Note: Descriptions are shown in the official language in which they were submitted.
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A SYSTEM AND METHOD FOR SELECTING AN ELECTRONIC COMMUNICATION
PATHWAY FROM A POOL OF POTENTIAL PATHWAYS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority of U.S.
Provisional Application No.
17/120,384, filed on December 14, 2020 and entitled "A SYSTEM AND METHOD FOR
SELECTING AN ELECTRONIC COMMUNICATION PATHWAY FROM A POOL OF
POTENTIAL PATHWAYS." This application claims the benefit of priority of U S.
Nonprovisional
Application No. 16/777,236, filed on January 30, 2020 and entitled "A SYSTEM
AND METHOD
FOR SELECTING AN ELECTRONIC COMMUNICATION PATHWAY FROM A POOL OF
POTENTIAL PATHWAYS." Each of U.S. Provisional Application No. 17/120,384 and
U.S.
Nonprovisional Application No. 16/777,236 is incorporated herein by reference.
FIELD OF THE INVENTION
[0002] The present invention generally relates to the field of
network communication. In
particular, the present invention is directed to a system and method for
selecting an electronic
communication pathway from a pool of potential pathways.
BACKGROUND
[0003] The Internet presents a plethora of potential communication
routes between devices, and
thus the users of those devices. The near-infinite possibilities present a
steadily broadening field of
both opportunities and challenges. One challenge among many is how direct
communications, such
as communications conferring rights to items of remunerative value, in a
manner that is equitable
while protecting the privacy and well-being of potential recipients and
preventing those same
potential recipients from diverting the communication through deceptive means.
While many
solutions to this and related challenges have been presented, none solves the
underlying problem in a
completely satisfactory manner.
SUMMARY OF THE DISCLOSURE
[0004] In an aspect, a system for selecting an electronic
communication pathway from a pool of
potential pathways includes at least a server. System includes a network
communication routing hub
operating on the at least a server, wherein the network communication routing
hub is configured to
identify a plurality of electronic communication pathways, wherein identifying
the plurality of
electronic communication pathways further includes receiving a plurality of
incoming
communications from a plurality of remote devices, wherein each remote device
of the plurality of
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remote devices is connected to the network communication routing hub by an
electronic
communication pathway, and each incoming communication of the plurality of
communications
contains a subject indicator linking the communication to the pool of
potential pathways. System
includes an authentication module operating on the at least a server, wherein
the authentication
module is configured to authenticate each device of the plurality of remote
devices, wherein
authenticating each device further includes determining at least a
verification element for each
remote device of the plurality of remote devices, wherein each verification
element of the plurality
of verification elements is further configured to include either at least an
authentication datum of
each remote device of the plurality of remote devices or at least a failed
authentication datum of each
remote device of the plurality of remote devices and transmit the at least a
verification element to
each device of the plurality of remote devices. System includes a pathway
selection module
operating on the at least a server, wherein the pathway selection module is
configured to select based
on a pathway probability variable a pathway from the plurality of electronic
communication
pathways, wherein the pathway probability variable operates as a function of a
pathway selection
algorithm, wherein the pathway selection algorithm is further configured to
determine the pathway
probability variable as a function of at least a seed value as a function of
each remote device and the
subject indicator, transmit a first outgoing communication over the selected
pathway to a remote
device of the plurality of remote devices associated with the selected
pathway, and transmit a second
outgoing communication over the non-selected pathways to the remaining remote
devices of the
plurality of remote devices.
100051 In another aspect, a method for selecting an electronic
communication pathway from a
pool of potential pathways is provided. The method includes identifying, by a
network
communication routing hub operating on the at least a server, a plurality of
electronic
communication pathways, wherein identifying the plurality of electronic
communication pathways
further includes receiving a plurality of incoming communications from a
plurality of remote
devices, wherein each remote device of the plurality of remote devices is
connected to the network
communication routing hub by an electronic communication pathway and each
incoming
communication of the plurality of communications contains a subject indicator
linking the
communication to the pool of potential pathways. The method includes
authenticating, by an
authentication module operating on the at least a server, each device of the
plurality of remote
devices wherein authenticating each device further includes determining at
least a verification
element of each device of the plurality of remote devices, wherein the
verification element is further
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configured to include either at least an authentication datum of each remote
device of the plurality of
remote devices or at least a failed authentication datum of each remote device
of the plurality of
remote devices and transmitting the at least a verification element to each
device of the plurality of
remote devices The method includes selecting, by a pathway selection module
operating on the at
least a server, based on a pathway probability variable a pathway from the
plurality of electronic
communication pathways, wherein the pathway probability variable operates as a
function of a
pathway selection algorithm, wherein the pathway selection algorithm includes
determining the
pathway probability variable as a function of at least a seed value as a
function of each remote
device and the subject indicator. The method includes transmitting, by a
pathway selection module
operating on at least a server, a first outgoing communication over the
selected pathway to a remote
device of the plurality of remote devices associated with the selected
pathway. The method includes
transmitting, by a pathway selection module operating on at least a server, a
second outgoing
communication over the non-selected pathways to the remaining remote devices
of the plurality of
remote devices.
100061 In another aspect, a system for selecting an electronic
communication pathway from a
pool of potential pathways is provided. The system includes at least a server.
The system includes a
network communication routing hub operating on the at least a server. The
network communication
routing hub is configured to identify a plurality of electronic communication
pathways, wherein
identifying the plurality of electronic communication pathways further
comprises receiving a
plurality of incoming communications from a plurality of remote devices. Each
remote device of the
plurality of remote devices is connected to the network communication routing
hub by an electronic
communication pathway. Each incoming communication of the plurality of
communications
contains a subject indicator linking the communication to the pool of
potential pathways. The
system includes an authentication module operating on the at least a server.
The authentication
module is configured to authenticate each device of the plurality of remote
devices wherein
authenticating each device further comprises determining at least a
verification element. The
verification element is further configured to include either at least an
authentication datum of each
remote device of the plurality of remote devices or at least a failed
authentication datum of each
remote device of the plurality of remote devices. The system includes a
pathway selection module
operating on the at least a server. The pathway selection module is configured
to select, based on a
pathway probability variable, a pathway from the plurality of electronic
communication pathways
wherein the pathway probability variable operates as a function of a pathway
selection algorithm.
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The pathway selection algorithm is configured to determine the pathway
probability variable, based
on the incoming communication of the plurality of communications, as a
function of each remote
device and the subject indicator and update the pathway probability variable
based on the pathway
selection algorithm The pathway selection module is further configured to
transmit a first outgoing
communication over the selected pathway to a remote device of the plurality of
remote devices
associated with the selected pathway and a second outgoing communication over
the non-selected
pathways to the remaining remote devices of the plurality of remote devices.
100071 In another aspect, a method for selecting an electronic
communication pathway from a
pool of potential pathways is provided. The method incudes identifying by a
network
communication routing hub operating on the at least a server a plurality of
electronic communication
pathways, wherein identifying the plurality of electronic communication
pathways further comprises
receiving a plurality of incoming communications from a plurality of remote
devices. Each remote
device of the plurality of remote devices is connected to the network
communication routing hub by
an electronic communication pathway. Each incoming communication of the
plurality of
communications contains a subject indicator linking the communication to the
pool of potential
pathways. The method includes authenticating by an authentication module
operating on the at least
a server each device of the plurality of remote devices wherein authenticating
each device further
comprises determining at least a verification element. The verification
element is further configured
to include either at least an authentication datum of each remote device of
the plurality of remote
devices or at least a failed authentication datum of each remote device of the
plurality of remote
devices. The method includes selecting by a pathway selection module operating
on the at least a
server, based on a pathway probability variable, a pathway from the plurality
of electronic
communication pathways wherein the pathway probability variable operates as a
function of a
pathway selection algorithm. The pathway selection algorithm is configured to
determine the
pathway probability variable, based on the incoming communication of the
plurality of
communications, as a function of each remote device and the subject indicator
and update the
pathway probability variable based on the pathway selection algorithm. The
method includes
transmitting by a pathway selection module operating on the at least a server
a first outgoing
communication over the selected pathway to a remote device of the plurality of
remote devices
associated with the selected pathway and a second outgoing communication over
the non-selected
pathways to the remaining remote devices of the plurality of remote devices
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100081 These and other aspects and features of non-limiting
embodiments of the present
invention will become apparent to those skilled in the art upon review of the
following description of
specific non-limiting embodiments of the invention in conjunction with the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
100091 For the purpose of illustrating the invention, the drawings
show aspects of one or more
embodiments of the invention. However, it should be understood that the
present invention is not
limited to the precise arrangements and instrumentalities shown in the
drawings, wherein:
FIG. 1 is a high-level block diagram illustrating an exemplary embodiment of a
system for selecting
an electronic communication pathway from a pool of potential pathways;
FIG. 2 is a schematic diagram illustrating an exemplary embodiment of a
predictive opportunities
engine and associated system elements;
FIG. 3 is a block diagram illustrating an exemplary embodiment of a first
label learner and
associated system elements;
FIG. 4 is a schematic diagram illustrating an exemplary embodiment of an
authentication module
and associated system components;
FIG. 5 is a depiction of an exemplary embodiment of a biometric database in
accordance with the
instant disclosure;
FIG. 6 is a depiction of an exemplary embodiment of an authentication database
in accordance with
the instant disclosure;
FIG. 7 is a schematic diagram illustrating an exemplary embodiment of a
pathway selection module
and associated system components;
FIG. 8 is a flow diagram illustrating an exemplary method of selecting an
electronic communication
pathway from a pool of potential pathways; and
FIG. 9 is a high-level block diagram of a computing system that can be used to
implement any one
or more of the methodologies disclosed herein and any one or more portions
thereof.
The drawings are not necessarily to scale and may be illustrated by phantom
lines, diagrammatic
representations and fragmentary views. In certain instances, details that are
not necessary for an
understanding of the embodiments or that render other details difficult to
perceive may have been
omitted.
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DETAILED DESCRIPTION
100101 Aspects of the present disclosure are directed to systems
and methods for assigning a
path on which to convey an outgoing communication using a selection process.
Selection may be
made from a pool of potential recipient devices that have conveyed previous
communications
linking each potential recipient device to a subject of the outgoing
communication, which may
include, for instance, the disbursal or shipment of an item of value; the
recipient device may convey
multiple communications linking each potential device to a subject of the
outgoing communication
correlating to the probability of selection for the recipient device; the pool
may be filtered to
eliminate duplication of users, devices that fail authentication measures, and
other checks to ensure
that the selection process is not subject to numerical bias or manipulation by
potential adversaries.
100111 Referring now to FIG. 1 an exemplary embodiment of a system
100 for selecting an
electronic communication pathway from a pool of potential pathways is
illustrated. System 100
includes at least a server 104. At least a server 104 may include any
computing device as described
herein, including without limitation a microcontroller, microprocessor,
digital signal processor
(DSP) and/or system on a chip (SoC) as described herein. At least a server 104
may be housed with,
may be incorporated in, or may incorporate one or more sensors of at least a
sensor. Computing
device may include, be included in, and/or communicate with a mobile device
such as a mobile
telephone or smartphone. At least a server 104 may include a single computing
device operating
independently, or may include two or more computing device operating in
concert, in parallel,
sequentially or the like; two or more computing devices may be included
together in a single
computing device or in two or more computing devices. At least a server 104
may communicate
with one or more additional devices as described below in further detail via a
network
communication routing hub 108. Network communication routing hub 108 may be
utilized for
connecting the at least a server 104 to electronic communication network 112
as described below,
and one or more devices. Examples of a network interface device include, but
are not limited to, a
network interface card (e.g., a mobile network interface card, a LAN card), a
modem, and any
combination thereof. Examples of a network include, but are not limited to, a
wide area network
(e.g., the Internet, an enterprise network), a local area network (e.g., a
network associated with an
office, a building, a campus or other relatively small geographic space), a
telephone network, a data
network associated with a telephone/voice provider (e.g., a mobile
communications provider data
and/or voice network), a direct connection between two computing devices, and
any combinations
thereof. A network may employ a wired and/or a wireless mode of communication.
In general, any
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network topology may be used. Information (e.g., data, software etc.) may be
communicated to
and/or from a computer and/or a computing device. At least a server 104 may
include but is not
limited to, for example, a at least a server 104 or cluster of computing
devices in a first location and
a second computing device or cluster of computing devices in a second
location. At least a server
104 may include one or more computing devices dedicated to data storage,
security, distribution of
traffic for load balancing, and the like. At least a server 104 may distribute
one or more computing
tasks as described below across a plurality of computing devices of computing
device, which may
operate in parallel, in series, redundantly, or in any other manner used for
distribution of tasks or
memory between computing devices. At least a server 104 may be implemented
using a "shared
nothing" architecture in which data is cached at the worker, in an embodiment,
this may enable
scalability of system 100 and/or computing device.
