KR20220034134A - Analysis of intellectual property data about products and services - Google Patents

Abstract

The techniques described herein relate to analyzing intellectual property data in accordance with the provision of various intellectual property related services to organizations. In certain implementations, information related to products and/or services may be obtained from multiple data sources. Additionally, information relating to intellectual property assets such as patents, trademarks, copyrights, trade secrets, and know-how may be obtained. In various situations, intellectual property assets may be mapped to individual products and/or services. Mappings between products and/or services and intellectual property assets are used to provide intellectual property-related services corresponding to the intellectual property assets, such as valuation services, strategy-related services, or risk-related services. can be

Description

Analysis of intellectual property data about products and services

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is, "Analysis Of Intellectual-Property Data In Relation To Products And Services," US Patent Application No. 16/503,107, filed on July 3, 2019, "Analysis Of Intellectual-Property Data In Relation To Products" U.S. Patent Application No. 16/503,126, filed on July 3, 2019, entitled "And Services," filed on July 3, 2019, entitled "Analysis Of Intellectual-Property Data In Relation To Products And Services." U.S. Patent Application No. 16/503,144, U.S. Patent Application No. 16/503,164, filed July 3, 2019, entitled "Analysis Of Intellectual-Property Data In Relation To Products And Services" U.S. Patent Application No. 16/503,187, filed on July 3, 2019, entitled "Property Data In Relation To Products And Services," claims priority, the entire contents of which are incorporated herein by reference.

Intellectual property is acquired by organizations to help safeguard innovation within them. Typically, information related to an organization's intellectual property can be difficult to analyze effectively and efficiently. For example, understanding the value of intellectual property, or understanding how intellectual property relates to products or services in a marketplace, can be difficult to achieve in an accurate and efficient manner using computer-implemented techniques.

A detailed description is set forth below with reference to the accompanying drawings. In the figures, the leftmost digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different drawings indicates similar or identical items. The systems shown in the accompanying drawings are not to scale, and components in the drawings may be drawn to scale with each other.
1 illustrates an example architecture for analyzing intellectual property data and utilizing the analysis of intellectual property data to provide multiple services, in accordance with some implementations.
2 illustrates an example environment for analyzing types of intellectual property data and product/service data to provide services related to intellectual property, in accordance with some implementations.
3 illustrates an example environment for creating mappings between products and intellectual property assets using a descriptive taxonomy, in accordance with some implementations.
4 illustrates an example system for generating valuations for intellectual property assets, in accordance with some implementations.
5 illustrates an example system for modifying mappings between intellectual property and taxonomy classifications and/or between intellectual property and products/services, in accordance with some implementations.
6 illustrates an example architecture for providing services to customers using mappings between products/services and intellectual property on a taxonomy, in accordance with some implementations.
7 illustrates an example framework for generating linguistic structures for claims in patent documents, in accordance with some implementations.
8 illustrates an example framework for determining a similarity metric between the linguistic structure of a product/service and the linguistic structure for a portion of a claim in a patent document, in accordance with some implementations.
9 illustrates an example framework for determining the value of an intellectual property feature corresponding to one or more products, in accordance with some implementations.
10 illustrates an example process for determining an intellectual property asset corresponding to a product and/or service, in accordance with some implementations.
11 illustrates an example process for determining an intellectual property asset corresponding to a product or service using a classification scheme, in accordance with some implementations.
12 illustrates an example process for performing qualitative and quantitative analysis of intellectual property data, in accordance with some implementations.
13 illustrates an example process for determining an intellectual property asset corresponding to a product and/or service using the linguistic structure of the intellectual property asset and the linguistic structure of the product and/or service, in accordance with some implementations. .
14 illustrates an example process for providing services to a customer based on relationships between a product and/or service and an intellectual property asset, in accordance with some implementations.

The techniques described herein relate to analyzing intellectual property data relating to products and/or services. As technological advances increase and the value of organizations has been characterized by the shift from tangible to intangible assets, the importance of intellectual property has also increased. Accordingly, organizations have taken various steps to protect their intellectual property, which may include, for example, patents, trademarks, copyrights, trade secrets, and/or know-how. However, few techniques, architectures, and frameworks have been developed for analyzing intellectual property data and generating useful information from an organization's intellectual property data. Additionally, due to the complexity of analyzing intellectual property data and the inability of conventional systems to effectively provide information to organizations about their intellectual property of value to organizations, using intellectual property to provide organizations The number of services available is also limited.

Implementations described herein relate to techniques, systems, and architectures for analyzing intellectual property data to create frameworks that can be used to provide services related to intellectual property assets. In certain implementations, an intellectual property service provider may obtain intellectual property data from multiple data sources. In various implementations, at least a portion of the data sources can include public data sources. Common data sources for storing intellectual property data include databases of patent offices of various jurisdictions, such as the United States Patent and Trademark Office (USPTO) database, the European Patent Office (EPO) database, and/or the World Intellectual Property (WIPO) database. Office) database. Additionally, intellectual property data may be stored in databases related to copyrights, such as the United States Copyright Office or the European Union Copyright Office. Intellectual property data may also be obtained from private data sources. Private data sources may include databases that store information related to an organization, maintained and/or controlled by the organization. Private data sources may also include databases of service providers that store information on behalf of the organization. Additionally, at least a portion of the organization's intellectual property data may be captured via one or more user interfaces. In some situations, one or more user interfaces may be rendered as part of customer portals accessible to customers of the intellectual property service provider. In examples, data sources may include a digital property registry that may be maintained and/or created by a system and/or institution other than an organization. For example, a digital property, such as a trade secret, is registered in a digital property registry utilizing one or more obfuscation values to represent the digital property and/or block values to represent a block within a distributed ledger in which the obfuscation values are registered. can be

The intellectual property service provider may also obtain data related to a number of products and/or services. Products and/or services may be provided for acquisition by the same organization from which intellectual property data is being acquired and analyzed. Additionally, products and/or services may be provided for acquisition by organizations different from the organization from which intellectual property data is being acquired and analyzed. Data related to products and/or services may include economic data related to products and/or services, manuals about products and/or services, specification sheets for products and/or services, product at least one of descriptions of products and/or services, and/or marketing materials related to products and/or services.

Data related to products and/or services may be obtained from multiple data sources. In certain implementations, data related to products and/or services may be obtained from various websites. In some scenarios, data related to products and/or services may be obtained from one or more websites of organizations that provide products and/or services for acquisition. In additional implementations, data related to products and/or services may be obtained from databases of organizations that provide products and/or services for acquisition. Additionally, data related to products and/or services may be obtained through one or more user interfaces, such as user interfaces provided by an intellectual property service provider as part of a customer portal.

Data related to an organization's intellectual property, and data related to products and/or services, may also be obtained through crowdsourcing. In certain implementations, an intellectual property service provider may post requests for information regarding intellectual property assets, and/or requests for information regarding products and/or services. Requests may be posted on one or more websites, sent to a group of individuals via one or more mobile device applications, or combinations thereof. In response to the requests, individuals 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 analyzes the information, and the intellectual property service provider may provide a number of services to the intellectual property service provider's customers. information can be organized in such a way that An intellectual property service provider may use machine learning techniques to analyze information obtained from data sources. In certain implementations, an intellectual property service provider may create one or more models that may be utilized to determine properties, characteristics, metrics, etc. relating to intellectual property assets and products and/or services. . In various implementations, an intellectual property service provider may implement machine learning techniques for determining relationships between intellectual property assets and products and/or services. In some examples, an intellectual property service provider may utilize relationships between intellectual property assets and products and/or services to estimate the value of intellectual property assets. An intellectual property service provider may also utilize machine learning techniques to determine exposure levels corresponding to intellectual property assets. Exposure levels associated with intellectual property assets may correspond to a probability that at least one of the coverage of the intellectual property assets may decrease or a litigation event relating to the intellectual property assets may occur.

An intellectual property service provider may utilize natural language processing techniques 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 for the words. In certain examples, the intellectual property service provider may determine relationships between words using parts of speech of words and grammatical relationships between words. An 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 documents that may cover one or more characteristics of products and/or services. In illustrative examples, an intellectual property service provider may utilize natural language processing techniques and machine learning techniques to determine probabilities that intellectual property assets may be enforceable with respect to corresponding products and/or services.

In certain implementations, the intellectual property service provider correlates the intellectual property documents using natural language processing techniques and/or machine learning techniques to determine relationships between words included in information related to the intellectual property assets. linguistic structures can be created. For example, an intellectual property service provider may determine verbs associated with actions performed in a claim of a patent document, and may also determine nouns and/or adjectives corresponding to actions. In some circumstances, an intellectual property service provider may utilize natural language processing techniques and machine learning techniques to determine elements of the claims of patent documents. In addition, an intellectual property service provider may use natural language processing techniques and machine learning techniques to create linguistic structures for products and/or services. In illustrative examples, the intellectual property service provider determines the actions to be performed in connection with products and/or services, verbs associated with the actions, and nouns, adjectives, and/or adverbs associated with verbs. You can create linguistic constructs that represent In various implementations, an intellectual property service provider may determine intellectual property assets corresponding to various products and/or services by comparing the intellectual property assets and respective linguistic structures of the products and/or services.

An intellectual property service provider may use the descriptive classification framework to determine intellectual property assets corresponding to characteristics of products and/or services. The descriptive classification framework may include a taxonomy comprising a plurality of classifications, each of which is associated with a plurality of criteria. Classifications for intellectual property documents may be determined according to a descriptive classification framework by performing a linguistic analysis of the intellectual property documents and determining characteristics of the intellectual property documents. The intellectual property service provider may then compare characteristics of the intellectual property documents against the criteria for classifications of the technical classification framework to determine individual classifications for the intellectual property documents. Additionally, the intellectual property service provider may also determine classifications for products and/or services according to a technical classification scheme. For example, an intellectual property service provider may perform a linguistic analysis of information related to products and/or services and determine characteristics of products and/or services. The intellectual property service provider may then compare characteristics of the products and/or services against criteria for the classifications of the technical classification framework to determine individual classifications for the products and/or services. In certain implementations, the intellectual property service provider determines intellectual property assets corresponding to products and/or services when the intellectual property assets and products and/or services are in the same or similar classification of a technical classification framework. can

In example implementations, an intellectual property service provider may create one or more models that map products and/or services to a technology classification framework and map intellectual property assets to a technology classification framework. The intellectual property service provider may then utilize natural language processing techniques and/or machine learning techniques to further develop one or more models by determining intellectual property assets corresponding to various products and/or services within a given classification. . In this way, the intellectual property service provider receives requests to identify intellectual property assets corresponding to the specified product and/or service, and utilizes one or more models to determine the intellectual property corresponding to the specified product and/or service. assets can be identified. The intellectual property service provider may then determine valuations for the intellectual property assets 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 revenue for a particular product and/or service attributable to the intellectual property asset, and based at least in part on the portion of revenue for the product and/or service attributable to the intellectual property asset, the intellectual property service provider You can estimate the value of your property. Intellectual property service providers may also utilize one or more model and technical classification frameworks to provide additional information to their customers. Illustratively, an intellectual property service provider may utilize one or more models and technical classification frameworks to determine the amount of exposure and/or loss with respect to intellectual property assets. An intellectual property service provider may also provide services to customers related to providing metrics for an organization's portfolio of intellectual property assets, using one or more model and descriptive classification frameworks. The metrics may indicate breadth and coverage measures for intellectual property documents. An intellectual property service provider may also use one or more model and technology classification frameworks to generate reports indicating technology characteristics around which an organization may acquire and/or develop additional intellectual property assets. Additionally, the intellectual property service provider may use one or more models and descriptive classification schemes to represent the intellectual property assets of competitors of the intellectual property service provider's clients and/or the intellectual property assets of competitors of the intellectual property service provider's clients. can generate reports indicating the metrics of

Conventional techniques and systems for analyzing intellectual property documents with respect to products and/or services are performed by individuals with the use of computers. For example, individuals may perform manual searches and online searches of intellectual property databases to identify information regarding products and/or services. Individuals may then perform manual analysis to determine intellectual property documents corresponding to products and/or services. In certain circumstances, individuals may also consult online resources related to the sale of intellectual property assets, litigation judgments against intellectual property assets, and/or intellectual property assets to determine the value of one or more intellectual property assets. Access to settlement agreements for proceedings relating to

However, conventional techniques and systems used to determine relationships between intellectual property assets and various products and/or services and to determine valuations for intellectual property assets are inefficient and often inaccurate. To illustrate, individuals are often incapable of searching and retrieving large amounts of data related to intellectual property assets and products and/or services. Typically, information is not overlooked or discovered in manual searches conducted online by individuals, where the information identifies intellectual property assets corresponding to individual goods and/or services and identifies the intellectual property assets. It can be useful in determining a valuation. Additionally, human-based analysis of the collected information can often miss relationships between intellectual property assets and products and/or services, or to intellectual property assets corresponding to various products and/or services. You may miss features covered by As such, conventional techniques and systems are labor intensive and often do not provide information that organizations can use to evaluate their intellectual property assets.

Additionally, implementing the techniques and systems described herein goes beyond simply collecting and organizing large amounts of data. 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, as well as providing useful information about the intellectual property assets described herein. Implemented implementations also utilize techniques and systems that generate accurate information supported by an analytical basis formed from the anomalous use of machine learning and natural language processing.

This disclosure provides a general understanding of the principles of 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 appreciate 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, including between systems and methods. Such modifications and variations are intended to be included within the scope of the appended claims.

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

1 illustrates an example architecture 100 for analyzing intellectual property data and utilizing the analysis of intellectual property data to provide a number of services, in accordance with some implementations. Architecture 100 may include an intellectual property services system 102 that analyzes data related to intellectual property assets. The data analyzed by the intellectual property services system 102 may be used by an intellectual property service provider to provide services related to the intellectual property assets. Intellectual property assets may include patents, trademarks, copyrights, trade secrets, and know-how. In various implementations, intellectual property assets may include portions of a patent, such as a claim of a patent. Additionally, intellectual property assets may include portions of copyright pertaining to portions of software code that correspond to particular features performed when the software code is executed.

In certain implementations, intellectual property assets may be associated with various forms of documentation indicating characteristics of the intellectual property assets. In situations where intellectual property assets include patents, patents may include utility patents, design patents, and/or plant patents. Patents may also include patent applications, such as provisional patent applications, utility patent applications, design patent applications, plant patent applications, or combinations thereof. In various scenarios, intellectual property assets may include trademark applications and granted trademark registrations. Intellectual property assets may also include documentation corresponding to copyright registrations and documentation including aspects of trade secrets. To illustrate, formulas, processes, and/or algorithms and software code subject to trade secrets may be documented. Actions taken to maintain the confidentiality of trade secrets may also be documented and included in intellectual property assets. In addition, intellectual property assets include the know-how of an organization, such as process improvements and innovations, new product designs, product improvements, brand names, logos, advertising slogans, website design, product appearance, product packaging, manufacturing This may include documentation of processes, engineering drawings, handling manuals, product catalogs, customer and supplier lists, and the like.

The intellectual property services system 102 may include an intellectual property mapping and learning system 104 . The intellectual property mapping and learning system 104 obtains information from multiple data sources, such as data sources 106 , and analyzes the information to discover relationships between intellectual property assets and products and/or services. can decide Data sources 106 may include customer portals 108 . The customer portals 108 are provided by the intellectual property services system 102 , including one or more user interface elements for capturing information related to the intellectual property service provider's customers, such as the intellectual property assets of the customer 110 . It may include one or more user interfaces generated. User interfaces associated with customer portals 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 customer portals 108 by representatives of customer 110 . In additional implementations, information may be entered into customer portals 108 by representatives of an intellectual property service provider.

Data sources 106 may also include one or more customer data sources 112 . The one or more customer data sources 112 are accessible to customers of the intellectual property service provider and may store data under the direction of the customers of the intellectual property service provider. That is, data stored by one or more customer data sources 112 may be under the control of individual customers of the intellectual property service provider. In some demonstrative examples, at least one customer data source 112 may be maintained on the premises of the customer 110 . In additional illustrative examples, the 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 by customer 110 .

Additionally, data sources 106 may include crowdsourced data sources 114 . Crowdsourced data sources 114 may include multiple individuals providing information to intellectual property services system 102 . In various implementations, the intellectual property services system 102 may post requests for information regarding intellectual property assets via at least one of one or more websites or one or more mobile device applications. The intellectual property services system 102 may also post requests for information regarding products and/or services that may correspond to intellectual property assets. In various implementations, individuals included in the crowdsourced data sources 114 use at least one computing device to access requests posted by the intellectual property services system 102 and receive responses to the requests. can provide The responses may include information regarding at least one of the intellectual property assets or products and/or services that were the subject of the requests.

Additionally, data sources 106 may include one or more common data sources 116 . The one or more public data sources 116 may include data sources that store data accessible to the general public. In some implementations, one or more public data sources 106 may store data accessible to individuals without any credentials. In additional implementations, the one or more public data sources 106 may store data accessible to individuals with credentials made publicly available by organizations that maintain the one or more public data sources 116 . . Data sources 116 that store data related to intellectual property assets may be accessible through one or more websites and/or one or more mobile device applications.

One or more public data sources 116 may include data sources that store data related to intellectual property assets. For example, the one or more public data sources 116 may include intellectual property organizations of various governmental jurisdictions, such as the United States Patent and Trademark Office, the European Patent Office, the World Intellectual Property Organization, or the Japanese Patent and Trademark Office. Intellectual property data stored by one or more public data sources 116 may include the content of intellectual property documents. For example, intellectual property data may include information contained in patent documents, such as claims, drawings, backgrounds, abstracts, descriptions of the drawings, and the like. In other examples, intellectual property data may include the content of trademark documents, such as descriptions of goods and services and/or classifications of goods and services. Additionally, intellectual property data may include information contained in copyright documents. Additionally, the intellectual property data may include information related to the review of intellectual property documents. For example, the intellectual property data may include history of patent applications and/or history of trademark applications. Intellectual property data may also include bibliographic information related to intellectual property documents, such as classification of patent documents, examiners assigned to examine patent and trademark applications, priority dates, filing dates, assigns, inventors, applicants , combinations thereof, and the like. In various implementations, intellectual property data can include data related to at least one of administrative procedures, litigation proceedings, settlement information, or licensing information for intellectual property assets.

The one or more public data sources 116 may also include data sources that store market and financial data. Market and financial data may relate to organizations that provide products and/or services for acquisition. For example, market and financial data may include the financial performance of organizations over a period of time. Additionally, market and financial data may also indicate classifications and industries for specific organizations. Market and financial data may also include financial performance of one or more industries over a period of time. Additionally, market and financial data may include data for financial markets, such as stock markets, over time.

