CN114303140A

CN114303140A - Analysis of intellectual property data related to products and services

Info

Publication number: CN114303140A
Application number: CN202080059995.2A
Authority: CN
Inventors: L·C·李; D·克劳斯; D·C·安德鲁斯; S·C·弗莱明
Original assignee: Aon Risk Services Inc of Maryland
Current assignee: Aon Risk Services Inc of Maryland
Priority date: 2019-07-03
Filing date: 2020-06-30
Publication date: 2022-04-08
Also published as: EP3994646A4; CA3145812A1; WO2021003187A1; EP3994646A1; KR20220034134A; JP2022538925A

Abstract

The techniques described herein relate to analyzing intellectual property data based on providing various intellectual property related services to an organization. In particular embodiments, information related to products and/or services may be obtained from multiple data sources. In addition, information related to intellectual property assets, such as patents, trademarks, copyrights, trade secrets and proprietary technology, may be obtained. In various instances, intellectual property assets can be mapped to corresponding products and/or services. The mapping between the products and/or services and the intellectual property assets can be used to provide intellectual property related services corresponding to the intellectual property assets, such as valuation services, policy related services, or risk related services.

Description

Analysis of intellectual property data related to products and services

Cross Reference to Related Applications

The present application claims an Analysis Of Integrated-Property Data In RelationToproducts accounts And Services titled "16/503,107 Of U.S. patent application No. 16/503,107 filed on 3.7.2019, an Analysis Of Integrated-Property Data In RelationToproducts Services titled" Of U.S. patent application No. 16/503,126 filed on 3.7.2019, an Analysis Of Integrated-Property Data In RelationToproducts Services titled "Of U.S. patent application No. 16/503,144 filed on 3.7.2019, a Data Of Integrated-Property Data In RelationToproducts Services And Services titled" Data Of Integrated-Property In Products Services "Of U.S. patent application No. 16/503,164 filed on 3.7.2019, an Analysis Of Integrated-Property In Products Services titled" Of U.S. patent application No. 16/503,187 filed on 2019, the entire contents of which are incorporated herein by reference.

Background

Intellectual property rights are acquired by an organization to help protect innovations within the organization. In general, information related to an organization's intellectual property may be difficult to analyze efficiently and effectively. For example, knowing the value of intellectual property or knowing how intellectual property is associated with a product or service on the market may be difficult to achieve in an accurate and efficient manner using computer-implemented techniques.

Drawings

The following detailed description refers to the accompanying drawings. 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 symbols in different drawings indicates similar or identical items. The systems depicted in the figures are not drawn to scale and the components in the figures may be depicted without scale to each other.

Fig. 1 illustrates an example architecture for analyzing intellectual property data and utilizing the analysis of intellectual property data to provide a variety of services in accordance with some embodiments.

Fig. 2 illustrates an example environment in which intellectual property data and types of product/service data are analyzed to provide services related to intellectual property according to some embodiments.

Fig. 3 illustrates an example environment for using technology taxonomy to generate a mapping between products and intellectual property assets in accordance with some embodiments.

Fig. 4 illustrates an example system that generates valuations for intellectual property assets in accordance with some embodiments.

Fig. 5 illustrates an example system that modifies a mapping between intellectual property and taxonomy and/or between intellectual property and products/services according to some embodiments.

FIG. 6 illustrates an example architecture for providing services to customers using a mapping between intellectual property and products/services associated with a classification system, according to some embodiments.

FIG. 7 illustrates an example framework for generating language constructs for claims of patent documents according to some embodiments.

FIG. 8 illustrates an example framework for determining similarity measures between the linguistic structure of a portion of the claims of a patent document and the linguistic structure of a product/service, in accordance with some embodiments.

Fig. 9 illustrates an example framework for determining a value of an intellectual property feature corresponding to one or more products, according to some embodiments.

Fig. 10 illustrates an example process for determining intellectual property assets corresponding to a product and/or service in accordance with some embodiments.

Fig. 11 illustrates an example process for determining intellectual property assets corresponding to a product or service using a classification system in accordance with some embodiments.

Fig. 12 illustrates an example process of performing qualitative and quantitative analysis of intellectual property data in accordance with some embodiments.

Fig. 13 illustrates an example process for determining an intellectual property asset corresponding to a product and/or service using the language structure of the intellectual property asset and the language structure of the product and/or service according to some embodiments.

Fig. 14 illustrates an example process of providing a service to a customer based on a relationship between a product and/or service and an intellectual property asset, according to some embodiments.

Detailed Description

The techniques described herein are directed to analyzing intellectual property data related to a product and/or service. As technology advances have increased, the value of organizations has shifted from tangible to intangible assets, with the importance of intellectual property increasing. Accordingly, organizations have taken various measures to protect their intellectual property rights, which may include patents, trademarks, copyrights, trade secrets and/or proprietary technology (know-how), for example. However, few techniques, architectures and frameworks have been developed for analyzing intellectual property data and generating useful information from the intellectual property data of an organization. Furthermore, the number of services provided to an organization using intellectual property rights is also limited due to the complexity of analyzing intellectual property data and the inability of conventional systems to efficiently provide information to the organization about the intellectual property rights that the organization has value.

Embodiments described herein relate to techniques, systems, and architectures that analyze intellectual property data to generate a framework that can be used to provide services related to intellectual property assets. In particular embodiments, an intellectual property service provider may obtain intellectual property data from multiple data sources. In various embodiments, at least a portion of the data sources may include a common data source. The common data source storing intellectual property data may include databases of different jurisdictions patent offices, such as the United States Patent and Trademark Office (USPTO) database, the European Patent Office (EPO) database, and/or the World Intellectual Property Office (WIPO) database. In addition, intellectual property data may be stored in a database associated with copyright, such as the U.S. copyright office or the European Union copyright office. Intellectual property data may also be obtained from private data sources. The private data source may include a database that stores information related to an organization, which is maintained and/or controlled by the organization. The private data source may also include a database of service providers that store information on behalf of the organization. Further, at least a portion of the intellectual property data of the organization may be captured via one or more user interfaces. In some cases, one or more user interfaces may be presented as part of a customer portal accessible to a customer of the intellectual property service provider. In an example, the data source can include a digital property registry, which can be maintained and/or generated by a system and/or entity other than an organization. For example, a digital asset such as a trade secret may be registered with a digital property registry using one or more obfuscated values to represent the digital asset and/or box values to represent boxes in the distributed ledger, where the obfuscated values are registered.

The intellectual property service provider may also obtain data related to a plurality of products and/or services. Products and/or services may be provided for acquisition by the same organization that acquired and analyzed the intellectual property data. Further, products and/or services may be provided for acquisition by an organization different from the organization for which intellectual property data is being acquired and analyzed. The data related to the product and/or service may include at least one of economic data related to the product and/or service, a manual about the product and/or service, a specification sheet for the product and/or service, a description of the product and/or service, and/or marketing material related to the product and/or service.

Data related to products and/or services may be obtained from a plurality of data sources. In particular embodiments, data related to products and/or services may be obtained from various websites. In some cases, data related to the product and/or service may be obtained from one or more websites of an organization that provides the product and/or service for acquisition. In further embodiments, data related to the product and/or service may be obtained from a database of an organization that provides the product and/or service for acquisition. Further, data related to the product and/or service may be obtained via one or more user interfaces, such as a user interface provided by an intellectual property service provider as part of a customer portal.

Data related to intellectual property rights of an organization and data related to products and/or services may also be obtained through crowd sourcing. In particular embodiments, an intellectual property service provider may issue a request for information about an intellectual property asset and/or a request for information about a product and/or service. The request may be posted on one or more websites, sent to a group of individuals, or a combination thereof via one or more mobile device applications. In response to the request, the individual may identify information corresponding to the request and send the information to the intellectual property service provider.

After obtaining information about products and/or services and obtaining intellectual property information, the intellectual property service provider may analyze and organize the information so that the intellectual property service provider may provide a plurality of services to customers of the intellectual property service provider. The intellectual property service provider may analyze the information obtained from the data source using machine learning techniques. In particular embodiments, an intellectual property service provider may generate one or more models that may be used to determine attributes, features, metrics, etc. about intellectual property assets and products and/or services. In various embodiments, an intellectual property service provider may implement machine learning techniques to determine relationships between intellectual property assets and products and/or services. In some examples, an intellectual property service provider may utilize the relationship between an intellectual property asset and a product and/or service to estimate the value of the intellectual property asset. The intellectual property service provider may also utilize machine learning techniques to determine the disclosure level corresponding to the intellectual property asset. The disclosure level associated with the intellectual property asset may correspond to a probability that at least one of the coverage areas of the intellectual property asset is likely to reduce or litigation events for the intellectual property asset occur.

Intellectual property service providers may utilize natural language processing techniques in order to analyze information obtained from data sources related to intellectual property assets and products and/or services. To illustrate, an intellectual property service provider may parse words included in information associated with products and/or services and information associated with intellectual property assets and determine parts of speech of the words. In some examples, an intellectual property service provider may use part of speech of words and grammatical relationships between words to determine relationships between words. The intellectual property service provider may also utilize natural language processing techniques and/or machine learning techniques to determine relationships between products and/or services and intellectual property assets. That is, an intellectual property service provider may utilize natural language processing techniques to determine intellectual property files that may cover one or more features of a product and/or service. In an illustrative example, an intellectual property service provider may utilize natural language processing techniques and machine learning techniques to determine a probability that an intellectual property asset may be implemented with respect to a corresponding product and/or service.

In particular embodiments, an intellectual property service provider may use natural language processing techniques and/or machine learning techniques to generate language structures associated with intellectual property files to determine relationships between words included in information associated with intellectual property assets. For example, an intellectual property service provider may determine verbs associated with actions performed in claims of a patent document, and may also determine nouns and/or adjectives corresponding to the actions. In some cases, intellectual property service providers may utilize natural language processing techniques and machine learning techniques to determine elements of patent document claims. Further, intellectual property service providers may use natural language processing techniques and machine learning techniques to generate language structures for products and/or services. In an illustrative example, an intellectual property service provider may determine actions to perform with respect to a product and/or service and generate a linguistic structure that indicates verbs related to the actions and specifications, adjectives, and/or adverbs related to the verbs. In various embodiments, an intellectual property service provider may determine intellectual property assets corresponding to various products and/or services by comparing the intellectual property assets to respective language structures of the products and/or services.

An intellectual property service provider may use a technology category framework to determine intellectual property assets that correspond to features of a product and/or service. The technology category framework may include a taxonomy comprising a plurality of classifications, wherein each classification is associated with a plurality of criteria. By linguistic analyzing the intellectual property files and determining the characteristics of the intellectual property files, the classification for the intellectual property files may be determined according to a technical classification framework. The intellectual property service provider may then compare the features of the intellectual property file to the classification criteria of the technical category framework to determine a corresponding classification of the intellectual property file. In addition, the intellectual property service provider may also determine a classification of the product and/or service based on the technology classification system. For example, an intellectual property service provider may perform linguistic analysis on information related to a product and/or service and determine characteristics of the product and/or service. The intellectual property service provider may then compare the characteristics of the product and/or service to the classification criteria of the technical category framework to determine a corresponding classification of the product and/or service. In particular embodiments, an intellectual property service provider may determine intellectual property assets corresponding to a product and/or service when the intellectual property assets and the product and/or service are in the same or similar classifications of the technology category framework.

In an illustrative implementation, an intellectual property service provider may generate one or more models that map products and/or services to a technology category framework and map intellectual property assets to a technology category framework. The intellectual property service provider may then further develop one or more models by determining intellectual property assets corresponding to various products and/or services within a given taxonomy using natural language processing techniques and/or machine learning techniques. In this manner, an intellectual property service provider may receive a request to identify intellectual property assets corresponding to a specified product and/or service and utilize one or more models to identify intellectual property assets corresponding to the specified product and/or service. The intellectual property service provider may then determine an estimate of the intellectual property asset based at least in part on the revenue of the specified product and/or service. For example, an intellectual property service provider may determine a portion of the revenue for a particular product and/or service attributable to an intellectual property asset and estimate a value of the intellectual property asset based at least in part on the portion of the revenue for the product and/or service attributable to the intellectual property asset. The intellectual property service provider may also provide additional information to the customer using one or more models and technical category frameworks. To illustrate, an intellectual property service provider may utilize one or more models and technical classification frameworks to determine the amount of disclosure and/or loss associated with an intellectual property asset. The intellectual property service provider may also provide services to customers using one or more models and technology category frameworks related to providing metrics for an organization's intellectual property asset portfolio. These metrics may indicate metrics on the extent and coverage of the intellectual property document. The intellectual property service provider may also generate reports using one or more models and a technology category framework that indicates the technical features around which an organization may acquire and/or develop additional intellectual property assets. Further, the intellectual property service provider may generate a report indicating intellectual property assets of a competitor of the customer of the intellectual property service provider and/or indicating metrics of intellectual property assets of a competitor of the customer of the intellectual property service provider using one or more models and technology classification systems.

Conventional techniques and systems for analyzing intellectual property files associated with products and/or services are performed by individuals using computers. For example, an individual may manually search an intellectual property database and an online search to identify information about products and/or services. The individual may then perform a manual analysis to determine intellectual property files corresponding to the product and/or service. In some cases, an individual may also access online resources related to the sale of intellectual property assets, intellectual property asset litigation adjudication, and/or resolution of litigation procedures related to intellectual property assets to determine one or more intellectual property assets.

However, conventional techniques and systems for determining relationships between intellectual property assets and various products and/or services and for determining intellectual property asset valuations are inefficient and often inaccurate. To illustrate, individuals are often unable to search and retrieve large amounts of data related to intellectual property assets and products and/or services. Often, a manual search performed online by an individual may ignore or not find information that may be helpful in identifying intellectual property assets corresponding to a corresponding good and/or service and in determining an valuation of the intellectual property assets. Furthermore, person-based analysis of the collected information may often miss relationships between intellectual property assets and products and/or services, or may miss features corresponding to various products and/or services that are covered by the intellectual property assets. Thus, conventional techniques and systems are labor intensive and generally do not provide information that an organization may use to evaluate an organization's intellectual property assets.

Moreover, implementing the techniques and systems described herein is not just a simple matter of collecting and organizing large amounts of data. Not only do the systems and techniques described herein provide useful information about intellectual property assets corresponding to products and/or services in a more efficient manner relative to conventional techniques and systems, but embodiments described herein also utilize generating accurate techniques and systems that are supported by an analytic foundation formed from non-conventional use of machine learning and natural language processing.

The present disclosure provides a complete understanding of the principles of the structure, function, manufacture, and use of the systems and methods disclosed herein. One or more examples of the 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. Features illustrated or described in connection with one embodiment may be combined with features of other embodiments, both in the system and in the method. Such modifications and variations are intended to be included herein within the scope of the appended claims.

Additional details are described below with reference to several exemplary embodiments.

Fig. 1 illustrates an example architecture 100 for analyzing intellectual property data and utilizing the analysis of intellectual property data to provide a variety of services, in accordance with some embodiments. The architecture 100 may include an intellectual property service system 102 that analyzes data related to intellectual property assets. The data analyzed by intellectual property service system 102 may be used by an intellectual property service provider to provide services related to intellectual property assets. Intellectual property assets may include patents, trademarks, copyrights, trade secrets and proprietary technology. In various embodiments, an intellectual property asset may comprise a portion of a patent, such as a patent claim. Further, the intellectual property asset may comprise a portion of a copyright that relates to a portion of software code that corresponds to a particular feature that is executed when the software code is executed.

In particular embodiments, an intellectual property asset may be associated with various forms of files that indicate characteristics of the intellectual property asset. Where the intellectual property asset comprises a patent, the patent may comprise a utility patent, a design patent, and/or a plant patent. 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 instances, an intellectual property asset may include a trademark application and an authorized trademark registration. The intellectual property asset may also include files corresponding to copyright registrations and files including aspects of trade secrets. For purposes of illustration, formulas, procedures, and/or algorithms and software code may be recorded as the subject of trade secrets. Actions taken to protect trade secrets may also be recorded on and included in intellectual property assets. Further, an intellectual property asset may include files of an organization's expertise, such as process improvements and innovations, new product designs, product improvements, brand names, logos, advertising banners, website designs, product appearances, product packaging, manufacturing processes, engineering drawings, instruction manuals, product catalogs, lists of customers and providers, and so forth.

Intellectual property service system 102 may include intellectual property mapping and learning system 104. Intellectual property mapping and learning system 104 may obtain information from multiple data sources, such as data source 106, and analyze the information to determine relationships between intellectual property assets and products and/or services. The data source 106 may include a customer portal 108. Customer portal 108 may include one or more user interfaces generated by intellectual property service system 102 that include one or more user interface elements to capture information related to intellectual property assets of customers of an intellectual property service provider, such as customer 110. The user interface associated with the customer portal 108 may be displayed as part of one or more websites of the intellectual property service provider and/or via one or more mobile device applications of the intellectual property service provider. In various implementations, information may be entered into the customer portal 108 by a representative of the customer 110. In further embodiments, the information may be entered into the customer portal 108 by a representative of the intellectual property service provider.

The data sources 106 may also include one or more customer data sources 112. One or more customer data sources 112 may be accessible to and store data under the direction of customers of the intellectual property service provider. That is, the data stored by one or more customer data sources 112 may be under the control of various customers of the intellectual property service provider. In some illustrative examples, at least one customer data source 112 may be maintained at the site of customer 110. In further 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, customer data source 112 may include a cloud-based data storage system accessible to customer 110.

Further, the data source 106 may include a crowd-sourced data source 114. The crowd-sourced data source 114 may include a plurality of individuals that provide information to the intellectual property service system 102. In various implementations, the intellectual property service system 102 can issue a request for information about an intellectual property asset via at least one of one or more websites or one or more mobile device applications. The intellectual property service system 102 may also issue a request for information that may correspond to a product and/or service of an intellectual property asset. In various implementations, individuals included in the crowd-sourced data source 114 may use at least one computing device to access requests issued by the intellectual property service system 102 and provide responses to the requests. The response may include information about at least one intellectual property asset or product and/or service that is the subject of the request.

Further, the data source 106 may include one or more common data sources 116. The one or more public data sources 116 may include data sources that store publicly accessible data. In some implementations, one or more public data sources 106 may store data that an individual may access without any credentials. In further embodiments, one or more public data sources 106 may store data accessible by individuals having credentials provided to the public by an organization that maintains one or more public data sources 116. The data source 116 storing data related to intellectual property assets may be accessed via one or more websites and/or one or more mobile device applications.

The one or more common 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 government jurisdictions, such as the U.S. 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 the content of intellectual property files. For example, intellectual property data may include information contained in patent documents, such as claims, drawings, background art, abstract, description of drawings, and the like. In other examples, the intellectual property data may include the content of the brand file, such as a description of the goods and services and/or a classification of the goods and services. Further, the intellectual property data may include information contained in the copyright file. Further, the intellectual property data may comprise information relating to the review of intellectual property files. To illustrate, the intellectual property data may include an examination history of patent applications and/or an examination history of trademark applications. Intellectual property data may also include bibliographic information relating to intellectual property files, such as classifications of patent files, examiners specifying examined patent and trademark applications, priority dates, application dates, assignee, inventors, applicants, combinations thereof, and the like. In various implementations, the intellectual property data may include data related to at least one of an administrative program, a litigation program, and a dispute or license information for the intellectual property asset.

The one or more public data sources 116 may also include data sources that store market and financial data. Market and financial data may be related to an organization that provides products and/or services for acquisition. For example, market and financial data may include financial performance of an organization over a period of time. In addition, market and financial data may also indicate the classification and industry of certain organizations. 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 of a financial market over time, such as a stock market.

Further, the one or more common data sources 116 may include data sources that store information about products and/or services. To illustrate, the one or more common data sources 116 may store data including descriptions of products and/or services, product specifications, 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.

In particular embodiments, intellectual property service system 102 may include a data acquisition system 118 to obtain data from data source 106. In various implementations, the data acquisition system 118 may extract information from multiple websites. For example, the data acquisition system 118 may include one or more web crawlers that access web sites and search for information corresponding to a given set of criteria and extract information corresponding to the criteria from the web sites. In an illustrative example, the data acquisition system 118 can obtain data corresponding to various products and/or services from one or more data sources 106. Further, the data acquisition system 118 may obtain data corresponding to a plurality of intellectual property assets from one or more data sources 106.

Further, data acquisition system 118 may perform one or more operations on data obtained from one or more data sources 106 before the data is stored by intellectual property data store 120. For example, the data acquisition system 118 can perform an optical character recognition operation on 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 some form of data obtained from one or more of the data sources 106, such as embedded scripts or fonts. The data acquisition system 118 can also add information to data obtained from one or more data sources 106. To illustrate, the data acquisition system 118 can add a timestamp to data obtained from one or more data sources 106. The data acquisition system 118 can also add one or more tags to data obtained from one or more of the data sources 106. The one or more tags may be associated with at least one of one or more organizations corresponding to the extracted data, one or more technology classes by the intellectual property service system 102, or one or more types of intellectual property assets (e.g., patents, trademarks, copyrights, trade secrets, proprietary technologies). Further, the data acquisition system 118 can apply tags to data obtained from 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 one or more data sources 106, data acquisition system 118 may store the data in intellectual property data store 120 in a manner that may efficiently retrieve and analyze the data.

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. Intellectual property mapping and learning system 104 may also generate data for providing intellectual property customer service 126 to a customer of an intellectual property service provider, such as customer 110. In particular embodiments, intellectual property mapping and learning system 104 may include language analysis system 122. The linguistic analysis system 122 may analyze words included in the information obtained from the one or more data sources 106 to determine a part-of-speech of the words. For example, the linguistic analysis system 122 may determine that words included in the information obtained from the one or more data sources 106 may be nouns, verbs, adverbs, adjectives, pronouns, articles, prepositions, conjunctions, and the like. The linguistic analysis system 122 may also determine relationships between words. To illustrate, in addition to linguistic analysis system 122 identifying verbs and adverbs that modify verbs, linguistic analysis system 122 can identify nouns and adjectives that modify nouns. Further, linguistic analysis system 122 may determine nouns and/or pronouns that correspond to the verbs that are performing the actions corresponding to the verbs.