[0012]
With continued reference to FIG. 1, at least a server 104 may be designed
and/or
configured to perform any method, method step, or sequence of method steps in
any embodiment
described in this disclosure, in any order and with any degree of repetition.
For instance at least a
server 104 may be configured to perform a single step or sequence repeatedly
until a desired or
commanded outcome is achieved; repetition of a step or a sequence of steps may
be performed
iteratively and/or recursively using outputs of previous repetitions as inputs
to subsequent
repetitions, aggregating inputs and/or outputs of repetitions to produce an
aggregate result, reduction
or decrement of one or more variables such as global variables, and/or
division of a larger processing
task into a set of iteratively addressed smaller processing tasks. At least a
server 104 may perform
any step or sequence of steps as described in this disclosure in parallel,
such as simultaneously
and/or substantially simultaneously performing a step two or more times using
two or more parallel
threads, processor cores, or the like; division of tasks between parallel
threads and/or processes may
be performed according to any protocol suitable for division of tasks between
iterations. Persons
skilled in the art, upon reviewing the entirety of this disclosure, will be
aware of various ways in
which steps, sequences of steps, processing tasks, and/or data may be
subdivided, shared, or
otherwise dealt with using iteration, recursion, and/or parallel processing.
[0013]
With continued reference to FIG. 1, server 104 may communicate with a
network
communication routing hub 108. Network communication routing hub 108 may
include, without
limitation a computing device, including any server as described herein.
Examples of a computing
device include, but are not limited to, an electronic book reading device, a
computer workstation, a
terminal computer, a server computer, a handheld device (e.g., a tablet
computer, a smartphone,
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etc.), a web appliance, a network router, a network switch, a network bridge,
any machine capable of
executing a sequence of instructions that specify an action to be taken by
that machine, and any
combinations thereof In one example, a computing device may include and/or be
included in a
kiosk. Network communication routing hub 108 may alternatively or additionally
include a desktop
computer, a handheld device or mobile device such as a smartphone or tablet,
and/or a special
purpose device; any such device may include or be included in network
communication routing hub
108 where configured as set forth in further detail below. Network
communication routing hub 108
may include two or more devices working in concert or in parallel; network
communication routing
hub 108 may include, for instance, a first server or cluster of servers in a
first location and a second
server or cluster of servers in a second location. Network communication
routing hub 108 may
include computing devices that are dedicated to particular tasks; for
instance, a single computing
device or cluster of computing devices may be dedicated to the operation of
queues described below,
while a separate computing device or cluster of computing devices may be
dedicated to storage
and/or production of dynamic data as described in further detail below.
Network communication
routing hub 108 may include one or more computing devices dedicated to data
storage, security,
distribution of traffic for load balancing, and the like. Network
communication routing hub 108 may
distribute one or more computing tasks as described below across a plurality
of computing devices
of network communication routing hub 108, which may operate in parallel, in
series, redundantly, or
in any other manner used for distribution of tasks or memory between computing
devices. Network
communication routing hub 108 may be implemented using a "shared nothing"
architecture in which
data is cached at the worker; in an embodiment, this may enable scalability of
system 100 and/or
network communication routing hub 108. In an embodiment, network communication
routing hub
108 may communicate locally or over a network to one or more remote devices to
perform one or
more embodiments of processes and/or process steps as disclosed in further
detail below;
communication may include, without limitation, communication with any other
device as described
herein.
[0014] Still referring to FIG. 1, network communication routing hub
104 may connect to an
electronic communication network 112. Electronic communication network 112,
may include any
network as described below in reference to FIG. 9 for conveying communications
between electronic
devices and/or computing devices as described below in reference to FIG. 9;
communications may
be performed, without limitation, using packet-based communication protocols.
Examples of packet-
based communication protocols include, without limitation, transmission
control protocol-internet
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protocol (TCP-IP), hypertext transfer protocol (HTTP), secure HTTP (HTTPS), or
the like.
Electronic communication network 108 may include, without limitation, a local
area network
(LAN), a wide area network (WAN), the Internet, or any other network as
consistent with
descriptions of a network as provided in this disclosure. Packets and/or
electronic communications
transmitted over electronic communication network 112 may be sent directly
from one device to
another via a wired or wireless transmission process or may be relayed through
one or more
intermediate devices including without limitation, models, routers, servers,
and the like.
100151 Continuing to refer to FIG. 1, network communication routing
hub 108 may implement
or utilize one or more aspects of a cryptographic system, for instance to
authenticate devices and/or
to protect communications and/or data as described in further detail below. In
one embodiment, a
cryptographic system is a system that converts data from a first form, known
as "plaintext," which is
intelligible when viewed in its intended format, into a second form, known as
"cyphertext," which is
not intelligible when viewed in the same way. Cyphertext may be unintelligible
in any format unless
first converted back to plaintext. In one embodiment, a process of converting
plaintext into
cyphertext is known as "encryption." Encryption process may involve the use of
a datum, known as
an "encryption key," to alter plaintext. Cryptographic system may also convert
cyphertext back into
plaintext, which is a process known as "decryption." Decryption process may
involve the use of a
datum, known as a "decryption key," to return the cyphertext to its original
plaintext form. In
embodiments of cryptographic systems that are "symmetric," decryption key is
essentially the same
as encryption key: possession of either key makes it possible to deduce the
other key quickly without
further secret knowledge. Encryption and decryption keys in symmetric
cryptographic systems may
be kept secret and shared only with persons or entities that the user of the
cryptographic system
wishes to be able to decrypt the cyphertext. One example of a symmetric
cryptographic system is the
Advanced Encryption Standard ("AES"), which arranges plaintext into matrices
and then modifies
the matrices through repeated permutations and arithmetic operations with an
encryption key.
100161 Still viewing FIG. 1, in embodiments of cryptographic
systems that are "asymmetric,"
either encryption or decryption key cannot be readily deduced without
additional secret knowledge,
even given the possession of a corresponding decryption or encryption key,
respectively; a common
example is a "public key cryptographic system," in which possession of the
encryption key does not
make it practically feasible to deduce the decryption key, so that the
encryption key may safely be
made available to the public. An example of a public key cryptographic system
is RSA, in which an
encryption key involves the use of numbers that are products of very large
prime numbers, but a
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decryption key involves the use of those very large prime numbers, such that
deducing the
decryption key from the encryption key requires the practically infeasible
task of computing the
prime factors of a number which is the product of two very large prime
numbers. Another example is
elliptic curve cryptography, which relies on the fact that given two points P
and Q on an elliptic
curve over a finite field, and a definition for addition where A + B = R, the
point where a line
connecting point A and point B intersects the elliptic curve, where "0,- the
identity, is a point at
infinity in a projective plane containing the elliptic curve, finding a number
k such that adding P to
itself k times results in Q is computationally impractical, given correctly
selected elliptic curve,
finite field, and P and Q.
100171 With continued reference to FIG. 1, network communication
routing hub 108 may
communicate, using electronic communication network 112, with a plurality of
remote network
devices 116a-n connected to electronic communication network 108. Each remote
network device of
the plurality of remote network devices 116a-n may include any computing
device as described
below in reference to FIG. 9, including without limitation any device suitable
for use as electronic
communication hub as described above. Each remote network device may include,
without
limitation, a mobile device, desktop device, or other terminal device
permitting a person to interact
with electronic communication network 112, network communication routing hub
108, and/or server
104 including without limitation by operation of a web browser or native
application instantiating
one or more user interfaces as directed, for instance, by server-side and/or
client-side programs
provided by network communication routing hub 108 in the form of a "website"
or similar network-
based application or suite of applications.
100181 Still referring to FIG. 1, network communication routing hub
108 may connect to a
plurality of remote network devices 116a-n via a plurality of electronic
communication pathways
120a-n. An electronic communication pathway of the plurality of electronic
network communication
pathways may be identified to network communication routing hub 108 by a
network address, which
may be a network address identified according to TCP-IP; network address may
include, without
limitation, a uniform resource locator (URL). An electronic communication
pathway of the plurality
of electronic communication pathways 120a-n may have varying intermediary
conduits or devices
for particular communications; however, endpoints of electronic communication
pathway between
network communication routing hub 108 and a remote device, of the plurality of
remote devices,
connected to network communication routing hub 104 via the electronic
communication pathway
remain at the remote device and the network communication routing hub 108.
Selection of an
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electronic communication pathway, for instance as set forth in further detail
below, may therefore be
made by reference to endpoints of the electronic communication pathway.
100191 Still referring to FIG. 1, an electronic communication
pathway of a plurality of
electronic communication pathways 120a-n may include a plurality of incoming
communications.
Each incoming communication of a plurality of incoming communications may be
received as a
single transmission or in one or more separate transmissions, each of which
may be preceded and/or
followed by communications transmitted by network communication routing hub
104. Each
incoming communication of the plurality of incoming communications may
include, without
limitation, any interface suitable for network communication routing hub 108
to communicate, using
electronic communication network 112, with a plurality of remote network
devices 116a-n
connected to electronic communication network 108. Each incoming communication
of the plurality
of incoming communications may include, without limitation, any transfer of
signs, signals, writing,
images, sounds, data, or intelligence of any nature transmitted in whole or in
part via a plurality of
electronic communication pathways 120 a-n. Each incoming communication may
include, without
limitation, an audio communication, video communication, text communication,
website
communication, electronic mail communication, or other communication interface
permitting each
remote device of the plurality of remote network devices 116a-n to correspond
with network
communication hub 108.
100201 With continued reference to FIG. 1, each incoming
communication of the plurality of
incoming communications includes at least a subject indicator. A subject
indicator may include,
without limitation, a numerical or other textual code associated in memory of
network
communication routing hub 108, application server 124, and/or server 104 with
a subject of
communication; wherein subject of communication includes an item of value to
be distributed and/or
with regards to which rights are being distributed, as set forth in further
detail below, a code
associated with a transaction whereby item of value and/or rights to item of
value are to be
distributed, as set forth in further detail below. Subject indicator may
include a verbal description of
a subject of communication, such as without limitation a name of an item of
value, transaction;
verbal description, which may include without limitation a name or title, may
be linked to a subject
in any manner suitable for linking a code to a subject as described above.
Subject matter indicator
may be included in an incoming communication by being typed into incoming
communication,
entered into a text-entry window as part of incoming communication, or the
like; selection of a link
or activation of one or more event handlers, forms, or the like by a remote
device and/or a user of
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remote device may insert subject matter indicator in an incoming communication
As a non-limiting
example, a user may navigate to a page associated with a transaction as
described above, fill in a
form or activate a button or the like, and cause the user and/or remote device
operated by the user to
transmit a communication network communication routing hub 108 including
subject matter
indicator; for instance, user may wish to attempt to acquire an item of value,
may navigate to a page
or other user interface indicating a transaction whereby item of value or
rights thereto may be
disbursed, and may post an entry indicating the user is entering the
transaction, as described in
further detail below.
100211 In an embodiment, and continuing to refer to FIG. 1, network
communication routing
hub 108 is further configured to detect a terminal condition. A terminal
condition is a condition
under which network communication routing hub 108 ceases to add electronic
communication
pathways to a plurality of electronic communication pathways 120a-n; for
instance incoming
communications may continuously be received at network communication routing
hub 108, such
that identification of the plurality of electronic communication pathways 120a-
n could take an
arbitrarily long time and/or produce an arbitrarily large plurality of
electronic communication
pathways 120a-n. Terminal condition may identify one or more data concerning
incoming
communications, the plurality of electronic communication pathways 120a-n, or
other circumstances
related to system 100 indicating that selection of additional pathways for
inclusion in the plurality of
electronic communication pathways 120a-n should cease. Network communication
routing hub 108
may thus identify the plurality of electronic communication pathways 120a-n
based on the detection
of terminal condition, for instance by determining that the terminal condition
is met, and that
electronic communication pathways already identified constitute a complete set
of electronic
communication pathways for the purposes of this embodiment of system 100.
100221 Still referring to FIG. 1, detection of terminal condition
may include determining that a
threshold number of pathways have been selected as above; comparison to a
threshold number may,
for instance, include a comparison to maximal number after filtering or
removal of inauthentic or
duplicate entries as described above. As a further non-limiting example, each
incoming
communication may include a number, such as without limitation a number
representing an amount
paid by a user of a remote device; the numbers provided in incoming
communications may be added
together or otherwise aggregated, and the result compared to a threshold. For
instance, and without
limitation, where an embodiment of system 100 involves a process whereby an
item of value or a
right thereto is to be transferred to a user, each user may be prompted to
deposit a share, such as
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without limitation an amount in a currency, into a "pot" for the item of
value. Once a particular "pot"
reaches "full," for instance when the sum of the payments or shares provided
adds up to a threshold
amount of currency such as a price for the item of value, application server
124, server 104 and/or
network communication routing hub 108 may determine that the pot has reached
maximum
shareholders and the item of value and/or rights thereto may be disbursed, for
instance as described
in further detail below. In an embodiment, submissions of shares and/or
payments may be non-
refundable; thus, a user to whom the item of value is not transferred may lose
the currency paid to
enter the share. Different pots may have different tiers of prices for shares.