In addition, the one or more public data sources 116 may include data sources that store information regarding products and/or services. Illustratively, one or more public data sources 116 may include descriptions of products and/or services, specifications for products, characteristics of products and/or services, images of products, products and/or may store data including videos related to services, pricing of products and/or services, organizations providing products and/or services, combinations thereof, and the like.

In certain implementations, the intellectual property services system 102 can include a data acquisition system 118 for obtaining data from data sources 106 . In various implementations, data acquisition system 118 may extract information from multiple websites. For example, data acquisition system 118 may include one or more web crawlers that access websites and retrieve information corresponding to a given set of criteria to extract information from websites corresponding to criteria. In illustrative examples, data acquisition system 118 may acquire data from one or more data sources 106 corresponding to various products and/or services. Additionally, data acquisition system 118 may acquire data from one or more data sources 106 corresponding to multiple intellectual property assets.

Additionally, data acquisition system 118 may perform one or more operations on data acquired from one or more data sources 106 before the data is stored by intellectual property knowledge data store 120 . For example, data acquisition system 118 may perform optical character recognition operations on at least a portion of data obtained from one or more data sources 106 . In other examples, data acquisition system 118 may remove information embedded in certain forms of data obtained from one or more data sources 106 , such as embedded scripts or fonts. Data acquisition system 118 may also append information to data obtained from one or more data sources 106 . To illustrate, data acquisition system 118 may append time stamps to data obtained from one or more data sources 106 . Data acquisition system 118 may also add one or more tags to data obtained from one or more data sources 106 . The one or more tags may include one or more organizations corresponding to the extracted data, one or more technical classifications utilized by the intellectual property services system 102 , or one or more categories of intellectual property assets (eg, patents, trademarks, copyrights). , trade secrets, know-how). Additionally, the data acquisition system 118 may include, in the data obtained from the one or more data sources 106 , that the data include economic data, market data, financial data, product and/or service description data, litigation-related data, licensing-related data. Tags indicating data, combinations thereof, etc. may be applied. By applying tags to data obtained from one or more data sources 106 , the data acquisition system 118 stores the data in the intellectual property knowledge data store 120 in such a way that the data can be efficiently retrieved and analyzed. can

The intellectual property mapping and learning system 104 may utilize natural language processing techniques and machine learning techniques to identify relationships between intellectual property assets and products and/or services. The intellectual property mapping and learning system 104 may also generate data for providing intellectual property customer services 126 to customers of the intellectual property service provider, such as customer 110 . In certain implementations, the intellectual property mapping and learning system 104 can include a language analysis system 122 . The language analysis system 122 may analyze words included in information obtained from one or more data sources 106 to determine parts of speech of the words. For example, language analysis system 122 may determine that words included in information obtained from one or more data sources 106 are nouns, verbs, adverbs, adjectives, pronouns, articles, prepositions, conjunctions, etc. You can decide that you can. The language analysis system 122 may also determine relationships between words. To illustrate, language analysis system 122 may identify nouns and adjectives that modify nouns in addition to verbs and adverbs that modify verbs. Additionally, language analysis system 122 may determine nouns and/or pronouns that perform actions corresponding to verbs.

In various implementations, language analysis system 122 may analyze information obtained from one or more data sources 106 to identify portions of intellectual property documents. For example, the language analysis system 122 analyzes the patent document to obtain at least one of a claim part of the patent document, a detailed description of the patent document, background art of the patent document, content of the invention of the patent document, summary of the patent document, etc. can be identified. Additionally, language analysis system 122 may determine individual elements of claims included in patent documents. In certain implementations, the language analysis system 122 can determine features included in the claims, which can relate to physical features of a device or system. In various implementations, features may relate to actions performed in connection with methods or processes or operations performed by devices or systems. Additionally, in scenarios where the claims relate to compositions of matter that correspond to molecules, the features may relate to various arrangements of atoms included in the compositions of matter, such as a phenyl functional group or a carboxyl functional group. . In some circumstances, elements of a claim may include multiple distinct features. In additional examples, language analysis system 122 may also analyze the trademark document to identify at least one of a description of goods and services or an international class of trademark.

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

Additionally, language analysis system 122 may analyze information related to products and/or services and determine characteristics of products and/or services. To illustrate, language analysis system 122 may determine physical components of devices and/or systems. The language analysis system 122 may also determine technical characteristics of devices and/or systems. Additionally, language analysis system 122 may also determine characteristics of processes and/or methods performed in connection with products and/or services.

In certain implementations, the language analysis system 122 is configured to relate to intellectual property assets, products, and/or services with respect to a library of words related to features of the intellectual property assets, products, and/or services. Analyzing the words may determine at least one of characteristics of intellectual property assets, characteristics of products, or characteristics of services. For example, the intellectual property mapping and learning system 104 may determine a particular set of words associated with each of a number of distinct features that may be associated with at least one of an intellectual property document, product, or service. To illustrate, the intellectual property mapping and learning system 104 may determine that words such as “screen,” “panel,” and “display” may indicate a display device characteristic of an electronic device. Continuing with this example, language analysis system 122 may parse intellectual property documents and/or information regarding products and/or services to identify words corresponding to words associated with a display device feature. can In situations where at least a threshold number of words included in intellectual property documents and/or information relating to products and/or services correspond to words associated with a display device feature, the language analysis system 122 may: It may be determined that a property document or a particular product and/or service includes a display device feature.

In various implementations, language analysis system 122 may also determine that proximity between words associated with a feature may indicate that the feature is present in an intellectual property document or in information about a product and/or service. there is. In some examples, when multiple words associated with a feature are within 3 words, within 5 words, within 10 words, or within 20 words of each other, the language analysis system 122 determines that the feature appears in the intellectual property document or may determine to be included in products and/or services. In additional examples, when multiple words associated with a feature are in the same sentence or in the same paragraph, the language analysis system 122 may determine that the feature is included in an intellectual property document or in a product and/or service.

Language analysis system 122 may also generate linguistic structures for intellectual property documents and linguistic features for information related to products and/or services. In illustrative examples, language analysis system 122 may generate linguistic structures for claims in patent documents. In certain scenarios, language analysis system 122 may generate linguistic structures for elements of claims in patent documents or features of claims in patent documents. For example, the language analysis system 122 may identify a verb corresponding to an action of an element of a claim in a patent document. The language analysis system 122 may also determine one or more nouns associated with the verb, and, in some circumstances, one or more adjectives corresponding to the one or more nouns. The linguistic analysis system 122 may then generate a linguistic structure showing relationships between the verb, one or more nouns, and/or one or more adjectives. Additionally, language analysis system 122 may generate linguistic constructs corresponding to actions performed in connection with products and/or services provided by an organization for acquisition. In certain implementations, the linguistic structure may include a tree structure, with a single node at the top of the tree structure as the initial node or root node, and subsequent nodes branching from the root node. A root node may contain a verb corresponding to an action, and branch nodes may correspond to nouns related to verbs, adjectives related to nouns, verbs and/or other words related to nouns, or combinations thereof. there is.

Additionally, the intellectual property mapping and learning system 104 may include an intellectual property (IP) model development system 124 that determines relationships between intellectual property documents and products and/or services. In various implementations, the IP knowledge model development system 124 may identify intellectual property assets corresponding to individual products and/or services. For example, IP knowledge model development system 124 may identify one or more patent claims, elements of a patent claim, and/or features of a patent claim corresponding to at least a portion of a product and/or service. In additional examples, the IP knowledge model development system 124 may be configured to store at least a portion of a trademark corresponding to a product and/or service, a copyright corresponding to the product and/or service, or a trade secret corresponding to the product and/or service. At least a portion is identifiable.

IP knowledge model development system 124 may determine that an intellectual property asset corresponds to a product and/or service by comparing the linguistic structures of the intellectual property assets to the linguistic structures of the products and/or services. In certain implementations, the IP knowledge model development system 124 may generate a first linguistic structure for a characteristic of the intellectual property asset and a second linguistic structure for a characteristic of the product and/or service. The IP knowledge model development system 124 may compare the first linguistic construct to the second linguistic construct to determine a similarity metric between the first linguistic construct and the second linguistic construct. In scenarios where the similarity metric between the first linguistic structure and the second linguistic structure is at least a threshold similarity metric, the IP knowledge model development system 124 is configured such that the characteristic of the intellectual property asset corresponds to the characteristic of the product and/or service. can decide to

The similarity metric may be based, at least in part, on words included in the first linguistic structure and words included in the second linguistic structure. The similarity metric may also be based, at least in part, on relationships between words included in the first linguistic structure and words included in the second linguistic structure. In example implementations, the first linguistic structure can include a first tree structure having a root node and a plurality of branch nodes arranged in a first configuration, wherein the second linguistic structure includes a root node and an additional number of It may include a second tree structure having branch nodes. In such situations, the IP knowledge model development system 124 may compare the first tree structure and the second tree structure to determine a similarity metric between the first linguistic structure and the second linguistic structure. For example, the IP knowledge model development system 124 compares the words included in the nodes of the first tree structure and the words included in the nodes of the second tree structure to obtain a first linguistic structure and a second linguistic structure. At least a portion of a similarity metric for the structure may be determined. Additionally, the IP knowledge model development system 124 compares the first configuration of the first tree structure with the second configuration of the second tree structure to obtain a similarity metric for the first linguistic structure and the second linguistic structure. At least a part can be determined. In various implementations, the IP knowledge model development system 124 compares positions of words and/or positions of nodes within the first tree structure and the second tree structure between the first linguistic structure and the second linguistic structure. can determine the similarity metric of

IP knowledge model development system 124 may also use the classification scheme to determine relationships between intellectual property assets and products and/or services. A classification scheme may include multiple classifications with individual classifications having one or more criteria for identifying intellectual property assets, products, and/or services to include in the individual classifications. In various implementations, the classifications of a classification scheme may include multiple skill groups. The classification scheme may, in some examples, be generated by the intellectual property mapping and learning system 104 . In additional examples, the taxonomy may be created by another organization, such as a government agency, an educational institution, a non-profit organization, a for-profit organization, or combinations thereof. In certain implementations, the IP knowledge model development system 124 may compare characteristics of individual intellectual property assets to criteria of multiple classifications included in the classification scheme, and determine one or more classifications to associate with the intellectual property assets. there is. Additionally, the IP knowledge model development system 124 compares characteristics of products and/or services to criteria of multiple classifications of the classification scheme, and identifies one or more classifications to associate with the products and/or services. can decide

In certain implementations, IP knowledge model development system 124 may determine intellectual property assets and products and/or services included in the same classification of a classification scheme. IP knowledge model development system 124 may then determine one or more relationships between intellectual property assets and products and/or services included in the same classification of the taxonomy. In this manner, the IP knowledge model development system 124 may develop one or more models representing intellectual property assets corresponding to products and/or services within the classification of the taxonomy. In the illustrative example, IP knowledge model development system 124 may develop a model for determining patent claims corresponding to display characteristics of mobile devices. In another illustrative example, IP knowledge model development system 124 may develop a model for determining brands corresponding to fitness tracker devices. In various implementations, use to determine classification scheme(s), relationships between intellectual property assets and products and/or services, and intellectual property assets that may be associated with particular products and/or services. The models to be used may be stored by the intellectual property knowledge data store 120 .

Relationships determined by IP knowledge model development system 124 between intellectual property assets and products and/or services within particular classifications, and intellectual property corresponding to products and/or services within a classification of the classification system. Models developed by IP knowledge model development system 124 for determining property assets may be used to provide a number of intellectual property customer services 126 . Intellectual property services 126 may include IP strategy related services 128 , IP exposure related services 130 , and IP valuation services 132 . In various implementations, intellectual property customer services 126 may be provided based on requests sent to intellectual property services system 102 for information regarding one or more intellectual property assets or one or more products and/or services. can The intellectual property services system 102 then utilizes the models, frameworks, and/or relationships between intellectual property assets and products and/or services generated by the intellectual property mapping and learning system 104 . so you can respond to requests. Requests may, in some circumstances, be sent by individuals associated with the intellectual property service provider, while in additional circumstances, the request may be sent by individuals associated with one or more customers 110 .

Intellectual property customer services 126 may include intellectual property (IP) strategy related services 128 . IP strategy related services 128 may include analysis of groups of intellectual property assets. In examples, IP strategy related services 128 include competitive landscaping 150 , IP benchmarking 152 , IP scoring & rating 154 , intelligence portfolio tool 156 , IP trend analyzer 158 , IP pruning and/or divestiture 160 , management reporting 162 , and/or strategic acquisition 164 . In certain implementations, IP strategy-related services 128 may include analysis of an organization's portfolio of intellectual property assets, such as analysis of a portfolio of customer 110's portfolio of intellectual property assets. In illustrative examples, IP strategy related services 128 may include analyzing a portfolio of patent documents and/or analyzing a portfolio of trademark documents. In various implementations, IP strategy related services 128 may include analyzing a portfolio of intellectual property documents of competitors of customer 110 , such as using competitive outlook 150 . For example, the intellectual property services system 102 may determine technical classifications for a competitor's intellectual property assets of the customer 110 , and provide prospective analysis showing the competitor's intellectual property documents pertaining to the individual technology classifications. You can create a document or report. In some examples, customer 110's intellectual property assets may be mapped to customer 110's competitor's intellectual property assets with respect to their respective technical classifications.

In other examples, IP strategy related services 128 may include determining scores and/or ratings of intellectual property assets, such as by IP scoring and rating component 154 . To illustrate, the intellectual property services system 102 may determine coverage measures and/or coverage measures of the customer 110's intellectual property assets or another organization's intellectual property assets. The intellectual property services system 102 may then rank the intellectual property assets based on scope measures and/or coverage measures. IP strategy related services 128 may also include identifying technology areas in which customer 110 may wish to develop intellectual property assets, such as using IP benchmarking component 152 . For example, the intellectual property services system 102 may determine technology classifications that relate to areas of technology in which the customer 110 has little or no intellectual property assets but is being developed by the customer 110 . Additionally, the intellectual property services system 102 , such as using the IP portfolio tool 156 , may include the number of a customer's intellectual property assets within specific technology areas and/or the intellectual property assets of one or more competitors of the customer 110 . Based on the number, future areas of research and development for customer 110 may be identified.

Additionally, IP strategy related services 128 may include identifying a customer's intellectual property assets to offer for sale or license to other organizations. Intellectual property services system 102 may also generate recommendations for intellectual property assets of customer 110 that may be abandoned or no longer maintained, such as by IP pruning and/or sale component 160 . there is. In certain implementations, the intellectual property services system 102 determines at least one of value measures, scope measures, or coverage measures for at least a portion of the customer 110 intellectual property assets, the individual measure Recommendations regarding at least one of sales opportunities, licensing opportunities, or cost savings opportunities (eg, waivers) of one or more intellectual property assets of a customer 110 , such as through the management reporting component 162 , using can create The intellectual property services system 102 may also be based on at least one of value measures, scope measures, coverage measures, or technology areas associated with organizations and/or intellectual property assets, such as a strategy component ( 164) to determine potential organizations and/or intellectual property assets that may be acquired by the customer. In addition, IP strategy related services 128 may include determining metrics for customer 110's intellectual property documents, such as by utilizing IP trend analyzer 158 . The metrics may indicate trends in at least one of the customer 110 's number of intellectual property assets pending or the customer 110 's number of intellectual property assets being granted.

The intellectual property services system 102 may also be utilized to provide IP risk related services 130 to the customer 110 . IP exposure related services 130 include IP liability 166, collateral protection 168, trade secret theft 170, IP litigation forwarding 172, source code diligence 174, and/or or design-around consulting. IP exposure related services 130 may relate to determining loss risk measures associated with customer 110 intellectual property assets utilizing IP liability component 166 . The risk of loss may correspond to at least one of a decrease in the value of the intellectual property assets, invalidation of at least a portion of the intellectual property asset, or theft of the intellectual property asset. In various implementations, IP exposure related services 130 may include determinations of liability measures for customer 110 intellectual property assets. The intellectual property services system 102 is based on at least one of the number of litigation cases for the customer's 110 intellectual property assets or the number of litigation cases for the intellectual property assets that are in the same technical class as one or more intellectual property assets of the customer 110 . to determine the measures of liability for intellectual property assets. A litigation case may include filing a request to initiate action on an intellectual property asset. Actions on intellectual property assets may include at least one of opposition procedures, procedures determined by an administrative agency, or procedures in a jurisdiction. In certain implementations, liability measures relating to intellectual property assets may correspond to a number of litigation cases involving intellectual property assets of customer 110 or other organization's intellectual property assets that occurred within a specified period of time. In some examples, liability measures for intellectual property assets utilize collateral protection component 168 to determine the terms of insurance policies issued to protect loans made to intellectual property assets as collateral. can be used

IP exposure related services 130 may also include determining measures for reducing risk of loss on intellectual property assets. For example, IP risk related services 130 may include utilizing trade secret theft component 170 to determine an amount of risk for theft of trade secrets of customer 110 . In certain implementations, the intellectual property services system 102 analyzes and analyzes security protocols or other security processes implemented by the customer 110 to protect the trade secrets and a risk of trade secret theft based at least in part on the analysis. quantity can be determined. IP exposure related services 130 may also utilize source code due diligence component 174 to protect processes and/or procedures for protecting source code developed by a customer, and intellectual property rights associated with the source code. may include determining actions and processes and/or procedures for the customer 110 to take to protect. Additionally, IP exposure related services 130 utilize the avoidance design consulting component 176 to provide options and/or options for customer 110 to design for avoidance against competitors' intellectual property assets. Competitors 110 may include determining options for designing hedging for customer 110 's intellectual property assets. In certain implementations, intellectual property services system 102 may analyze multiple intellectual property assets and determine characteristics of intellectual property assets that correspond to characteristics of products and/or services. The intellectual property services system 102 may then identify features of the products and/or services that may be modified to avoid features of the intellectual property assets associated with the products and/or services.

Additionally, IP exposure related services 130 may include utilizing IP litigation forwarding component 172 to determine a strategy in intellectual property litigation actions. Illustratively, the intellectual property services system 102 may analyze a sequence of events that have occurred for a pending litigation action in relation to those that occurred in the previous litigation actions, and may make recommendations for future decisions in a pending litigation. can decide In illustrative examples, the intellectual property services system 102 may determine motions to file in a pending lawsuit to increase the probability of a favorable outcome for the customer. Intellectual property services system 102 may also determine recommendations for settlement negotiations, such as timing of settlement proposals and/or amounts to be proposed in connection with the settlement negotiations. In addition, the intellectual property services system 102 may generate recommendations for litigation counsel to hold in a particular litigation action and/or to generate recommendations regarding changes to litigation counsel to hold.

