Note: Descriptions are shown in the official language in which they were submitted.
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ANALYSIS OF INTELLECTUAL-PROPERTY DATA IN RELATION TO PRODUCTS AND SERVICES
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Patent Application No.
16/503,107, filed July 03, 2019, titled
"Analysis Of Intellectual-Property Data In Relation To Products And Services,"
and U.S. Patent Application No.
16/503,126, filed July 03, 2019, titled "Analysis Of Intellectual-Property
Data In Relation To Products And Services,"
and U.S. Patent Application No. 16/503,144, filed July 03, 2019, titled
"Analysis Of Intellectual-Property Data In Relation
To Products And Services," and U.S. Patent Application No. 16/503,164, filed
July 03, 2019, titled "Analysis Of
Intellectual-Property Data In Relation To Products And Services," and U.S.
Patent Application No. 16/503,187, filed July
03, 2019, titled "Analysis Of Intellectual-Property Data In Relation To
Products And Services," the entire contents of
which are incorporated herein by reference.
BACKGROUND
[0002] Intellectual property is obtained by organizations to help protect
innovation within the organizations.
Typically, information related to intellectual property of an organization can
be difficult to effectively and efficiently
analyze. For example, understanding the value of the intellectual property or
understanding how intellectual property
relates to products or services in the marketplace can be difficult to achieve
in an accurate and efficient manner using
computer-implemented techniques.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] The detailed description is set forth below with reference to the
accompanying figures. In the figures, the
left-most digit(s) of a reference number identifies the figure in which the
reference number first appears. The use of the
same reference numbers in different figures indicates similar or identical
items. The systems depicted in the
accompanying figures are not to scale and components within the figures may be
depicted not to scale with each other.
[0004] FIG. 1 illustrates an example architecture to analyze intellectual-
property data and utilize the analysis of the
intellectual-property data to provide a number of services according to some
implementations.
[0005] FIG. 2 illustrates an example environment to analyze types of
intellectual-property data and product/service
data to provide services related to intellectual property according to some
implementations.
[0006] FIG. 3 illustrates an example environment to generate mappings
between products and intellectual-property
assets using a technology taxonomy according to some implementations.
[0007] FIG. 4 illustrates an example system to generate valuations for
intellectual-property assets according to some
implementations.
[0008] FIG. 5 illustrates an example system to modify mappings between
intellectual property and taxonomy
classifications and and/or between intellectual property and products/services
according to some implementations.
[0009] FIG. 6 illustrates an example architecture to provide services to
customers using mappings between
intellectual property and products/services in relation to a classification
system according to some implementations.
[0010] FIG. 7 illustrates an example framework to generate linguistic
structures for claims of patent documents
according to some implementations.
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[0011] FIG. 8 illustrates an example framework to determine a similarity
metric between a linguistic structure for a
portion of a claim of a patent document and a linguistic structure of a
product/service according to some implementations.
[0012] FIG. 9 illustrates an example framework to determine a value of an
intellectual-property feature that
corresponds to one or more products according to some implementations.
[0013] FIG. 10 illustrates an example process to determine an intellectual-
property asset that corresponds to a product
and/or service according to some implementations.
[0014] FIG. 11 illustrates an example process to determine an
intellectual-property asset that corresponds to a product
or service using a classification system according to some implementations.
[0015] FIG. 12 illustrates an example process to perform a qualitative
analysis and a quantitative analysis of
intellectual-property data according to some implementations.
[0016] FIG. 13 illustrates an example process to determine an
intellectual-property asset that corresponds to a product
and/or service using a linguistic structure of the intellectual-property asset
and a linguistic structure of the product and/or
service according to some implementations.
[0017] FIG. 14 illustrates an example process to provide services to a
customer based on relationships between a
product and/or service and an intellectual-property asset according to some
implementations.
DETAILED DESCRIPTION
[0018] Techniques described herein are directed to analyzing intellectual-
property data in relation to products and/or
services. As technological advancement has increased, and the value of
organizations has been characterized by the shift
from tangible assets to intangible assets, the importance of intellectual
property has also increased. Thus, organizations
have taken various measures to safeguard their intellectual property, which
may include patents, trademarks, copyrights,
trade secrets, and/or know-how, for example. However, there have been few
techniques, architectures, and frameworks
developed to analyze intellectual-property data and generate useful
information from an organization's intellectual-
property data. Additionally, the number of services provided to organizations
using intellectual property is also limited
due to the complexity of analyzing intellectual-property data and the
inability of conventional systems to effectively
provide information to organizations regarding their intellectual property
that is of value to the organizations.
[0019] The implementations described herein are directed to techniques,
systems, and architectures to analyze
intellectual-property data to generate frameworks that may be used to provide
services related to intellectual-property
assets. In particular implementations, an intellectual-property services
provider may obtain intellectual-property data from
a number of data sources. In various implementations, at least a portion of
the data sources may include public data
sources. Public data sources storing intellectual-property data may include
databases of patent offices of various
jurisdictions, such as the Unites States Patent and Trademark Office (USPTO)
database, the European Patent Office (EPO)
database, and/or the World Intellectual Property Office (WIPO) database.
Additionally, intellectual-property data may be
stored in databases related to copyrights, such as the United States Copyright
Office or the European Union Copyright
Office. The intellectual-property data may also be obtained from private data
sources. The private data sources may
include databases that store information related to an organization that are
maintained and/or controlled by the
organization. The private data sources may also include databases of service
providers that store information on behalf of
an organization. Further, at least a portion of the intellectual-property data
of an organization may be captured via one or
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more user interfaces. In some situations, the one or more user interfaces may
be rendered as part of customer portals that
are accessible to customers of the intellectual-property services provider. In
examples, the data sources may include a
digital-property registry, which may be maintained and/or generated by a
system and/or entity other than the organization.
For example, a digital property, such as a trade secret, may be registered
with the digital-property registry utilizing one or
more obfuscation values to represent the digital property and/or block values
to represent a block in a distributed ledger
where the obfuscation value is registered.
[0020] The intellectual-property services provider may also obtain data
related to a number of products and/or
services. The products and/or services may be offered for acquisition by a
same organization for which the intellectual-
property data is being obtained and analyzed. Additionally, the products
and/or services may be offered for acquisition
by organizations differing from an organization for which the intellectual-
property data is being obtained and analyzed.
The data related to the products and/or services may include at least one of
economic data related to the products and/or
services, manuals regarding the products and/or services, specification sheets
for the products and/or services, descriptions
of the products and/or services, and/or marketing materials related to the
products and/or services.
[0021] The data related to products and/or services may be obtained from
a number of data sources. In particular
implementations, the data related to products and/or services may be obtained
from various websites. In some scenarios,
the data related to the products and/or services may be obtained from one or
more websites of organizations that are
offering the products and/or services for acquisition. In additional
implementations, the data related to the products and/or
services may be obtained from databases of the organizations offering the
products and/or services for acquisition. Further,
the data related to the products and/or services may be obtained via one or
more user interfaces, such as user interfaces
provided by the intellectual-property services provider as part of a customer
portal.
[0022] Data related to intellectual property of an organization and data
related to products and/or services may also
be obtained through crowdsourcing. In particular implementations, the
intellectual-property services provider may publish
requests for information about intellectual-property assets and/or requests
for information about products and/or services.
The requests may be published on one or more websites, via one or more mobile
device applications, sent to a group of
individuals, or combinations thereof. In response to the requests, individuals
may identify information that corresponds
to the request and send the information to the intellectual-property services
provider.
[0023] After obtaining information about products and/or services and
obtaining intellectual-property information,
an intellectual-property services provider may analyze the information and
organize the information in such a way that
the intellectual-property services provider may provide a number of services
to customers of the intellectual-property
services provider. The intellectual-property services provider may analyze the
information obtained from the data sources
using machine learning techniques. In particular implementations, the
intellectual-property services provider may generate
one or more models that may be utilized to determine properties,
characteristics, metrics, and the like with respect to
intellectual-property assets and products and/or services. In various
implementations, the intellectual-property services
provider may implement machine learning techniques to determine relationships
between intellectual-property assets and
products and/or services. In some examples, the intellectual-property services
provider may utilize the relationships
between intellectual-property assets and products and/or services to estimate
the value of intellectual-property assets. The
intellectual-property services provider may also utilize machine learning
techniques to determine levels of exposure
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corresponding to intellectual-property assets. The levels of exposure
associated with the intellectual-property assets may
correspond to a probability that at least one of coverage of the intellectual-
property assets may decrease or that a litigation
event with respect to the intellectual-property assets occurs.
[0024] The intellectual-property services provider may utilize natural
language processing techniques in order to
analyze the information obtained from the data sources related to the
intellectual-property assets and the products and/or
services. To illustrate, the intellectual-property services provider may parse
words included in information associated with
products and/or services and information associated with the intellectual-
property assets and determine parts of speech
for the words. In certain examples, the intellectual-property services
provider may determine relationships between words
using the parts of speech of the words and grammatical relationships between
the words. The intellectual-property services
provider may utilize natural language processing techniques and/or machine
learning techniques to also determine
relationships between products and/or services and intellectual-property
assets. That is, the intellectual-property services
provider may utilize natural language processing techniques to determine
intellectual-property documents that may cover
one or more features of the products and/or services. In illustrative
examples, the intellectual-property services provider
may utilize natural language processing techniques and machine learning
techniques to determine probabilities that
intellectual-property assets may be enforced with respect to corresponding
products and/or services.
[0025] In particular implementations, the intellectual-property services
provider may generate linguistic structures
that correlate to intellectual-property documents using natural language
processing techniques and/or machine learning
techniques to determine relationships between words included in the
information related to the intellectual-property assets.
For example, the intellectual-property services provider may determine verbs
related to actions performed in a claim of a
patent document and also determine nouns and/or adjectives that correspond to
the actions. In some situations, the
intellectual-property services provider may utilize natural language
processing techniques and machine learning
techniques to determine elements of claims of patent documents. In addition,
the intellectual-property services provider
may generate linguistic structures for products and/or services using natural
language processing techniques and machine
learning techniques. In illustrative examples, the intellectual-property
services provider may determine actions performed
with respect to products and/or services and generate linguistic structures
that indicate verbs related to the actions and
nouns, adjectives, and/or adverbs that are related to the verbs. In various
implementations, the intellectual-property
services provider may determine intellectual-property assets that correspond
to various products and/or services by
comparing the respective linguistic structures of the intellectual-property
assets and the products and/or services.
[0026] The intellectual-property services provider may determine
intellectual-property assets that correspond with
features of products and/or services using a technology-classification
framework. The technology-classification
framework may include a taxonomy that includes a number of classifications
with each of the classifications being
associated with a number of criteria. Classifications for intellectual-
property documents may be determined according to
the technology-classification framework by performing a linguistic analysis of
the intellectual-property documents and
determining features of the intellectual-property documents. The intellectual-
property services provider may then compare
the features of the intellectual-property documents against the criteria for
the classifications of the technology-
classification framework to determine respective classifications for the
intellectual-property documents. Additionally, the
intellectual-property services provider may also determine classifications for
products and/or services according to the
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technology-classification system. For example, the intellectual-property
services provider may perform a linguistic
analysis of information related to products and/or services and determine
features of the products and/or services. The
intellectual-property service provider may then compare features of the
products and/or services in relation to criteria for
the classifications of the technology-classification framework to determine
respective classifications for the products
and/or service. In particular implementations, the intellectual-property
services provider may determine intellectual-
property assets that correspond to products and/or services when the
intellectual-property assets and the products and/or
services are in a same or similar classification of the technology-
classification framework.
[0027] In illustrative implementations, the intellectual-property
services provider may generate one or more models
that map products and/or services to a technology-classification framework and
that map intellectual-property assets to
the technology-classification framework. The intellectual-property services
provider may then utilize natural language
processing techniques and/or machine learning techniques to further develop
the one or more models by determining
intellectual-property assets that correspond to various products and/or
services within a given classification. In this way,
the intellectual-property services provider may receive requests to identify
intellectual-property assets corresponding to a
specified product and/or service and utilize the one or more models to
identify the intellectual-property assets that
correspond to the specified product and/or service. The intellectual-property
services provider may then determine
valuations for the intellectual-property assets based at least partly on
revenue of the specified product and/or service. For
example, the intellectual-property services provider may determine a portion
of the revenue of a particular product and/or
service that is attributable to an intellectual-property asset and estimate a
value of the intellectual-property asset based at
least in part on the portion of revenue of the product and/or service
attributable to the intellectual-property asset. The
intellectual-property services provider can also utilize the one or more
models and the technology-classification
framework to provide additional information to customers. To illustrate, the
intellectual-property services provider may
utilize the one or more models and the technology-classification framework to
determine an amount of exposure and/or
loss with respect to intellectual-property assets. The intellectual-property
services provider may also provide services to
customers using the one or more models and the technology-classification
framework related to providing metrics for a
portfolio of intellectual-property assets of an organization. The metrics may
indicate measures of breadth and coverage
with respect to the intellectual-property documents. The intellectual-property
services provider may also generate reports
using the one or more models and the technology-classification framework
indicating technology features around which
an organization may acquire and/or develop additional intellectual-property
assets. Additionally, the intellectual-property
services provider may generate reports using the one or more models and the
technology-classification system indicating
intellectual-property assets of competitors of customers of the intellectual-
property services provider and/or indicating
metrics of the intellectual-property assets of competitors of customers of the
intellectual-property services provider.
[0028] Conventional techniques and systems that analyze intellectual-
property documents with respect to products
and/or services are performed by individuals with the use of computers. For
example, the individuals may perform manual
searches of intellectual-property databases and online searches to identify
information about products and/or services. The
individuals may then perform a manual analysis to determine intellectual-
property documents that correspond to products
and/or services. In certain situations, individuals may also access online
resources related to the sale of intellectual-
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property assets, litigation verdicts for intellectual-property assets, and/or
settlement agreements for litigation proceedings
with respect to intellectual-property assets to determine the value of one or
more intellectual-property assets.
[0029] However, the conventional techniques and systems used to determine
relationships between intellectual-
property assets and various products and/or services and to determine
valuations for intellectual-property assets are
inefficient and, often, inaccurate. To illustrate, individuals are often
unable to search and retrieve large amounts of data
relating to intellectual-property assets and products and/or services.
Typically, information is overlooked or not found in
manual searches performed online by individuals, where the information may be
useful in identifying intellectual-property
assets that correspond to respective goods and/or services and in determining
valuations of intellectual-property assets.
Further, a human-based analysis of the information collected may often miss
relationships between intellectual-property
assets and products and/or services or may miss features covered by
intellectual-property assets that correspond to various
products and/or services. Thus, conventional techniques and systems are labor
intensive and often do not provide
information that is usable by organizations to evaluate the intellectual-
property assets of the organizations.
[0030] Additionally, implementing the techniques and systems described
herein is more than simply collecting and
organizing large amounts of data. The systems and techniques described herein
not only provide useful information
regarding intellectual-property assets that correspond to products and/or
services in a more efficient way with respect to
conventional techniques and systems, but the implementations described herein
also utilize techniques and systems that
generate accurate information that is supported by an analytical basis formed
from the unconventional use of machine
learning and natural language processing.
[0031] The present disclosure provides an overall understanding of the
principles of the structure, function,
manufacture, and use of the systems and methods disclosed herein. One or more
examples of the present disclosure are
illustrated in the accompanying drawings. Those of ordinary skill in the art
will understand that the systems and methods
specifically described herein and illustrated in the accompanying drawings are
non-limiting embodiments. The features
illustrated or described in connection with one embodiment may be combined
with the features of other embodiments,
including as between systems and methods. Such modifications and variations
are intended to be included within the
scope of the appended claims.
[0032] Additional details are described below with reference to several
example embodiments.
[0033] FIG. 1 illustrates an example architecture 100 to analyze
intellectual-property data and utilize the analysis of
the intellectual-property data to provide a number of services according to
some implementations. The architecture 100
may include an intellectual-property services system 102 that analyzes data
related to intellectual-property assets. The
data analyzed by the intellectual-property services system 102 may be used by
an intellectual-property services provider
to provide services related to intellectual-property assets. The intellectual-
property assets may include patents, trademarks,
copyrights, trade secrets, and know-how. In various implementations, the
intellectual-property assets may include a
portion of a patent, such as a claim of a patent. Additionally, the
intellectual-property assets may include a portion of a
copyright that is directed to a portion of software code that corresponds to a
particular feature that is performed when the
software code is executed.
[0034] In particular implementations, intellectual-property assets may be
associated with various forms of
documentation that indicate features of the intellectual-property assets. In
situations where the intellectual-property assets
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include patents, the patents may include utility patents, design patents,
and/or plant patents. The patents may also include
patent applications, such as provisional patent applications, utility patent
applications, design patent applications, plant
patent applications, or combinations thereof In various scenarios, the
intellectual-property assets may include trademark
applications and granted trademark registrations. The intellectual-property
assets may also include documentation
corresponding to copyright registrations and documentation including aspects
of trade secrets. To illustrate, formulas,
processes, and/or algorithms and software code that are the subject of trade
secrets may be documented. Actions taken to
preserve the secrecy of trade secrets may also be documented and included in
the intellectual-property assets. In addition,
the intellectual-property assets may include documentation of know-how of the
organization, such as process
improvements and innovations, new product designs, product improvements, brand
names, logos, ad slogans, website
design, product appearance, product packaging, manufacturing processes,
engineering drawings, instruction manuals,
product catalogs, customer and supplier lists, and so forth.
[0035] The intellectual-property services system 102 may include an
intellectual-property mapping and learning
system 104. The intellectual-property mapping and learning system 104 may
obtain information from a number of data
sources, such as data sources 106, and analyze the information to determine
relationships between intellectual-property
assets and products and/or services. The data sources 106 may include customer
portals 108. The customer portals 108
may include one or more user interfaces generated by the intellectual-property
services system 102 that include one or
more user interface elements to capture information related to intellectual-
property assets of customers of an intellectual-
property services provider, such as a customer 110. The user interfaces
associated with the customer portals 108 may be
displayed as part of one or more websites of an intellectual-property services
provider and/or via one or more mobile
device applications of the intellectual-property services provider. In various
implementations, information may be entered
into the customer portals 108 by a representative of the customer 110. In
additional implementations, information may be
entered into the customer portals 108 by representatives of an intellectual-
property services provider.
[0036] The data sources 106 may also include one or more customer data
sources 112. The one or more customer
data sources 112 may be accessible to the customers of the intellectual-
property services provider and store data under
the direction of the customers of the intellectual-property services provider.
That is, the data stored by the one or more
customer data sources 112 may be under the control of respective customers of
the intellectual-property services provider.
In some illustrative examples, at least one customer data source 112 may be
maintained on premises of the customer 110.
In additional illustrative examples, at least one customer data source 112 may
be maintained by an additional organization,
such as an organization that provides remote data storage services. For
example, a customer data source 112 may include
a cloud-based data storage system that is accessible by the customer 110.
[0037] Additionally, the data sources 106 may include crowdsourcing data
sources 114. The crowdsourcing data
sources 114 may include a number of individuals that provide information to
the intellectual-property services system
102. In various implementations, the intellectual-property services system 102
may publish requests for information about
intellectual-property assets via at least one of one or more websites or one
or more mobile device applications. The
intellectual-property service system 102 may also publish requests for
information about products and/or services that
may correspond to intellectual-property assets. In various implementations,
individuals included in the crowdsourcing
data sources 114 may access the requests published by the intellectual-
property services system 102 using at least one
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computing device and provide responses to the requests. The responses may
include information about at least one of
intellectual-property assets or products and/or services that were the subject
of the requests.
[0038] Further, the data sources 106 may include one or more public data
sources 116. The one or more public data
sources 116 may include data sources that store data that is accessible to the
general public. In some implementations, the
one or more public data sources 106 may store data that is accessible to
individuals without any credentials. In additional
implementations, the one or more public data sources 106 may store data that
is accessible to individuals with credentials
that are made available to the public by organizations maintaining the one or
more public data sources 116. The data
sources 116 that store data related to intellectual-property assets may be
accessible via one or more websites and/or one
or more mobile device applications.
[0039] The one or more public data sources 116 may include data sources
that store data related to intellectual-
property assets. For example, the one or more public data sources 116 may
include intellectual property organizations of
various governmental jurisdictions, such as the United States Patent and
Trademark Office, the European Patent Office,
the World Intellectual Property Organization, or the Japanese Patent Office.
The intellectual-property data stored by the
one or more public data sources 116 may include content of intellectual-
property documents. For example, the intellectual-
property data may include information contained in patent documents, such as
claims, drawings, backgrounds, abstracts,
descriptions of drawings, and the like. In other examples, the intellectual-
property data may include content of trademark
documents, such as descriptions of goods and services and/or classifications
of goods and services. Additionally, the
intellectual-property data may include information included in copyright
documents. Further, the intellectual-property
data may include information related to the examination of intellectual-
property documents. To illustrate, the intellectual-
property data may include prosecution histories of patent applications and/or
prosecution histories of trademark
applications. The intellectual-property data may also include bibliographic
information related to intellectual-property
documents, such as classification of patent documents, examiners assigned to
examine patent and trademark applications,
priority dates, filing dates, assignees, inventors, applicants, combinations
thereof, and the like. In various implementations,
the intellectual-property data may include data related to at least one of
administrative proceedings, litigation proceedings,
settlement information, or licensing information for intellectual-property
assets.
[0040] The one or more public data sources 116 may also include data
sources that store market and financial data.
The market and financial data may be related to organizations offering
products and/or services for acquisition. For
example, the market and financial data may include financial performance of
organizations over a period of time.
Additionally, the market and financial data may also indicate classifications
and industries for certain organizations. The
market and financial data may also include financial performance of one or
more industries over a period of time. Further,
the market and financial data may include data for financial markets, such as
stock markets, over time.
[0041] In addition, the one or more public data sources 116 may include
data sources that store information about
products and/or services. To illustrate, the one or more public data sources
116 may store data that includes descriptions
of products and/or services, specifications for products, features of products
and/or services, images of products, videos
related to products and/or services, pricing of products and/or services,
organizations that provide products and/or
services, combinations thereof, and so forth.
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[0042] In particular implementations, the intellectual-property services
system 102 may include a data acquisition
system 118 to obtain data from the data sources 106. In various
implementations, the data acquisition system 118 may
extract information from a number of websites. For example, the data
acquisition system 118 may include one or more
web crawlers that access websites and search for information that corresponds
to a given set of criteria and extracts the
information from the websites that correspond to the criteria. In illustrative
examples, the data acquisition system 118
may obtain data from the one or more data sources 106 corresponding to various
products and/or services. Additionally,
the data acquisition system 118 may obtain data from the one or more data
sources 106 corresponding to a number of
intellectual-property assets.
[0043] Further, the data acquisition system 118 may perform one or more
operations with respect to the data obtained
from the one or more data sources 106 before the data is stored by the
intellectual-property knowledge data store 120. For
example, the data acquisition system 118 may perform optical character
recognition operations with respect to at least a
portion of the data obtained from the one or more data sources 106. In other
examples, the data acquisition system 118
may remove information embedded in certain forms of data obtained from the one
or more data sources 106, such as
embedded scripts or fonts. The data acquisition system 118 may also add
information to data obtained from the one or
more data sources 106. To illustrate, the data acquisition system 118 may add
time stamps to data obtained from the one
or more data sources 106. The data acquisition system 118 may also add one or
more tags to data obtained from the one
or more data sources 106. The one or more tags may be related to at least one
of one or more organizations that correspond
to the extracted data, one or more technology classifications utilized by the
intellectual-property services system 102, or
one or more categories of intellectual-property assets (e.g., patents,
trademarks, copyrights, trade secrets, know-how).
Additionally, the data acquisition system 118 may apply tags to data obtained
from the one or more data sources 106
indicating that the data is economic data, market data, financial data,
product and/or service description data, litigation
related data, licensing related data, combinations thereof, and so forth. By
applying tags to data obtained from the one or
more data sources 106, the data acquisition system 118 may store the data in
the intellectual-property knowledge data
store 120 in such a way that the data may be retrieved and analyzed
efficiently.
[0044] The intellectual-property mapping and learning system 104 may
utilize natural language processing
techniques and machine learning techniques to identify relationships between
intellectual-property assets and products
and/or services. The intellectual-property mapping and learning system 104 may
also generate data for providing
intellectual property customer services 126 to customers of an intellectual-
property services provider, such as the customer
110. In particular implementations, the intellectual-property mapping and
learning system 104 may include a language
analysis system 122. The language analysis system 122 may analyze words
included in information obtained from the one
or more data sources 106 to determine parts of speech of the words. For
example, the language analysis system 122 may
determine that words included in information obtained from the one or more
data sources 106 may be nouns, verbs,
adverbs, adjectives, pronouns, articles, prepositions, conjunctions, and so
forth. The language analysis system 122 may
also determine relationships between words. To illustrate, the language
analysis system 122 may identify nouns and
adjectives that modify the nouns in addition to verbs and adverbs that modify
the verbs. Further, the language analysis
system 122 may determine nouns and/or pronouns that are performing actions
corresponding to verbs.