As a non-limiting
illustrative example, a pot for an electronic device having a $500 value, and
a terminal condition
associated with submitted shares adding up to that value, may be associated
with a first pot option
wherein each share is 50 Dollars; this pot may only fit 15 shareholders before
it is "full" and a single
shareholder takes the win for this product, creating a 1/15 chance of
receiving the item for each
shareholder. Continuing the illustrative example in a second pot option each
share may cost 20
Dollars; this pot may fit 40 shareholders before it is "full" and a single
shareholder may receive the
product as before, with each shareholder's probability of receipt at 1/40.
Further continuing the
above-described example a third pot option may set the price per share at 5
Dollars. This pot may fit
150 shareholders before it is "full" and a single shareholder takes receives
the product; each
shareholder may have odds of receiving the product of 1/150. The above-
described examples are for
illustrative purposes only, and the disclosure is not intended to be limited
to these examples. Share
prices may be fixed per "pot," or may vary per "pot"; in the latter case, for
instance, a user's odds of
receipt may be weighted according to a size of contribution. Persons skilled
in the art, upon
reviewing the entirety of this disclosure, will be aware of various
alternative terminal conditions that
may be applied in embodiments of system 100.
100231 Continuing to refer to FIG. 1, in an embodiment, network
communication routing hub
104 may convey communication from remote devices of a plurality of remote
network devices 116a-
n and an application server 124. Application server 124 may operate an
application with regard to
which communications performed according to methods described in this
disclosure may be
conveyed; for instance, application may generate communications regarding an
item of value to be
transferred, monetary value associated with the item of value, increments of
monetary value, and/or
terminal conditions for selection of the plurality of electronic communication
pathways 120a-n as
described in further detail below. An item of value may include any tangible
or intangible unit of
property, ranging for instance from $1.00 household items to automobiles &
real estate. Application
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server 124 may include, be included in, or be identical to network
communication routing hub 108;
persons skilled in the art, upon reviewing the entirety of this disclosure,
will be aware of various
ways in which application server 124 and/or network communication routing hub
108 may
interrelate and/or communicate as consistent with this disclosure. Network
communication routing
hub 108 may be designed and configured to perform any method and/or method
steps as described
herein, including without limitation methods and/or method steps described in
this disclosure, in any
order or combination, or with any degree of iteration or repetition. For
instance, and without
limitation, network communication routing hub 108 may be designed and/or
configured to identify a
plurality of electronic communication pathways 120a-n, wherein identifying the
plurality of
electronic communication pathways 120a-n further comprises receiving a
plurality of incoming
communications from a plurality of remote network devices 116a-n, wherein each
remote network
device of the plurality of remote network devices 116a-n is connected to the
network communication
routing hub 108 by an electronic communication pathway and each incoming
communication of the
plurality of communications contains a subject indicator linking the
communication to the pool of
potential pathways, to authenticate each device of the plurality of remote
network devices 116a-n to
verify uniqueness of each user of the plurality of remote network devices 116a-
n, to select a pathway
from the plurality of electronic communication pathways 120a-n, to transmit a
first outgoing
communication over the selected pathway to a remote network device of the
plurality of remote
network devices 116a-n associated with the selected pathway, and/or to
transmit a second outgoing
communication over the non-selected pathways to the remaining remote network
devices of the
plurality of remote network devices 116a-n for instance as set forth in
further detail below.
100241 Continuing to refer to FIG. 1, network communication routing
hub 108 may connect to
at least an authentication module 128 executing on the at least a server 104.
At least an
authentication module 128 may include any suitable hardware or software. In an
embodiment, at
least an authentication module 128 is designed and configured to authenticate
each device of the
plurality of remote devices 116a-n. Authentication module 128 may determine at
least a verification
element of each device of the plurality of remote devices and transmit the
verification element of
each device to each device of the plurality of remote devices 116a-n. Each
verification element of
the plurality of verification elements may include at least an authentication
datum of each remote
device of the plurality of remote devices or at least a failed authentication
datum of each remote
device of the plurality of remote devices. At least a verification element may
be determined
including without limitation any process for determination as described in
this disclosure. At least a
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verification element of each device may be transmitted to each remote device
of the plurality of
remote devices 116a-n via network communication routing hub 108 and electronic
communication
pathways 120a-n.
100251 Each of the above processes may be performed utilizing
pathway numeric verification
unit, pathway age verification unit, and/or pathway biometric verification
unit, as described in more
detail below in FIGS. 4-6. In an embodiment, authentication module 128 may
transmit the at least a
verification element to each device of the plurality of remote devices 116a-n
as described in more
detail below. An exemplary embodiment of authentication module 128 is
described in more detail
below in reference to FIG. 4.
100261 With continued reference to FIG. 1, network communication
routing hub 108 may
connect to pathway selection module 132 executing on the at least a server
104. Pathway selection
module 132 may include any suitable hardware or software module. In an
embodiment, pathway
selection module 132 is designed and configured to select, based on a pathway
probability variable,
a pathway from the plurality of electronic communication pathways 120a-n,
transmit a first outgoing
communication over the selected pathway to a remote device of the plurality of
remote devices
116a-n associated with the selected pathway, and transmit a second outgoing
communication over
the non-selected pathways to the remaining remote network devices of the
plurality of remote
network devices 116a-n. The pathway probability variable may include without
limitation the
weighted selection probability of each pathway of the plurality of electronic
communication
pathways 120a-n. Pathway probability variable may operate as a function of a
pathway selection
algorithm, for instance as described in this disclosure in reference to FIG.
7. First outgoing
communication may, as a non-limiting example, inform a user associated with a
selected electronic
communication pathway that the user is a chosen recipient of an item of value
or a right thereto.
Second outgoing communication may, as a non-limiting example, inform users
associated with
selected electronic communication pathways that the users were not chosen as
the recipient of an
item of value or right thereto. Outgoing communication may include a link,
code, or other event
handler or element of data that a receiving user may be able to use to acquire
item of value and/or
right thereto. User may be required to submit authentication information
again; authentication
module 128 and/or network communication routing hub 108 may authenticate
remote device and/or
user a second time prior to conveying item and/or right thereto to user.
Conveyance may include,
without limitation, sending via mail or parcel service, transmitting
intangible property and/or a code,
document, or other element of data redeemable for tangible property via
electronic means, or any
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other suitable form of conveyance that may occur to a user skilled in the art
upon reviewing the
entirety of this disclosure.
100271 Still viewing FIG. 1, each of the above processes may be
performed utilizing machine
learning processes, and/or the pathway probability algorithm operating on
pathway selection module
132, as described in more detail below in FIG. 7. In an embodiment, the
pathway probability
algorithm is configured to determine pathway probability variable based on the
incoming
communication of the plurality of incoming communications as a function of
each remote device and
the subject indicator and update the pathway probability variable based on the
pathway selection
algorithm. An exemplary embodiment of the pathway probability algorithm is
described in more
detail below in reference to FIG 7.
100281 Still referring to FIG. 1, system 100 includes a predictive
opportunities engine 136
operating on the at least a server 104, wherein the predictive opportunities
engine 136 is configured
to receive at least a least a first training set and an incoming communication
of the plurality of
incoming communications from each remote device of the plurality of remote
devices 116a-n and
generate at least a compatible output. At least a server 104, predictive
opportunities engine 136,
and/or one or more modules operating thereon may be designed and/or configured
to perform any
method, method step, or sequence of method steps in any embodiment described
in this disclosure,
in any order and with any degree of repetition. For instance, at least a
server 104 and/or predictive
opportunities engine 136 may be configured to perform a single step or
sequence repeatedly until a
desired or commanded outcome is achieved; repetition of a step or a sequence
of steps may be
performed iteratively and/or recursively using outputs of previous repetitions
as inputs to subsequent
repetitions, aggregating inputs and/or outputs of repetitions to produce an
aggregate result, reduction
or decrement of one or more variables such as global variables, and/or
division of a larger processing
task into a set of iteratively addressed smaller processing tasks. At least a
server 104 and/or
predictive opportunities engine 136 may perform any step or sequence of steps
as described in this
disclosure in parallel, such as simultaneously and/or substantially
simultaneously performing a step
two or more times using two or more parallel threads, processor cores, or the
like; division of tasks
between parallel threads and/or processes may be performed according to any
protocol suitable for
division of tasks between iterations. Persons skilled in the art, upon
reviewing the entirety of this
disclosure, will be aware of various ways in which steps, sequences of steps,
processing tasks, and/or
data may be subdivided, shared, or otherwise dealt with using iteration,
recursion, and/or parallel
processing.
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100291 Referring now to FIG. 2, at least a server 104 and/or
pathway opportunities engine 136
may be designed and configured to receive training data. Training data, as
used herein, is data
containing correlation that a machine-learning process may use to model
relationships between two
or more categories of data elements. For instance, and without limitation,
training data may include a
plurality of data entries, each entry representing a set of data elements that
were recorded, received,
and/or generated together; data elements may be correlated by shared existence
in a given data entry,
by proximity in a given data entry, or the like. Multiple data entries in
training data may evince one
or more trends in correlations between categories of data elements; for
instance, and without
limitation, a higher value of a first data element belonging to a first
category of data element may
tend to correlate to a higher value of a second data element belonging to a
second category of data
element, indicating a possible proportional or other mathematical relationship
linking values
belonging to the two categories. Multiple categories of data elements may be
related in training data
according to various correlations; correlations may indicate causative and/or
predictive links
between categories of data elements, which may be modeled as relationships
such as mathematical
relationships by machine-learning processes as described in further detail
below. Training data may
be formatted and/or organized by categories of data elements, for instance by
associating data
elements with one or more descriptors corresponding to categories of data
elements. As a non-
limiting example, training data may include data entered in standardized forms
by persons or
processes, such that entry of a given data element in a given field in a form
may be mapped to one or
more descriptors of categories. Elements in training data may be linked to
descriptors of categories
by tags, tokens, or other data elements; for instance, and without limitation,
training data may be
provided in fixed-length formats, formats linking positions of data to
categories such as comma-
separated value (CSV) formats and/or self-describing formats such as
extensible markup language
(XML), enabling processes or devices to detect categories of data.
100301 Alternatively or additionally, and still referring to FIG.
2, training data may include one
or more elements that are not categorized; that is, training data may not be
formatted or contain
descriptors for some elements of data. Machine-learning algorithms and/or
other processes may sort
training data according to one or more categorizations using, for instance,
natural language
processing algorithms, tokenizati on, detection of correlated values in raw
data and the like;
categories may be generated using correlation and/or other processing
algorithms. As a non-limiting
example, in a corpus of text, phrases making up a number "n" of compound
words, such as nouns
modified by other nouns, may be identified according to a statistically
significant prevalence of n-
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grams containing such words in a particular order; such an n-gram may be
categorized as an element
of language such as a "word" to be tracked similarly to single words,
generating a new category as a
result of statistical analysis. Similarly, in a data entry including some
textual data, a person's name
and/or a description of an item of value may be identified by reference to a
list, dictionary, or other
compendium of terms, permitting ad-hoc categorization by machine-learning
algorithms, and/or
automated association of data in the data entry with descriptors or into a
given format. The ability to
categorize data entries automatedly may enable the same training data to be
made applicable for two
or more distinct machine-learning algorithms as described in further detail
below.
100311 Still referring to FIG. 2, categorization device may be
configured to receive a first
training set 200 including a plurality of first data entries, each first data
entry of the first training set
200 including at least an element of subject indicator data 204 and at least a
correlated compatible
label 208. At least an element of subject indicator data 204 may include any
data indicative of any
identifying a subject of communication; wherein subject of communication
includes an item of value
to be distributed and/or with regards to which rights are being distributed,
item of value may include
any tangible or intangible unit of property, as described above in reference
to FIG. 1. Subject
indicator data 204 may include a singular item of value and/or a grouping of
multiple items of value.
100321 Subject indicator data 204 may include, without limitation,
identifying data for items
ranging from a $1.00 household item to real estate. Subject indicator data 204
may include, without
limitation, identifying data for an option for an overnight trip, such as an
overnight trip to Alaska,
which may include a flight, dining, lodging, and/or an excursion. Subject
indicator data 204 may
include identifying data for a voucher, such as a gift card, which may include
a disclosed or
undisclosed value, such as an exchange for a particular good or service.