In various implementations, language analysis system 122 may analyze information obtained from one or more data sources 106 to identify portions of an intellectual property file. For example, the linguistic analysis system 122 may analyze the patent document to identify at least one of: the claims section of the patent document, the detailed description of the patent document, the background of the patent document, the abstract of the patent document, the summary of the invention of the patent document, the abstract of the patent document, etc. In addition, linguistic analysis system 122 may determine various elements of a claim included in the patent document. In particular embodiments, linguistic analysis system 122 may determine features included in the claims that may relate to physical characteristics of a device or system. In various implementations, these features may relate to actions being performed in relation to methods or processes or actions performed by devices or systems. Furthermore, where the claims refer to a composition of matter corresponding to a molecule, the features may refer to various arrangements of atoms included in the composition of matter, such as phenyl functionality or carboxyl functionality. In some cases, an element of a claim may include multiple individual features. In further examples, the language analysis system 122 may also analyze the trademark file to identify at least one of a description of goods and services or an international category of trademarks.

In some implementations, language analysis system 122 may analyze intellectual property files obtained from one or more data sources 106 and generate modified intellectual property files. Language analysis system 122 may generate a modified intellectual property file by removing portions of the original intellectual property file. For example, language analysis system 122 may remove at least one of the conjunctions or articles from the intellectual property file. In further examples, language analysis system 122 may generate the modified intellectual property file by indicating parts of speech and/or relationships between words in the original intellectual property file.

Further, linguistic analysis system 122 may analyze information related to the product and/or service and determine characteristics of the product and/or service. To illustrate, the linguistic analysis system 122 may determine physical components of the device and/or system. Linguistic analysis system 122 may also determine technical characteristics of the devices and/or systems. Further, linguistic analysis system 122 may also determine characteristics of processes and/or methods related to the product and/or service.

In particular embodiments, language analysis system 122 may determine at least one of a characteristic of an intellectual property asset, a characteristic of a product, or a characteristic of a service by analyzing words related to the intellectual property asset, product, and/or service with respect to a thesaurus related to the characteristic of the intellectual property asset, product, and/or service. For example, the intellectual property mapping and learning system 104 may determine a particular set of words related to each of a plurality of individual features that may be associated with at least one of the intellectual property files, products or services. To illustrate, intellectual property mapping and learning system 104 may determine that words such as "screen," "panel," and "display" may indicate display device characteristics of an electronic device. Continuing with this example, language analysis system 122 may parse the intellectual property file and/or information about the product and/or service to identify words that correspond to words associated with the display device characteristics. In the event that at least a threshold number of words included in the intellectual property file and/or information about the product and/or service correspond to words associated with display device characteristics, language analysis system 122 may determine that the particular intellectual property file or the particular product and/or service includes display device functionality.

In various embodiments, linguistic analysis system 122 may also determine that the proximity between words associated with the features may indicate that the features are present in the intellectual property file or information about the product and/or service. In some examples, the language analysis system 122 may determine that a feature is included in an intellectual property file or in a product and/or service when a plurality of words associated with the feature are within 3 words, 5 words, 10 words, or 20 words of each other. In further examples, the language analysis system 122 may determine that a feature is included in an intellectual property file or in a product and/or service when multiple words associated with the feature are within the same sentence or within the same paragraph.

The language analysis system 122 may also generate language constructs for intellectual property files and language features for information related to products and/or services. In an illustrative example, linguistic analysis system 122 may generate a linguistic structure for a claim of a patent document. In a particular scenario, linguistic analysis system 122 may generate linguistic structures for elements of or features of claims of patent documents. For example, linguistic analysis system 122 may identify verbs that correspond to actions of elements of claims of the patent document. Linguistic analysis system 122 may also determine one or more nouns related to the verb, and in some cases, one or more adjectives corresponding to the one or more nouns. Linguistic analysis system 122 may then generate linguistic structures that show relationships between verbs, one or more nouns, and/or one or more adjectives. Additionally, the linguistic analysis system 122 may generate linguistic structures that correspond to actions performed with respect to products and/or services provided by the organization for acquisition. In particular embodiments, the language structure may include a tree structure in which a single node serves as the initial 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 corresponding to an action, and the branch node may correspond to a noun related to the verb, an adjective related to the noun, other words related to the verb and/or the noun, or a combination thereof.

Further, the intellectual property mapping and learning system 104 may include an Intellectual Property (IP) model development system 124 that determines relationships between intellectual property files and products and/or services. In various embodiments, IP intellectual model development system 124 may identify intellectual property assets corresponding to respective products and/or services. For example, the IP knowledge model development system 124 can identify one or more patent claims, elements of a patent claim, and/or features of a patent claim that correspond to at least a portion of a product and/or service. In further examples, IP knowledge model development system 124 may identify a trademark corresponding to the product and/or service, at least a portion corresponding to a copyright of the product and/or service, or at least a portion of a business secret corresponding to the product and/or service.

The IP intellectual model development system 124 may determine that an intellectual property asset corresponds to a product and/or service by comparing the language structure of the intellectual property asset to the language structure of the product and/or service. In particular embodiments, IP intellectual model development system 124 may generate a first language structure for features of intellectual property assets and a second language structure for features of products and/or services. The IP knowledge model development system 124 can compare the first language construct to the second language construct to determine a similarity measure between the first language construct and the second language construct. In the event that the similarity measure between the first language construct and the second language construct is at least a threshold similarity measure, the IP intellectual model development system 124 can determine that the feature of the intellectual property asset corresponds to a feature of a product and/or service.

The similarity measure may be based at least in part on the words included in the first language structure and the words included in the second language structure. The similarity metric may also be based at least in part on a relationship between words included in the first language structure and words included in the second language structure. In an illustrative embodiment, the first language structure may include a first tree structure having a root node and a plurality of branch nodes arranged in a first configuration, and the second language structure may include a second tree structure having a root node and an additional number of branch nodes. In these cases, the IP knowledge model development system 124 can compare the first tree structure and the second tree structure to determine a similarity measure between the first language structure and the second language structure. To illustrate, the IP knowledge model development system 124 may compare words included in nodes of the first tree structure with words included in nodes of the second tree structure to determine at least a portion of a similarity measure of the first language structure and the second language structure. Additionally, the IP knowledge model development system 124 can compare the first configuration of the first tree structure to the second configuration of the second tree structure to determine at least a portion of a similarity measure of the first language structure and the second language structure. In various implementations, the IP knowledge model development system 124 may compare the locations of words and/or nodes within the first tree structure to the locations of words and/or nodes within the second tree structure to determine a similarity measure between the first language structure and the second language structure.

The IP intellectual model development system 124 may also use a classification system to determine relationships between intellectual property assets and products and/or services. The classification system may include a plurality of classifications, where each classification has one or more criteria for identifying intellectual property assets, products, and/or services for inclusion in the respective classification. In various embodiments, the classification of the classification system may include a plurality of technology groups. In some examples, the classification system may be generated by intellectual property mapping and learning system 104. In further examples, the classification system may be generated by another entity, such as a governmental entity, an educational organization, a non-profit organization, a profit organization, or a combination thereof. In particular embodiments, IP intellectual model development system 124 may compare the characteristics of individual intellectual property assets to the criteria of a plurality of classifications included in a classification system and determine one or more classifications to associate with the intellectual property assets. In addition, the IP knowledge model development system 124 may compare the characteristics of the product and/or service to the criteria of multiple classifications of the classification system and determine one or more classifications to associate with the product and/or service.

In particular embodiments, IP intellectual model development system 124 may determine intellectual property assets and products and/or services that are included in the same category of the classification system. IP intellectual model development system 124 may then determine one or more relationships between products and/or services included in the same category of intellectual property assets and the classification system. In this manner, the IP intellectual model development system 124 may develop one or more models that indicate intellectual property assets corresponding to products and/or services within a taxonomy of the taxonomy system. In an illustrative example, the IP knowledge model development system 124 may develop a model to determine patent claims corresponding to display characteristics of a mobile device. In another illustrative example, the IP knowledge model development system 124 may develop models to determine the brand of the device corresponding to the fitness tracker device. In various embodiments, a classification system, a relationship between intellectual property assets and products and/or services, and a model for determining intellectual property assets that may be related to a particular product and/or service may be stored by intellectual property data store 120.

The relationships between products and/or services and intellectual property assets within a particular taxonomy as determined by the IP intellectual model development system 124, and the models developed by the IP intellectual model development system 124 to determine intellectual property assets corresponding to products and/or services within the taxonomy of the taxonomy system, may be used to provide a plurality of intellectual property customer services 126. Intellectual property services 126 may include IP policy related services 128, IP disclosure related services 130, and IP valuation services 132. In various embodiments, the intellectual property customer service 126 may be provided based on a request sent to the intellectual property service system 102 for information about one or more intellectual property assets or one or more products and/or services. The intellectual property service system 102 may then respond to the request with a model, framework, and/or relationship between the intellectual property assets and the products and/or services generated by the intellectual property mapping and learning system 104. In some cases, the request may be sent by an individual associated with the intellectual property service provider, while in other cases, the request may be sent by an individual associated with one or more customers 110.

Intellectual property customer service 126 may include Intellectual Property (IP) policy related service 128. The IP policy correlation service 128 may include analysis of intellectual property asset groupings. In an example, the IP policy related services 128 may include competitive landscaping 150, IP benchmarking 152, IP scoring and rating 154, intelligence gathering tools 156, IP trend analyzers 158, IP pruning and/or stripping 160, executive reports 162, and/or strategic acquisitions 164. In particular embodiments, IP policy correlation service 128 may include an analysis of an organization's selection of intellectual property assets (portfolios), such as an analysis of a client's 110 selection of intellectual property assets. In an illustrative example, the IP policy correlation service 128 may include analyzing a selection of patent files and/or analyzing a selection of trademark files. In various embodiments, IP policy correlation service 128 may include analyzing a selection of intellectual property files of competitors of customer 110, such as by using race condition optimization 150. For example, intellectual property service system 102 may determine a classification of intellectual property assets of a competitor of customer 110 and generate one or more files or reports that provide a situational analysis showing the intellectual property files of the competitor relative to various technical categories. In some cases, intellectual property assets of customer 110 may be mapped with intellectual property assets of competitors of customer 110 in terms of their respective technology categories.

In other examples, the IP policy correlation service 128 may include determining a rating and/or rating of the intellectual property asset, such as by the IP rating and rating component 154. To illustrate, intellectual property service system 102 can determine a measure of the extent and/or a measure of coverage of an intellectual property asset of customer 110 or an intellectual property asset of another organization. The intellectual property service system 102 may then rank the intellectual property assets based on the measure of breadth and/or the measure of coverage. IP policy correlation service 128 may also include identifying technical areas in which customer 110 may want to develop intellectual property assets, such as by using IP benchmarking component 152. For example, intellectual property service system 102 may determine a technology category in which customer 110 has few or no intellectual property assets, but is related to the technical field customer 110 is developing. Additionally, intellectual property service system 102 may identify future research and development areas for customer 110, for example, based on multiple intellectual property assets of the customer and/or multiple intellectual property assets of one or more competitors of customer 110 in certain technical areas, by using IP election tool 156.

In addition, the IP policy correlation service 128 may include identifying intellectual property assets of the customer for sale or licensing to other organizations. Intellectual property service system 102 may also generate recommendations for intellectual property assets of customer 110 that may be abandoned or no longer maintained by, for example, IP pruning and/or stripping component 160. In particular embodiments, intellectual property service system 102 can determine at least one of a measure of value, a measure of breadth, or a measure of coverage of at least a portion of intellectual property assets of customer 110 and utilize the respective measures to generate recommendations regarding at least one of sales opportunities, licensing opportunities, or cost savings opportunities (e.g., relinquishes) of one or more intellectual property assets of customer 110, for example, via execution reporting component 162. Intellectual property service system 102 can also determine potential organizations and/or intellectual property assets that are likely to acquire, for example, by policy component 164, by the customer based on a value measure, an extent measure, a coverage measure, or at least one measure in a technical field associated with the organization and/or intellectual property asset. Further, the IP policy correlation service 128 may include determining metrics for intellectual property files of the customer 110, such as by utilizing an IP trend analyzer 158. The metrics may indicate a trend of at least one of the number of intellectual property assets of the submitted customer 110 or the number of intellectual property assets of the authorized customer 110.

Intellectual property service system 102 may also be used to provide IP risk related services 130 to customer 110. The IP disclosure-related services 130 may include IP accountability 166, mortgage protection 168, secret theft of commerce 170, IP litigation transfer 172, source code due diligence 174, and/or consulting around the design. The IP disclosure related service 130 may be related to utilizing the IP responsibility component 166 to determine a measure of loss risk associated with an intellectual property asset of a customer 110. The risk of loss may correspond to at least one of: a decline in value of the intellectual property asset, an invalidity of at least a portion of the intellectual property asset, or a theft of the intellectual property asset. In various embodiments, IP disclosure-related service 130 may include determining liability measures related to intellectual property assets of customer 110. The intellectual property service system 102 can determine a measure of liability for an intellectual property asset based on at least one of: multiple litigation events for an intellectual property asset of client 110, or multiple litigation events for an intellectual property asset in the same technical category as one or more intellectual property assets of client 110. Litigation events may include submitting a request to initiate litigation for an intellectual property asset. Litigation for an intellectual property asset may include at least one of an objection program, a program of administrative decisions, or a program within a jurisdiction. In particular embodiments, the measure of liability for an intellectual property asset may be related to a plurality of litigation events that occur within a specified time period, related to an intellectual property asset of customer 110, or related to an intellectual property asset of another organization. In some cases, with mortgage protection component 168, measures of liability associated with an intellectual property asset may be used to determine insurance policy terms issued for protecting a loan conducted using the intellectual property asset as a mortgage.

The IP disclosure related service 130 may also include determining measures for reducing the risk of loss with respect to intellectual property assets. For example, IP risk related service 130 may include determining an amount of risk that a business secret of customer 110 is stolen using theft of business secret component 170. In particular embodiments, intellectual property service system 102 may analyze the security protocols or other security processes implemented by customer 110 to protect the trade secret and determine the amount of risk of the trade secret being stolen based at least in part on the analysis. IP disclosure-related service 130 may also include processes and/or programs that utilize source code due diligence component 174 to determine actions for protecting source code developed by the customer and customer 110, as well as processes and/or programs that are undertaken to protect intellectual property related to the source code. Further, IP publication related service 130 may include an option to determine that customer 110 is designing around a competitor's intellectual property asset and/or an option to determine that a competitor of customer 110 is designing around a competitor's intellectual property asset of customer 110 using consulting component 176 around the design. In particular embodiments, intellectual property service system 102 may analyze a plurality of intellectual property assets and determine characteristics of the intellectual property assets that correspond to characteristics of a product and/or service. The intellectual property service system 102 may then identify features of the product and/or service that may be modified to avoid features of intellectual property assets associated with the product and/or service.

Further, the IP disclosure related service 130 may include utilizing the IP litigation transfer component 172 to determine policies in intellectual property litigation actions. To illustrate, the intellectual property service system 102 can analyze a series of events related to events occurring in previous litigation actions that have occurred with respect to a pending litigation action to determine a suggestion for future decisions in the pending litigation. In an illustrative example, the intellectual property service system 102 can determine a move to submit in a pending litigation to increase the likelihood of a favorable outcome for the customer. Intellectual property service system 102 may also determine recommendations for and disputes, such as an amount of an offer and/or a time of the dispute offer in connection with the dispute. Further, the intellectual property service system 102 can generate suggestions for litigant attorneys to retain in particular litigation actions and/or generate suggestions regarding modifications to retained litigant attorneys.

In various embodiments, intellectual property customer service 126 provided via intellectual property service system 102 may include IP valuation service 132. The IP valuation service 132 can include IP stack valuation 178, M & A seller and buyer service 180, asset supported lending 182, and/or value allocation 184. IP valuation service 132 can include determining a value metric for an intellectual property asset using IP stack valuation 178. In particular embodiments, intellectual property service system 102 may determine a value metric for an intellectual property asset for a customer or determine a value metric for an intellectual property asset of another organization. In some examples, intellectual property service system 102 may determine a value metric for an intellectual property asset that may be purchased or licensed by customer 110. The intellectual property service system 102 may also utilize the M & a seller and buyer service 180 to determine a value metric for an organization's intellectual property assets that may be purchased or otherwise obtained by the customer 110. In further embodiments, intellectual property service system 102 may determine a value metric for an intellectual property asset of customer 110 in connection with an acquisition of customer 110 by another organization or a merger of customer 110 with another organization. Further, the intellectual property service system 102 may determine a value measure of the intellectual property assets of the customer 110 with respect to one or more loans made to the customer 110 with the intellectual property assets of the customer 110 using the asset supported lending service 182, wherein the intellectual property assets of the customer 110 are used as mortgages for at least a portion of the loan amount.

Intellectual property service system 102 can utilize value expression service 184 to determine a value metric for an intellectual property asset based on a measure of the breadth of the intellectual property asset. Additionally, the intellectual property service system 102 can determine a value metric for the intellectual property asset based on the revenue of the product and/or service corresponding to the intellectual property asset. To determine a measure of the extent and/or partial revenue of a product and/or service corresponding to an intellectual property asset, intellectual property service system 102 can utilize one or more language analysis techniques and one or more machine learning techniques.

Fig. 2 illustrates an example environment 200 for analyzing multiple types of intellectual property data and product/service data to provide services related to intellectual property according to some embodiments. Environment 200 may include intellectual property mapping and learning system 104, one or more data sources 106, and intellectual property data store 120. Intellectual property mapping and learning system 104 may be implemented by one or more computing devices 202. The one or more computing devices 202 may be included in a cloud computing architecture that operates the one or more computing devices 202 on behalf of an intellectual property service provider. In these scenarios, the cloud computing architecture may implement one or more virtual machine instances on one or more computing devices 202 on behalf of an intellectual property service provider. The cloud computing architecture may be located remotely from the intellectual property service provider. In further embodiments, one or more computing devices 202 may be under the direct control of an intellectual property service provider. For example, an intellectual property service provider may maintain 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.

Intellectual property data store 120 may store information that may be used by intellectual property mapping and learning system 104 in providing services related to intellectual property assets. In particular embodiments, intellectual property data store 120 may store Intellectual Property (IP) data 204. 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 a combination thereof. The IP data 204 may also include customer IP data 206 corresponding to data stored by the intellectual property data store 120 relating to the customer obtaining services from the intellectual property service provider. In some embodiments, customer IP data 216 may be stored separately from IP data of other organizations in intellectual property data store 120.

In various embodiments, the IP data 204 may include data related to intellectual property assets, such as trademarks, copyrights, patents, and trade secrets. IP data 204 may include files containing information related to various types of intellectual property. For example, the IP data 204 may include patent applications, published patent applications, and issued or approved patents. IP data 204 may also include trademark applications and submissions related to copyright protection. In addition, IP data 204 may include files containing business secrets and files supporting protection of business secrets. To illustrate, IP data 204 may include employment agreements, employee manuals, policies, and/or procedures of an organization that may be used to support the business privacy state of the organization's innovation.

The IP data 204 may also include bibliographic information for intellectual property files. In an illustrative example, the IP data 204 may include a particular date associated with the intellectual property file (e.g., application date, release date, priority date), an assignee of the intellectual property file, transfer history of the intellectual property file, important individuals associated with the intellectual property file (e.g., inventors, reviewers, etc.), third party classifications associated with the intellectual property file, priority file indications for certain intellectual property files, intellectual property status jurisdictions or reviewing organizations for the intellectual property file, combinations thereof, and the like. Further, the IP data 204 may include information related to the review history of the intellectual property file. The review history may include various events related to reviewing the intellectual property file. To illustrate, the IP data 204 may include a date that the file was submitted during intellectual property file review, such as a date that a reply was submitted, a date that the reviewer issued a review comment notice or review report, a grant date, an authorization date, combinations thereof, and so forth. Further, IP data 204 may include files that were archived and/or submitted during review of the intellectual property file. In an illustrative example, the IP data 204 may include review notes, review note replies, information disclosure statements, application data sheets, statements, samples supporting brand use, complaint summaries, reviewer replies to complaint summaries, reply summaries, complaint decisions, permit notifications, disagreement files, copyright submissions, interview summary files, combinations thereof, and the like.

IP data 204 may also include statistics and/or metrics related to various reviewers reviewing intellectual property assets. To illustrate, the IP data 204 may include the number of intellectual property assets permitted over a period of time, the average number of review comment notices provided during review of intellectual property assets, the number of complaints over a period of time, decisions regarding complaints, the average length of time that review comment notices are provided, the age of experience, the number of intellectual property assets reviewed over a period of time, combinations thereof, and the like. Further, IP data 204 may include statistics and/or metrics related to a censor group reviewing intellectual property assets. IP data 204 may also include statistics and/or metrics for individual inspectors with respect to statistics and/or metrics for a group of inspectors. For example, IP data 204 may include the number of reviews that an individual patent reviewer provides per grant item relative to the average number of reviews that are provided per grant item for a patent reviewer group that includes the individual patent reviewer, such as a field-specific unit or a group of patent reviewers within a particular technology category.

In various implementations, the IP data 204 may include data related to litigation procedures and/or pseudo-litigation procedures associated with intellectual property assets. In some embodiments, the IP data 204 may include files submitted during litigation procedures, such as, for example, requests, responses, complaints, trends, discovery requests, discovery responses, expert opinions, court decisions, co-party directives, combinations thereof, and the like. In further implementations, the IP data 204 may include a copy of litigation programs. For example, the IP data 204 may include a copy of a court action and/or a copy of a witness. In further embodiments, the IP data 204 may include files submitted during a litigation procedure, such as a multiparty review procedure at the U.S. patent and trademark office or an objection procedure at the european patent office.

