation data and real-world vibration measurement history data) to identify
vibration
signatures associated with a machine and/or machine component. The foregoing
examples are
non-limiting examples and the cognitive processes system 15510 may be used for
any other
suitable AI/machine-learning related tasks that are performed with respect to
industrial
facilities.
[00597] In embodiments, the environment control system 15512 controls one or
more
aspects of industrial facilities. In some of these embodiments, the
environment control system
15512 may control one or more devices within an industrial environment. For
example, the
environment control system 15512 may control one or more machines within an
environment, robots within an environment, an HVAC system of the environment,
an alarm
system of the environment, an assembly line in an environment, or the like. In
embodiments,
the environment control system 15512 may leverage the digital twin simulation
system
15506, the digital twin dynamic model system 15508, and/or the cognitive
processes system
15510 to determine one or more control instructions. In embodiments, the
environment
control system 15512 may implement a rules-based and/or a machine-learning
approach to
determine the control instructions. In response to determining a control
instruction, the
environment control system 15512 may output the control instruction to the
intended device
within a specific environment via the digital twin I/0 system 15504.
[00598] FIG. 156 illustrates an example digital twin management system 15502
according to
some embodiments of the present disclosure. In embodiments, the digital twin
management
system 15502 may include, but is not limited to, a digital twin creation
module 15564, a
digital twin update module 15566, and a digital twin visualization module
15568.
[00599] In embodiments, the digital twin creation module 15564 may create a
set of new
digital twins of a set of environments using input from users, imported data
(e.g., blueprints,
specifications, and the like), image scans of the environment, 3D data from a
LIDAR device
and/or SLAM sensor, and other suitable data sources. For example, a user
(e.g., a user
affiliated with an organization/customer account) may, via a client
application 15570, provide
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input to create a new digital twin of an environment. In doing so, the user
may upload 2D or
3D image scans of the environment and/or a blueprint of the environment. The
user may also
upload 3D data, such as taken by a camera, a LIDAR device, an IR scanner, a
set of SLAM
sensors, a radar device, an EMF scanner, or the like. In response to the
provided data, the
digital twin creation module 15564 may create a 3D representation of the
environment, which
may include any objects that were captured in the image data/detected in the
3D data. In
embodiments, the cognitive processes system 15572 may analyze input data
(e.g., blueprints,
image scans, 3D data) to classify rooms, pathways, equipment, and the like to
assist in the
generation of the 3D representation. In some embodiments, the digital twin
creation module
15564 may map the digital twin to a 3D coordinate space (e.g., a Cartesian
space having x, y,
and z axes).
[00600] In some embodiments, the digital twin creation module 15564 may output
the 3D
representation of the environment to a graphical user interface (GUI). In some
of these
embodiments, a user may identify certain areas and/or objects and may provide
input relating
to the identified areas and/or objects. For example, a user may label specific
rooms,
equipment, machines, and the like. Additionally or alternatively, the user may
provide data
relating to the identified objects and/or areas. For example, in identifying a
piece of
equipment, the user may provide a make/model number of the equipment. In some
embodiments, the digital twin creation module 15564 may obtain information
from a
manufacturer of a device, a piece of equipment, or machinery. This information
may include
one or more properties and/or behaviors of the device, equipment, or
machinery. In some
embodiments, the user may, via the GUI, identify locations of sensors
throughout the
environment. For each sensor, the user may provide a type of sensor and
related data (e.g.,
make, model, IP address, and the like). The digital twin creation module 15564
may record
the locations (e.g., the x, y, z coordinates of the sensors) in the digital
twin of the
environment. In embodiments, the digital twin system 15500 may employ one or
more
systems that automate the population of digital twins. For example, the
digital twin system
15500 may employ a machine vision-based classifier that classifies makes and
models of
devices, equipment, or sensors. Additionally or alternatively, the digital
twin system 15500
may iteratively ping different types of known sensors to identify the presence
of specific
types of sensors that are in an environment. Each time a sensor responds to a
ping, the digital
twin system 15500 may extrapolate the make and model of the sensor.
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[00601] In some embodiments, the manufacturer may provide or make available
digital
twins of their products (e.g., sensors, devices, machinery, equipment, raw
materials, and the
like). In these embodiments, the digital twin creation module 15564 may import
the digital
twins of one or more products that are identified in the environment and may
embed those
digital twins in the digital twin of the environment. In embodiments,
embedding a digital twin
within another digital twin may include creating a relationship between the
embedded digital
twin with the other digital twin. In these embodiments, the manufacturer of
the digital twin
may define the behaviors and/or properties of the respective products. For
example, a digital
twin of a machine may define the manner by which the machine operates, the
inputs/outputs
of the machine, and the like. In this way, the digital twin of the machine may
reflect the
operation of the machine given a set of inputs.
[00602] In embodiments, a user may define one or more processes that occur in
an
environment. In these embodiments, the user may define the steps in the
process, the
machines/devices that perform each step in the process, the inputs to the
process, and the
outputs of the process.
[00603] In embodiments, the digital twin creation module 15564 may create a
graph
database that defines the relationships between a set of digital twins. In
these embodiments,
the digital twin creation module 15564 may create nodes for the environment,
systems and
subsystems of the environment, devices in the environment, sensors in the
environment,
workers that work in the environment, processes that are performed in the
environment, and
the like. In embodiments, the digital twin creation module 15564 may write the
graph
database representing a set of digital twins to the digital twin datastore
15516.
[00604] In embodiments, the digital twin creation module 15564 may, for each
node, include
any data relating to the entity in the node representing the entity. For
example, in defining a
node representing an environment, the digital twin creation module 15564 may
include the
dimensions, boundaries, layout, pathways, and other relevant spatial data in
the node.
Furthermore, the digital twin creation module 15564 may define a coordinate
space with
respect to the environment. In the case that the digital twin may be rendered,
the digital twin
creation module 15564 may include a reference in the node to any shapes,
meshes, splines,
surfaces, and the like that may be used to render the environment. In
representing a system,
subsystem, device, or sensor, the digital twin creation module 15564 may
create a node for
the respective entity and may include any relevant data. For example, the
digital twin creation
module 15564 may create a node representing a machine in the environment. In
this example,
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the digital twin creation module 15564 may include the dimensions, behaviors,
properties,
location, and/or any other suitable data relating to the machine in the node
representing the
machine. The digital twin creation module 15564 may connect nodes of related
entities with
an edge, thereby creating a relationship between the entities. In doing so,
the created
relationship between the entities may define the type of relationship
characterized by the
edge. In representing a process, the digital twin creation module 15564 may
create a node for
the entire process or may create a node for each step in the process. In some
of these
embodiments, the digital twin creation module 15564 may relate the process
nodes to the
nodes that represent the machinery/devices that perform the steps in the
process. In
embodiments, where an edge connects the process step nodes to the
machinery/device that
performs the process step, the edge or one of the nodes may contain
information that
indicates the input to the step, the output of the step, the amount of time
the step takes, the
nature of processing of inputs to produce outputs, a set of states or modes
the process can
undergo, and the like.