100331 Continuing to refer to FIG. 2, each element of first
training set 200 includes at least a
compatible label 208. A compatible label, as described herein, is an element
of data identifying
and/or describing a current, incipient, or future subject indicator of
interest to a person; subject
indicator may include a subject of communication; wherein subject of
communication includes an
item of value to be distributed and/or with regards to which rights are being
distributed, item of
value may include any tangible or intangible unit of property, as described
above in reference to
FIG. 1. At least a compatible label may be associated with a singular subject
indicator or a grouping
of items of value that may be associated with one or more elements of subject
indicator data 204 as
described in further detail below. Items of value associated with compatible
labels may include,
without limitation, one or more goods and/or services. Items of value
associated with compatible
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labels may include, without limitation, one or more technology products or
services, including
without limitation cell phones, laptops, computers, tablets, smart phones,
charging cables,
televisions, radios, mp3 players, electronic book readers, video game
consoles, virtual reality
headsets, software, programming training, technical support, streaming
subscriptions, virtual
assistants, website building, or the like. Compatible labels may be associated
with one or more
housewares products or services, including without limitation pots, pants,
dishes, flatware, cooking
utensils, tablecloth, placemats, glassware, towels, shower curtains, rugs,
bathmats, sheets,
pillowcases, blankets, mattress pads, curtains, mirrors, wall hangings, shoe
racks, jewelry boxes,
clocks, candles, lamps, couches, chairs, dining table, desks, fans, or the
like. Compatible labels may
be associated with one or more small appliances including without limitation
blender, coffee pot,
microwave, toaster, toaster oven, panini press, waffle maker, crock pot, bread
makers, can openers,
electric toothbrushes, hair dryers, digital scales, waterproof radio, space
heaters, food processor, rice
cookers, juicers, alarm clocks, or the like. Compatibility labels may be
associated with one or more
large appliances including without limitation refrigerator, freezer,
convection oven, grill, washing
machine and dryer, kitchen stove, water heaters, and the like Compatible
labels may be associated
with one or more leisure services including without limitation massage
parlors, manicurists,
facialists, water sport activities, guided excursions, sporting activities,
concerts, museums,
monuments, guided tours, car rentals, shopping trips, personal training, or
the like. Compatible
labels may be associated with one or more clothing products or services
including without limitation
tailoring, pants, dresses, special occasion styling, shoes, custom designing,
clothing subscription
services, dry cleaning services, t-shirts, handbags, and the like. Compatible
labels may be associated
with one or more healthcare products or services including without limitation
cosmetic procedures,
genetic testing, nutritional supplements, blood pressure monitors, fever
thermometers, blood glucose
monitors, dietician services, over-the-counter medication, screening tests for
diseases; such as heart
disease, colon cancer, and/or the like, personal-care items, in-home senior
care, marriage counselor
services, chiropractor services, and/or the like.
[0034]
Compatible labels may include items for which a person may have determined
an
arbitrary value, such as a person who has created an item; for instance, a
person may be an artist and
specialize in portraits, the item of value associated with compatible labels
may be an individualized
self-portrait. The above-described examples are presented for illustrative
purposes only and are not
intended to be exhaustive. Persons skilled in the art, upon reviewing the
entirety of this disclosure,
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will be aware of various additional examples of conditions that may be
associated with prognostic
labels as described in this disclosure.
[0035] Still referring to FIG. 2, at least a compatible label may
be stored in any suitable data
and/or data type. For instance, and without limitation, at least a compatible
label may include textual
data, such as numerical, character, and/or string data. Textual data may
include a standardized name
and/or code for an item, product, service, goods or the like; codes may
include without limitation,
manufacturing codes, production codes, selling codes, RDIF codes, which may
include without
limitation codes used in classification systems such as Global Product
Classification (GPC)
standards. In general, there is no limitation on forms textual data or non-
textual data used as at least
a compatible label may take; persons skilled in the art, upon reviewing the
entirety of this disclosure,
will be aware of various forms which may be suitable for use as at least a
compatible label
consistently with this disclosure.
100361 With continued reference to FIG. 2, at least a compatible
label 208 may be stored as
image data, such as for example an image of a particular product such as a
photograph of a particular
sunscreen product or an image of a particular book. Image data may be stored
in various forms
including for example, joint photographic experts group (JPEG), exchangeable
image file format
(Exif), tagged image file format (TIFF), graphics interchange format (GIF),
portable network
graphics (PNG), netpbm format, portable bitmap (PBM), portable any map (PNM),
high efficiency
image file format (I-IEIF), still picture interchange file format (SPIFF),
better portable graphics
(BPG), drawn filed, enhanced compression wavelet (ECW), flexible image
transport system (FITS),
free lossless image format (FLIF), graphics environment manage (GEM), portable
arbitrary map
(PAM), personal computer exchange (PCX), progressive graphics file (PGF),
gerber formats, 2
dimensional vector formats, 3 dimensional vector formats, compound formats
including both pixel
and vector data such as encapsulated postscript (EPS), portable document
format (PDF), and stereo
formats.
100371 With continued reference to FIG. 2, in each first data
element of first training set 200, at
least an element of subject indicator data 204 is correlated with a compatible
label 208 where the
element of subject indicator data is located in the same data element and/or
portion of data element
as the compatible label 208; for example, and without limitation, an element
of subject indicator data
is correlated with a correlated element where both element of subject
indicator data and correlated
element are contained within the same first data element of the first training
set. As a further
example, an element of subject indicator data is correlated with a correlated
element where both
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share a category label as described in further detail below, where each is
within a certain distance of
the other within an ordered collection of data in data element, or the like.
Still further, an element of
subject indicator data may be correlated with a correlated element where the
element of subject
indicator data and the correlated element share an origin, such as being data
that was collected with
regard to a single person or the like. In an embodiment, a first datum may be
more closely correlated
with a second datum in the same data element than with a third datum contained
in the same data
element; for instance, the first element and the second element may be closer
to each other in an
ordered set of data than either is to the third element, the first element and
second element may be
contained in the same subdivision and/or section of data while the third
element is in a different
subdivision and/or section of data, or the like. Persons skilled in the art,
upon reviewing the entirety
of this disclosure, will be aware of various forms and/or degrees of
correlation between subject
indicator data 204 and compatible label 208 that may exist in first training
set 200 and/or first data
element consistently with this disclosure.
100381 In an embodiment, and still referring to FIG. 2, predictive
opportunities engine 136 may
be designed and configured to associate at least an element of subject
indicator data 204 with at least
a category from a list of significant categories of subject indicator data
204. Significant categories of
subject indicator data 204 may include labels and/or descriptors describing
types of subject indicator
data 204 that are identified as being of high relevance in identifying
compatible labels. As a non-
limiting example, one or more categories may identify significant categories
of subject indicator data
204 based on degree of relevance to one or more product specifications and/or
within one or more
industry, which may include without limitation industries listed in
classification systems such as
North American Industry Classification System (NAICS). For instance, and
without limitation, a
particular product, good, and/or service may be recognized in a given industry
as complimentary of
various products, good, and/or service within a relevant field. As a non-
limiting example, and
without limitation, subject indicator data describing sneakers, such as
running sneakers, tennis shoes,
cleats, and/or other performance shoes may be recognized as useful for
identifying various products,
goods, and/or services such as socks, massage therapists, physical therapists,
personal trainers,
athletic clothing, and/or nutritional supplements. As an additional example,
subject indicator data
describing a beach accessories, such as beach towels, umbrellas, chairs may be
useful in selecting
compatible label 208 that include products, goods, and/or services such as
sunscreen, coolers, hats,
floatation devices, yard games, water sport activities, sailing lessons, boat
rentals, kayaks, and/or
tanning services. Similarly, snow sport equipment, such as skis, snowboards,
snowshoes,
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snowmobile and/or other snow sport apparatus may be recognized as useful in
identifying products,
goods and/or services such as lift tickets, lodging, winter outdoor
excursions, local food vouchers,
rental vouchers, snow sport lessons, hand warmers, winter apparel, gloves,
hats, and/or other winter
accessories. Persons skilled in the art, upon reviewing the entirety of this
disclosure, will be aware of
various additional categories of subject indicator data that may be used
consistently with this
disclosure.
100391 Continuing to refer to FIG. 2, whether an entry indicating
significance of a category of
subject indicator data and/or a given relationship of such categories to
compatible labels, an entry or
entries may be aggregated to indicate an overall degree of significance. For
instance, each category
of subject indicator data, relationship of such categories to compatible
labels, and/or category of
compatible labels may be given an overall significance score; overall
significance score may, for
instance, be incremented each time an expert submission and/or paper indicates
significance as
described above. Persons skilled in the art, upon reviewing the entirety of
this disclosure will be
aware of other ways in which scores may be generated using a plurality of
entries, including
averaging, weighted averaging, normalization, and the like Significance scores
may be ranked; that
is, all categories of subject indicator data, relationships of such categories
to compatible labels,
and/or categories of compatible labels may be ranked according significance
scores, for instance by
ranking categories of subject indicator data, relationships of such categories
to compatible labels,
and/or categories of compatible labels higher according to higher significance
scores and lower
according to lower significance scores. Categories of subject indicator data,
relationships of such
categories to compatible labels, and/or categories of compatible labels may be
eliminated from
current use if they fail a threshold comparison, which may include a
comparison of significance
score to a threshold number, a requirement that significance score belong to a
given portion of
ranking such as a threshold percentile, quartile, or number of top-ranked
scores. Significance scores
may be used to filter outputs as described in further detail below; for
instance, where a number of
outputs are generated and automated selection of a smaller number of outputs
is desired, outputs
corresponding to higher significance scores may be identified as more probable
and/or selected for
presentation while other outputs corresponding to lower significance scores
may be eliminated.
100401 Still referring to FIG. 2, predictive opportunities engine
136 may detect further
significant categories of subject indicator data, relationships of such
categories to compatible labels,
and/or categories of compatible labels using machine-learning processes,
including without
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limitation unsupervised machine-learning processes as described in further
detail below; such newly
identified categories may be added to pre-populated lists of categories as
described above.
100411 Continuing to refer to FIG. 2, in an embodiment, predictive
opportunities engine 136
may be configured, for instance as part of receiving the first training set
200, to associate at least
correlated compatible label 208 with at least a category from a list of
significant categories of
compatible labels. Significant categories of compatible labels may be
acquired, determined, and/or
ranked as described above. As a non-limiting example, compatible labels may be
organized
according to relevance to and/or association with a list of significant
products, goods and/or services.
A list of significant products, goods, and/or services may include, without
limitation, products,
goods, and/or services having generally acknowledged correlation with office
spaces; this may be
determined, as a non-limiting example, by a product of relative frequency of a
good, service, and/or
product being associated with an office space, such as use, affiliated
purchases, similarity, and the
like, within the population with years of life and/or years of able-bodied
existence lost, on average,
as a result of the condition. A list of products, goods, and/or services may
be modified for a given
person, without limitation, to reflect a current financial status; for
instance, a person with a
significant amount of wealth, such as a fixed income, significant preservation
of funds, diverse
investment portfolio, and the like, may have a higher probability of interest
in such products, goods,
and/or services than a typical person from the general population, and as a
result predictive
opportunities engine 136 may modify list of significant categories to reflect
this difference.
100421 With continued reference to FIG. 2, predictive opportunities
engine 136 may include a
first label learner 212 operating on the predictive opportunities engine 136,
the first label learner 212
designed and configured to generate the at least a compatible output as a
function of the first training
set 200 and the at least an incoming communication of the plurality of
incoming communications.
First label learner 212 may include any hardware and/or software module. First
label learner 212 is
designed and configured to generate outputs using machine learning processes.
A machine learning
process is a process that automatedly uses a body of data known as "training
data" and/or a "training
set" to generate an algorithm that will be performed by a computing
device/module to produce
outputs given data provided as inputs; this is in contrast to a non-machine
learning software program
where the commands to be executed are determined in advance by a user and
written in a
programming language.
100431 Still referring to FIG. 2, first label learner 212 may be
designed and configured to
generate at least a compatible output by creating at least a first machine-
learning model 216 relating
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subject indicator data 204 to compatible labels 208 using the first training
set 200 and generating the
at least a compatible output using the first machine-learning model 216; at
least a first machine-
learning model 216 may include one or more models that determine a
mathematical relationship
between subject indicator data 204 and compatible labels 208. Such models may
include without
limitation model developed using linear regression models. Linear regression
models may include
ordinary least squares regression, which aims to minimize the square of the
difference between
predicted outcomes and actual outcomes according to an appropriate norm for
measuring such a
difference (e.g. a vector-space distance norm); coefficients of the resulting
linear equation may be
modified to improve minimization. Linear regression models may include ridge
regression methods,
where the function to be minimized includes the least-squares function plus
term multiplying the
square of each coefficient by a scalar amount to penalize large coefficients.