Intellectual property intellectual data store 120 may also store IP valuation data 208. The IP valuation data 208 can be used by the intellectual property mapping and learning system 104 to determine the value of an intellectual property asset or a portion of an intellectual property asset. In particular embodiments, the IP estimation data 208 may include estimates that occur during litigation procedures or pseudo-litigation procedures and are agreed upon during de-negotiation. Further, the IP valuation data 208 can include licensing terms obtained with respect to the intellectual property asset or a portion of the intellectual property asset. The IP valuation data 208 can also include an arbitration provided by a judge, a co-auditor, other judicial organization, or an administrative organization that indicates the value of an intellectual property asset or a portion of an intellectual property asset. In various embodiments, at least a portion of the IP valuation data 208 can include information related to a customer of the intellectual property service provider that is not publicly available. In further embodiments, the IP valuation data 208 can include information that can be used to determine the value of an intellectual property asset or a publicly available part of an intellectual property asset.

Further, intellectual property data store 120 may store business data 210. The business data 210 may include product/service data 212 and economic data 214. Product/service data 212 may include data associated with products and/or services, which may be purchased by various organizations. Product/service data 212 may include a description of the product and/or service, specifications of the product and/or service, product manuals, pricing of the product and/or service, quantity sold for the product and/or service, descriptions of various products and/or services for the organization provided, combinations thereof, and the like. Product/service data 212 may include customer product/service data 216 that includes information related to products and/or services offered by customers of an intellectual property service provider. In some embodiments, customer product/service data 216 may be stored in intellectual property data store 120 separately from product/service data of other organizations.

The economic data 214 may include information indicative of the financial performance of an organization that provides products and/or services for acquisition. The financial performance information may include revenue of the organization over a period of time, profits of the organization over a period of time, spending of the organization over a period of time, a prediction of financial performance, or a combination thereof. The economic data 214 may also include an amount of revenue for the organization corresponding to the sale of one or more products and/or services. The economic data 214 may include customer economic data 218, the customer economic data 218 including economic data corresponding to a customer of the intellectual property service provider. In some embodiments, customer economic data 218 may be stored in intellectual property intellectual data store 120 separately from economic data of other organizations.

In addition, the economic data 204 may also include industry financial data. For example, the economic data 204 may include revenue, profit, expense, etc. of certain industries that provide goods and/or services for acquisition, such as the retail industry, the semiconductor industry, or the transportation industry. Further, the economic data 204 may include economic data for individual states, counties, countries, or other political jurisdictions. To illustrate, the economic data 204 may include domestic total production data, employment data, trade data, combinations thereof, and the like. In some cases, the economic data 204 may indicate the number of domestic total production values attributed to countries or political jurisdictions of one or more industry sectors.

Further, intellectual property data store 120 may store at least one technology category 220. Technology category 220 may include multiple classifications of products and/or services. The technology class 220 may also include one or more criteria associated with the respective classifications of the technology class 220. For example, to classify according to a particular classification of technology classes 220, a product and/or service may correspond to at least a threshold number of criteria for the particular classification. In various implementations, the technology category 220 may indicate products and/or services associated with various categories. That is, products and/or services that have been previously assigned to a category may be included in technology category 220.

In some implementations, the technology categories 220 may be generated by the intellectual property mapping and learning system 104. Further, in particular embodiments, at least a portion of technology classes 220 may be generated by additional organizations. To illustrate, the technology category 220 may include a classification included in a classification system of a government organization and/or a classification system of an industry organization. In an illustrative example, at least a portion of the classifications of technology categories 220 may correspond to the U.S. patent and trademark office technology categories. In other illustrative examples, at least a portion of the classifications included in technology category 220 may correspond to technology categories included in International Patent Classification (IPC), loganin classification, nice classification, and/or vienna classification.

In various embodiments, intellectual property data store 120 may store an Intellectual Property (IP) to product and/or service mapping 222. The IP-to-product and/or service mapping 222 may indicate that an intellectual property asset or portion of intellectual property has been mapped to an asset of a product and/or service. In an illustrative example, the IP-to-product and/or service map 222 may indicate claims of a patent document that correspond to features of a mobile device (e.g., a microphone of the mobile device). In another illustrative example, the IP-to-product and/or service mapping 222 may indicate a trademark corresponding to a remote data storage service. The IP-to-product/service map 222 may also indicate an organization that provides the corresponding product and/or service for acquisition. Further, IP-to-product and/or service mapping 222 may indicate the owner of the intellectual property asset mapped to a particular product and/or service.

IP-to-product and/or service mapping 222 may include a customer mapping 224 indicating a mapping between products and/or services of a customer of an intellectual property service provider and intellectual property assets of the customer of the intellectual property service provider. In further implementations, customer mapping 224 may include a mapping between intellectual property assets of a customer of an intellectual property service provider and products and/or services provided by an organization that is not a customer of an intellectual property service provider. Further, the customer mapping may include a mapping between products and/or services provided by a customer of the intellectual property service provider and intellectual property assets of an organization that are not customers of the intellectual property service provider.

Intellectual property data store 120 may also store previous customer service data 226. Previous customer service data 226 may include data generated by an intellectual property service provider in providing services to one or more customers. For example, the prior customer service data 226 may include data generated by an intellectual property service provider in providing the IP policy related service 128, the IP disclosure related service 130, and/or the IP valuation service 132, described with respect to FIG. 1. In an illustrative example, prior customer service data 226 may include an estimate of an intellectual property asset determined by an intellectual property service provider. In a further illustrative example, prior customer service data 226 may include a determination of risk of intellectual property assets regarding a customer of an intellectual property service provider. In a further illustrative example, the prior customer service data 226 may include claim forms, policy IP analysis, and/or portfolio analysis data generated by an intellectual property service provider when providing services to a customer. In some embodiments, the previous customer service data 226 may be used to provide subsequent services to the customers of the intellectual property service provider. In this way, the intellectual property generated by an intellectual property service provider can be increased and used to more efficiently and accurately service customers of the intellectual property service provider.

Fig. 3 illustrates an example environment 300 for generating a mapping between a product and an intellectual property asset using technology classes, according to some embodiments. Environment 300 may include an intellectual property mapping and learning system 104 implemented via one or more computing devices 202. Environment 300 may also include a customer 110 of an intellectual property service provider and a group of intellectual property assets 302 of customer 110. Intellectual property asset set 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, through an nth IP asset 314. The

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, proprietary technology, or other categories of intellectual property. In further 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 in a set of claims of a patent document.

In various implementations, one or more of the

IP assets

304, 306, 308, 310, 312, 314 may correspond to a different intellectual property classification than at least one other of the

IP assets

304, 306, 308, 310, 312, 314. For example, first IP asset 304 may correspond to a trademark, while second IP asset 306 may correspond to a trade secret. Further, in some implementations, each of the

IP assets

304, 306, 308, 310, 312, 314 may correspond to the same type of intellectual property classification. To illustrate, the

IP assets

304, 306, 308, 310, 312, 314 may each correspond to at least a portion of a patent or patent application, such as a patent collection of the customer 110. In another illustrative example,

IP assets

304, 306, 308, 310, 312, 314 may each correspond to a patent or a claim of a patent application. In a further illustrative example,

IP assets

304, 306, 308, 310, 312, 314 can each correspond to a business secret. In a further illustrative example,

IP assets

304, 306, 308, 310, 312, 314 may each correspond to a trademark. In other illustrative examples,

IP assets

304, 306, 308, 310, 312, 314 may each correspond to a copyright.

Additionally, environment 300 may include technology class 220 of FIG. 2. The technology class 220 may include a plurality of classes, such as a first class 316, a second class 318, a third class 320, through an nth class 322. The

individual classifications

316, 318, 320, 322 of the technology category 220 may be related to a set of individual criteria that characterize the item associated with a particular classification of the technology category 220. At least one of the intellectual property assets, products or services may be classified according to at least one classification of the technology classes 220. In an illustrative embodiment, the intellectual property mapping and learning system 104 may determine characteristics of the first intellectual property asset 304 and compare the characteristics of the first intellectual property asset 304 to the set of criteria of the first classification 316. In particular embodiments, intellectual property mapping and learning system 104 may determine a metric that indicates a degree of similarity between the features of first intellectual property asset 304 and the set of criteria of first classification 316.

In various implementations, the degree of similarity between the features of the first IP asset 304 and the set of criteria of the first classification 316 may indicate a plurality of features of the first IP asset 304 corresponding to one or more criteria of the first classification 316. In an illustrative implementation, intellectual property mapping and learning system 304 may determine a degree of similarity between the features of first IP asset 304 and first classification 316 by comparing the words of one or more features of first IP asset 304 with the words of first classification 316. Intellectual property mapping and learning system 304 may determine the characteristics of first IP asset 304 and first classification 316 based at least on a threshold number of words of the characteristics of first IP asset 304 corresponding to words of first classification 316. In some scenarios, when the spelling of the words of first IP asset 304 is the same as the spelling of the words of first classification 316, intellectual property mapping and learning system 304 may determine that the words of the features of first IP asset 304 correspond to the words of the first classification. In further cases, intellectual property mapping and learning system 304 may determine that the words of the features of first IP asset 304 correspond to the words of first classification 316 based on the words of first IP asset 304 being synonyms of the words of first classification 316. In a further example, intellectual property mapping and learning system 104 may determine that the words of the features of first IP asset 304 correspond to the words of first classification 316 based on the words of first IP asset 304 being derivatives of the words of first classification 316. For example, the words of the first IP asset 304 may be different tenses of the words of the first classification 306. In other examples, the words of first IP asset 304 may be in the plural or singular form of first category word 316.

Intellectual property mapping and learning system 104 may determine a first degree of similarity between the features of first IP asset 304 and first classification 316 based on determining that the individual features of first IP asset 304 correspond to the individual criteria of first classification 316. Additionally, the intellectual property mapping and learning system 104 may determine a second degree of similarity between the first IP asset 304 and the first classification 316 based on at least one criterion that determines that two features of the first intellectual property asset 304 correspond to the first classification 316. In some implementations, intellectual property mapping and learning system 104 may determine that first IP asset 304 corresponds to first classification 316 based on a degree of similarity between features of first IP asset 304 and criteria of first classification 316 being above a threshold degree of similarity. In further embodiments, intellectual property mapping and learning system 104 may determine that first IP asset 304 corresponds to the first classification based on the degree of similarity between the features of first IP asset 304 and first classification 316 being greater than the degree of similarity between the features of first IP asset 304 and the respective sets of criteria of the additional classifications of technology class 220 (e.g., the sets of criteria of second classification 318, third classification 320, through nth classification 322).

Intellectual property mapping and learning system 104 may also determine a classification of technical categories 220 for a plurality of products and/or services, such as first product 324, second product 326, and third product 328. Intellectual property mapping and learning system 104 may determine the classification of technical categories 220 for

products

324, 326, 328 themselves. In further embodiments, the intellectual property mapping and learning system 104 may determine a classification of the technology category 220 corresponding to one or more features of the

product

324, 326, 328. In an illustrative example, intellectual property mapping and learning system 104 may determine that first product 324 corresponds to the transportation classification of technology class 220, second product 326 corresponds to the mobile communication device classification of technology class 220, and third product 328 corresponds to the printing device classification of technology class 220. In a further illustrative example, intellectual property mapping and learning system 104 may determine a classification of features common to

products

324, 326, 328, such as a display device included in first product 324, a display device of second product 326, and a display device of third product 328. The intellectual property mapping and learning system 104 may also determine a classification of additional individual features of the

products

324, 326, 328 relative to the technology category 220.

In particular embodiments, intellectual property mapping and learning system 104 may determine a classification of a technology category 220 of a

product

324, 326, 328 and/or a feature of a

product

324, 326, 328 based at least in part on words describing the

product

324, 326, 328 and/or words describing features of the

product

324, 326, 328 in relation to a set of criteria for the classification of the technology category 220, such as respective combinations of criteria for the

classifications

316, 318, 320, 322. For example, the intellectual property mapping and learning system 104 may determine a description of the

product

324, 326, 328 and/or a degree of similarity between the features of the

product

324, 326, 328 and the classification criteria of the technology category 220.

In an illustrative embodiment, the degree of similarity between the characteristics of the first product 324 and the set of criteria of the first category 316 may indicate that a plurality of words describing the characteristics of the first product 324 correspond to one or more criteria of the first category 316. That is, the intellectual property mapping and learning system 104 may compare one or more words describing the features of the first product 324 with words regarding the first classification 316 and determine that the plurality of words describing the features of the first product 324 correspond to words of the one or more criteria of the first classification 316. When the spelling of the words of the features of the first product 324 is the same as the words associated with the first category 316, the intellectual property mapping and learning system 304 may determine that the words describing the features of the first product 324 correspond to the words associated with the first category 316. In further cases, the intellectual property mapping and learning system 104 may determine that the words describing the features of the first product 324 correspond to the words associated with the first classification 316 based on the words describing the features of the first product 324 being synonyms of the words associated with the first classification 316. In a further example, the intellectual property mapping and learning system 104 may determine that the words describing the features of the first product 324 correspond to the words associated with the first classification 316 based on the words describing the features of the first product 324 being derivatives of the words associated with the first classification 316. For example, the words that describe the characteristics of the first product 324 may be different tenses of the words associated with the first category 306. In other examples, the words that describe the characteristics of the first product 324 may be in the plural or singular form of the words associated with the first classification 316.

Intellectual property mapping and learning system 304 may determine that the feature of first product 324 corresponds to first category 316 based at least on a threshold number of words describing the feature of first product 324 corresponding to the word associated with first category 316. In some scenarios, the intellectual property mapping and learning system 104 may determine a degree of similarity between words describing features of the first product 324 and words associated with the first classification 316 to determine whether the features of the first product 324 are classified according to the first classification 316. In particular embodiments, intellectual property mapping and learning system 104 may determine that the feature of first product 324 corresponds to first category 316 based on a degree of similarity between words describing the feature of first product 324 and words of first category 316 being above a degree of similarity threshold. In further embodiments, the intellectual property mapping and learning system 104 may determine that the feature of the first product 324 corresponds to the first category 316 based on the degree of similarity between the words describing the feature of the first product 324 and the words of the first category 316 being greater than the degree of similarity between the words describing the feature of the first product 324 and the words associated with the respective criteria of the additional categories of technology categories 220 (e.g., the words associated with the set of criteria of the second category 318, the third category 320, through the nth category 322).

Intellectual property mapping and learning system 104 may also determine a mapping 330 between products and/or services and intellectual property asset set 302. In particular embodiments, intellectual property mapping and learning system 104 may utilize technology classes 220 to determine characteristics of IP asset set 302 that correspond to characteristics of one or more products and/or services. In various implementations, the intellectual property mapping and learning system 104 may determine a mapping between features of intellectual property assets included in the intellectual property asset group 302 and features of products and/or services classified according to the same classification of technology classes 220. The mapping 330 may indicate that the intellectual property asset may cover a feature of the product. In an illustrative implementation, the mapping 330 may indicate that intellectual property assets may be claimed in a judicial program and/or an administrative program for a respective product.

The illustrative example of fig. 3 includes a first mapping 332 between the first product 324 and a set of intellectual property assets including the first IP asset 304 and the third IP asset 308. The mapping 330 may also include a second mapping 334 between the second product 326 and another set of intellectual property assets including the first IP asset 304, the second IP asset 306, and the fourth IP asset 310. Further, mapping 330 may include a third mapping 336 between third product 328 and an additional set of intellectual property assets including first IP asset 304 and fifth IP asset 312.

Intellectual property mapping and learning system 104 may determine

mappings

332, 334, 336 by determining similarities between

products

324, 326, 328 and/or features of

products

324, 326, 328 and features of

IP assets

304, 306, 308, 310, 312, 314 and/or

IP assets

304, 306, 08, 310, 312, 314. In particular embodiments, the intellectual property mapping and learning system 104 may determine a mapping between features of the

intellectual property assets

304, 306, 308, 310, 312, 314 and features of the

products

324, 326, 328 classified according to the same classification of the technology class 220. In various implementations, the intellectual property mapping and learning system 104 may determine the mapping between the features of the first IP asset 304 and the features of the first product 324 by determining a degree of similarity between words of the features of the first IP asset and words describing the features of the first product 324.

In an illustrative example, the intellectual property mapping and learning system 104 may determine a similarity between elements of the claims related to the first IP asset 304 and features of the first product 324. In another illustrative example, intellectual property mapping and learning system 104 may determine a degree of similarity between the trademark associated with first IP asset 304 and words or phrases used in marketing and branding of first product 324. Intellectual property mapping and learning system 104 may determine a degree of similarity between a first word of a feature of first IP asset 304 and a second word of a feature of first product 324 by performing a comparison between the first word and the second word. The degree of similarity between a first word and a second word may be based on the number of words that are identical between the first word and the second word, the number of words that are synonyms between the first word and the second word, and/or the number of derivatives between the first word and the second word.

In further implementations, the intellectual property mapping and learning system 104 may determine a mapping between an intellectual property asset and a product and/or service based at least in part on the language structure generated for the intellectual property asset and the language structure generated for the product and/or product. The language structure may indicate a relationship between words of the intellectual property asset and words describing the product. In various implementations, the intellectual property mapping and learning system 104 may generate language structures for the features of the intellectual property asset and generate language structures for the features of the product and compare the language structures of the features of the intellectual property asset to the features of the product.

In particular embodiments, the intellectual property mapping and learning system 104 may determine a degree of similarity between the language structure of the features of the intellectual property asset and the language structure of the features of the product. In some implementations, the intellectual property mapping and learning system 104 may compare words included in the language structure of a feature of an intellectual property asset (e.g., a feature of the first intellectual property asset 304) with words included in the language structure of a feature of a product (e.g., a feature of the first product 324). Further, the intellectual property mapping and learning system 104 may compare the arrangement of the language structure of the features of the first intellectual property asset 104 with the arrangement of the language structure of the features of the first product 324. The configuration of the language structure of the feature of the first intellectual property asset 304 may indicate a first relationship between words related to the feature of the first intellectual property asset 304 and the configuration of the language structure of the feature of the first product 324 may indicate a relationship between words describing the feature of the first product 324. The intellectual property mapping and learning system 104 may generate a mapping between the feature of the intellectual property asset and the feature of the product based at least in part on a degree of similarity between a language structure of the feature of the intellectual property asset and a language structure of the feature of the product being greater than a threshold degree of similarity.

Fig. 4 illustrates an example system 400 that generates valuations for intellectual property assets in accordance with some implementations. System 400 may include intellectual property mapping and learning system 104 and one or more computing devices 202 that may implement intellectual property mapping and learning system 104. The system 400 may also include a first data store storing Intellectual Property (IP) valuation data 402 and a second data store storing business data 404. IP valuation data 402 and business data 404 can include information corresponding to customers of an intellectual property service provider. The IP valuation data 402 and business data 404 can also include information corresponding to an organization that is not a customer of the intellectual property service provider.

The IP valuation data 402 can include information that can be used to determine the value of an intellectual property asset. In particular embodiments, the IP valuation data 402 can include decisions indicative of damage indemnities awarded during judicial procedures associated with intellectual property assets. The IP valuation data 402 can also include the amount of money to license the intellectual property asset. Further, the IP valuation data 402 can include an amount paid as part of an explanation relating to a judicial program and/or an administrative program that occurs with respect to an intellectual property asset. Business data 404 may include information indicating revenue obtained by an organization regarding products and/or services provided by the organization. Business data 404 may also include other financial information related to an organization, such as total revenue over a period of time, amount of revenue within a particular area of technology over a period of time, profits earned over a period of time, expenses over a period of time, combinations thereof, and the like. In various implementations, the expenses included in the business data 404 may indicate expenses for an organization to provide one or more products and/or services to a consumer for acquisition.

Intellectual property mapping and learning system 104 may utilize at least one of IP valuation data 402 or business data 404 to determine an valuation of one or more intellectual property assets. In an illustrative example, intellectual property mapping and learning system 104 may determine an estimate of an intellectual property asset corresponding to second product 326 of fig. 3. In particular, intellectual property mapping and learning system 104 may determine an estimate of intellectual property mapping features of second product 326, such as first IP asset 304, second IP asset 306, and fourth IP asset 310. For example, intellectual property mapping and learning system 104 may determine a first valuation 406 of first IP asset 304, a second valuation 408 of second IP asset 306, and a third valuation 410 of fourth IP asset 310. Valuations 406, 408, 410 can indicate that an organization owning rights to the

respective IP asset

304, 306, 310 can obtain a monetary value in exchange for rights to the

IP asset

304, 306, 310 from one or more additional organizations. In various implementations, the valuations 406, 408, 410 may indicate one or more monetary values that an organization owning the rights to the

respective IP asset

304, 306, 310 may obtain in one or more approval transactions involving the

IP asset

304, 306, 310. In further embodiments, the valuations 406, 408, 410 may indicate one or more monetary values that an organization owning the rights to the

respective IP asset

304, 306, 310 may obtain when selling the

IP asset

304, 306, 310. Further, the valuations 406, 408, 410 can indicate one or more monetary values of the

respective IP asset

304, 306, 310 relative to another organization during a merger or acquisition of the organization owning the right to own the

IP asset

304, 306, 310. In other implementations, the valuations 406, 408, 410 may indicate one or more monetary values of the

respective IP assets

304, 306, 310 as mortgages of a loan to an organization that owns the rights to the

IP assets

304, 306, 310.