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[0045] In various implementations, the language analysis system 122 may
analyze information obtained from the
one or more data sources 106 to identify portions of intellectual-property
documents. For example, the language analysis
system 122 may analyze a patent document to identify at least one of a claims
portion of the patent document, a detailed
description of the patent document, a background of the patent document, a
summary of the patent document, an abstract
of the patent document, and so forth. Additionally, the language analysis
system 122 may determine individual elements
of claims included in patent documents. In particular implementations, the
language analysis system 122 may determine
features included in claims that may be directed to physical features of a
device or system. In various implementations,
the features may be directed to actions that are being performed in relation
to the methods or processes or actions
performed by devices or systems. Further, in scenarios where the claims are
directed to compositions of matter that
correspond to molecules, the features may be directed to various arrangements
of atoms included in the compositions of
matter, such as a phenyl functional group or a carboxyl functional group. In
some situations, the elements of a claim may
include a number of individual features. In additional examples, the language
analysis system 122 may also analyze a
trademark document to identify at least one of a description of goods and
services or international class of the trademark.
[0046] In certain implementations, the language analysis system 122 may
analyze intellectual-property documents
obtained from the one or more data sources 106 and generate modified
intellectual-property documents. The language
analysis system 122 may generate the modified intellectual-property documents
by removing portions of the original
intellectual-property documents. For example, the language analysis system 122
may remove at least one of conjunctions
or articles from intellectual-property documents. In additional examples, the
language analysis system 122 may generate
the modified intellectual-property documents by indicating parts of speech
and/or relationships between words in the
original intellectual-property documents.
[0047] Additionally, the language analysis system 122 may analyze
information related to products and/or services
and determine features of the products and/or services. To illustrate, the
language analysis system 122 may determine
physical components of devices and/or systems. The language analysis system
122 may also determine technical features
of devices and/or systems. Further, the language analysis system 122 may also
determine features of processes and/or
methods performed in relation to products and/or services.
[0048] In particular implementations, the language analysis system 122
may determine at least one of features of
intellectual-property assets, features of products, or features of services by
analyzing words related to intellectual-property
assets, products, and/or services with respect to a library of words related
to features of intellectual-property assets,
products, and/or services. For example, the intellectual-property mapping and
learning system 104 may determine a
particular set of words that are related to each of a number of individual
features that may be associated with at least one
of an intellectual-property document, a product, or a service. To illustrate,
the intellectual-property mapping and learning
system 104 may determine that words, such as "screen", "panel", and "display"
may indicate a display device feature of
an electronic device. Continuing with this example, the language analysis
system 122 may parse intellectual-property
documents and/or information about products and/or services to identify words
that correspond to the words associated
with a display device feature. In situations where at least a threshold number
of words included in the intellectual-property
documents and/or the information about products and/or services corresponds to
the words associated with the display
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device feature, the language analysis system 122 may determine that a
particular intellectual-property document or a
particular product and/or service includes the display device feature.
[0049] In various implementations, the language analysis system 122 may
also determine that proximity between
words associated with a feature may indicate that a feature is present in an
intellectual-property document or in information
about a product and/or service. In some examples, when a number of words
associated with a feature are within 3 words,
within 5 words, within 10 words, or within 20 words of each other, the
language analysis system 122 may determine that
the feature is included in an intellectual-property document or in a product
and/or service. In additional examples, when
a number of words associated with a feature are within a same sentence or
within a same paragraph, the language analysis
system 122 may determine that the feature is included in an intellectual-
property document or in a product and/or service.
[0050] The language analysis system 122 may also generate linguistic
structures for intellectual-property documents
and linguistic features for information related to products and/or services.
In illustrative examples, the language analysis
system 122 may generate linguistic structures for claims of patent documents.
In particular scenarios, the language
analysis system 122 may generate linguistic structures for elements of claims
of patent documents or features of claims
of patent documents. For example, the language analysis system 122 may
identify a verb that corresponds to an action of
an element of a claim of a patent document. The language analysis system 122
may also determine one or more nouns
related to the verb and, in some situations, one or more adjectives that
correspond to the one or more nouns. The language
analysis system 122 may then generate a linguistic structure that shows
relationships between the verb, the one or more
nouns, and/or the one or more adjectives. Additionally, the language analysis
system 122 may generate linguistic
structures that correspond to actions performed with respect to products
and/or services offered by an organization for
.. acquisition. In particular implementations, the linguistic structure may
include a tree structure with a single node as an
initial node or root node at the top of the tree structure and subsequent
nodes branching from the root node. The root node
may include a verb that corresponds to an action and the branch nodes may
correspond to nouns related to the verb,
adjectives related to the nouns, other words related to the verb and/or nouns,
or combinations thereof
[0051] Additionally, the intellectual-property mapping and learning
system 104 may include an intellectual-property
(IP) model development system 124 that determines relationships between
intellectual-property documents and products
and/or services. In various implementations, the IP knowledge model
development system 124 may identify intellectual-
property assets that correspond to respective products and/or services. For
example, the IP knowledge model development
system 124 may identify one or more patent claims, an element of a patent
claim, and/or a feature of a patent claim that
corresponds to at least a portion of a product and/or service. In additional
examples, the IP knowledge model development
system 124 may identify a trademark that corresponds to a product and/or a
service, at least a portion of a copyright that
corresponds to a product and/or service, or at least portion of a trade secret
that corresponds to a product and/or service.
[0052] The IP knowledge model development system 124 may determine that
an intellectual-property asset
corresponds to a product and/or service by comparing linguistic structures of
intellectual-property assets with linguistic
structures of products and/or services. In particular implementations, the IP
knowledge model development system 124
may generate a first linguistic structure for a feature of an intellectual-
property asset and a second linguistic structure for
a feature of a product and/or service. The IP knowledge model development
system 124 may compare the first linguistic
structure with the second linguistic structure to determine a similarity
metric between the first linguistic structure and the
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second linguistic structure. In scenarios where the similarity metric between
the first linguistic structure and the second
linguistic structure is at least a threshold similarity metric, the IP
knowledge model development system 124 may
determine that the feature of the intellectual-property asset corresponds to
the feature of the product and/or service.
[0053] The similarity metric may be based at least partly on words
included in the first linguistic structure and words
included in the second linguistic structure. The similarity metric may also be
based at least partly on relationships between
words included in the first linguistic structure and words included in the
second linguistic structure. In illustrative
implementations, the first linguistic structure may include a first tree
structure with a root node and a number of branch
nodes arranged in a first configuration and the second linguistic structure
may include a second tree structure with a root
node and an additional number of branch nodes. In these situations, the IP
knowledge model development system 124
may compare the first tree structure and the second tree structure to
determine the similarity metric between the first
linguistic structure and the second linguistic structure. To illustrate, the
IP knowledge model development system 124
may compare words included in the nodes of the first tree structure and words
included in the nodes of the second tree
structure to determine at least a portion of the similarity metric for the
first linguistic structure and the second linguistic
structure. Additionally, the IP knowledge model development system 124 may
compare the first configuration of the first
tree structure with the second configuration of the second tree structure to
determine at least a portion of the similarity
metric for the first linguistic structure and the second linguistic structure.
In various implementations, the IP knowledge
model development system 124 may compare the locations of words and/or
locations of nodes within the first tree
structure and the second tree structure to determine a similarity metric
between the first linguistic structure and the second
linguistic structure.
[0054] The IP knowledge model development system 124 may also determine
relationships between intellectual-
property assets and products and/or services using a classification system.
The classification system may include a number
of classifications with individual classifications having one or more criteria
to identify intellectual-property assets,
products, and/or services to include in the respective classifications. In
various implementations, the classifications of the
classification system may include a number of technology groups. The
classification system may be generated by the
intellectual-property mapping and learning system 104, in some examples. In
additional examples, the classification
system may be generated by another entity, such as a governmental entity, an
educational institution, a non-profit
organization, a for-profit organization, or combinations thereof In particular
implementations, the IP knowledge model
development system 124 may compare features of individual intellectual-
property assets with criteria of a number of
classifications included in the classification system and determine one or
more classifications to associate with the
intellectual-property assets. Additionally, the IP knowledge model development
system 124 may compare features of
products and/or services with criteria of a number of classifications of the
classification system and determine one or more
classifications to associate with the products and/or services.
[0055] In particular implementations, the IP knowledge model development
system 124 may determine intellectual-
property assets and products and/or services included in a same classification
of the classification system. The IP
knowledge model development system 124 may then determine one or more
relationships between intellectual-property
assets and products and/or services included in the same classification of the
classification system. In this way, the IP
knowledge model development system 124 may develop one or more models
indicating intellectual-property assets that
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correspond to products and/or services within a classification of the
classification system. In an illustrative example, the
IP knowledge model development system 124 may develop a model to determine
patent claims that correspond to display
features of mobile devices. In another illustrative example, the IP knowledge
model development system 124 may develop
a model to determine trademarks that correspond to fitness tracker devices. In
various implementations, the classification
system(s), the relationships between intellectual-property assets and products
and/or services, and the models used to
determine intellectual-property assets that may be related to particular
products and/or services may be stored by the
intellectual-property knowledge data store 120.
[0056] The relationships determined by the IP knowledge model development
system 124 between products and/or
services and intellectual-property assets within particular classifications
and the models developed by the IP knowledge
model development system 124 to determine intellectual-property assets that
correspond with products and/or services
within a classification of the classification system may be used to provide a
number of intellectual property customer
services 126. The intellectual-property services 126 may include IP strategy-
related services 128, IP exposure-related
services 130, and IP valuation services 132. In various implementations, the
intellectual property customer services 126
may be provided based on requests sent to the intellectual-property services
system 102 for information regarding one or
more intellectual-property assets or one or more products and/or services. The
intellectual-property services system 102
may then utilize the models, frameworks, and/or relationships between
intellectual-property assets and products and/or
services generated by the intellectual-property mapping and learning system
104 to respond to the requests. The requests
may be sent, in some situations, by individuals associated with an
intellectual-property services provider, while in
additional situations, the request may be sent by individuals associated with
one or more customers 110.
[0057] The intellectual property customer services 126 may include
intellectual property (IP) strategy-related
services 128. The IP strategy-related services 128 may include analysis of
groups of intellectual-property assets. In
examples, the IP strategy-related services 128 may include competitive
landscaping 150, IP benchmarking 152, IP scoring
& rating 154, an intelligence portfolio tool 156, an IP trend analyzer 158, IP
pruning and/or divestiture 160, executive
reporting 162, and/or strategic acquisition 164. In particular
implementations, the IP strategy-related services 128 may
.. include the analysis of a portfolio of intellectual-property assets of an
organization, such as the analysis of a portfolio of
intellectual-property assets of the customer 110. In illustrative examples,
the IP strategy-related services 128 may include
analyzing a portfolio of patent documents and/or analyzing a portfolio of
trademark documents. In various
implementations, the IP strategy-related services 128 may include analyzing a
portfolio of intellectual-property documents
of competitors of the customer 110, such as by using the competitive
landscaping 150. For example, the intellectual-
property services system 102 may determine technology classifications for
intellectual-property assets of a competitor of
the customer 110 and generate one or more documents or a report that provides
a landscape analysis showing the
intellectual-property documents of the competitor with respect to individual
technology classifications. In some instances,
the intellectual-property assets of the customer 110 may be mapped against the
intellectual-property assets of a competitor
of the customer 110 with regard to their respective technology
classifications.
[0058] In other examples, the IP strategy-related services 128 may include
determining scores and/or ratings of
intellectual-property assets, such as by the IP scoring and rating component
154. To illustrate, the intellectual-property
services system 102 may determine measures of breadth and/or measures of
coverage of intellectual-property assets of
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the customer 110 or intellectual-property assets of another organization. The
intellectual-property services system 102
may then rank the intellectual-property assets based on the measures of
breadth and/or measures of coverage. The IP
strategy-related services 128 may also include identifying technology areas in
which the customer 110 may want to
develop intellectual-property assets, such as by using the IP benchmarking
component 152. For example, the intellectual-
property services system 102 may determine technology classifications in which
the customer 110 has few or no
intellectual-property assets, but are related to technology areas that are
being developed by the customer 110. Additionally,
the intellectual-property services system 102 may identify future areas of
research and development for the customer 110,
such as by using the IP portfolio tool 156, based on a number of intellectual-
property assets of the customer and/or a
number of intellectual-property assets of one or more competitors of the
customer 110 in certain technology areas.
[0059] Further, the IP strategy-related services 128 may include
identifying intellectual-property assets of the
customer to offer for sale or license to other organizations. The intellectual-
property service system 102 may also generate
recommendations for intellectual-property assets of the customer 110 that may
be abandoned or no longer maintained,
such as by the IP pruning and/or divestiture component 160. In particular
implementations, the intellectual-property
services system 102 may determine at least one of measures of value, measures
of breadth, or measures of coverage for
at least a portion of the intellectual-property assets of the customer 110 and
utilize the respective measures to generate
recommendations, such as via the executive reporting component 162, regarding
at least one of sales opportunities,
licensing opportunities, or cost savings opportunities (e.g., abandonment) of
one or more intellectual-property assets of
the customer 110. The intellectual-property services system 102 may also
determine potential organizations and/or
intellectual-property assets that may be acquired, such as by the strategic
component 164, by the customer based on at
least one of the measures of value, measures of breadth, measures of coverage,
or the technology areas associated with
the organizations and/or the intellectual-property assets. In addition, the IP
strategy-related services 128 may include
determining metrics for intellectual-property documents of the customer 110,
such as by utilizing the IP trend analyzer
158. The metrics may indicate trends in at least one of the number of
intellectual-property assets of the customer 110
being filed or the number of intellectual-property assets of the customer 110
being granted.
[0060] The intellectual-property services system 102 may also be utilized
to provide IP risk-related services 130 to
the customer 110. The IP Exposed-Related Services 130 may include IP liability
166, collateral protection 168, theft of
trade secrets 170, IP litigation transfer 172, source code diligence 174,
and/or design-around consulting. The IP exposure-
related services 130 may be related to determining, utilizing the IP liability
component 166, measures of risk of loss
related to intellectual-property assets of the customer 110. The risk of loss
may correspond to at least one of a decrease in
value of an intellectual-property assets, invalidation of at least a portion
of an intellectual-property asset, or theft of an
intellectual-property asset. In various implementations, the IP exposure-
related services 130 may include determinations
of measures liability with respect to intellectual-property assets of the
customer 110. The intellectual-property services
system 102 may determine measures of liability of intellectual-property assets
based on at least one of a number of
litigation events of intellectual-property assets of the customer 110 or a
number of litigation events of intellectual-property
assets that are in a same technology classification as one or more
intellectual-property assets of the customer 110. A
litigation event may include filing of a request to initiate an action against
an intellectual-property asset. Actions against
intellectual-property assets may include at least one of opposition
proceedings, proceedings decided by an administrative
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body, or proceedings in a judicial jurisdiction. In particular
implementations, measures of liability with respect to
intellectual-property assets may correspond to a number of litigation events
related to intellectual-property assets of the
customer 110 or intellectual-property assets of another organization that have
taken place within a specified period of
time. In some instances, measures of liability with respect to intellectual-
property assets may be used to determine,
utilizing the collateral protection component 168, terms of insurance policies
issued to protect loans made with
intellectual-property assets as collateral.
[0061] The IP exposure-related services 130 may also include determining
measures to reduce risk of loss with
respect to intellectual-property assets. For example, the IP risk-related
services 130 may include determining, utilizing
the theft of trade secrets component 170, an amount of risk for the theft of
trade secrets of the customer 110. In particular
implementations, the intellectual-property services system 102 may analyze
security protocols or other security processes
implemented by the customer 110 to protect trade secrets and determine an
amount of risk of trade secret theft based at
least partly on the analysis. The IP exposure-related services 130 may also
include determining, utilizing the source code
diligence component 174, processes and/or procedures to safeguard source code
developed by the customer and actions
for the customer 110 and processes and/or procedures to take to protect the
intellectual property rights related to the source
code. Additionally, the IP exposure-related services 130 may include
determining, utilizing the design-around consulting
component 176, options for the customer 110 to design around intellectual-
property assets of competitors and/or options
for competitors of the customer 110 to design around intellectual-property
assets of the customer 110. In particular
implementations, the intellectual-property services system 102 may analyze a
number of intellectual-property assets and
determine features of the intellectual-property assets that correspond to
features of products and/or services. The
intellectual-property services system 102 can then identify features of the
products and/or services that can be modified
to avoid the features of the intellectual-property assets related to the
products and/or services.
[0062] Further, the IP exposure-related services 130 may include
determining, utilizing the IP litigation transfer
component 172, strategy in intellectual property litigation actions. To
illustrate, the intellectual-property services system
102 may analyze a series of events that has taken place with respect to a
pending litigation action in relation to the events
that took place in previous litigation actions to determine recommendations
for future decisions in the pending litigation.
In illustrative examples, the intellectual-property services system 102 may
determine that motions to file in a pending
litigation to increase the probability of a favorable outcome for the
customer. The intellectual-property services system
102 may also determine recommendations for settlement negotiations, such as
amounts to offer in relation to settlement
negotiations and/or timing of settlement offers. In addition, the intellectual-
property services system 102 may generate
recommendations for litigation counsel to retain in a particular litigation
action and/or generate recommendations
regarding modifications to the litigation counsel being retained.
[0063] In various implementations, the intellectual property customer
services 126 provided via the intellectual-
property services system 102 may include IP valuation services 132. The IP
valuation services 132 may include IP stack
valuation 178, M&A sell-side and buy-side services 180, asset-backed lending
182, and/or value articulation 184. The IP
valuation services 132 may include determining, utilizing the IP stack
valuation 178, measures of value of intellectual-
property assets. In particular implementations, the intellectual-property
services system 102 may determine measures of
value of intellectual-property assets for the customer or determine measures
of value of intellectual-property assets of
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another organization. In some examples, the intellectual-property services
system 102 may determine measures of value
of intellectual-property assets that may be purchased or licensed by the
customer 110. The intellectual-property services
system 102 may also determine, utilizing the M&A sell-side and buy-side
services 180, measures of value of intellectual-
property assets of an organization that may be purchased or otherwise acquired
by the customer 110. In additional
implementations, the intellectual-property services system 102 may determine
measures of value of intellectual-property
assets of the customer 110 in conjunction with an acquisition of the customer
110 by another organization or merger of
the customer 110 with another organization. Further, the intellectual-property
services system 102 may determine,
utilizing the asset-backed lending services 182, measures of value of
intellectual-property assets of the customer 110 in
relation to one or more loans made to the customer 110 with the intellectual-
property assets of the customer 110 being
used as collateral for at least a portion of the loan amount.
[0064] The intellectual-property services system 102 may determine,
utilizing the value articulation services 184,
measures of value of intellectual-property assets based on measures of breadth
of the intellectual-property assets.
Additionally, the intellectual-property services system 102 may determine
measures of value of intellectual-property
assets based on revenue of products and/or services that correspond to the
intellectual-property assets. In order to
determine the measures of breadth and/or portions of revenue of the products
and/or services corresponding to the
intellectual-property assets, the intellectual-property services system 102
may utilize one or more linguistic analysis
techniques and one or more machine learning techniques.
[0065] FIG. 2 illustrates an example environment 200 to analyze a number
of types of intellectual-property data and
product/service data to provide services related to intellectual property
according to some implementations. The
environment 200 may include the intellectual-property mapping and learning
system 104, the one or more data sources
106, and the intellectual-property knowledge data store 120. The intellectual-
property mapping and learning system 104
may be implemented by one or more computing devices 202. The one or more
computing devices 202 can be included in
a cloud computing architecture that operates the one or more computing devices
202 on behalf of an intellectual-property
services provider. In these scenarios, the cloud computing architecture may
implement one or more virtual machine
instances on behalf of the intellectual-property services provider on the one
or more computing devices 202. The cloud
computing architecture may be located remotely from the intellectual-property
services provider. In additional
implementations, the one or more computing devices 202 can be under the direct
control of the intellectual-property
services provider. For example, the intellectual-property services provider
may maintain the one or more computing
devices 202 in one or more geographic locations to perform operations related
to analyzing intellectual-property data and
data related to products and/or services.
[0066] The intellectual-property knowledge data store 120 may store
information that may be utilized by the
intellectual-property mapping and learning system 104 in providing services
related to intellectual-property assets. In
particular implementations, the intellectual-property knowledge data store 120
may store intellectual-property (IP) data
204. The IP data 204 may include data related to intellectual-property assets.
The IP data 204 may be obtained via one or
more publicly accessible data sources, one or more private data sources, or
combinations thereof. The IP data 204 may
also include customer IP data 206 that corresponds to data stored by the
intellectual-property knowledge data store 120
that is related to customers obtaining services from the intellectual-property
services provider. In some implementations,
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the customer IP data 216 may be stored separately from IP data of other
organizations in the intellectual-property
knowledge data store 120.
[0067] In various implementations, the IP data 204 may include data
related to intellectual-property assets, such as
trademarks, copyrights, patents, and trade secrets. The IP data 204 may
include documents that include information related
to various types of intellectual property. For example, the IP data 204 may
include patent applications, published patent
applications, and issued or granted patents. The IP data 204 may also include
trademark applications and submissions
made in conjunction with the protection of copyrights. Additionally, the IP
data 204 may include documents that include
trade secrets and documents that support the protection of trade secrets. To
illustrate, the IP data 204 may include
employment agreements, employee manuals, policies, and/or procedures of
organizations that may be used to support the
trade secret status of innovation of the organizations.
[0068] The IP data 204 may also include bibliographic information for
intellectual-property documents. In
illustrative examples, the IP data 204 may include information particular
dates related to intellectual-property documents
(e.g., filing dates, issue dates, priority dates), assignees of intellectual-
property documents, assignment history of
intellectual-property documents, significant individuals related to the
intellectual-property documents (e.g., inventors,
examiners, etc.), third-party classifications related to intellectual-property
documents, indications of priority documents
for certain intellectual-property documents, status of an intellectual-
property document with an intellectual property
jurisdiction or examining organization, combinations thereof, and the like. In
addition, the IP data 204 may include
information related to prosecution history of intellectual-property documents.
The prosecution history may include various
events that took place with respect to the examination of intellectual-
property documents. To illustrate, the IP data 204
may include dates that documents were filed during examination of intellectual-
property documents, such as dates when
responses were filed, dates that examiners issued office actions or
examination reports, dates of allowance, dates of
issuance, combinations thereof, and so forth. Further, the IP data 204 may
include documents that were filed and/or
submitted during prosecution of intellectual-property documents. In
illustrative examples, the IP data 204 may include
office actions, office action responses, information disclosure statements,
application data sheets, declarations, specimens
to support use of trademarks, appeal briefs, examiner answers to appeal
briefs, reply briefs, decisions on appeal, notices
of allowances, opposition documents, copyright submissions, interview summary
documents, combinations thereof, and
the like.
[0069] The IP data 204 may also include statistics and/or metrics related
to individual examiners that examine
intellectual-property assets. To illustrate, the IP data 204 may include
number of intellectual-property assets allowed over
a period of time, average number of office actions provided during examination
of intellectual-property assets, number of
appeals over a period of time, decisions on appeal, average length of time to
provide office actions, years of experience,
number of intellectual-property assets examined over a period of time,
combinations thereof, and so forth. Further, the IP
data 204 may include statistics and/or metrics related to groups of examiners
that examiner intellectual-property assets.
The IP data 204 may also include statistics and/or metrics of individual
examiners with respect to the statistics and/or
metrics of a group of examiners. For example, the IP data 204 may include
number of office actions provided per allowed
matter for an individual patent examiner with respect to an average number of
office actions provided per allowed matter
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for a group of patent examiners that includes the individual patent examiner,
such as a group of patent examiners in a
particular art unit or a particular technology classification.
[0070] In various implementations, the IP data 204 may include data
related to litigation proceedings and/or pseudo-
litigation proceedings associated with intellectual-property assets. In
certain implementations, the IP data 204 may include
documents filed during litigation proceedings, such as petitions, answers,
pleadings, motions, discovery requests,
discovery responses, expert opinions, decisions by a court, jury verdicts,
jury charges, combinations thereof, and the like.
In additional implementations, the IP data 204 may include transcripts of
litigations proceedings. For example, the IP data
204 may include transcripts of court proceedings and/or transcripts of
depositions. In further implementations, the IP data
204 may include documents filed during pseudo-litigation proceedings, such as
inter partes review proceedings in the
United States Patent and Trademark Office or opposition proceedings in the
European Patent Office.
[0071] The intellectual-property knowledge data store 120 may also store
IP valuation data 208. The IP valuation
data 208 may be used by the intellectual-property mapping and learning system
104 to determine the value of intellectual-
property assets or portions of intellectual-property assets. In particular
implementations, the IP valuation data 208 may
include values reached during settlement negotiations that took place during
litigation proceedings or pseudo-litigation
.. proceedings. Additionally, the IP valuation data 208 may include terms of
licenses obtained with respect to intellectual-
property assets or portions of intellectual-property assets. The IP valuation
data 208 may also include verdicts provided
by judges, juries, other judicial bodies, or administrative bodies that
indicate value of intellectual-property assets or
portions of intellectual-property assets. In various implementations, at least
a portion of the IP valuation data 208 may
include information related to customers of the intellectual-property services
provider that is not publicly available. In
additional implementations, the IP valuation data 208 may include information
that may be used to determine the value
of intellectual-property assets or portions of intellectual-property assets
that is publicly available.