Linear regression models
may include least absolute shrinkage and selection operator (LASSO) models, in
which ridge
regression is combined with multiplying the least-squares term by a factor of
1 divided by double the
number of samples. Linear regression models may include a multi-task lasso
model wherein the
norm applied in the least-squares term of the lasso model is the Frobenius
norm amounting to the
square root of the sum of squares of all terms. Linear regression models may
include the elastic net
model, a multi-task elastic net model, a least angle regression model, a LARS
lasso model, an
orthogonal matching pursuit model, a Bayesian regression model, a logistic
regression model, a
stochastic gradient descent model, a perceptron model, a passive aggressive
algorithm, a robustness
regression model, a Huber regression model, or any other suitable model that
may occur to persons
skilled in the art upon reviewing the entirety of this disclosure. Linear
regression models may be
generalized in an embodiment to polynomial regression models, whereby a
polynomial equation
(e.g. a quadratic, cubic or higher-order equation) providing a best predicted
output/actual output fit is
sought; similar methods to those described above may be applied to minimize
error functions, as will
be apparent to persons skilled in the art upon reviewing the entirety of this
disclosure.
100441 Continuing to refer to FIG. 2, machine-learning algorithm
used to generate first
machine-learning model 216 may include, without limitation, linear
discriminant analysis. Machine-
learning algorithm may include quadratic discriminate analysis. Machine-
learning algorithms may
include kernel ridge regression. Machine-learning algorithms may include
support vector machines,
including without limitation support vector classification-based regression
processes. Machine-
learning algorithms may include stochastic gradient descent algorithms,
including classification and
regression algorithms based on stochastic gradient descent. Machine-learning
algorithms may
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include nearest neighbors algorithms. Machine-learning algorithms may include
Gaussian processes
such as Gaussian Process Regression. Machine-learning algorithms may include
cross-
decomposition algorithms, including partial least squares and/or canonical
correlation analysis.
Machine-learning algorithms may include naïve Bayes methods. Machine-learning
algorithms may
include algorithms based on decision trees, such as decision tree
classification or regression
algorithms. Machine-learning algorithms may include ensemble methods such as
bagging meta-
estimator, forest of randomized tress, AdaBoost, gradient tree boosting,
and/or voting classifier
methods. Machine-learning algorithms may include neural net algorithms,
including convolutional
neural net processes.
100451 Still referring to FIG. 2, first label learner 212 may
generate compatible output using
alternatively or additional artificial intelligence methods, including without
limitation by creating an
artificial neural network, such as a convolutional neural network comprising
an input layer of nodes,
one or more intermediate layers, and an output layer of nodes. Connections
between nodes may be
created via the process of "training" the network, in which elements from a
training dataset are
applied to the input nodes, a suitable training algorithm (such as Levenherg-
Marquardt, conjugate
gradient, simulated annealing, or other algorithms) is then used to adjust the
connections and weights
between nodes in adjacent layers of the neural network to produce the desired
values at the output
nodes. This process is sometimes referred to as deep learning. This network
may be trained using
first training set 200 the trained network may then be used to apply detected
relationships between
elements of subject indicator data 204 and compatible labels 208.
100461 Referring now to FIG. 3, an exemplary embodiment of first
label learner is illustrated.
Machine-learning algorithms used by first label learner 212 may include
supervised machine-
learning algorithms, which may, as a non-limiting example be executed using a
supervised learning
module 300 executing on at least a server 104 and/or on another computing
device in
communication with at least a server 104, which may include any hardware or
software module.
Supervised machine learning algorithms, as defined herein, include algorithms
that receive a training
set relating a number of inputs to a number of outputs, and seek to find one
or more mathematical
relations relating inputs to outputs, where each of the one or more
mathematical relations is optimal
according to some criterion specified to the algorithm using some scoring
function. For instance, a
supervised learning algorithm may use elements of subject indicator data as
inputs, compatible label
208 as outputs, and a scoring function representing a desired form of
relationship to be detected
between elements of subject indicator data and compatible label 208; scoring
function may, for
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instance, seek to maximize the probability that a given element of subject
indicator data and/or
combination of elements of subject indictor data is associated with a given
compatible label 208
and/or combination of compatible label 208 to minimize the probability that a
given element of
subject indictor data and/or combination of elements of subject indictor data
is not associated with a
given compatible label 208 and/or combination of compatible label 208. Scoring
function may be
expressed as a risk function representing an "expected loss- of an algorithm
relating inputs to
outputs, where loss is computed as an error function representing a degree to
which a prediction
generated by the relation is incorrect when compared to a given input-output
pair provided in first
training set. Persons skilled in the art, upon reviewing the entirety of this
disclosure, will be aware of
various possible variations of supervised machine learning algorithms that may
be used to determine
relation between elements of subject indictor data and compatible label 208.
In an embodiment, one
or more supervised machine-learning algorithms may be restricted to a
particular domain for
instance, a supervised machine-learning process may be performed with respect
to a given set of
parameters and/or categories of parameters that have been suspected to be
related to a given set of
compatible label 208, and/or are specified as linked to a consumer good and/or
industry covering a
particular set of compatible label 208. As a non-limiting example, a
particular set of accessories or
services may be typically used in conjunction with a distinct item of
technology, and a supervised
machine-learning process may be performed to relate those particular
accessories and/or services to
the distinct item of technology and correlated compatible products; in an
embodiment, domain
restrictions of supervised machine-learning procedures may improve accuracy of
resulting models
by ignoring artifacts in training data. Domain restrictions may be suggested
by experts and/or
deduced from known purposes for particular evaluations and/or known tests used
to evaluate
compatible label 208. Additional supervised learning processes may be
performed without domain
restrictions to detect, for instance, previously unknown and/or unsuspected
relationships between
physiological data and compatible label 208.
100471 With continued reference to FIG. 3, machine-learning
algorithms may include
unsupervised processes; unsupervised processes may, as a non-limiting example,
be executed by an
unsupervised learning module 304 executing on at least a server 104 and/or on
another computing
device in communication with at least a server 104, which may include any
hardware or software
module. An unsupervised machine-learning process, as used herein, is a process
that derives
inferences in datasets without regard to labels; as a result, an unsupervised
machine-learning process
may be free to discover any structure, relationship, and/or correlation
provided in the data. For
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instance, and without limitation, first label learner 212 and/or at least a
server 104 may perform an
unsupervised machine learning process on first training set, which may cluster
data of first training
set 200 according to detected relationships between elements of the first
training set, including
without limitation correlations of elements of subject indictor data to each
other and correlations of
compatible label 208 to each other; such relations may then be combined with
supervised machine
learning results to add new criteria for first label learner 212 to apply in
relating compatible output to
compatible label 208. As a non-limiting, illustrative example, an unsupervised
process may
determine that a first element of subject indictor data acquired in an
incoming communication
correlates closely with a second element of subject indicator data, where the
first element has been
linked via supervised learning processes to a given compatible label 208, but
the second has not; for
instance, the second element may not have been defined as an input for the
supervised learning
process, or may pertain to a domain outside of a domain limitation for the
supervised learning
process. Continuing the example a close correlation between first element of
subject indictor data
and second element of subject indictor data may indicate that the second
element is also a good
predictor for the compatible label 208; second element may be included in a
new supervised process
to derive a relationship or may be used as a synonym or proxy for the first
subject indictor data
element by first label learner 212.
100481 Still referring to FIG. 3, at least a server 104 and/or
first label learner 132 may detect
further significant categories of subject indictor data, relationships of such
categories to compatible
label 208, and/or categories of compatible label 208 using machine-learning
processes, including
without limitation unsupervised machine-learning processes as described above;
such newly
identified categories, may be added to pre-populated lists of categories, as
described above. In an
embodiment, as additional data is added to system 100, first label learner 212
and/or at least a server
104 may continuously or iteratively perform unsupervised machine-learning
processes to detect
relationships between different elements of the added and/or overall data; in
an embodiment, this
may enable system 100 to use detected relationships to discover new
correlations between known
consumer goods and services, and/or compatible label 208 and one or more
elements of data in large
bodies of data, such as product properties, product specifications, offering
description-related data,
enabling future supervised learning and/or lazy learning processes as
described in further detail
below to identify relationships between, e.g., particular clusters of
household products and services
and particular compatible label 208 and/or suitable compatible label 208. Use
of unsupervised
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learning may greatly enhance the accuracy and detail with which system may
detect compatible label
208.
100491 With continued reference to FIG. 3, unsupervised processes
may be subjected to domain
limitations. For instance, and without limitation, an unsupervised process may
be performed
regarding a comprehensive set of data regarding one person, such as a
comprehensive user profile,
search history, user subject indicator history, and/or personal data such as
social media profiles,
location data, billing statements and/or other data concerning that persons.
As another non-limiting
example, an unsupervised process may be performed on data concerning a
particular cohort of
persons; cohort may include, without limitation, a demographic group such as a
group of people
having a shared age range, ethnic background, nationality, sex, and/or gender.
Cohort may include,
without limitation, a group of people having a shared value for an element
and/or category of subject
indicator data, a group of people having a shared value for an element and/or
category of compatible
label 208; as illustrative examples, cohort could include all people having an
allergy to cotton, all
people over the age of 65, all people of Italian descent, or the like. Persons
skilled in the art, upon
reviewing the entirety of this disclosure, will be aware of a multiplicity of
ways in which cohorts
and/or other sets of data may be defined and/or limited for a particular
unsupervised learning
process.
100501 Still referring to FIG. 3, first label learner 212 may
alternatively or additionally be
designed and configured to generate at least a compatible output 308 by
executing a lazy learning
process as a function of the first training set 212 and/or at least a subject
indicator; lazy learning
processes may be performed by a lazy learning module 312 executing on at least
a server 104 and/or
on another computing device in communication with at least a server 104, which
may include any
hardware or software module. A lazy-learning process and/or protocol, which
may alternatively be
referred to as a "lazy loading" or "call-when-needed" process and/or protocol,
may be a process
whereby machine learning is conducted upon receipt of an input to be converted
to an output, by
combining the input and training set to derive the algorithm to be used to
produce the output on
demand. For instance, an initial set of simulations may be performed to cover
a "first guess" at a
compatible label 208 associated with a particular consumer good or service,
using first training set.
As a non-limiting example, an initial heuristic may include a ranking of
compatible label 208
according to relation to a service type of at least a particular service
sample, one or more categories
of subject indicator data identified in service type of at least a particular
service sample, and/or one
or more values detected in at least a specific service sample; ranking may
include, without
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limitation, ranking according to significance scores of associations between
elements of subject
indicator data and compatible label 208, for instance as calculated as
described above. Heuristic may
include selecting some number of highest-ranking associations and/or
compatible label 208. First
label learner 212 may alternatively or additionally implement any suitable
"lazy learning" algorithm,
including without limitation a K-nearest neighbors algorithm, a lazy naive
Bayes algorithm, or the
like; persons skilled in the art, upon reviewing the entirety of this
disclosure, will be aware of
various lazy-learning algorithms that may be applied to generate compatible
outputs 808 as
described in this disclosure, including without limitation lazy learning
applications of machine-
learning algorithms as described in further detail below.
100511 Referring now to FIG. 4, an exemplary embodiment of an
authentication module 128, as
pictured in FIG. 1 is illustrated in detail. Authentication module 128 may
include any suitable
hardware or software module. Authentication module 128 includes pathway
numeric verification
unit 400. Pathway numeric verification unit 400 may be configured to perform
pathway numeric
verification of each device of the plurality of remote devices 116a-n. Pathway
numeric verification
may include validating the user's financial ability to participate in an
embodiment of system 100
and/or a transaction as described herein for a transfer of an item of value
and/or rights thereto is
assessed; ability to participate may include ability to absorb losses from
participation in transactions
Validation may include any process whereby a user's financial ability to
participate in an
embodiment of system 100 and/or a transaction as described herein for a
transfer of an item of value
and/or rights thereto is assessed; ability to participate may include ability
to absorb losses from
participation in transactions. Pathway numeric verification may include a
credit check or check of a
user's consumer report, a check of one or more account balances of accounts as
provided by user,
verification of income or assets, or the like. Pathway numeric verification
may include a check
against a limit of transactions a user may participate in per time period; for
instance, user may be
forbidden to participate in more than some threshold number of transactions
per day, week, month,
and/or year. Threshold and/or limit may be user-specific; for instance, a user
with lower income or a
lower credit score may have a lower threshold number, while a user with
superior credit, a high
income, or more assets may have a higher threshold number. Threshold may be
applied to number of
participations, or to number of losses, as described in further detail below.
Pathway numeric
verification unit 400 may include updating the at least a verification element
of each device of the
plurality of remote devices 116a-n. Updating the verification element may
include transmitting the
authentication datum or failed authentication datum to each remote device of
the plurality of remote
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devices 116a-n through network communication hub 108, as described above. For
instance, and
without limitation, one or more pathway numeric verification described above
determined a user
unable to participate in a particular transaction; the verification element of
the user's remote device
of the plurality of remote devices 116a-n would update to contain an element
of failed authentication
datum. In an embodiment, use of pathway numeric verification may prevent users
from harming
themselves financially, for instance due to addictive behavior.