In various implementations, the intellectual property customer services 126 provided through the intellectual property services system 102 may include IP valuation services 132 . IP valuation services 132 include IP stack valuation 178 , M&A sell segment and buy segment services 180 , asset-backed loan 182 , and/or articulation 184 . can do. IP valuation services 132 may include utilizing IP stack valuation 178 to determine value measures of intellectual property assets. In certain implementations, the intellectual property services system 102 may determine measures of value of intellectual property assets to a customer or may determine measures of value of intellectual property assets of another organization. In some examples, intellectual property services system 102 may determine measures of value of intellectual property assets that may be purchased or licensed by customer 110 . Intellectual property services system 102 also utilizes M&A sell-segment and buy-segment services 180 to determine value measures of an organization's intellectual property assets that may be purchased or otherwise acquired by customer 110 . can In additional implementations, the intellectual property services system 102 may provide measures of the value of the customer 110's intellectual property assets in connection with the acquisition of the customer 110 by another organization or the merging of the customer 110 with another organization. can decide Additionally, the intellectual property services system 102 may utilize asset-backed loan services 182 to provide the customer 110 with the customer 110's intellectual property assets being used as collateral for at least a portion of the loan amount. Value measures of the intellectual property assets of the customer 110 may be determined in connection with one or more loans made to the customer 110 .

Intellectual property services system 102 may utilize value association services 184 to determine value measures of intellectual property assets based on scope measures of the intellectual property assets. Additionally, the intellectual property services system 102 may determine measures of value of the intellectual property assets based on the revenue of products and/or services corresponding to the intellectual property assets. To determine scope measures and/or portions of revenue of products and/or services corresponding to intellectual property assets, the intellectual property services system 102 may employ one or more linguistic analysis techniques and one or more machine learning techniques. can be used

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, in accordance with some implementations. Environment 200 may include an intellectual property mapping and learning system 104 , one or more data sources 106 , and an intellectual property knowledge data repository 120 . The intellectual property mapping and learning system 104 may be implemented by one or more computing devices 202 . One or more computing devices 202 may be included in a cloud computing architecture that operates one or more computing devices 202 on behalf of an intellectual property service provider. In such scenarios, the cloud computing architecture may implement one or more virtual machine instances on behalf of the intellectual property service provider on one or more computing devices 202 . The cloud computing architecture may be located remotely from the intellectual property service provider. In additional implementations, 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.

The intellectual property knowledge data repository 120 may store information that may be utilized by the intellectual property mapping and learning system 104 in providing services related to the intellectual property assets. In certain implementations, the intellectual property knowledge data store 120 may store intellectual property (IP) data 204 . IP data 204 may include data related to intellectual property assets. IP data 204 may be obtained via one or more publicly accessible data sources, one or more private data sources, or combinations thereof. IP data 204 may also include customer IP data 206 corresponding to data stored by intellectual property knowledge data store 120 , relating to customers obtaining services from intellectual property service providers. In some implementations, customer IP data 216 may be stored separately from IP data of other organizations in intellectual property knowledge data repository 120 .

In various implementations, IP data 204 may include data related to intellectual property assets, such as trademarks, copyrights, patents, and trade secrets. IP data 204 may include documents containing information related to various types of intellectual property. For example, IP data 204 may include patent applications, published patent applications, and issued or granted patents. IP data 204 may also include trademark applications and submissions made with protection of copyrights. Additionally, IP data 204 may include documents containing trade secrets and documents supporting protection of trade secrets. To illustrate, IP data 204 may include organizations' employment contracts, employee manuals, policies, and/or procedures that may be used to support the trade secret status of organizations' innovation.

IP data 204 may also include bibliographic information for intellectual property documents. In illustrative examples, IP data 204 may include specific dates associated with intellectual property documents (eg, filing dates, publication dates, priority dates), assignees of intellectual property documents, and transfer histories of intellectual property documents. , significant individuals related to intellectual property documents (eg, inventors, examiners, etc.), third party classifications related to intellectual property documents, indications of priority documents over certain intellectual property documents, intellectual property jurisdiction It may include information such as the status of intellectual property documents according to the review organization, combinations thereof, and the like. In addition, the IP data 204 may include information related to the filing history of intellectual property documents. The filing history may include various incidents that occur in relation to the examination of intellectual property documents. Illustratively, IP data 204 may include dates for which documents were filed during examination of intellectual property documents, such as dates for which replies were filed, dates on which examiners issued office action or examination reports, registration decision dates, publication dates, combinations thereof, and the like. Additionally, IP data 204 may include documents filed and/or filed during the filing of intellectual property documents. In illustrative examples, IP data 204 may include interim cases, interim case answers, information disclosure statements, application data sheets, declarations, samples to support the use of trademarks, reasons for adjudication, adjudication may include examiner responses to reasons for claim, replies, decisions on appeals, notices of allowances, objections documents, copyright submissions, interview summary documents, combinations thereof, and the like. .

IP data 204 may also include statistics and/or metrics related to individual judges reviewing intellectual property assets. Illustratively, the IP data 204 may include the number of intellectual property assets determined to be registered over a period of time, the average number of interim cases presented during the examination of intellectual property assets, the number of appeals over a period of time, decisions on appeals, interim cases may include the average length of time they provide them, the number of years of experience, the number of intellectual property assets assessed over a period of time, combinations thereof, and the like. Additionally, IP data 204 may include statistics and/or metrics related to groups of judges reviewing intellectual property assets. The IP data 204 may also include statistics and/or metrics of individual judges relating to the statistics and/or metrics of the group of judges. For example, IP data 204 may include an average number of intermediate cases presented per case allowed for a group of patent examiners, including individual patent examiners, such as a particular art unit or group of patent examiners of a particular technical classification. may include the number of intermediate cases provided per case allowed for individual patent examiners regarding

In various implementations, IP data 204 may include data related to litigation and/or pseudo-litigation procedures associated with intellectual property assets. In certain implementations, IP data 204 may include lawsuits, answers, pleadings, petitions for injunction, discovery requests, discovery responses, expert opinions, decisions by a court, a jury. may include documents submitted during proceedings such as verdicts, jury charges, combinations thereof, and the like. In additional implementations, IP data 204 may include transcripts of proceedings. For example, IP data 204 may include records of court proceedings and/or records of testimony transcripts. In further implementations, IP data 204 may include documents submitted during pseudo-litigation procedures, such as the US Patent and Trademark Office's inter-party review procedures or the European Patent and Trademark Office's objection procedures.

The intellectual property knowledge data store 120 may also store IP valuation data 208 . The IP valuation data 208 may be used by the intellectual property mapping and learning system 104 to determine the value of intellectual property assets or portions of intellectual property assets. In certain implementations, IP valuation data 208 may include values reached during settlement negotiations that occurred during litigation or pseudo-litigation. Additionally, IP valuation data 208 may include terms of licenses obtained with respect to intellectual property assets or portions of intellectual property assets. IP valuation data 208 may also include verdicts provided by judges, juries, other judicial bodies, or administrative bodies indicating the value of intellectual property assets or portions of intellectual property assets. can In various implementations, at least a portion of the IP valuation data 208 may include information relating to customers of an intellectual property service provider that is not publicly available. In additional implementations, IP valuation data 208 can include information that can be used to determine the value of publicly available intellectual property assets or portions of intellectual property assets.

In addition, the intellectual property knowledge data store 120 may store business data 210 . 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 offered for acquisition by various organizations. Product/service data 212 may include descriptions of products and/or services, specifications of products and/or services, product manuals, pricing of products and/or services, and descriptions of products and/or services. number of sales, descriptions of organizations providing various products and/or services, combinations thereof, and the like. Product/service data 212 may include customer product/service data 216 including information relating to products and/or services provided by customers of the intellectual property service provider. In some implementations, customer product/service data 216 may be stored separately from product/service data of other organizations in intellectual property knowledge data repository 120 .

Economic data 214 may include information indicative of the financial performance of organizations that provide products and/or services for acquisition. Financial performance information may include revenues of organizations over a period of time, profits of organizations over a period of time, costs of organizations over a period of time, expectations of financial performance, or combinations thereof. Economic data 214 may also include an amount of revenue for organizations corresponding to sales of one or more products and/or services. Economic data 214 may include customer economic data 218 comprising economic data corresponding to customers of the intellectual property service provider. In some implementations, the customer economic data 218 may be stored separately from the economic data of other organizations in the intellectual property knowledge data repository 120 .

Additionally, economic data 204 may also include industry financial data. For example, economic data 204 may include revenue, profits, costs, etc. for certain industries that provide goods and/or services for acquisition, such as retail industry, semiconductor industry, or transportation industry. . Additionally, economic data 204 may include economic data of various states, counties, countries, or other political jurisdictions. To illustrate, economic data 204 may include gross domestic product data, employment data, business data, combinations thereof, and the like. In some examples, economic data 204 may indicate an amount of gross domestic product of a country or political jurisdiction attributable to one or more industry sectors.

Additionally, the intellectual property knowledge data storage 120 may store at least one technical taxonomy 220 . The technology taxonomy 220 may include multiple classifications for products and/or services. The descriptive taxonomy 220 may also include one or more criteria associated with individual classifications of the descriptive taxonomy 220 . For example, to be classified according to a particular classification of the descriptive taxonomy 220 , a product and/or service may correspond to at least a threshold number of criteria of the particular classification. In various implementations, the technology taxonomy 220 may indicate products and/or services associated with the respective classifications. That is, products and/or services previously assigned to a classification may be included in the technical taxonomy 220 .

The descriptive taxonomy 220 may, in some implementations, be generated by the intellectual property mapping and learning system 104 . Moreover, in certain implementations, at least a portion of the descriptive taxonomy 220 may be generated by an additional organization. To illustrate, the technical taxonomy 220 may include classifications included in the classification systems of government organizations and/or classification systems of industrial organizations. In illustrative examples, at least a portion of the classifications of the descriptive taxonomy 220 may correspond to descriptive classifications of the US Patent and Trademark Office. In other illustrative examples, at least a portion of the classifications included in the technical taxonomy 220 are included in the International Patent Classification (IPC), the Locarno Classification, the Nice Classification, and/or the Vienna Classification. It can correspond to technical classifications.

In various implementations, the intellectual property knowledge data store 120 may store mappings 222 of intellectual property (IP) to products and/or services. Mappings of IP to products and/or services 222 may indicate intellectual property assets or portions of intellectual property assets mapped to the product and/or service. In the illustrative example, mappings 222 of IP to products and/or services may indicate a claim in a patent document that corresponds to a feature of the mobile device, such as a microphone of the mobile device. In another illustrative example, mappings 222 of IP to products and/or services may indicate a trademark corresponding to a remote data storage service. Mappings of IP to products/services 222 may also indicate an organization providing individual products and/or services for acquisition. Additionally, IP-to-products and/or services mappings 222 may indicate owners of intellectual property assets mapped to particular products and/or services.

The IP to products and/or services mappings 222 are customer mappings representing mappings between the intellectual property service provider's customers' products and/or services and the intellectual property service provider's customers' intellectual property assets. may include 224 . In additional implementations, customer mappings 224 may be a mapping between intellectual property assets of customers of the intellectual property service provider and products and/or services provided by organizations that are not customers of the intellectual property service provider. may include Additionally, customer mappings may include mappings between products and/or services provided by customers of the intellectual property service provider and intellectual property assets of organizations that are not customers of the intellectual property service provider.

The intellectual property knowledge data store 120 may also store historical customer service data 226 . Previous customer service data 226 may include data generated by an intellectual property service provider when providing services to one or more customers. For example, prior customer service data 226 may include IP strategy related services 128 , IP exposure related services 130 , and/or IP valuation services 132 , as described with respect to FIG. 1 . The provision may include data generated by intellectual property service providers. In illustrative examples, historical customer service data 226 may include valuations of intellectual property assets determined by the intellectual property service provider. In additional illustrative examples, historical customer service data 226 may include determinations of risk with respect to intellectual property assets of the intellectual property service provider's customers. In further illustrative examples, historical customer service data 226 may include claim charts, strategic IP analyzes, and/or portfolio analysis data generated by an intellectual property service provider when providing services to customers. there is. In certain implementations, previous customer service data 226 may be utilized to provide subsequent services to customers of the intellectual property service provider. In this way, the knowledge generated by the intellectual property service provider is increased and can be used to more efficiently and accurately provide services to the intellectual property service provider's customers.

3 illustrates an example environment 300 for creating mappings between products and intellectual property assets using a descriptive taxonomy, in accordance with some implementations. Environment 300 may include an intellectual property mapping and learning system 104 implemented via one or more computing devices 202 . The environment 300 may also include a customer 110 of an intellectual property service provider and a group 302 of the customer 110's intellectual property assets. The group of intellectual property assets 302 includes 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 , At most, the N-th IP asset 314 may be included. IP assets 304 , 306 , 308 , 310 , 312 , 314 may include various types of intellectual property. For example, IP assets 304 , 306 , 308 , 310 , 312 , 314 may include trademarks, patents, trade secrets, copyrights, know-how, or other classifications of intellectual property. In additional examples, at least a portion of IP assets 304 , 306 , 308 , 310 , 312 , 314 may correspond to a portion of an intellectual property asset, such as one or more claims of a set of claims in a patent document.

In various implementations, one or more of IP assets 304 , 306 , 308 , 310 , 312 , 314 may have different intellectual property than at least another one of IP assets 304 , 306 , 308 , 310 , 312 , 314 . It can correspond to classification. For example, a first IP asset 304 may correspond to a trademark and a second IP asset 306 may correspond to a trade secret. Additionally, in some implementations, each of IP assets 304 , 306 , 308 , 310 , 312 , 314 may correspond to the same type of intellectual property classification. To illustrate, IP assets 304 , 306 , 308 , 310 , 312 , 314 may each correspond to patents or patent applications, such as at least a portion of customer 110 's patent portfolio. In another illustrative example, IP assets 304 , 306 , 308 , 310 , 312 , 314 may each correspond to claims of a patent or patent application. In a further illustrative example, IP assets 304 , 306 , 308 , 310 , 312 , 314 may each correspond to trade secrets. In a further illustrative example, IP assets 304 , 306 , 308 , 310 , 312 , 314 may each correspond to trademarks. In other illustrative examples, IP assets 304 , 306 , 308 , 310 , 312 , 314 may each correspond to copyrights.

Additionally, environment 300 may include descriptive taxonomy 220 of FIG. 2 . The descriptive classification system 220 may include a number of classifications, such as a first classification 316 , a second classification 318 , a third classification 320 , and at most an N-th classification 322 . Individual classifications 316 , 318 , 320 , 322 of descriptive taxonomy 220 may be associated with separate sets of criteria that characterize items associated with a particular classification of descriptive taxonomy 220 . At least one of the intellectual property assets, products, or services may be classified according to at least one classification of the technology taxonomy 220 . In example implementations, the intellectual property mapping and learning system 104 determines characteristics of the first intellectual property asset 304 and assigns the characteristics of the first intellectual property asset 304 to a criterion set of a first classification 316 . can be compared to In certain implementations, the intellectual property mapping and learning system 104 can determine a metric indicative of an amount of similarity between the characteristics of the first intellectual property asset 304 and the criterion set of the first classification 316 .

In various implementations, the amount of similarity between the characteristics of the first IP asset 304 and the set of criteria of the first classification 316 is the first IP asset 304 corresponding to one or more criteria of the first classification 316 . can indicate the number of features of In example implementations, the intellectual property mapping and learning system 304 may configure the first IP asset 304 by comparing words of one or more features of the first IP asset 304 to words of a first classification 316 . The amount of similarity between the characteristic of , and the first classification 316 may be determined. The intellectual property mapping and learning system 304 is configured to, based on at least a threshold number of words of the characteristic of the first IP asset 304 corresponding to words of the first classification 316 , the first IP asset 304 . It may be determined that the characteristic of ? corresponds to the first classification 316 . In some scenarios, the intellectual property mapping and learning system 304 determines a characteristic of the first IP asset 304 when the spelling of a word in the first IP asset 304 is the same as a word in the first classification 316 . It may be determined that the word of ' corresponds to the word of the first classification. In additional situations, the intellectual property mapping and learning system 304 may be configured to, based on the word of the first IP asset 304 being a synonym of a word of the first classification 316 , It may be determined that the word of the feature corresponds to a word of the first classification 316 . In further examples, the intellectual property mapping and learning system 104 may determine the characteristic of the first IP asset 304 based on the word of the first IP asset 304 being a derivative of a word of the first classification 316 . It may be determined that the word corresponds to a word of the 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 word of the first IP asset 304 may be a plural or singular version of the word of the first classification 316 .

The intellectual property mapping and learning system 104 is configured to determine a characteristic of the first IP asset 304 and the A first amount of similarity between the first classifications 316 may be determined. Additionally, the intellectual property mapping and learning system 104 is configured to, based on determining that two characteristics of the first intellectual property asset 304 correspond to at least one criterion of the first classification 316 , the first A second amount of similarity between the IP asset 304 and the first classification 316 may be determined. In certain implementations, the intellectual property mapping and learning system 104 is configured to, based on the amount of similarity between the characteristic of the first IP asset 304 and the criteria of the first classification 316 exceed a threshold similarity amount, It may be determined that the first IP asset 304 corresponds to the first classification 316 . In additional implementations, the intellectual property mapping and learning system 104 determines that an amount of similarity between a characteristic of the first IP asset 304 and the first classification 316 is a characteristic and a technical classification of the first IP asset 304 . greater than the amounts of similarity between the individual criterion sets of the additional classifications of the scheme 220 , such as the criterion sets of the second classification 318 , the third classification 320 , and at most the Nth classification 322 . Based on that, it may be determined that the first IP asset 304 corresponds to the first classification.