[0072] In addition, the intellectual-property knowledge data store 120
may store business data 210. The business
data 210 may include product/service data 212 and economic data 214. The
product/service data 212 may include data
associated with products and/or services that are offered for acquisition by
various organizations. The product/service
data 212 may include descriptions of products and/or services, specifications
of products and/or services, product manuals,
pricing of products and/or services, number of sales of products and/or
services, descriptions of organizations that provide
various products and/or services, combinations thereof, and the like. The
product/service data 212 may include customer
product/service data 216 that includes information that is related to products
and/or services offered by customers of the
intellectual-property services provider. In some implementations, the customer
product/service data 216 may be stored
.. separately from product/service data of other organizations in the
intellectual-property knowledge data store 120.
[0073] The economic data 214 may include information indicating financial
performance of organizations offering
products and/or services for acquisition. The financial performance
information may include revenue of organizations
over a period of time, profit of organizations over a period of time, expenses
of organizations over a period of time,
projections of financial performance, or combinations thereof. The economic
data 214 may also include amount of revenue
of organizations that corresponds to sales of one or more products and/or
services. The economic data 214 may include
customer economic data 218 that includes economic data that corresponds to
customers of the intellectual-property
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services provider. In some implementations, the customer economic data 218 may
be stored separately from economic
data of other organizations in the intellectual-property knowledge data store
120.
[0074] Additionally, the economic data 204 may also include industry
financial data. For example, the economic
data 204 may include revenue, profit, expenses, and the like for certain
industries that provide goods and/or services for
acquisition, such as a retail industry, a semiconductor industry, or
transportation industry. Further, the economic data 204
may include economic data of various states, counties, countries, or other
political jurisdictions. To illustrate, the
economic data 204 may include gross domestic product data, employment data,
trade data, combinations thereof, and so
forth. In some instances, the economic data 204 may indicate an amount of
gross domestic product of a country or political
jurisdiction attributed to one or more industry segments.
[0075] Further, the intellectual-property knowledge data store 120 may
store at least one technology taxonomy 220.
The technology taxonomy 220 may include a number of classifications for
products and/or services. The technology
taxonomy 220 may also include one or more criteria associated with individual
classifications of the technology taxonomy
220. For example, to be classified according to a particular classification of
the technology taxonomy 220, a product
and/or service may correspond to at least a threshold number of criteria of a
particular classification. In various
implementations, the technology taxonomy 220 may indicate products and/or
services that are associated with individual
classifications. That is, products and/or services that have previously been
assigned to a classification may be included in
the technology taxonomy 220.
[0076] The technology taxonomy 220 may be generated by the intellectual-
property mapping and learning system
104, in some implementations. In addition, in particular implementations, at
least a portion of the technology taxonomy
220 may be generated by an additional organization. To illustrate, the
technology taxonomy 220 may include
classifications that are included in classification systems of governmental
organizations and/or classification systems of
industry organizations. In illustrative examples, at least a portion of the
classifications of the technology taxonomy 220
may correspond to technology classifications of the United States Patent and
Trademark Office. In other illustrative
examples, at least a portion of the classifications included in the technology
taxonomy 220 may correspond to technology
classifications included in the International Patent Classification (IPC), the
Locarno Classification, the Nice Classification,
and/or the Vienna Classification.
[0077] In various implementations, the intellectual-property knowledge
data store 120 may store intellectual property
(IP) to products and/or services mappings 222. The IP to products and/or
services mappings 222 may indicate intellectual-
property assets or portions of intellectual-property assets that have been
mapped to a product and/or service. In an
illustrative example, the IP to products and/or services mappings 222 may
indicate a claim of a patent document that
corresponds to a feature of a mobile device, such as a microphone of the
mobile device. In another illustrative example,
the IP to products and/or services mappings 222 may indicate a trademark that
corresponds to a remote data storage
service. The IP to products/services mappings 222 may also indicate an
organization that offers the respective products
and/or services for acquisition. Additionally, the IP to products and/or
services mappings 222 may indicate the owners of
the intellectual-property assets mapped to particular products and/or
services.
[0078] The IP to products and/or services mappings 222 may include
customer mappings 224 that indicate mappings
between products and/or services of customers of an intellectual-property
services provider and intellectual-property
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assets of customers of the intellectual-property services provider. In
additional implementations, the customer mappings
224 may include mappings between intellectual-property assets of customers of
the intellectual-property services provider
and products and/or services offered by organizations that are not customers
of the intellectual-property services provider.
Further, the customer mappings may include mappings between products and/or
services offered by customers of the
intellectual-property services provider and intellectual-property assets of
organizations that are not customers of the
intellectual-property services provider.
[0079] The intellectual-property knowledge data store 120 may also store
previous customer service data 226. The
previous customer service data 226 may include data that was generated by an
intellectual-property services provider
when providing services to one or more customers. For example, the previous
customer service data 226 may include data
generated by the intellectual-property services provider in providing the IP
strategy-related services 128, the IP exposure-
related services 130, and/or the IP valuation services 132 described in
relation to FIG. 1. In illustrative examples, the
previous customer service data 226 may include valuations of intellectual-
property assets that were determined by the
intellectual-property services provider. In additional illustrative examples,
the previous customer service data 226 may
include determinations of risk with respect to intellectual-property assets of
customers of the intellectual-property services
provider. In further illustrative examples, the previous customer service data
226 may include claim charts, strategic IP
analyses, and/or portfolio analysis data generated by the intellectual-
property services provider when providing services
to customers. In certain implementations, the previous customer service data
226 may be utilized to provide subsequent
services to customers of the intellectual-property services provider. In this
way, the knowledge generated by the
intellectual-property services provider may increase and be used to more
efficiently and accurately provide services to
customers of the intellectual-property services provider.
[0080] FIG. 3 illustrates an example environment 300 to generate mappings
between products and intellectual-
property assets using a technology taxonomy according to some implementations.
The environment 300 may include the
intellectual-property mapping and learning system 104 that is implemented via
one or more computing devices 202. The
environment 300 may also include the customer 110 of the intellectual-property
services provider and a group of
intellectual-property assets 302 of the customer 110. The group of
intellectual-property assets 302 may include a first IP
asset 304, a second IP asset 306, a third IP asset 308, a fourth IP asset 310,
a fifth IP asset 312, up to an Nth IP asset 314.
The IP assets 304, 306, 308, 310, 312, 314 may include various types of
intellectual property. For example, the IP assets
304, 306, 308, 310, 312, 314 may include trademarks, patents, trade secrets,
copyrights, know-how, or other classifications
of intellectual property. In additional examples, at least a portion of the IP
assets 304, 306, 308, 310, 312, 314 may
correspond to a portion of an intellectual-property asset, such as one or more
claims of a set of claims of a patent document.
[0081] In various implementations, one or more of the IP assets 304, 306,
308, 310, 312, 314 may correspond to a
different classification of intellectual property than at least another one of
the IP assets 304, 306, 308, 310, 312, 314. For
example, the first IP asset 304 may correspond to a trademark and the second
IP asset 306 may correspond to a trade
secret. Additionally, in some implementations, each of the IP assets 304, 306,
308, 310, 312, 314 may correspond to a
same type of classification of intellectual property. To illustrate, the IP
assets 304, 306, 308, 310, 312, 314 may each
correspond to patents or patent applications, such as at least a portion of a
patent portfolio of the customer 110. In another
illustrative example, the IP assets 304, 306, 308, 310, 312, 314 may each
correspond to claims of a patent or patent
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application. In an additional illustrative example, the IP assets 304, 306,
308, 310, 312, 314 may each correspond to trade
secrets. In a further illustrative example, the IP assets 304, 306, 308, 310,
312, 314 may each correspond to trademarks.
In other illustrative examples, the IP assets 304, 306, 308, 310, 312, 314 may
each correspond to copyrights.
[0082] Additionally, the environment 300 may include the technology
taxonomy 220 of FIG. 2. The technology
taxonomy 220 may include a number of classifications, such as a first
classification 316, a second classification 318, a
third classification 320, up to an Nth classification 322. The individual
classifications 316, 318, 320,322 of the technology
taxonomy 220 may be related to individual sets of criteria that characterize
items associated with a particular classification
of the technology taxonomy 220. At least one of intellectual-property assets,
products, or services may be classified
according to at least one classification of the technology taxonomy 220. In
illustrative implementations, the intellectual-
property mapping and learning system 104 may determine features of the first
intellectual-property asset 304 and compare
the features of the first intellectual-property asset 304 to the set of
criteria of the first classification 316. In particular
implementations, the intellectual-property mapping and learning system 104 may
determine a metric indicating an amount
of similarity between the features of the first intellectual-property asset
304 and the set of criteria of the first classification
316.
[0083] In various implementations, the amount of similarity between the
features of the first IP asset 304 and the set
of criteria of the first classification 316 may indicate a number of features
of the first IP asset 304 that correspond to one
or more criteria of the first classification 316. In illustrative
implementations, the intellectual-property mapping and
learning system 304 may determine the amount of similarity between a feature
of the first IP asset 304 and the first
classification 316 by comparing words of one or more features of the first IP
asset 304 with words of the first classification
316. The intellectual-property mapping and learning system 304 may determine
that a feature of the first IP asset 304
corresponds to the first classification 316 based on at least a threshold
number of words of a feature of the first IP asset
304 corresponding to words of the first classification 316. In some scenarios,
the intellectual-property mapping and
learning system 304 may determine that a word of a feature of the first IP
asset 304 corresponds to a word of the first
classification when a spelling of the word of the first IP asset 304 is the
same as a word of the first classification 316. In
additional situations, the intellectual-property mapping and learning system
304 may determine that a word of a feature
of the first IP asset 304 corresponds to a word of the first classification
316 based on the word of the first IP asset 304
being a synonym of the word of the first classification 316. In further
examples, the intellectual-property mapping and
learning system 104 may determine that a word of a feature of the first IP
asset 304 corresponds to a word of the first
classification 316 based on the word of the first IP asset 304 being a
derivative of the word of the first classification 316.
.. For example, the word of the first IP asset 304 may be a different tense of
the word of the first classification 306. In other
examples, the word of the first IP asset 304 may be a plural or singular
version of the word of the first classification 316.
[0084] The intellectual-property mapping and learning system 104 may
determine a first amount of similarity
between a feature of the first IP asset 304 and the first classification 316
based on determining that a single feature of the
first IP asset 304 corresponds to a single criteria of the first
classification 316. Additionally, the intellectual-property
mapping and learning system 104 may determine a second amount of similarity
between the first IP asset 304 and the first
classification 316 based on determining that two features of the first
intellectual-property asset 304 correspond to at least
one criteria of the first classification 316. In certain implementations, the
intellectual-property mapping and learning
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system 104 may determine that the first IP asset 304 corresponds to the first
classification 316 based on an amount of
similarity between a feature of the first IP asset 304 and the criteria of the
first classification 316 being above a threshold
amount of similarity. In additional implementations, the intellectual-property
mapping and learning system 104 may
determine that the first IP asset 304 corresponds to the first classification
based on an amount of similarity between a
feature of the first IP asset 304 and the first classification 316 is greater
than amounts of similarity between the feature of
the first IP asset 304 and respective sets of criteria of the additional
classifications of the technology taxonomy 220, such
as the sets of criteria of the second classification 318, the third
classification 320, up to the Nth classification 322.
[0085] The intellectual-property mapping and learning system 104 may also
determine classifications of the
technology taxonomy 220 for a number of products and/or services, such as a
first product 324, a second product 326,
and a third product 328. The intellectual-property mapping and learning system
104 may determine classifications of the
technology taxonomy 220 for the products 324, 326, 328 themselves. In
additional implementations, the intellectual-
property mapping and learning system 104 may determine classifications of the
technology taxonomy 220 that correspond
to one or more features of the products 324, 326, 328. In an illustrative
example, the intellectual-property mapping and
learning system 104 may determine that the first product 324 corresponds to a
transportation classification of the
technology taxonomy 220, the second product 326 corresponds to a mobile
communication device classification of the
technology taxonomy 220, and the third product 328 corresponds to a printing
device classification of the technology
taxonomy 220. In additional illustrative examples, the intellectual-property
mapping and learning system 104 may
determine a classification for a feature common to the products 324, 326, 328,
such as a display device included in the
first product 324, a display device of the second product 326, and a display
device of the third product 328. The
intellectual-property mapping and learning system 104 may also determine
classifications of additional individual features
of the products 324, 326, 328 with respect to the technology taxonomy 220.
[0086] In particular implementations, the intellectual-property mapping
and learning system 104 may determine
classifications of the technology taxonomy 220 for the products 324, 326, 328
and/or features of the products 324, 326,
328 based at least partly on words describing the products 324, 326, 328
and/or words describing the features of the
products 324, 326, 328 in relation to the sets of criteria of the
classifications of the technology taxonomy 220, such as the
respective sets of criteria of the classifications 316, 318, 320, 322. For
example, the intellectual-property mapping and
learning system 104 may determine an amount of similarity between descriptions
of the products 324, 326, 328 and/or
features of the products 324, 326, 328 and the criteria of the classifications
of the technology taxonomy 220.
[0087] In illustrative implementations, the amount of similarity between
a feature of the first product 324 and the set
of criteria of the first classification 316 may indicate a number of words
describing the feature of the first product 324
that correspond to one or more criteria of the first classification 316. That
is, the intellectual-property mapping and
learning system 104 may compare one or more words describing the feature of
the first product 324 with words related to
the first classification 316 and determine a number of words of the
description of the feature of the first product 324 that
correspond to words of one or more criteria of the first classification 316.
The intellectual-property mapping and learning
system 304 may determine that a word describing a feature of the first product
324 corresponds to a word associated with
the first classification 316 when a spelling of the word of the feature of the
first product 324 is the same as a word
associated with the first classification 316. In additional situations, the
intellectual-property mapping and learning system
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104 may determine that a word describing a feature of the first product 324
corresponds to a word associated with the first
classification 316 based on the word describing the feature of the first
product 324 being a synonym of the word associated
with the first classification 316. In further examples, the intellectual-
property mapping and learning system 104 may
determine that a word describing a feature of the first product 324
corresponds to a word associated with the first
.. classification 316 based on the word describing the feature of the first
product 324 being a derivative of the word
associated with the first classification 316. For example, the word describing
the feature of the first product 324 may be
a different tense of the word associated with the first classification 306. In
other examples, the word describing the feature
of the first product 324 may be a plural or singular version of the word
associated with the first classification 316.
[0088] The intellectual-property mapping and learning system 304 may
determine that a feature of the first product
324 corresponds to the first classification 316 based on at least a threshold
number of words describing the feature of the
first product 324 corresponding to words associated with the first
classification 316. In some scenarios, the intellectual-
property mapping and learning system 104 may determine an amount of similarity
between words describing a feature of
the first product 324 and words associated with the first classification 316
to determine whether or not the features of the
first product 324 is to be classified according to the first classification
316. In particular implementations, the intellectual-
property mapping and learning system 104 may determine that a feature of the
first product 324 corresponds to the first
classification 316 based on an amount of similarity between words describing
the feature of the first product 324 and
words of the first classification 316 being above a threshold amount of
similarity. In additional implementations, the
intellectual-property mapping and learning system 104 may determine that a
feature of the first product 324 corresponds
to the first classification 316 based on an amount of similarity between words
describing a feature of the first product 324
.. and words of the first classification 316 being greater than amounts of
similarity between the words describing the feature
of the first product 324 and words associated with the respective criteria of
the additional classifications of the technology
taxonomy 220, such as words associated with the sets of criteria of the second
classification 318, the third classification
320, up to the Nth classification 322.
[0089] The intellectual-property mapping and learning system 104 may also
determine mappings 330 between
products and/or services and the group of intellectual-property assets 302. In
particular implementations, the intellectual-
property mapping and learning system 104 may utilize the technology taxonomy
220 to determine features of the group
of IP assets 302 that correspond to features of one or more products and/or
services. In various implementations, the
intellectual-property mapping and learning system 104 may determine mappings
between features of an intellectual-
property asset included in the group of intellectual-property assets 302 and
features of a product and/or service that are
.. classified according to a same classification of the technology taxonomy
220. The mappings 330 may indicate that an
intellectual-property asset may cover a feature of a product. In illustrative
implementations, the mappings 330 may
indicate that an intellectual-property asset may be asserted in a judicial
proceeding and/or an administrative proceeding
against the corresponding product.
[0090] The illustrative example of FIG. 3 includes a first mapping 332
between the first product 324 and a group of
intellectual-property assets that includes the first IP asset 304 and the
third IP asset 308. The mappings 330 may also
include a second mapping 334 between the second product 326 and another group
of intellectual-property assets that
includes the first IP asset 304, the second IP asset 306, and the fourth IP
asset 310. In addition, the mappings 330 may
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include a third mapping 336 between the third product 328 and an additional
group of intellectual-property assets including
the first IP asset 304 and the fifth IP asset 312.
[0091] The intellectual-property mapping and learning system 104 may
determine the mappings 332, 334, 336 by
determining similarities between the products 324, 326, 328 and/or features of
the products 324, 326, 328 and the IP assets
304, 306, 308, 310, 312, 314 and/or features of the IP assets 304, 306, 308,
310, 312, 314. In particular implementations,
the intellectual-property mapping and learning system 104 may determine
mappings between features of intellectual-
property assets 304, 306, 308, 310-, 312, 314 and features of products 324,
326, 328 that are classified according to a
same classification of the technology taxonomy 220. In various
implementations, the intellectual-property mapping and
learning system 104 may determine the mapping between a feature of the first
IP asset 304 and a feature of the first
product 324 by determining an amount of similarity between words of the
feature of the first IP asset 304 and words
describing the feature of the first product 324.
[0092] In an illustrative example, the intellectual-property mapping and
learning system 104 may determine an
amount of similarity between an element of a claim related to the first IP
asset 304 and a feature of the first product 324.
In another illustrative example, the intellectual-property mapping and
learning system 104 may determine an amount of
similarity between a trademark related to the first IP asset 304 and a word or
group of words used in the marketing and
branding of the first product 324. The intellectual-property mapping and
learning system 104 may determine the amount
of similarity between the first words of the feature of the first IP asset 304
and second words of a feature of the first
product 324 by performing a comparison between the first words and the second
words. The amount of similarity between
the first words and the second words may be based on a number of words that
are the same between the first words and
the second words, a number of words that are synonyms between the first words
and the second words, and/or a number
of words that are derivatives between the first words and the second words.
[0093] In additional implementations, the intellectual-property mapping
and learning system 104 may determine
mappings between intellectual-property assets and products and/or services
based at least partly on linguistic structures
generated for the intellectual-property assets and linguistic structures
generated for the products and/or services. The
linguistic structures may indicate relationships between words of the
intellectual-property assets and words describing the
products. In various implementations, the intellectual-property mapping and
learning system 104 may generate linguistic
structures for features of the intellectual-property assets and generate
linguistic structures for features of the products and
compare the linguistic structures of the features of the intellectual-property
assets and the features of the products.
[0094] In particular implementations, the intellectual-property mapping
and learning system 104 may determine an
amount of similarity between a linguistic structure of a feature of an
intellectual-property asset and a linguistic structure
of a feature of a product. In certain implementations, the intellectual-
property mapping and learning system 104 may
compare words included in the linguistic structure of the feature of the
intellectual-property asset, such as a feature of the
first intellectual-property asset 304, with words included in the linguistic
structure of a feature of a product, such as a
feature of the first product 324. Additionally, the intellectual-property
mapping and learning system 104 may compare a
configuration of the linguistic structure of the feature of the first
intellectual-property asset 104 with a configuration of
the linguistic structure of the feature of the first product 324. The
configuration of the linguistic structure of the feature of
the first intellectual-property asset 304 may indicate first relationships
between words related to the feature of the first
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intellectual-property asset 304 and the configuration of the linguistic
structure of the feature of the first product 324 may
indicate relationships between words describing the feature of the first
product 324. The intellectual-property mapping
and learning system 104 may generate a mapping between a feature of an
intellectual-property asset and a feature of a
product based at least partly on an amount of similarity between a linguistic
structure of the feature of the intellectual-
.. property asset and a linguistic structure of a feature of the product being
greater than a threshold amount of similarity.
[0095] FIG. 4 illustrates an example system 400 to generate valuations
for intellectual-property assets according to
some implementations. The system 400 may include the intellectual-property
mapping and learning system 104 and one
or more computing devices 202 that may implement the intellectual-property
mapping and learning system 104. The
system 400 may also include a first data store that stores intellectual-
property (IP) valuation data 402 and a second data
.. store that stores business data 404. The IP valuation data 402 and the
business data 404 may include information
corresponding to customers of an intellectual-property service provider. The
IP valuation data 402 and the business data
404 may also include information corresponding to organizations that are not
customers of the intellectual-property
services provider.
[0096] The IP valuation data 402 may include information that may be used
to determine values of intellectual-
property assets. In particular implementations, the IP valuation data 402 may
include verdicts indicating damages awarded
during judicial proceedings related to intellectual-property assets. The IP
valuation data 402 may also include amounts
for licensing intellectual-property assets. In addition, the IP valuation data
402 may include amounts paid as part of
settlements related to judicial proceedings and/or administrative proceedings
that took place with regard to intellectual-
property assets. The business data 404 may include information indicating
revenue obtained by organizations with respect
to products and/or services offered by the organizations. The business data
404 may also include other financial
information related to organizations, such as overall revenue over a period of
time, amount of revenue within a particular
technology area over a period of time, profit obtained over a period of time,
expenditures over a period of time,
combinations thereof, and so forth. In various implementations, the
expenditures included in the business data 404 may
indicate expenditures of an organization to offer one or more products and/or
services for acquisition to consumers.
[0097] The intellectual-property mapping and learning system 104 may
utilize at least one of the IP valuation data
402 or the business data 404 to determine valuations for one or more
intellectual-property assets. In an illustrative
example, the intellectual-property mapping and learning system 104 may
determine valuations for intellectual-property
assets that corresponds to the second product 326 of FIG. 3. In particular,
the intellectual-property mapping and learning
system 104 may determine valuations for intellectual-property assets that are
mapped to features of the second product
326, such as the first IP asset 304, the second IP asset 306, and the fourth
IP asset 310. For example, the intellectual-
property mapping and learning system 104 may determine a first valuation 406
for the first IP asset 304, a second valuation
408 for the second IP asset 306, and a third valuation 410 for the fourth IP
asset 310. The valuations 406, 408, 410 may
indicate a monetary value that the organization(s) that own the rights to the
respective IP assets 304, 306, 310 may obtain
from one or more additional organizations in exchange for rights to the IP
assets 304, 306, 310. In various
implementations, the valuations 406, 408, 410 may indicate one or more
monetary values that the organization(s) that
own the rights to the respective IP assets 304, 306, 310 may obtain in one or
more licensing transactions that involve the
IP assets 304, 306, 310. In additional implementations, the valuations 406,
408, 410 may indicate one or more monetary
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values that the organization(s) that own the rights to the respective IP
assets 304, 306, 310 may obtain with respect to a
sale of the IP assets 304, 306, 310. Further, the valuations 406, 408, 410 may
indicate one or more monetary values of the
respective IP assets 304, 306, 310 during a merger or an acquisition of an
organization that owns the rights to the IP assets
304, 306, 310 with respect to an additional organization. In still other
implementations, the valuations 406, 408, 410 may
indicate one or more monetary values of the respective IP assets 304, 306, 310
as collateral for a loan to an organization
that owns the rights to the IP assets 304, 306, 310.
[0098] The intellectual-property mapping and learning system 104 may
determine valuations of intellectual-property
assets by determining an amount of revenue of a product and/or service to
attribute to the intellectual-property assets. In
particular implementations, the intellectual-property mapping and learning
system 104 may determine an amount of
revenue of a product and/or service to attribute to an intellectual-property
asset based at least partly on a breadth of the
intellectual-property asset covering the product and/or service with respect
to other intellectual-property assets included
in a same classification of a framework of classifications, such as the
technology taxonomy 220 of FIG. 2. In additional
implementations, the intellectual-property mapping and learning system 104 may
determine an amount of revenue of a
product and/or service to attribute to an intellectual-property asset based at
least partly on a breadth of an intellectual-
property asset covering the product and/or service with respect to other
intellectual-property assets covering the product
and/or service.
[0099] Breadth of an intellectual-property asset may be determined based
on word count of the intellectual-property
asset and/or commonality of words of the intellectual-property asset. In
particular implementations, the number of unique
words and the frequency with which those words appear in other intellectual-
property assets may be utilized to determine
a breadth value for a given intellectual-property asset. For example, for a
given intellectual-property asset, the word count
of the intellectual-property asset is compared to the word count of other
intellectual-property assets, such as a number of
additional intellectual-property assets included in a same classification as
the given intellectual-property asset or a number
of additional intellectual-property assets covering a same product and/or
service as the given intellectual-property asset.
Additionally, a commonness score may be determined for a given intellectual-
property asset based on the commonality
of words in the intellectual-property asset as compared to the commonality of
words in other intellectual-property assets.