100521 With continued reference to FIG. 4, authentication module
128 includes pathway age
verification unit 404 configured to perform pathway age verification of each
remote device of the
plurality of remote devices 116a-n. Pathway age verification is configured to
receive the user's birth
datum to determine if the user surpasses at least a threshold age, such as a
minimum age requirement
to participate in an embodiment of system 100 and/or a transaction as
described herein for a transfer
of an item of value and/or rights thereto is assessed; ability to participate
may include verifying the
user is over the age of 18. Threshold age and/or lower age limit may be
subject indicator specific; for
instance, a product, good, or service containing explicit content may have a
higher threshold age,
while a product, good, or service containing adolescent targeted content may
have a lower threshold
age. Pathway age verification may include comparison of the threshold age to,
without limitation,
user's birth datum, such as without limitation user input of the user's date
of birth, user's date of
birth connected to a social medial profile, user's date of birth associated
with a connected bank
account, and the like. Pathway age verification unit 404 may include updating
the at least a
verification element of each device of the plurality of remote devices 116a-n.
Updating the
verification element may include transmitting the authentication datum or
failed authentication
datum to each remote device of the plurality of remote devices 116a-n through
network
communication hub 108, as described above. For instance, and without
limitation, pathway age
verification determined a user's age to be below the threshold for a
particular subject indicator
causing the user to be unable participate in the particular transaction; the
verification element of the
user's remote device of the plurality of remote devices 116a-n would update to
contain an element of
failed authentication datum. In an embodiment, use of pathway age verification
may prevent users
under the age of 18 from accessing inappropriate content, for instance due to
sexually explicit
content.
100531 Still referring to FIG. 4, authentication module 128
includes pathway biometric
verification unit 408 configured to perform pathway biometric verification of
each device of the
plurality of remote devices 116a-n to participate in an embodiment of system
100 and/or a
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transaction as described herein for a transfer of an item of value and/or
rights thereto is assessed;
ability to participate may include verifying the identity of the user. Pathway
biometric verification is
configured to receive at least a biometric datum from each device of the
plurality of remote devices
116a-n and match the at least a biometric datum for each remote device of the
plurality of remote
devices 116a-n to a correlated biometric datum stored within biometric
database 412. Authentication
module 128 may include or communicate with biometric database 412. Biometric
database 412 may
be implemented as any database and/or datastore suitable for use as a
biometric database. An
exemplary embodiment of a biometric database 412 is provided below in FIG. 5.
Biometric datum
may include, without limitation, any body measurement or calculation, such as
physiological
characteristics and/or behavioral characteristics. In an embodiment, without
limitation, a biometric
datum may include without limitation fingerprint, palm veins, face
recognition, DNA, palm print,
hand geometry, iris recognition, retina, odor/scent, typing rhythm, gait,
voice, and the like. Persons
skilled in the art, upon reviewing the entirety of this disclosure, will be
aware of various additional
examples for biometric datum that may be received from each remote device of
the plurality of
remote devices consistently with this disclosure.
[0054] Continuing to refer to FIG. 4, pathway biometric
verification unit 408 may include
updating the at least a verification element of each device of the plurality
of remote devices 116a-n.
Updating the verification element may include transmitting the authentication
datum or failed
authentication datum to each remote device of the plurality of remote devices
116a-n through
network communication hub 108, as described above. For instance, and without
limitation,
fingerprint scan data determined the user was not the authenticated user for
the selected remote
device of the plurality of remote devices 116a-n causing the selected remote
device to be unable to
participate in a particular transaction; the verification element of the
user's remote device of the
plurality of remote devices 116a-n would update to contain an element of
failed authentication
datum.
100551 Referring now to FIG. 5, an exemplary embodiment of
biometric database 412 is
illustrated. Biometric database 412 may include any data structure for ordered
storage and retrieval
of data, which may be implemented as a hardware or software module. Biometric
database 412 may
be implemented, without limitation, as a relational database, a key-value
retrieval datastore such as a
NOSQL database, or any other format or structure for use as a datastore that a
person skilled in the
art would recognize as suitable upon review of the entirety of this
disclosure. Biometric database 412
may include a plurality of data entries and/or records corresponding to
elements of biometric datum
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as described above. Data entries and/or records may describe, without
limitation, data concerning
particular physiological characteristics and/or behavioral characteristics
that have been collected.
Data entries in a biometric database 412 may be flagged with or linked to one
or more additional
elements of information, which may be reflected in data entry cells and/or in
linked tables such as
tables related by one or more indices in a relational database; one or more
additional elements of
information may include data associating a biometric and/or a person from whom
a biological
extraction was extracted or received with one or more cohorts, including
demographic groupings
such as ethnicity, sex, age, income, geographical region, or the like.
Additional elements of
information may include one or more categories of biometric datum as described
above. Persons
skilled in the art, upon reviewing the entirety of this disclosure, will be
aware of various ways in
which data entries in a biometric database 412 may reflect categories,
cohorts, and/or populations of
data consistently with this disclosure.
100561 Still referring to FIG. 5, one or more database tables in
biometric database 412 may
include, as a non-limiting example, a fingerprint data table 500. Fingerprint
data table 500 may be a
table matching biometric datum input from a remote device of the plurality of
remote devices 116a-n
as described above to fingerprint data. For instance, and without limitation,
biometric database 412
may include a fingerprint data table 500 listing samples acquired from a user
correlated to a remote
device of the plurality of remote devices 116a-n having allowed the system 100
to retrieve
fingerprint data from the user's remote device through fingerprint scanner,
such as optical scanners
or capacitive scanners, one or more rows recording such an entry may be
inserted in fingerprint data
table 500.
100571 With continued reference to FIG. 5, biometric database 412
may include tables listing
one or more samples according to sample source. For instance, and without
limitation, biometric
database 412 may include a typing rhythm database 504 listing samples acquired
from a user by
obtaining the user's keystroke dynamics when typing characters on a keyboard
and/or keypad, such
as the time to get to and depress a key, time the key is held down, use of
caps-lock, pace of typing
characters, misspellings, or the like. As another non-limiting example,
biometric database 412 may
include a face recognition data table 508, which may list samples acquired
from a user correlated to
a remote device of the plurality of remote devices 116a-n having allowed the
system 100 to obtain
digital images or video frames of the user's facial demographics, such as
relative position, size,
and/or shape of the eyes, nose, cheekbones, jaw, and/or the like. As a further
non-limiting example,
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biometric database 412 may include a voice recognition data table 512, which
may list samples
acquired from a user correlated to a remote device of the plurality of remote
devices 116a-n having
allowed the system 100 to retrieve the user's unique voice patterns though a
microphone located on
the user's remote device, such as dictation variants, common phrases, volume
level, dialect, pitch,
format frequencies, and/or the like. As a further example, also non-limiting,
biometric database 412
may include an iris scan data table 516, which may list samples acquired from
a user correlated to a
remote device of the plurality of remote devices 116a-n having allowed the
system 100 to retrieve a
user's iris scan from a camera located on a user's remote device, including
without limitation images
of the detailed structures of the iris which are visible externally. As
another non-limiting example,
biometric database 412 may include a retinal scan data table 520, which may
include samples
acquired from a user correlated to a user's remote device of the plurality of
remote devices 116a-n
having allowed system 100 to extract a user's retinal scan; retinal scans may
include an image of the
complex and unique structure of an individual's capillaries in the retina.
Tables presented above are
presented for exemplary purposes only; persons skilled in the art will be
aware of various ways in
which data may be organized in biometric database 412 consistently with this
disclosure.
[0058] Referring again to FIG. 4, authentication module 128 may
include or communicate with
authentication database 416. Authentication database 416 may be implemented as
any database
and/or datastore suitable for use as an authentication database. An exemplary
embodiment of an
authentication database 416 is provided below in FIG. 6. Authentication module
128 is configured to
store each verification element of each remote device of the plurality of
remote devices within
authentication database 416. Storage of each verification element of each
remote device of the
plurality of remote devices is based on the identification of the verification
element within
authentication, as described above. Verification element may include an
authentication datum or a
failed authentication datum, as described above in reference to FIG. 1.
Persons skilled in the art,
upon reviewing the entirety of this disclosure, will be aware of various
additional examples for
verification elements that may be received from authentication module 128
consistently with this
disclosure.
[0059] Referring now to FIG. 6, an exemplary embodiment of
authentication database 416 is
illustrated. Authentication database 416 may include any data structure for
ordered storage and
retrieval of data, which may be implemented as a hardware or software module.
Authentication
database 416 may be implemented, without limitation, as a relational database,
a key-value retrieval
datastore such as a NOSQL database, or any other format or structure for use
as a datastore that a
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person skilled in the art would recognize as suitable upon review of the
entirety of this disclosure.
Authorization database 416 may include a plurality of data entries and/or
records corresponding to
verification elements as described above. Data entries and/or records may
describe, without
limitation, data concerning authentication datum and failed authentication
datum.
[0060] Still referring to FIG. 6, one or more database tables in
authentication database 416 may
include, as a non-limiting example, an authentication datum table 600.
Authentication datum table
600 may be a table storing and/or matching authentication datum from each
verification element to
each remote device of the plurality of remote devices 116a-n. For instance,
and without limitation,
authentication database 416 may include an authentication datum table 600
listing samples acquired
from each verification element for each remote device of the plurality of
remote devices 116a-n
generated in authentication module 128, such as an element of authentication
datum.
[0061] Still referring to FIG. 6, one or more database tables in
authentication database 416 may
include, as a non-limiting example, a failed authentication datum table 604.
Failed authentication
datum table 604 may be a table storing and/or matching failed authentication
datum from each
verification element to each remote device of the plurality of remote devices
116a-n. For instance,
and without limitation, authentication database 416 may include a failed
authentication datum table
604 listing samples acquired from each verification element for each remote
device of the plurality
of remote devices 116a-n generated in authentication module 128, such as an
element of failed
authentication datum. Tables presented above are presented for exemplary
purposes only; persons
skilled in the art will be aware of various ways in which data may be
organized in authentication
database 416 consistently with this disclosure.
[0062] Referring now to FIG. 7, an exemplary embodiment of a
pathway selection module 132
is illustrated. Pathway selection module 132 may include any suitable hardware
or software module.
Pathway selection module 132 may include a pathway selection machine-learning
algorithm 700.
Pathway selection machine-learning algorithm 700 and/or pathway selection
module may be
configured to calculate for each remote device of the plurality of remote
devices 116a-n a pathway
probability variable 704 for each pathway of the plurality of electronic
communication pathways
120a-n for each subject indicator. Calculating the pathway probability
variable 704 may include
multiplying the selected electronic communication pathway's probability
variable, and/or a seed
value representing and/or associated with the selected electronic
communication pathway's
probability variable, for each subject indicator by the volume of incoming
communications for the
selected electronic communication pathway of the plurality of electronic
communication pathways
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120a-n associated to the selected subject indicator and dividing the selected
electronic
communication pathway's pathway probability variable 704 for each subject
indicator by the total
count of pathway probability variables 704 for each subject indicator. For
instance, without
limitation, a specific remote device of the plurality of remote devices 116a-n
accounts for five
electronic communication pathways for a specific subject indicator out of a
total fifty electronic
communication pathways for the specific subject indicator. The specific remote
device's pathway
probability variable is multiplied by the five electronic communication
pathways for the subject
indicator. The pathway probability variable may initially begin with a numeric
value of one, and/or
one multiplied by a seed value, wherein after multiplication the remote
device's pathway probability
variable is five, and/or five multiplied by a seed value. The remote device's
pathway probability
variable for the specific subject indicator of five is divided by fifty, the
total electronic
communication pathways for the specific subject indicator. The remote device's
pathway probability
variable for the specific subject indicator is updated to 0.1 to reflect
weighted chance of selecting the
electronic communication pathway of the plurality of electronic communication
pathways 120a-n for
the specific remote device for the specific subject indicator.
100631 Further referring to FIG. 7, pathway selection module 132
may be configured to
calculate pathway probability variable based on one or more event sources,
defined as sources of
random, pseudorandom, and/or unpredictable data. One or more sources may be
internal; for
instance, and without limitation, a random number generator and/or
pseudorandom number
generator may produce a seed value which may be used to generate pathway
probability variable. A
seed value may, as a non-limiting example, be compared to a value
corresponding to a particular
specific remote device; remote device may be assigned value automatically
and/or may generate
and/or transmit value. For instance, and without limitation, specific remote
device may transmit a
number to system 100, which number and/or representation of number may be
compared to a
number used as seed value and/or a representation thereof. A seed value may be
selected from a
range of possible values, such as a random or pseudorandom number on a
specified interval. A seed
value may include a set of two or more numbers. Alternatively or additionally,
a seed value may be
directly compared to an identifier of specific remote device.