The intellectual property mapping and learning system 104 also provides a descriptive taxonomy for a number of products and/or services, such as a first product 324 , a second product 326 , and a third product 328 . 220) can be determined. The intellectual property mapping and learning system 104 may determine the classifications of the descriptive taxonomy 220 for the products 324 , 326 , 328 itself. In additional implementations, the intellectual property mapping and learning system 104 may determine classifications of the descriptive taxonomy 220 that correspond to one or more characteristics of the products 324 , 326 , 328 . In the illustrative example, the intellectual property mapping and learning system 104 is configured such that a first product 324 corresponds to a transport classification of the technology taxonomy 220 , and the second product 326 corresponds to a transport classification of the technology taxonomy 220 . Corresponds to the mobile communication device classification, and may determine that the third product 328 corresponds to the printing device classification of the technology taxonomy 220 . In additional illustrative examples, the intellectual property mapping and learning system 104 may include a display device included in the first product 324 , a display device in the second product 326 , and a display device in the third product 328 . Classification of features common to products 324 , 326 , and 328 may be determined. The intellectual property mapping and learning system 104 may also determine classifications of additional individual characteristics of the products 324 , 326 , 328 with respect to the descriptive taxonomy 220 .

In certain implementations, the intellectual property mapping and learning system 104 relates to criteria sets of classifications of the descriptive taxonomy 220 , such as respective sets of criteria of the classifications 316 , 318 , 320 , 322 . products 324 , 326 , 328 and/or products 324 , 326 , 328 and or determine the classifications of the descriptive taxonomy 220 for characteristics of the products 324 , 326 , 328 . For example, the intellectual property mapping and learning system 104 may base the classifications of the descriptive taxonomy 220 and the descriptions of the features of the products 324 , 326 , 328 and/or the products 324 , 326 , 328 . The amount of similarity between them can be determined.

In example implementations, an amount of similarity between a characteristic of the first product 324 and the set of criteria of the first classification 316 determines a characteristic of the first product 324 that corresponds to one or more criteria of the first classification 316 . Can indicate the number of words to describe. That is, the intellectual property mapping and learning system 104 compares one or more words describing a characteristic of the first product 324 with words associated with the first classification 316 , and selects one of the first classifications 316 . It is possible to determine the number of words in the description of the feature of the first product 324 that correspond to the words of the above criteria. The intellectual property mapping and learning system 304 is configured to provide a word describing a characteristic of the first product 324 when the spelling of a word of the characteristic of the first product 324 is the same as the word associated with the first classification 316 . may be determined to correspond to a word associated with the first classification 316 . In additional contexts, the intellectual property mapping and learning system 104 may configure the first product ( It may be determined that the word describing the characteristic of 324 corresponds to the word associated with the first classification 316 . In other examples, the intellectual property mapping and learning system 104 may configure the first product 324 based on the word describing the characteristic of the first product 324 being a derivative of a word associated with the first classification 316 . It may be determined that a word describing the characteristics of ? corresponds to a word associated with the first classification 316 . For example, a word describing a characteristic of the first product 324 may be a different tense of a word associated with the first classification 306 . In other examples, the word describing the characteristic of the first product 324 may be a plural or singular version of the word associated with the first classification 316 .

The intellectual property mapping and learning system 304 is configured to, based on at least a threshold number of words describing a characteristic of the first product 324 correspond to words associated with the first classification 316 , the first product 324 . ) corresponds to the first classification 316 . In some scenarios, the intellectual property mapping and learning system 104 determines an amount of similarity between words describing a characteristic of the first product 324 and words associated with the first classification 316 , such that the first It may be determined whether characteristics of the product 324 are to be classified according to the first classification 316 . In certain implementations, the intellectual property mapping and learning system 104 is configured to determine if an amount of similarity between the words describing the characteristic of the first product 324 and the words of the first classification 316 exceeds a threshold similarity amount. Based on that, it may be determined that the characteristic of the first product 324 corresponds to the first classification 316 . In additional implementations, the intellectual property mapping and learning system 104 determines that the amount of similarity between the words describing the characteristic of the first product 324 and the words of the first classification 316 is determined by the first product 324 . . Based on being greater than amounts of similarity between words associated with the sets of criteria of the classification 322 , it may be determined that the characteristic of the first product 324 corresponds to the first classification 316 .

The intellectual property mapping and learning system 104 may also determine mappings 330 between the products and/or services and the group of intellectual property assets 302 . In certain implementations, the intellectual property mapping and learning system 104 utilizes the descriptive taxonomy 220 to map characteristics of a group of IP assets 302 that correspond to characteristics of one or more products and/or services. can decide In various implementations, the intellectual property mapping and learning system 104 provides features of intellectual property assets included in the group of intellectual property assets 302 and products classified according to the same classification of the descriptive taxonomy 220 . /or determine mappings between features of the service. Mappings 330 may indicate that an intellectual property asset may cover a feature of a product. In example implementations, the mappings 330 may indicate that the intellectual property asset may be asserted in judicial and/or administrative proceeding with respect to the corresponding product.

The illustrative example of FIG. 3 includes a first mapping 332 between a first product 324 and a group of intellectual property assets including a first IP asset 304 and a third IP asset 308 . . Mappings 330 are also between second product 326 and another group of intellectual property assets including first IP asset 304 , second IP asset 306 , and fourth IP asset 310 . and a second mapping 334 of In addition, the mappings 330 include a third mapping 336 between the third product 328 and the additional group of intellectual property assets including the first IP asset 304 and the fifth IP asset 312 . may include

Intellectual property mapping and learning system 104 may include products 324 , 326 , 328 and/or features of products 324 , 326 , 328 and IP assets 304 , 306 , 308 , 310 , 312 , 314 ) and/or mappings 332 , 334 , 336 by determining similarities between features of IP assets 304 , 306 , 308 , 310 , 312 , 314 . In certain implementations, the intellectual property mapping and learning system 104 classifies according to the characteristics of the intellectual property assets 304 , 306 , 308 , 310 , 312 , 314 and the same classification of the descriptive taxonomy 220 . Mappings between features of products 324 , 326 , 328 can be determined. In various implementations, the intellectual property mapping and learning system 104 determines an amount of similarity between words of a characteristic of the first IP asset 304 and words that describe a characteristic of the first product 324 , such as: A mapping may be determined between the characteristics of the first IP asset 304 and the characteristics of the first product 324 .

In the illustrative example, the intellectual property mapping and learning system 104 can determine an amount of similarity between the features of the first product 324 and elements of the claim associated with the first IP asset 304 . In another illustrative example, the intellectual property mapping and learning system 104 may provide a similarity between a brand associated with the first IP asset 304 and a word or group of words used in marketing and branding of the first product 324 . quantity can be determined. The intellectual property mapping and learning system 104 is configured to: between the first words and second words of the feature of the first IP asset 304 and second words of the feature of the first product 324 . The amount of similarity can be determined by performing a comparison between them. The amount of similarity between the first words and the second words is the number of words that are identical between the first words and the second words, the number of words that are synonymous between the first words and the second words, and/or It may be based on the number of words derived between the first words and the second words.

In additional implementations, the intellectual property mapping and learning system 104 is based at least in part on linguistic constructs created for intellectual property assets and linguistic constructs created for products and/or services. to determine mappings between intellectual property assets and products and/or services. Linguistic structures may indicate relationships between words of intellectual property assets and words that describe products. In various implementations, the intellectual property mapping and learning system 104 generates linguistic structures for features of intellectual property assets and linguistic structures for features of products, such that the language of features of intellectual property assets is generated. The mechanical structures and characteristics of products can be compared.

In certain implementations, the intellectual property mapping and learning system 104 may determine an amount of similarity between the linguistic structure of a feature of the intellectual property asset and the linguistic structure of a feature of the product. In certain implementations, the intellectual property mapping and learning system 104 may convert words included in a linguistic structure of a characteristic of an intellectual property asset, such as a characteristic of a first intellectual property asset 304 , to a characteristic of a product, such as a first Words included in the linguistic structure of the feature of the product 324 may be compared. Additionally, the intellectual property mapping and learning system 104 may compare the construction of the linguistic structure of the features of the first intellectual property asset 104 to the construction of the linguistic structure of the features of the first product 324 . . The configuration of the linguistic structure of the characteristic of the first intellectual property asset 304 may indicate first relationships between words related to the characteristic of the first intellectual property asset 304 , the characteristic of the first product 324 . The construction of the linguistic structure of may indicate relationships between words that describe a characteristic of the first product 324 . The intellectual property mapping and learning system 104 is configured to determine a characteristic of an intellectual property asset based, at least in part, on a similarity amount between the linguistic structure of a characteristic of the intellectual property asset and a linguistic structure of a product characteristic is greater than a threshold similarity amount. You can create mappings between product features.

4 illustrates an example system 400 for generating valuations for intellectual property assets, in accordance with some implementations. System 400 may include an intellectual property mapping and learning system 104 , and one or more computing devices 202 that may implement the intellectual property mapping and learning system 104 . System 400 may also include a first data store to store intellectual property (IP) valuation data 402 and a second data store to store business data 404 . IP valuation data 402 and business data 404 may include information corresponding to customers of the intellectual property service provider. IP valuation data 402 and business data 404 may also include information corresponding to organizations that are not customers of the intellectual property service provider.

IP valuation data 402 may include information that may be used to determine values of intellectual property assets. In certain implementations, IP valuation data 402 may include verdicts indicative of damages awarded during judicial proceedings related to intellectual property assets. IP valuation data 402 may also include amounts for licensing intellectual property assets. In addition, IP valuation data 402 may include amounts paid as part of settlements related to judicial and/or administrative proceedings arising out of or relating to intellectual property assets. The business data 404 may include information indicative of revenue earned by organizations with respect to products and/or services provided by the organizations. The business data 404 may also include other financial information related to organizations, such as total revenue over a period of time, amount of revenue within a particular technology area over a period of time, profits earned over a period of time, and a period of time. expenditures over , combinations thereof, and the like. In various implementations, expenditures included in business data 404 may be indicative of an organization's expenditures for providing one or more products and/or services for acquisition to consumers.

The intellectual property mapping and learning system 104 may utilize at least one of the IP valuation data 402 or the business data 404 to determine valuations for one or more intellectual property assets. In the illustrative example, the intellectual property mapping and learning system 104 may determine valuations for intellectual property assets corresponding to the second product 326 of FIG. 3 . In particular, the intellectual property mapping and learning system 104 maps to features of the second product 326 , such as the first IP asset 304 , the second IP asset 306 , and the fourth IP asset 310 . can determine valuations for intellectual property assets. For example, the intellectual property mapping and learning system 104 may include a first valuation 406 of a first IP asset 304 , a second valuation 408 of a second IP asset 306 , and a fourth A third valuation 410 of the IP asset 310 may be determined. Valuations 406 , 408 , 410 indicate that the organization(s) owning rights to individual IP assets 304 , 306 , 310 compares them with rights to IP assets 304 , 306 , 310 . May indicate monetary value that can be obtained from one or more additional organizations in exchange. In various implementations, valuations 406 , 408 , 410 are determined by the organization(s) that own rights to the respective IP assets 304 , 306 , 310 . may indicate one or more monetary values that may be obtained in one or more licensing transactions involving In additional implementations, valuations 406 , 408 , 410 are determined by the organization(s) owning rights to the respective IP assets 304 , 306 , 310 . ) may indicate one or more monetary values that can be obtained in connection with the sale. Additionally, valuations 406 , 408 , 410 may be calculated for individual IP assets 304 during a merger or acquisition of an organization that owns rights to IP assets 304 , 306 , 310 with respect to the additional organization. , 306, 310) may indicate one or more monetary values. In yet other implementations, valuations 406 , 408 , 410 may include individual IP assets 304 , as collateral for a loan to an organization that owns rights to IP assets 304 , 306 , 310 . 306, 310) may indicate one or more monetary values.

The intellectual property mapping and learning system 104 may determine valuations of intellectual property assets by determining the amount of revenue to be attributed to the intellectual property assets. In certain implementations, the intellectual property mapping and learning system 104 may provide products and/or services for other intellectual property assets included in the same classification of a framework of classifications, such as the descriptive taxonomy 220 of FIG. 2 . Based at least in part on the scope of the intellectual property assets that it covers, an amount of revenue for products and/or services attributable to the intellectual property assets may be determined. In additional implementations, the intellectual property mapping and learning system 104 is based, at least in part, on the scope of the intellectual property asset covering the product and/or service relative to other intellectual property assets covering the product and/or service. to determine the amount of revenue for products and/or services attributable to intellectual property assets.

The scope of the intellectual property asset may be determined based on the number of words in the intellectual property asset and/or the commonality of the words in the intellectual property asset. In certain implementations, the number of unique words and the frequency with which they appear in other intellectual property assets may be utilized to determine a range value for a given intellectual property asset. For example, for a given intellectual property asset, the word count of the intellectual property asset is the same as other intellectual property assets, such as multiple additional intellectual property assets included in the same classification as the given intellectual property asset or the same product as the given intellectual property asset. and/or a number of additional intellectual property assets covering the service. Additionally, a commonality score may be determined based on, for a given intellectual property asset, the commonality of words of that intellectual property asset compared to the commonality of words of other intellectual property assets.

In situations where a given intellectual property asset is a patent claim, the scope value of a claim may be in the same classification as other patent claims, such as other patent claims covering the same product and/or service as the given patent claim or in the same classification as the given patent claim. may express the estimated scope of its intellectual property rights to other patent claims classified accordingly. In certain implementations, the scope value 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 comprising a preamble with a closed connective phrase may have a smaller range value than a patent claim comprising a preamble with an open connective phrase. Additionally, patent claims that contain specific words, such as absolute, exemplary or relative words, may have lower range values than patent claims that do not include these types of words.

The word count may include the number of words of or part of the intellectual property asset. In various implementations, the word count may be determined after duplicate words are removed from the initial list of words included in the intellectual property asset. In this way, the word count may be the number of unique words of the intellectual property asset. Additionally, the word count may include the number of words in the intellectual property asset after removal of stop words. Stopwords may include the most common words in a language. Illustratively, stopwords may include short functional words (eg, "the", "is", "at", "which", and "on" in the International Application Specification), as well as others. The intellectual property mapping and learning system 104 may access one or more lists of stopwords for one or more languages. Additionally, the word count may be determined before or after converting acronyms and abbreviations into their full word representations. The word count may also include or exclude words from the preamble. In some implementations, a number of different word counts, such as a first word count that includes the number of unique words and a second word count that excludes stopwords, can be used to determine the scope of the intellectual property asset.

The commonality of words may correspond to the frequency with which a given word is found within a corpus of documents or within a group of intellectual property assets. Words with higher commonality, ie words that are more common words within a corpus of words, may correspond to a larger range, whereas the presence of rarely used words within a corpus of words may indicate a reduced range. In the context of patent claims, words often found in the art are generally considered broader or less restrictive than uncommon words.

In the example implementation, the intellectual property mapping and learning system 104 configures the first intellectual property asset 304 and the first intellectual property asset 304 to range values of other intellectual property assets included in the same classification of the technical taxonomy 220 . A range value of the asset 304 may be determined. The intellectual property mapping and learning system 104 utilizes the relative coverage score of the first intellectual property asset 304 to determine a portion of the revenue of the second product 326 attributable to the first intellectual property asset 304 . can The intellectual property mapping and learning system 104 is also configured for range values of other intellectual property assets covering the second product 326 , such as the second intellectual property asset 306 and the fourth intellectual property asset 310 . By determining the additional range value of the first intellectual property asset 304 , it is possible to determine an additional relative range score for the first intellectual property asset 304 . In a particular illustrative example, the intellectual property mapping and learning system 104 may determine that the portion of the revenue of the second product 326 attributable to the first intellectual property asset 304 is 0.00625%.

In additional implementations, the intellectual property mapping and learning system 104 may determine valuations for the intellectual property asset based on licensing information, settlement information, damages, or combinations thereof. For example, the intellectual property mapping and learning system 104 may analyze the IP valuation data 402 to determine at least one intellectual property asset covering a product and/or service, such as a second product 326 . Identify characteristics of products and/or services that were the subject of licensing transactions, agreements, and/or damages corresponding to characteristics of intellectual property assets 304 , 306 , 310 . The intellectual property mapping and learning system 104 is then based on the monetary values of agreements, licensing transactions, and/or damages regarding characteristics of particular products and/or services that may correspond to intellectual property assets. to determine valuations for one or more intellectual property assets. In the illustrative example, the intellectual property mapping and learning system 104 is configured to provide a first intellectual property asset 304 that includes at least one feature having at least a critical similarity to a feature of a product that was the subject of a claim for damages in a judicial proceeding. of the claims can be identified. The intellectual property mapping and learning system 104 may then determine the first valuation 406 based on an amount of similarity between the claimed features of the first IP asset 304 and the features of the product that was the subject of indemnification. .

In additional implementations, the intellectual property mapping and learning system 104 may resolve damages, licensing transactions, and/or agreements related to a classification of products and/or services corresponding to a classification of the first intellectual property asset. can decide The intellectual property mapping and learning system 104 then analyzes damages, licensing transactions, and/or agreements of products and/or services that are in the same class as the first IP asset 304 for a first valuation (406) can be determined. In certain implementations, the intellectual property mapping and learning system 104 determines an average amount of monetary value for agreements, damages, and/or licensing transactions in the descriptive classification of the first IP asset 304 . and may determine the first valuation 406 based on the average amount of monetary values. Additionally, the intellectual property mapping and learning system 104 may be configured to provide at least one characteristic of the first IP asset 304 and damages, agreements, and/or licensing in the same classification as the first IP asset 304 . It is possible to determine the similarity between the features that were the subject of the transactions. The intellectual property mapping and learning system 104 may determine a percentage or percentage of damages, agreements, and/or licensing transactions to assign to the at least one characteristic of the first IP asset 304 based on the amount of similarity. there is.

5 illustrates an example system 500 for modifying mappings between intellectual property and taxonomy classifications and/or between intellectual property and products/services, in accordance with some implementations. System 500 may include an intellectual property mapping and learning system 104 implemented by one or more computing devices 202 . System 500 can 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 the first user 504 or the second user 508 may be a representative of an intellectual property service provider. In additional implementations, at least one of the first user 504 or the second user 508 may not be a representative of the intellectual property service provider. For example, at least one of the first user 504 or the second user 508 may be a representative of another organization or part of a crowdsourcing group. In various implementations, the first user 504 and the second user 508, via the first computing device 502 and the second computing device 506 , respectively, provide classifications of intellectual property assets and technology classification schemes. may provide input regarding mappings between and/or provide input regarding mappings between intellectual property assets and products and/or services.

In certain implementations, the intellectual property mapping and learning system 104 can determine the mapping 510 of IP assets to classifications. The mapping of IP asset to classification may indicate that the IP asset has been classified according to a particular classification of a framework of classifications, such as descriptive taxonomy 220 of FIG. 2 . In the illustrative example, the mapping 510 of IP asset to class may indicate that the claim of the patent is classified according to a class related to mobile device batteries. In another illustrative example, mapping 510 of IP assets to classification may indicate that the trademark is classified according to a classification associated with the online gaming platform. The intellectual property mapping and learning system 104 may send the IP asset to class mapping 510 to the first computing device 502 along with a request for input regarding the IP asset to class mapping 510 . The request for input may relate to a query as to whether the classification of the IP asset is correct. In various implementations, when the mapping 510 of an IP asset to a class is incorrect, the request for input may request a different class to assign to the IP asset.