[00100] In situations where a given intellectual-property asset is a
patent claim, the breadth value of the claim may
represent an estimated scope of an intellectual property right relative to
other patent claims, such as other patent claims
that cover a same product and/or service as the given patent claim or other
patent claims that are classified according to a
same classification as the given patent claim. In particular implementations,
the breadth value of a patent claim may be
based at least partly on a type of preamble included in the patent claim. For
example, a patent claim including a preamble
having a closed transition phrase may have a breadth value that is less than a
patent claim including a preamble having an
open transition phrase. Additionally, patent claims that include certain
words, such as an absolute word, exemplary word,
or relative word, may have lower breadth values than patent claims that do not
include these types of words.
[00101] Word count may include the number of words of an intellectual-
property asset or a portion of an intellectual-
property asset. In various implementations, a word count may be determined
after duplicate words are removed from an
initial list of words included in the intellectual-property asset. In this
way, the word count may be a count of unique words
of an intellectual-property asset. Additionally, a word count may include a
number of words of the intellectual-property
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asset after the removal of stop words. Stop words may include the most common
words in a language. To illustrate, stop
words may include short function words such as "the" "is," "at," "which," and
"on," as well as others. The intellectual-
property mapping and learning system 104 may have access to one or more lists
of stop words for one or more languages.
Further, a word count may be determined before or after converting acronyms
and abbreviations into their full word
representations. The word count may also include or exclude words in the
preamble. In some implementations, a number
of different word counts may be used to determine breadth of an intellectual-
property asset, such as a first word count that
includes a number of unique words and a second word count that excludes stop
words.
[0102] Commonality of words may correspond to the frequency that a given
word is found within a corpus of
documents or within a group of intellectual-property assets. Words that have a
higher commonality, that is words that are
more common words within a corpus of words, may correspond to greater breadth
while the presence of infrequently used
words within a corpus of words may indicate reduced breadth. In the context of
patent claims, words that are often found
in the technical field are generally considered broader, or less limiting,
than uncommon words.
[0103] In illustrative implementations, the intellectual-property mapping
and learning system 104 may determine a
breadth value of the first intellectual-property asset 304 relative to breadth
values of other intellectual-property assets
included in a same classification of the technology taxonomy 220 as the first
intellectual-property asset 304. The
intellectual-property mapping and learning system 104 may utilize the relative
breadth score of the first intellectual-
property asset 304 to determine a portion of the revenue of the second product
326 to attribute to the first intellectual-
property asset 304. The intellectual-property mapping and learning system 104
may also determine an additional relative
breadth score for the first intellectual-property asset 304 by determining an
additional breadth value for the first
intellectual-property asset 304 relative to breadth values of other
intellectual-property assets that cover the second product
326, such as the second intellectual-property asset 306 and the fourth
intellectual-property asset 310. In a particular
illustrative example, the intellectual-property mapping and learning system
104 may determine that the portion of revenue
of the second product 326 to attribute to the first intellectual-property
asset 304 is 0.00625%.
[0104] In additional implementations, the intellectual-property mapping
and learning system 104 may determine
valuations for intellectual-property assets based on licensing information,
settlement information, damages awards, or
combinations thereof For example, the intellectual-property mapping and
learning system 104 may analyze the IP
valuation data 402 to identify features of products and/or services that have
been the subject of licensing deals, settlements,
and/or damages awards that correspond to features of at least one intellectual-
property asset that covers a product and/or
service, such as the intellectual-property assets 304, 306, 310 that cover the
second product 326. The intellectual-property
mapping and learning system 104 may then determine valuations for one or more
intellectual-property assets based on the
monetary values of settlements, licensing deals, and/or damages awards
regarding features of particular products and/or
services that may correspond to the intellectual-property assets. In an
illustrative example, the intellectual-property
mapping and learning system 104 may identify a claim of the first intellectual-
property asset 304 that includes at least one
feature that has at least a threshold similarity to a feature of a product
that was the subject of a damages award in a judicial
proceeding. The intellectual-property mapping and learning system 104 may then
determine the first valuation 406 based
on the amount of similarity between the feature of the claim of the first IP
asset 304 and the feature of the product that
was the subject of the damages award.
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[0105] In additional implementations, the intellectual-property mapping
and learning system 104 may determine
damages awards, licensing deals, and/or settlements related to a
classification of products and/or services that corresponds
to a classification of the first intellectual-property asset. The intellectual-
property mapping and learning system 104 may
then analyze the damages awards, licensing deals, and/or settlements of the
products and/or services in the same
.. classification as the first IP asset 304 to determine the first valuation
406. In particular implementations, the intellectual-
property mapping and learning system 104 may determine an average amount of
monetary value for settlements, damages
awards, and/or licensing deals in a technology classification of the first IP
asset 304 and determine the first valuation 406
based on the average amount of monetary value. Further, the intellectual-
property mapping and learning system 104 may
determine a similarity between at least one feature of the first IP asset 304
and features that were the subject of damages
awards, settlements, and/or licensing deals in a same classification as the
first IP asset 304. The intellectual-property
mapping and learning system 104 may then determine a percentage or proportion
of the damages awards, settlements,
and/or licensing deals to allocate to the at least one feature of the first IP
asset 304 based on the amount of similarity.
[0106] FIG. 5 illustrates an example system 500 to modify mappings
between intellectual property and taxonomy
classifications and mappings between intellectual property and
products/services according to some implementations. The
system 500 may include the intellectual-property mapping and learning system
104 that is implemented by one or more
computing devices 202. The system 500 may also include a first computing
device 502 that is operated by a first user 504
and a second computing device 506 that is operated by a second user 508. In
some implementations, at least one of the
first user 504 or the second user 508 may be representatives of an
intellectual-property services provider. In additional
implementations, at least one of the first user 504 or the second user 508 may
not be representatives of the intellectual-
.. property services provider. For example, at least one of the first user 504
or the second user 508 may be a representative
of another organization or part of a crowdsourcing group. In various
implementations, the first user 504 and the second
user 508 may, via the first computing device 502 and the second computing
device 506, respectively, provide input
regarding mappings between intellectual-property assets and classifications of
a technology classification system and/or
provide input regarding mappings between intellectual-property assets and
products and/or services.
[0107] In particular implementations, the intellectual-property mapping and
learning system 104 may determine an
IP asset to classification mapping 510. The IP asset to classification mapping
may indicate that the IP asset has been
classified according to a particular classification of a framework of
classifications, such as the technology taxonomy 220
of FIG. 2. In an illustrative example, the IP asset to classification mapping
510 may indicate that a claim of a patent is
classified according to a classification related to mobile device batteries.
In another illustrative example, the IP asset to
classification mapping 510 may indicate that a trademark is classified
according to a classification related to an online
gaming platform. The intellectual-property mapping and learning system 104 may
send the IP asset to classification
mapping 510 to the first computing device 502 in conjunction with a request
for input regarding the IP asset to
classification mapping 510. The request for input may be directed to an
inquiry whether the classification of the IP asset
is correct or not. In various implementations when the IP asset to
classification mapping 510 is not correct, the request for
input may ask for a different classification to assign to the IP asset.
[0108] In certain implementations, the intellectual-property mapping and
learning system 104 may generate one or
more user interfaces that may be displayed by the first computing device 502
and may include at least one user interface
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element to capture input from the first user 504 regarding the IP asset to
classification mapping 510. For example, the one
or more user interfaces may include at least one user interface element to
capture input indicating that the IP asset to
classification mapping 510 is to be modified, at least one user interface
element to capture input indicating that the IP
asset to classification mapping 510 is not to be modified, at least one user
interface element to capture input indicating a
different classification for the IP asset, or combinations thereof The first
user 504 may provide IP asset to classification
mapping feedback 512 to the intellectual-property mapping and learning system
104 via the one or more user interfaces.
[0109] The intellectual-property mapping and learning system 104 may
analyze the IP asset to classification mapping
feedback 512 to determine whether the IP asset to classification mapping 510
is to be modified. To illustrate, the
intellectual-property mapping and learning system 104 may analyze the IP asset
to classification mapping feedback 512
to determine whether the IP asset to classification mapping feedback 512
indicates that the IP asset to classification
mapping 510 is correct or whether the IP asset is to be classified according
to a different classification. The intellectual-
property mapping and learning system 104 may utilize the IP asset to
classification mapping feedback 512 to modify a
framework of classifications. For example, the intellectual-property mapping
and learning system 104 may modify one
or more criteria of a classification of the framework of classifications based
on the IP asset to classification mapping
feedback 512. In an illustrative example, the intellectual-property mapping
and learning system 104 may add one or more
criteria or remove one or more criteria from a classification based on the IP
asset to classification mapping feedback 512
indicating that the IP asset to classification mapping 510 is to be modified.
[0110] In additional illustrative examples, the intellectual-property
mapping and learning system 104 may modify a
model that determines classifications of IP assets based on the IP asset to
classification mapping feedback 512. The model
may include a number of factors and respective weightings of factors that may
be used to determine classifications of IP
assets. In particular implementations, the model may be generated using one or
more machine learning techniques. In
various implementations, the intellectual-property mapping and learning system
104 may modify a model utilized to
determine classifications of IP assets by removing one or more factors
included in the model, adding one or more factors
to the model, modifying weightings of one or more factors included in the
model, or combinations thereof.
[0111] In the illustrative example of FIG. 5, the intellectual-property
mapping and learning system 104 may
determine a modified IP asset to classification mapping 514. The modified IP
asset to classification mapping 514 may
indicate that the IP asset is associated with a different classification than
the classification for the IP asset in the IP asset
to classification mapping 510. The intellectual-property mapping and learning
system 104 may determine a different
classification for the IP asset based on the IP asset to classification
mapping feedback 512. For example, in situations
where the IP asset to classification mapping feedback 512 indicates that the
classification of the IP asset is to be modified
to a particular, different classification, the intellectual-property mapping
and learning system 104 may change the
classification of the IP asset to the classification indicated in the IP asset
to classification mapping feedback 512. In
additional implementations, the intellectual-property mapping and learning
system 104 may analyze the input included in
the IP asset to classification mapping feedback 512 to modify a model that
determines classifications of IP assets and then
implement the modified model with respect to the IP asset. The modified model
may then generate the modified IP asset
to classification mapping 514.
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[0112] Additionally, the intellectual-property mapping and learning
system 104 may determine an IP asset to
product/service mapping 516. The IP asset to product/service mapping 516 may
indicate that at least a portion of the IP
asset covers at least a portion of a product and/or service. For example, the
IP asset to product/service mapping 516 may
indicate that a claim of a patent covers a user interface feature of an audio
application executed by a mobile communication
device. In another example, the IP asset to product/service mapping 516 may
indicate that a trade secret corresponds to a
process of manufacturing a food product. The intellectual-property mapping and
learning system 104 may send the IP
asset to product/service mapping 516 to the second computing device 506 to
request input from the second user 508
regarding the IP asset to product/service mapping 516. The request for input
may be directed to an inquiry whether the
mapping between the IP asset and the product/service is correct or not. In
various implementations when the IP asset to
product/service mapping 516 is not correct, the request for input may ask for
a different product/service to assign to the
IP asset.
[0113] The intellectual-property mapping and learning system 104 may
generate one or more user interfaces that
may be displayed by the second computing device 506 and may include at least
one user interface element to capture
input from the second user 508 regarding the IP asset to product/service
mapping 516. For example, the one or more user
.. interfaces may include at least one user interface element to capture input
indicating that the IP asset to product/service
mapping 516 is to be modified, at least one user interface element to capture
input indicating that the IP asset to
product/service mapping 516 is not to be modified, at least one user interface
element to capture input indicating a different
product/service that corresponds to the IP asset, or combinations thereof The
second user 508 may provide IP asset to
product/service mapping feedback 518 to the intellectual-property mapping and
learning system 104 via the one or more
user interfaces.
[0114] The intellectual-property mapping and learning system 104 may
analyze the IP asset to product/service
mapping feedback 518 to determine whether the IP asset to product/service
mapping 516 is to be modified. To illustrate,
the intellectual-property mapping and learning system 104 may analyze the IP
asset to product/service mapping feedback
518 to determine whether the IP asset to product/service mapping feedback 518
indicates that the IP asset to
.. product/service mapping 516 is correct or whether the IP asset is to be
associated with another product and/or service.
The intellectual-property mapping and learning system 104 may utilize the IP
asset to product/service mapping feedback
518 to modify a framework of classifications. For example, the intellectual-
property mapping and learning system 104
may modify one or more criteria of a classification of the framework of
classifications based on the IP asset to
product/service mapping feedback 518. In an illustrative example, the
intellectual-property mapping and learning system
104 may add one or more criteria or remove one or more criteria from a
classification of a framework of classifications
based on the IP asset to product/service mapping feedback 518 indicating that
the IP asset to product/service mapping 516
is to be modified.
[0115] Further, the intellectual-property mapping and learning system 104
may modify a model that determines
products and/or services that correspond to IP assets based on the IP asset to
product/service mapping feedback 518. The
model may include a number of factors and respective weightings of factors
that may be used to determine products and/or
services that are covered by IP assets. The model may be generated using one
or more machine learning techniques. In
particular implementations, the intellectual-property mapping and learning
system 104 may modify a model utilized to
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determine products and/or services covered by IP assets by removing one or
more factors included in the model, adding
one or more factors to the model, modifying weightings of one or more factors
included in the model, or combinations
thereof
[0116] In the illustrative example of FIG. 5, the intellectual-property
mapping and learning system 104 may
determine a modified IP asset to product/service mapping 520. The modified IP
asset to product/service mapping 520
may indicate that the IP asset is associated with a different product and/or
service than the product and/or service
associated with the IP asset in the IP asset to product/service mapping 516.
The intellectual-property mapping and learning
system 104 may determine a different product and/or service covered by the IP
asset based on the IP asset to
product/service mapping feedback 518. For example, in situations where the IP
asset to product/service mapping feedback
518 indicates that the product and/or service associated with the IP asset is
to be modified to be associated with a different
product and/or service, the intellectual-property mapping and learning system
104 may change the product and/or service
associated with the IP asset to the product and/or service indicated in the IP
asset to product/service mapping feedback
518. In additional implementations, the intellectual-property mapping and
learning system 104 may analyze the input
included in the IP asset to product/service mapping feedback 518 to modify a
model that determines products and/or
services covered by IP assets and then implement the modified model with
respect to the IP asset. The modified model
may then generate the modified IP asset to product/service mapping 520.
[0117] FIG. 6 illustrates an example architecture 600 to provide
intellectual property related services to customers
using mappings between intellectual property and products/services in relation
to a classification system according to
some implementations. The architecture 600 may include an intellectual-
property services provider 602. The intellectual-
property services provider 602 may provide services to customers, such as the
customer 110, that are related to intellectual-
property assets. The intellectual-property assets may be associated with the
customers of the intellectual-property services
provider 602, in some scenarios. For example, customers of the intellectual-
property services provider 602 may request
that the intellectual-property services provider 602 provide one or more
services with regard to intellectual-property assets
for which the customers of the intellectual-property services provider 602
hold the ownership rights. In additional
examples, the customers of the intellectual-property services provider 602 may
request that the intellectual-property
services provider 602 provide services with regard to intellectual-property
assets with ownership rights held by
organizations that are not customers of the intellectual-property services
provider 602.
[0118] At least a portion of the operations performed by the intellectual-
property services provider 602 may be
performed by one or more computing devices 604. The one or more computing
devices 604 may be any suitable type of
computing device, e.g., portable, semi-portable, semi-stationary, or
stationary. Some examples of the one or more
computing devices 604 may include tablet computing devices; smart phones and
mobile communication devices; laptops,
netbooks and other portable computers or semi-portable computers; desktop
computing devices, terminal computing
devices and other semi-stationary or stationary computing devices; dedicated
register devices; wearable computing
devices, or other body-mounted computing devices; augmented reality devices;
or other computing devices capable of
sending communications and performing the functions according to the
techniques described herein.
[0119] The one or more computing devices 604 may include one or more
servers or other types of computing devices
that can be embodied in any number of ways. For example, in the example of a
server, the modules, other functional
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components, and data may be implemented on a single server, a cluster of
servers, a server farm or data center, a cloud-
hosted computing service, a cloud-hosted storage service, and so forth,
although other computer architectures can
additionally or alternatively be used.
[0120] Further, while the figures illustrate the components and data of
the one or more computing devices 604 as
being present in a single location, these components and data can
alternatively be distributed across different computing
devices and different locations in any manner. Consequently, the functions
performed by the one or more computing
devices 604 may be implemented by one or more server computing devices, with
the various functionality described
above distributed in various ways across the different computing devices.
Multiple computing devices 604 may be located
together or separately, and organized, for example, as virtual servers, server
banks and/or server farms. The described
functionality may be provided by the servers of an intellectual-property
services provider, or may be provided by the
servers and/or services of multiple different organizations.
[0121] In the illustrated example, the one or more computing devices 604
may include one or more processors 606,
one or more computer-readable media 608, one or more communication interfaces
610, and one or more input/output
devices 612. Each processor 606 may be a single processing unit or a number of
processing units, and may include single
or multiple computing units or multiple processing cores. The processor(s) 606
may be implemented as one or more
microprocessors, microcomputers, microcontrollers, digital signal processors,
central processing units, state machines,
logic circuitries, and/or any devices that manipulate signals based on
operational instructions. For example, the
processor(s) 606 may be one or more hardware processors and/or logic circuits
of any suitable type specifically
programmed or configured to execute the algorithms and processes described
herein. The processor(s) 606 may be
configured to fetch and execute computer-readable instructions stored in the
computer-readable media 608, which can
program the processor(s) 606 to perform the functions described herein.
[0122] The computer-readable media 608 may include volatile and
nonvolatile memory and/or removable and non-
removable media implemented in any type of technology for storage of
information, such as computer-readable
instructions, data structures, program modules, or other data. Such computer-
readable media 608 may include, but is not
.. limited to, RAM, ROM, EEPROM, flash memory or other memory technology,
optical storage, solid state storage,
magnetic tape, magnetic disk storage, RAID storage systems, storage arrays,
network attached storage, storage area
networks, cloud storage, or any other medium that may be used to store the
desired information and that may be accessed
by a computing device. Depending on the configuration of the one or more
computing devices 604, the computer-readable
media 608 may be a type of computer-readable storage media and/or may be a
tangible non-transitory media to the extent
that when mentioned, non-transitory computer-readable media exclude media such
as energy, carrier signals,
electromagnetic waves, and signals per se.
[0123] The computer-readable media 608 may be used to store any number of
functional components that are
executable by the processor(s) 606. In many implementations, these functional
components comprise instructions or
programs that are executable by the processor(s) 606 and that, when executed,
specifically configure the one or more
processors 606 to perform the actions attributed above to the intellectual-
property services provider 602. Functional
components stored in the computer-readable media 608 may include the
intellectual-property services system 104, the
data acquisition system 118, the language analysis system 122, the IP
knowledge model development system 124,
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intellectual property (IP) valuation tools 614, IP strategy tools 616, and IP
risk tools 616. The computer-readable media
608 may also store the data of the intellectual-property knowledge data store
120.
[0124] In at least one example, the computer-readable media 608 may
include or maintain other functional
components and data, such as other modules and data, which may include
programs, drivers, one or more operating
systems, etc., and the data used or generated by the functional components.
Further, the one or more computing devices
604 may include many other logical, programmatic and physical components, of
which those described above are merely
examples that are related to the discussion herein.
[0125] The communication interface(s) 610 may include one or more
interfaces and hardware components for
enabling communication with various other devices, such as over one or more
network(s). For example, communication
interface(s) 610 may enable communication through one or more of the Internet,
cable networks, cellular networks,
wireless networks (e.g., Wi-Fi) and wired networks, as well as close-range
communications such as Bluetooth0,
Bluetooth0 low energy, and the like, as additionally enumerated elsewhere
herein.
[0126] The one or more computing devices 604 may further be equipped with
various input/output (I/O) devices 612.
The I/O devices 612 can include speakers, a microphone, a camera, a display
(e.g., a liquid crystal display, a plasma
display, a light emitting diode display, an OLED (organic light-emitting
diode) display, an electronic paper display, or
any other suitable type of display able to present digital content thereon),
and various user controls (e.g., buttons, a
joystick, a keyboard, a keypad, etc.), a haptic output device, and so forth.
Further, in particular implementations, the one
or more computing devices 604 may include one or more sensors, such as an
accelerometer, gyroscope, compass,
proximity sensor, camera, microphone, and/or a switch, a GPS sensor, etc.
[0127] In particular implementations, the intellectual-property services
provider 602 may generate various mappings
620 that may be used to provide intellectual property related services to
customer of the intellectual-property services
provider 602 For example, the mappings 620 may include one or more
intellectual-property asset to classification
mappings 622. The individual intellectual-property classification mappings 622
may indicate a relationship between an
intellectual-property asset and a classification of a classification
framework, such as a classification of the technology
taxonomy 220. Additionally, the mappings 620 may include one or more
intellectual-property asset to product/service
mappings 624. The individual intellectual-property asset to product/service
mappings 624 may indicate a relationship
between an intellectual-property asset and a product and/or service. Further,
the mappings 620 may include one or more
product/service to economic data mappings 626. The individual product/service
to economic data mappings 626 may
indicate specific economic data that is related to at least one product or
service. To illustrate, a product/service to economic
data mapping 626 may indicate revenue of a product and/or service.
[0128] The intellectual-property services provider 602 may receive
requests for IP-related services and the
intellectual-property services system 104 may utilize the mappings, data
stored by the intellectual-property knowledge
data store, and/or additional information, such as one or more classification
frameworks, to provide the services associated
with the request. For example, the intellectual-property services provider 602
may receive requests from customers to
obtain services related to intellectual property valuation, intellectual
property strategy, and intellectual property risk. In
particular implementations, the intellectual-property services provider 602
may utilize the IP valuation tools 614 to
provide intellectual property valuation services to customers. The IP
valuation tools 614 may include at least one of one
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or more user interfaces, one or more scripts, or one or more applications that
may be used to analyze data related to
intellectual-property assets and provide information corresponding to the
values of intellectual-property assets of
customers of the intellectual-property services provider 602. Additionally,
the intellectual-property services provider 602
may utilize IP strategy tools 616 to provide IP strategy services to
customers. The IP strategy tools 616 may include at
least one of one or more user interfaces, one or more scripts, or one or more
applications that may be used to analyze data
related to intellectual-property assets and provide strategy related
information to the customers of the intellectual-property
services provider 602. Further, the intellectual-property services provider
602 may utilize the IP exposure tools 618 to
provide IP risk services to customers. The IP exposure tools 618 may include
at least one of one or more user interfaces,
one or more scripts, or one or more applications that may be used to analyze
data related to intellectual-property assets
.. and provide risk related information to the customers of the intellectual-
property services provider 602.
[0129] In an illustrative implementation, the customer 110 may send a
request for IP-related services 628 to the
intellectual-property services provider 602. The request for IP-related
services 628 may be sent electronically to the
intellectual-property services provider 602. For example, the customer 110 may
send a communication, such as an email
or message, to the intellectual-property services provider 602 that includes
the request for IP-related services 628. In
additional examples, the customer 110 may access one or more user interfaces
provided by the intellectual-property
services provider 602 to generate the request for IP-related services 628. The
intellectual-property services provider 602
may communicate one or more aspects of the request for IP-related services 628
to an additional computing device 630
that is operated by a user 632. The user 632 may be a representative of the
intellectual-property services provider 602. In
particular implementations, the request for IP-related services 628 may
include a number of aspects, such as requests for
one or more IP valuation services, for one or more IP strategy services,
and/or one or more IP risk services. Individual
aspects of the request may be provided to a single representative of the
intellectual-property service provider 602 or to a
number of representatives of the intellectual-property services provider 602.
In particular illustrative implementations, the
request for IP-related services 628 may include a first request for valuations
of a portfolio of intellectual-property assets,
a second request for a patent landscape analysis related to an electronic
device manufactured by the customer 110, a third
.. request for a risk assessment related to invalidation of a number of
intellectual-property assets of the customer 110, and a
fourth request for a trade secret theft assessment related to trade secrets of
the customer 110. In this scenario, the
intellectual-property services provider 602 may, in some instances, assign the
user 632 to provide services related to the
first request, the second request, the third request, and the fourth request.
In additional instances, the intellectual-property
services provider 602 may assign the user 632 to provide services related to
one of the first request, the second request,
the third request, or the fourth request, and assign the tasks related to
providing services associated with the remaining
requests to other representatives of the intellectual-property services
provider 602.
[0130] In a situation where the user 632 is assigned to perform services
related to the valuation of intellectual-
property assets, the user 632 may operate the additional computing device 630
to access the IP valuation tools 614. In
various implementations, the intellectual-property services provider 602 may
obtain identifiers of the intellectual-property
assets for which the valuations are being determined from the additional
computing device 630. The identifiers may
include identifiers provided by intellectual property jurisdictions (e.g.,
EPO, USPTO, JPO, etc.), such as application
numbers, registration numbers, patent numbers, publication numbers, or
combinations thereof. The identifiers may also
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include titles of intellectual-property assets. Further, the identifiers may
be alphanumeric strings generated by the
intellectual-property services provider 602 that correspond to individual
intellectual-property assets. In addition, the
intellectual-property services provider 602 may obtain a situation or type of
valuation to be determined. For example, the
intellectual-property services provider 602 may receive information from the
additional computing device 630 indicating
.. that at least one of a valuation is to be determined for the sale of the
intellectual-property assets of the customer 110, a
valuation is to be determined for the licensing of the intellectual-property
assets of the customer 110, or a valuation is to
be determined for the intellectual-property assets of the customer 110 to be
used as collateral for a loan.