[00605] In embodiments, the digital twin update module 15566 updates sets of
digital twins
based on a current status of one or more industrial entities. In some
embodiments, the digital
twin update module 15566 receives sensor data from a sensor system 15530 of an
industrial
environment and updates the status of the digital twin of the industrial
environment and/or
digital twins of any affected systems, subsystems, devices, workers,
processes, or the like. As
discussed, the digital twin I/0 system 15504 may receive the sensor data in
one or more
sensor packets. The digital twin I/O system 15504 may provide the sensor data
to the digital
twin update module 15566 and may identify the environment from which the
sensor packets
were received and the sensor that provided the sensor packet. In response to
the sensor data,
the digital twin update module 15566 may update a state of one or more digital
twins based
on the sensor data. In some of these embodiments, the digital twin update
module 15566 may
update a record (e.g., a node in a graph database) corresponding to the sensor
that provided
the sensor data to reflect the current sensor data. In some scenarios, the
digital twin update
module 15566 may identify certain areas within the environment that are
monitored by the
sensor and may update a record (e.g., a node in a graph database) to reflect
the current sensor
data. For example, the digital twin update module 15566 may receive sensor
data reflecting
different vibrational characteristics of a machine and/or machine components.
In this
example, the digital twin update module 15566 may update the records
representing the
vibration sensors that provided the vibration sensor data and/or the records
representing the
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machine and/or the machine components to reflect the vibration sensor data. In
another
example, in some scenarios, workers in an industrial environment (e.g.,
manufacturing
facility, industrial storage facility, a mine, a drilling operation, or the
like) may be required to
wear wearable devices (e.g., smart watches, smart helmets, smart shoes, or the
like). In these
embodiments, the wearable devices may collect sensor data relating to the
worker (e.g.,
location, movement, heartrate, respiration rate, body temperature, or the
like) and/or the
environment surrounding the worker and may communicate the collected sensor
data to the
digital twin system 15500 (e.g., via the real-time sensor API 15514) either
directly or via an
aggregation device of the sensor system. In response to receiving the sensor
data from the
wearable device of a worker, the digital twin update module 15566 may update a
digital twin
of a worker to reflect, for example, a location of the worker, a trajectory of
the worker, a
health status of the worker, or the like. In some of these embodiments, the
digital twin update
module 15566 may update a node representing a worker and/or an edge that
connects the
node representing the environment with the collected sensor data to reflect
the current status
of the worker.
[00606] In some embodiments, the digital twin update module 15566 may provide
the sensor
data from one or more sensors to the digital twin dynamic model system 15508,
which may
model a behavior of the environment and/or one or more industrial entities to
extrapolate
additional state data.
[00607] In embodiments, the digital twin visualization module 15568 receives
requests to
view a visual digital twin or a portion thereof. In embodiments, the request
may indicate the
digital twin to be viewed (e.g., an environment identifier). In response, the
digital twin
visualization module 15568 may determine the requested digital twin and any
other digital
twins implicated by the request. For example, in requesting to view a digital
twin of an
environment, the digital twin visualization module 15568 may further identify
the digital
twins of any industrial entities within the environment. In embodiments, the
digital twin
visualization module 15568 may identify the spatial relationships between the
industrial
entities and the environment based on, for example, the relationships defined
in a graph
database. In these embodiments, the digital twin visualization module 15568
can determine
the relative location of embedded digital twins within the containing digital
twin, relative
locations of adjoining digital twins, and/or the transience of the
relationship (e.g., is an object
fixed to a point or does the object move). The digital twin visualization
module 15568 may
render the requested digital twins and any other implicated digital twin based
on the
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identified relationships. In some embodiments, the digital twin visualization
module 15568
may, for each digital twin, determine the surfaces of the digital twin. In
some embodiments,
the surfaces of a digital may be defined or referenced in a record
corresponding to the digital
twin, which may be provided by a user, determined from imported images, or
defined by a
manufacturer of an industrial entity. In the scenario that an object can take
different poses or
shapes (e.g., an industrial robot), the digital twin visualization module
15568 may determine
a pose or shape of the object for the digital twin. The digital twin
visualization module 15568
may embed the digital twins into the requested digital twin and may output the
requested
digital twin to a client application.
[00608] In some of these embodiments, the request to view a digital twin may
further
indicate the type of view. As discussed, in some embodiments, digital twins
may be depicted
in a number of different view types. For example, an environment or device may
be viewed
in a "real-world" view that depicts the environment or device as they
typically appear, in a
"heat" view that depicts the environment or device in a manner that is
indicative of a
temperature of the environment or device, in a "vibration" view that depicts
the machines
and/or machine components in an industrial environment in a manner that is
indicative of
vibrational characteristics of the machines and/or machine components, in a
"filtered" view
that only displays certain types of objects within an environment or
components of a device
(such as objects that require attention resulting from, for example,
recognition of a fault
condition, an alert, an updated report, or other factor), an augmented view
that overlays data
on the digital twin, and/or any other suitable view types. In embodiments,
digital twins may
be depicted in a number of different role-based view types. For example, a
manufacturing
facility device may be viewed in an "operator" view that depicts the facility
in a manner that
is suitable for a facility operator, a "C-Suite" view that depicts the
facility in a manner that is
suitable for executive-level managers, a "marketing" view that depicts the
facility in a
manner that is suitable for workers in sales and/or marketing roles, a "board"
view that
depicts the facility in a manner that is suitable for members of a corporate
board, a
"regulatory" view that depicts the facility in a manner that is suitable for
regulatory
managers, and a "human resources" view that depicts the facility in a manner
that is suitable
for human resources personnel. In response to a request that indicates a view
type, the digital
twin visualization module 15568 may retrieve the data for each digital twin
that corresponds
to the view type. For example, if a user has requested a vibration view of a
factory floor, the
digital twin visualization module 15568 may retrieve vibration data for the
factory floor
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(which may include vibration measurements taken from different machines and/or
machine
components and/or vibration measurements that were extrapolated by the digital
twin
dynamic model system 15508 and/or simulated vibration data from digital twin
simulation
system 15506) as well as available vibration data for any industrial entities
appearing on the
factory floor. In this example, the digital twin visualization module 15568
may determine
colors corresponding to each machine component on a factory floor that
represent a vibration
fault level state (e.g., red for alarm, orange for critical, yellow for
suboptimal, and green for
normal operation). The digital twin visualization module 15568 may then render
the digital
twins of the machine components within the environment based on the determined
colors.