In certain implementations, the intellectual property mapping and learning system 104 may generate one or more user interfaces that may be displayed by the first computing device 502 , and may generate one or more user interfaces relating to the mapping 510 of IP assets to classifications. 1 at least one user interface element for capturing input from a user 504 . For example, the one or more user interfaces may indicate that at least one user interface element for capturing an input indicating that the mapping of IP assets to classes 510 should be modified, that the mapping of IP assets to classes 510 should not be modified. at least one user interface element for capturing input indicating the The first user 504 may provide feedback 512 of the mapping of IP assets to classification to the intellectual property mapping and learning system 104 via one or more user interfaces.

The intellectual property mapping and learning system 104 may analyze the feedback 512 of the mapping of IP assets to classes to determine whether the mapping 510 of IP assets to classes should be modified. Illustratively, the intellectual property mapping and learning system 104 analyzes the feedback 512 of the mapping of IP assets to classes such that the feedback 512 of the mapping of IP assets to classifications 512 is the mapping of IP assets to classes 510 . ) is correct, or whether IP assets should be classified according to different classifications. The intellectual property mapping and learning system 104 may utilize the feedback 512 of the mapping of IP assets to classifications to modify the framework of classifications. For example, the intellectual property mapping and learning system 104 may modify one or more criteria of a classification in the framework of classifications based on the feedback 512 of the mapping of IP asset to classification. In the illustrative example, the intellectual property mapping and learning system 104 selects one of the classifications, based on the feedback 512 of the mapping of IP assets to classifications indicating that the mappings 510 of IP assets to classifications should be modified. One or more criteria can be added or one or more criteria can be removed.

In additional illustrative examples, the intellectual property mapping and learning system 104 may modify a model for determining classifications of IP assets based on feedback 512 of the mapping of IP assets to classifications. The model may include a number of factors that may be used to determine classifications of IP assets and their respective weights. In certain implementations, the model may be generated using one or more machine learning techniques. In various implementations, the intellectual property mapping and learning system 104 is configured to remove one or more factors included in the model, add one or more factors to the model, modify weights of one or more factors included in the model. , or combinations thereof, may modify the model utilized to determine the classifications of IP assets.

In the illustrative example of FIG. 5 , the intellectual property mapping and learning system 104 may determine a mapping 514 of modified IP assets to classification. The modified IP asset to classification mapping 514 may indicate that the IP asset is associated with a different classification than the classification for the IP asset in the IP asset to classification mapping 510 . The intellectual property mapping and learning system 104 may determine a different classification for an IP asset based on the feedback 512 of the mapping of IP assets to classification. For example, in situations where the feedback 512 of the mapping of IP assets to classification indicates that the classification of IP assets should be modified to a different particular classification, the intellectual property mapping and learning system 104 may It may change to the classification shown in Feedback 512 of the mapping of IP assets to classification. In additional implementations, the intellectual property mapping and learning system 104 analyzes the input included in the feedback 512 of the mapping of IP asset to classification to revise the model that determines the classifications of IP assets, and then: For IP assets, a modified model can be implemented. The modified model may then generate a mapping 514 of modified IP assets to classification.

Additionally, the intellectual property mapping and learning system 104 may determine a mapping 516 of IP assets to products/services. Mapping 516 of IP asset to product/service may indicate that at least a portion of the IP asset covers at least a portion of the product and/or service. For example, mapping 516 of IP assets to products/services 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, mapping 516 of IP assets to products/services may indicate that the trade secret corresponds to a process of manufacturing a food product. The intellectual property mapping and learning system 104 provides a mapping 516 of IP assets to products/services to request input from a second user 508 regarding the mapping 516 of IP assets to products/services. 2 to the computing device 506 . The request for input may relate to a query as to whether the mapping between IP assets and products/services is correct. In various implementations, when the mapping 516 of an IP asset to a product/service is incorrect, the request for input may request a different product/service to assign to the IP asset.

The intellectual property mapping and learning system 104 may generate one or more user interfaces that may be displayed by the second computing device 506 , wherein the second user ( at least one user interface element for capturing input from 508). For example, the one or more user interfaces may include at least one user interface element for capturing input indicating that the mapping 516 of IP assets to products/services should be modified, mapping 516 of IP assets to products/services. at least one user interface element for capturing input indicating that it should not be modified, at least one user interface element for capturing input indicating a different product/service corresponding to the IP asset, or combinations thereof can The second user 508 may provide feedback 518 of the mapping of IP assets to products/services to the intellectual property mapping and learning system 104 via one or more user interfaces.

The intellectual property mapping and learning system 104 may analyze the feedback 518 of the mapping of IP assets to products/services to determine whether the mapping 516 of IP assets to products/services should be modified. Illustratively, the intellectual property mapping and learning system 104 may analyze the feedback 518 of the mapping of IP assets to products/services such that the feedback 518 of the mapping of IP assets to products/services is such that the feedback 518 of the mapping of IP assets to products/services is Determine whether the mapping 516 of /service indicates that it is correct, or whether the IP asset should be associated with another product and/or service. The intellectual property mapping and learning system 104 may utilize the feedback 518 of the mapping of IP assets to products/services to modify the framework of classifications. For example, the intellectual property mapping and learning system 104 may modify one or more criteria of a classification in the framework of classifications based on the feedback 518 of the mapping of IP assets to products/services. In the illustrative example, the intellectual property mapping and learning system 104 based on the feedback 518 of the mapping of IP assets to products/services indicating that the mapping 516 of IP assets to products/services should be modified. , one or more criteria may be added or one or more criteria removed from the classification of the framework of classifications.

Additionally, the intellectual property mapping and learning system 104 may modify a model for determining products and/or services corresponding to IP assets based on feedback 518 of the mapping of IP assets to products/services. there is. The model may include a number of factors and respective weights of factors that may be used to determine products and/or services covered by IP assets. The model may be created using one or more machine learning techniques. In certain implementations, the intellectual property mapping and learning system 104 is configured to remove one or more factors included in the model, add one or more factors to the model, modify weights of one or more factors included in the model. , or combinations thereof, may modify the model utilized to determine products and/or services covered by IP assets.

In the illustrative example of FIG. 5 , the intellectual property mapping and learning system 104 can determine a mapping 520 of modified IP assets to products/services. The modified IP asset-to-product/service mapping 520 is configured to associate the IP asset 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 mapping 516 . can indicate that The intellectual property mapping and learning system 104 may determine the different products and/or services covered by the IP assets based on the feedback 518 of the mapping of IP assets to products/services. For example, in situations where feedback 518 of mapping of IP assets to products/services indicates that products and/or services associated with IP assets should be modified to be associated with different products and/or services, intellectual property mapping and Learning system 104 may change products and/or services associated with IP assets into products and/or services indicated in feedback 518 of the mapping of IP assets to products/services. In additional implementations, the intellectual property mapping and learning system 104 analyzes the input included in the feedback 518 of the mapping of IP assets to products/services, such that products covered by IP assets and/or It is possible to modify the model for determining services, and then, for IP assets, to implement the modified model. The modified model may then create a mapping 520 of modified IP assets to products/services.

6 illustrates an example architecture 600 for providing intellectual property related services to customers using mappings between intellectual property and products/services relative to a taxonomy, in accordance with some implementations. 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 . Intellectual property assets may, in some scenarios, be associated with customers of the intellectual property service provider 602 . For example, customers of the intellectual property service provider 602 can provide one or more services in connection with intellectual property assets in which the customers of the intellectual property service provider 602 have titles. you can request In additional examples, customers of the intellectual property service provider 602 provide services with respect to intellectual property assets in which the intellectual property service provider 602 has titles held by organizations that are not customers of the intellectual property service provider 602 . may be requested to provide.

At least a portion of the operations performed by the 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-portable, semi-stationary, or stationary. Some examples of one or more computing devices 604 include tablet computing devices; smart phones and mobile communication devices; laptops, netbooks and other portable or semi-portable computers; desktop computing devices, terminal computing devices and other semi-stationary or stationary computing devices; dedicated register devices; wearable computing devices, or other body mounted computing devices; augmented reality devices; or other computing devices capable of transmitting 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 implemented in any number of ways. For example, in the example of a server, modules, other functional components, and data may be implemented on a single server, cluster of servers, server farm or data center, cloud-hosted computing service, cloud-hosted storage service, etc. However, other computer architectures may additionally or alternatively be used.

Additionally, although the figures illustrate that the components and data of one or more computing device 604 are present in a single location, such components and data may alternatively be present in different computing devices and in different locations in any manner. can be distributed across the Consequently, the functions performed by the one or more computing devices 604 may be implemented by one or more server computing devices with the various functionality described above distributed in various ways across different computing devices. Multiple computing devices 604 may be located together or separately, and may be organized as, for example, virtual servers, server banks, and/or server farms. The described functionality may be provided by servers of an intellectual property service provider, or may be provided by servers and/or services of a number of different organizations.

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

Computer readable medium 608 includes volatile and non-volatile memory and/or implemented in any tangible technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. or removable and non-removable media. Such computer readable medium 608 may include 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, may include, but is not limited to, storage area networks, cloud storage, or any other medium that may be used to store 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 tangible type of computer readable storage medium and/or, when referred to, a non-transitory computer readable medium comprising energy, carrier signals, etc. to the extent excluding media such as , electromagnetic waves, and signals themselves, may be a tangible, non-transitory medium.

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

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

Communication interface(s) 610 may include one or more interfaces and hardware components, such as to enable communication with various other devices over one or more network(s). For example, communication interface(s) 610 may include the Internet, cable networks, cellular networks, wireless networks (eg, Wi-Fi), and wired networks, as additionally listed elsewhere herein. rather than short-range communications, such as communication via one or more of Bluetooth®, Bluetooth® low energy, and the like.

One or more computing devices 604 may further include various input/output (I/O) devices 612 . I/O devices 612 may include speakers, microphones, cameras, displays (eg, liquid crystal displays, plasma displays, light emitting diode displays, organic light emitting diode (OLED) displays, electronic paper displays, or digital content on them). any other suitable type of display), and various user controls (eg, buttons, joystick, keyboard, keypad, etc.), a haptic output device, and the like. Additionally, in certain implementations, one or more computing devices 604 may include one or more sensors, such as an accelerometer, gyroscope, compass, proximity sensor, camera, microphone, and/or switch, GPS sensor, etc. .

In certain implementations, the intellectual property service provider 602 may create various mappings 620 that may be used to provide intellectual property related services to a customer of the intellectual property service provider 602 . For example, the mappings 620 may include one or more intellectual property asset-to-class mappings 622 . Individual intellectual property classification mappings 622 may indicate a relationship between a classification of intellectual property assets and a classification framework, such as a classification of technical taxonomy 220 . Additionally, the mappings 620 may include a mapping 624 of one or more intellectual property assets to products/services. Individual intellectual property asset to product/service mappings 624 may indicate a relationship between the intellectual property asset and the product and/or service. Additionally, the mappings 620 may include a mapping 626 of one or more products/services to economic data. The individual product/service to economic data mappings 626 may indicate specific economic data associated with at least one product or service. Illustratively, the mapping 626 of product/service to economic data may indicate a revenue for the product and/or service.

The intellectual property service provider 602 may receive requests for IP related services, and the intellectual property services system 104 may include mappings, data stored by an intellectual property knowledge data store, and/or additional information; For example, one or more classification frameworks may be utilized to provide services associated with the request. For example, intellectual property service provider 602 may receive requests from customers to obtain services related to intellectual property valuation, intellectual property strategy, and intellectual property risk. In certain implementations, the intellectual property service provider 602 may utilize IP valuation tools 614 to provide intellectual property valuation services to customers. IP valuation tools 614 are one or more user interfaces that can be used to analyze data related to intellectual property assets and provide information corresponding to the values of intellectual property assets of clients of the intellectual property service provider 602 . , one or more scripts, or one or more applications. Additionally, the intellectual property service provider 602 may utilize the IP strategy tools 616 to provide IP strategy services to customers. IP strategy tools 616 may include one or more user interfaces, one or more scripts, or one or more user interfaces that may be used to analyze data related to intellectual property assets and provide strategy-related information to clients of the intellectual property service provider 602 . It may include at least one of the applications. Additionally, the intellectual property service provider 602 may utilize IP exposure tools 618 to provide IP risk services to customers. IP exposure tools 618 may include one or more user interfaces, one or more scripts, or one or more user interfaces 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 . It may include at least one of the applications.

In an example implementation, customer 110 may send a request for IP related services 628 to intellectual property service provider 602 . The request for IP related services 628 may be electronically sent to the intellectual property service provider 602 . For example, customer 110 may send a communication, such as an email or message, including a request for IP related services 628 to intellectual property service provider 602 . In additional examples, customer 110 may access one or more user interfaces provided by intellectual property service provider 602 to create a request for IP related services 628 . The intellectual property service provider 602 may communicate one or more aspects of the request for IP related services 628 to the additional computing device 630 operated by the user 632 . User 632 may be a representative of intellectual property service provider 602 . In certain implementations, the request for IP related services 628 includes multiple aspects, such as requests for one or more IP valuation services, one or more IP strategy services, and/or one or more IP risk services. can do. Individual aspects of the request may be provided to a single representative of the intellectual property service provider 602 or to multiple representatives of the intellectual property service provider 602 . In certain example implementations, the request for IP-related services 628 includes a first request for valuations of a portfolio of intellectual property assets, a patent outlook analysis related to an electronic device manufactured by the customer 110 , a second request to the customer 110 , a third request to assess a risk related to invalidation of a plurality of intellectual property assets of the customer 110 , and a fourth request to an assessment of theft of trade secrets related to the customer 110 's trade secrets can In such a scenario, the intellectual property service provider 602 may assign, in some examples, to the user 632 to provide services related to the first request, the second request, the third request, and the fourth request. . In additional examples, the intellectual property service provider 602 assigns the user 632 to provide services related to one of the first request, the second request, the third request, or the fourth request, and the remaining request Tasks related to providing services associated with the IP service providers may be assigned to other representatives of the intellectual property service provider 602 .

In a situation where user 632 is assigned to perform services related to the valuation of intellectual property assets, user 632 may operate additional computing device 630 to access IP valuation tools 614 . there is. In various implementations, the intellectual property service provider 602 can obtain identifiers of the intellectual property assets whose valuations are being determined from the additional computing device 630 . Identifiers may include identifiers provided by intellectual property jurisdictions (eg, EPO, USPTO, JPO, etc.), such as application numbers, registration numbers, patent numbers, publication numbers, or combinations thereof. there is. Identifiers may also include titles of intellectual property assets. Additionally, identifiers may be alphanumeric strings generated by intellectual property service provider 602 that correspond to individual intellectual property assets. In addition, the intellectual property service provider 602 may obtain the type or context of the valuation to be determined. For example, the intellectual property service provider 602 may determine that a valuation should be determined for the sale of the customer 110's intellectual property assets, and that a valuation should be determined for the licensing of the customer's 110 intellectual property assets. , or information indicating that a valuation should be determined for the intellectual property assets of the customer 110 to be used as collateral for the loan.

After obtaining input from the additional computing device 630 via the IP valuation tools 614 , the intellectual property services system 104 provides the intellectual property associated with the valuation of the intellectual property assets requested by the customer 110 . To provide customer services 634 , mappings 620 , data stored by intellectual property knowledge data store 120 , models generated by intellectual property service provider 602 , machine learning algorithms, or You can access combinations of these. Depending on the type of valuation being performed and the amount of information the intellectual property service provider 602 has already obtained regarding the intellectual property assets of the customer 110 for which valuations are being performed, the intellectual property services system 104 may , mappings 622 of intellectual property asset to classification, intellectual property asset to product/service, to determine valuations of intellectual property assets of customer 110 that are the subject of a request for IP related services 628 . Mappings 624 of , or mappings 626 of product/service to economic data may be accessed.

In additional situations where user 632 is assigned to perform strategy-related services on customer 110's intellectual property assets, user 632 operates additional computing device 630 to implement IP strategy tools. 616 can be accessed. In such situations, the intellectual property services system 104 may obtain identifiers of the intellectual property assets from the additional computing device, as well as indications of the type of strategic related services to provide. The intellectual property services system 104 then performs mappings 620 , the intellectual property data stored by the knowledge data repository 120 , models generated by the intellectual property service provider 602 , machine learning algorithms, or combinations thereof.

In additional scenarios where user 632 is assigned to perform risk-related services on customer 110's intellectual property assets, user 632 operates additional computing device 630 to use IP risk tools ( 618) can be accessed. In such situations, the intellectual property services system 104 may obtain identifiers of the intellectual property assets from the additional computing device, as well as indications of the type of risk-related services to provide. Intellectual property services system 104 then performs mappings 620, intellectual property, to provide to customer 110 intellectual property customer services 634 related to IP risk services requested by customer 110 . data stored by the knowledge data repository 120 , models generated by the intellectual property service provider 602 , machine learning algorithms, or combinations thereof.

In an example implementation, the intellectual property service provider 602 may receive a request for IP-related services 628 from a customer 110 , and the request for IP-related services 628 is a request from the customer 110 . It may include a request for a valuation of the intellectual property asset 636 . The intellectual property service provider 602 can provide a request to the additional computing device 630 for a valuation of the intellectual property asset 636 . In response to the request for valuation services, user 632 may operate additional computing device 630 to access IP valuation tools 614 . IP valuation tools 614 provide one or more user interface elements that include one or more user interface elements for capturing information that can be used by intellectual property service provider 602 to determine a valuation for IP asset 636 . You can create interfaces. In various implementations, IP valuation tools 614 may include a user interface element for capturing the identifier of the IP asset 636 and the type of valuation to be determined. In certain illustrative examples, IP asset 636 may be a US patent, and additional computing device 630 includes an identifier of IP asset 636 , such as a patent number of IP asset 636 , and a type of valuation. An input may be obtained indicating that this corresponds to a sale of IP asset 636 .