[0131] After obtaining input from the additional computing device 630 via
the IP valuation tools 614, the intellectual-
property services system 104 may access the mappings 620, data stored by the
intellectual-property knowledge data store
120, models generated by the intellectual-property services provider 602,
machine learning algorithms, or combinations
thereof, to provide intellectual property customer services 634 associated
with the valuation of intellectual-property assets
requested by the customer 110. Depending on the type of valuation being
performed and the amount of information that
the intellectual-property services provider 602 has already obtained with
respect to the intellectual-property assets of the
customer 110 for which valuations are being performed, the intellectual-
property services system 104 may access one or
more of the intellectual-property asset to classification mappings, 622, the
intellectual-property asset to product/service
mappings 624, or the product/service to economic data mappings 626 to
determine valuations for the intellectual-property
assets of the customer 110 that are the subject of the request for IP-related
services 628.
[0132] In additional situations where the user 632 is assigned to perform
strategy related services for intellectual-
property assets of the customer 110, the user 632 may operate the additional
computing device 630 to access the IP strategy
tools 616. In these situations, the intellectual-property services system 104
may obtain identifiers of intellectual-property
assets from the additional computing device, as well as indications of the
type of strategy related services to provide. The
intellectual-property services system 104 may then access the mappings 620,
data stored by the intellectual-property
knowledge data store 120, models generated by the intellectual-property
services provider 602, machine learning
algorithms, or combinations thereof, to provide intellectual-property customer
services 634 to the customer 110 related to
the IP strategy services requested by the customer 110.
[0133] In further scenarios where the user 632 is assigned to perform
risk related services for intellectual-property
assets of the customer 110, the user 632 may operate the additional computing
device 630 to access the IP risk tools 618.
In these situations, the intellectual-property services system 104 may obtain
identifiers of intellectual-property assets from
the additional computing device, as well as indications of the type of risk
related services to provide. The intellectual-
property services system 104 may then access the mappings 620, data stored by
the intellectual-property knowledge data
store 120, models generated by the intellectual-property services provider
602, machine learning algorithms, or
combinations thereof, to provide intellectual-property customer services 634
to the customer 110 related to the IP risk
services requested by the customer 110.
[0134] In an illustrative implementation, the intellectual-property
services provider 602 may receive the request for
IP-related services 628 from the customer 110, and the request for IP-related
services 628 may include a request for
valuation of an intellectual-property asset 636 of the customer 110. The
intellectual-property services provider 602 may
provide the request for valuation of the intellectual-property asset 636 to
the additional computing device 630. In response
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to the request for valuation services, the user 632 may operate the additional
computing device 630 to access the IP
valuation tools 614. The IP valuation tools 614 may generate one or more user
interfaces that include one or more user
interface elements to capture information that may be used by the intellectual-
property services provider 602 to determine
a valuation for the IP asset 636. In various implementations, the IP valuation
tools 614 may include a user interface
element to capture an identifier of the IP asset 636 and a type of valuation
to be determined. In a particular illustrative
example, the IP asset 636 may be a US patent and the additional computing
device 630 may obtain an identifier of the IP
asset 636, such as a patent number of the IP asset 636, and input indicating
that the type of valuation corresponds to a sale
of the IP asset 636.
[0135] Based on the input obtained from the additional computing device
630, the intellectual-property services
system 104 may determine whether the mappings 620 include one or more mappings
related to the IP asset 636. For
example, the intellectual-property services system 104 may have previously
determined a classification related to the IP
asset 636 and generated an intellectual-property asset to classification
mapping 622 for the IP asset 636. In another
example, the intellectual-property services system 104 may have previously
determined a product and/or service that
corresponds to the IP asset 636 and generated an intellectual-property asset
to product/service mapping 624 for the IP
.. asset 636. In additional examples, the intellectual-property services
system 104 may have previously determined economic
data that corresponds to the IP asset 636 and generated a product/service to
economic data mapping 626. In these
situations, one or more of the mappings 620 related to the IP asset 636 may be
stored by the intellectual-property
knowledge data store 120 and the intellectual-property services system 104 may
utilize the identifier of the IP asset 636
to retrieve the mappings 620 that correspond to the IP asset 636. In
situations where the mappings 620 do not include one
.. or more mappings used to determine the valuation of the IP asset 636, the
intellectual-property services system 104 may
generate at least one of an intellectual-property asset to classification
mapping 622 for the IP asset 636, an intellectual-
property asset to product/service mapping 624 for the IP asset 636, or a
product/service to economic data mapping 626
for the IP asset 636.
[0136] Continuing with the illustrative example from above, the
intellectual-property services system 104 may
determine an intellectual-property asset to classification mapping 622 for the
IP asset 636 to determine a classification of
the IP asset 636. The intellectual-property services system 104 may then
identify additional intellectual-property assets
having the same classification as the IP asset 636. The intellectual-property
services system 104 may determine the breadth
of the intellectual-property asset 636 with respect to the breadth of the
other intellectual-property assets included in the
same classification of the IP asset 636. The breadth of the IP asset 636
relative to the breadth of additional IP assets in the
same classification as the IP asset 636 may be used to determine the valuation
of the IP asset 636. In various
implementations, the intellectual-property services system 104 may also obtain
licensing data, damages awards, and/or
settlement data for additional intellectual-property assets included in the
same classification as the IP asset 636 and utilize
the data to determine a valuation for the IP asset 636.
[0137] Additionally, the intellectual-property services system 104 may
determine an intellectual-property asset to
.. product/service mapping 624 for the IP asset 636 that indicates a product
and/or service that corresponds to the IP asset
636. In some situations, the intellectual-property services system 104 may
identify multiple intellectual-property asset to
product/service mappings 624 related to the IP asset 636. In particular
implementations, revenue related to one or more
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of the products and/or services corresponding to the IP asset 636 may be used
to determine the valuation of the IP asset
636. Further, the intellectual-property services system 104 may determine a
product/service to economic data mapping
626 for the IP asset 636. The product/service to economic data mapping 626 for
the IP asset 636 may indicate the financial
data associated with the one or more products and/or services corresponding to
the IP asset 636 and may be used by the
intellectual-property services system 104 to determine a valuation for the IP
asset 636 in response to the request received
from the additional computing device 630
[0138] In particular implementations, the intellectual-property services
system 104 may generate one or more user
interfaces that include one or more valuations for the IP asset 636 and make
the one or more user interfaces accessible to
the additional computing device 630. In certain implementations, the
intellectual-property services system 104 may
provide a notification to the additional computing device 630, such as an
email, message, and the like, to indicate that the
one or more valuations for the IP asset 636 have been determined.
[0139] In addition, the intellectual-property services system 104 may
provide access to the mappings 620 related to
the IP asset 636. In these situations, the user 632 may utilize additional
computing device 630 to provide input regarding
the mappings 620 to use in determining one or more valuations for the IP asset
636. In an illustrative example, the
.. intellectual-property services system 104 may provide a first intellectual-
property asset to classification mapping
indicating that the IP asset 636 is associated with a first classification and
a second intellectual-property asset to
classification mapping indicating that the IP asset 626 is associated with a
second classification. The additional computing
device 630 may send input to the intellectual-property services provider 602
indicating selection of the first intellectual-
property asset to classification mapping or the second intellectual-property
asset to classification mapping. The
intellectual-property services system 604 may also provide multiple
intellectual-property asset to product/service
mappings 624 related to the IP asset 636 to the additional computing device
630 and obtain input from the additional
computing device 630 indicating at least one intellectual-property asset to
product/service mapping 624 to utilize to
determine a valuation for the IP asset 636. Further, the intellectual-property
services system 104 may provide multiple
product/service to economic data mappings 626 corresponding to one or more
products and/or services related to the IP
.. asset 636 to the additional computing device 630 and obtain input from the
additional computing device 630 indicating at
least one product/service to economic data mapping 626 to utilize to determine
a valuation for the IP asset 636.
[0140] FIG. 7 illustrates an example framework 700 to generate linguistic
structures for claims of patent documents
according to some implementations. The framework 700 includes an intellectual-
property asset 702. In the illustrative
example of FIG. 7, the intellectual-property asset 702 is a claim of a patent
or patent application. At 704, a parsing and
.. linguistic analysis 704 may be performed with respect to the intellectual-
property asset 702. In various implementations,
the parsing and linguistic analysis 704 may be performed by the intellectual-
property services system 104. In particular
implementations, the parsing and linguistic analysis 704 may include
identifying words of the intellectual-property asset
702 and categorizing the words of the intellectual-property asset. In
illustrative examples, the parsing and linguistic
analysis 704 may generate a linguistic analysis 706 for the intellectual-
property asset 702 that indicates parts of speech of
at least a portion of the words included in the intellectual-property asset
702. For example, the linguistic analysis 706 may
indicate verbs, nouns, and adjectives of the intellectual-property asset 702.
In additional scenarios, the linguistic analysis
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706 may also indicate adverbs, conjunctions, prepositions, pronouns, stop
words, common words, unique words, or
combinations thereof, of the intellectual-property asset.
[0141] Additionally, the framework 700 may include, at 708, generating
one or more linguistic structures for the
intellectual-property asset 702. In particular examples, the intellectual-
property services system 104 may generate the one
or more linguistic structures at 708. The one or more linguistic structures
may indicate relationships between words of the
intellectual-property asset 702. In various implementations, multiple
linguistic structures may be generated for the
intellectual-property asset 702. In illustrative implementations, a linguistic
structure may be generated for a plurality of
features of the intellectual-property asset 702. For example, a linguistic
structure may be generated for actions that are
taking place in a claim. In certain implementations, the linguistic structures
may be generated for individual elements
included in a claim of a patent or patent application.
[0142] In the illustrative example of FIG. 7, a linguistic structure 710
may be generated for the feature of the
intellectual-property asset 702 starting with "displaying a portion of web
page content ...". This feature may include one
element of a claim of the intellectual-property asset 702. The linguistic
structure 710 may be a tree structure that includes
a root node 712 and a number of branch nodes 714, 716, 718. The root node 712
of the linguistic structure 710 includes
the word "display", which is a verb corresponding to the feature for which the
linguistic structure 710 is being generated.
The nodes 714 and 716 correspond to nouns that are related to the verb in the
root node 712. Additionally, the node 718
corresponds to the noun and adjective included in the node 716. Although the
illustrative example of the linguistic
structure 710 includes a single root node with three branch nodes, the
linguistic structure 710, and other linguistic
structures, may include additional nodes that correspond to different words of
a feature of the intellectual-property asset
702. The root node 712 may be included in a first level of the linguistic
structure 710, the second node 714 and the third
node 716 may be included in a second level of the linguistic structure 710,
and the fourth node 718 may be included in a
third level of the linguistic structure 710.
[0143] FIG. 8 illustrates an example framework 800 to determine a
similarity metric between a linguistic structure
for a portion of a claim of a patent document and a linguistic structure of a
product/service according to some
implementations. The framework 800 includes the linguistic structure 710 from
FIG. 7 that represents a portion of a claim
of the intellectual-property asset 702. Additionally, at 802, linguistic
structures may be generated for a number of products
and/or services using product/service data 804. The product/service data 804
may include data that includes descriptions
of products and/or services. The product/service data 804 may be analyzed and
parsed using natural language processing
techniques to determine classifications for words included in the
product/service data 804. Additionally, the
product/service data 804 may be analyzed to generate linguistic structures for
various features of products included in the
product/service data 804. For example, a first linguistic structure 806 may be
generated for at least one feature of a first
product 808, a second linguistic structure 810 may be generated for at least
one feature of a second product 812, and a
third linguistic structure 814 may be generated for at least one feature of a
third product 816. The linguistic structures
806, 810, 814 may include tree structures with a root node and one or more
branch nodes.
[0144] At 818, the framework 800 may include determining similarity metrics
820 between the linguistic structure
710 and the linguistic structures 806, 810, 814. In various implementations,
the similarity metrics 820 may indicate an
amount of similarity between linguistic structures. The similarity metrics 820
may be determined based on similarities
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between words included in the linguistic structure 710 and words included in
the linguistic structures 806, 810, 814.
Additionally, the similarity metrics 820 may be determined based on
similarities between the arrangement of nodes
included in the linguistic structure 710 and the respective arrangements of
nodes included in the linguistic structures 806,
810, 814. In particular, the similarity metrics 820 may include a first
similarity metric 822 that corresponds to an amount
of similarity between the linguistic structure 710 and the first linguistic
structure 806. Additionally, a second similarity
metric 824 may correspond to an amount of similarity between the linguistic
structure 710 and the second linguistic
structure 810. Further, the similarity metrics 820 may include a third
similarity metric 826 that corresponds to an amount
of similarity between the linguistic structure 710 and the third linguistic
structure 814. In various implementations, the
similarity metrics 820 may include numerical representations of amounts of
similarity between linguistic structures. In
particular implementations, the similarity metrics 820 may be designated along
a numerical scale, such as 1 to 10 or 1 to
100 or represented by a percentage that indicates amounts of similarity
between linguistic structures.
[0145] The amounts of similarity between the linguistic structure 710 and
the linguistic structures 806, 810, 814 may
be used to determine one or more of the products 808, 812, 816 that may
correspond to the intellectual-property asset 702.
That is, in situations where a similarity metric 822, 824, 826 is greater than
a threshold amount of similarity, a mapping
or other indicator of correspondence between the intellectual-property asset
702 and a respective product 806, 810, 814
may be generated. The mappings may then be used to provide various services to
organizations, such as IP valuations
services, IP risk-related services, and/or IP strategy-related services.
[0146] FIG. 9 illustrates an example framework 900 to a value of an
intellectual property feature that corresponds to
one or more products according to some implementations. The framework 900 may
include a first product 902 that
corresponds to a first IP feature 904 and a second product 906 that
corresponds to a second IP feature 908. The first
product 902 may be linked to the first IP feature 904 based on an amount of
similarity between a linguistic structure of
the product 902 and a linguistic structure of the first IP feature 904.
Additionally, the second product 906 may be linked
to the second IP feature 908 based on an amount of similarity between a
linguistic structure of the second product 906
and a linguistic structure of the second IP feature 908. In illustrative
examples, the first IP feature 904 may be an element
of a claim of a patent or patent application and the second IP feature may be
an element of a claim of another patent or
patent application.
[0147] The framework 900 also includes the intellectual-property services
system 104 and the intellectual-property
knowledge data store 120. The intellectual-property services system 104 may
retrieve financial data 910 from the
intellectual-property knowledge data store 120. The financial data 910 may
include information related to revenue
generated by sales of various products and/or services, such as revenue
information for the first product 902 and revenue
information for the second product 906. The intellectual-property services
system 104 may also, at 914, determine a
portion of the value of a product and/or service that corresponds to an IP
feature associated with the product and/or service.
For example, the intellectual-property services system 104 may determine a
portion of the amount of revenue of the first
product 902 to attribute to the first IP feature 904. In various
implementations, the amount of revenue of the first product
902 to attribute to the first IP feature 904 may be based on a measure of
breadth of the IP feature 904. To illustrate, the
intellectual-property services system 104 may determine a breadth of the first
IP feature 904 with respect to additional
intellectual property features included in a same technology classification as
the first IP feature 904. Based on the measure
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of breadth of the first IP feature 904 in relation to the breadth of other IP
features, the intellectual-property services system
104 may determine an amount of revenue of the first product 902 to attribute
to the first IP feature 904. In certain situations,
the higher the value of the measure of breadth of the first IP feature 904,
the higher the percentage of revenue of the first
product 902 to attribute to the first IP feature 904. Further, the lower the
value of the measure of breadth of the first IP
feature 904, the lower the percentage of revenue of the first product 902 to
attribute to the first IP feature 904.
[0148] At 914, the framework 900 includes determining a value 916 for the
IP feature 914. In particular
implementations, the intellectual-property services system 104 may determine
the value 916 of the first IP feature 904
based on the amount of revenue of the first product 902 and a portion of the
revenue of the first product 902 attributed to
the first IP feature 904. In various implementations, the intellectual-
property services system 104 may multiple the portion
of the revenue of the first product 902 attributed to the first IP feature 904
by revenue information of the first product 902
to determine the value 916 of the first IP feature 904.
[0149] FIG. 10-14 illustrate example processes of analyzing intellectual-
property data. The processes described
herein are illustrated as collections of blocks in logical flow diagrams,
which represent a sequence of operations, some or
all of which may be implemented in hardware, software or a combination thereof
In the context of software, the blocks
may represent computer-executable instructions stored on one or more computer-
readable media that, when executed by
one or more processors, program the processors to perform the recited
operations. Generally, computer-executable
instructions include routines, programs, objects, components, data structures
and the like that perform particular functions
or implement particular data types. The order in which the blocks are
described should not be construed as a limitation,
unless specifically noted. Any number of the described blocks may be combined
in any order and/or in parallel to
implement the process, or alternative processes, and not all of the blocks
need be executed. For discussion purposes, the
processes are described with reference to the environments, architectures and
systems described in the examples herein,
such as, for example those described with respect to FIGS. 1-9, although the
processes may be implemented in a wide
variety of other environments, architectures and systems.
[0150] FIG. 10 illustrates an example process 1000 to determine an
intellectual-property asset that corresponds to a
product or service according to some implementations.
[0151] At 1002, the process 1000 includes receiving, from one or more
data sources, information about products. In
particular implementations, the one or more data sources may include a
publicly accessible data source. Publicly
accessible data sources may include websites that include information that may
be accessed by the general public without
credentials issued by the organizations maintaining and/or controlling access
to the websites. For example. publicly
accessible data sources may include uniform resource locators (URLs) that are
available to the public without the URL(s)
being first provided to individuals by the organizations themselves. In
contrast, access to private data sources may be
controlled more strictly than access to public data sources by restricting
access to LaLs associated with the private data
sources and/or by requiring specific credentials to access the private data
sources. In some situations, an organization may
maintain and/or control a website that includes both publicly accessible
information that may be accessible to the general
public and privately accessible information that is accessible to customers,
employees, and other individuals that have
specifically been granted access by the organization. The publicly accessible
data sources may include government
websites, intellectual-property databases maintained by intellectual property
jurisdictions, websites of companies offering
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products and/or services, combinations thereof, and the like. In these
situations, obtaining data related to a product and/or
service from the publicly accessible data source may include determining a
plurality of keywords associated with the
product and/or service and parsing the publicly accessible data source to
identify data that corresponds to at least one
keyword of the plurality of keywords. Additionally, the data that corresponds
to the at least one keyword may be extracted
from the publicly accessible data source and the data that corresponds to the
at least one keyword may be stored in a data
store of a service provider. In certain implementations, an intellectual-
property services provider may obtain information
from public data sources using web crawlers or other applications that may
identify websites and parse the websites for
specified information.
[0152] In additional implementations, the one or more data sources may
include a data source of an organization that
offers a product and/or service for acquisition. A data source of an
organization may be a private data source that is
accessible to an intellectual-property services provider based at least partly
on the organization granting access by the
intellectual-property service provider to the data source of the organization.
The data source of the organization may be
accessible via a database management application and an intellectual-property
services provider may utilized the database
management application to parse the data source of the organization for at
least one keyword of a plurality of keywords
associated with a product and/or service and to extract data corresponding to
the at least one keyword from the data source
of the organization. The intellectual-property services provider may then
store the data obtained from the data source of
the organization in a data store of an additional organization, such as a data
store of the intellectual-property services
provider. In various implementations, data related to a product and/or service
that is stored in a data store of an
organization that is offering the product and/or service for sale may be
stored such that relationships between intellectual-
property assets and respective product and/or services are identifiable. That
is, the organization may have tracked the
intellectual-property assets that are associated with particular products
and/or services and stored data indicating this
relationship. In this way, an intellectual-property services provider may
search a data store of an organization and identify
intellectual-property assets that correspond to products and/or services
offered for sale by the organization using data
generated by the organization.
[0153] In further implementations, data relating to products and/or
services may be obtained using crowdsourcing
techniques. To illustrate, an intellectual-property services provider may
cause a request for information about a product
and/or service to be published on a website. Individuals accessing the website
may submit responses to the request via the
website. In additional implementations, an intellectual-property services
provider may send requests to particular
individuals to obtain information about products and/or services. The requests
may be included in one or more types of
communication, such as email, mobile device message, instant messaging
notification, phone call, combinations thereof,
and so forth. In various implementations, the intellectual-property services
provider may identify one or more groups of
individuals to obtain information about one or more products and/or services.
For example, the intellectual-property
services provider may identify individuals that may be considered experts
and/or have at least a threshold amount of
knowledge about various products and/or services and the intellectual-property
services provider may contact a respective
group of individuals when the intellectual-property services provider would
like to obtain information about a product
and/or service for which the respective group has knowledge. In these
scenarios, at least a portion of the individuals
contacted by the intellectual-property services provider may provide
information about one or more products and/or
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services to the intellectual-property services provider in response to the
request(s). In some cases, information obtained
about a product and/or service may indicate one or more sources of information
about the product and/or service, such as
one or more websites or publications that may include information about the
product and/or service. Additionally,
information about a product and/or service may include at least one of a
description of the product and/or service, pricing
information related to the product and/or service, financial information of
the product and/or service.
[0154] In various implementations, an intellectual-property services
provider may also provide one or more portals
for individuals to submit information. For example, the intellectual-property
services provider may generate one or more
user interfaces that include at least one user interface element to capture
information about products and/or services offered
for sale by an organization and/or to capture information about intellectual-
property assets of the organization. The portals
.. may be accessible by representatives of at least One of the intellectual-
property services provider or the organization. In
particular implementations, the intellectual-property services provider may
provide a portal that may be used to obtain
information about trade secrets of the organization. In additional
implementations, the intellectual-property services
provider may provide a portal that may be used to obtain information about
patent documents of the organization. In
further implementations, the intellectual-property services provider may
provide a portal that may be used to obtain
information about products and/or services offered by the organization.
[0155] At block 1004, the process 1000 includes identifying intellectual-
property assets. For example, the
intellectual-property assets may be identified from publicly-available
resources and/or from resources associated with one
or more organizations.
[0156] At 1006, the process 1000 includes determining one or more
relationships between individual ones of the
products and individual ones of the intellectual-property assets. The
relationships between the individual products or
services and the individual intellectual-property assets may be determined by
identifying features of the products or
services and features of the intellectual-property assets. The features of the
products or services may be determined by
parsing descriptions of the products or services and identifying functional
features, physical features, and/or technical
features of the products or services. In various implementations, videos
and/or images related to the products or services
may be analyzed using one or more object recognition techniques to determine
features of the products or services. An
intellectual-property services provider may analyze features of the
intellectual-property asset and features of products or
services to determine similarities between the features of the products or
services and the features of the intellectual-
property asset. In some cases, the amount of similarity may be based on
similarities of words associated with the products
and or services and the intellectual-property asset. The amount of similarity
may also be based on similarities in
.. relationships between words related to features of the products or services
and relationships between words related to
features of the intellectual-property asset. The intellectual-property
services provider may determine that there is a
relationship between a product or service and an intellectual-property asset
based on a similarity between the features of
the product or service and the features of the intellectual-property asset is
at least a threshold similarity. In an illustrative
example, an intellectual-property services provider may determine features of
a claim of a patent document and features
of a product or service. The intellectual-property service provider may then
identify a relationship between the claim and
the product or service based on similarities between the features of the claim
of the patent document and features of the
product or service.
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[0157] At 1008, the process 1000 includes generating, based at least in
part on the one or more relationships,
association data indicating the one or more relationships between the
individual ones of the products and the individual
ones of the intellectual-property assets. For example, the association data
may include a framework of relationships
between individual products or services and at least one intellectual-property
asset that is mapped to the product or service.
The framework may also indicate individual intellectual-property assets and at
least one product or service that is
associated with the intellectual-property asset. In this way, the framework
may be searchable based on intellectual-
property asset or based on product or service in order to identify products or
services and intellectual-property assets that
are related.
[0158] The association data may include a mapping included in the
framework that indicates an intellectual-property
asset that corresponds to a product or service. In various implementations, an
intellectual-property services provider may
receive a request to identify one or more products or services and one or more
intellectual-property assets that are related.
In these situations, the intellectual-property services provider may parse the
framework based on identifiers of the
intellectual-property assets or identifiers of the intellectual-property
assets to determine relationships between products
or services and intellectual-property assets. The relationships between the
products or services and the intellectual-
property assets may be utilized by the intellectual-property services provider
to provide various intellectual property
related services to a customer of the intellectual-property services provider.
In particular implementations, the intellectual
property related services may include valuation services for intellectual-
property assets. In these scenarios, the
intellectual-property services provider may determine one or more metrics
related to the intellectual-property asset where
the one or more metrics including at least one of a measure of breadth of one
or more portions of the intellectual-property
asset, a measure of risk with respect to the one or more portions of the
intellectual-property asset, or a measure of coverage
of the one or more portions of the intellectual-property asset. The
intellectual-property services provider may also
determine revenue obtained for the product or service over a period of time
and then determine, based at least partly on
the one or more metrics, an amount of the revenue of the product or service to
attribute to one or more portions of the
intellectual-property asset. After determining the amount of revenue of the
product or service to attribute to the
intellectual-property asset, the intellectual-property services provider may
determine a value of the intellectual-property
asset based at least partly the amount of revenue of the product or service
obtained over the period of time and the portion
of the amount of revenue of the product or service attributed to the
intellectual-property asset.