Additionally or alternatively, the digital twin visualization module 15568 may
render the
digital twins of the machine components within the environment with indicators
having the
determined colors. For instance, if the vibration fault level state of an
inbound bearing of a
motor is suboptimal and the outbound bearing of the motor is critical, the
digital twin
visualization module 15568 may render the digital twin of the inbound bearing
having an
indicator in a shade of yellow (e.g., suboptimal) and the outbound bearing
having an indicator
in a shade of orange (e.g., critical). It is noted that in some embodiments,
the digital twin
system 15500 may include an analytics system (not shown) that determine the
manner by
which the digital twin visualization system 15500presents information to a
human user. For
example, the analytics system may track outcomes relating to human
interactions with real-
world environments or objects in response to information presented in a visual
digital twin. In
some embodiments, the analytics system may apply cognitive models to determine
the most
effective manner to display visualized information (e.g., what colors to use
to denote an
alarm condition, what kind of movements or animations bring attention to an
alarm condition,
or the like) or audio information (what sounds to use to denote an alarm
condition) based on
the outcome data. In some embodiments, the analytics system may apply
cognitive models to
determine the most suitable manner to display visualized information based on
the role of the
user. In embodiments, the visualization may include display of information
related to the
visualized digital twins, including graphical information, graphical
information depicting
vibration characteristics, graphical information depicting harmonic peaks,
graphical
information depicting peaks, vibration severity units data, vibration fault
level state data,
recommendations from cognitive intelligence system 15510, predictions from
cognitive
intelligence system 15510, probability of failure data, maintenance history
data, time to
failure data, cost of downtime data, probability of downtime data, cost of
repair data, cost of
machine replace data, probability of shutdown data, manufacturing KPIs, and
the like.
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[00609] In another example, a user may request a filtered view of a digital
twin of a process,
whereby the digital twin of the process only shows components (e.g., machine
or equipment)
that are involved in the process. In this example, the digital twin
visualization module 15568
may retrieve a digital twin of the process, as well as any related digital
twins (e.g., a digital
twin of the environment and digital twins of any machinery or devices that
impact the
process). The digital twin visualization module 15568 may then render each of
the digital
twins (e.g., the environment and the relevant industrial entities) and then
may perform the
process on the rendered digital twins. It is noted that as a process may be
performed over a
period of time and may include moving items and/or parts, the digital twin
visualization
module 15568 may generate a series of sequential frames that demonstrate the
process. In this
scenario, the movements of the machines and/or devices implicated by the
process may be
determined according to the behaviors defined in the respective digital twins
of the machines
and/or devices.
[00610] As discussed, the digital twin visualization module 15568 may output
the requested
digital twin to a client application 15570. In some embodiments, the client
application 15570
is a virtual reality application, whereby the requested digital twin is
displayed on a virtual
reality headset. In some embodiments, the client application 15570 is an
augmented reality
application, whereby the requested digital twin is depicted in an AR-enabled
device. In these
embodiments, the requested digital twin may be filtered such that visual
elements and/or text
are overlaid on the display of the AR-enabled device.
[00611] It is noted that while a graph database is discussed, the digital twin
system 15500
may employ other suitable data structures to store information relating to a
set of digital
twins. In these embodiments, the data structures, and any related storage
system, may be
implemented such that the data structures provide for some degree of feedback
loops and/or
recursion when representing iteration of flows.
[00612] FIG. 131 illustrates an example of a digital twin I/O system 15504
that interfaces
with the environment 15520, the digital twin system 15500, and/or components
thereof to
provide bi-directional transfer of data between coupled components according
to some
embodiments of the present disclosure.
[00613] In embodiments, the transferred data includes signals (e.g., request
signals,
command signals, response signals, etc.) between connected components, which
may include
software components, hardware components, physical devices, virtualized
devices, simulated
devices, combinations thereof, and the like. The signals may define material
properties (e.g.,
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physical quantities of temperature, pressure, humidity, density, viscosity,
etc.), measured
values (e.g., contemporaneous or stored values acquired by the device or
system), device
properties (e.g., device ID or properties of the device's design
specifications, materials,
measurement capabilities, dimensions, absolute position, relative position,
combinations
thereof, and the like), set points (e.g., targets for material properties,
device properties,
system properties, combinations thereof, and the like), and/or critical points
(e.g., threshold
values such as minimum or maximum values for material properties, device
properties,
system properties, etc.). The signals may be received from systems or devices
that acquire
(e.g., directly measure or generate) or otherwise obtain (e.g., receive,
calculate, look-up,
filter, etc.) the data, and may be communicated to or from the digital twin
I/O system 15504
at predetermined times or in response to a request (e.g., polling) from the
digital twin I/O
system 15504. The communications may occur through direct or indirect
connections (e.g.,
via intermediate modules within a circuit and/or intermediate devices between
the connected
components). The values may correspond to real-world elements 157302r (e.g.,
an input or
output for a tangible vibration sensor) or virtual elements 157302v (e.g., an
input or output
for a digital twin 157302d and/or a simulated element 157302s that provide
vibration data).
[00614] In embodiments, the real-world elements 157302r may be elements within
the
industrial environment 15520. The real-world elements 157302r may include, for
example,
non-networked objects 15522, the devices 15524 (smart or non-smart), sensors
15526, and
humans 15528. The real-world elements 151302r may be process or non-process
equipment
within the industrial environments 15520. For example, process equipment may
include
motors, pumps, mills, fans, painters, welders, smelters, etc., and non-process
equipment may
include personal protective equipment, safety equipment, emergency stations or
devices (e.g.,
safety showers, eyewash stations, fire extinguishers, sprinkler systems,
etc.), warehouse
features (e.g., walls, floor layout, etc.), obstacles (e.g., persons or other
items within the
environment 15520, etc.), etc.
[00615] In embodiments, the virtual elements 157302v may be digital
representations of or
that correspond to contemporaneously existing real-world elements 157302r.
Additionally or
alternatively, the virtual elements 157302v may be digital representations of
or that
correspond to real-world elements 157302r that may be available for later
addition and
implementation into the environment 15520. The virtual elements may include,
for example,
simulated elements 175302s and/or digital twins 157302d. In embodiments, the
simulated
elements 157302s may be digital representations of real-world elements 157302s
that are not
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present within the industrial environment 15520. The simulated elements
157302s may
mimic desired physical properties which may be later integrated within the
environment
15520 as real-world elements 157302r (e.g., a "black box" that mimics the
dimensions of a
real-world elements 157302r). The simulated elements 157302s may include
digital twins of
existing objects (e.g., a single simulated element 151302s may include one or
more digital
twins 151302d for existing sensors). Information related to the simulated
elements 157302s
may be obtained, for example, by evaluating behavior of corresponding real-
world elements
157302r using mathematical models or algorithms, from libraries that define
information and
behavior of the simulated elements 131302s (e.g., physics libraries, chemistry
libraries, or the
like).
[00616] In embodiments, the digital twin 157302d may be a digital
representation of one or
more real-world elements 157302r. The digital twins 157302d are configured to
mimic, copy,
and/or model behaviors and responses of the real-world elements 157302r in
response to
inputs, outputs, and/or conditions of the surrounding or ambient environment.