100641 In an embodiment, and still referring to FIG. 7, pathway
selection module 132 may
generate a seed value as a function of an internal process that iteratively
compares transmissions
from one or more remote devices to additional transmissions of one or more
remote devices and/or
to one or more internal or external sources of random, pseudorandom, and/or
unpredictable behavior,
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where "unpredictable" as used in this context indicates behavior having
outcomes based on data
unavailable to one or more remote devices. A series of iteratively processed
transmissions may be
conceptualized, in a non-limiting example, as a "game" in which a remote
device submits inputs
iteratively for comparison to one or more internal or external sources of
random, pseudorandom,
and/or unpredictable behavior; one or more internal or external sources of
random, pseudorandom,
and/or unpredictable behavior may be or be based on a single value and/or
event, and/or one or more
iteratively generated values and/or events. Values and/or events may be
generated based on a
stateless process performed per iteration, a stateful process performed per
iteration based on states
and/or values generated in previous iterations and/or inputs received from one
or more remote
devices in previous iterations and/or in a current iteration, or the like.
Remote devices may be
configured by system 100 to display representations of a current state to
users of remote devices, for
instance in the form of visual representations of states, inputs from other
remote devices, inputs of a
remote device performing display, or the like; inputs may be keyed by user
selections of one or more
displayed options, user entry of commands, numbers, or other data values, or
the like.
100651 With further reference to FIG. 7, an external source of
random, pseudorandom and/or
unpredictable data may be used by pathway selection module to generate a seed
value. For instance,
and without limitation, an input may be received describing an outcome of an
external event, which
input may be used to generate seed value. A seed value may subsequently be
used to generate
pathway probability variable, for instance as a function of one or more
previous, concurrent, or
subsequently received inputs from one or more remote devices, which inputs may
be matched to
and/or otherwise compared to seed values. One or more inputs from a remote
device may be
conceptualized, as non-limiting example, as a prediction and/or guess, whether
automatically or
user-generated, concerning an outcome of an external event.
100661 With continued reference to FIG. 7, pathway selection
machine-learning algorithm 700
and/or other algorithms as described in this disclosure may be performed by a
computing
device/module to produce outputs given data provided as inputs. Pathway
selection machine-
learning algorithm used to generate for each remote device of the plurality of
remote devices 116a-n
a pathway probability variable for each pathway of the plurality of electronic
communication
pathways 120a-n for each subject indicator may include, without limitation,
linear discriminant
analysis. Machine-learning algorithm may include quadratic discriminate
analysis. Machine-learning
algorithms may include kernel ridge regression. Machine-learning algorithms
may include support
vector machines, including without limitation support vector classification-
based regression
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processes. Machine-learning algorithms may include stochastic gradient descent
algorithms,
including classification and regression algorithms based on stochastic
gradient descent. Machine-
learning algorithms may include nearest neighbors algorithms. Machine-learning
algorithms may
include Gaussian processes such as Gaussian Process Regression. Machine-
learning algorithms may
include cross-decomposition algorithms, including partial least squares and/or
canonical correlation
analysis. Machine-learning algorithms may include naive Bayes methods. Machine-
learning
algorithms may include algorithms based on decision trees, such as decision
tree classification or
regression algorithms. Machine-learning algorithms may include ensemble
methods such as bagging
meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting,
and/or voting classifier
methods. Machine-learning algorithms may include neural net algorithms,
including convolutional
neural net processes.
[0067] Referring now to FIG. 8, an exemplary embodiment of a method
800 of selecting an
electronic communication pathway from a pool of potential pathways is
illustrated. At step 805, a
network communication routing hub 108 identifies a plurality of electronic
communication pathways
120a-n. Network communication routing hub 108 may include, without limitation,
a network
communication routing hub 108 as described above in reference to FIG. 1.
Identifying the plurality
of electronic communication pathways 120a-n may include receiving a plurality
of incoming
communications from a plurality of remote network devices 116a-n; the
plurality of remote network
devices 116a-n may include, without limitation, a plurality of remote network
devices 116a-n as
described above in reference to FIG. 1. Each incoming communication may be
received as a single
transmission or in one or more separate transmissions, each of which may be
preceded and/or
followed by communications transmitted by network communication routing hub
108. For instance,
and without limitation, each incoming communication may include each incoming
communication
as described above in reference to FIGS. 1-7. Each remote network device of
the plurality of remote
network devices 112a-n may be connected to the network communication routing
hub 104 by an
electronic communication pathway, for instance as described above in reference
to FIGS. 1.
[0068] In an embodiment, and continuing to refer to FIG. 8, each
incoming communication of
the plurality of incoming communications may contain a subject indicator
linking the
communication to a pool of potential pathways; the pool of potential pathways
may be a set of
electronic communication pathways from which a plurality of electronic
communication pathways
120a-n may be selected. For instance, and without limitation, pool of
potential pathways may
include one or more pathways with regard to which subsequent steps such as
authentication, filtering
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for duplicates, or the like may not have been performed yet; the pool of
potential pathways may be
reduced by such steps as one or more pathways in the pool is removed for
failing authentication, as
representing a duplicate user and/or device, or the like. A subject indicator
may include, without
limitation, a numerical or other textual code associated in memory of network
communication
routing hub 108, application server 120, and/or server 104 with a subject of
communication, for
instance as described above in reference to FIGS. 1-2. Step 810 requires a
detection of a terminal
condition. Terminal conditional may include, without limitation, terminal
conditions as described
above in reference to FIG. 1. If NO, program flow continues back to step 805
wherein network
communication hub 108 continues to identify a plurality of electronic
communication pathways to a
plurality of remote devices. If YES, the program flow continues on to step
815.
100691 At step 815, and still referring to FIG. 8, authentication
module 128 operating on the at
least a server 104 authenticates each device of the plurality of remote
network devices 116a-n.
Authentication, as used herein, is a determination that remote device of the
plurality of remote
devices and/or a user of the remote device is permitted to participate in a
current iteration of method
800; authentication may include a determination that remote device and/or user
is permitted to
participate in a transaction, such as a transaction for distribution of an
item of value and/or rights to
an item of value as set forth in further detail below. Any remote device that
fails authentication
and/or is associated with a user that fails authentication, may have its
associated network
communication pathway removed from pool of potential pathways, where failure
of authentication
may include a conclusion, in any test or series of tests performed in the
course of authentication as
described in this disclosure, that remote device and/or user is not permitted
to participate in
embodiment of method 800.
100701 With continued reference to FIG. 8, authentication may
include authentication of remote
device itself. Authentication of remote device may include, without
limitation, comparison of a
remote device to a "device fingerprint" describing one or more settings of
remote device; such a
fingerprint may be used, without limitation to verify that the remote device
matches a device
previously used by user seeking to be authenticated, that the remote device
does not match a device
previously used by a user that is not authenticated for any reason described
below (e.g., a user
attempting to skirt authentication with a pseudonym or fake identity), or the
like. Device
authentication may include receipt of a digital signature and/or digital
certificate as described above
from remote device; authentication module 128 may evaluate such digital
signature and/or digital
certificate as described above. Device authentication may include comparison
of an intemet protocol
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(IP) address or other network address currently used by remote device to a
past address; device
authentication may include an approximate determination of a geographical
location of remote
device, for instance via IP geolocation processes matching IP addresses to
geographical locations.
Each of the above-described data concerning remote device may be compared to
one another and/or
to user authentication data and/or user data; for instance, and without
limitation, identity and/or
location of device as determined by the above-described methods may be
compared to information
stored regarding and/or provided by a user, to verify that user-provided data
is or is not accurate, to
identify a user that is not permitted to engage in transaction, or the like.
100711 Still viewing FIG. 8, authentication may include user
authentication. User authentication
may, for instance, include receiving in an incoming communication, user logon
credentials such as,
without limitation, a user identifier or "username" and/or a user password.
User authentication may
include receiving one or more items of secret information from a user; for
instance, and without
limitation, authentication module 128 may communicate with network
communication routing hub
108 to send user one or more "security questions" asking user for secret
information, which user
may have previously submitted to system 100.
100721 Still viewing FIG. 8, authentication may include pathway
numeric verification whereby
pathway numeric verification unit 400 comprises validating the user's
financial ability to participate
in an embodiment of method 800 and/or a transaction as described herein for a
transfer of an item of
value and/or rights thereto is assessed; ability to participate may include
ability to absorb losses from
participation in transactions. Pathway numeric verification may include,
without limitation, a credit
check or check of a user's consumer report, a check of one or more account
balances of accounts as
provided by user, verification of income or assets, or the like for instance
as described above in
reference to FIG 1-6. Authentication may further include pathway age
verification whereby pathway
age verification unit 404 comprises determining if a user's birth datum
surpasses at least a threshold
age, such as a minimum age requirement to participate in an embodiment of
method 800 and/or a
transaction as described herein for a transfer of an item of value and/or
rights thereto is assessed.
Pathway age verification may include comparison of the threshold age to,
without limitation, user's
birth datum, such as without limitation user input of the user's date of birth
for instance as described
above in reference to FIGS. 1-6.
100731 With continued reference to FIG. 8, authentication may
further include pathway
biometric verification whereby pathway biometric verification unit 408
comprises performing
pathway biometric verification of each device of the plurality of remote
devices 116a-n to participate
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in an embodiment of method 800 and/or a transaction as described herein for a
transfer of an item of
value and/or rights thereto is assessed; ability to participate may include
verifying the identity of the
user. Pathway biometric verification comprises receiving at least a biometric
datum from each
device of the plurality of remote devices 116a-n and matching the at least a
biometric datum for each
remote device of the plurality of remote devices 116a-n to a correlated
biometric datum stored
within biometric database 412, as described above in reference to FIGS 1-6.
100741 Still referencing FIG. 8, step 820 requires a determination
of a failed authentication
datum during authenticating each device of the plurality of remote devices of
method 800, as
described in detail above. If NO, program flow continues back to step 815 and
authentication of each
remote device of the plurality of remote devices continues until a failed
authentication datum is
determined for each remote device of the plurality of remote devices. If YES,
a failed authentication
datum for the specific remote device of the plurality of remote devices 116a-n
is detected, program
flows on to step 825. In step 825, the determined failed authentication datum
is stored in
authentication database 416, as described above in reference to FIGS. 4-6.
Step 830 comprises
terminating the electronic communication pathway 120a-n for each device of the
plurality of remote
devices associated to the failed authentication datum. Terminating may include
for instance without
limitation eliminating the electronic communication pathway 120a-n for each
device of the plurality
of remote devices associated to the failed authentication datum from the pool
of electronic
communication pathways as described above.
100751 Continuing to refer to FIG. 8, step 835 requires a
determination of authentication datum
during authentication of each device of the plurality of remote devices of
method 800, as described
in detail above. If NO, program flow continues back to step 815 and
authentication of each remote
device of the plurality of remote devices continues until an authentication
datum is determined for
each remote device of the plurality of remote devices. If YES, an
authentication datum for the
specific remote device of the plurality of remote devices 116a-n is detected,
program flows to step
840. In step 840, the determined authentication datum for the specific remote
device of the plurality
of remote devices 116a-n is stored in an authentication database 416, as
described above in reference
to FIGS. 4-6.
100761 At step 845, and continuing to refer to FIG. 8, network
communication routing hub 108
may verify uniqueness of each user of the plurality of remote network devices
116a-n.
Determination of uniqueness may include, without limitation, checking user
authentication data to
ensure that each user only participates a single time. Determination of
uniqueness may also include,
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without limitation, identification of remote device, including by processes
described above for
device authentication, as associated with a particular user, to eliminate the
use of pseudonyms, fake
identities, and/or "sock puppet" accounts. Verification of uniqueness may, for
instance, prevent a
single user from "gaming" a transaction by overrepresenting him or herself
within an iteration of an
embodiment of method 800, a step or set of steps of method 800 and/or a
transaction as described
herein for a transfer of an item of value and/or rights.
100771 At step 850, and still referring to FIG. 8, pathway
selection module 132 operating on the
at least a server 104 selects a pathway from the plurality of electronic
communication pathways
120a-n based on pathway probability variable 704. Pathway probability variable
704 operates as a
function of pathway selection algorithm 700. The pathway probability variable
704 may include
without limitation the weighted selection probability of each pathway of the
plurality of electronic
communication pathways 120a-n, as described above in reference to FIGS. 1-7.