[0159] At block 1010, the process 1000 includes receiving a request to
identify an intellectual-property asset of the
intellectual-property assets that corresponds to a product of the products.
For example, a user, using a user interface, may
provide input indicating a request to identify an asset that corresponds to a
given product of the products. Input data
corresponding to the input may be received as the request.
[0160] At block 1012, the process 1000 includes identifying, based at
least in part on the association data, the
intellectual-property asset that corresponds to the product. For example, the
system may be utilized to determine, using
the association data, which products have relationships with the intellectual-
property asset.
[0161] At 1014, the process 1000 includes generating a response to the
request, the response indicating that the
intellectual-property asset is associated with the product. In some
implementations, the user interface may also include
one or more user interface elements to provide input regarding the
relationships between the intellectual-property asset
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and the product or service. In certain implementations, input may be obtained
via the user interface or via an additional
user interface indicating one or modifications to the relationship between the
intellectual-property asset and the product
or service.
[0162] FIG. 11 illustrates an example process 1100 to determine an
intellectual-property asset that corresponds to a
product or service using a classification system according to some
implementations.
[0163] At 1102, the process 1100 includes generating a classification
system that includes classifications, individual
ones of the classifications corresponding to a technology group. In various
implementations, individual classifications
may be associated with one or more criteria. In illustrative implementations,
the individual classifications may be
associated with one or more words and each classification may be associated
with different groups of words. Additionally,
the individual classifications of the classification system may be associated
with one or more physical features, one or
more technical features, or combinations thereof In certain implementations,
the one or more physical features and/or the
one or more technical features may each be related to a set of words.
[0164] At 1104, the process 1100 includes receiving infor
_______________________ nation about a product offered for acquisition by an
organization, the information obtained from at least one of: a datastore of
the organization; a website of the organization;
.. or input via a user interface.
[0165] At 1106, the process 1100 includes determining, based at least in
part on the information, a first feature of the
product. The information about the product or service may be analyzed by
parsing the information about the product or
service to determine one or more words associated with the product or service.
In particular implementations, the
information about the product or service may be analyzed to determine at least
one of one or more physical features or
.. one or more technical features of the product or service. The one or more
physical features and/or the one or more technical
features of the product or service may be identified based at least partly on
comparing words of at least one technical
feature and/or words of at least one physical feature to words included in the
information obtained about the product or
service. In an illustrative implementation, a physical feature of the product
or service may be identified based at least
partly on at least one word related to the physical feature being included in
the information about the product or service.
In addition, a technical feature of a product or service may be identified
based at least partly on at least one word related
to the technical feature being included in the information about the product
or service.
[0166] At 1108, the process 1100 includes determining that the product
corresponds to a classification of the
classifications based at least partly on the first feature corresponding to a
reference feature associated with the
classification. In various implementations, words associated with the first
features of the product or service may be
compared to additional words associated with second features of the
classification. In certain implementations, a
classification may be assigned to a product or service based at least partly
on at least a threshold number of words of the
first features of the product or service corresponding to a number of words of
the second features of the classification. In
particular implementations, a model may be used to determine classifications
for product or services. The model may
receive input including words corresponding to features of products or service
and words corresponding to classifications
and determine probabilities that products or services correspond to
classifications of the classification system. In
illustrative implementations, a classification may be assigned to a product or
service when a probability that the product
or service corresponds to a classification is greater than a threshold
probability. In additional implementations, a
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classification may be assigned to a product or service when a probability that
the product or service corresponds to the
classification is a highest probability among a plurality of probabilities
that have been determined for the product or
service using the model for a plurality of classifications.
[0167] At 1110, the process 1100 includes identifying an intellectual-
property asset associated with the organization.
The intellectual-property asset of the organization may be identified based on
information obtained from the organization.
In particular implementations, an intellectual-property services provider may
obtain information about the intellectual-
property asset that includes a document that corresponds to the intellectual-
property asset, such as a trade secret document,
a patent application, a utility patent, a design patent, a plant patent, a
trademark application, or a copyright submission. In
additional implementations, an organization may provide identifiers of
intellectual-property assets of the organization and
the intellectual-property services provider may obtain information about the
intellectual-property assets from one or more
databases based on the identifiers.
[0168] At 1112, the process 1100 includes determining a second feature of
the intellectual-property asset. The
features of the intellectual-property asset may be determined by analyzing
information related to the intellectual-property
asset, such as documents related to the intellectual-property asset. In
particular implementations, the intellectual-property
asset may be a claim of a patent or patent application, and the features of
the intellectual-property asset may be identified
by analyzing words of the claim. Additionally, when the intellectual-property
asset is a claim of a patent or patent
application, the features of the intellectual-property asset may be identified
by analyzing words of elements of the claim.
Further, when the intellectual-property asset is a trademark, the features of
the trademark may be identified by analyzing
words of a description of goods or services associated with the trademark. In
various implementations, the features of the
intellectual-property asset may be identified by comparing words included in
documents associated with the intellectual-
property asset with words associated with physical features and/or technical
features. An intellectual-property services
provider may assign words to individual physical features and individual
technical features. In certain implementations,
an intellectual-property services provider may determine that an intellectual-
property asset includes a technical feature or
a physical feature when at least one word associated with the intellectual-
property asset corresponds to at least one
additional word related to the technical feature or at least one additional
word related to the physical feature.
[0169] At 1114, the process 1100 includes determining that the
intellectual-property asset corresponds to the
classification based at least paitly OD the second feature of the intellectual-
property asset corresponding to the reference
feature associated with the classification. An intellectual-property services
provider may determine that one or more third
features of the intellectual-property asset correspond to the at least one
fourth feature associated with the classification by
comparing words of the one or more third features to words of the at least one
fourth feature. In various implementations,
the intellectual-property services provider may determine that the one or more
third features of the intellectual-property
asset correspond to the at least one fourth feature based at least partly on
at least a threshold number of words of the one
or more third features correspond to words of the at least one fourth feature.
[0170] In particular implementations, a model may be used to determine
classifications for intellectual-property
assets. The model may receive input including words corresponding to features
of intellectual-property assets and words
corresponding to classifications and determine probabilities that intellectual-
property assets correspond to classifications
of the classification system. In illustrative implementations, a
classification may be assigned to an intellectual-property
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asset when a probability that the intellectual-property asset corresponds to
the classification is greater than a threshold
probability. In additional implementations, a classification may be assigned
to an intellectual-property asset when a
probability that the intellectual-property asset corresponds to the
classification is a highest probability among a plurality
of probabilities that have been determined for the intellectual-property asset
using the model for a plurality of
classifications.
101711 In various implementations, models used to determine
classifications for intellectual-property assets and
models used to determine classifications for intellectual-property assets may
be modified. For example, an intellectual-
property services provider may request input regarding a classification of an
intellectual-property asset. In some cases,
the input may indicate that the intellectual-property asset should be
classified according to a different classification. In
other situations, the input may indicate that the intellectual-property asset
is classified correctly. The intellectual-property
services provider may then modify the model used to classify intellectual-
property asset based the input. Additionally, the
intellectual-property services provider may request input regarding a
classification of a product or service. The input may
indicate that the product or service should be classified according to a
different classification. In other scenarios, the input
may indicate that the product or service is classified correctly. The
intellectual-property services provider may then modify
the model used to classify the product or service based on the input.
101721 FIG. 12 illustrates an example process 1200 to perform a
qualitative analysis and a quantitative analysis of
intellectual-property data according to some implementations.
101731 At 1202, the process 1200 includes receiving information
indicating revenue associated with a product. The
information may include financial data such as information regarding revenue
obtained by one or more organizations
through sales of the product or service. The financial data may be obtained
from a variety of sources. For example, an
intellectual-property services provider may provide a portal that captures
information regarding financial data of products
and/or services. To illustrate, the intellectual-property services provider
may generate one or more user interfaces that
include one or more user interface elements to capture one or more portions of
the financial data. In additional
implementations, the intellectual-property- services provider may implement
software tools to parse a datastore of an
organization that offers the product or service for sale to identify portions
of the financial data corresponding to the product
or service. In further implementations, the intellectual-property services
provider may analyze information from one or
more websites to identify at least a portion of the financial data
corresponding to the product or service. In illustrative
examples, the intellectual-property services provider may utilize web crawlers
and other website parsing tools to analyze
information included in websites, including websites of one or more
organizations offering the product or service for
acquisition and/or third-party websites, to identify- at least a portion of
the fmancial data corresponding to the product or
service.
101741 At block 1204, the process 1200 includes determining a
classification of the product based at least partly on
a technical feature of the product. For example, the intellectual-property
services provider may determine a classification
for the product or service by determining features of the product or service
and comparing the features of the product or
service to criteria for a number of classifications of the classification
system.
101751 At block 1206, the process 1200 includes identifying a patent
claim that corresponds to the product based at
least partly on the patent claim being associated with the classification. For
example, intellectual-property assets may be
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associated with the classification, Those intellectual-property assets may
include patents, which may include claims.
Additionally, the process 1200 may include identifying, generally, an
intellectual-property asset of an organization. The
intellectual-property asset of the organization may include one or more
intellectual-property assets having legal rights that
may be enforced by the organization. In various implementations, the
intellectual-property asset may be assigned to the
organization. In additional implementations, the organization may have a
license with respect to the intellectual-property
asset. An intellectual-property services provider may determine that the
intellectual-property asset corresponds to the
organization based at least partly on information obtained from the
organization. For example, the organization may
provide a list of intellectual-property assets to the intellectual-property
services provider. The list may be stored in a data
store of the organization that is accessible to the intellectual-property
services provider and the intellectual-property
.. services provider may parse the data store to obtain the list. In further
implementations, the organization may provide the
list of intellectual-property assets to the intellectual-property services
provider via a communication, such as an email or
message. Also, the intellectual-property services organization may provide a
customer portal by which the organization
may provide a list of intellectual-property assets of the organization. In
particular implementations, the intellectual-
property services provider may analyze information available from public data
sources, such as patent jurisdiction
databases, to identify intellectual-property assets of the organization. To
illustrate, the intellectual-property services
provider may parse a publicly accessible datastore to identify intellectual-
property assets that are assigned to the
organization, intellectual-property assets where the organization is an
applicant, intellectual-property assets having
inventors that are related to the organization, or combinations thereof.
101761 At block 1208, the process 1200 includes identifying words
included in the patent claim. For example, the
data representing the patent may be parsed andior textual recognition
techniques may be performed to identify the words
that make up the patent claim.
[0177] At block 1210, the process 1200 includes determining a breadth of
the patent claim, In some implementations,
the intellectual-property services provider may determine the breadth of the
intellectual-property asset relative to the
breadth of other intellectual-property assets, such as intellectual-property
assets in a same classification as the intellectual-
property asset, to determine the portion of revenue of the product or service
to attribute to the intellectual-property asset.
101781 At block 1212, the process 1200 includes determining a portion of
the revenue to apportion to the patent claim
based at least partly on the breadth of the patent claim. For example, in
order to determine the measures of breadth and/or
portions of revenue of the products and/or services corresponding to the
intellectual-property assets, the intellectual-
property services system may utilize one or more linguistic analysis
techniques and one or more machine learning
techniques. An intellectual-property services provider may determine a portion
of the revenue for the product or service
to attribute to the intellectual-property asset based on an amount of features
of the product or service that are covered by
the intellectual-property asset. For example, if a product or service has a
number of features, the portion of the number of
features covered by the intellectual-property asset with respect to the total
number of features may correspond to the
portion of the revenue for the product or service to attribute to the
intellectual-property asset. In an illustrative example,
an intellectual-property asset may cover 2% of the features of a product or
service, and the intellectual-property services
provider may determine that 2% of the revenue of the product or service is to
be attributed to the intellectual-property
asset. In particular implementations, the proportion of the features of the
product or service covered by the intellectual-
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property asset may serve as a starting point for determining the portion of
revenue for the product or service to attribute
to the intellectual-property asset. In various implementations, an
intellectual-property services provider may modify an
initial portion of the amount of revenue of the product or service attributed
to the intellectual-property asset based on a
number of discount factors, which will be discussed in more detail below. In
additional implementations, an intellectual-
property services provider may determine a portion of the revenue for the
product or service to attribute to the intellectual-
property asset based on a breadth of the intellectual-property asset. In some
implementations, the intellectual-property
services provider may determine the breadth of the intellectual-property asset
relative to the breadth of other intellectual-
property assets, such as intellectual-property assets in a same classification
as the intellectual-property asset, to determine
the portion of revenue of the product or service to attribute to the
intellectual-property asset.
[0179] At block 1214, the process 1200 includes determining a measure of
value of the patent claim based at least
partly on the portion of the revenue apportioned to the patent claim. For
example, the measure of value for the intellectual-
property asset may be determined by multiplying the revenue for the product or
service by the portion of the revenue of
the product or service attributed to the intellectual-property asset. In
various implementations, one or more discount factors
may also be used to determine the measure of value for the intellectual-
property asset. The discount factors may be applied
to at least one of the amount of revenue for the product or service used to
determine the measure of value or the portion
of the revenue for the product or service attributed to the intellectual-
property asset. The one or more discount factors
may reduce an initial measure of the value of the intellectual-property asset
to a modified measure of value of the
intellectual-property asset. In illustrative examples, one or more discount
factors may be based at least partly on a first
risk corresponding to invalidation of the intellectual-property asset and a
second risk corresponding to a probability of
litigation with respect to the intellectual-property asset. In particular
implementations, the intellectual-property asset may
include a patent claim and the first risk may be based at least partly on
prosecution history events related to the patent
claim. Additionally, in situations where the intellectual-property asset
includes a patent claim, the first risk may be based
at least partly on metrics of an examiner related to the patent claim relative
to additional metrics of additional examiners
included in a same art unit as the examiner, the metrics corresponding to at
least one of a number of notices of allowance
produced over a period of time, an average number of office actions before
producing a notice of allowance, a number of
notices of appeal filed over the period of time, a number of reversals in
appeal decisions over the period of time, or
combinations thereof. Further, the second risk based at least partly on a
first number of litigation events taking place with
respect to a number of intellectual-property assets having a same
classification as the intellectual-property asset relative
to a second number of litigation events taking place with respect to an
additional plurality of intellectual-property assets
included in a different classification of a classification system. In some
illustrative examples where the intellectual
-
property asset includes a patent claim, a discount factor may be determined
based at least partly on a number of additional
patent claims assigned to the organization that correspond to the product or
service. In illustrative examples where the
intellectual-property asset includes a trademark, a discount factor may be
based at least partly on at least one of a number
of litigation events related to trademark assets included in a same
classification as the trademark asset, a number of
oppositions related to the trademark assets included in the same
classification as the trademark asset, or metrics of an
examiner associated with the trademark asset in relation to additional metrics
of additional examiners associated with
additional trademark assets included in the classification.
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[0180] Additionally, or alternatively, the process 1200 may include
determining that the product or service
corresponds to the intellectual-property asset. An intellectual-property
services provider may determine that the product
or service corresponds to the intellectual-property asset based on obtaining
input indicating that the product or service
corresponds to the intellectual-property asset. For example, a representative
of the organization may access a customer
portal provided by the intellectual-property services provider to enter
information via a user interface indicating that the
product or service corresponds to the intellectual-property asset. In other
examples, a representative of the intellectual-
property services provider may enter information into a user interface
indicating that the intellectual-property asset
corresponds to the product or service. In additional implementations, the
organization may store data indicating
relationships between intellectual-property assets and products and/or
services offered by the organization for sale. To
illustrate, for each product or service of the organization, the organization
may store a list of intellectual-property assets
that are related to one or more features of the respective product or service.
In these scenarios, the intellectual-property
services provider may parse a datastore of the organization or a website of
the organization that includes the list of
intellectual-property assets that are related to one or more products and/or
services of the organization.
[0181] In additional implementations, an intellectual-property services
provider may determine a product or service
that corresponds to an intellectual-property asset of the organization by
determining an amount of similarity between the
product or service and the intellectual-property asset. In various
implementations, the intellectual-property service
provider may parse an intellectual-property document associated with the
intellectual-property asset to determine
individual first words of the intellectual-property document and parse
information related to the product or service to
determine individual second words included in the information. The
intellectual-property services provider may then
determine a similarity metric between at least a portion of the individual
first words and at least a portion of the individual
second words. The intellectual-property services provider may determine that
the product or service corresponds to the
intellectual-property asset based at least partly on determining that the
similarity metric is at least a threshold similarity
metric. In further implementations, the intellectual-property services
provider may analyze information about the product
or service and information about the intellectual-property asset to determine
physical and/or technical features of the
product or service and physical and/or teclmical features of the intellectual-
property asset. The intellectual-property
services provider may determine that the intellectual-property asset
corresponds to the product or service based at least
partly on similarities between physical features andlor technical features of
the product or service and physical features
and/or technical features of the intellectual-property asset.
[0182] In particular implementations, the intellectual-property services
provider may determine that both the product
or service and the intellectual-property asset are associated with a same
classification of a classification system before
analyzing the information of the product or service and the information of the
intellectual-property asset to determine
similarities between the intellectual-property asset and the product or
service. In various implementations, the intellectual
-
property services provider may determine a classification for the product or
service by determining features of the product
or service and comparing the features of the product or service to criteria
for a number of classifications of the
classification system. Additionally, the intellectual-property services
provider may determine a classification for the
intellectual-property asset by determining features of the intellectual-
property asset and comparing the features of the
intellectual-property asset to criteria for a number of classifications of the
classification system. In various
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implementations, the intellectual-property services provider may determine
first similarity metrics indicating amounts of
similarity between the features of the product or service and the criteria of
the classifications and determine second
similarity metrics indicating amounts of similarity between the features of
the intellectual-property asset and the criteria
of the classifications. The intellectual-property services provider may then
utilize the first similarity metrics to determine
a classification for the product or service and the second similarity metrics
to determine a classification for the intellectual-
property asset. The intellectual-property services provider may determine a
classification for the product or service and a
classification for the intellectual-property asset based on a threshold
similarity metric such that a first similarity metric
and/or a second similarity metric for a particular classification that is
above the threshold similarity metric may indicate
that product or service and/or the intellectual-property asset corresponds to
the classification. In additional
implementations, the intellectual-property services provider may determine a
first similarity metric having a highest value
among the first similarity metrics to determine that the classification
related to the highest value first similarity metric
corresponds to the product or service. The intellectual-property services
provider triay also determine a second similarity
metric having a highest value among the second similarity metrics to determine
that the classification related to the highest
value second similarity metric corresponds to the intellectual-property asset.
[0183] FIG. 13 illustrates an example process 1300 to determine an
intellectual-property asset that corresponds to a
product or service using a linguistic structure of the intellectual-property
asset and a linguistic structure of the product or
service according to some implementations.
[0184] At 1302, the process 1300 includes deteimining first parts of
speech for first words included in first
information associated with a product. In various implementations, natural
language processing techniques may be used
to determine the individual words included in the first information and the
parts of speech associated with the individual
words. In particular implementations, an intellectual-property services
provider may determine at least one of nouns,
verbs, adjectives, adverbs, prepositions, conjunctions, or pronouns included
in the first information. Additionally, the
intellectual-property services provider may determine relationships between
the words included in the first information.
For example, the intellectual-property services provider may identify the
words included in a same sentence. The
intellectual-property services provider may also identify words included in a
same paragraph. Additionally, the
intellectual-property services provider may identify one or more adjectives
that modify individual nouns and one or more
adverbs that modify individual verbs. Further, the intellectual-property
services provider may store data indicating the
relationships between words. To illustrate, the intellectual-property services
provider may assign an identifier to individual
words included in the first information and assign codes or classes to the
individual words. In a particular example, the
intellectual-property services provider may assign a code to a word included
in the first information indicating that the
word is a noun and also store in a table related to the word, an identifier of
an adjective related to the word. The table may
also include identifiers of words in a same sentence or element as the noun.
[0185] At block 1304, the process 1300 includes determining second parts
of speech for second words included in
second information corresponding to a claim of a patent document. Determining
the second parts of speech may be
performed in the same or a similar manner as determining the first parts of
speech, as described above.
[0186] At block 1306, the process 1300 includes determining a portion of
the first words that correspond to a feature
of the product. For example, a catalog of features may be associated with the
product, and the intellectual-property
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services provider may analyze the first words in association with the features
to determining which of the words
correspond to at least one of the features associated with the product.
[0187] At block 1308, the process 1300 includes determining, based at
least partly on the first parts of speech, a first
action performed with respect to the feature. For example, the intellectual-
property services provider may determine
.. which of the words is a verb acting on a given feature. The verb may
indicate the action performed with respect to the
feature.
[0188] At 1310, the process 1300 includes generating, based at least
partly on the first action, a first linguistic
structure for the feature, the first linguistic structure indicating one or
more first relationships between the first action and
one or more first nouns included in the first information. In particular
examples, the linguistic structure may include a tree
structure with a root node and one or more branch nodes. The root node may be
in a first level of the tree structure and
the one or more branch nodes may be included in subsequent levels of the tree
structure. In the tree structure, each node
that is a branch of another node is related to the initial node. That is, the
tree structure may include parent nodes and child
nodes that are related to the parent nodes. In an illustrative example, a noun
included in a first node on a first level of the
tree structure may be associated with a first adjective included in a second
node and a second adjective in a third node of
the tree structure, where the second node and third node are child nodes of
the first node and are included in a second
level of the tree structure. In various implementations, a linguistic
structure of the intellectual-property asset may be
generated with respect to an action related to the intellectual-property asset
in a root node with the words corresponding
to the action being included in the branch nodes. In an illustrative example,
a verb corresponding to the action may be
included in a root node on a first level of the linguistic structure and nouns
and adjectives related to the verb may be
included in branch nodes of the linguistic structure on second and/or third
levels of the linguistic structure. In situations
where the intellectual-property asset is a patent claim, the intellectual-
property services provider may generate a linguistic
structure for individual elements included in the patent claim.
[0189] At 1312, the process 1300 includes determining, based at least
partly on the second parts of speech. a second
action included in the claim. That is, an intellectual-property services
provider may analyze the words included in the
second information and identify at least one of nouns, verbs, adjectives,
adverbs, prepositions, conjunctions, or pronouns
included in the second information. In particular implementations, the
intellectual-property services provider may utilize
natural language processing techniques to determine the individual words and
the respective parts of speech of the words
included in the second information.
[0190] At 1314, the process 1300 includes generating, based at least
partly on the second action, a second linguistic
structure for the claim, the second linguistic structure indicating one or
more second relationships between the second
action and one or more second nouns included in the claim. The second and/or
additional linguistic structure generated
based on the second information may have a tree structure with a root node and
one or more branch nodes. The root node
may be in a first level of the tree structure and the one or more branch nodes
may be included in subsequent levels of the
tree structure. In the tree structure, each node that is a branch of another
node is related to the initial node. That is, the tree
structure may include parent nodes and child nodes that are related to the
parent nodes. In an illustrative example, a noun
included in a first node on a first level of the tree structure may be
associated with a first adjective included in a second
node and a second adjective in a third node of the tree structure, where the
second node and third node are child nodes of
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the first node and are included in a second level of the tree structure. In
various implementations, a linguistic structure of
the product or service may be generated with respect to an action performed
with respect to the product or service being
in a root node with the additional words corresponding to the action being
included in the branch nodes. In an illustrative
example, a verb corresponding to the action may be included in a root node on
a first level of the additional linguistic
structure and nouns and adjectives related to the verb may be included in
branch nodes of the additional linguistic structure
on second and/or third levels of the additional linguistic structure. In some
implementations, the intellectual-property
services provider may generate a linguistic structure for individual technical
features of the product or service, for
individual physical features of the product or service, or both.
[0191] At 1316, the process 1300 includes determining a similarity metric
between the first linguistic structure and
.. the second linguistic structure. For example, OM or more components of the,
first linguistic structure may be compared
to one or more components of the second linguistic structure. When the
components of the linguistic structures correspond
to each other, the similarity metric may indicate a high degree of similarity.
When the components do not correspond
and/or differences exist as between the linguistic structures, the similarity
metric may indicate a low degree of similarity.
The measure of similarity between the first linguistic structure and the
second linguistic structure may be determined by
comparing similarities in the configuration of the first linguistic structure
and the configuration of the second linguistic
structure. For example, an intellectual-property services provider may
determine the measure of similarity based on a
number of levels included in the first linguistic structure and a number of
levels included in the second linguistic structure.
The intellectual-property services provider may also determine the measure of
similarity based on a number of nodes in
each level of the first linguistic structure and a number of nodes in each
level of the second linguistic structure. To
.. illustrate, the intellectual-property services provider may compare a
number of nodes in a second level of the first linguistic
structure with a number of nodes in a second level of the second linguistic
structure.