The intellectual property mapping and learning system 104 may determine an estimate of an intellectual property asset by determining an amount of revenue attributed to a product and/or service of the intellectual property asset. In particular embodiments, the intellectual property mapping and learning system 104 may determine the amount of revenue attributed to a product and/or service of an intellectual property asset covering the product and/or service based at least in part on the extent of the intellectual property asset relative to other intellectual property assets included in the same classification (e.g., the technology category 220 of fig. 2) of the classification framework. In further embodiments, the intellectual property mapping and learning system 104 may determine an amount of revenue attributed to a product and/or service of an intellectual property asset based at least in part on an extent of the intellectual property asset covering the product and/or service relative to other intellectual property assets covering the product and/or service.

The breadth of the intellectual property asset may be determined based on the number of words of the intellectual property asset and/or the commonality of the words of the intellectual property asset. In particular embodiments, the number of unique words and the frequency with which those words appear in other intellectual property assets may be used to determine the breadth value of a given intellectual property asset. For example, for a given intellectual property asset, the number of words of the intellectual property asset is compared to the number of words of other intellectual property assets, e.g., a plurality of additional intellectual property assets included in the same classification as the given intellectual property asset or covering the same product and/or service as the given intellectual property asset. Further, a commonality score for a given intellectual property asset may be determined based on the commonality of words in the intellectual property asset with the commonality of words in other intellectual property assets.

Where a given intellectual property asset is a patent claim, the breadth value of the claim may represent the estimated scope of the intellectual property right relative to other patent claims, such as other patent claims covering the same product and/or service as the given patent claim or other patent claims classified according to the same classification as the given patent claim. In particular embodiments, the breadth values of a patent claim may be based, at least in part, on the type of preamble included in the patent claim. For example, a patent claim including a preamble with a closed transitional phrase may have a smaller breadth value than a patent claim including a preamble with an open transitional phrase. Furthermore, patent claims containing certain words (e.g., absolute words, exemplary words, or relative words) may have lower breadth values than patent claims that do not include these types of words.

The number of words may comprise a number of words of the intellectual property asset or a portion of the intellectual property asset. In various implementations, the word count may be determined after removing duplicate words from the initial list of words included in the intellectual property asset. Thus, the word count may be a unique word of the intellectual property asset. Further, the number of words may include the number of words of the intellectual property asset after the stop words are removed. Stop words may include the most common words in a language. For purposes of illustration, stop words may include short function words such as "the", "is", "at", "which", and "on". Intellectual property mapping and learning system 104 may access one or more stop word lists in one or more languages. Furthermore, the number of words may be determined before or after converting acronyms and abbreviations into their full-word representation. The word count may also include or exclude words in the prologue. In some implementations, a plurality of different word counts may be used to determine the breadth of an intellectual property asset, such as a first word count including a plurality of unique words and a second word count not including stop words.

The commonality of words may correspond to the frequency with which a given word is found in a corpus of documents or a set of intellectual property assets. Words with higher commonalities, i.e., words that are more common words in the lexicon, may correspond to a greater breadth, while the presence of words that are less frequently used in the lexicon may indicate a reduced breadth. In the context of patent claims, words that appear frequently in the technical field are generally considered to be broader or less restrictive than the less common words.

In an illustrative embodiment, the intellectual property mapping and learning system 104 may determine an extent value of the first intellectual property asset 304 relative to other intellectual property assets included in the same category as the technology category 220 of 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 for attributing to the first intellectual property asset 304. Intellectual property mapping and learning system 104 may also determine an additional relative breadth score for first intellectual property asset 304 by determining an additional breadth value for first intellectual property asset 304 relative to the breadth values of other intellectual property assets (e.g., second intellectual property asset 306 and fourth intellectual property asset 310) that cover second product 326. In a particular illustrative example, the intellectual property mapping and learning system 104 may determine that the revenue portion of the second product 326 due to the first intellectual property asset 304 is 0.00625%

In further embodiments, the intellectual property mapping and learning system 104 can determine an estimate of an intellectual property asset based on licensing information, settlement information, damage compensation, or a combination thereof. For example, the intellectual property mapping and learning system 104 can analyze the IP valuation data 402 to identify features of products and/or services that have become licensing trades, and settlement and/or damage compensation targets that correspond to features of at least one intellectual property asset covering a product and/or service, such as

intellectual property assets

304, 306, 310 covering a second product 326. The intellectual property mapping and learning system 104 may then determine an estimate of one or more intellectual property assets based on characteristics of the monetary value of the settlement, approval of the transaction, and/or damage of the indemnity of a particular product and/or service that may correspond to the intellectual property asset. In an illustrative example, the intellectual property mapping and learning system 104 may identify a claim for 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 is the subject of claims sanction in the judicial program. Intellectual property mapping and learning system 104 may then determine first valuation 406 based on the degree of similarity between the claimed features of first IP asset 304 and the features of the product that are damage claims.

In further embodiments, the intellectual property mapping and learning system 104 may determine damage indemnity, license transaction, and/or settlement related to the classification of the product and/or service corresponding to the classification of the first intellectual property asset. Intellectual property mapping and learning system 104 may then analyze the damage indemnity, licensing transaction, and/or dispute for the same category of products and/or services as first IP asset 304 to determine first valuation 406. In particular embodiments, intellectual property mapping and learning system 104 may determine an average monetary value amount of the settlement, damage to the indemnity, and/or license transaction in the technical category of the first IP asset 304 and determine the first valuation 406 based on the average amount of the monetary value. Further, intellectual property mapping and learning system 104 can determine a similarity between at least one feature of first IP asset 304 and a feature that is the subject of a damage indemnity, and/or a license transaction of the same classification as the first IP asset. Intellectual property mapping and learning system 104 may then determine a percentage or proportion of damage indemnity, and/or license transactions to assign to the at least one feature of first IP asset 304 based on the degree of similarity.

Fig. 5 illustrates an example system 500 that modifies a mapping between intellectual property and taxonomy and a mapping between intellectual property and products/services according to some embodiments. System 500 may include intellectual property mapping and learning system 104 implemented by one or more computing devices 202. The system 500 may also include a first computing device 502 operated by a first user 504 and a second computing device 506 operated by a second user 508. In some implementations, at least one of first user 504 or second user 508 may be a representative of an intellectual property service provider. In further embodiments, at least one of first user 504 or second user 508 may not be a representative of an intellectual property service provider. For example, at least one of first user 504 or second user 508 may be a representative of another organization or part of a crowd-sourced group. In various implementations, the first user 504 and the second user 508 may provide input regarding mapping between intellectual property assets and classifications of technology category systems and/or provide input regarding mapping between intellectual property assets and products and/or services via the first computing device 502 and the second computing device 506, respectively.

In particular embodiments, intellectual property mapping and learning system 104 may determine IP asset to classification mapping 510. The IP asset-to-classification map may indicate that the IP asset has been classified according to a particular classification of a classification framework, such as the technology class 220 of fig. 2. In an illustrative example, the IP asset to class mapping 510 may indicate that claims of a patent are classified according to a class associated with a mobile device battery. In another illustrative example, the IP asset to category mapping 510 may indicate that trademarks are categorized according to a category associated with the online gaming platform. Intellectual property mapping and learning system 104 may send IP asset to classification map 510 to first computing device 502 along with an input request for IP asset to classification map 510. The input request may be a query as to whether the classification of the IP asset is correct. In various implementations, when the IP asset-to-class mapping 510 is incorrect, the input request may require that a different class be assigned to the IP asset.

In some implementations, intellectual property mapping and learning system 104 may generate one or more user interfaces that may be displayed by first computing device 502 and that may include at least one user interface element to capture input assets from first user 504 regarding an IP 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 map 510 is to be modified, at least one user interface element to capture input indicating that the IP asset-to-classification map 510 is not to be modified, at least one user interface element to capture input indicating a different classification of an IP asset, or a combination thereof. First user 504 may provide IP asset to taxonomy mapping feedback 512 to intellectual property mapping and learning system 104 via one or more user interfaces.

Intellectual property mapping and learning system 104 may analyze IP asset to classification mapping feedback 512 to determine whether to modify IP asset to classification mapping 510. 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 intellectual property asset is to be classified according to different classifications. Intellectual property mapping and learning system 104 may utilize IP asset to classification mapping feedback 512 to modify the classification framework. For example, intellectual property mapping and learning system 104 may modify the classification criteria of the IP asset based classification framework into classification mapping feedback 512. In an illustrative example, intellectual property mapping and learning system 104 may add or remove one or more criteria from the classifications based on IP asset to classification mapping feedback 512, which indicates that IP asset to classification mapping 510 is to be modified.

In a further illustrative example, intellectual property mapping and learning system 104 may modify a model that determines a classification of an IP asset based on IP asset to classification mapping feedback 512. The model may include a number of factors and corresponding weights for the factors that may be used to determine the IP asset classification. In particular embodiments, 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 the model used to determine the IP asset classification by removing one or more factors included in the model, adding one or more factors to the model, modifying weights of one or more factors included in the model, or a combination thereof.

In the illustrative example of fig. 5, intellectual property mapping and learning system 104 may determine a modified IP asset to classification mapping 514. Modified IP asset-to-classification map 514 may indicate that an IP asset is associated with a classification different from the classification of the IP asset in IP asset-to-classification map 510. Intellectual property mapping and learning system 104 may determine different classifications of IP assets based on IP asset to classification mapping feedback 512. For example, in the event that IP asset to classification mapping feedback 512 indicates that the classification of the IP asset is to be modified to a particular different classification, intellectual property mapping and learning system 104 may change the classification of the IP asset to the classification indicated in IP asset to classification mapping feedback 512. In further implementations, the intellectual property mapping and learning system 104 may analyze the inputs included in the IP asset to classification mapping feedback 512 to modify the model that determines the classification of the IP asset and then implement the modified model for the IP asset. The modified model may then generate a modified IP asset to classification map 514.

Additionally, 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 an IP product and/or service. For example, the IP asset to product/service mapping 516 may indicate that the claims of the patent cover user interface features of an audio application executed by the mobile communication device. In another example, the IP asset to product/service map 516 may indicate that the trade secret corresponds to a process of manufacturing a food product. Intellectual property mapping and learning system 104 may send IP asset to product/service mapping 516 to second computing device 506 to request input from second user 508 regarding IP asset to product/service mapping 516. The request for input may be directed to ask whether the mapping between the IP asset and the product/service is correct. In various implementations, when the IP asset to product/service mapping 516 is incorrect, the input request may require that a different product/service be assigned to the IP asset.

Intellectual property mapping and learning system 104 may generate one or more user interfaces that may be displayed by second computing device 506 and that may include at least one user interface element to capture input from second user 508 regarding 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 corresponding to the IP asset, or a combination thereof. Second user 508 may provide IP asset to product/service mapping feedback 518 to intellectual property mapping and learning system 104 via one or more user interfaces.

Intellectual property mapping and learning system 104 may analyze the IP asset to product/service mapping feedback 518 to determine whether to modify the IP asset to product/service mapping 516. 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 associated with other products and/or services. Intellectual property mapping and learning system 104 may utilize IP asset to product/service mapping feedback 518 to modify the classification framework. For example, the intellectual property mapping and learning system 104 may modify one or more criteria of the classification based on the classification framework of the IP asset to product/service mapping feedback 518. In an illustrative example, intellectual property mapping and learning system 104 may add one or more criteria to or remove one or more criteria from the classifications of the classification framework based on IP asset to product/service mapping feedback 518, indicating that IP asset to product/service mapping 516 is to be modified.

Further, the intellectual property mapping and learning system 104 may modify the model that determines the product and/or service corresponding to the IP asset based on the IP asset to product/service mapping feedback 518. The model may include a plurality of factors and respective weights of the factors that may be used to determine the products and/or services covered by the IP asset. The model may be generated using one or more machine learning techniques. In particular embodiments, intellectual property mapping and learning system 104 may modify the products and/or services covered by the IP asset by removing one or more factors contained in the model, adding one or more factors to the model, modifying the weight of one or more factors contained in the model, or a combination thereof.

In the illustrative example of fig. 5, 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 map 520 may indicate that the IP asset is associated with a product and/or service that is different from the product and/or service associated with the IP asset in the IP asset to product/service map 516. Intellectual property mapping and learning system 104 may determine the different products and/or services that the IP asset covers based on IP asset to product/service mapping feedback 518. For example, where the IP asset to product/service mapping feedback 518 indicates that a 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 to product/service mapping feedback 518 indicated in the IP asset. In further embodiments, the intellectual property mapping and learning system 104 may analyze the inputs included in the IP asset to product/service mapping feedback 518 to modify the model of the product and/or service that the IP asset is determined to cover, and then implement the modified model for the IP asset. The modified model may then generate a modified IP asset to product/service mapping 520.

Fig. 6 illustrates an example architecture 600 for providing intellectual property related services to a customer using a mapping between intellectual property and products/services related to a classification system, according to some embodiments. Architecture 600 may include an intellectual property service provider 602. Intellectual property service provider 602 may provide services related to intellectual property assets to customers, such as customer 110. In some scenarios, an intellectual property asset may be associated with a customer of intellectual property service provider 602. For example, a customer of intellectual property service provider 602 may request that intellectual property service provider 602 provide one or more services regarding intellectual property assets that the customer of intellectual property service provider 602 has ownership. In further examples, a customer of intellectual property service provider 602 may request that intellectual property service provider 602 provide a service on an intellectual property asset for which ownership is held by an organization that is not a customer of intellectual property service provider 602.

At least a portion of the operations performed by intellectual property service 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, such as portable, semi-stationary, or stationary. Some examples of the one or more computing devices 604 may include a tablet computing device; smart phones and mobile communication devices; notebook computers, netbooks and other portable or semi-portable computers; desktop computing devices, terminal computing devices, and other semi-fixed or fixed computing devices; a special purpose register device; wearable computing devices, or other body-mounted computing devices; augmented reality devices; or other computing devices capable of sending communications and performing functions in accordance with the techniques described herein.

The one or more computing devices 604 may include one or more servers or other types of computing devices that may be embodied in any number of ways. For example, in the server example, modules, other functional components, and data may be executed at 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 may additionally or alternatively be used.

Further, while the figures illustrate the components and data of one or more computing devices 604 as residing in a single location, these components and data may alternatively be distributed in any manner across different computing devices and different locations. Thus, the functions performed by one or more computing devices 604 may be implemented by one or more server computing devices, with the various functions described above being distributed across different computing devices in various ways. The multiple computing devices 604 may be located together or separately and organized, for example, as virtual servers, server groups, and/or server farms. The described functionality may be provided by a server of an intellectual property service provider or may be provided by a plurality of servers and/or services of different organizations.

In the illustrated example, one or more computing devices 604 can 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 multiple processing units and may include a single or multiple computing units or multiple processing cores. Processor 606 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitry, and/or any devices that manipulate signals based on operational instructions. For example, the processor 606 may be one or more hardware processors and/or any suitable type of logic circuitry specifically programmed or configured to perform the algorithms and processes described herein. The processor 606 may be configured to retrieve and execute computer-readable instructions stored in a computer-readable medium 608, which may program the processor 606 to perform the functions described herein.

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 medium 608 may be a computer-readable storage medium and/or may be a tangible, non-transitory medium that does not include media such as energy, carrier wave signals, electromagnetic waves, and signals per se.

Computer-readable media 608 may be used to store any number of functional components that may be executed by processor 606. In many implementations, these functional components include instructions or programs that are executable by processor 606, and upon execution, specifically configure one or more processors 606 to perform the actions attributed above to intellectual property service provider 602. The functional components stored in the computer-readable medium 608 may include an intellectual property service system 104, a data acquisition system 118, a language analysis system 122, an IP intellectual model development system 124, an Intellectual Property (IP) valuation tool 614, an IP policy tool 616, and an IP risk tool 616. The computer readable medium 608 may also store intellectual property data store 120.

In at least one example, computer-readable media 608 can include or maintain other functional components and data, such as other modules and data, which can include programs, drivers, one or more operating systems, etc., as well as data used or generated by the functional components. Further, one or more computing devices 604 may include many other logical, program, and physical components, of which the above are merely examples relevant to the discussion herein.

The communication interface 610 may include one or more interfaces and hardware components for enabling communications with various other devices, such as over one or more networks. For example, the communication interface 610 may communicate over one or more of the internet, a wired network, a cellular network, a wireless network (e.g., Wi-Fi), and a wired network, and the like

Short-range communication of low energy, etc., as described elsewhere hereinAre otherwise recited herein.

One or more computing devices 604 may also be equipped with various input/output (I/O) devices 612. The I/O devices 612 may include speakers, microphones, cameras, displays (e.g., liquid crystal displays, plasma displays, light emitting diode displays, OLED (organic light emitting diode) displays, electronic paper displays, or any other suitable type of display capable of presenting digital content thereon), as well as various user controls (e.g., buttons, joysticks, keyboards, keypads, etc.), tactile output devices, and the like. Further, in particular embodiments, one or more computing devices 604 may include one or more sensors, such as accelerometers, gyroscopes, compasses, proximity sensors, cameras, microphones and/or switches, GPS sensors, and/or the like.

In particular embodiments, intellectual property service provider 602 may generate various mappings 620 that may be used to provide intellectual property related services to customers of intellectual property service provider 602. For example, mapping 620 may include one or more intellectual property asset to category mappings 622. An individual intellectual property classification map 622 may indicate a relationship between the intellectual property assets and the classifications of the classification framework, such as the classifications of the technology classes 220. Additionally, mapping 620 may include one or more intellectual property asset to product/service mappings 624. The individual intellectual property asset to product/service mapping 624 may indicate a relationship between an intellectual property asset and a product and/or service. Further, mapping 620 can include one or more product/service to economic data mappings 626. The individual product/service-to-economic data map 626 may indicate specific economic data related to at least one product or service. To illustrate, the product/service-to-economic data map 626 may indicate revenue for the product and/or service.

Intellectual property service provider 602 may receive a request for an IP related service and intellectual property service system 104 may utilize a mapping, data stored by an intellectual property data store, and/or additional information such as one or more classification frames to provide the service related to the request. For example, intellectual property service provider 602 may receive a request from a customer to obtain services related to intellectual property valuations, intellectual property policies, and intellectual property risks. In particular embodiments, intellectual property service provider 602 may utilize IP valuation tool 614 to provide intellectual property valuation services to customers. IP valuation tool 614 may comprise 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 an intellectual property asset and provide information corresponding to the value of the intellectual property asset of a customer of intellectual property service provider 602. In addition, intellectual property service provider 602 may utilize IP policy tool 616 to provide IP policy services to customers. IP policy tool 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 policy related information to customers of intellectual property service provider 602. In addition, intellectual property service provider 602 may utilize IP publishing tool 618 to provide IP risk services to customers. IP publishing tool 618 may comprise 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 customers of intellectual property service provider 602.

In an illustrative implementation, customer 110 may send IP-related service request 628 to intellectual property service provider 602. IP-related service request 628 may be electronically sent to intellectual property service provider 602. For example, customer 110 may send a communication, such as an email or message, to intellectual property service provider 602 that includes a request for IP related services 628. In a further example, customer 110 may access one or more user interfaces provided by intellectual property service provider 602 to generate request 628 for an IP-related service. Intellectual property service provider 602 may communicate one or more aspects of IP-related service request 628 to additional computing device 630 operated by user 632. User 632 may be a representative of intellectual property service provider 602. In particular embodiments, request 628 for IP-related services may include aspects, such as a request for one or more IP valuation services, one or more IP policy services, and/or one or more IP risk services. Aspects of the request may be provided to a single representative of intellectual property service provider 602 or multiple representatives of intellectual property service provider 602. In a particular illustrative implementation, request 628 for IP-related services may include a first request to evaluate a selection of intellectual property assets, a second request to analyze a patent layout related to electronic devices manufactured by customer 110, a third request to evaluate risks associated with invalidation of a plurality of intellectual property assets by customer 110, and a fourth request to evaluate theft of a trade secret associated with the trade secret of customer 110. In this case, intellectual property service provider 602 may in some cases assign user 632 to provide services related to the first request, the second request, the third request, and the fourth request. In further cases, intellectual property service provider 602 may assign user 632 to provide a service related to one of the first request, the second request, the third request, or the fourth request, and allocate tasks related to providing the services associated with the remaining requests to other representatives of intellectual property service provider 602.

In the case where user 632 is assigned to perform services related to intellectual property asset valuation, user 632 may operate additional computing device 630 to access IP valuation tool 614. In various implementations, intellectual property service provider 602 may obtain an identifier of the intellectual property asset for which an valuation is being determined from additional computing device 630. The identifier may include an identifier provided by an intellectual property jurisdiction (e.g., EPO, USPTO, JPO, etc.), such as an application number, registration number, patent number, publication number, or a combination thereof. The identifier may also include a name of the intellectual property asset. Further, the identifiers may be alphanumeric strings generated by intellectual property service provider 602 corresponding to individual intellectual property assets. In addition, intellectual property service provider 602 may obtain an assessment situation or type to be determined. For example, the intellectual property service provider 602 may receive information from the additional computing device 630 indicating that an appraisal of selling the intellectual property assets of the customer 110 is to be determined, an appraisal is to be determined for the licensing of the intellectual property assets of the customer 110, or an appraisal of the intellectual property assets of the customer 110 is to be determined for use as at least one of a collateral for the loan.

After obtaining input from additional computing devices 630 via IP valuation tool 614, intellectual property service system 104 can access mapping 620, data stored by intellectual property data store 120, models generated by intellectual property service provider 602, machine learning algorithms, or combinations thereof to provide intellectual property customer service 634 in association with an valuation of an intellectual property asset requested by customer 110. Depending on the type of valuation being performed and the amount of information that intellectual property service provider 602 has obtained about the intellectual property asset of customer 110 performing the valuation, intellectual property service system 104 can access one or more intellectual property assets to a classification map 622, an intellectual property asset to product/service map 624, or a product/service to economic data map 626 to determine the valuation of the intellectual property asset of customer 110 that is the subject of the IP related service 628 request.