Data related to
physical properties and responses of the real-world elements 157302r may be
obtained, for
example, via user input, sensor input, and/or physical modeling (e.g.,
thermodynamic models,
electrodynamic models, mechanodynamic models, etc.). Information for the
digital twin
157302d may correspond to and be obtained from the one or more real-world
elements
157302r corresponding to the digital twin 157302d. For example, in some
embodiments, the
digital twin 131302d may correspond to one real-world element 157302r that is
a fixed digital
vibration sensor 15536 on a machine component, and vibration data for the
digital twin
131302d may be obtained by polling or fetching vibration data measured by the
fixed digital
vibration sensor on the machine component. In a further example, the digital
twin 157302d
may correspond to a plurality of real-world elements 157302r such that each of
the elements
can be a fixed digital vibration sensor on a machine component, and vibration
data for the
digital twin 157302d may be obtained by polling or fetching vibration data
measured by each
of the fixed digital vibration sensors on the plurality of real-world elements
157302r.
Additionally or alternatively, vibration data of a first digital twin 157302d
may be obtained
by fetching vibration data of a second digital twin 157302d that is embedded
within the first
digital twin 157302d, and vibration data for the first digital twin 157302d
may include or be
derived from vibration data for the second digital twin 157302d. For example,
the first digital
twin may be a digital twin 157302d of an environment 15520 (alternatively
referred to as an
"environmental digital twin") and the second digital twin 157302d may be a
digital twin
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157302d corresponding to a vibration sensor disposed within the environment
15520 such
that the vibration data for the first digital twin 157302d is obtained from or
calculated based
on data including the vibration data for the second digital twin 157302d.
[00617] In embodiments, the digital twin system 15500 monitors properties of
the real-world
elements 157302r using the sensors 15526 within a respective environment 15520
that is or
may be represented by a digital twin 157302d and/or outputs of models for one
or more
simulated elements 157302s. In embodiments, the digital twin system 15500 may
minimize
network congestion while maintaining effective monitoring of processes by
extending polling
intervals and/or minimizing data transfer for sensors corresponding that
correspond to
affected real-world elements 157302r and performing simulations (e.g., via the
digital-twin
simulation system 15506) during the extended interval using data that was
obtained from
other sources (e.g., sensors that are physically proximate to or have an
effect on the affected
real-world elements 157302r). Additionally or alternatively, error checking
may be
performed by comparing the collected sensor data with data obtained from the
digital-twin
simulation system 15506. For example, consistent deviations or fluctuations
between sensor
data obtained from the real-world element 157302r and the simulated element
157302s may
indicate malfunction of the respective sensor or another fault condition.
[00618] In embodiments, the digital twin system 15500 may optimize features of
the
environment through use of one or more simulated elements 157302s. For
example, the
digital twin system 15500 may evaluate effects of the simulated elements
157302s within a
digital twin of an environment to quickly and efficiently determine costs
and/or benefits
flowing from inclusion, exclusion, or substitution of real-world elements
157302r within the
environment 15520. The costs and benefits may include, for example, increased
machinery
costs (e.g., capital investment and maintenance), increased efficiency (e.g.,
process
optimization to reduce waste or increase throughput), decreased or altered
footprint within
the environment 15520, extension or optimization of useful lifespans,
minimization of
component faults, minimization of component downtime, etc.
[00619] In embodiments, the digital twin I/0 system 15504 may include one or
more
software modules that are executed by one or more controllers of one or more
devices (e.g.,
server devices, user devices, and/or distributed devices) to affect the
described functions. The
digital twin I/O system 15504 may include, for example, an input module
157304, an output
module 157306, and an adapter module 157308.
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[00620] In embodiments, the input module 157304 may obtain or import data from
data
sources in communication with the digital twin I/O system 15504, such as the
sensor system
15530 and the digital twin simulation system 15506. The data may be
immediately used by or
stored within the digital twin system 15500. The imported data may be ingested
from data
streams, data batches, in response to a triggering event, combinations
thereof, and the like.
The input module 157304 may receive data in a format that is suitable to
transfer, read,
and/or write information within the digital twin system 15500.
[00621] In embodiments, the output module 157306 may output or export data to
other
system components (e.g., the digital twin datastore 15516, the digital twin
simulation system
15506, the cognitive intelligence system 15510, etc.), devices 15524, and/or
the client
application 15570. The data may be output in data streams, data batches, in
response to a
triggering event (e.g., a request), combinations thereof, and the like. The
output module
157306 may output data in a format that is suitable to be used or stored by
the target element
(e.g., one protocol for output to the client application and another protocol
for the digital twin
datastore 15516).
[00622] In embodiments, the adapter module 157308 may process and/or convert
data
between the input module 157304 and the output module 157306. In embodiments,
the
adapter module 157308 may convert and/or route data automatically (e.g., based
on data
type) or in response to a received request (e.g., in response to information
within the data).
[00623] In embodiments, the digital twin system 15500 may represent a set of
industrial
workpiece elements in a digital twin, and the digital twin simulation system
15506 simulates
a set of physical interactions of a worker with the workpiece elements.
[00624] In embodiments, the digital twin simulation system 15506 may determine
process
outcomes for the simulated physical interactions accounting for simulated
human factors. For
example, variations in workpiece throughput may be modeled by the digital twin
system
15500 including, for example, worker response times to events, worker fatigue,
discontinuity
within worker actions (e.g., natural variations in human-movement speed,
differing
positioning times, etc.), effects of discontinuities on downstream processes,
and the like. In
embodiments, individualized worker interactions may be modeled using
historical data that is
collected, acquired, and/or stored by the digital twin system 15500. The
simulation may begin
based on estimated amounts (e.g., worker age, industry averages, workplace
expectations,
etc.). The simulation may also individualize data for each worker (e.g.,
comparing estimated
amounts to collected worker-specific outcomes).
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[00625] In embodiments, information relating to workers (e.g., fatigue rates,
efficiency rates,
and the like) may be determined by analyzing performance of specific workers
over time and
modeling said performance.
[00626] In embodiments, the digital twin system 15500 includes a plurality of
proximity
sensors within the sensor system 15530. The proximity sensors are or may be
configured to
detect elements of the environment 15520 that are within a predetermined area.
For example,
proximity sensors may include electromagnetic sensors, light sensors, and/or
acoustic
sensors.
[00627] The electromagnetic sensors are or may be configured to sense objects
or
interactions via one or more electromagnetic fields (e.g., emitted
electromagnetic radiation or
received electromagnetic radiation). In embodiments, the electromagnetic
sensors include
inductive sensors (e.g., radio-frequency identification sensors), capacitive
sensors (e.g.,
contact and contactless capacitive sensors), combinations thereof, and the
like.
[00628] The light sensors are or may be configured to sense objects or
interactions via
electromagnetic radiation in, for example, the far-infrared, near-infrared,
optical, and/or
ultraviolet spectra. In embodiments, the light sensors may include image
sensors (e.g.,
charge-coupled devices and CMOS active-pixel sensors), photoelectric sensors
(e.g., through-
beam sensors, retroreflective sensors, and diffuse sensors), combinations
thereof, and the like.