[0192] The intellectual-property services provider may also determine the
measure of similarity based on similarities
between words included in the first linguistic structure and the words
included in the second linguistic structure. To
illustrate, the intellectual-property services provider may compare one or
more words included in a root node of the first
linguistic structure with one or more words included in a root node of the
second linguistic structure. In these situations,
the measure of similarity may be based on whether the one or more words
included in the root node of the first linguistic
structure and the one or more words included in the root node of the second
linguistic structure are the same, similar,
synonyms, and the like. Additionally, the intellectual-property services
provider may compare words in the branch nodes
of the first linguistic structure and the words in the branch nodes of the
second linguistic structure to determine the measure
of similarity. In particular implementations, the intellectual-property
services provider may compare words included in
individual levels of the first linguistic structure with words included in
individual levels of the second linguistic structure.
[0193] At 1318, the process 1300 includes determining, based at least
partly on the similarity metric, that the claim
corresponds to the product. In some illustrative examples, the intellectual-
property services provider may determine that
the product or service and the intellectual-property asset are in a same
classification of a classification system before
.. comparing the first linguistic structure and the second linguistic
structure. Additionally, in various situations, the
intellectual-property service provider may generate multiple linguistic
structures for the product or service and multiple
linguistic structures for the intellectual-property asset. In these scenarios,
the intellectual-property services provider may
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compare one or more linguistic structures of the product or service with one
or more linguistic structures of the intellectual-
property asset to determine a measure of similarity between the product or
service and the intellectual-property asset. In
further implementations, the measure of similarity, such as a similarity
metric, between the first linguistic structure and
the second linguistic structure may be modified based on user input. For
example, the intellectual-property services
provider may receive input indicating that the product or service does not
correspond with the intellectual-property asset.
In these situations, the intellectual-property services provider may modify
the measure of similarity and/or modify a model
used to generate the measure of similarity based on the input. In additional
implementations, the intellectual-property
services provider may determine that an intellectual-property asset and a
product or service do not correspond to each
other and the intellectual-property services provider may receive input
indicating that the additional product or service
and the additional intellectual-property asset do correspond to one another.
Accordingly, the intellectual-property services
provider may modify an additional measure of similarity between one or more
linguistic structures of the product or
service and one or more linguistic structures of the intellectual-property
asset or a mode used to generate the additional
measure of similarity based on the input.
[0194] FIG. 14 illustrates an example process 1400 to provide services to
a customer based on relationships between
a product or service and an intellectual-property asset according to some
implementations.
[0195] At 1402, the process 1400 includes receiving, from one or more
data sources, first information regarding
products offered for acquisition. The information may include details
associated with the products and/or the sources of
the products, for example.
[0196] At 1404, the process 1400 includes receiving, from the one or more
data sources, second information
regarding intellectual-property assets. For example. the information may
include documents andlor data associated with
the intellectual-property assets and/or that correspond to the intellectual-
property assets.
[0197] At 1406, the process 1400 includes determining, based at least in
part on the first information and the second
information, that an intellectual-property asset of the intellectual-property
assets corresponds to a feature associated with
a product of the products. The comparisons between the individual intellectual-
property assets and the individual products
and/or services may be used by the intellectual-property services provider to
determine similarity metrics between the
individual intellectual-property assets and the individual products and/or
services. In situations, where a similarity metric
is greater than a threshold metric or has a highest value among a number of
similarity metrics associated with a particular
classification, the intellectual-property services provider may determine that
there is a relationship between the
intellectual-property asset and the product or service. In illustrative
implementations, the intellectual-property services
provider may generate linguistic structures using natural language processing
techniques to determine similarity metrics
for respective intellectual-property assets and respective products or
services.
[0198] In various implementations, an intellectual-property services
provider may generate a framework that
indicates relationships between intellectual-property assets and products
and/or services. In these scenarios, the
intellectual-property services provider may receive a request to determine a
product or service that corresponds to an
intellectual-property asset. The intellectual-property services provider may
receive an identifier of the product or service
and then parse the framework using the identifier of the product or service to
identify one or more intellectual-property
assets that the framework indicates have a relationship with the product or
service. Additionally, the intellectual-property
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services provider may receive a request including an identifier of an
intellectual-property asset. In these cases, the
intellectual-property services provider may parse the framework using the
identifier and identify one or more products or
services that the framework indicates have a relationship with the
intellectual-property asset.
[0199] At 1408, the process 1400 includes receiving a first request to
determine a value of the intellectual-property
asset. In various implementations, the request may be provided via one or more
tools offered by the intellectual-property
services provider. In various implementations, the intellectual-property
services provider may generate one or more user
interfaces by which requests for services may be made by customers of the
intellectual-property services provider and/or
by representatives of the intellectual-property services provider.
[0200] At 1410, the process 1400 includes identifying, based at least
partly on receiving the first request, economic
data indicating revenue of an organization that is associated with the
product. The economic data may indicate, for a
given product, the amount of the organization's revenue that is attributable
to the product.
[0201] At block 1412, the process 1400 includes determining a portion of
the revenue attributable to the intellectual-
property asset. In illustrative examples, the intellectual-property services
provider may determine a breadth of the
intellectual-property asset with respect to the breadth of additional
intellectual-property assets, such as intellectual-
.. property assets included in a same technology category as the intellectual-
property asset. In these situations, the
intellectual-property services provider may determine the portion of revenue
of the product or service to attribute to the
intellectual-property asset based at least partly on the breadth of the
intellectual-property asset relative to the breadth of
the additional intellectual-property assets. A higher relative breadth score
of the intellectual-property asset with respect to
the additional intellectual-property assets may cause the intellectual-
property services provider to apportion a larger
amount of the revenue of the product or service to the intellectual-property
asset in relation to an amount of the revenue
of the product or service attributed to the intellectual-property asset in
situations where the relative breadth of the
intellectual-property asset is lower.
[0202] At 1414, the process 1400 includes detenuirting, based at least
partly on the portion of the revenue, a measure
of value of the intellectual-property asset. For example, the portion of the
revenue attributable to the intellectual-property
may be utilized as a factor to determine an overall value of the asset, in
addition to, in examples, revenues of other products
attributable to the asset and/or characteristics of the asset, such as
breadth, coverage, and/or exposure factors.
[0203] At 1416, the process 1400 includes receiving a second request to
determine at least OM of: a first exposure
value representing loss of coverage with respect to the intellectual-property
asset; or a second exposure value representing
a litigation event with respect to the intellectual-property- asset.
[0204] At 1418, the process 1400 includes determining, based at least in
part on receiving the second request, at least
one of the first exposure value or the second exposure value. The exposure
value associated with the intellectual-property
asset may be based on a probability of a litigation event occurring with
respect to the intellectual-property asset. In
additional implementations, the exposure value associated with the
intellectual-property asset may correspond to a
probability that the scope of the intellectual-property asset may be reduced.
In further implementations, the amount of
exposure associated with the intellectual-property asset may correspond to a
probability that the intellectual-property asset
may be invalidated in whole or in part. In illustrative examples, the higher
the amount of exposure related to the
intellectual-property asset, the higher the discount applied to the portion of
the revenue of the product or service attributed
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to the intellectual-property asset. In situations where the intellectual-
property asset is a trade secret, the intellectual-
property services provider may determine a discount to apply to the portion of
the revenue of the product or service
attributed to the intellectual-property asset based on probability of theft of
the trade secret.
[0205] At 1420, the process 1400 includes causing display, via one or
more user interfaces, of an indicator of the
measure of value of the product and the at least one of the first exposure
value or the second exposure value. For example,
the measure of value of the intellectual-property asset may be determined
using the revenue of the product or service
received by one or more organizations via sales of the product or service over
a period of time and the portion of revenue
of the product or service attributed to the intellectual-property asset. In
particular implementations, the measure of value
may be updated. For example, as the intellectual-property services provider
obtains updated revenue information for the
product or service, the intellectual-property services provider may update the
measure of value for the intellectual-property
asset based on the updated revenue. Additionally, the intellectual-property
services provider may obtain information that
may be used to update the discount applied to the portion of the revenue of
the product or service to attribute to the
intellectual-property asset and the intellectual-property service system may
corresponding update the measure of value
based on the modified discount to apply. In certain implementation, the
intellectual-property services provider may obtain
feedback indicating an accuracy of the measure of value and modify the measure
of value based on the feedback.
[0206] In various implementations, the measure of value for the
intellectual-property asset may be based on a type
of valuation for the intellectual-property asset. To illustrate, a first
measure of value may be determined when the
intellectual-property asset is being valued as part of a sale of the
intellectual-property asset and a second measure of value
may be determined when the intellectual-property asset is being valued as
collateral for a loan. In other examples, a third
measure of value may be determined when the intellectual-property asset is
being valued as part of a sale of an organization
or a merger of an organization that holds the legal rights to enforce the
intellectual-property asset.
[0207] In particular implementations, additional services may be provided
by the intellectual-property services
provider. For example, the intellectual-property services provider may receive
a request to identify a number of
intellectual-property assets of an organization that are associated with a
particular technology group. In other examples,
the intellectual-property services provider may receive requests to determine
one or more risks corresponding to the
intellectual-property services provider. In additional examples, the
intellectual-property services provider may receive a
request to identify one or more organizations that have intellectual property
in a particular technology group or in a
particular classification of a system of classifications. In illustrative
examples, the intellectual-property services provider
may utilize a framework indicating relationships between intellectual-property
assets and products or services to provide
response to the requests. In various situations, the intellectual-property
services provider may obtain identifiers of
intellectual-property assets, identifiers of organizations, identifiers of
products or services, identifiers of technology
groups, or combinations thereof to utilize to parse the framework and provide
responses to the requests for services. In
particular illustrative scenarios, the various identifiers may include
alphanumeric strings that include a series of characters.
In additional implementations, the requests for services may include keywords
that the intellectual-property services
provider may utilize to parse the framework and generate the responses to the
requests for services.
[0208] CLAUSES
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[0209] 1. A method comprising: receiving, from one or more data sources,
information about products; identifying
intellectual-property assets; determining one or more relationships between
individual ones of the products and individual
ones of the intellectual-property assets; generating, based at least in part
on the one or more relationships, association data
indicating the one or more relationships between the individual ones of the
products and the individual ones of the
intellectual-property assets; receiving a request to identify an intellectual-
property asset of the intellectual-property assets
that corresponds to a product of the products; identifying, based at least in
part on the association data, the intellectual-
property asset that corresponds to the product; and generating a response to
the request, the response indicating that the
intellectual-property asset is associated with the product.
[0210] 2. The method of clause 1, wherein the data sources include a
publicly -accessible data source, and the method
further comprises: detennining a keyword associated with the product;
identifying, based at least in part on the publicly-
accessible data source, data corresponding to the keyword; and extracting the
data that corresponds to the keyword from
the publicly-accessible data source.
[0211] 3. The method of clauses 1 or 2, wherein the data sources include
a data store associated with a first
organization offering the product for acquisition, and the method further
comprises: determining, by a second
organization, a keywords associated with the product; identifying, by the
second organization and from the data store of
the first organization, data that corresponds to the keyword; and extracting,
by the second organization, the data that
corresponds to the keyword.
[0212] 4. The method of any of clauses 1, 2, or 3, further comprising:
identifying, utilizing the data store, data
indicating a relationship between the intellectual-property asset and the
product; and wherein generating the association
data comprises generating the association data based at least partly on the
data indicating the relationship between the
intellectual-property asset and the product.
[0213] 5. The method of any of clauses 1, 2, 3, or 4, wherein the
request comprises a first request, and the method
further comprises: causing a second request for information about the product
to be at least one of: published on a website
accessible to computing devices; or sent to the computing devices; and
receiving, in response to the second request. data
indicating at least one of a source of the information or the information.
[0214] 6. The method of any of clauses 1, 2, 3, 4, or 5, further
comprising: generating a user interface including a
user-interface element configured to receive input representing information
about the intellectual-property asset;
receiving, utilizing the user-interface element, the input: and wherein
generating the association data comprises generating
the association data based at least in part on the input.
[0215] 7. The method of any of clauses 1, 2, 3, 4, 5, or 6, further
comprising: determining a metric associated the
intellectual-property asset, the metric including at least one of: a measure
of breadth of at least a portion of the intellectual-
property asset; a measure of exposure associated with at least a portion of
the intellectual-property asset; or a measure of
coverage of at least a portion of the intellectual-property asset; determining
revenue associated with the product over a
period of time; and determining, based at least partly on the metric, an
amount of the revenue to attribute to at least a
portion of the intellectual-property asset.
[0216] 8. A system comprising: one or more processors; and one or more
computer-readable media storing
instructions executable by the one or more processors, wherein the
instructions, when executed by the one or more
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processors, cause the one or more processors to perform operations comprising:
receiving information about at least one
of products or services, at least a portion of the information including
economic data; determining a relationship been
between a product of the products or a service of the services and an
intellectual-property asset; generating association
data indicating the relationship between the product or the service and the
intellectual-property asset; and identifying,
based at least in part on the association data, the intellectual-property
asset of multiple intellectual-property assets that
corresponds to the product or the service.
[0217] 9. The system of clause 8, wherein the information about the at
least one of the products or the services
includes a description of at least one of the product or the service, and the
operations further comprise determining a
feature of the at least one of the product or the service based at least
partly on the description.
[0218] 10. The system of clauses 8 or 9, wherein the feature comprises a
first feature, and the operations further
comprise: identifying, based at least in part on at least one of a publicly-
accessible data source or a data source of an
organization offering at least one of the product or the service, information
about the intellectual-property asset;
determining a second feature of the intellectual-property asset based at least
partly on the information about the
intellectual-property asset; and wherein generating the association data
comprises generating the association data based
at least in part on the first feature and the second feature.
[0219] 11. The system of any of clauses 8, 9, or 10, wherein the
intellectual-property asset comprises a patent
document, and the operations further comprise: receiving a description of at
least one of the product or the service, the
description including words related to the at least one of the product or the
service; determining that at least a portion of
the words are included in a claim of the patent document; and wherein the
association data indicates that the claim
corresponds to the at least one of the product or the service based at least
partly on the at least the portion of the words
being included in the claim.
[0220] 12. The system of any of clauses 8, 9, 10, or 11, the operations
further comprising generating a user interface
including one or more user-interface elements configured to capture
information about the multiple intellectual-property
assets, the one or more user-interface elements including at least one of: a
first element configured to receive first
information associated with trade secret documents; a second element
configured to receive second information associated
with trademark documents; or a third element configured to receive third
information associated with copyright
documents.
[0221] 13. The system of any of clauses 8, 9, 10, 11, or 12, the
operations further comprising: causing a request for
information about at least one of the product or the service to be at least
one of: published on a website accessible to
computing devices; or sent to the computing devices: and receiving, in
response to the request, data indicating at least one
of a source of the information or the information.
[0222] 14. The system of any of clauses 8, 8, 10, 11, U. or 13, the
operations further comprising: determining, based
at least partly on the economic data, an amount of revenue associated with at
least one of the product or the service
obtained over a period of time; determining a portion of the amount of revenue
to attribute to the intellectual-property
.. asset; and determining a value of the intellectual-property asset based at
least partly on the portion of the amount of the
revenue.
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[0223] 15. A method comprising: receiving information about at least one
of products or services, at least a portion
of the information including economic data associated with the at least one of
the products or the services; determining
relationships between individual ones of the at least one of the products or
services and individual ones of intellectual-
property assets; generating association data indicating the relationships
between the individual ones of the products and
the individual ones of the intellectual-property assets; identifying, based at
least in part on the association data, an
intellectual-property asset of the intellectual-property assets that
corresponds to at least one of a product or a service of
the at least one of the products or services; and generating data indicating
that the intellectual-property asset is associated
with the at least one of the product or the service.
[0224] 16. The method of clause 15, further comprising: causing a
request for information about the at least one of
the product or the service to be at least one of: published on a website
accessible to computing devices; or sent to the
computing devices; and receiving, in response to the request, data indicating
at least one of a source of the information or
the information.
[0225] 17. The method of clauses 15 or 16, further comprising: receiving
input data indicating that at least one of the
products or services does not correspond to at least one of the intellectual-
property assets; and causing the association
data to indicate that the at least one of the products or services does not
correspond to the at least one of the intellectual-
property assets.
[0226] 18. The method of any of clauses 15, 16, or 17, wherein the
intellectual-property assets include a patent
document and a trademark document, and the method further comprises: receiving
a description of the product or the
service; determining a first relationship between the patent document and the
product or the service based at least partly
on a first number of words included in a claim of the patent document
corresponding to a second number of words included
in the description of the product or the service; determining a second
relationship between the trademark document and
the product or the service based at least partly on a third number of words
included in a description of goods and services
of the trademark document corresponding to the second number of words included
in the description of the product or the
service; and wherein the association data includes: a first association
between the claim of the patent document and the
product or the service; and a second association between the trademark
document and the product or the service.
[0227] 19. The method of any of clauses 15, 16, 17, or 18, wherein the
economic data includes revenue of the product
or the service over a period of time, and the method further comprising:
determining, based at least partly on a first
measure of breadth of the claim of the patent document, a first portion of the
revenue to attribute to the claim of the patent
document; and determining, based at least partly on a second measure of
breadth of the description of goods and services
included in the trademark document, a second portion of the revenue to
attribute to the trademark document.
[0228] 20. The method of aiw of clauses 15, 16, 17, 18, or 19, wherein
the intellectual-property assets include a trade
secret and a trademark docwnent, and the method further comprises: receiving a
description of the product or the service;
determining a relationship between the trade secret document and the product
or the service based at least partly on a first
number of words included in the trade secret document corresponding to a
second number of words included in the
description of the product or the service; and wherein the relationships
include a relationship between the trade secret
document and the product or the service.
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[0229] 21. A method comprising: generating a classification system that
includes classifications, individual ones of
the classifications corresponding to a technology group; receiving information
about a product offered for acquisition by
an organization, the information obtained from at least one of: a datastore of
the organization; a website of the
organization; or input via a user interface; determining, based at least in
part on the information, a first feature of the
product; determining that the product corresponds to a classification of the
classifications based at least partly on the first
feature corresponding to a reference feature associated with the
classification; identifying an intellectual-property asset
associated with the organization; determining a second feature of the
intellectual-property asset; and determining that the
intellectual-property asset corresponds to the classification based at least
partly on the second feature of the intellectual-
property asset corresponding to the reference feature associated with the
classification.
[0230] 22. The method of clause 21, further comprising: determining first
words to associate with the classification;
determining second words included in the information; determining that at
least a threshold number of words of the second
words are included in the first words; and wherein determining that the
product corresponds to the classification comprises
determining that the product corresponds to the classification based at least
in part on the threshold number of words of
the second words being included in the first words.
[0231] 23. The method of clauses 21 or 22, further comprising: determining
a physical feature of the product, the
physical feature associated with a first word; determining a technical feature
of the product, the technical feature
associated with a second word; and wherein the first feature corresponds to
the physical feature or the technical feature.
[0232] 24. The method of any of clauses 21, 22, or 23, further
comprising: determining that at least one of the first
word or the second word is associated with the classification; and wherein
determining that the product corresponds to
the classification comprises determining that the product corresponds to the
classification based at least in part on the at
least one of the first word or the second word being associated with the
classification.
[0233] 25. The method of any of clauses 21, 22, 23, or 24, further
comprising: determining that at least one of the
first word or the second word is associated with the intellectual-property
asset; and determining that the product is
associated with the intellectual-property asset based at least in part on the
at least one of the first word or the second word
being associated with the intellectual-property asset.
[0234] 26. The method of any of clauses 21,22, 23,24, or 25, further
comprising: generating a first model configured
to determine a first probability that individual ones of the products
correspond to individual ones of the classifications;
and generating a second model configured to determine a second probability
that individual ones of the intellectual-
property assets correspond to the individual ones of the classifications.
[0235] 27. The method of any of clauses 21, 22, 23, 24, 25, or 26, further
comprising: sending a re quest for feedback
related to the classification system; receiving input data indicating that the
product does not correspond to the
classification; and training the first model based at least in part on the
input data.
[0236] 28. The method of any of clauses 21, 22, 23, 24, 25, 26, or 27,
further comprising: sending a request for
feedback related to the classification system; receiving input data indicating
that the intellectual-property asset does not
correspond to the classification; and training the second model based at least
in part on the input data.
[0237] 29. A system comprising: one or more processors; and one or more
computer-readable media storing
instructions executable by the one or more processors, wherein the
instructions, when executed by the one or more
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processors, cause the one or more processors to perform operations comprising:
generating a model configured to
determine classifications of at least one of products or services; receiving
information about at least one of a product or a
service offered by an organization; identifying a word associated with the at
least one of the product or the service included
in the information; determining, based at least partly on the word and
utilizing the model, a probability that the at least
one of the product or the service corresponds to a classification of the
classifications; determining that the at least one of
the product or the service corresponds to the classification based at least
partly on the probability; identifying an
intellectual-property asset; determining a feature of the intellectual-
property asset; and determining, utilizing the model,
that the intellectual-property asset corresponds to the classification based
at least partly on the feature corresponding to
the word.
[0238] 30. The system of clause 29, wherein the probability comprises a
first probability, the classification comprises
a first classification, and the operations further comprise: determining,
based at least partly on the word and utilizing the
model, a second probability that the at least one of the product or the
service corresponds to a second classification of the
classifications, the first probability being greater than the second
probability; and wherein determining that the at least
one of the product or the service corresponds to the first classification
comprises determining that the at least one of the
.. product or the service corresponds to the first classification based at
least in part on the first probability being greater than
the second probability.
[0239] 31. The system of clauses 29 or 30, the operations further
comprising: receiving input data indicating that the
at least one of the product or the service does not correspond to the
classification; and training the model based at least
partly on the input data.
[0240] 32. The system of any of clauses 29,30, or 31, wherein the
probability is based at least partly on determining
that a first number of words included in the information corresponds to a
second number of words of the classification.
[0241] 33. The system of any of clauses 29, 30, 31, or 32, the operations
further comprising: determining a feature
of the product based at least partly on words included in the information
corresponding to words included in a library of
features; and wherein: the classification is associated with features; and the
probability is based at least partly on the
feature of the product being included in the features of the classification.
[0242] 34. The system of any of clauses 29, 30, 31, 32, or 33, wherein
the word comprises a first word, the
classification is associated with words, and the operations further comprise:
determining that a second word included in
the information associated with the at least one of the product or the service
is included in the words; and wherein the
probability is based at least partly on the second word being included in the
words.
[0243] 35. The system of any of clauses 29, 30, 31, 32, or 33, the
operations further comprising: determining a
proximity associated with the first word and the second word, the proximity
based at least in part on at least one of: a
number of intervening words between the first word and the second word; the
second word being in a same sentence as
the first word; or the second word being in a different sentence than the
first word; and wherein the probability is based
at least in part on the proximity.
[0244] 36. A method comprising: generating a model configured to determine
classifications of individual ones of
intellectual-property assets; receiving an intellectual-property asset:
determining a word included in the intellectual-
property asset; determining, based at least partly on the word and utilizing
the model, a probability that at least a portion
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of the intellectual-property asset corresponds to a classification of the
classifications; determining that the at least the
portion of the intellectual-property asset correspond to the classification
based at least partly on the probability; receiving
information associated with at least one of a product or a service;
identifying a feature of the information; and detennining,
utilizing the model, that the at least one of the product or the service
corresponds to the classification based at least partly
on the feature corresponding to the word.
[0245] 37. The method of clause 36, wherein the intellectual-property
asset includes a patent document, and the
method further comprises: identifying a claim of the patent document;
determining words included in the claim;
determining that a first number of the words included in the claim correspond
to a second number of words of the
classification; and wherein the probability is based at least partly on the
first number of the words included in the claim
corresponding to the second number of the words of the classification.
[0246] 38. The method of clauses 36 or 37, wherein the model comprises a
first model, the probability comprises a
first probability, and the method further comprises: generating a second model
configured to identify products that
correspond to the intellectual-property assets; identifying a product included
in the classification; determining, based at
least in part on the second model, a second probability that the product
corresponds to the intellectual-property asset; and
determining, based at least partly on the second probability, that the product
corresponds to the intellectual-property asset.
[0247] 39. The method of any of clauses 36, 37, or 38, wherein the
intellectual-property asset includes a trademark
document, and the method further comprises: identifying a description of goods
and services of the trademark document;
determining words included in the description of goods and services;
determining that a first number of the word included
in the description of the goods and the services corresponds to a second
number of words of the classification; and wherein
the probability is based at least partly on the first number of the words
included in the description of the goods and the
services corresponds to the second number of the words of the classification.
[0248] 40. The method of any of clauses 36, 37, 38, or 39, further
comprising: receiving input data indicating that at
least a portion of the intellectual-property asset is =associated with the
classification; and training the model based at
least partly on the input data.
[0249] 41. A method comprising: determining first parts of speech for first
words included in first information
associated with a product; determining second parts of speech for second words
included in second information
corresponding to a claim of a patent document; determining a portion of the
first words that correspond to a feature of the
product; determining, based at least partly on the first parts of speech, a
first action performed with respect to the feature;
generating, based at least partly on the first action, a first linguistic
structure for the feature, the first linguistic structure
indicating one or more first relationships between the first action and one or
more first nouns included in the first
information; determining, based at least partly on the second parts of speech,
a second action included in the claim;
generating, based at least partly on the second action, a second linguistic
structure for the claim, the second linguistic
structure indicating one or more second relationships between the second
action and one or more second nouns included
in the claim; determining a similarity metric between the first linguistic
structure and the second linguistic structure; and
determining, based at least partly on the similarity metric, that the claim
corresponds to the product.