In the additional case where user 632 is assigned to perform policy-related services for intellectual property assets of customer 110, user 632 may operate additional computing device 630 to access IP policy tool 616. In these cases, the intellectual property service system 104 may obtain an identifier of the intellectual property asset from the additional computing device, along with an indication of the type of policy-related service to be provided. Intellectual property service system 104 may then access mapping 620, data stored by intellectual property data store 120, models generated by intellectual property service provider 602, machine learning algorithms, or combinations thereof to provide intellectual property customer service 634 to customer 110 in connection with the IP policy service requested by customer 110.

In other scenarios where user 632 is assigned to perform risk-related services for intellectual property assets of customer 110, user 632 may operate additional computing device 630 to access IP risk tool 618. In these cases, the intellectual property service system 104 can obtain an identifier of the intellectual property asset from the additional computing device, along with an indication of the type of risk-related service to be provided. Intellectual property service system 104 may then access mapping 620, data stored by intellectual property data store 120, models generated by intellectual property service provider 602, machine learning algorithms, or combinations thereof to provide intellectual property customer service 634 to customer 110 in connection with the intellectual property risk service requested by customer 110.

In an illustrative implementation, intellectual property service provider 602 may receive a request for IP related services 628 from customer 110, and the request for IP related services 628 may include an valuation request for intellectual property assets 636 of customer 110. Intellectual property service provider 602 may provide an valuation request for intellectual property asset 636 to additional computing device 630. In response to the request for valuation services, the user 632 can operate an additional computing device 630 to access the IP valuation tool 614. IP valuation tool 614 can generate one or more user interfaces that include one or more user interface elements to capture information that can be used by intellectual property service provider 602 to determine valuations of IP assets 636. In various implementations, the IP evaluation tool 614 may include a user interface element to capture an identifier of the IP asset 636 and the valuation type to be determined. In a particular illustrative example, the IP asset 636 can be a U.S. patent, and the additional computing device 630 can obtain an identifier of the IP asset 636, such as a patent number of the IP asset 636, and an input indicating that the valuation type corresponds to a sale of the IP asset 636.

Based on input obtained from the additional computing device 630, the intellectual property service system 104 can determine whether the mapping 620 includes one or more mappings related to the IP asset 636. For example, the intellectual property service system 104 may have previously determined a classification associated with the IP asset 636 and generated an intellectual property asset to classification map 622 for the IP asset 636. In another example, the intellectual property service system 104 may have previously determined a product and/or service corresponding to the IP asset 636 and generated an intellectual property asset to product/service mapping 624 for the IP asset 636. In an additional example, intellectual property service system 104 may have previously determined economic data corresponding to IP asset 636 and generated product/service to economic data map 626. In these cases, one or more mappings 620 related to the IP asset 636 may be stored by the intellectual property data store 120, and the intellectual property service system 104 may retrieve the mapping 620 corresponding to the IP asset 636 utilizing the identifier of the IP asset 636. In the event that mapping 620 does not include one or more mappings for determining valuations of IP asset 636, intellectual property service system 104 can generate at least one of an intellectual property asset to classification mapping 622 of IP asset 636, an intellectual property asset to product/service mapping 624 of IP asset 636, or a product/service to economic data mapping 626 of IP asset 636.

Continuing with the illustrative example above, intellectual property service system 104 can determine intellectual property asset to classification mapping 622 of IP asset 636 to determine a classification of IP asset 636. The intellectual property service system 104 may then identify additional intellectual property assets having the same classification as the IP asset 636. The intellectual property service system 104 can determine the extent of the intellectual property asset 636 relative to other intellectual property assets included in the same classification of IP assets 636. The extent of IP asset 636 can be used to determine an estimate of IP asset 636 relative to the extent of additional IP assets of the same category as IP asset 636. In various embodiments, the intellectual property service system 104 can also obtain licensing data, damage reimbursements, and/or settlement data for other intellectual property assets included in the same category as the IP asset 636 and utilize the data to determine an appreciation for the IP asset 636.

In addition, intellectual property service system 104 can determine an intellectual property asset to product/service mapping 624 for IP asset 636 that indicates the products and/or services corresponding to IP asset 636. In some cases, intellectual property service system 104 can identify a plurality of intellectual property asset to product/service mappings 624 related to IP assets 636. In particular embodiments, revenue associated with one or more products and/or services corresponding to an IP asset 636 may be used to determine an estimate of the IP asset 636. Further, intellectual property service system 104 determines a product/service to economic data mapping 626 for IP asset 636. The product/service-to-economic data map 626 of the IP asset 636 may indicate financial data associated with one or more products and/or services corresponding to the IP asset 636 and may be used by the intellectual property service system 104 to determine an valuation of the IP asset 636 in response to a request received from the additional computing device 630.

In particular embodiments, intellectual property service system 104 may generate one or more user interfaces that include one or more valuations of IP assets 636 and make the one or more user interfaces accessible to additional computing devices 630. In some implementations, the intellectual property service system 104 can provide a notification, e.g., an email, message, etc., to the additional computing device 630 to indicate that one or more valuations for the IP asset 636 have been determined.

Further, intellectual property service system 104 can provide access to mapping 620 associated with IP asset 636. In these cases, user 632 may utilize additional computing device 630 to provide input regarding mapping 620 for determining one or more valuations of IP assets 636. In an illustrative example, the intellectual property service system 104 may provide a first intellectual property asset to classification map indicating that the IP asset 636 is associated with a first classification and a second intellectual property asset to classification map indicating that the IP asset 626 is associated with a second classification. The additional computing device 630 may send an input to the intellectual property service provider 602 indicating a selection of the first intellectual property asset to the taxonomy mapping or the second intellectual property asset to the taxonomy mapping. The intellectual property service system 604 may also provide a plurality of intellectual property asset to product/service mappings 624 associated with the IP assets 636 to the additional computing device 630 and obtain an input from the additional computing device 630 indicative of at least one intellectual property asset to product/service mapping 624 for determining an valuation of the IP assets 636. Further, the intellectual property service system 104 can provide a plurality of product/service to economic data maps 626, communicate the IP assets 636 to the additional computing device 630 corresponding to one or more product and/or service related, and obtain input from the additional computing device 630 indicative of at least one product/service to economic data maps 626 for determining an estimate of the IP assets 636. Further, the intellectual property service system 104 can provide a plurality of product/service to economic data maps 626 corresponding to one or more products and/or services related to the IP asset 636 to the additional computing device 630 and obtain an input from the additional computing device 630 indicative of at least one product/service to economic data map 626 for determining an valuation of the IP asset 636.

FIG. 7 illustrates an example framework 700 for generating language constructs for claims of patent documents, according to some embodiments. Framework 700 includes intellectual property assets 702. In the illustrative example of fig. 7, intellectual property asset 702 is a claim of a patent or patent application. At 704, parsing and language analysis 704 may be performed for intellectual property asset 702. In various embodiments, parsing and language analysis 704 may be performed by intellectual property service system 104. In particular embodiments, parsing and language analysis 704 may include identifying words of intellectual property assets 702 and classifying words of intellectual property assets. In an illustrative example, parsing and linguistic analysis 704 may generate linguistic analysis 706 for intellectual property asset 702 that indicates a part-of-speech of at least a portion of words included in intellectual property asset 702. For example, linguistic analysis 706 may indicate verbs, specifications, and adjectives of intellectual property asset 702. In further scenarios, language analysis 706 may also indicate adverb, conjunctions, prepositions, pronouns, stop words, common words, unique words of the intellectual property asset, or combinations thereof.

Further, framework 700 may include generating one or more language constructs for intellectual property asset 702 at 708. In a particular example, intellectual property service system 104 may generate one or more language constructs at 708. One or more language constructs may indicate relationships between words of intellectual property asset 702. In various embodiments, multiple language structures may be generated for intellectual property asset 702. In an illustrative embodiment, language constructs may be generated for a plurality of features of intellectual property asset 702. For example, a language construct may be generated for the actions that occur in the claims. In some embodiments, linguistic structures may be generated for individual elements included in a patent or patent application claim.

In the illustrative example of fig. 7, a language structure 710 may be generated for a feature of intellectual property asset 702, starting with "display a portion … … of web page content". The feature may comprise an element of a claim of intellectual property asset 702. The language structure 710 may be a tree structure including a root node 712 and a plurality 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 was generated.

Nodes

714 and 716 correspond to nouns related to verbs in root node 712. Additionally, node 718 corresponds to nouns and adjectives included in node 716. Although the illustrative example of language structure 710 includes a single root node having three branch nodes, language structure 710 and other language structures may include additional nodes corresponding to different words of a feature of intellectual property asset 702. The root node 712 may be included in a first level of the language construct 710, the second node 714 and the third node 716 may be included in a second level of the language construct 710, and the fourth node 718 may be included in a third level of the language construct 710.

FIG. 8 illustrates an example framework 800 for determining similarity measures between the linguistic structure of a portion of the claims of a patent document and the linguistic structure of a product/service, in accordance with some embodiments. Framework 800 includes language constructs 710 from fig. 7 that represent a portion of a claim for intellectual property asset 702. Additionally, at 802, a language structure can be generated for a plurality of products and/or services using product/service data 804. Product/service data 804 may include data including descriptions of products and/or services. Product/service data 804 may be analyzed and parsed using natural language processing techniques to determine a classification of words included in product/service data 804. In addition, product/service data 804 can be analyzed to generate language constructs for various characteristics of products included in product/service data 804. For example, a first language structure 806 may be generated for at least one feature of a first product 808, a second language structure 810 may be generated for at least one feature of a second product 812, and a third language structure 814 may be generated for at least one feature of a third product 816. The language structures 806, 810, 814 may include a tree structure having a root node and one or more branch nodes.

At 818, framework 800 can include determining similarity measures 820 between language construct 710 and language constructs 806, 810, 814. In various implementations, the similarity metric 820 may indicate a degree of similarity between language structures. The similarity measure 820 may be determined based on similarity between words included in the language structure 710 and words included in the language structures 806, 810, 814. Additionally, similarity metric 820 may be determined based on the similarity between the arrangement of nodes included in linguistic structure 710 and the respective arrangements of nodes included in linguistic structures 806, 810, 814. In particular, the similarity metric 820 may include: the first similarity metric 822 corresponds to a degree of similarity between the language construct 710 and the first language construct 806. Additionally, the second similarity metric 824 may correspond to a similarity metric between the language structure 710 and the second language structure 810. Further, the similarity metrics 820 may include a third similarity metric 826 corresponding to a degree of similarity between the language construct 710 and the third language construct 814. In various implementations, the similarity measure 820 may include a numerical representation of the similarity between language structures. In particular embodiments, the similarity measure 820 may be specified along a numerical scale, such as 1 to 10 or 1 to 100, or represented by a percentage indicating the amount of similarity between language structures.

The similarity between language construct 710 and language constructs 806, 810, 814 may be used to determine one or more of

products

808, 812, 816 that may correspond to intellectual property asset 702. That is, where the similarity measures 822, 824, 826 are greater than a threshold degree of similarity, a mapping or other indicator of correspondence between the intellectual property asset 702 and the respective products 806, 810, 814 may be generated. These mappings may then be used to provide various services to the organization, such as IP valuation services, IP risk related services, and/or IP policy related services.

Fig. 9 illustrates an example framework 900 corresponding to values of intellectual property features of one or more products according to some implementations. Framework 900 may include a first product 902 corresponding to a first IP feature 904 and a second product 906 corresponding to a second IP feature 908. The first product 902 may be linked to the first IP feature 904 based on the degree of similarity between the language structure of the product 902 and the language structure of the first IP feature 904. Additionally, the second product 906 may be linked to the second IP feature 908 based on a degree of similarity between the language structure of the second product 906 and the language structure of the second IP feature 908. In an illustrative example, 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.

The framework 900 also includes an intellectual property service system 104 and an intellectual property data store 120. Intellectual property service system 104 may retrieve financial data 910 from intellectual property data store 120. Financial data 910 may include information related to revenue generated by the sale of various products and/or services, such as revenue information for first product 902 and revenue information for second product 906. Intellectual property service system 104 may also determine a portion of the product and/or service value corresponding to the IP feature associated with the product and/or service at 914. For example, intellectual property service system 104 may determine that a portion of the amount of revenue for first product 902 is due to first IP feature 904. In various implementations, the amount of revenue for the first product 902 due to the first IP feature 904 may be based on a breadth metric of the IP feature 904. To illustrate, the intellectual property service system 104 may determine the breadth of the first IP feature 904 with respect to additional intellectual property features included in the same technology category as the first IP feature 904. Based on the measure of the extent of the first IP feature 904 relative to the extent of the other IP features, the intellectual property service system 104 can determine an amount of revenue for the first product 902 to attribute to the first IP feature 904. In some cases, the higher the breadth metric value of the first IP feature 904, the higher the percentage of revenue of the first product 902 due to the first IP feature 904. Further, the lower the measure of extent of the first IP feature 904, the lower the percentage of revenue of the first product 902 due to the first IP feature 904.

At 914, framework 900 includes determining a value 916 of IP feature 914. In particular embodiments, intellectual property service system 104 may determine a value 916 of first IP feature 904 based on an amount of revenue for first product 902 and a portion of the revenue for first product 902 due to first IP feature 904. In various implementations, the intellectual property service system 104 may multiply the revenue portion of the first product 902 attributed to the first IP feature 904 by the revenue information of the first product 902 to determine the value 916 of the first IP feature 904.

Fig. 10-14 illustrate an example process of analyzing intellectual property data. The processes described herein are illustrated as a collection of blocks in a logical flow graph, 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, which, 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 so forth 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 otherwise specified. Any number of the described blocks may be combined in any order and/or in parallel to implement a process or an alternative process, and not all blocks need to be performed. For purposes of discussion, the processes are described with reference to the environments, architectures, and systems described in examples herein, such as those described with reference to fig. 1-9, although the processes may implement the environments, architectures, and systems in a wide variety of other ways.

Fig. 10 illustrates an example process 1000 for determining intellectual property assets corresponding to a product or service in accordance with some embodiments.

At 1002, process 1000 includes receiving information about a product from one or more data sources. In particular embodiments, the one or more data sources may include publicly accessible data sources. Publicly accessible data sources may include websites containing information accessible to the public without the need for organization issued credentials to maintain and/or control access to the websites. For example, publicly accessible data sources may include Uniform Resource Locators (URLs) that are publicly available without the organization first providing the URLs to individuals by itself. Conversely, by restricting access to URLs associated with private data sources and/or by requiring specific credentials to access private data sources, access to private data sources may be more tightly controlled than access to public data sources. In the event of a knock, an organization may maintain and/or control a website that includes both public access information that is accessible to the public, and private access information that is accessible to customers, employees, and other individuals to whom the organization specifically grants access. The publicly accessible data sources may include government websites, intellectual property databases maintained by intellectual property jurisdictions, websites of companies that provide products and/or services, combinations thereof, and the like, in which case obtaining data related to the products and/or services from the publicly accessible data sources may include determining a plurality of keywords associated with the products and/or services and parsing the publicly accessible data sources to identify data corresponding to at least one of the plurality of keywords. In addition, data corresponding to the at least one keyword can be extracted from a publicly accessible data source and stored in a data store of the service provider. In some embodiments, the intellectual property service provider may obtain information from a common data source using a web crawler or other application that may identify a website and parse the website to obtain specified information.

In further embodiments, the one or more data sources may include data sources of an organization that provides products and/or services for acquisition. The data source of the organization may be a private data source accessible to the intellectual property service provider based at least in part on the organization granting the intellectual property service provider access to the data source of the organization. The data source of the organization may be accessed via a database management application, and the intellectual property service provider may utilize the database management application to parse the data source of the organization to obtain at least one keyword of a plurality of keywords associated with the product and/or service and extract data corresponding to the at least one keyword from the data source of the organization. The intellectual property service provider may then store the data obtained from the organization's data sources in a data store of an additional organization, such as the intellectual property service provider's data store. In various embodiments, data related to a product and/or service stored in a data store of an organization that provides the product and/or service for sale may be stored such that a relationship between an intellectual property asset and the corresponding product and/or service is identifiable. That is, an organization may have tracked intellectual property assets associated with a particular product and/or service and stored data indicating such a relationship in such a manner that an intellectual property service provider may search the organization's data store and use the data generated by the organization to identify intellectual property assets corresponding to the product and/or service sold by the organization.

In further embodiments, data related to products and/or services may be obtained using crowd sourcing techniques. To illustrate, an intellectual property service provider may cause a request for information about a product and/or service to be issued on a website via which an individual accessing the website may submit a response to the request. In further embodiments, an intellectual property service provider may send a request to a particular individual to obtain information about a product and/or service. The request may be included in one or more types of communications, such as an email, a mobile device message, an instant messaging notification, a phone call, combinations thereof, and so forth. In various embodiments, an intellectual property service provider may identify one or more groups of individuals to obtain information about one or more products and/or services. For example, an intellectual property service provider may identify individuals who may be considered experts and/or who have at least a threshold amount of knowledge about various products and/or services, and when the intellectual property service provider wants to obtain information about products and/or services known to the respective group, the intellectual property service provider may contact the respective group of individuals. In these scenarios, at least a portion of the individuals contacted by the intellectual property service provider may provide information regarding one or more products and/or services to the intellectual property service provider in response to a request. In some cases, the obtained information about the 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, the information about the 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.

In various embodiments, the intellectual property service provider may also provide one or more portals for individuals to submit information. For example, an intellectual property service provider may generate one or more user interfaces that include at least one user interface element to obtain information about products and/or services offered for sale by an organization and/or to obtain information about intellectual property assets. The portal may be accessible by a representative of at least one of the intellectual property service provider or the organization, and in particular embodiments, the intellectual property service provider may provide a portal that may be used to obtain information about the organization's trade secret. In further embodiments, an intellectual property service provider may provide a portal that may be used to obtain information about the organization's patent files. In a further embodiment, an intellectual property service provider may provide a portal that may be used to obtain information about products and/or services offered by the organization.

At block 1004, process 1000 includes identifying an intellectual property asset. For example, intellectual property assets may be identified from publicly available resources and/or resources associated with one or more organizations.

At 1006, process 1000 includes determining one or more relationships between the individual product and the individual intellectual property asset. The relationship between an individual product or service and an individual intellectual property asset may be determined by identifying characteristics of the product or service and characteristics of the intellectual property asset. The characteristics of the product or service may be determined by parsing the description of the product or service and identifying functional, physical, and/or technical characteristics of the product or service. In various embodiments, videos and/or images associated with a product or service may be analyzed using one or more object recognition techniques to determine characteristics of the product or service. The intellectual property service provider may analyze the characteristics of the intellectual property asset and the characteristics of the product or service to determine similarities between the characteristics of the product or service and the characteristics of the intellectual property asset. In some cases, the degree of similarity may be based on the similarity of words associated with the product and/or service and the intellectual property asset. The degree of similarity may also be based on the similarity of the relationship between words related to the characteristics of the product or service and the relationship between words related to the characteristics of the intellectual property asset. The intellectual property service provider may determine that a relationship exists between the product or service and the intellectual property asset based on the similarity between the features of the product or service and the features of the intellectual property asset being at least a threshold similarity. In an illustrative example, an intellectual property service provider may determine the characteristics of a claim of a patent document and the characteristics of a product or service. The intellectual property service provider may then identify the relationship between the claim and the product or service based on the similarity between the features of the claim and the features of the product or service of the patent document.

At 1008, process 1000 includes generating association data indicative of one or more relationships between the individual product and the individual intellectual property asset based at least in part on the one or more relationships. For example, the association data may include a framework of relationships between individual products or services and intellectual property assets mapped to at least one of the products or services. The framework may also indicate an individual intellectual property asset and at least one product or service associated with the intellectual property asset. In this manner, the framework may search based on intellectual property assets or based on products or services to identify related products or services and intellectual property assets.

The association data may include a mapping contained in the framework indicating that the intellectual property asset corresponds to a product or service. In various embodiments, an intellectual property service 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 cases, the intellectual property service provider may parse the framework based on the identifier of the intellectual property asset or the identifier of the intellectual property asset to determine the relationship between the product or service and the intellectual property asset. The intellectual property service provider may utilize the relationship between the product or service and the intellectual property asset to provide various intellectual property related services to the customers of the intellectual property service provider. In particular embodiments, the intellectual property related services may include valuation services for intellectual property assets. In these scenarios, the intellectual property service provider may determine one or more metrics related to the intellectual property asset, wherein the one or more metrics include at least one of: an extent metric of one or more portions of the intellectual property asset, a risk metric with respect to one or more portions of the intellectual property asset, or a measure of coverage of one or more portions of the intellectual property asset. The intellectual property service provider may also determine revenue that the product or service earns over a period of time and determine an amount of revenue for the product or service attributed to one or more portions of the intellectual property asset based at least in part on one or more metrics. After determining the amount of revenue attributed to the product or service of the intellectual property asset, the intellectual property service provider may determine the value of the intellectual property asset based at least in part on the amount of revenue the product or service obtains over a period of time and the amount of revenue attributed to the portion of the product or service of the intellectual property asset.

At block 1010, the process 1000 includes receiving a request to identify one of the intellectual property assets corresponding to one of the products. For example, a user may provide input indicating a request to identify assets corresponding to a given one of the products using the user interface. Input data corresponding to the input may be received as a request.

At block 1012, the process 1000 includes identifying intellectual property assets corresponding to the product based at least in part on the association data. For example, the system may be used to determine which products are related to intellectual property assets using association data.

At 1014, process 1000 includes generating a response to the request 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 relationship between the intellectual property asset and the product or service. In some implementations, input indicating one or more modifications to the relationship between the intellectual property asset and the product or service may be obtained via the user interface or via another user interface.

Fig. 11 illustrates an example process 1100 for determining intellectual property assets corresponding to a product or service using a classification system, according to some embodiments.