In an embodiment,
pathway selection algorithm 700 comprises determining the pathway probability
variable 704 based
on incoming communication of the plurality of incoming communications as a
function of each
remote device of the plurality of remote devices 116a-n and a specific subject
indicator, updating the
pathway probability variable for each pathway of each remote device of the
plurality of remote
devices 116a-n based on pathway selection algorithm 700, as described above in
reference to FIG. 7
As a non-limiting example, where method 800 includes a process for selecting a
recipient of an item
of value and/or a right thereto, selection of electronic communication pathway
may include selection
of a pathway associated with a user who will receive the item and/or right
thereto. Selection of
pathway may include selection of a user account and/or of user identification
and/or authentication
information associated with a user that is to receive the item of value and/or
right thereto; for
instance, arrangement of electronic communication pathways according to
indices as described
above may be accomplished by arrangement of user-identifying information
according to indices.
100781 Still referring to FIG. 8, pathway probability variable may
be generated according to any
process, algorithm, and/or methodology described above, including without
limitation generation of
a seed value and use of seed value to generate pathway probability variable,
for instance and without
limitation by comparison to one or more one-time and/or iteratively generated
outputs and/or states,
one or more one-time or iteratively received inputs from remote device and/or
remote devices, or the
like. Input process may include gamification as described above, which
gamification may be
represented to a user of a remote device as any game, including without
limitation card games such
as poker, blackjack, bridge, pinochle, or the like, tile games such as
mahjong, dominoes, or the like,
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board games such as checkers, chess, backgammon, go, or the like, dice games,
bingo, and/or any
other game of chance or skill, or virtual representation thereof which one,
two or more players may
engage in. Seed value may alternatively or additionally be compared to one or
more numbers and/or
values generated for and/or received from one or more remote devices; such
values may be
represented as "lottery- submissions, and/or submissions to a lottery process,
an outcome of which
may represent selection and/or generation of pathway probability variable.
100791 With further reference to FIG. 8, a seed value may be
generated, selected, and/or
received as a function of one or more external sources of random,
pseudorandom, and/or
unpredictable data such sources may include outcomes of athletic events, video
game competitions,
business ventures, elections, court cases, weather, and/or any other process
about which predictions
may be made as a function of luck or skill. One or more inputs from a remote
device may be
conceptualized, as non-limiting example, as a prediction and/or guess, whether
automatically or
user-generated, concerning an outcome of an external event. Submissions from
remote devices may
include predictions concerning such outcomes and/or include a "bet" on an
outcome, or a series of
such predictions, bets, or the like Submissions may include selection of one
or more event
generation participants, processes, or the like, such as without limitation
selection of one or more
teams, players on one or more teams, or the like performance of which may be
used as inputs to a
game such as a virtual sporting event. For instance, and without limitation,
submissions from one or
more remote devices may concern, and/or be compared to external athletic or
other events according
to a sports fantasy process such as "fantasy football," "fantasy baseball," or
the like. Submissions
may include selections of teams and/or players to virtual rosters, submissions
guessing which team
will win and/or exceed a point spread, which player and/or team will score
first, achieve a given
statistical achievement, or the like, or other such predictions.
100801 Still referencing FIG. 8, step 855 requires a determination
of whether the electronic
communication pathway is the selected electronic communication pathway of the
plurality of
electronic communication pathways 120a-n of step 850. If YES, the pathway is
the selected
electronic communication pathway of the plurality of electronic communication
pathways, program
flows to step 860. If NO, program flows to step 865.
100811 At step 860, and continuing to refer to FIG. 8, pathway
selection module 132 transmits a
first outgoing communication over selected pathway to a remote network device
of the plurality of
remote network devices 116a-n associated with selected pathway via network
communication
routing hub 108. First outgoing communication may, as a non-limiting example,
inform a user
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associated with a selected electronic communication pathway that the user is a
chosen recipient of an
item of value or a right thereto, as described above in reference to FIG. 1.
Step 865 comprises
pathway selection module 132 operating on at least a server 104 transmits a
second outgoing
communication over the non-selected pathways to the remaining remote devices
of the plurality of
remote devices within the pool of communications associated with a specified
subject indicator.
Second outgoing communication may, as a non-limiting example, inform users
associated with
selected electronic communication pathways that the users were not chosen as
the recipient of an
item of value or right thereto, as described above in reference to FIG. 1.
100821 It is to be noted that any one or more of the aspects and
embodiments described herein
may be conveniently implemented using one or more machines (e.g., one or more
computing devices
that are utilized as a user computing device for an electronic document, one
or more server devices,
such as a document server, etc.) programmed according to the teachings of the
present specification,
as will be apparent to those of ordinary skill in the computer art.
Appropriate software coding can
readily be prepared by skilled programmers based on the teachings of the
present disclosure, as will
be apparent to those of ordinary skill in the software art. Aspects and
implementations discussed
above employing software and/or software modules may also include appropriate
hardware for
assisting in the implementation of the machine executable instructions of the
software and/or
software module.
100831 Such software may be a computer program product that employs
a machine-readable
storage medium. A machine-readable storage medium may be any medium that is
capable of storing
and/or encoding a sequence of instructions for execution by a machine (e.g., a
computing device)
and that causes the machine to perform any one of the methodologies and/or
embodiments described
herein. Examples of a machine-readable storage medium include, but are not
limited to, a magnetic
disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical
disk, a read-only
memory "ROM" device, a random-access memory "RAM" device, a magnetic card, an
optical card,
a solid-state memory device, an EPROM, an EEPROM, and any combinations
thereof. A machine-
readable medium, as used herein, is intended to include a single medium as
well as a collection of
physically separate media, such as, for example, a collection of compact discs
or one or more hard
disk drives in combination with a computer memory. As used herein, a machine-
readable storage
medium does not include transitory forms of signal transmission.
100841 Such software may also include information (e.g., data)
carried as a data signal on a data
carrier, such as a carrier wave. For example, machine-executable information
may be included as a
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data-carrying signal embodied in a data carrier in which the signal encodes a
sequence of instruction,
or portion thereof, for execution by a machine (e.g., a computing device) and
any related information
(e.g., data structures and data) that causes the machine to perform any one of
the methodologies
and/or embodiments described herein.
100851 Examples of a computing device include, but are not limited
to, an electronic book
reading device, a computer workstation, a terminal computer, a server
computer, a handheld device
(e.g., a tablet computer, a smartphone, etc.), a web appliance, a network
router, a network switch, a
network bridge, any machine capable of executing a sequence of instructions
that specify an action
to be taken by that machine, and any combinations thereof. In one example, a
computing device may
include and/or be included in a kiosk.
100861 FIG. 9 shows a diagrammatic representation of one embodiment
of a computing device
in the exemplary form of a computer system 900 within which a set of
instructions for causing a
control system to perform any one or more of the aspects and/or methodologies
of the present
disclosure may be executed. It is also contemplated that multiple computing
devices may be utilized
to implement a specially configured set of instructions for causing one or
more of the devices to
perform any one or more of the aspects and/or methodologies of the present
disclosure. Computer
system 900 includes a processor 904 and a memory 908 that communicate with
each other, and with
other components, via a bus 912. Bus 912 may include any of several types of
bus structures
including, but not limited to, a memory bus, a memory controller, a peripheral
bus, a local bus, and
any combinations thereof, using any of a variety of bus architectures.
100871 Processor 904 may include any suitable processor, such as
without limitation a processor
incorporating logical circuitry for performing arithmetic and logical
operations, such as an arithmetic
and logic unit (ALU), which may be regulated with a state machine and directed
by operational
inputs from memory and/or sensors; processor 904 may be organized according to
Von Neumann
and/or Harvard architecture as a non-limiting example. Processor 904 may
include, incorporate,
and/or be incorporated in, without limitation, a microcontroller,
microprocessor, digital signal
processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable
Logic Device
(CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor
Processing Unit (TPU),
analog or mixed signal processor, Trusted Platform Module (TPM), a floating-
point unit (FPU),
and/or system on a chip (SoC)
100881 Memory 908 may include various components (e.g., machine-
readable media) including,
but not limited to, a random-access memory component, a read only component,
and any
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combinations thereof. In one example, a basic input/output system 916 (BIOS),
including basic
routines that help to transfer information between elements within computer
system 900, such as
during start-up, may be stored in memory 908. Memory 908 may also include
(e.g., stored on one or
more machine-readable media) instructions (e.g., software) 920 embodying any
one or more of the
aspects and/or methodologies of the present disclosure. In another example,
memory 908 may
further include any number of program modules including, but not limited to,
an operating system,
one or more application programs, other program modules, program data, and any
combinations
thereof.
100891 Computer system 900 may also include a storage device 924.
Examples of a storage
device (e.g., storage device 924) include, but are not limited to, a hard disk
drive, a magnetic disk
drive, an optical disc drive in combination with an optical medium, a solid-
state memory device, and
any combinations thereof. Storage device 924 may be connected to bus 912 by an
appropriate
interface (not shown). Example interfaces include, but are not limited to,
SCSI, advanced technology
attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394
(FIREWIRE), and any
combinations thereof. In one example, storage device 924 (or one or more
components thereof) may
be removably interfaced with computer system 900 (e.g., via an external port
connector (not
shown)). Particularly, storage device 924 and an associated machine-readable
medium 928 may
provide nonvolatile and/or volatile storage of machine-readable instructions,
data structures,
program modules, and/or other data for computer system 900. In one example,
software 920 may
reside, completely or partially, within machine-readable medium 928. In
another example, software
920 may reside, completely or partially, within processor 904.
100901 Computer system 900 may also include an input device 932. In
one example, a user of
computer system 900 may enter commands and/or other information into computer
system 900 via
input device 932. Examples of an input device 932 include, but are not limited
to, an alpha-numeric
input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an
audio input device (e.g.,
a microphone, a voice response system, etc.), a cursor control device (e.g., a
mouse), a touchpad, an
optical scanner, a video capture device (e.g., a still camera, a video
camera), a touchscreen, and any
combinations thereof. Input device 932 may be interfaced to bus 912 via any of
a variety of
interfaces (not shown) including, but not limited to, a serial interface, a
parallel interface, a game
port, a USB interface, a FIREWIRE interface, a direct interface to bus 912,
and any combinations
thereof. Input device 932 may include a touch screen interface that may be a
part of or separate from
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display 936, discussed further below. Input device 932 may be utilized as a
user selection device for
selecting one or more graphical representations in a graphical interface as
described above.
100911 A user may also input commands and/or other information to
computer system 900 via
storage device 924 (e.g., a removable disk drive, a flash drive, etc.) and/or
network interface
device 940. A network interface device, such as network interface device 940,
may be utilized for
connecting computer system 900 to one or more of a variety of networks, such
as network 944, and
one or more remote devices 948 connected thereto. Examples of a network
interface device include,
but are not limited to, a network interface card (e.g., a mobile network
interface card, a LAN card), a
modem, and any combination thereof. Examples of a network include, but are not
limited to, a wide
area network (e.g., the Internet, an enterprise network), a local area network
(e.g., a network
associated with an office, a building, a campus or other relatively small
geographic space), a
telephone network, a data network associated with a telephone/voice provider
(e.g., a mobile
communications provider data and/or voice network), a direct connection
between two computing
devices, and any combinations thereof A network, such as network 944, may
employ a wired and/or
a wireless mode of communication. In general, any network topology may be
used. Information
(e.g., data, software 920, etc.) may be communicated to and/or from computer
system 900 via
network interface device 940.
100921 Computer system 900 may further include a video display
adapter 952 for
communicating a displayable image to a display device, such as display device
936. Examples of a
display device include, but are not limited to, a liquid crystal display
(LCD), a cathode ray tube
(CRT), a plasma display, a light emitting diode (LED) display, and any
combinations thereof.
Display adapter 952 and display device 936 may be utilized in combination with
processor 904 to
provide graphical representations of aspects of the present disclosure. In
addition to a display device,
computer system 900 may include one or more other peripheral output devices
including, but not
limited to, an audio speaker, a printer, and any combinations thereof. Such
peripheral output devices
may be connected to bus 912 via a peripheral interface 956. Examples of a
peripheral interface
include, but are not limited to, a serial port, a USB connection, a FIREW1RE
connection, a parallel
connection, and any combinations thereof
100931 The foregoing has been a detailed description of
illustrative embodiments of the
invention. Various modifications and additions can be made without departing
from the spirit and
scope of this invention. Features of each of the various embodiments described
above may be
combined with features of other described embodiments as appropriate in order
to provide a
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multiplicity of feature combinations in associated new embodiments.
Furthermore, while the
foregoing describes a number of separate embodiments, what has been described
herein is merely
illustrative of the application of the principles of the present invention.
Additionally, although
particular methods herein may be illustrated and/or described as being
performed in a specific order,
the ordering is highly variable within ordinary skill to achieve methods,
systems, and software
according to the present disclosure. Accordingly, this description is meant to
be taken only by way
of example, and not to otherwise limit the scope of this invention.
100941 Exemplary embodiments have been disclosed above and
illustrated in the accompanying
drawings. It will be understood by those skilled in the art that various
changes, omissions and
additions may be made to that which is specifically disclosed herein without
departing from the
spirit and scope of the present invention.
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