[0250] 42. The method of clause 41, wherein: the first linguistic
structure includes a first tree structure having: a first
level including a first node corresponding to the first action; and a second
level including a second node; and the second
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linguistic structure includes a second tree structure having: a third level
including a third node corresponding to the second
action; and a fourth level including a fourth node.
[0251] 43. The method of clauses 41 or 42, further comprising:
determining a first amount of similarity between the
first action and the second action; determining a second amount of similarity
between the first level, the second level, the
third level, and the fourth level; and determining a third amount of
similarity between the first node, the second node, the
third node, and the fourth node; and wherein the similarity metric is
determined at least partly based on the first amount
of similarity, the second amount of similarity, and the third amount of
similarity.
[0252] 44. The method of any of clauses 41, 42, or 43, further
comprising: determining an amount of similarity
between a first word of the second node and a second word of a fifth node
associated with the second node; and wherein
the similarity metric is determined at least partly based on the amount of
similarity.
[0253] 45. The method of any of clauses 41, 42, 43, or 44, wherein the
claim includes elements, the action
corresponds to an element of the elements, and the method further comprises
determining that the element corresponds to
the feature based at least partly on the similarity metric.
[0254] 46. The method of any of clauses 41, 42, 43, 44, or 45, further
comprising: receiving input data corresponding
to an amount of similarity between the claim and the product; and modifying,
based at least partly on the amount of
similarity, the similarity metric.
[0255] 47. A system comprising: one or more processors; and one or more
computer-readable media storing
instructions executable by the one or more processors, wherein the
instructions, when executed by the one or more
processors, cause the one or more processors to perform operations comprising:
receiving information including a claim
of a patent document; determining words included in the claim; determining
parts of speech for the words; detennining,
based at least partly on the parts of speech, an action included in the claim,
the action associated with a verb and the parts
of speech include a noun that corresponds to the verb; and generating a
linguistic structure for the claim, the linguistic
structure indicating one or more relationships between the verb and one or
more additional words included in the claim.
[0256] 48. The system of clause 47, wherein, the action comprises a first
action, the verb comprises a first verb, the
noun comprises a first noun, the one or more additional words comprise one or
more first additional words, the linguistic
structure comprises a first linguistic structure, the one or more
relationships comprise first one or more relationships, and
the operations further comprise: determining, based at least partly on the
parts of speech, a second action included in the
claim, the second action associated with a second verb and a second noun
included in the claim; and generating a second
linguistic structure for the claim, the second linguistic structure indicating
one or more second relationships between the
second verb and one or more second additional words included in the claim.
[0257] 49. The system of clauses 47 or 48, wherein the information
comprises first information, the action comprises
a first action, and the operations further comprise: receiving second
information corresponding to at least one of a product
or a service, the second information including a second action performed with
respect to a feature of the at least one of
the product or the service; generating a third linguistic structure for the at
least one of the product or the service, the third
linguistic structure indicating one or more third relationships between the
second action and one or more third additional
words included in the second information; and determining, based at least
partly on the first linguistic structure, the second
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linguistic structure, and the third linguistic structure, a similarity metric
between the at least one of the product or the
service and the claim.
[0258] 50. The system of any of clauses 47, 48, or 49, the operations
further comprising: detennining, based at least
partly on the similarity metric being less than a threshold similarity metric,
that the at least one of the product or the
service is unassociated with the claim; receiving input data indicating that
the at least one of the product or the service
corresponds with the claim; and increasing a value of the similarity metric
based at least partly on the input data.
[0259] 51. The system of any of clauses 47, 48, 49, or 50, the operations
further comprising: determining, based at
least partly on the similarity metric being at least a threshold similarity
metric, that the at least one of the product or the
service corresponds to the claim; receiving input data indicating that the at
least one of the product or the service is
unassociated with the claim; and decreasing a value of the similarity metric
based at least partly on the input data.
[0260] 52. The system of any of clauses 47, 48, 49, 50, or 51, wherein
the linguistic structure comprises a first
linguistic structure, and the operations further comprises: performing a
comparison between the first linguistic structure
and a second linguistic structure, the second linguistic structure associated
with at least one of a product or a service; and
determining, based at least partly on the comparison, that the at least one of
the product or the service corresponds to the
claim.
[0261] 53. The system of any of clauses 47, 48, 49, 50, 51, or 52, the
operations further comprising: determining a
classification for the claim; and determining that the at least one of the
product or the service is associated with the
classification; and determining, based at least in part on the at least one of
the product or the service being associated with
the classification, that the claim is associated with the at least one of the
product or the service.
[0262] 54. The system of any of clauses 47, 48, 49, 50, 51, 52, or 53,
wherein determining that the at least one of the
product or service is associated with the classification is based at least in
part on the comparison.
[0263] 55. A method comprising: receiving information corresponding to at
least one of a product or a service;
determining words included in the information; determining parts of speech for
the words; determining a portion of the
individual words that correspond to a feature of the at least one of the
product or the service; determining, based at least
partly on the parts of speech, an action performed with respect to the
feature; and generating, based at least partly on the
parts of speech, a linguistic structure for the at least one of the product or
the service, the linguistic structure indicating
one or more relationships between the action and the words.
[0264] 56. The method of clause 55, wherein the linguistic structure
comprises a tree structure having levels,
individual ones of the levels having one or more nodes.
[0265] 57. The method of clauses 55 or 56, wherein: a first level of the
levels includes a first node; the first node of
the one or more nodes corresponds to a verb of the words, the verb
corresponding to the action; and a second level of the
levels includes a second node of the one or more nodes, the second node
indicating a noun that corresponds to the verb.
[0266] 58. The method of any of clauses 55, 56, or 57, wherein: the noun
comprises a first noun; the second node
indicates an adjective that corresponds to the first noun; the second level
includes a third node that indicates a second
noun that corresponds to the verb; and a third level of the levels includes a
fourth node that indicates a third noun that is
associated with the first noun.
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[0267] 59. The method of any of clauses 55, 56, 57, or 58, wherein the
linguistic structure comprises a first linguistic
structure, the one or more relationships comprise one or more second
relationships, the verb comprises a first verb, and
the method farther comprises: generating a second linguistic structure for a
claim included in a patent document;
performing a comparison between the first linguistic structure and the second
linguistic structure; and determining, based
at least partly on the comparison, that the at least one of the product or the
service corresponds to the claim.
[0268] 60. The method of any of clauses 55, 56, 57, 58, or 59, wherein
the linguistic structure includes a first tree
structure having a first level with a first node and a second level with a
second node, the linguistic structure comprises a
first linguistic structure, and the method farther comprises: generating a
second linguistic structure for an intellectual-
property asset, the second linguistic structure including a second tree
structure having a third level with a third node and
a fourth level with a fourth node; performing a first comparison between a
first word indicated by the first node and a
second word indicated by the third node; performing a second comparison
between a first number of nodes included in
the second level and a second number of nodes included in the fourth level;
and determining that the at least one of the
product or the service corresponds to the intellectual-property asset based at
least partly on the first comparison and the
second comparison.
[0269] 61. A method comprising: receiving information indicating revenue
associated with a product; determining a
classification of the product based at least partly on a technical feature of
the product; identifying a patent claim that
corresponds to the product based at least partly on the patent claim being
associated with the classification; identifying
words included in the patent claim; determining a breadth of the patent claim;
determining a portion of the revenue to
apportion to the patent claim based at least partly on the breadth of the
patent claim; and determining a measure of value
of the patent claim based at least partly on the portion of the revenue
apportioned to the patent claim.
[0270] 62. The method of clause 61, further comprising applying a
discount factor to the measure of value, the
discount factor based at least partly on: a first exposure value corresponding
to invalidation of the patent claim; and a
second exposure value corresponding to a probability of litigation with
respect to the patent claim.
[0271] 63. The method of clauses 61 or 62, wherein the classification
comprises a first classification, and the method
further comprises: determining the second exposure value based at least partly
on a first number of litigation events taking
place with respect to patents having the first classification relative to a
second number of litigation events taking place
with respect to patents having a second classification.
[0272] 64. The method of any of clauses 61, 62, or 63, further comprising
determining the first exposure value based
at least partly on a prosecution history event related to the patent claim.
[0273] 65. The method of any of clauses 61, 62, 63, or 64. further
comprising determining the first exposure value
based at least partly on a first metric associated with a first examiner
related to the patent claim relative to a second metric
of a second examiner included in an art unit that is associated with the first
examiner, at least one of the first metric or the
second metric including at least one of: a number of notices of allowance
produced over a period of time; an average
number of office actions before producing a notice of allowance; a number of
notices of appeal filed over the period of
time; or a number of reversals in appeal decisions over the period of time.
[0274] 66. The method of any of clauses 61, 62, 63, 64, or 65, wherein:
the patent claim is assigned to an organization
and the revenue of the product is provided to the organization; and
determining the discount factor comprises determining
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the discount factor based at least partly on a number of patent claims other
than the patent claim that are assigned to the
organization and that correspond to the product.
[0275] 67. A system comprising: one or more processors; and one or more
computer-readable media storing
instructions executable by the one or more processors, wherein the
instructions, when executed by the one or more
processors, cause the one or more processors to perform operations comprising:
receiving financial data corresponding to
at least one of a product or a service, the financial data indicating revenue
associated with the at least one of the product
or the service; identifying an intellectual-property asset of an organization,
the intellectual-property asset including a
patent asset, a trademark asset, a copyright asset, or a trade-secret asset;
determining that the at least one of the product
or the service corresponds to the intellectual-property asset; determining a
portion of the revenue to attribute to the
intellectual-property asset; and determining a measure of value of the
intellectual-property asset based at least partly on
the portion of the revenue attributed to the intellectual-property asset.
[0276] 68. The system of clause 67, wherein determining that the at least
one of the product or the service corresponds
to the intellectual-property asset includes at least one of: receiving input
data indicating that the at least one of the product
or the service corresponds to the intellectual-property asset; or identifying
information indicating that the at least one of
the product or the service corresponds to the intellectual-property asset, the
information at least one of: stored in a datastore
of the organization; or accessible via a website of the organization.
[0277] 69. The system of clauses 67 or 68, wherein receiving the
financial data includes at least one of: generating a
user interface including one or more user-interface elements configured to
receive the financial data; identifying, utilizing
a datastore of the organization, a portion of the financial data corresponding
to the at least one of the product or the service;
or identifying, utilizing information from one or more websites, the portion
of the financial data corresponding to the at
least one of the product or the service.
[0278] 70. The system of any of clauses 67, 68, or 69, wherein
determining that the at least one of the product or the
service corresponds to the intellectual-property asset includes: determining,
based at least in part on an intellectual-
property document associated with the intellectual-property asset, first words
of the intellectual-property document;
determine, based at least in part on information related to the at least one
of the product or the service, second words
included in the information; determining a similarity metric between at least
a portion of the first words and at least a
portion of the second words; and determining that the similarity metric meets
at least a threshold similarity metric.
[0279] 71. The system of any of clauses 67, 68, 69, or 70, the operations
further comprising: determining, based at
least in part on infonnation corresponding to the at least one of the product
or the service, a feature of the at least one of
the product or the service; determining, based at least in part on the feature
and first criteria associated with a first
classification, a first similarity metric; determining, based at least in part
on the feature and second criteria associated with
a second classification, a second similarity metric; and determining that the
at least one of the product or the service
corresponds to the first classification based at least partly on the first
similarity metric being at least a threshold value and
the second similarity metric being less than the threshold value.
[0280] 72. The system of any of clauses 67, 68, 69, 70, or 71, wherein the
feature comprises a first feature, and the
operations further comprise: determining a second feature of the intellectual-
property asset; determining, based at least in
part on the second feature and the first criteria, a third similarity metric;
determining, based at least in part on the second
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feature and the second criteria, a fourth similarity metric; and determining
that the intellectual-property asset corresponds
to the first classification based at least partly on the third similarity
metric being at least the threshold value and the fourth
similarity metric being less than the threshold value.
[0281] 73. The system of any of clauses 67, 68, 69, 70, 71, or 72, the
operations further comprising: determining a
discount factor to associate with the first classification, the discount
factor based at least partly on a first degree of exposure
corresponding to invalidation of the patent claim and a second degree of
exposure corresponding to probability of litigation
with respect to the patent claim; and determining, based at least partly on
the discount factor, a modified amount of the
revenue to use in determining the measure of value.
[0282] 74. The system of any of clauses 67, 68, 69, 70, 71, 72, or 73,
the operations further comprising: determining
.. a measure of breadth of the intellectual-property asset; and wherein the
portion of the revenue attributed to the intellectual-
property asset is based at least partly on the measure of breadth.
[0283] 75. A method comprising: receiving financial data corresponding to
at least one of a product or a service, the
financial data indicating revenue for the at least one of the product or the
service; identifying an intellectual-property asset
of an organization; determining first features of the at least one of the
product or the service, the first features including
.. at least one of: a first physical feature of the at least one of the
product or the service; or a first technical feature of the at
least one of the product or the service; determining second features of the
intellectual-property asset, the second features
including at least one of: a second physical feature of the intellectual-
property asset; or a second technical feature of the
intellectual-property asset; determining a similarity metric between the at
least one of the product or the service and the
intellectual-property asset based at least partly on an analysis of the first
features and the second features; and determining,
based at least partly on the similarity metric, that the at least one of the
product or the service corresponds to the
intellectual-property asset.
[0284] 76. The method of clause 75, further comprising: receiving
information about the at least one of the product
or the service from at least one of: a website associated with the at least
one of the product or the service; a datastore of
the organization; or a user interface including one or more user-interface
elements configured to capture data related to at
.. least one of products or services; and wherein at least one of the first
features is determined based at least in part on the
information.
[0285] 77. The method of clauses 75 or 76, further comprising:
determining a classification of multiple classifications
to associate with the at least one of the product or the service based at
least partly on the first features, the classification
being associated with at least one of first features or the second features;
and determining that the intellectual-property
.. asset corresponds to the classification based at least partly on the
classification being associated with the at least one of
the first features or the second features.
[0286] 78. The method of any of clauses 75, 76, or 77, wherein the
intellectual-property asset comprises a first
intellectual-property asset, and the method further comprise: determining a
first measure of breadth of the first intellectual-
property asset based at least partly on at least one of a first number of
physical features of the first intellectual-property
asset in relation to a second number of physical features of a second
intellectual-property asset included in the
classification; determining a second measure of breadth of the first
intellectual-property asset based at least partly on at
least one of a first number of technical features of the first intellectual-
property asset in relation to a second number of
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technical features of the second intellectual-property asset; determining an
third measure of breadth of the first intellectual-
property asset based at least partly on the first measure of breadth and the
second measure of breadth; and determining
the portion of the revenue of the product attributed to the intellectual-
property asset based at least partly on the third
measure of breadth.
[0287] 79. The method of any of clauses 75, 76, 77, or 78, wherein the
intellectual-property asset includes a
trademark asset, and the method further comprises determining the second
features based at least in part on a description
of goods and services of the registered trademark.
[0288] 80. The method of any of clauses 75, 76, 77, 78, or 79, further
comprising determining an amount of discount
to apply to the portion of the revenue attributed to the trademark asset based
at least partly on at least one of: a number of
litigation events related to trademark assets included in a classification
associated with the trademark asset; a number of
oppositions related to the trademark assets included in the classification; or
first metrics of an examiner associated with
the trademark asset in relation to second metrics of examiners associated with
the trademark assets included in the
classification.
[0289] 81. A method comprising: receiving, from one or more data sources,
first information regarding products
.. offered for acquisition; receiving, from the one or more data sources,
second information regarding intellectual-property
assets; determining, based at least in part on the first information and the
second infoimation, that an intellectual-property
asset of the intellectual-property assets corresponds to a feature associated
with a product of the products; receiving a first
request to deteimine a value of the intellectual-property asset; identifying,
based at least partly on receiving the first
request, economic data indicating revenue of an organization that is
associated with the product; determining a portion of
the revenue attributable to the intellectual-property asset; determining,
based at least partly on the portion of the revenue,
a measure of value of the intellectual-property asset; receiving a second
request to determine at least one of: a first
exposure value representing loss of coverage with respect to the intellectual-
property asset; or a second exposure value
representing a litigation event with respect=to the intellectual-property
asset; determining, based at least in part on
receiving the second request, at least one of the first exposure value or the
second exposure value; and causing display,
via one or more user interfaces, of an indicator of the measure of value of
the product and the at least one of the first
exposure value or the second exposure value.
[0290] 82. The method of clause 81, further comprising: receiving, from
the one or more data sources, third
information regarding the products, the third inforination representing an
update of the first information and including
second economic data; increasing or decreasing the portion of the revenue
based at least in part on the third information;
and determining a second measure of value of the intellectual-property asset
based at least partly on increasing or
decreasing the portion of the revenue.
[0291] 83. The method of clauses 81 or 82, further comprising: receiving,
from the one or more data sources, fourth
information representing an update to the second information; and determining,
based at least partly on the fourth
information, at least of: a third exposure value associated with the loss of
coverage; or a fourth exposure value associated
with the litigation event.
[0292] 84. The method of any of clauses 81, 82, or 83, further
comprising: generating a classification system that
includes classifications, individual ones of the classifications corresponding
to one or more technology groups;
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determining one or more first features of the product based at least partly on
a first linguistic analysis of a portion of the
first information corresponding to the product; determining one or more second
features of the intellectual-property asset
based at least partly on a second linguistic analysis of a portion of the
second information corresponding to the intellectual-
property asset; determining, based at least partly on the one or more first
features, that the product is included in a
classification of the classifications; and determining, based at least partly
on the one or more second features. that the
intellectual-property asset is included in the classification.
[0293] 85. The method of any of clauses 81, 82, 83, or 84, further
comprising: generating a framework that indicates
relationships between first products associated with the classification and
intellectual-property assets associated with the
classification; and wherein determining that the intellectual-property asset
corresponds to the product includes
determining that the framework indicates a relationship between the product
and the intellectual-property asset.
[0294] 86. The method of any of clauses 81, 82, 83, 84, or 85, further
comprising: determining a first feature of the
one or more first features based at least in part on a first noun associated
with the intellectual-property asset corresponding
to a first action performed by the product; determining a first linguistic
structure for the first feature indicating a first
relationship between the first action and the first noun; determining a second
feature of the one or more second features
based at least in part on a second noun associated with the intellectual-
property asset corresponding to a second action;
determining a second linguistic structure for the second feature indicating a
second relationship between the second action
and the second noun; and determining a similarity metric indicating an amount
of similarity between the first linguistic
structure and the second linguistic structure; and wherein determining that
the intellectual-property asset corresponds to
the product is based at least partly on the similarity metric.
[0295] 87. The method of any of clauses 81, 82, 83, 84, 85, or 86, wherein
the second exposure value associated with
the litigation event is determined based at least partly on a number of
litigation events corresponding to intellectual-
property assets having the classification.
[0296] 88. The method of any of clauses 81, 82, 83, 84, 85, 86, or 87,
wherein the measure of value comprises a first
measure of value, and the method further comprises: sending a third request
for feedback regarding an accuracy of the
first measure of value; receiving input data indicating that the first measure
of value is to be modified; and determining,
based at least partly on the input data, a second measure of value of the
intellectual-property asset.
[0297] 89. A system comprising: one or more processors; and one or more
computer-readable media storing
instructions executable by the one or more processors, wherein the
instructions, when executed by the one or more
processors, cause the one or more processors to perform operations comprising:
determining that an intellectual-property
.. asset corresponds to at least one of a product or a service; receiving a
request to determine a value of the intellectual-
property asset; receiving economic data associated with the at least one of
the product or the service, the economic data
including a revenue of an organization associated with the at least one of the
product or the service; determining a portion
of the revenue attributable to the intellectual-property asset; determining,
based at least partly on the portion of the
revenue, a first measure of value of the intellectual-property asset;
receiving input data indicating that the first measure
of value of the intellectual-property asset is to be modified; determining,
based at least partly on the input data, a second
measure of value of the intellectual-property asset; and causing display, via
a user interface, of an indication of the second
measure of value.
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[0298] 90. The system of clause 89, wherein the intellectual-property
asset is a patent document, and the operations
further comprise: determining a measure of breadth of a claim of the patent
document; and wherein the portion of the
revenue is based at least partly on the measure of breadth.
[0299] 91. The system of clauses 89 or 90, wherein the request comprises
a first request, and the operations further
comprise: receiving a second request to determine a first value measure for
individual ones of intellectual-property assets
associated with a loan to an organization, the intellectual-property assets
associated with the organization; determining
the first value measure based at least in part on first criteria; receiving a
third request to determine a second value measure
for the individual ones of the intellectual-property assets associated with at
least one of a sale of at least a portion of the
organization or a merger of the organization with an additional organization;
and determining the second value measure
based at least in part on second criteria, wherein the second value measure
differs from the first value measure.
[0300] 92. The system of any of clauses 89, 90, or 91, wherein the
request comprises a first request, and the operations
further comprise: generating a framework that indicates one or more
relationships between individual products or services
and individual patents; receiving a second request to identify one or more
patents related to one or more of the products
or the services; determining, utilizing the framework, the one or more of the
patents related to the one or more of the
products or the services; and causing display, via a user interface, of an
indication of the one or more patents.
[0301] 93. The system of any of clauses 89, 90, 91, or 92, the operations
further comprising: determining a feature
of a product or a service of the products or the services; determining, based
at least partly on the framework, a claim of
the one or more patents that relates to the feature; and causing display, via
the user interface, of an indication of the claim.
[0302] 94. The system of any of clauses 89, 90, 91, 92, or 93, wherein
the request comprises a first request, and the
operations further comprise: generating a classification system including
classifications, individual ones of the
classifications associated with one or more technology groups; receiving a
second request to identify one or more
organizations that are associated with claims of patent documents associated
with products or services included in a
classification of the classifications; determining, based at least partly on
the framework, organizations associated with
claims that correspond to the products or the services included in the
classification; and causing display, via the user
interface, of an indication of a number of claims associated with individual
organizations of the organizations that
correspond to the products or the services included in the classification.
[0303] 95. The system of any of clauses 89, 90, 91, 92, 93, or 94,
wherein the economic data comprises first economic
data, the revenue comprises first revemie, the measure of value comprises a
first measure of value, and the operations
further comprising: receiving second economic data associated with the product
or the service, the second economic data
including second revenue of the organization associated with the product or
the service; and determining, based at least
partly on the second revenue, a second measure of value of the intellectual-
property asset.
[0304] 96. A method comprising: determining that an intellectual-property
asset corresponds to a product or a
service; receiving a request to determine a value of the intellectual-property
asset; receiving first economic data including
a first revenue of an organization associated with the product or the service;
determining a portion of the first revenue
attributable to the intellectual-property asset; determining, based at least
partly on the portion of the first revenue, a first
measure of value of the intellectual-property asset; receiving second economic
data including second revenue of the
organization associated with the product or the service; deteimining, based at
least partly on the second revenue, a second
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measure of value of the intellectual-property asset; and causing display, via
a user interface, of an indication of the second
measure of value of the intellectual-property asset.
[0305] 97. The method of clause 96, further comprising: receiving, from
at least one of a public data source or a data
source of the organization, information corresponding to the intellectual-
property asset; determining, based at least partly
on the information, at least one of: a first exposure value associated with
loss of coverage with respect to the intellectual-
property asset; or a second exposure value associated with a litigation event
with respect to the intellectual-property asset.
[0306] 98. The method of clauses 96 or 97, wherein the intellectual-
property asset comprises a trade secret, and the
method further comprises: determining a probability of theft of the trade
secret based at least partly on a portion of the
information determining the first exposure value based at least partly on the
probability of theft of the trade secret; and
causing display, via the user interface, of an indication of the first
exposure value.
[0307] 99. The method of any of clauses 96, 97, or 98, further
comprising: determining a discount amount to reduce
the second amount of the revenue based at least partly on the at least one of
the first exposure value or the second exposure
value; and wherein the second measure of value is based at least partly on a
difference between the second amount of the
revenue and the discount amount.
[0308] 100. The method of any of clauses 96, 97, 98, or 99, further
comprising: determining an intellectual-property
category associated with the intellectual-property asset, the intellectual-
property category being at least one of a patent
category, a trademark category, a copyright category, or a trade-secret
category; and wherein determining the second
measure of value comprises determining the second measure of value based at
least in part on the intellectual-property
category associated with the intellectual-property asset.
[0309] Furthermore, the foregoing is merely illustrative of the principles
of this disclosure and various modifications
can be made by those skilled in the art without departing from the scope of
this disclosure. The above described examples
are presented for purposes of illustration and not of limitation. The present
disclosure also can take many forms other
than those explicitly described herein. Accordingly, it is emphasized that
this disclosure is not limited to the explicitly
disclosed methods, systems, and apparatuses, but is intended to include
variations to and modifications thereof, which are
within the spirit of the following claims.
[0310] As a further example, variations of apparatus or process
parameters (e.g., dimensions, configurations,
components, process step order, etc.) can be made to further optimize the
provided structures, devices and methods, as
shown and described herein. In any event, the structures and devices, as well
as the associated methods, described herein
have many applications. Therefore, the disclosed subject matter should not be
limited to any single example described
herein, but rather should be construed in breadth and scope in accordance with
the appended claims.