Based on the input obtained from the additional computing device 630 , the intellectual property services system 104 can determine whether the mappings 620 include one or more mappings associated with the IP asset 636 . For example, the intellectual property services system 104 may have previously determined a classification associated with the IP asset 636 and generated a mapping 622 of the intellectual property asset to the classification for the IP asset 636 . In another example, the intellectual property services system 104 has previously determined a product and/or service corresponding to the IP asset 636 and a mapping 624 of the intellectual property asset to the product/service to the IP asset 636 . may have created In additional examples, the intellectual property services system 104 may have previously determined economic data corresponding to the IP asset 636 and generated a mapping 626 of product/service to economic data. In such circumstances, one or more of the mappings 620 associated with the IP asset 636 may be stored by the intellectual property knowledge data store 120 , and the intellectual property services system 104 to the IP asset 636 . The identifier of the IP asset 636 may be utilized to retrieve the corresponding mappings 620 . In situations where the mappings 620 do not include one or more mappings used to determine the valuation of the IP asset 636 , the intellectual property services system 104 may at least one of mapping ( 622 ) to classifications, mapping ( 624 ) of intellectual property assets to products/services to IP assets ( 636 ), or mapping ( 626 ) of products/services to economic data to IP assets ( 636 ). can create

Continuing the illustrative example above, the intellectual property services system 104 can determine a mapping 622 of an intellectual property asset to a classification for the IP asset 636 to determine the classification of the IP asset 636 . . The intellectual property services system 104 may then identify additional intellectual property assets that have the same classification as the IP asset 636 . The intellectual property services system 104 may determine the scope of the intellectual property asset 636 relative to the scope of other intellectual property assets included in the same classification of the IP asset 636 . The range of IP asset 636 relative to a range of additional IP assets in the same classification as IP asset 636 may be used to determine a valuation of IP asset 636 . In various implementations, the intellectual property services system 104 also obtains licensing data, damages, and/or settlement data for additional intellectual property assets included in the same classification as the IP asset 636 , The data may be used to determine a valuation for the IP asset 636 .

Additionally, the intellectual property services system 104 may provide a mapping 624 of intellectual property assets to products/services for IP assets 636 , representing products and/or services corresponding to IP assets 636 . can decide In some situations, the intellectual property services system 104 may identify multiple intellectual property asset to product/service mappings 624 associated with the IP asset 636 . In certain implementations, revenue associated with one or more of the products and/or services corresponding to IP asset 636 may be used to determine a valuation of IP asset 636 . Additionally, the intellectual property services system 104 may determine a mapping 626 of product/service to economic data to IP assets 636 . Mapping 626 of product/service to economic data for IP asset 636 may represent financial data associated with one or more products and/or services corresponding to IP asset 636 , including additional computing devices ( may be used by intellectual property services system 104 to determine a valuation for IP asset 636 in response to a request received from 630 .

In certain implementations, the intellectual property services system 104 generates one or more user interfaces that include one or more valuations for the IP asset 636 , the one or more user interfaces accessing the additional computing device 630 . can make it possible In certain implementations, the intellectual property services system 104 may provide a notification, such as 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. can

In addition, the intellectual property services system 104 may provide access to mappings 620 associated with the IP asset 636 . In such situations, user 632 may utilize additional computing device 630 to provide input regarding mappings 620 to use in determining one or more valuations for IP asset 636 . there is. In the illustrative example, the intellectual property services system 104 provides a mapping of a first intellectual property asset to class indicating that the IP asset 636 is associated with the first class, and the IP asset 626 with the second class. A mapping of a second intellectual property asset to class indicating that it is related may be provided. The additional computing device 630 can send an input to the intellectual property service provider 602 indicating a selection of a mapping of a first intellectual property asset to class or a mapping of a second intellectual property asset to class. The intellectual property services system 604 also provides multiple intellectual property asset to product/service mappings 624 associated with the IP asset 636 to the additional computing device 630 and to the IP asset 636 . An input may be obtained from the additional computing device 630 representing a mapping 624 of at least one intellectual property asset to a product/service to utilize in determining a valuation for it. Additionally, the intellectual property services system 104 may store multiple product/service to economic data mappings 626 corresponding to one or more products and/or services associated with the IP asset 636 to the additional computing device 630 . ) and may obtain input from the additional computing device 630 representing a mapping 626 of at least one product/service to economic data to utilize in determining a valuation for the IP asset 636 . .

7 illustrates an example framework 700 for generating linguistic structures for claims in patent documents, in accordance with some implementations. The framework 700 includes intellectual property assets 702 . In the illustrative example of FIG. 7 , the intellectual property asset 702 is a claim of a patent or patent application. At 704 , a syntactic and linguistic analysis 704 may be performed on the intellectual property asset 702 . In various implementations, the parsing and linguistic analysis 704 may be performed by the intellectual property services system 104 . In certain implementations, parsing and linguistic analysis 704 may include identifying words in intellectual property asset 702 and categorizing words in intellectual property asset. In illustrative examples, the syntactic and linguistic analysis 704 is a linguistic analysis 706 for the intellectual property asset 702, indicating parts of speech of at least some of the words included in the intellectual property asset 702 . can create For example, linguistic analysis 706 may indicate verbs, nouns, and adjectives of intellectual property asset 702 . In additional scenarios, linguistic analysis 706 may also indicate adverbs, conjunctions, prepositions, pronouns, stopwords, common words, proper words, or combinations thereof of intellectual property assets. there is.

Additionally, the framework 700 can include, at 708 , generating one or more linguistic structures for the intellectual property asset 702 . In certain examples, the intellectual property services system 104 may generate one or more linguistic constructs, at 708 . One or more linguistic constructs may indicate relationships between words in intellectual property asset 702 . In various implementations, multiple linguistic structures may be created for intellectual property asset 702 . In example implementations, a linguistic structure may be generated for a plurality of features of intellectual property asset 702 . For example, a linguistic structure may be created for actions taking place in a claim. In certain implementations, linguistic structures may be created for individual elements included in a claim of a patent or patent application.

In the illustrative example of FIG. 7 , a linguistic structure 710 may be created for a characteristic of the intellectual property asset 702 that begins with "displaying a portion of web page content...". Such features may include one element of the claims of intellectual property asset 702 . The linguistic 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 contains the word "display", which is a verb corresponding to the feature for which the linguistic structure 710 is being created. Nodes 714 and 716 correspond to verb-related nouns in root node 712 . Additionally, node 718 corresponds to the nouns and adjectives included in node 716 . Although the illustrative example of a linguistic structure 710 includes a single root node with three branch nodes, the linguistic structure 710 and other linguistic structures correspond to different words of a characteristic of the intellectual property asset 702 . Additional nodes may be included. Root node 712 may be included in a first level of linguistic structure 710 , second node 714 and third node 716 may be included in second level of linguistic structure 710 , A fourth node 718 may be included in the third level of the linguistic structure 710 .

8 illustrates an example framework 800 for determining a similarity metric between the linguistic structure of a product/service and the linguistic structure for a portion of a claim in a patent document, in accordance with some implementations. The framework 800 includes a linguistic structure 710 from FIG. 7 representing a portion of the claims of the intellectual property asset 702 . Additionally, at 802 , linguistic structures may be created for multiple 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 classifications for words included in product/service data 804 . Additionally, product/service data 804 may be analyzed to generate linguistic structures for various characteristics of products included in product/service data 804 . For example, a first linguistic structure 806 may be generated for at least one feature of a first product 808 , and a second linguistic structure 810 for at least one feature of a second product 812 . can be generated, and a third linguistic structure 814 can be generated for at least one characteristic of the third product 816 . Linguistic structures 806 , 810 , 814 may include tree structures having a root node and one or more branch nodes.

At 818 , the framework 800 can include determining similarity metrics 820 between the linguistic structure 710 and the linguistic structures 806 , 810 , 814 . In various implementations, similarity metrics 820 may indicate an amount of similarity between linguistic constructs. Similarity metrics 820 may be determined based on similarities between words included in linguistic structures 710 and words included in linguistic structures 806 , 810 , 814 . Additionally, the similarity metrics 820 are based on similarities between the arrangement of nodes included in the linguistic structure 710 and the respective arrangements of nodes included in the linguistic structures 806 , 810 , 814 . can be determined based on In particular, the similarity metrics 820 may include a first similarity metric 822 corresponding to an amount of similarity between the linguistic structure 710 and the first linguistic structure 806 . Additionally, the second similarity metric 824 may correspond to an amount of similarity between the linguistic structure 710 and the second linguistic structure 810 . Additionally, the similarity metrics 820 can include a third similarity metric 826 corresponding to an amount of similarity between the linguistic structure 710 and the third linguistic structure 814 . In various implementations, similarity metrics 820 can include numerical representations of similarity quantities between linguistic structures. In certain implementations, the similarity metrics 820 may be expressed as a percentage, specified along a numerical scale such as 1 to 10 or 1 to 100, or indicating amounts of similarity between linguistic structures.

The amounts of similarity between the linguistic structure 710 and the linguistic structures 806 , 810 , 814 are used to determine one or more of the products 808 , 812 , 816 that may correspond to the intellectual property asset 702 . can be used That is, in situations where the similarity metric 822 , 824 , 826 is greater than a threshold similarity amount, a mapping or other indicator of the correspondence between the intellectual property asset 702 and the individual product 806 , 810 , 814 will be generated. can The mappings may then be used to provide various services to organizations, such as IP valuation services, IP risk related services, and/or IP strategy related services.

9 illustrates an example framework 900 for the value of intellectual property features corresponding to one or more products, in accordance with some implementations. The framework 900 may include a first product 902 corresponding to the first IP characteristic 904 and a second product 906 corresponding to the second IP characteristic 908 . The first product 902 may be linked to the first IP feature 904 based on an amount of similarity between the linguistic structure of the product 902 and the linguistic structure of the first IP feature 904 . Additionally, the second product 906 is linked to the second IP characteristic 908 based on an amount of similarity between the linguistic structure of the second product 906 and the linguistic structure of the second IP characteristic 908 . can be In illustrative examples, the first IP characteristic 904 may be an element of a claim of a patent or patent application, and the second IP characteristic may be an element of a claim of another patent or patent application.

The framework 900 also includes an intellectual property services system 104 and an intellectual property knowledge data repository 120 . The intellectual property services system 104 may retrieve the financial data 910 from the intellectual property knowledge data store 120 . Financial data 910 includes information related to revenue generated by sales of various products and/or services, such as revenue information for a first product 902 and revenue information for a second product 906 . can do. The intellectual property services system 104 may also determine, at 914 , a value portion of the product and/or service that corresponds to an IP characteristic associated with the product and/or service. For example, the intellectual property services system 104 can determine the portion of the revenue amount of the first product 902 attributable to the first IP characteristic 904 . In various implementations, the amount of revenue to be attributed to the first IP characteristic 904 of the first product 902 may be based on a range measure of the IP characteristic 904 . To illustrate, the intellectual property services system 104 can determine the scope of the first IP characteristic 904 to additional intellectual property characteristics included in the same technical classification as the first IP characteristic 904 . Based on the range scale of the first IP characteristic 904 to a range of other IP characteristics, the intellectual property services system 104 calculates the amount of revenue for the first product 902 attributable to the first IP characteristic 904 . can decide In certain circumstances, the higher the value of the range measure of the first IP characteristic 904 , the higher the percentage of revenue of the first product 902 that will be attributed to the first IP characteristic 904 . Additionally, the lower the value of the range measure of the first IP characteristic 904 , the lower the percentage of revenue of the first product 902 attributable to the first IP characteristic 904 .

At 914 , the framework 900 includes determining a value 916 for the IP characteristic 914 . In certain implementations, the intellectual property services system 104 may configure the first IP based on the amount of revenue of the first product 902 and the portion of the revenue of the first product 902 attributable to the first IP characteristic 904 . A value 916 of the feature 904 may be determined. In various implementations, the intellectual property services system 104 multiplies the portion of the revenue of the first product 902 attributable to the first IP characteristic 904 by the revenue information of the first product 902 to the first IP characteristic. A value 916 of 904 may be determined.

10-14 illustrate example processes for analyzing intellectual property data. The processes described herein are illustrated as sets of blocks of a logic flow diagram that represent a series of operations, some or all of which may be implemented in hardware, software, or a combination thereof. In the context of software, blocks may represent computer-executable instructions stored on one or more computer-readable media that, when executed by one or more processors, program the processors to perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, etc. that perform particular functions or implement particular data types. The order in which the blocks are described should not be construed as limiting unless specifically stated. Any number of the described blocks may be combined in any order and/or in parallel to implement a process or alternative processes, and not all of the blocks need be executed. For discussion purposes, processes are described with reference to environments, architectures, and systems described in the examples herein, eg, such as those described in connection with FIGS. may be implemented in environments, architectures, and systems.

10 illustrates an example process 1000 for determining an intellectual property asset corresponding to a product or service, in accordance with some implementations.

At 1002 , process 1000 includes receiving information about products from one or more data sources. In certain implementations, the one or more data sources may include a publicly accessible data source. Publicly accessible data sources may include websites containing information that can be accessed by the general public without credentials issued by organizations that maintain and/or control access to the websites. . For example, publicly accessible data sources may include publicly available URLs without the unified resource locator (URL)(s) being first provided to individuals by the organizations themselves. In contrast, access to private data sources is more important than access to public data sources by restricting access to URLs associated with private data sources and/or by requiring specific credentials to access private data sources. It can be more tightly controlled. In some situations, an organization provides information that includes both publicly accessible information that may be accessible to the general public and personally accessible information that is accessible to customers, employees, and other individuals specifically authorized access to the organization. maintain and/or control the website; Publicly accessible data sources may include government websites, intellectual property databases maintained by intellectual property jurisdictions, websites of companies providing products and/or services, combinations thereof, and the like. there is. In such situations, obtaining data related to a product and/or service from a publicly accessible data source may include determining a plurality of keywords associated with the product and/or service and parsing the publicly accessible data source to It may include identifying data corresponding to at least one keyword among the plurality of keywords. Additionally, data corresponding to the at least one keyword may be extracted from a publicly accessible data source, and data corresponding to the at least one keyword may be stored in a data store of a service provider. In certain implementations, an intellectual property service provider may obtain information from public data sources using web crawlers or other applications that can identify websites and parse websites for specified information.

In additional implementations, the one or more data sources may include data sources of organizations that provide products and/or services for acquisition. The organization's data source may be a private data source accessible to the intellectual property service provider based at least in part upon the organization authorizing access by the intellectual property service provider to the organization's data source. The organization's data source may be accessible through a database management application, and the intellectual property service provider may utilize the database management application to query the organization's data source for at least one 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 data obtained from the organization's data sources in an additional organization's data repository, such as the intellectual property service provider's data repository. In various implementations, data related to a product and/or service stored in a data repository of an organization that provides a product and/or service for sale may include information relating to intellectual property assets and the relationships between the individual products and/or services. It may be stored so as to be identifiable. That is, an organization may have tracked intellectual property assets associated with particular products and/or services and stored data indicative of such relationships. In this way, the intellectual property service provider searches the organization's data repository and uses the data generated by the organization to retrieve intellectual property assets corresponding to products and/or services offered for sale by the organization. can be identified.

In further implementations, data related to products and/or services may be obtained using crowdsourcing techniques. Illustratively, an intellectual property service provider may cause a request for information regarding a product and/or service to be posted on a website. Individuals accessing the website may submit responses to the request through the website. In additional implementations, the intellectual property service provider may send requests to specific individuals to obtain information regarding products and/or services. Requests may be involved in one or more types of communication, such as email, mobile device message, instant messaging notification, phone call, combinations thereof, and the like. In various implementations, an intellectual property service provider may identify one or more groups of individuals to obtain information regarding one or more products and/or services. For example, an intellectual property service provider may identify individuals who may be considered experts and/or may have at least a threshold amount of knowledge regarding various products and/or services, and the intellectual property service provider may, Individuals in an individual group may be contacted when an intellectual property service provider wishes to obtain information about a product and/or service for which the group has knowledge. In such scenarios, at least a portion of the individuals contacted by the intellectual property service provider may provide information about one or more products and/or services to the intellectual property service provider in response to the request(s). In some cases, information obtained about a product and/or service may include one or more of the information about the product and/or service, such as one or more websites or publications that may contain information about the product and/or service. You can indicate the source. Additionally, the information regarding 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, and financial information of the product and/or service.

In various implementations, 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 have at least one user interface element for capturing information about products and/or services offered for sale by an organization and/or for capturing information about an organization's intellectual property assets. It is possible to create one or more user interfaces including The portals may be accessible by representatives of at least one of an intellectual property service provider or organization. In certain implementations, an intellectual property service provider can provide a portal that can be used to obtain information about an organization's trade secrets. In additional implementations, an intellectual property service provider can provide a portal that can be used to obtain information about an organization's patent documents. In further implementations, an intellectual property service provider may provide a portal that may be used to obtain information regarding products and/or services provided by an organization.

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

At 1006 , process 1000 includes determining one or more relationships between individual products of products and individual intellectual property assets of intellectual property assets. Relationships between individual products or services and individual intellectual property assets may be determined by identifying characteristics of the products or services and characteristics of the intellectual property assets. Characteristics of products or services may be determined by parsing descriptions of the products or services and identifying functional characteristics, physical characteristics, and/or technical characteristics of the products or services. In various implementations, videos and/or images associated with the products or services may be analyzed using one or more object recognition techniques to determine characteristics of the products or services. An intellectual property service provider may analyze characteristics of the intellectual property asset and characteristics of the products or services to determine similarities between features of the products or services and characteristics of the intellectual property asset. In some cases, the amount of similarity may be based on similarities of words associated with products and/or services and intellectual property assets. The amount of similarity may also be based on similarities in relationships between words related to features of products or services and relationships between words related to features of an 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 critical similarity. In an illustrative example, an intellectual property service provider may determine features of a claim in a patent document and features of a product or service. The intellectual property service provider may then identify a relationship between the claim and the product or service based on similarities between the features of the product or service and features of the claim in the patent document.

At 1008 , the process 1000 includes generating association data indicative of one or more relationships between individual products of products and individual intellectual property assets of intellectual property assets 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 at least one intellectual property asset mapped to the product or service. The framework may also represent individual intellectual property assets and at least one product or service associated with the intellectual property asset. In this way, the framework may be searchable based on the intellectual property asset or based on the product or service to identify related products or services and intellectual property assets.