At 1102, process 1100 includes generating a classification system that includes classifications, individual ones of which correspond to technology groupings. In various embodiments, the individual classifications may be associated with one or more criteria. In an illustrative implementation, individual classifications may be associated with one or more words, and each classification may be associated with a different phrase. Furthermore, the individual classifications of the classification system may be associated with one or more physical characteristics, one or more technical characteristics, or a combination thereof. In some embodiments, one or more physical features and/or one or more technical features may each be associated with a group of words.

At 1104, process 1100 includes receiving information about a product offered for procurement by an organization, the information being obtained from at least one of: a data store of the organization; organizing a website; or via a user interface.

At 1106, process 1100 includes determining a first characteristic of the product based at least in part on the information. 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 embodiments, information about a product or service may be analyzed to determine at least one of one or more physical characteristics or one or more technical characteristics of the product or service. One or more physical characteristics and/or one or more technical characteristics of the product or service may be identified based at least in part on comparing words of the at least one technical characteristic and/or words of the at least one physical characteristic to words included in the obtained information about the product or service. In an illustrative implementation, a physical characteristic of a product or service may be identified based, at least in part, on at least one word related to the physical characteristic included in information about the product or service. Further, technical features of the product or service may be identified based at least in part on at least one word related to the technical features included in the information about the product or service.

At 1108, the process 1100 includes determining that the product corresponds to one of the classifications based at least in part on the first feature corresponding to a reference feature associated with the classification. In various implementations, words associated with a first feature of a product or service may be compared to additional words associated with a second feature of a category. In some implementations, a classification may be assigned to a product or service based at least in part on at least a threshold number of words of a first feature of the product or service corresponding to a number of words of a second feature of the classification. In particular implementations, the model may be used to determine a classification of a product or service. The model may receive input including words corresponding to features of the product or service and words corresponding to the classification, and determine a probability that the product or service corresponds to the classification of the classification system. In an illustrative implementation, a classification may be assigned to a product or service when the probability that the product or service corresponds to the classification is greater than a threshold probability. In further embodiments, a classification may be assigned to a product or service when the probability that the product or service corresponds to the classification is the highest probability of a plurality of probabilities that have been determined for the product or service using a model for the plurality of classifications.

At 1110, process 1100 includes identifying intellectual property assets associated with an organization. The intellectual property assets of an organization may be identified based on information obtained from the organization. In particular embodiments, an intellectual property service provider may obtain information about an intellectual property asset, including files corresponding to the intellectual property asset, such as trade secret files, patent applications, utility patents, design patents, plant patents, trademark applications, or copyright submissions. In further embodiments, the organization may provide an identifier of the intellectual property asset of the organization, and the intellectual property service provider may obtain information about the intellectual property asset from one or more databases based on the identifier.

At 1112, the process 1100 includes determining a second characteristic of the intellectual property asset. The characteristics of the intellectual property asset may be determined by analyzing information related to the intellectual property asset, such as files related to the intellectual property asset. In a particular implementation, an intellectual property asset may be a claim of a patent or patent application, and a characteristic of the intellectual property asset may be identified by analyzing the words of the claim. In addition, when an intellectual property asset is a claim of a patent or patent application, a feature of the intellectual property asset may be identified by analyzing the words of the claim element. Further, when the intellectual property asset is a trademark, the characteristics of the trademark may be identified by analyzing descriptive words of the goods or services associated with the trademark. In various implementations, the characteristics of the intellectual property asset may be identified by comparing words included in a file associated with the intellectual property asset with words associated with physical and/or technical characteristics. An intellectual property service provider may assign words to individual physical features and individual technical features. In some embodiments, the intellectual property service provider may determine that the intellectual property asset comprises a technical feature or a physical feature when the 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.

At 1114, the process 1100 includes determining that the intellectual property asset corresponds to the classification based at least in part on the second feature of the intellectual property asset corresponding to the classification related reference feature. The intellectual property service provider may determine that the one or more third features of the intellectual property asset correspond to the at least one fourth feature associated with the classification by comparing the words of the one or more third features with the words of the at least one fourth feature. In various embodiments, the intellectual property service 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 in part, on at least a threshold number of words of the one or more third features corresponding to words of the at least one fourth feature.

In particular embodiments, the model may be used to determine a classification of an intellectual property asset. The model may receive an input including words corresponding to features of the intellectual property asset and words corresponding to the classifications and determine a probability that the intellectual property asset corresponds to a classification of the classification system. In an illustrative implementation, a classification may be assigned to an intellectual property asset when the probability that the intellectual property asset corresponds to the classification is greater than a threshold probability. In further embodiments, a classification may be assigned to an intellectual property asset when the probability that the intellectual property asset corresponds to the classification is the highest probability of a plurality of probabilities determined for the intellectual property asset using a model for the plurality of classifications.

In various embodiments, the model used to determine the intellectual property asset classification and the model used to determine the intellectual property asset classification may be modified. For example, an intellectual property service provider may request input regarding a classification of intellectual property assets. In some cases, the input may indicate that the intellectual property asset should be classified according to different classifications. In other cases, the input may indicate that the intellectual property asset is correctly classified. The intellectual property service provider may then modify the model used to classify the intellectual property assets based on the input. Further, the intellectual property service provider may request input regarding the classification of the product or service. The input may indicate that the product or service should be classified according to different classifications. In other scenarios, the input may indicate that the product or service is correctly classified. The intellectual property service provider may then modify the model used to classify the product or service based on the input.

Fig. 12 illustrates an example process 1200 of performing qualitative and quantitative analysis of intellectual property data in accordance with some embodiments.

At 1202, process 1200 includes receiving information indicative of revenue associated with a product. The information may include financial data, such as information regarding revenue obtained by one or more organizations selling the product or service. Financial data may be obtained from a variety of sources. For example, an intellectual property service provider may provide a portal that obtains information about financial data for products and/or services. To illustrate, the intellectual property service provider may generate one or more user interfaces that include one or more user interface elements to capture one or more portions of financial data. In further embodiments, the intellectual property service provider may implement a software tool to parse the data store of the organization that provides the product or service for sale to identify portions of the financial data corresponding to the product or service. In further embodiments, the intellectual property service 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 an illustrative example, an intellectual property service provider may utilize web crawlers and other website parsing tools to analyze information contained in a website, including websites of one or more organizations that provide a product or service for acquisition and/or third party websites to identify at least a portion of financial data corresponding to the product or service.

At block 1204, the process 1200 includes determining a classification of the product based at least in part on the technical features of the product. For example, an intellectual property service provider may determine a classification of a product or service by determining characteristics of the product or service and comparing the characteristics of the product or service to criteria of a plurality of classifications of a classification system.

At block 1206, process 1200 includes identifying a patent claim corresponding to the product based at least in part on the patent claim being associated with the classification. For example, intellectual property assets may be associated with a category. These intellectual property assets may include patents, which may include claims. Additionally, process 1200 may generally include identifying intellectual property assets of an organization. An organization's intellectual property assets may include one or more intellectual property assets having legal rights that may be enforced by the organization. In various embodiments, intellectual property assets may be assigned to an organization. In further implementations, an organization may have a license for an intellectual property asset. The intellectual property service provider may determine that the intellectual property asset corresponds to an organization based, at least in part, on information obtained from the organization. For example, an organization may provide a list of intellectual property assets to an intellectual property service provider. The list may be stored in a data store of an organization accessible to the intellectual property service provider, and the intellectual property service provider may parse the data store to obtain the list. In a further embodiment, the organization may provide the intellectual property asset list to the intellectual property service provider via a communication such as an email or message. In addition, an intellectual property service organization may provide a customer portal through which the organization may provide a list of intellectual property assets of the organization. In particular embodiments, an intellectual property service provider may analyze information available from a public data source, such as a patent jurisdictional database, to identify intellectual property assets of an organization. To illustrate, an intellectual property service provider may parse a publicly accessible data store to identify intellectual property assets assigned to an organization, intellectual property assets of an organization that are applicants, intellectual property assets that have inventors related to the organization, or a combination thereof.

At block 1208, the process 1200 includes identifying a word included in the patent claim. For example, data representing a patent may be parsed and/or text recognition techniques may be performed to identify the words that make up a patent claim.

At block 1210, the process 1200 includes determining the breadth of the patent claims. In some implementations, the intellectual property service provider can determine the extent of the intellectual property asset relative to the extent of other intellectual property assets, such as intellectual property assets that are in the same category as the intellectual property asset, to determine the portion of revenue attributed to the product or service of the intellectual property asset.

At block 1212, the process 1200 includes determining a portion of revenue to allocate to the patent claim based at least in part on the breadth of the patent claim. For example, to determine a measure of the extent and/or portion of revenue of a product and/or service corresponding to an intellectual property asset, the intellectual property service system may utilize one or more language analysis techniques and one or more machine learning techniques. The intellectual property service provider may determine the portion of the revenue of a product or service attributed to an intellectual property asset based on the amount of the feature of the product or service covered by the intellectual property asset. For example, if a product or service has multiple features, the portion of the amount of the feature covered by the intellectual property asset relative to the total number of features may correspond to the portion of the revenue of the product or service attributed to the intellectual property asset. In one illustrative example, an intellectual property asset may cover 2% of the features of a product or service, and an intellectual property service provider may determine that 2% of the revenue for the product or service will be attributed to the intellectual property asset. In particular embodiments, the proportion of the features of the product or service covered by the intellectual property asset may be used as a starting point for determining the revenue portion of the product or service attributed to the intellectual property asset. In various embodiments, the intellectual property service provider may modify an initial portion of the amount of revenue for a product or service based on a plurality of discount factors, which are discussed in more detail below. In further embodiments, the intellectual property service provider may determine the portion of the revenue of a product or service attributed to an intellectual property asset based on the extent of the intellectual property asset. In some implementations, the intellectual property service provider can determine the extent of the intellectual property asset relative to the extent of other intellectual property assets, such as intellectual property assets that are in the same category as the intellectual property asset, to determine the portion of revenue attributed to the product or service of the intellectual property asset.

At block 1214, process 1200 includes determining a value metric for the patent claim based at least in part on the portion of revenue allocated to the patent claim. For example, a value metric for an intellectual property asset may be determined by multiplying the revenue of the product or service by the portion of the revenue of the product or service attributed to the intellectual property asset. In various embodiments, one or more discount factors may also be used to determine a value metric for an intellectual property asset. The discount factor may be applied to one of: an amount of revenue for the product or service used to determine the value metric, or a portion of the revenue for the product or service attributed to the intellectual property asset. The one or more discount factors may reduce the initial measure of value of the intellectual property asset to a modified measure of value of the intellectual property asset. In an illustrative example, the one or more discount factors may be based, at least in part, on a first risk corresponding to the intellectual property asset being invalid and a second risk corresponding to a litigation probability for the intellectual property asset. In particular embodiments, the intellectual property asset may comprise a patent claim, and the first risk may be based at least in part on an examination history associated with the patent claim. Further, where the intellectual property asset includes a patent claim, the first risk may be based at least in part on additional measures of the reviewer associated with the patent claim relative to other reviewers included in the same technical unit as the reviewer, the measures corresponding to at least one of a number of permit notices generated over a period of time, an average number of review comment notices prior to generation of the permit notice, a number of complaint notices submitted over a period of time, a complaint decision of a number of withdrawals over a period of time, or a combination thereof. Further, the second risk is based at least in part on a first number of litigation events occurring with respect to a plurality of intellectual property assets having the same classification as the intellectual property asset, the additional plurality of intellectual property assets in a different classification of the classification system being included with respect to a second number of litigation events occurring with respect to a company. In some illustrative examples where the intellectual property asset comprises a patent claim, the discount factor may be determined based at least in part on a number of additional patent claims assigned to an organization corresponding to the product or service. In an illustrative example where the intellectual property asset comprises a trademark, the discount factor may be based at least in part on one of: multiple litigation events related to brand assets that belong to the same category as the brand asset, multiple objections related to brand assets that belong to the same category as the brand asset, or the indicators of reviewers associated with the brand asset are related to additional indicators of other reviewers associated with other brand assets included in the taxonomy.

Additionally or alternatively, process 1200 may include determining that the product or service corresponds to an intellectual property asset. The intellectual property service provider may determine that the product or service corresponds to an intellectual property asset based on obtaining input indicating that the product or service corresponds to the intellectual property asset. For example, a representative of an organization may access a customer portal provided by an intellectual property service provider to input information via a user interface indicating that a product or service corresponds to an intellectual property asset. In other examples, a representative of an intellectual property service provider may enter information into a user interface indicating that an intellectual property asset corresponds to a product or service. In further embodiments, an organization may store data indicating relationships between intellectual property assets and products and/or services offered for sale by the organization. To illustrate, for each product or service of an organization, the organization may store a list of intellectual property assets associated with one or more features of the respective product or service. In these scenarios, an intellectual property service provider may parse an organization's data store or an organization's website, which includes a list of intellectual property assets related to one or more products and/or services of the organization.

In further embodiments, the intellectual property service provider may determine the product or service corresponding to the intellectual property asset of the organization by determining a degree of similarity between the product or service and the intellectual property asset. In various embodiments, an intellectual property service provider may parse an intellectual property file associated with an intellectual property asset to determine respective first words of the intellectual property file and parse information related to a product or service to determine respective second words contained in the information. The intellectual property service provider may then determine a similarity measure between at least a portion of the individual first words and at least a portion of the individual second words. The intellectual property service provider may determine that the product or service corresponds to an intellectual property asset based at least in part on determining that the similarity metric is at least a threshold similarity metric. In further embodiments, the intellectual property service provider may analyze the information about the product or service and the information about the intellectual property asset to determine physical and/or technical characteristics of the product or service and the intellectual property. The intellectual property service provider may determine that the intellectual property asset corresponds to a product or service based at least in part on a similarity between the physical and/or technical features of the product or service and the physical and/or technical features of the intellectual property asset.

In particular embodiments, the intellectual property service provider may determine the similarity of the intellectual property asset to the product or service to determine that the product or service and the intellectual property asset are both associated with the same classification of the classification system prior to analyzing the product or service information and the intellectual property asset information. In various embodiments, an intellectual property service provider may determine a classification of a product or service by determining characteristics of the product or service and comparing the characteristics of the product or service to criteria of a plurality of classifications of a classification system. Further, the intellectual property service provider may determine the classification of the intellectual property asset by determining characteristics of the intellectual property asset and comparing the characteristics of the intellectual property asset to criteria of a plurality of classifications of the classification system. In various implementations, an intellectual property service provider may determine a first similarity metric indicative of a similarity between a feature of a product or service and a classification criterion, and determine a second similarity metric indicative of a similarity between a feature of an intellectual property asset and a classification criterion. The intellectual property service provider may then determine a classification of the product or service using the first similarity metric and determine a classification of the intellectual property asset using the second similarity metric. The intellectual property service provider may determine a classification of the product or service and a classification of the intellectual property asset based on the threshold similarity metric such that a first similarity metric and/or a second similarity metric for a particular classification that is higher than the threshold similarity may indicate that the product or service and/or intellectual property asset corresponds to the classification. In further embodiments, the intellectual property service provider may determine the similarity measure having the highest value among the first similarity measures to determine that the classification associated with the highest value first similarity measure corresponds to a product or service. The intellectual property service provider may also determine the second similarity measure having the highest value among the second similarity measures to determine that the classification associated with the highest value second similarity measure corresponds to the intellectual property asset.

Fig. 13 illustrates an example process 1300 of using a language structure of an intellectual property asset and a language structure of a product or service to determine an intellectual property asset corresponding to the product or service according to some implementations.

At 1302, process 1300 includes determining a first part of speech of a first word included in first information associated with a product. In various implementations, the respective words included in the first information and the parts of speech associated with the respective words may be determined using natural language processing techniques. In particular embodiments, the intellectual property service provider may determine at least one of a noun, a verb, an adjective, an adverb, a preposition, a conjunctive, or a pronoun included in the first information. Further, the intellectual property service provider may determine a relationship between words included in the first information. For example, an intellectual property service provider may identify words contained in the same sentence. The intellectual property service provider may also identify words contained in the same paragraph. In addition, the intellectual property service provider may identify one or more adjectives that modify the individual's specifications and one or more adverbs that modify the individual's verbs. Further, the intellectual property service provider may store data indicating relationships between words. To illustrate, the intellectual property service provider may assign identifiers to the individual words included in the first information and assign codes or categories to the individual words. In a particular example, the intellectual property service provider may assign a code to a word included in the first information indicating that the word is a noun, and also store an identifier of an adjective associated with the word in a table associated with the word. The table may also include identifiers of words in the same sentences or elements as the nouns.

At block 1304, process 1300 includes determining a second part of speech of a second word included in second information corresponding to a claim of the patent document. As described above, determining the second part of speech may be performed in the same or similar manner as determining the first part of speech.

At block 1306, the process 1300 includes determining a portion of the first word that corresponds to the product feature. For example, a catalog of features may be associated with a product, and an intellectual property service provider may analyze first words associated with the features to determine which words correspond to at least one of the features associated with the product.

At block 1308, process 1300 includes determining a first action to perform with respect to the feature based at least in part on the first part-of-speech. For example, an intellectual property service provider may determine which word is a verb that acts on a given feature. Verbs may indicate actions to be performed with respect to features.

At 1310, process 1300 includes generating a first language structure of the feature based at least in part on the first action, the first language structure indicating one or more first relationships between the first action and one or more first nouns included in the first information. In a particular example, the language structure may include a tree structure having 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 a subsequent level of the tree structure. In the tree structure, each node that is a branch of another node is associated with an initial node. That is, the tree structure may include a parent node and child nodes associated with the parent node. In an illustrative example, a specification included in a first node at a first level of the tree structure may be associated with a first adjective included in a second node of the tree structure and a second adjective in a third node of the tree structure, where the second node and the third node are children of the first node, contained at a second level of the tree structure. In various embodiments, a language structure of an intellectual property asset may be generated in a root node for an action related to the intellectual property asset, where a word corresponding to the action is included in a branch node. In an illustrative example, verbs corresponding to actions may be included in a root node of a first level of linguistic structure, and adjectives associated with the verbs may be included in branch nodes of linguistic structures of a second and/or third level of linguistic structure. Where the intellectual property asset is a patent claim, the intellectual property service provider may generate language constructs for the individual elements contained in the patent claim.

At 1312, process 1300 includes a second action in determining a claim based at least in part on the inclusion in the second part of speech. That is, the intellectual property service provider may analyze the words included in the second information and identify at least one of a norm, a verb, an adjective, an adverb, a preposition, a conjunctive, or a pronoun included in the second information. In particular embodiments, the intellectual property service provider may utilize natural language processing techniques to determine individual words and corresponding parts of speech of the words included in the second information.

At 1314, the process 1300 includes generating, based at least in part on the second action, a second language construct of the claim that indicates one or more second relationships between the second action and one or more second nouns included in the claim. The second and/or additional language 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 a subsequent level of the tree structure. In the tree structure, each node that is a branch of another node is associated with an initial node. That is, the tree structure may include a parent node and child nodes associated with the parent node. In an illustrative example, a noun included in a first node at a first level of the tree structure may be associated with a first adjective included in a second node and a second adjective included in a third node of the tree structure, where the second node and the third node are children of the first node, contained at a second level of the tree structure. In various implementations, a language structure of a product or service may be generated for an action performed in a root node for the product or service, with additional words corresponding to the action included in branch nodes. In an illustrative example, verbs corresponding to actions may be included in a root node of a first level of the additional linguistic structure, and nouns and adjectives related to the verbs may be included in branch nodes of the second and/or additional linguistic structures and/or in a third level of the additional linguistic structure. In some embodiments, an intellectual property service provider may generate language constructs for individual technical features of a product or service, individual physical features of a product or service, or both.

At 1316, the process 1300 includes determining a similarity measure between the first language construct and the second language construct. For example, one or more components of a first language construct may be compared to one or more components of a second language construct. The similarity measure may indicate a high degree of similarity when the components of the language structure correspond to each other. The similarity measure may indicate a low degree of similarity when the components do not correspond and/or there is a difference between language structures. A similarity measure between the first language construct and the second language construct may be determined by comparing the similarity of the configuration of the first language construct and the configuration of the second language construct. For example, an intellectual property service provider may determine a similarity metric based on a plurality of levels included in a first language structure and a plurality of levels included in a second language structure. The intellectual property service provider may also determine a similarity metric based on the number of nodes in each level of the first language structure and the number of nodes in each level of the second language structure. To illustrate, the intellectual property service provider may compare the number of nodes in the second level of the first language construct with the number of nodes in the second level of the second language construct.

The intellectual property service provider may also determine a similarity measure based on a similarity between words included in the first language structure and words included in the second language structure. To illustrate, the intellectual property service provider may compare one or more words included in the root node of the first language structure with one or more words included in the root node of the second language structure. In these cases, the similarity metric may be based on whether the one or more words included in the root node of the first language construct are the same, similar, synonymous, etc. as the one or more words included in the root node of the second language construct. Further, the intellectual property service provider may compare words in the branching nodes of the first language structure with words in the branching nodes of the second language structure to determine a similarity measure. In particular embodiments, an intellectual property service provider may compare words included in various levels of a first language structure with words included in various levels of a second language structure.

At 1318, process 1300 includes determining that the claim corresponds to a product based at least in part on the similarity metric. In some illustrative examples, the intellectual property service provider may determine that the product or service and the intellectual property asset are in the same category of the taxonomy system prior to comparing the first language construct and the second language construct. Further, in various instances, an intellectual property service provider may generate multiple language constructs for a product or service and multiple language constructs for an intellectual property asset. In these cases, the intellectual property service provider may compare the one or more language constructs of the product or service to the one or more language constructs of the intellectual property asset to determine a similarity measure between the product or service and the intellectual property. In further embodiments, a similarity measure, such as a similarity measure, between the first language construct and the second language construct may be modified based on user input. For example, an intellectual property service provider may receive input indicating that a product or service does not correspond to an intellectual property asset. In these cases, the intellectual property service provider may modify the similarity measure and/or modify the model used to generate the similarity measure based on the input. In further embodiments, the intellectual property service provider may determine that the intellectual property asset and the product or service do not correspond to each other, and the intellectual property service provider may receive input indicating that the additional product or service and the additional intellectual property asset do correspond to each other. Thus, the intellectual property service provider may modify additional similarity measures between one or more language constructs of the product or service and one or more language constructs of the intellectual property asset, or a pattern for generating additional similarity measures from the input.