Further, the light sensors may be implemented as part of a system or
subsystem, such as a
light detection and ranging ("LIDAR") sensor.
[00629] The acoustic sensors are or may be configured to sense objects or
interactions via
sound waves that are emitted and/or received by the acoustic sensors. In
embodiments, the
acoustic sensors may include infrasonic, sonic, and/or ultrasonic sensors.
Further, the
acoustic sensors may be grouped as part of a system or subsystem, such as a
sound navigation
and ranging ("SONAR") sensor.
[00630] In embodiments, the digital twin system 15500 stores and collects data
from a set of
proximity sensors within the environment 15520 or portions thereof. The
collected data may
be stored, for example, in the digital twin datastore 15516 for use by
components the digital
twin system 15500 and/or visualization by a user. Such use and/or
visualization may occur
contemporaneously with or after collection of the data (e.g., during later
analysis and/or
optimization of processes).
[00631] In embodiments, data collection may occur in response to a triggering
condition.
These triggering conditions may include, for example, expiration of a static
or a dynamic
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predetermined interval, obtaining a value short of or in excess of a static or
dynamic value,
receiving an automatically generated request or instruction from the digital
twin system
15500 or components thereof, interaction of an element with the respective
sensor or sensors
(e.g., in response to a worker or machine breaking a beam or coming within a
predetermined
distance from the proximity sensor), interaction of a user with a digital twin
(e.g., selection of
an environmental digital twin, a sensor array digital twin, or a sensor
digital twin),
combinations thereof, and the like.
[00632] In some embodiments, the digital twin system 15500 collects and/or
stores RFID
data in response to interaction of a worker with a real-world element 157302r.
For example,
in response to a worker interaction with a real-world environment, the digital
twin will collect
and/or store RFID data from RFID sensors within or associated with the
corresponding
environment 15520. Additionally or alternatively, worker interaction with a
sensor-array
digital twin will collect and/or store RFID data from RFID sensors within or
associated with
the corresponding sensor array. Similarly, worker interaction with a sensor
digital twin will
collect and/or store RFID data from the corresponding sensor. The RFID data
may include
suitable data attainable by RFID sensors such as proximate RFID tags, RFID tag
position,
authorized RFID tags, unauthorized RFID tags, unrecognized RFID tags, RFID
type (e.g.,
active or passive), error codes, combinations thereof, and the like.
[00633] In embodiments, the digital twin system 15500 may further embed
outputs from one
or more devices within a corresponding digital twin. In embodiments, the
digital twin system
15500 embeds output from a set of individual-associated devices into an
industrial digital
twin. For example, the digital twin I/O system 15504 may receive information
output from
one or more wearable devices 15554 or mobile devices (not shown) associated
with an
individual within an industrial environment. The wearable devices may include
image capture
devices (e.g., body cameras or augmented-reality headwear), navigation devices
(e.g., GPS
devices, inertial guidance systems), motion trackers, acoustic capture devices
(e.g.,
microphones), radiation detectors, combinations thereof, and the like.
[00634] In embodiments, upon receiving the output information, the digital
twin I/O system
15504 routes the information to the digital twin creation module 15564 to
check and/or
update the environment digital twin and/or associated digital twins within the
environment
(e.g., a digital twin of a worker, machine, or robot position at a given
time). Further, the
digital twin system 15500 may use the embedded output to determine
characteristics of the
environment 15520.
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[00635] In embodiments, the digital twin system 15500 embeds output from a
LIDAR point
cloud system into an industrial digital twin. For example, the digital twin
I/0 system 15504
may receive information output from one or more Lidar devices 15538 within an
industrial
environment. The Lidar devices 15538 is configured to provide a plurality of
points having
associated position data (e.g., coordinates in absolute or relative x, y, and
z values). Each of
the plurality of points may include further LIDAR attributes, such as
intensity, return number,
total returns, laser color data, return color data, scan angle, scan
direction, etc. The Lidar
devices 15538 may provide a point cloud that includes the plurality of points
to the digital
twin system 15500 via, for example, the digital twin I/0 system 15504.
Additionally or
alternatively, the digital twin system 15500 may receive a stream of points
and assemble the
stream into a point cloud, or may receive a point cloud and assemble the
received point cloud
with existing point cloud data, map data, or three dimensional (3D)-model
data.
[00636] In embodiments, upon receiving the output information, the digital
twin I/O system
15504 routes the point cloud information to the digital twin creation module
15564 to check
and/or update the environment digital twin and/or associated digital twins
within the
environment (e.g., a digital twin of a worker, machine, or robot position at a
given time). In
some embodiments, the digital twin system 15500 is further configured to
determine closed-
shape objects within the received LIDAR data. For example, the digital twin
system 15500
may group a plurality of points within the point cloud as an object and, if
necessary, estimate
obstructed faces of objects (e.g., a face of the object contacting or adjacent
a floor or a face of
the object contacting or adjacent another object such as another piece of
equipment). The
system may use such closed-shape objects to narrow search space for digital
twins and
thereby increase efficiency of matching algorithms (e.g., a shape-matching
algorithm).
[00637] In embodiments, the digital twin system 15500 embeds output from a
simultaneous
location and mapping ("SLAM") system in an environmental digital twin. For
example, the
digital twin I/O system 15504 may receive information output from the SLAM
system, such
as Slam sensor 15562, and embed the received information within an environment
digital
twin corresponding to the location determined by the SLAM system. In
embodiments, upon
receiving the output information from the SLAM system, the digital twin I/O
system 15504
routes the information to the digital twin creation module 15564 to check
and/or update the
environment digital twin and/or associated digital twins within the
environment (e.g., a
digital twin of a workpiece, furniture, movable object, or autonomous object).
Such updating
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provides digital twins of non-connected elements (e.g., furnishings or
persons) automatically
and without need of user interaction with the digital twin system 15500.
[00638] In embodiments, the digital twin system 15500 can leverage known
digital twins to
reduce computational requirements for the Slam sensor 15562 by using
suboptimal map-
building algorithms. For example, the suboptimal map-building algorithms may
allow for a
higher uncertainty tolerance using simple bounded-region representations and
identifying
possible digital twins. Additionally or alternatively, the digital twin system
15500 may use a
bounded-region representation to limit the number of digital twins, analyze
the group of
potential twins for distinguishing features, then perform higher precision
analysis for the
distinguishing features to identify and/or eliminate categories of, groups of,
or individual
digital twins and, in the event that no matching digital twin is found,
perform a precision scan
of only the remaining areas to be scanned.