Associated data may include mappings included in the framework representing intellectual property assets corresponding to products or services. In various implementations, an intellectual property service provider may receive a request to identify one or more products or services and one or more intellectual property assets with which it relates. In such situations, the intellectual property service provider parses the framework based on the identifiers of the intellectual property assets or the identifiers of the intellectual property assets to determine relationships between the products or services and the intellectual property assets. can do. The relationships between products or services and intellectual property assets may be utilized by an intellectual property service provider to provide a variety of intellectual property related services to the intellectual property service provider's customers. In certain implementations, intellectual property related services may include valuation services for intellectual property assets. In such scenarios, the intellectual property service provider may determine one or more metrics related to the intellectual property asset, wherein the one or more metrics are: at least one of a risk measure relating to, or a measure of the coverage of one or more portions of the intellectual property asset. The intellectual property service provider may also determine revenue earned for the product or service over a period of time, and then based at least in part on one or more metrics, of the product or service attributable to one or more portions of the intellectual property asset. You can determine the amount of revenue. After determining the amount of revenue from the product or service attributable to the intellectual property asset, the intellectual property service provider, can determine the value of an intellectual property asset based at least in part on

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

At block 1012 , process 1000 includes identifying, based at least in part on the associated data, an intellectual property asset corresponding to the product. For example, the system may be utilized to determine which products have relationships with intellectual property assets, using the associative data.

At 1014 , process 1000 includes generating a response to the request, the response indicating that the intellectual property asset is associated with the product. In some implementations, the user interface can also include one or more user interface elements for providing input regarding relationships between the intellectual property asset and the product or service. In certain implementations, the input may be obtained through a user interface or through an additional user interface that indicates one modification or modifications to the relationship between the intellectual property asset and the product or service.

11 illustrates an example process 1100 for determining an intellectual property asset corresponding to a product or service using a classification scheme, in accordance with some implementations.

At 1102 , process 1100 includes generating a classification system including classifications, wherein individual classifications of classifications correspond to a description group. In various implementations, individual classifications may be associated with one or more criteria. In example implementations, individual classifications may be associated with one or more words, and each classification may be associated with different groups of words. Additionally, individual classifications of a classification system may be associated with one or more physical characteristics, one or more technical characteristics, or combinations thereof. In certain implementations, one or more physical characteristics and/or one or more technical characteristics may each be associated with a set of words.

At 1104 , process 1100 includes receiving information regarding a product being provided for acquisition by an organization, the information comprising: an organization's data store; the organization's website; or input through a user interface.

At 1106 , process 1100 includes determining, based at least in part on the information, a first characteristic of the product. 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 certain implementations, information regarding 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. The one or more physical characteristics and/or the one or more technical characteristics of the product or service combine words of at least one technical characteristic and/or words of at least one physical characteristic with words contained in information obtained about the product or service. may be identified based, at least in part, on the comparison. In an example implementation, a physical characteristic of the 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. Furthermore, the technical characteristic of the product or service may be identified based at least in part on at least one word related to the technical characteristic included in the information about the product or service.

At 1108 , process 1100 includes determining that the product corresponds to one of the classifications based at least in part on the first characteristic corresponding to a reference characteristic associated with the classification. In various implementations, words associated with first features of a product or service may be compared to additional words associated with second features of a classification. In certain implementations, the classification may be assigned to the product or service based at least in part on that at least a threshold number of words in first features of the product or service correspond to multiple words in second features of the classification. In certain implementations, a model may be used to determine classifications for a product or services. The model may receive input comprising words corresponding to classifications and words corresponding to features of products or services, and determine probabilities that the products or services correspond to classifications of a classification scheme. In example implementations, when a probability that the product or service corresponds to a classification is greater than a threshold probability, the classification may be assigned to the product or service. In further implementations, a classification is assigned to a product or service when a probability that the product or service corresponds to a classification is a highest probability among a plurality of probabilities determined for the product or service using the model for the plurality of classifications. can be assigned

At 1110 , process 1100 includes identifying an intellectual property asset associated with the organization. An organization's intellectual property assets may be identified based on information obtained from the organization. In certain implementations, the intellectual property service provider may access the intellectual property asset in a document corresponding to the intellectual property asset, such as a trade secret document, a patent application, a utility patent, a design patent, a plant patent, a trademark application, or a copyright filing. information can be obtained. In additional implementations, an organization can provide identifiers of the organization's intellectual property assets, and the intellectual property service provider can obtain information about the intellectual property assets from one or more databases based on the identifiers.

At 1112 , process 1100 includes determining a second characteristic of the intellectual property asset. Characteristics of the intellectual property asset may be determined by analyzing information related to the intellectual property asset, such as documents related to the intellectual property asset. In certain implementations, the intellectual property asset can be a claim of a patent or patent application, and features of the intellectual property asset can be identified by analyzing words in the claim. Additionally, when the intellectual property asset is a patent or claim of a patent application, characteristics of the intellectual property asset can be identified by analyzing words of elements of the claim. Additionally, when the intellectual property asset is a trademark, characteristics of the trademark can be identified by analyzing words in the description of goods or services associated with the trademark. In various implementations, characteristics of an intellectual property asset may be identified by comparing words included in documents associated with the intellectual property asset to words associated with physical characteristics and/or technical characteristics. The intellectual property service provider may assign words to individual physical characteristics and individual technical characteristics. In certain implementations, the intellectual property service provider determines that when the at least one word associated with the intellectual property asset corresponds to at least one additional word related to a technical feature or at least one additional word related to a physical feature, the intellectual property asset is It may be determined to include technical features or physical features.

At 1114 , process 1100 includes determining that the intellectual property asset corresponds to the classification based at least in part on a second characteristic of the intellectual property asset corresponding to a reference characteristic associated with the classification. The intellectual property service provider determines that the one or more third characteristics of the intellectual property asset correspond to the at least one fourth characteristic associated with the classification by comparing the words of the one or more third characteristics to the words of the at least one fourth characteristic. can decide In various implementations, the intellectual property service provider is configured to: based, at least in part, on the at least a threshold number of words of the one or more third characteristics corresponding to words of the at least one fourth characteristic, the one or more third It may be determined that the feature corresponds to the at least one fourth feature.

In certain implementations, a model may be used to determine classifications for intellectual property assets. The model may receive input including words corresponding to classifications and words corresponding to characteristics of intellectual property assets, and determine probabilities that the intellectual property assets correspond to classifications of the classification scheme. In example implementations, when a probability that an intellectual property asset corresponds to a classification is greater than a threshold probability, the classification may be assigned to the intellectual property asset. In additional implementations, a classification is assigned to an intellectual property asset when a probability that the intellectual property asset corresponds to a classification is a highest probability among a plurality of probabilities determined for the intellectual property asset using the model for the plurality of classifications. can be assigned

In various implementations, the models used to determine classifications for intellectual property assets and models used to determine classifications for intellectual property assets may be modified. For example, an intellectual property 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 a different classification. In other situations, 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. Additionally, intellectual property service providers may request input regarding the classification of products or services. The input may indicate that the product or service should be classified according to a different classification. In other scenarios, the input may indicate that the product or service is classified correctly. The intellectual property service provider may then modify the model used to classify the product or service based on the input.

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

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 through sales of products or services. Financial data may be obtained from a variety of sources. For example, an intellectual property service provider may provide a portal that captures information regarding financial data of products and/or services. To illustrate, an intellectual property service provider may create one or more user interfaces that include one or more user interface elements for capturing one or more portions of financial data. In additional implementations, an intellectual property service provider may implement software tools for parsing an organization's data repository that provides a product or service for sale to identify portions of financial data corresponding to the product or service. there is. In further implementations, 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 illustrative examples, an intellectual property service provider provides a product or service for acquisition utilizing web crawlers and other website parsing tools to identify at least a portion of financial data corresponding to the product or service. Analyze information contained in websites including one or more organization websites and/or third party websites.

At block 1204 , process 1200 includes determining a classification of the product based at least in part on technical characteristics of the product. For example, an intellectual property service provider may determine a classification for a product or service by determining characteristics of the product or service and comparing the characteristics of the product or service to criteria for multiple classifications in 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 classification. Such intellectual property assets may include patents, which may include claims. Additionally, process 1200 may generally include identifying an organization's intellectual property assets. An organization's intellectual property assets may include one or more intellectual property assets that have legal rights enforceable by the organization. In various implementations, an intellectual property asset may be assigned to an organization. In additional implementations, an organization may have a license to an intellectual property asset. The intellectual property service provider may determine, based at least in part on information obtained from the organization, that the intellectual property assets correspond to the organization. For example, an organization may provide a list of intellectual property assets to an intellectual property service provider. The inventory may be stored in an organization's data store accessible to the intellectual property service provider, and the intellectual property service provider may parse the data store to obtain the inventory. In further implementations, an organization may provide an inventory of intellectual property assets to an intellectual property service provider via communication such as email or message. Additionally, an intellectual property services organization may provide a customer portal by which the organization may provide a listing of the organization's intellectual property assets. In certain implementations, an intellectual property service provider may analyze information available from public data sources, such as patent jurisdiction databases, to identify an organization's intellectual property assets. Illustratively, an intellectual property service provider has public access to identify intellectual property assets assigned to an organization, intellectual property assets to which the organization is an applicant, intellectual property assets having inventors associated with the organization, or combinations thereof. Any possible datastore can be parsed.

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

At block 1210 , process 1200 includes determining the scope of a patent claim. In some implementations, the intellectual property service provider determines the portion of revenue for a product or service that is attributable to the intellectual property asset to a range of other intellectual property assets, such as intellectual property assets in the same class as the intellectual property asset. You can determine the scope of your intellectual property assets.

At block 1212 , process 1200 includes determining, based at least in part on the scope of the patent claim, a portion of the revenue to be allocated to the patent claim. For example, to determine scope measures and/or portions of revenue of products and/or services corresponding to intellectual property assets, the intellectual property services system may utilize one or more linguistic analysis techniques and one or more machine learning techniques. can The intellectual property service provider may determine, based on the amount of features of the product or service covered by the intellectual property asset, the portion of revenue for the product or service attributable to the intellectual property asset. For example, if a product or service has multiple features, the portion of the number of features covered by the intellectual property asset relative to the total number of features may correspond to the portion of revenue for the product or service attributable to the intellectual property asset. In the illustrative example, the intellectual property asset may cover 2% of features of a product or service, and the intellectual property service provider may determine that 2% of the revenue of the product or service will be attributable to the intellectual property asset. In certain implementations, a ratio of features of a product or service covered by the intellectual property asset can serve as a starting point for determining the portion of revenue for the product or service that is attributable to the intellectual property asset. In various implementations, the intellectual property service provider may modify an initial portion of the amount of revenue for the product or service attributable to the intellectual property asset based on a number of discount factors, which will be discussed in greater detail below. In additional implementations, the intellectual property service provider may determine, based on the scope of the intellectual property asset, the portion of revenue for the product or service attributable to the intellectual property asset. In some implementations, the intellectual property service provider determines the portion of revenue for a product or service attributable to the intellectual property asset to a range of other intellectual property assets, such as intellectual property assets in the same class as the intellectual property asset. You can determine the scope of your intellectual property assets.

At block 1214 , process 1200 includes determining, based at least in part on a portion of the revenue allocated to the patent claim, a measure of the value of the patent claim. For example, a measure of value for an intellectual property asset may be determined by multiplying the return on the product or service by the portion of the revenue for the product or service attributable to the intellectual property asset. In various implementations, one or more deduction factors may also be used to determine a measure of value for the intellectual property asset. The deduction factors may be applied to at least one of a measure of value for the product or service attributable to the intellectual property asset or the amount of revenue for the product or service used to determine the portion of revenue. The one or more deduction factors may reduce an initial measure of the value of the intellectual property asset to a modified measure of the value of the intellectual property asset. In illustrative examples, the one or more deduction factors may be based, at least in part, on a first risk corresponding to invalidation of the intellectual property asset and a second risk corresponding to a probability of litigation relating to the intellectual property asset. In certain implementations, the intellectual property asset may include a patent claim, and the first risk may be based at least in part on historical events related to the patent claim. Additionally, in situations where the intellectual property asset comprises a patent claim, the first risk is at least in the examiner's metrics relative to the additional metrics of additional examiners included in the same examination room as the examiner associated with the particular claim. The metrics may be based in part on: the number of registration decision notifications generated over a period of time, the average number of intermediate events before generating a notification of a registration decision, the number of notifications of appeals submitted over a period of time, the number of notifications of appeals submitted over a period of time corresponds to at least one of the number of overturns of transport decisions over , or combinations thereof. Additionally, a second risk is a plurality of intellectual property assets having the same classification as the intellectual property asset for a second number of litigation cases arising against an additional plurality of intellectual property assets included in a different classification of the classification system. based at least in part on the number of first litigation cases arising against In some demonstrative examples where the intellectual property asset includes a patent claim, the deduction factor may be determined based, at least in part, on the number of additional patent claims assigned to the organization corresponding to the product or service. In the illustrative examples where the intellectual property asset includes a trademark, the deductible factor is the number of litigation cases involving trademark assets included in the same classification as the trademark asset, and the number of objections relating to trademark assets included in the same classification as the trademark asset. , or based at least in part on at least one of the examiner's metrics associated with the trademark asset with respect to the additional examiner's additional metrics associated with the additional trademark assets included in the classification.

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

In additional implementations, an intellectual property service provider may determine a product or service corresponding to an organization's intellectual property asset by determining an amount of similarity between the product or service and the intellectual property asset. In various implementations, the intellectual property service provider parses the intellectual property document associated with the intellectual property asset to determine individual first words of the intellectual property document, and parses the information related to the product or service to determine individual first words included in the information. Second words may be determined. The intellectual property service provider may determine a similarity metric between at least a portion of the respective first words and at least a portion of the respective second words. The intellectual property service provider may determine that the product or service corresponds to the intellectual property asset based at least in part on determining that the similarity metric is at least a threshold similarity metric. In further implementations, the intellectual property service provider analyzes the information about the product or service and the information about the intellectual property asset to obtain physical and/or technical characteristics of the product or service and physical and/or technical characteristics of the intellectual property asset. can decide An intellectual property service provider determines that an intellectual property asset is a product or You can decide to respond to the service.

In certain implementations, the intellectual property service provider, prior to analyzing the information in the product or service and the information in the intellectual property asset, determines that both the product or service and the intellectual property asset are associated with the same classification in the classification scheme, A property may determine similarities between an asset and a product or service. In various implementations, an intellectual property service provider may determine a classification for a product or service by determining characteristics of the product or service and comparing the characteristics of the product or service to criteria for multiple classifications in a classification scheme. Additionally, the intellectual property service provider may determine a classification for an intellectual property asset by determining characteristics of the intellectual property asset and comparing the characteristics of the intellectual property asset to criteria for multiple classifications of the classification system. In various implementations, the intellectual property service provider determines first similarity metrics indicative of amounts of similarity between the criteria of classifications and characteristics of the product or service, and determines between the characteristics of the intellectual property asset and the criteria of classifications. Second similarity metrics indicative of similarity amounts may be determined. The intellectual property service provider may then utilize the first similarity metrics to determine a classification for the product or service, and utilize the second similarity metrics to determine a classification for the intellectual property asset. The intellectual property service provider may be configured such that a first similarity metric and/or a second similarity metric for a particular classification exceeding the threshold similarity metric may indicate that a product or service and/or intellectual property asset corresponds to the classification; Based on the metrics, you can determine a classification for a product or service and a classification for intellectual property assets. In additional implementations, the intellectual property service provider determines a first similarity metric having a highest value among the first similarity metrics to determine that a classification associated with the highest valued first similarity metric corresponds to a product or service. can The intellectual property service provider may also determine a second similarity metric having a highest value among the second similarity metrics to determine that a classification associated with the second highest valued similarity metric corresponds to the intellectual property asset.

13 illustrates an example process 1300 for determining an intellectual property asset corresponding to a product or service using the linguistic structure of the intellectual property asset and the linguistic structure of the product or service, in accordance with some implementations.

At 1302 , process 1300 includes determining first parts of speech for first words included in first information associated with the product. In various implementations, natural language processing techniques may be used to determine individual words included in the first information and parts of speech associated with the individual words. In certain implementations, the intellectual property service provider can determine at least one of nouns, verbs, adjectives, adverbs, prepositions, conjunctions, or pronouns included in the first information. Additionally, the intellectual property service provider may determine relationships between words included in the first information. For example, an intellectual property service provider may identify words included in the same sentence. The intellectual property service provider may also identify words contained in the same paragraph. Additionally, the intellectual property service provider may identify one or more adjectives that modify individual nouns and one or more adverbs that modify individual verbs. Additionally, the intellectual property service provider may store data indicative of relationships between words. To illustrate, the intellectual property service provider may assign an identifier to individual words included in the first information and assign codes or classes to the individual words. In a specific example, the intellectual property service provider may assign a code indicating that the word is a noun to the word included in the first information, and further store an identifier of an adjective associated with the word in a table associated with the word. A table may also contain identifiers of words in the same sentence or element as the noun.

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

At block 1306 , the process 1300 includes determining a portion of the first words corresponding to a characteristic of the product. For example, a catalog of features may be associated with a product, and the intellectual property service provider may analyze first words in association with the features to determine which of the words correspond to at least one of the features associated with the product. can

At block 1308 , process 1300 includes determining, based at least in part on the first parts of speech, a first action to be performed with respect to the feature. For example, an intellectual property service provider may determine which of the words are verbs that act on a given characteristic. A verb may indicate an action to be performed in relation to a feature.

At 1310 , process 1300 includes generating, based at least in part on a first action, a first linguistic structure for the feature, the first linguistic structure comprising in the first action and the first information. indicates one or more first relationships between one or more first nouns. In certain examples, the linguistic structure may include a tree structure having a root node and one or more branch nodes. The root node may be at a first level of the tree structure, and one or more branch nodes may be included at subsequent levels of the tree structure. In a tree structure, each node that is a branch of another node is associated with an initial node. That is, the tree structure may include parent nodes and child nodes related to the parent nodes. In an illustrative example, a noun contained in a first node on a first level of the tree structure may be associated with a first adjective contained in a second node of the tree structure and a second adjective contained in a third node, wherein the second The node and the third node are child nodes of the first node and are included in the second level of the tree structure. In various implementations, a linguistic structure of an intellectual property asset may be created for an action related to the intellectual property asset at the root node, and words corresponding to the action are included in the branch nodes. In an illustrative example, the verb corresponding to the action may be included in a root node on a first level of the linguistic structure, and the nouns and adjectives associated with the verb are linguistically on the second and/or third levels of the linguistic structure. It can be included in branch nodes of the structure. In situations where the intellectual property asset is a patent claim, the intellectual property service provider may create a linguistic structure for the individual elements included in the patent claim.