Fig. 14 illustrates an example process 1400 for providing a service to a customer based on a relationship between a product or service and an intellectual property asset, in accordance with some embodiments.

At 1402, process 1400 includes receiving first information about a product offered for acquisition from one or more data sources. For example, the information may include details associated with the product and/or the source of the product.

At 1404, process 1400 includes receiving second information about the intellectual property asset from the one or more data sources. For example, the information may include files and/or data associated with and/or corresponding to the intellectual property asset.

At 1406, process 1400 includes determining, based at least in part on the first information and the second information, that one of the intellectual property assets corresponds to a feature associated with one of the products. The intellectual property service provider may use the comparison between individual intellectual property assets and individual products and/or services to determine a similarity measure between individual intellectual property assets and individual products and/or services. The intellectual property service provider may determine that a relationship exists between the intellectual property asset and the product or service where the similarity metric is greater than a threshold metric or has the highest value among a plurality of similarity metrics associated with a particular classification. In an illustrative implementation, an intellectual property service provider may generate language constructs using natural language processing techniques to determine similarity measures of respective intellectual property assets to respective products or services.

In various embodiments, an intellectual property service provider may generate a framework indicating the relationship between intellectual property assets and products and/or services. In these scenarios, an intellectual property service provider may receive a request to determine a product or service corresponding to an intellectual property asset. An intellectual property service provider may receive an identifier of a product or service and then parse a frame using the identifier of the product or service to identify one or more intellectual property assets that the frame indicates have a relationship to the product or service. Further, the intellectual property service provider may receive a request including an intellectual property asset identifier. In these cases, the intellectual property service provider may use the identifier resolution framework and identify one or more products or services that the framework indicates have a relationship to the intellectual property asset.

At 1408, process 1400 includes receiving a first request for determining a value of an intellectual property asset. In various embodiments, the request may be provided via one or more tools provided by the intellectual property service provider. In various embodiments, the intellectual property service provider may generate one or more user interfaces through which a customer of the intellectual property service provider and/or a representative of the intellectual property service provider may make the service request.

At 1410, process 1400 includes identifying economic data indicative of revenue for an organization associated with the product based at least in part on receiving the first request. For a given product, the economic data may indicate an amount of revenue attributable to an organization of the product.

At block 1412, process 1400 includes determining a portion of revenue attributable to the intellectual property asset. In an illustrative example, an intellectual property service provider may determine the breadth of an intellectual property asset relative to the breadth of additional intellectual property assets (e.g., intellectual property assets included in the same technical category as the intellectual property asset). In these cases, the intellectual property service provider may determine the portion of revenue attributed to the product or service of the intellectual property asset based at least in part on the extent of the intellectual property asset relative to the extent of the additional intellectual property. A higher relative extent score of an intellectual property asset relative to an additional intellectual property asset may cause an intellectual property service provider to allocate a greater amount of revenue for a product or service to the intellectual property asset than the amount of revenue for a product or service attributed to the intellectual property asset if the relative extent of the intellectual property asset is lower.

At 1414, process 1400 includes determining a value measure for the intellectual property asset based at least in part on the portion of revenue. For example, in addition to revenue attributable to the asset and/or other products of the asset's characteristics (e.g., extent, coverage, and/or exposure factors), a portion of the revenue attributable to the intellectual property rights may be used as a factor in determining the total value of the asset.

At 1416, process 1400 includes receiving a second request to determine at least one of: a first public value representing a loss of coverage with respect to an intellectual property asset; or a second public value representing a litigation event related to the intellectual property asset.

At 1418, the process 1400 includes determining at least one of the first public value or the second public value based at least in part on receiving the second request. The public value associated with an intellectual property asset may be based on the probability of occurrence of a litigation event with respect to the intellectual property asset. In further embodiments, the public value associated with the intellectual property asset may correspond to a probability that the scope of the intellectual property asset may be narrowed. In further embodiments, the disclosure amount associated with an intellectual property asset may correspond to a probability that the intellectual property asset may be fully or partially invalid. In an illustrative example, the higher the amount of risk associated with an intellectual property asset, the higher the discount applied to the portion of the revenue for the product or service attributed to the intellectual property asset. In the case where the intellectual property asset is a trade secret, the intellectual property service provider may determine a discount applicable to the product or service income component belonging to the intellectual property asset based on the likelihood of theft of the intellectual property.

At 1420, process 1400 includes causing an indicator of a value metric for the product and at least one of the first public value or the second public value to be displayed via one or more user interfaces. For example, the value metric for an intellectual property asset may be determined using a portion of the revenue of a product or service obtained by one or more organizations over a period of time via the sale of the product or service and the revenue of the product or service attributed to the intellectual property asset. In particular embodiments, the value metric may be updated. For example, when an intellectual property service provider obtains updated revenue information for a product or service, the intellectual property service provider may update a value metric for an intellectual property asset based on the updated revenue. Further, the intellectual property service provider may obtain information that may be used to update the discount to apply to the portion of the revenue of the product or service attributed to the intellectual property asset, and the intellectual property service system may update the value metric accordingly for application based on the modified discount. In some implementations, the intellectual property service provider can obtain feedback indicating the accuracy of the value metric and modify the value metric based on the feedback.

In various embodiments, the value metric for an intellectual property asset may be based on an valuation type of the intellectual property asset. To illustrate, a first value metric may be determined when an intellectual property asset is valued as part of the sale of the intellectual property asset, and a second value metric may be determined when the intellectual property asset is evaluated as a collateral for a loan. In other examples, the third value metric may be determined when the intellectual property asset is valued as part of a merger of an organization that sells or owns a legal right to execute the intellectual property asset.

In particular embodiments, additional services may be provided by an intellectual property service provider. For example, an intellectual property service provider may receive a request to identify a plurality of intellectual property assets of an organization associated with a particular technology group. In other examples, an intellectual property service provider may receive a request to determine one or more risks corresponding to the intellectual property service provider. In further examples, an intellectual property service provider may receive a request to identify one or more organizations that have intellectual property in a particular taxonomy of a particular technology grouping or taxonomy system. In an illustrative example, an intellectual property service provider may provide a response to a request with a framework that indicates the relationship between intellectual property assets and products or services. In various instances, an intellectual property service provider may obtain an identifier of an intellectual property asset, an identifier of an organization, an identifier of a product or service, an identifier of a technology group, or a combination thereof for parsing the framework and providing a response to a service request. In a particular illustrative scenario, the various identifiers may comprise an alphanumeric string comprising a series of characters. In further embodiments, the service request may include keywords that the intellectual property service provider may use to parse the framework and generate a response to the service request.

Clause and subclause

1. A method, comprising: receiving information about a product from one or more data sources; identifying an intellectual property asset; determining one or more relationships between individual products and individual intellectual property assets; generating association data based at least in part on the one or more relationships, the association data indicating one or more relationships between the individual product and the individual intellectual property asset; receiving a request to identify one of the intellectual property assets corresponding to one of the products; identifying an intellectual property asset corresponding to the product based at least in part on the association data; and generating a response to the request, the response indicating that the intellectual property asset is associated with the product.

2. The method of clause 1, wherein the data source comprises a publicly accessible data source, the method further comprising: determining a keyword related to the product; identifying data corresponding to the keyword based at least in part on the publicly accessible data source; and extracting data corresponding to the keyword from the publicly accessible data source.

3. The method of clause 1 or 2, wherein the data source comprises a data store associated with a first organization that provides the product for acquisition, and the method further comprises: determining, by the second organization, a keyword associated with the product; identifying, by the second organization and from the data store of the first organization, data corresponding to the keyword; and extracting, by the second organization, data corresponding to the keyword.

4. The method of clause 1, 2 or 3, further comprising: identifying, with a data store, data indicative of a relationship between an intellectual property asset and a product; and wherein generating the association data comprises generating the association data based at least in part on the data indicative of the relationship between the intellectual property asset and the product.

5. The method of clause 1, 2, 3 or 4, wherein the request comprises a first request, the method further comprising: such that the second request for information about the product is at least one of: publishing on a website accessible to the computing device; or to a computing device; and receiving, in response to the second request, data indicating at least one of a source of the information or the information.

6. The method of clauses 1, 2, 3, 4, or 5, further comprising: generating a user interface comprising a user interface element configured to receive an input representing information about an intellectual property asset; receiving an input with a user interface element; and wherein generating the association data comprises generating the association data based at least in part on the input.

7. The method of clauses 1, 2, 3, 4, 5, or 6, further comprising: determining a metric related to the intellectual property asset, the metric comprising at least one of: a measure of the extent of at least a portion of the intellectual property asset; a measure of disclosure 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 an amount of revenue attributed to at least a portion of the intellectual property asset based at least in part on the metric.

8. A system, comprising: one or more processors; and one or more computer-readable media storing instructions executable by 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 about at least one of a product or a service, at least a portion of the information including economic data; determining a relationship between a product of the products or a service of the services and the intellectual property asset; generating association data indicative of a relationship between the product or service and the intellectual property asset; and identifying an intellectual property asset of the plurality of intellectual property assets corresponding to the product or service based at least in part on the association data.

9. The system of clause 8, wherein the information about the at least one of the product or the service includes a description of the at least one of the product or the service, and the operations further comprise determining a characteristic of the at least one of the product or the service based, at least in part, on the description.

10. The system of clause 8 or 9, wherein the characteristic comprises a first characteristic, the operations further comprising: identifying information about the intellectual property asset based at least in part on at least one of a publicly accessible data source or a data source of an organization providing at least one product or service; determining a second characteristic of the intellectual property asset based at least in part 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 characteristic and the second characteristic.

11. The system of clause 8, 9 or 10, wherein the intellectual property asset comprises a patent document, the operations further comprising: receiving a description of at least one of a product or a service, the description including words related to the at least one of the product or the service; at least a portion of the determinants is contained in the claims of the patent document; and wherein the association data indicates that the claim corresponds to at least one of a product or service based, at least in part, on at least a portion of the word being included in the claim.

12. The system of clause 8, 9, 10 or 11, the operations further comprising generating a user interface comprising one or more user interface elements configured to capture information about the plurality of intellectual property assets, the one or more user interface elements comprising at least one of: a first element configured to receive first information related to a trade secret file; a second element configured to receive second information associated with a brand file; or a third element configured to receive third information associated with the copyright file.

13. The system of clause 8, 9, 10, 11, or 12, the operations further comprising: causing the request for information for the at least one product or service to be at least one of: publishing on a website accessible to the computing device; or to a computing device; and receiving, in response to the request, data indicative of at least one of a source of the information or the information.

14. The system of clause 8, 9, 10, 11, 12, or 13, the operations further comprising: determining an amount of revenue associated with at least one product or service obtained over a period of time based at least in part on economic data; determining a portion of an amount of revenue attributed to the intellectual property asset; and determining a value of the intellectual property asset based at least in part on the portion of the amount of revenue.

15. A method, comprising: receiving information about at least one of a product or a service, at least a portion of the information comprising economic data associated with the at least one of the product or the service; determining a relationship between an individual product or individual service of the at least one of the products or services and an individual one of intellectual property assets; generating association data indicating the relationship between the individual ones of the products and the individual ones of the intellectual property assets; identifying one of the intellectual property assets corresponding to one of a product or a service of the at least one of a product or a service based at least in part on the association data; and generating data indicating that the intellectual property asset is associated with the at least one of the product or the service.

16. The method of clause 15, further comprising: such that the request for information about at least one of the products or services is at least one of: publishing on a website accessible to the computing device; or to a computing device; and receiving data indicating at least one of a source of the information or the information in response to the request.

17. The method of clause 15 or 16, further comprising: receiving input data indicating that at least one product or service does not correspond to at least one intellectual property asset; and causing the association data to indicate that the at least one of a product or a service does not correspond to the at least one of the intellectual property assets.

18. The method of any of clauses 15, 16 or 17, wherein the intellectual property asset comprises a patent file and a trademark file, the method further comprising: receiving a description of the product or service; determining a first relationship between the patent document and the product or service based, at least in part, on the first number of words included in the claims of the patent document corresponding to a second number of words included in the description of the product or service; determining a second relationship between the brand file and the product or service based, at least in part, on the third number of words contained in the goods and service description of the brand file corresponding to the second number of words contained in the product or service description; and wherein the associated data comprises: a first association of a claim of a patent document with a product or service; and a second association between the brand file and the product or service.

19. The method of any of clauses 15, 16, 17 or 18, wherein the economic data comprises revenue for the product or service over a period of time, and the method further comprises: determining a first portion of revenue attributed to patent document claims based, at least in part, on the first breadth metric of the patent document claims; and determining a second portion of revenue attributed to the trademark file based at least in part on the second measure of extent of the description of the goods and services in the trademark file.

20. The method of any of clauses 15, 16, 17, 18 or 19, wherein the intellectual property asset comprises trade secrets and brand files, the method further comprising: receiving a description of a product or service; determining a relationship between the trade secret file and the product or service based at least in part on the first number of words included in the trade secret file corresponding to the second number of words included in the description of the product or service; and wherein the relationship comprises a relationship between the trade secret document and the product or service.

21. A method, comprising: generating a classification system comprising classifications, each of the classifications corresponding to a technology group; receiving information about a product provided by an organization for acquisition, the information being obtained from at least one of: a data store of the organization; the web site of the organization; or via a user interface; determining a first characteristic of the product based at least in part on the information; determining that the product corresponds to a classification in the classifications based, at least in part, on a first feature corresponding to a reference feature associated with the classification; identifying an intellectual property asset associated with an organization; determining a second characteristic of the intellectual property asset; and determining that the intellectual property asset corresponds to the classification based at least in part on the second feature of the intellectual property asset corresponding to the reference feature associated with the classification.

22. The method of clause 21, further comprising: determining a first word associated with the classification; determining a second word contained in the information; determining that at least a threshold number of words of the second word are contained in the first word; 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 a threshold number of words of the second word included in the first word.

23. The method of clause 21 or 22, further comprising: determining a physical characteristic of the product, the physical characteristic being associated with the first word; determining a technical characteristic of the product, the technical characteristic being associated with the second word; and wherein the first feature corresponds to a physical feature or a technical feature.

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 a 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 at least one of the first word or the second word associated with the classification.

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 an intellectual property asset; and determining that the product is associated with the intellectual property asset based at least in part on at least one of the first word or the second word associated with the intellectual property asset.

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 an individual product of the products corresponds to an individual product in the classification; and generating a second model configured to determine a second probability that an individual asset of the intellectual property assets corresponds to an individual asset of the taxonomy.

27. The method of any of clauses 21, 22, 23, 24, 25, or 26, further comprising: sending a feedback request related to the classification system; receiving input data indicating that the product is not eligible for classification; and training the first model based at least in part on the input data.

28. The method of any of clauses 21, 22, 23, 24, 25, 26, or 27, further comprising: sending a feedback request related to the classification system; receiving input data indicating that the intellectual property asset does not comply with the classification; and training the second model based at least in part on the input data.

29. A system, comprising: one or more processors; 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: generating a model configured to determine a classification of at least one product or service; receiving information about at least one product or service provided by an organization; identifying a word associated with at least one of a product or service contained in the information; determining a probability that at least one of the product or service corresponds to a classification of the classifications based at least in part on the word and using the model; determining, based at least in part on the probability, that at least one of the product or service corresponds to a classification; identifying an intellectual property asset; determining characteristics of an intellectual property asset; and determining, using the model, that the intellectual property asset corresponds to the classification based at least in part on the features corresponding to the word.

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 in part on the word and using the model, a second probability that at least one of the product or service corresponds to a second one of the classifications, the first probability being greater than the second probability; and wherein determining that at least one of the product or service corresponds to the first classification comprises determining that at least one of the product or service corresponds to the first classification based at least in part on the first probability being greater than the second probability.

31. The system of clause 29 or 30, the operations further comprising: receiving input data indicating that at least one of the products or services does not correspond to the classification; and training the model based at least in part on the input data.

32. The system of any of clauses 29, 30 or 31, wherein the probability is based at least in part on determining that the first number of words included in the information correspond to the categorized second number of words.

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 in part on words included in the information corresponding to words included in the feature library; and wherein: the classification is associated with a feature; and the probability is based at least in part on the product feature included in the classification feature.

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 the word, and the operations further comprise: determining that a second word included in information associated with at least one of the products or services is included in the word; and wherein the probability is based at least in part on a second word included in the words.

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 intermediate words between the first word and the second word; the second word and the first word are in the same sentence; or the second word is in a different sentence from the first word; and wherein the probability is based at least in part on the proximity.

36. A method, comprising: generating a model configured to determine a classification of an individual intellectual property asset; receiving an intellectual property asset; determining words contained in the intellectual property asset; determining a probability that at least a portion of the intellectual property asset corresponds to the classified classification based at least in part on the words and using the model; determining, based at least in part on the probability, that at least a portion of the intellectual property asset corresponds to a classification; receiving information related to at least one of a product or a service; identifying a characteristic of the information; and determining, with the model, that at least one of the product or service corresponds to the classification based at least in part on the features corresponding to the word.

37. The method of clause 36, wherein the intellectual property asset comprises a patent document, the method further comprising: identifying a claim of the patent document; determining words contained in the claims; determining that a first number of words included in the claims corresponds to a second number of words of the taxonomy; and wherein the probability is based at least in part on the first number of words included in the claims corresponding to the second number of words classified.

38. The method of clause 36 or 37, wherein the model comprises a first model and the probabilities comprise first probabilities, the method further comprising: generating a second model for identifying a product corresponding to the intellectual property asset: identifying products included in the classification; determining a second probability that the product corresponds to the intellectual property asset based at least in part on the second model; and determining that the product corresponds to an intellectual property asset based at least in part on the second probability.

39. The method of any of clauses 36, 37 or 38, wherein the intellectual property asset comprises a trademark file, the method further comprising: identifying descriptions of goods and services in the brand file; determining words contained in the description of goods and services; determining that a first number of words contained in the description of goods and services corresponds to a second number of words of the category; and wherein the probability is based at least in part on the first number of words contained in the goods and services description corresponding to the second number of classified words.

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 assets are not related to the taxonomy; and training the model based at least in part on the input data.

41. A method, comprising: determining a first part of speech of a first word included in first information associated with a product; determining a second part of speech of a second word contained in second information corresponding to a claim of a patent document; determining a portion of the first word that corresponds to the product characteristic; determining a first action to perform with respect to the feature based at least in part on the first part-of-speech; generating a first language structure of the feature based at least in part on the first action, the first language structure indicating one or more first relationships between the first action and one or more first nouns included in the first information; determining a second action to include in the claim based at least in part on the second part of speech; generating a second language structure of the claim based at least in part on the second action, the second language structure indicating one or more second relationships between the second action and one or more second nouns included in the claim; determining a similarity measure between the first language construct and the second language construct; and determining that the claim corresponds to the product based at least in part on the similarity metric.

42. The method of clause 41, wherein: the first language structure includes a first tree structure having: the first level includes a first node corresponding to a first action; the second stage includes a second node; the second language construct includes a second free construct having: a third stage comprising a third node corresponding to the second action; and a fourth stage comprising a fourth node.

43. The method of clause 41 or 42, further comprising: determining a first degree of similarity between the first action and the second action; determining a second degree of similarity between the first stage, the second stage, the third stage, and the fourth stage; determining the similarity degree among the first node, the second node, the third node and the fourth node; and wherein the similarity metric is determined based at least in part on the first similarity metric, the second similarity metric, and the third similarity metric.

44. The method of any of clauses 41, 42 or 43, further comprising: determining a degree of similarity between the 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 based at least in part on the degree of similarity.

45. The method of any of clauses 41, 42, 43 or 44, wherein the claim includes an element, 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 in part on the similarity measure.

46. The method of any of clauses 41, 42, 43, 44 or 45, further comprising: receiving input data corresponding to a degree of similarity between a claim and a product; and modifying the similarity metric based at least in part on the similarity metric.

47. A system, comprising: one or more processors; 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: receiving information comprising a claim of a patent document; determining words contained in the claims; determining the part of speech of a word; determining an action included in the claim based at least in part on the part-of-speech, the action and the part-of-speech associated with the verb including a noun corresponding 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.

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 a first one or more relationships, and the operations further comprise: determining a second action included in the claim based at least in part on the part of speech, the second action being 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.

49. The system of clause 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 a product or a service; generating a third language structure for at least one of the product or the service, the third language 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 a similarity measure between the at least one of the product or service and the claim based at least in part on the first language construct, the second language construct, and the third language construct.

50. The system of any of clauses 47, 48 or 49, the operations further comprising: determining that at least one of the product or service is not related to the claim based at least in part on the similarity metric being less than a threshold similarity metric; receiving input data indicating that at least one of a product or service corresponds to a claim; and increasing a value of the similarity metric based at least in part on the input data.

51. The system of any of clauses 47, 48, 49 or 50, the operations further comprising: determining that at least one of the product or service corresponds to a claim based at least in part on the similarity metric being at least a threshold similarity metric; receiving input data indicating that at least one of the product or service is not dependent on the claim; and reducing the value of the similarity measure based at least in part on the input data.

52. The system of any of clauses 47, 48, 49, 50, or 51, wherein the language construct comprises a first language construct, and the operations further comprise: comparing between the first language construct and a second language construct, the second language construct being associated with at least one of a product or a service; and determining, based at least in part on the comparison, that at least one of the product or service corresponds to a claim.