[00639] In embodiments, the digital twin system 15500 may further reduce
compute
required to build a location map by leveraging data captured from other
sensors within the
environment (e.g., captured images or video, radio images, etc.) to perform an
initial map-
building process (e.g., a simple bounded-region map or other suitable
photogrammetry
methods), associate digital twins of known environmental objects with features
of the simple
bounded-region map to refine the simple bounded-region map, and perform more
precise
scans of the remaining simple bounded regions to further refine the map. In
some
embodiments, the digital twin system 15500 may detect objects within received
mapping
information and, for each detected object, determine whether the detected
object corresponds
to an existing digital twin of a real-world-element. In response to
determining that the
detected object does not correspond to an existing real-world-element digital
twin, the digital
twin system 15500 may use, for example, the digital twin creation module 15564
to generate
a new digital twin corresponding to the detected object (e.g., a detected-
object digital twin)
and add the detected-object digital twin to the real-world-element digital
twins within the
digital twin datastore. Additionally or alternatively, in response to
determining that the
detected object corresponds to an existing real-world-element digital twin,
the digital twin
system 15500 may update the real-world-element digital twin to include new
information
detected by the simultaneous location and mapping sensor, if any.
[00640] In embodiments, the digital twin system 15500 represents locations of
autonomously or remotely moveable elements and attributes thereof within an
industrial
digital twin. Such movable elements may include, for example, workers,
persons, vehicles,
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autonomous vehicles, robots, etc. The locations of the moveable elements may
be updated in
response to a triggering condition. Such triggering conditions may include,
for example,
expiration of a static or a dynamic predetermined interval, receiving an
automatically
generated request or instruction from the digital twin system 15500 or
components thereof,
interaction of an element with a respective sensor or sensors (e.g., in
response to a worker or
machine breaking a beam or coming within a predetermined distance from a
proximity
sensor), interaction of a user with a digital twin (e.g., selection of an
environmental digital
twin, a sensor array digital twin, or a sensor digital twin), combinations
thereof, and the like.
[00641] In embodiments, the time intervals may be based on probability of the
respective
movable element having moved within a time period. For example, the time
interval for
updating a worker location may be relatively shorter for workers expected to
move frequently
(e.g., a worker tasked with lifting and carrying objects within and through
the environment
15520) and relatively longer for workers expected to move infrequently (e.g.,
a worker tasked
with monitoring a process stream). Additionally or alternatively, the time
interval may be
dynamically adjusted based on applicable conditions, such as increasing the
time interval
when no movable elements are detected, decreasing the time interval as or when
the number
of moveable elements within an environment increases (e.g., increasing number
of workers
and worker interactions), increasing the time interval during periods of
reduced
environmental activity (e.g., breaks such as lunch), decreasing the time
interval during
periods of abnormal environmental activity (e.g., tours, inspections, or
maintenance),
decreasing the time interval when unexpected or uncharacteristic movement is
detected (e.g.,
frequent movement by a typically sedentary element or coordinated movement,
for example,
of workers approaching an exit or moving cooperatively to carry a large
object),
combinations thereof, and the like. Further, the time interval may also
include additional,
semi-random acquisitions. For example, occasional mid-interval locations may
be acquired
by the digital twin system 15500 to reinforce or evaluate the efficacy of the
particular time
interval.
[00642] In embodiments, the digital twin system 15500 may analyze data
received from the
digital twin I/O system 15504 to refine, remove, or add conditions. For
example, the digital
twin system 15500 may optimize data collection times for movable elements that
are updated
more frequently than needed (e.g., multiple consecutive received positions
being identical or
within a predetermined margin of error).
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[00643] In embodiments, the digital twin system 15500 may receive, identify,
and/or store a
set of states 15840a-n related to the environment 15520. The states 15840a-n
may be, for
example, data structures that include a plurality of attributes 158404a-n and
a set of
identifying criteria 158406a-n to uniquely identify each respective state
15840a-n. In
embodiments, the states 15840a-n may correspond to states where it is
desirable for the
digital twin system 15500 to set or alter conditions of real-world elements
157302r and/or the
environment 15520 (e.g., increase/decrease monitoring intervals, alter
operating conditions,
etc.).
[00644] In embodiments, the set of states 15840a-n may further include, for
example,
minimum monitored attributes for each state 15840a-n, the set of identifying
criteria
158406a-n for each state 15840a-n, and/or actions available to be taken or
recommended to
be taken in response to each state 15840a-n. Such information may be stored
by, for example,
the digital twin datastore 15516 or another datastore. The states 15840a-n or
portions thereof
may be provided to, determined by, or altered by the digital twin system
15500. Further, the
set of states 15840a-n may include data from disparate sources. For example,
details to
identify and/or respond to occurrence of a first state may be provided to the
digital twin
system 15500 via user input, details to identify and/or respond to occurrence
of a second state
may be provided to the digital twin system 15500 via an external system,
details to identify
and/or respond to occurrence of a third state may be determined by the digital
twin system
15500 (e.g., via simulations or analysis of process data), and details to
identify and/or
respond to occurrence of a fourth state may be stored by the digital twin
system 15500 and
altered as desired (e.g., in response to simulated occurrence of the state or
analysis of data
collected during an occurrence of and response to the state).
[00645] In embodiments, the plurality of attributes 158404a-n includes at
least the attributes
158404a-n needed to identify the respective state 15840a-n. The plurality of
attributes
158404a-n may further include additional attributes that are or may be
monitored in
determining the respective state 15840a-n, but are not needed to identify the
respective state
15840a-n. For example, the plurality of attributes 158404a-n for a first state
may include
relevant information such as rotational speed, fuel level, energy input,
linear speed,
acceleration, temperature, strain, torque, volume, weight, etc.
[00646] The set of identifying criteria 158406a-n may include information for
each of the set
of attributes 158404a-n to uniquely identify the respective state. The
identifying criteria
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158406a-n may include, for example, rules, thresholds, limits, ranges, logical
values,
conditions, comparisons, combinations thereof, and the like.
[00647] The change in operating conditions or monitoring may be any suitable
change. For
example, after identifying occurrence of a respective state 158406a-n, the
digital twin system
15500 may increase or decrease monitoring intervals for a device (e.g.,
decreasing
monitoring intervals in response to a measured parameter differing from
nominal operation)
without altering operation of the device. Additionally or alternatively, the
digital twin system
15500 may alter operation of the device (e.g., reduce speed or power input)
without altering
monitoring of the device. In further embodiments, the digital twin system
15500 may alter
operation of the device (e.g., reduce speed or power input) and alter
monitoring intervals for
the device (e.g., decreasing monitoring intervals).
[00648] FIG. 158 illustrates an example set of identified states 15840a-n
related to industrial
environments that the digital twin system 15500 may identify and/or store for
access by
intelligent systems (e.g., the cognitive intelligence system 15510) or users
of the digital twin
system 15500, according to some embodiments of the present disclosure. The
states 15840a-n
may include operational states (e.g., suboptimal, normal, optimal, critical,
or alarm operation
of one or more components), excess or shortage states (e.g., supply-side or
output-side
quantities), combinations thereof, and the like.
[00649] In embodiments, the digital twin system 15500 may monitor attributes
158404a-n of
real-world elements 157302r and/or digital twins 157302d to determine the
respective state
15840a-n. The attributes 158404a-n may be, for example, operating conditions,
set points,
critical points, status indicators, other sensed information, combinations
thereof, and the like.
For example, the attributes 158404a-n may include power input 158404a,
operational speed
158404b, critical speed 158404c, and operational temperature 158404d of the
monitored
elements. While the illustrated example illustrates uniform monitored
attributes, the
monitored attributes may differ by target device (e.g., the digital twin
system 15500 would
not monitor rotational speed for an object with no rotatable components).
[00650] Each of the states 15840a-n includes a set of identifying criteria
158406a-n meeting
particular criteria that are unique among the group of monitored states 13240a-
n. The digital
twin system 15500 may identify the overspeed state 15540a, for example, in
response to the
monitored attributes 158404a-n meeting a first set of identifying criteria
158406a (e.g.,
operational speed 158404b being higher than the critical speed 158404c, while
the
operational temperature 158404d is nominal).
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[00651] In response to determining that one or more states 15840a-n exists or
has occurred,
the digital twin system 15500 may update triggering conditions for one or more
monitoring
protocols, issue an alert or notification, or trigger actions of subcomponents
of the digital
twin system 15500. For example, subcomponents of the digital twin system 15500
may take
actions to mitigate and/or evaluate impacts of the detected states 15540a-n.
When attempting
to take actions to mitigate impacts of the detected states 15540a-n on real-
world elements
157302r, the digital twin system 15500 may determine whether instructions
exist (e.g., are
stored in the digital twin datastore 15516) or should be developed (e.g.,
developed via
simulation and cognitive intelligence or via user or worker input). Further,
the digital twin
system 15500 may evaluate impacts of the detected states 15540a-n, for
example,
concurrently with the mitigation actions or in response to determining that
the digital twin
system 15500 has no stored mitigation instructions for the detected states
15540a-n.
[00652] In embodiments, the digital twin system 15500 employs the digital twin
simulation
system 15506 to simulate one or more impacts, such as immediate, upstream,
downstream,
and/or continuing effects, of recognized states. The digital twin simulation
system 15506 may
collect and/or be provided with values relevant to the evaluated states 15540a-
n. In
simulating the impact of the one or more states 15540a-n, the digital twin
simulation system
15506 may recursively evaluate performance characteristics of affected digital
twins 157302d
until convergence is achieved. The digital twin simulation system 15506 may
work, for
example, in tandem with the cognitive intelligence system 15510 to determine
response
actions to alleviate, mitigate, inhibit, and/or prevent occurrence of the one
or more states
15540a-n. For example, the digital twin simulation system 15506 may
recursively simulate
impacts of the one or more states 15540a-n until achieving a desired fit
(e.g., convergence is
achieved), provide the simulated values to the cognitive intelligence system
15510 for
evaluation and determination of potential actions, receive the potential
actions, evaluate
impacts of each of the potential actions for a respective desired fit (e.g.,
cost functions for
minimizing production disturbance, preserving critical components, minimizing
maintenance
and/or downtime, optimizing system, worker, user, or personal safety, etc.).
[00653] In embodiments, the digital twin simulation system 15506 and the
cognitive
intelligence system 15510 may repeatedly share and update the simulated values
and
response actions for each desired outcome until desired conditions are met
(e.g., convergence
for each evaluated cost function for each evaluated action). The digital twin
system 15500
may store the results in the digital twin datastore 15516 for use in response
to determining
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that one or more states 15540a-n has occurred. Additionally, simulations and
evaluations by
the digital twin simulation system 15506 and/or the cognitive intelligence
system 15510 may
occur in response to occurrence or detection of the event.
[00654] In embodiments, simulations and evaluations are triggered only when
associated
actions are not present within the digital twin system 15500. In further
embodiments,
simulations and evaluations are performed concurrently with use of stored
actions to evaluate
the efficacy or effectiveness of the actions in real time and/or evaluate
whether further actions
should be employed or whether unrecognized states may have occurred. In
embodiments, the
cognitive intelligence system 15510 may also be provided with notifications of
instances of
undesired actions with or without data on the undesired aspects or results of
such actions to
optimize later evaluations.
[00655] In embodiments, the digital twin system 15500 evaluates and/or
represents the
impact of machine downtime within a digital twin of a manufacturing facility.
For example,
the digital twin system 15500 may employ the digital twin simulation system
15506 to
simulate the immediate, upstream, downstream, and/or continuing effects of a
machine
downtime state 15540b. The digital twin simulation system 15506 may collect or
be provided
with performance-related values such as optimal, suboptimal, and minimum
performance
requirements for elements (e.g., real-world elements 157302r and/or nested
digital twins
157302d) within the affected digital twins 157302d, and/or characteristics
thereof that are
available to the affected digital twins 157302d, nested digital twins 157302d,
redundant
systems within the affected digital twins 157302d, combinations thereof, and
the like.
[00656] In embodiments, the digital twin system 15500 is configured to:
simulate one or
more operating parameters for the real-world elements in response to the
industrial
environment being supplied with given characteristics using the real-world-
element digital
twins; calculate a mitigating action to be taken by one or more of the real-
world elements in
response to being supplied with the contemporaneous characteristics; and
actuate, in response
to detecting the contemporaneous characteristics, the mitigating action. The
calculation may
be performed in response to detecting contemporaneous characteristics or
operating
parameters falling outside of respective design parameters or may be
determined via a
simulation prior to detection of such characteristics.
[00657] Additionally or alternatively, the digital twin system 15500 may
provide alerts to
one or more users or system elements in response to detecting states.
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[00658] In embodiments, the digital twin I/0 system 15504 includes a pathing
module
157310. The pathing module 157310 may ingest navigational data from the
elements 157302,
provide and/or request navigational data to components of the digital twin
system 15500
(e.g., the digital twin simulation system 15506, the digital twin behavior
system , and/or the
cognitive intelligence system 15510), and/or output navigational data to
elements 157302
(e.g., to the wearable devices 15554). The navigational data may be collected
or estimated
using, for example, historical data, guidance data provided to the elements
157302,
combinations thereof, and the like.
[00659] For example, the navigational data may be collected or estimated using
historical
data stored by the digital twin system 15500. The historical data may include
or be processed
to provide information such as acquisition time, associated elements 157302,
polling
intervals, task performed, laden or unladen conditions, whether prior guidance
data was
provided and/or followed, conditions of the environment 15520, other elements
157302
within the environment 15520, combinations thereof, and the like. The
estimated data may be
determined using one or more suitable pathing algorithms. For example, the
estimated data
may be calculated using suitable order-picking algorithms, suitable path-
search algorithms,
combinations thereof, and the like. The order-picking algorithm may be, for
example, a
largest gap algorithm, an s-shape algorithm, an aisle-by-aisle algorithm, a
combined
algorithm, combinations thereof, and the like. The path-search algorithms may
be, for
example, Dijkstra's algorithm, the A* algorithm, hierarchical path-finding
algorithms,
incremental path-finding algorithms, any angle path-finding algorithms, flow
field
algorithms, combinations thereof, and the like.