53. The system of any of clauses 47, 48, 49, 50, 51, or 52, the operations further comprising: determining a claim classification; determining that at least one of the product or service is associated with a classification; and determining that the claim is associated with at least one of the product or service based at least in part on the at least one of the product or service associated with the classification.

54. The system of any of clauses 47, 48, 49, 50, 51, 52, or 53, wherein determining that at least one of the product or service is associated with a classification is based, at least in part, on the comparison.

55. A method, comprising: receiving information corresponding to at least one of a product or a service; determining words contained in the information; determining the part of speech of a word; determining a portion of each word corresponding to a characteristic of at least one of a product or a service; determining an action to perform with respect to the feature based at least in part on the part of speech; and generating a language structure for at least one of the product or service based at least in part on the part of speech, the language structure indicating one or more relationships between the actions and the words.

56. The method of clause 55, wherein the language structure comprises a tree structure having levels, each level in the levels having one or more nodes.

57. The method of clause 55 or 56, wherein: the first hierarchy includes a first node; a first node of the one or more nodes corresponds to a verb of a word, and the verb corresponds to an action; the second level includes a second node of the one or more nodes, the second node indicating a noun corresponding to the verb.

58. The method of any of clauses 55, 56 or 57, wherein: the noun includes a first noun; the second node represents an adjective corresponding to the first noun; the second level includes a third node representing a second noun corresponding to the verb; the third level includes a fourth node indicating a third noun associated with the first noun.

59. The method of any of clauses 55, 56, 57 or 58, wherein the linguistic structure includes a first linguistic structure, the one or more relationships include one or more second relationships, the verb includes a first verb, and the method further comprises: generating a second language construct for a claim contained in the patent document; performing a comparison between the first language construct and the second language construct; and determining, based at least in part on the comparison, that at least one of the product or service corresponds to a claim.

60. The method of any of clauses 55, 56, 57, 58 or 59, wherein the language structure comprises a first tree structure having a first level with first nodes and a second level with second nodes, the language structure comprising a first language structure, and the method further comprises: generating a second language structure for the intellectual property asset, the second language structure comprising a second tree structure having a third level with third nodes and a fourth level with fourth nodes; 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 the first number of nodes included in the second stage and the second number of nodes included in the fourth stage; and determining that at least one of the product or service corresponds to an intellectual property asset based at least in part on the first comparison and the second comparison.

61. A method, comprising: receiving information indicative of revenue associated with the product; determining a classification of the product based at least in part on the technical features of the product; identifying a patent claim corresponding to the product based at least in part on the patent claim associated with the classification; identifying words contained in patent claims; determining the breadth of the patent claims; determining a portion of revenue allocated to a patent claim based at least in part on the breadth of the patent claim; and determining a value metric for the patent claim based at least in part on the revenue portion allocated to the patent claim.

62. The method of clause 61, further comprising applying a discount factor to the value metric, the discount factor based at least in part on: a first disclosure value corresponding to the invalidity of a patent claim; and a second published value corresponding to the litigation probability of the patent claim.

63. The method of clause 61 or 62, wherein the classification comprises a first classification, and the method further comprises: the second disclosure value is determined based at least in part on a first number of litigation events related to a patent having a first classification relative to a second number of litigation events occurring for a patent having a second classification.

64. The method of any of clauses 61, 62, or 63, further comprising determining the first disclosure value based, at least in part, on an examination history event associated with the patent claim.

65. The method of any of clauses 61, 62, 63 or 64, further comprising determining a first disclosure value based at least in part on a first metric associated with a first reviewer associated with the patent claim relative to a second metric of a second reviewer included in a technical unit associated with the first reviewer, at least one of the first metric or the second metric including at least one of: some notifications of subsidies that occur over a period of time; the average number of times of the examination opinion notices before the permission notice is issued; a number of complaint notices submitted over a period of time; or multiple withdrawal of the complaint decision over a period of time.

66. The method of any of clauses 61, 62, 63, 64, or 65, wherein: the patent claims are assigned to an organization to which product revenue is provided; and determining the discount factor comprises determining the discount factor based at least in part on a number of patent claims, other than patent claims, assigned to the organization and corresponding to the product.

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 the products or services, the financial data indicating revenue associated with the at least one of the products or services; identifying an organization's intellectual property assets, including patent assets, trademark assets, copyright assets, or trade secret assets; determining that at least one of a product or a service corresponds to an intellectual property asset; determining a portion of revenue attributed to the intellectual property asset; a value metric for the intellectual property asset is determined based at least in part on the revenue component attributed to the intellectual property asset.

68. The system of clause 67, wherein determining that at least one of a product or a service corresponds to an intellectual property asset comprises at least one of: receiving input data indicating that at least one of a product or a service corresponds to an intellectual property asset; or identifying information indicating that at least one of the product or service corresponds to an intellectual property asset, the information being at least one of: stored in an organized data store; or accessed via the organization's website.

69. The system of clauses 67 or 68, wherein receiving financial data comprises at least one of: generating a user interface comprising one or more user interface elements configured to receive financial data; identifying a portion of financial data corresponding to at least one of a product or service with a data store of an organization; or identify a financial data portion corresponding to at least one of the product or service using information from one or more websites.

70. The system of any of clauses 67, 68, or 69, wherein determining that at least one of a product or a service corresponds to an intellectual property asset comprises: determining a first word of an intellectual property document based at least in part on the intellectual property document associated with the intellectual property asset; determining a second word contained in the information based at least in part on information related to at least one of the product or the service; determining a similarity measure between at least a portion of the first word and at least a portion of the second word; and determining that the similarity metric satisfies at least one threshold similarity metric.

71. The system of any of clauses 67, 68, 69, or 70, the operations further comprising: determining a characteristic of at least one of the products or services based, at least in part, on information corresponding to the at least one of the products or services; determining a first similarity metric based at least in part on the features associated with the first classification and a first criterion; determining a second similarity metric based at least in part on the features associated with the second classification and a second criterion; and determining that at least one of the product or service corresponds to the first classification based at least in part on the first similarity metric being at least a threshold and the second similarity metric being less than the threshold.

72. The system of any of clauses 67, 68, 69, 70, or 71, wherein the feature comprises a first feature, the operations further comprising: determining a second characteristic of the intellectual property asset; determining a third similarity metric based at least in part on the second feature and the first criterion; determining a fourth similarity metric based at least in part on the second feature and a second criterion; and determining that the intellectual property asset corresponds to the first classification based at least in part on the third similarity metric being at least a threshold and the fourth similarity metric being less than the threshold.

73. The system of any of clauses 67, 68, 69, 70, 71, or 72, the operations further comprising: determining a discount factor associated with the first classification, the discount factor based at least in part on a first degree of disclosure corresponding to the patent claim being invalid and a second degree of disclosure corresponding to a probability of litigation regarding the patent claim; an amount of modification to determine revenue for the value metric is determined based at least in part on the discount factor.

74. The system of any of clauses 67, 68, 69, 70, 71, 72, or 73, the operations further comprising: determining an extent measure of the intellectual property asset; and wherein the portion of the revenue attributed to the intellectual property asset is based at least in part on the extent metric.

75. A method, comprising: receiving financial data corresponding to at least one of the products or services, the financial data indicating revenue for the at least one of the products or services; identifying an organized intellectual property asset; determining a first characteristic of at least one of the product or service, the first characteristic comprising at least one of: a first physical characteristic of at least one of a product or a service; or a first technical characteristic of at least one of the product or service; determining a second characteristic of the intellectual property asset, the second characteristic comprising at least one of: a second physical characteristic of the intellectual property asset; or a second technical characteristic of the intellectual property asset; determining a similarity metric between at least one of the product or service and the intellectual property asset based at least in part on the analysis of the first and second features; and determining that at least one of the product or service corresponds to an intellectual property asset based at least in part on the similarity metric.

76. The method of clause 75, further comprising: receiving information about at least one of the product or service from at least one of: a website associated with at least one of the products or services; a data store of the organization; or a user interface comprising one or more user interface elements configured to capture data related to at least one of a product or a service; and wherein at least one of the first features is determined based at least in part on the information.

77. The method of clause 75 or 76, further comprising: determining a classification of a plurality of classifications associated with at least one of a product or a service based, at least in part, on the first feature, the classification being associated with at least one of the first feature or the second feature; and determining that the intellectual property asset corresponds to the classification based at least in part on the classification associated with at least one of the first feature or the second feature.

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 comprises: determining an extent metric for a first intellectual property asset based at least in part on at least one of a first number of physical features of the first intellectual property asset related to a second number of physical features of a second intellectual property asset included in the classification; determining a second breadth metric for the first intellectual property asset based at least in part on at least one of the first number of technical features of the first intellectual property asset that are related to the second number of technical features of the second intellectual property; determining a third breadth metric for the first intellectual property asset based at least in part on the first breadth metric and the second breadth metric; and determining a product revenue portion attributed to the intellectual property asset based at least in part on the third breadth metric.

79. The method of any of clauses 75, 76, 77 or 78, wherein the intellectual property asset comprises a trademark asset, and the method further comprises determining the second characteristic based at least in part on a description of goods and services of the registered trademark.

80. The method of any of clauses 75, 76, 77, 78, or 79, further comprising determining a discount amount applicable to the portion of the revenue attributed to the brand asset based at least in part on at least one of: some litigation events related to brand assets contained in the category related to brand assets; some objections relating to brand assets contained in the taxonomy; or a first metric of an auditor associated with the brand asset and a second metric of an auditor associated with the brand asset contained in the classification.

81. A method, comprising: receiving first information about a product offered for acquisition from one or more data sources; receiving second information about the intellectual property asset from one or more data sources; determining that one of the intellectual property assets corresponds to a feature associated with one of the products based at least in part on the first information and the second information; receiving a first request to determine a value of an intellectual property asset; based at least in part on receiving the first request, identifying economic data indicative of revenue for an organization associated with the product; determining revenue attributed to the portion of the intellectual property asset; determining a value metric for the intellectual property asset based at least in part on a portion of the revenue; receiving a second request to determine at least one of: a first public value representing a loss of coverage of the intellectual property asset; or a second public value representing a litigation event related to the intellectual property asset; determining at least one of the first public value or the second public value based at least in part on receiving the second request; and cause display of an indicator of a value metric of the product and at least one of the first public value or the second public value via one or more user interfaces.

82. The method of clause 81, further comprising: receiving third information about the product from the one or more data sources, the third information representing an update to the first information and including second economic data; increasing or decreasing a portion of the revenue based at least in part on the third information; determining a second value metric for the intellectual property asset based at least in part on increasing or decreasing a portion of the revenue.

83. The method of clause 81 or 82, further comprising: receiving fourth information representing an update to the second information from one or more data sources; and determine, based at least in part on the fourth information, at least the following: a third disclosed value associated with a loss of coverage; or a fourth published value associated with a litigation event.

84. The method of any of clauses 81, 82 or 83, further comprising: generating a classification system, the classification corresponding to a respective classification of one or more technology groups; determining one or more first characteristics of the product based, at least in part, on a first linguistic analysis of a portion of first information corresponding to the product; determining one or more second characteristics of the intellectual property asset based at least in part on a second language analysis of a portion of second information corresponding to the intellectual property asset; determining that the product is included in the category of categories based at least in part on the one or more first features; and determining that the intellectual property asset is included in the classification based at least in part on the one or more second features.

85. The method of any of clauses 81, 82, 83 or 84, further comprising: generating a frame indicating a relationship between the first product associated with the category and the intellectual property asset associated with the category; and wherein determining that the intellectual property asset corresponds to the product comprises determining that the frame indicates a relationship between the product and the intellectual property asset.

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 the first action performed by the product; determining a first language construct indicative of a first feature of a first relationship between a first action and a 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 the second action; determining a second language construct indicative of a second feature of a second relationship between a second action and a second noun; and determining a similarity measure indicative of a degree of similarity between the first language construct and the second language construct; and wherein determining that the intellectual property asset corresponds to a product is based at least in part on the similarity metric.

87. The method of any of clauses 81, 82, 83, 84, 85, or 86, wherein the second public value associated with the litigation event is determined based at least in part on a number of litigation events corresponding to the intellectual property asset having the classification.

88. The method of any of clauses 81, 82, 83, 84, 85, 86 or 87, wherein the value metric comprises a first value metric, and the method further comprises: sending a third request to obtain feedback on the accuracy of the first value metric; receiving input data indicating that the first value metric is to be modified; and determining a second value metric for the intellectual property asset based at least in part on the input data.

89. A system, comprising: one or more processors; 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: determining that the intellectual property asset corresponds to at least one of a product or a service; receiving a request to determine a value of an intellectual property asset; receiving economic data related to at least one of the product or service, the economic data including revenue for an organization related to the at least one of the product or service; determining a portion of revenue attributed to the intellectual property asset; determining a first value metric for the intellectual property asset based at least in part on a portion of the revenue; receiving input data indicating that a first value measure of an intellectual property asset is to be modified; determining a second value metric for the intellectual property asset based at least in part on the input data; and displaying, via the user interface, an indication of the second value metric.

90. The system of clause 89, wherein the intellectual property asset is a patent document, the operations further comprising: determining a measure of breadth of the patent document claims; and wherein a portion of the revenue is based at least in part on the extent metric.

91. The system of clause 89 or 90, wherein the request comprises a first request, and the operations further comprise: receiving a second request to determine a first value metric for individual ones of the intellectual property assets associated with the loan to the organization, the intellectual property assets associated with the organization; determining a first value metric based at least in part on a first criterion; receiving a third request to determine a second value measure for the individual intellectual property assets relevant to at least one of selling at least a portion of the organization or a merger of the organization with other organizations; and determining a second value metric based at least in part on the second criterion, wherein the second value metric is different from the first value metric.

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 indicating 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 products or services; determining, using the framework, one or more patents related to one or more products or services; and causing display of an indication of the one or more patents via the user interface.

93. The system of any of clauses 89, 90, 91, or 92, the operations further comprising: determining a characteristic of the product or service; determining, based at least in part on the framework, one or more patent claims related to the feature; and causing display of an indication of a claim via the user interface.

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 comprising classifications, each of the classifications associated with one or more technology groups; receiving a second request to identify one or more organizations associated with claims of patent documents associated with the product or service contained in the taxonomy; based at least in part on the framework, determining an organization to which the claim corresponding to the product or service included in the taxonomy relates; and displaying, via the user interface, indications of the plurality of claims associated with respective ones of the organizations corresponding to the products or services included in the taxonomy.

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 revenue, the value metric comprises a first value metric, and the operations further comprise: receiving second economic data associated with the product or service, the second economic data comprising second revenue for an organization related to the product or service; and determining a second value metric for the intellectual property asset based at least in part on the second revenue.

96. A method, comprising: determining that the intellectual property asset corresponds to a product or service; receiving a request to determine a value of an intellectual property asset; receiving first economic data comprising first revenue for an organization associated with a product or service; determining a portion of the first revenue attributed to the intellectual property asset; determining a first value metric for the intellectual property asset based at least in part on the portion of the first revenue; receiving second economic data comprising second revenue for an organization associated with the product or service; determining a second value metric for the intellectual property asset based at least in part on the second revenue; and cause display, via the user interface, of an indication of the second value metric for the intellectual property asset.

97. The method of clause 96, further comprising: receiving information corresponding to the intellectual property asset from at least one of a public data source or an organized data source; determining, based at least in part on the information, at least one of: a first public value associated with a loss of coverage of an intellectual property asset; or a second public value associated with a litigation event associated with the intellectual property asset.

98. The method of clause 96 or 97, wherein the intellectual property asset comprises a trade secret, and the method further comprises: determining a first public value based at least in part on a probability that a commercial secret was stolen; and causing display of an indication of the first public value via the user interface.

99. The method of any of clauses 96, 97 or 98, further comprising: determining a discount amount to reduce the amount of the second revenue based at least in part on at least one of the first public value or the second public value; and wherein the second value metric is based at least in part on a difference between the amount of the second revenue and the discount amount.

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 commercial confidentiality category; and wherein determining the second value metric comprises determining the second value metric based at least in part on the intellectual property category associated with the intellectual property asset.

In addition, the foregoing is illustrative only of the principles of the disclosure, and various modifications can be made by those skilled in the art without departing from the scope of the disclosure. The above examples are presented for purposes of illustration and not limitation. The present disclosure may also take many forms other than those explicitly described herein. It is therefore emphasized that the present disclosure is not limited to the explicitly disclosed methods, systems and devices, but is intended to encompass variations and modifications thereof which are within the spirit of the appended claims.

As another example, variations in equipment or process parameters (e.g., dimensions, configurations, components, sequence of process steps, etc.) may be made to further optimize the provided structures, devices, and methods, as shown and described herein. In any event, the structures and devices described herein, and the associated methods, have many applications. Accordingly, the disclosed subject matter should not be limited to any single example described herein, but rather construed in breadth and scope in accordance with the appended claims.

Claims

1. A method, comprising:

receiving information about a product from one or more data sources;

identifying an intellectual property asset;

determining one or more relationships between individual ones of the products and individual ones of the intellectual property assets;

generating association data indicative of the one or more relationships between the individual ones of the products and the individual ones of the intellectual property assets based at least in part on the one or more relationships;

receiving a request to identify one of the intellectual property assets that corresponds to one of the products;

Identifying the intellectual property asset corresponding to the product based at least in part on the association data; and

generating a response to the request, the response indicating that the intellectual property asset is associated with the product.

2. The method of claim 1, wherein the data source comprises a publicly accessible data source, and the method further comprises:

determining a keyword associated with the product;

identifying data corresponding to the keyword based at least in part on the publicly accessible data source; and

extracting the data corresponding to the keyword from the publicly accessible data source.

3. The method of claim 1 or 2, wherein the data source comprises a data store associated with a first organization that provides the product for retrieval, and the method further comprises:

determining, by a second organization, a keyword associated with the product;

identifying, by the second organization and from the data store of the first organization, data corresponding to the keyword; and

extracting, by the second organization, the data corresponding to the keyword.

4. The method of claim 3, further comprising:

Identifying, with the data store, data indicative of a relationship between the intellectual property asset and the product; and is

Wherein generating the association data comprises generating the association data based at least in part on the data indicative of the relationship between the intellectual property asset and the product.

5. The method of claim 1, 2, 3, or 4, wherein the request comprises a first request, and the method further comprises:

such that the second request for information about the product is at least one of:

publishing on a website accessible to the computing device; or

Sending to the computing device; and

receiving, in response to the second request, data indicating at least one of a source of the information or the information.

6. The method of claim 1, 2, 3, 4, or 5, further comprising:

generating a user interface comprising a user interface element configured to receive an input representing information about the intellectual property asset;

receiving the input with the user interface element; and is

Wherein generating the association data comprises generating the association data based at least in part on the input.

7. The method of claim 1, 2, 3, 4, 5, or 6, further comprising:

determining metrics associated with the intellectual property asset, the metrics including at least one of:

a measure of the extent of at least a portion of the intellectual property asset;

a measure of disclosure 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 an amount of revenue attributed to at least a portion of the intellectual property asset based at least in part on the metric.

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 processors, cause the one or more processors to perform operations comprising:

receiving information about at least one of a product or a service, at least a portion of the information comprising economic data;

determining a relationship between a product of the products or a service of the services and intellectual property assets;

Generating association data indicative of the relationship between the product or the service and the intellectual property asset; and

identifying the intellectual property asset of a plurality of intellectual property assets corresponding to the product or the service based at least in part on the association data.

9. The system of claim 8, wherein the information about the at least one of the product or the service includes a description of the at least one of the product or the service, and the operations further comprise determining a characteristic of the at least one of the product or the service based at least in part on the description.

10. The system of claim 8 or 9, wherein the feature comprises a first feature, and the operations further comprise:

identifying information about the intellectual property asset based at least in part on at least one of a publicly accessible data source or a data source of an organization providing at least one of the product or the service;

determining a second characteristic of the intellectual property asset based at least in part on the information about the intellectual property asset; and is

Wherein generating the association data comprises generating the association data based at least in part on the first characteristic and the second characteristic.

11. The system of claim 8, 9 or 10, wherein the intellectual property asset comprises a patent file, and the operations further comprise:

receiving a description of at least one of the product or the service, the description including words associated with the at least one of the product or the service;

determining that at least a portion of the word is contained in the claims of the patent document; and is

Wherein the association data indicates that the claim corresponds to the at least one of the product or the service based, at least in part, on the at least a portion of the word being included in the claim.

12. The system of claim 8, 9, 10, or 11, the operations further comprising generating a user interface comprising one or more user interface elements configured to capture information about the plurality of intellectual property assets, the one or more user interface elements comprising at least one of:

a first element configured to receive first information associated with a trade secret file;

a second element configured to receive second information associated with a brand file; or

A third element configured to receive third information associated with the copyright file.

13. The system of claim 8, 9, 10, 11, or 12, the operations further comprising:

such that the request for information about at least one of the product or the service is at least one of:

publishing on a website accessible to the computing device; or

Sending to the computing device; and

receiving, in response to the request, data indicating at least one of a source of the information or the information.

14. The system of claim 8, 9, 10, 11, 12, or 13, the operations further comprising:

determining an amount of revenue associated with at least one of the product or the service obtained over a period of time based at least in part on the economic data;

determining a portion of an amount of revenue attributed to the intellectual property asset; and

determining a value of the intellectual property asset based at least in part on a portion of the amount of revenue.

15. A method, comprising:

receiving information about at least one of a product or a service, at least a portion of the information comprising economic data associated with the at least one of the product or the service;

Determining a relationship between an individual product or individual service of the at least one of the products or services and an individual one of intellectual property assets;

generating association data indicating the relationship between the individual ones of the products and the individual ones of the intellectual property assets;

identifying one of the intellectual property assets corresponding to one of a product or a service of the at least one of a product or a service based at least in part on the association data; and

generating data indicating that the intellectual property asset is associated with the at least one of the product or the service.