[00660] Additionally or alternatively, the navigational data may be collected
or estimated
using guidance data of the worker. The guidance data may include, for example,
a calculated
route provided to a device of the worker (e.g., a mobile device or the
wearable device 15554).
In another example, the guidance data may include a calculated route provided
to a device of
the worker that instructs the worker to collect vibration measurements from
one or more
locations on one or more machines along the route. The collected and/or
estimated
navigational data may be provided to a user of the digital twin system 15500
for
visualization, used by other components of the digital twin system 15500 for
analysis,
optimization, and/or alteration, provided to one or more elements 157302,
combinations
thereof, and the like.
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[00661] In embodiments, the digital twin system 15500 ingests navigational
data for a set of
workers for representation in a digital twin. Additionally or alternatively,
the digital twin
system 15500 ingests navigational data for a set of mobile equipment assets of
an industrial
environment into a digital twin.
[00662] In embodiments, the digital twin system 15500 ingests a system for
modeling traffic
of mobile elements in an industrial digital twin. For example, the digital
twin system 15500
may model traffic patterns for workers or persons within the environment
15520, mobile
equipment assets, combinations thereof, and the like. The traffic patterns may
be estimated
based on modeling traffic patterns from and historical data and
contemporaneous ingested
data. Further, the traffic patterns may be continuously or intermittently
updated depending on
conditions within the environment 15520 (e.g., a plurality of autonomous
mobile equipment
assets may provide information to the digital twin system 15500 at a slower
update interval
than the environment 15520 including both workers and mobile equipment
assets).
[00663] The digital twin system 15500 may alter traffic patterns (e.g., by
providing updated
navigational data to one or more of the mobile elements) to achieve one or
more
predetermined criteria. The predetermined criteria may include, for example,
increasing
process efficiency, decreasing interactions between laden workers and mobile
equipment
assets, minimizing worker path length, routing mobile equipment around paths
or potential
paths of persons, combinations thereof, and the like.
[00664] In embodiments, the digital twin system 15500 may provide traffic data
and/or
navigational information to mobile elements in an industrial digital twin. The
navigational
information may be provided as instructions or rule sets, displayed path data,
or selective
actuation of devices. For example, the digital twin system 15500 may provide a
set of
instructions to a robot to direct the robot to and/or along a desired route
for collecting
vibration data from one or more specified locations on one or more specified
machines along
the route using a vibration sensor. The robot may communicate updates to the
system
including obstructions, reroutes, unexpected interactions with other assets
within the
environment 15520, etc.
[00665] In some embodiments, an ant-based system 15574 enables industrial
entities,
including robots, to lay a trail with one or more messages for other
industrial entities,
including themselves, to follow in later journeys. In embodiments, the
messages include
information related to vibration measurement collection. In embodiments, the
messages
include information related to vibration sensor measurement locations. In some
embodiments,
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the trails may be configured to fade over time. In some embodiments, the ant-
based trails
may be experienced via an augmented reality system. In some embodiments, the
ant-based
trails may be experienced via a virtual reality system. In some embodiments,
the ant-based
trails may be experienced via a mixed reality system. In some embodiments, ant-
based
tagging of areas can trigger a pain-response and/or accumulate into a warning
signal. In
embodiments, the ant-based trails may be configured to generate an information
filtering
response. In some embodiments, the ant-based trails may be configured to
generate an
information filtering response wherein the information filtering response is a
heightened
sense of visual awareness. In some embodiments, the ant-based trails may be
configured to
generate an information filtering response wherein the information filtering
response is a
heightened sense of acoustic awareness. In some embodiments, the messages
include
vectorized data.
[00666] In embodiments, the digital twin system 15500 includes design
specification
information for representing a real-world element 157302r using a digital twin
157302d. The
digital may correspond to an existing real-world element 157302r or a
potential real-world
element 157302r. The design specification information may be received from one
or more
sources. For example, the design specification information may include design
parameters set
by user input, determined by the digital twin system 15500 (e.g., the via
digital twin
simulation system 15506), optimized by users or the digital twin simulation
system 15506,
combinations thereof, and the like. The digital twin simulation system 15506
may represent
the design specification information for the component to users, for example,
via a display
device or a wearable device. The design specification information may be
displayed
schematically (e.g., as part of a process diagram or table of information) or
as part of an
augmented reality or virtual reality display. The design specification
information may be
displayed, for example, in response to a user interaction with the digital
twin system 15500
(e.g., via user selection of the element or user selection to generally
include design
specification information within displays). Additionally or alternatively, the
design
specification information may be displayed automatically, for example, upon
the element
coming within view of an augmented reality or virtual reality device. In
embodiments, the
displayed design specification information may further include indicia of
information source
(e.g., different displayed colors indicate user input versus digital twin
system 15500
determination), indicia of mismatches (e.g., between design specification
information and
operational information), combinations thereof, and the like.
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[00667] In embodiments, the digital twin system 15500 embeds a set of control
instructions
for a wearable device within an industrial digital twin, such that the control
instructions are
provided to the wearable device to induce an experience for a wearer of the
wearable device
upon interaction with an element of the industrial digital twin. The induced
experience may
be, for example, an augmented reality experience or a virtual reality
experience. The
wearable device, such as a headset, may be configured to output video, audio,
and/or haptic
feedback to the wearer to induce the experience. For example, the wearable
device may
include a display device and the experience may include display of information
related to the
respective digital twin. The information displayed may include maintenance
data associated
with the digital twin, vibration data associated with the digital twin,
vibration measurement
location data associated with the digital twin, financial data associated with
the digital twin,
such as a profit or loss associated with operation of the digital twin,
manufacturing KPIs
associated with the digital twin, information related to an occluded element
(e.g., a sub-
assembly) that is at least partially occluded by a foreground element (e.g., a
housing), a
virtual model of the occluded element overlaid on the occluded element and
visible with the
foreground element, operating parameters for the occluded element, a
comparison to a design
parameter corresponding to the operating parameter displayed, combinations
thereof, and the
like. Comparisons may include, for example, altering display of the operating
parameter to
change a color, size, and/or display period for the operating parameter.
[00668] In some embodiments, the displayed information may include indicia for
removable
elements that are or may be configured to provide access to the occluded
element with each
indicium being displayed proximate to or overlying the respective removable
element.
Further, the indicia may be sequentially displayed such that a first indicium
corresponding to
a first removable element (e.g., a housing) is displayed, and a second
indicium corresponding
to a second removable element (e.g., an access panel within the housing) is
displayed in
response to the worker removing the first removable element. In some
embodiments, the
induced experience allows the wearer to see one or more locations on a machine
for optimal
vibration measurement collection. In an example, the digital twin system 15500
may provide
an augmented reality view that includes highlighted vibration measurement
collection
locations on a machine and/or instructions related to collecting vibration
measurements.
Furthering the example, the digital twin system 15500 may provide an augmented
reality
view that includes instructions related to timing of vibration measurement
collection.
Information utilized in displaying the highlighted placement locations may be
obtained using
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