At 1312 , process 1300 includes determining, based at least in part on the second parts of speech, a second action included in the claim. That is, the intellectual property service provider analyzes the words included in the second information and identifies at least one of nouns, verbs, adjectives, adverbs, prepositions, conjunctions, or pronouns included in the second information. can do. In certain implementations, the intellectual property service provider may utilize natural language processing techniques to determine individual words and individual parts of speech of words included in the second information.

At 1314 , process 1300 includes generating, based at least in part on a second action, a second linguistic structure for the claim, the second linguistic structure comprising: the second action and one included in the claim Indicate one or more second relationships between two or more second nouns. The second and/or additional linguistic structure generated based on the second information may have a tree structure having a root node and one or more branch nodes. The root node may be at a first level of the tree structure, and one or more branch nodes may be included at subsequent levels of the tree structure. In a tree structure, each node that is a branch of another node is associated with an initial node. That is, the tree structure may include parent nodes and child nodes related to the parent nodes. In an illustrative example, a noun contained in a first node on a first level of the tree structure may be associated with a first adjective contained in a second node of the tree structure and a second adjective contained in a third node, wherein the second The node and the third node are child nodes of the first node and are included in the second level of the tree structure. In various implementations, a linguistic structure of a product or service may be generated for an action performed in relation to the product or service at the root node, and additional words corresponding to the action are included in the branch nodes. In an illustrative example, the verb corresponding to the action may be included in a root node on a first level of the additional linguistic structure, and the nouns and adjectives associated with the verb are on the second and/or third level of the additional linguistic structure. It may be included in branch nodes of additional linguistic structures on the . In some implementations, an intellectual property service provider can create a linguistic structure for individual technical characteristics of a product or service, individual physical characteristics of a product or service, or both.

At 1316 , process 1300 includes determining a similarity metric between the first linguistic construct and the second linguistic construct. For example, one or more components of a first linguistic structure may be compared to one or more components of a second linguistic structure. When the components of the linguistic structures correspond to each other, the similarity metric may indicate a high level of similarity. When components do not correspond and/or differences exist between linguistic structures, the similarity metric may indicate a low level of similarity. A measure of similarity between the first linguistic construct and the second linguistic construct may be determined by comparing similarities in the construct of the first linguistic construct and the construct of the second linguistic construct. For example, the intellectual property service provider may determine the similarity measure based on the number of levels included in the first linguistic structure and the number of levels included in the second linguistic structure. The intellectual property service provider may also determine a similarity measure based on the number of nodes at each level of the first linguistic structure and the number of nodes at each level of the second linguistic structure. To illustrate, the intellectual property service provider may compare the number of nodes at the second level of the first linguistic structure to the number of nodes at the second level of the second linguistic structure.

The intellectual property service provider may also determine a similarity measure based on similarities between words included in the first linguistic structure and words included in the second linguistic structure. As an example, the intellectual property service provider may compare one or more words included in a root node of a first linguistic structure with one or more words included in a root node of a second linguistic structure. In these situations, the similarity measure is, whether the one or more words included in the root node of the first linguistic structure and the one or more words included in the root node of the second linguistic structure are identical, similar, synonyms, etc. can be based on Additionally, the intellectual property service provider may compare the words in the branching nodes of the first linguistic structure with the words in the branching nodes of the second linguistic structure to determine a similarity measure. In certain implementations, the intellectual property service provider may compare words included in distinct levels of a first linguistic structure to words included in distinct levels of a second linguistic structure.

At 1318 , process 1300 includes determining, based at least in part on the similarity metric, that the claim corresponds to a product. In some demonstrative examples, the intellectual property service provider may determine, prior to comparing the first linguistic structure and the second linguistic structure, that the product or service and the intellectual property asset are in the same classification of the taxonomy. Additionally, in various circumstances, an intellectual property service provider may create multiple linguistic constructs for a product or service and multiple linguistic constructs for an intellectual property asset. In such scenarios, the intellectual property service provider may compare one or more linguistic structures of the product or service to one or more linguistic structures of the intellectual property asset to determine a measure of similarity between the product or service and the intellectual property asset. . In further implementations, a similarity measure, such as a similarity metric, between the first linguistic structure and the second linguistic structure may be modified based on user input. For example, an intellectual property service provider may receive an input indicating that a product or service does not correspond to an intellectual property asset. In such situations, the intellectual property service provider may, based on the input, modify the similarity measure and/or modify the model used to generate the similarity measure. In additional implementations, 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 indicate that the additional product or service and the additional intellectual property asset correspond to each other. input can be received. Accordingly, the intellectual property service provider uses, based on the input, to generate additional similarity measures, or additional similarity measures, between one or more linguistic structures of a product or service and one or more linguistic structures of intellectual property assets. mode can be modified.

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

At 1402 , process 1400 includes receiving, from one or more data sources, first information regarding products being provided for acquisition. The information may include, for example, details associated with products and/or sources of products.

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

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 characteristic associated with the one of the products. Comparisons between individual intellectual property assets and individual products and/or services may be used by an intellectual property service provider to determine similarity metrics between individual intellectual property assets and individual products and/or services. . In situations where the similarity metric is greater than the threshold metric or has the highest value among multiple similarity metrics associated with a particular classification, the intellectual property service provider may determine that a relationship exists between the intellectual property asset and the product or service. In example implementations, an intellectual property service provider may generate linguistic constructs using natural language processing techniques to determine similarity metrics for individual intellectual property assets and individual products or services.

In various implementations, an intellectual property service provider may create a framework that represents relationships between intellectual property assets and products and/or services. In such scenarios, the intellectual property service provider may receive a request to determine a product or service corresponding to the intellectual property asset. The intellectual property service provider receives an identifier of the product or service, and then parses the framework using the identifier of the product or service to determine one or more intellectual property assets that the framework indicates as having a relationship with the product or service. can be identified. Additionally, the intellectual property service provider may receive a request comprising an identifier of the intellectual property asset. In such cases, the intellectual property service provider may use the identifier to parse the framework and identify one or more products or services that the framework indicates as having a relationship with the intellectual property asset.

At 1408 , process 1400 includes receiving a first request to determine a value of an intellectual property asset. In various implementations, the request may be provided via one or more tools provided by the intellectual property service provider. In various implementations, the intellectual property service provider may create one or more user interfaces through which requests for services may be made by customers of the intellectual property service provider and/or by representatives of the intellectual property service provider.

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

At block 1412 , process 1400 includes determining a portion of revenue attributable to the intellectual property asset. In illustrative examples, the intellectual property service provider may determine the scope of the intellectual property asset to additional intellectual property assets, such as a range of intellectual property assets included in the same technical category as the intellectual property asset. In such circumstances, the intellectual property service provider may determine, based at least in part on the scope of the intellectual property asset to the scope of the additional intellectual property assets, the portion of revenue for the product or service to be attributed to the intellectual property asset. . A higher relative coverage score of that intellectual property asset relative to additional intellectual property assets allows the intellectual property service provider to generate a return on a product or service attributable to the intellectual property asset in situations where the relative coverage of the intellectual property asset is lower. may result in more allocation of the revenue of a product or service to intellectual property assets compared to the amount of revenue.

At 1414 , process 1400 includes determining, based at least in part on the portion of the revenue, a measure of the value of the intellectual property asset. For example, the portion of revenue attributable to intellectual property may be, in examples, an asset's revenues, in addition to those of itself and/or other products attributable to characteristics of the asset, such as scope, coverage, and/or exposure factors. It can be used as a factor for determining the overall value.

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

At 1418 , process 1400 includes determining at least one of a first exposure value or a second exposure value based at least in part on receiving the second request. The exposure value associated with the intellectual property asset may be based on a probability of a litigation event occurring in relation to the intellectual property asset. In a further implementation, an exposure value associated with the intellectual property asset may correspond to a probability that the scope of the intellectual property asset may be reduced. In further implementations, an amount of exposure associated with the intellectual property asset may correspond to a probability that the intellectual property asset may be fully or partially invalidated. In illustrative examples, the higher the amount of exposure associated with an intellectual property asset, the higher the deduction applied to the portion of the revenue of a product or service attributable to the intellectual property asset. In situations where the intellectual property asset is a trade secret, the intellectual property service provider may determine, based on the probability of theft of the trade secret, a deduction to apply to the portion of the revenue of the product or service attributable to the intellectual property asset.

At 1420 , process 1400 includes displaying, via one or more user interfaces, an indicator of the value measure of the product, and at least one of a first exposure value or a second exposure value. For example, a measure of the value of an intellectual property asset may be calculated using the revenue from the product or service received by one or more organizations through sales of the product or service over a period of time, and the portion of the revenue from the product or service attributable to the intellectual property asset. can be decided. In certain implementations, the value measure may be updated. For example, when the intellectual property service provider obtains updated revenue information for a product or service, the intellectual property service provider may update a value measure for the intellectual property asset based on the updated revenue. Additionally, intellectual property service providers may obtain information that may be used to update deductions that apply to the portion of revenue from products or services attributable to intellectual property assets, and the intellectual property service system may determine which modifications to apply Based on the deductions made, the value measure can be updated correspondingly. In certain implementations, the intellectual property service provider may obtain feedback indicative of the accuracy of the value measure and revise the value measure based on the feedback.

In various implementations, the measure of value for the intellectual property asset may be based on a type of valuation for the intellectual property asset. For example, a first measure of value may be determined when the intellectual property asset is valued as part of a sale of the intellectual property asset, and a second measure of value may be determined when the intellectual property asset is valued as collateral for a loan. there is. In other examples, the third measure of value may be determined when the intellectual property asset is valued as part of a sale or merger of an organization that has legal rights to enforce the intellectual property asset.

In certain implementations, additional services may be provided by the intellectual property service provider. For example, an intellectual property service provider may receive a request to identify multiple intellectual property assets of an organization that are associated with a particular technology group. In other examples, the intellectual property service provider may receive requests to determine one or more risks corresponding to the intellectual property service provider. In additional examples, an intellectual property service provider may receive a request to identify one or more organizations that have intellectual property in a particular technology group or in a particular classification of a system of classifications. In illustrative examples, an intellectual property service provider may utilize a framework that indicates relationships between intellectual property assets and products or services to provide a response to requests. In various situations, the intellectual property service provider will utilize identifiers of intellectual property assets, identifiers of organizations, identifiers of products or services, to parse the framework and utilize to provide responses to requests for services. , identifiers of technical groups, or combinations thereof. In certain example scenarios, the various identifiers may include alphanumeric strings that include a sequence of characters. In additional implementations, requests for services may include keywords that the intellectual property service provider may utilize to parse the framework and generate responses to requests for services.

items

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

2. The method of item 1, wherein the data sources comprise a publicly accessible data source, the method comprising: determining a keyword associated with the product; identifying, based at least in part on a publicly accessible data source, data corresponding to the keyword; and extracting data corresponding to the keyword from a publicly accessible data source.

3. The method of item 1 or item 2, wherein the data sources comprise a data store associated with a first organization that provides a product for acquisition, the method comprising: determining, by the second organization, keywords associated with the product; identifying, by the second organization and from the data repository of the first organization, data corresponding to the keyword; and extracting, by the second organization, data corresponding to the keyword.

4. The method of any one of item 1, item 2, or item 3 further comprising: utilizing the data repository to identify data indicative of a relationship between the intellectual property asset and the product, wherein the associated data is generated The step of generating includes generating the association data based at least in part on the data indicative of a relationship between the intellectual property asset and the product.

5. The method of any one of item 1, item 2, item 3, or item 4, wherein the request comprises a first request, and wherein the method comprises: wherein the second request for information about the product is accessible to the computing devices. causing at least one of being posted on a website or transmitted to computing devices; and in response to the second request, receiving data indicative of at least one of a source or information of the information.

6. The method of any one of item 1, item 2, item 3, item 4, or item 5 generates a user interface comprising a user interface element configured to receive an input representing information relating to an intellectual property asset. to do; and utilizing the user interface element to receive an input, wherein generating the associated data includes generating the associated data based at least in part on the input.

7. The method of any one of item 1, item 2, item 3, item 4, item 5, or item 6 includes determining a metric associated with the intellectual property asset, the metric comprising: a range of at least a portion of the intellectual property asset comprising at least one of a measure, an exposure measure associated with at least a portion of the intellectual property asset, or a coverage measure of at least a portion of the intellectual property asset; determining a revenue associated with the product over a period of time; and determining, based at least in part on the metric, an amount of revenue attributable to at least a portion of the intellectual property asset.

8. The system comprises: one or more processors; and one or more computer-readable media storing instructions executable by the one or more processors, which, when executed by the one or more processors, cause the one or more processors to cause information regarding at least one of products or services. receiving, at least a portion of the information including economic data, determining a relationship between one of the products or services and the intellectual property asset, and the relationship between the product or service and the intellectual property asset; generate association data representing

9. The system of item 8, wherein the information about the at least one of the products or services comprises a description of the at least one of the products or services, and wherein the actions are based at least in part on the at least one of the products or services. further comprising determining a characteristic for

10. The system of item 8 or item 9, wherein the feature comprises the first feature, and wherein the operations are performed 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 a product or service. based on identifying information about the intellectual property asset; and determining a second characteristic of the intellectual property asset based at least in part on the information about the intellectual property asset, wherein generating the associated data comprises: at least in part the first characteristic and the second characteristic. includes creating

11. The system of any one of items 8, 9, or 10, wherein the intellectual property asset comprises a patent document, and the operations include receiving a description of at least one of the product or service, wherein the description is the product or containing words related to at least one of the services; and determining that at least a portion of the words are included in a claim of the patent document, wherein the associated data is based at least in part on inclusion of at least a portion of the words in the claim, wherein the claim corresponds to at least one of the product or service. indicate that

12. The system of any one of items 8, 9, 10, or 11, wherein the operations include: a user interface comprising one or more user interface elements configured to capture information regarding a plurality of intellectual property assets; The method further comprises generating, the one or more user interface elements comprising: a first element configured to receive first information associated with trade secret documents; a second element configured to receive second information associated with the trademark documents; or a third element configured to receive third information associated with the copyright documents.

13. The system of any one of item 8, item 9, item 10, item 11, or item 12, wherein the operations include: a request for information regarding at least one of a product or service is performed on a website accessible to computing devices. at least one of causing it to be posted to, or transmitted to, computing devices; and in response to the request, receiving data indicative of at least one of a source or information of the information.

14. The system of any one of item 8, item 8, item 10, item 11, item 12, or item 13, wherein the actions are based, at least in part, on economic data, the product or service obtained over a period of time. determining an amount of revenue associated with at least one of; determining the portion of the amount of revenue attributable to intellectual property assets; and determining the value of the intellectual property asset based at least in part on the portion of the amount of revenue.

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

16. The method of item 15, wherein the request for information regarding at least one of the product or service is made to at least one of causing the computing devices to be posted on an accessible website or transmitted to the computing devices. to do; and in response to the request, receiving data indicative of at least one of a source or information of the information.

17. The method of item 15 or item 16, further comprising: receiving input data indicating that at least one of the products or services does not correspond to at least one of the intellectual property assets; and causing the associated data to indicate that at least one of the products or services does not correspond to at least one of the intellectual property assets.

18. The method of any one of items 15, 16, or 17, wherein the intellectual property assets include patent documents and trademark documents, the method comprising: receiving a description of the product or service; determine a first relationship between the patent document and the product or service based at least in part on that the first number of words included in the claims of the patent document correspond to the second number of words included in the description of the product or service to do; between the trademark document and the product or service based at least in part on that the third number of words included in the description of the goods and services in the trademark document correspond to the second number of words included in the description of the product or service; The method further comprises determining a second relationship, wherein the association data includes a first association between a claim in the patent document and the product or service, and a second association between the trademark document and the product or service.

19. The method of any one of item 15, item 16, item 17, or item 18, wherein the economic data comprises revenue of a product or service over a period of time, the method comprising: a first scope of a claim in the patent document; determining, based at least in part on the measure, a first portion of revenue attributable to claims in the patent document; and determining, based at least in part on a second scale measure of a description of goods and services included in the trademark document, a second portion of revenue attributable to the trademark document.

20. The method of any one of items 15, 16, 17, 18, or 19, wherein the intellectual property assets include trade secrets and trademark documents, the method comprising: receiving a description of the product or service ; determining a relationship between the trade secret document and the product or service based at least in part on that the first number of words included in the trade secret document correspond to a second number of words included in a description of the product or service and wherein the relationships include a relationship between the trade secret document and the product or service.

21. The method includes generating a classification system comprising classifications, wherein individual classifications of the classifications correspond to a description group; receiving information regarding a product offered for acquisition by the organization, wherein the information is obtained from at least one of the organization's data repository, the organization's website, or input through a user interface; determining, based at least in part on the information, a first characteristic of the product; determining that the product corresponds to one of the classifications based at least in part on the first characteristic corresponding to a reference characteristic associated with the classification; identifying intellectual property assets associated with the 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 characteristic of the intellectual property asset corresponding to the reference characteristic associated with the classification.

22. The method of item 21 further comprising: determining first words to associate with a classification; determining second words included in the information; and determining that at least a threshold number of words of the second words are included in the first words, wherein determining that the product corresponds to the classification comprises: at least a threshold number of words of the second words determining, based at least in part on inclusion in the first words, that the product corresponds to the classification.

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

24. The method of any one of items 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 determining that the product corresponds to the classification. The act includes determining that the product corresponds to the classification based at least in part on which at least one of the first word or the second word is associated with the classification.

25. The method of any one of items 21, 22, 23, or 24 comprises: 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 the association of the at least one of the first word or the second word with the intellectual property asset.

26. The method of any one of item 21, item 22, item 23, item 24, or item 25 generates a first model configured to determine a first probability that individual products of products correspond to respective classifications of classifications. to do; and generating a second model configured to determine a second probability that individual intellectual property assets of the intellectual property assets will correspond to respective classifications of the classifications.

27. The method of any of items 21, 22, 23, 24, 25, or 26 comprises: sending a request for feedback related to a classification scheme; receiving input data indicating that the product does not correspond to a classification; and training the first model based at least in part on the input data.

28. The method of any one of items 21, 22, 23, 24, 25, 26, or 27 comprises: sending a request for feedback related to a classification scheme; receiving input data indicating that the intellectual property asset does not correspond to a classification; and training the second model based at least in part on the input data.

29. The system comprises: one or more processors; and one or more computer-readable media storing instructions executable by the one or more processors, which, when executed by the one or more processors, cause the one or more processors to cause at least one classification of products or services. generating a model configured to determine, receiving information regarding at least one of a product or service provided by an organization, identifying a word associated with at least one of the product or service included in the information, at least in the word determining, based in part on and utilizing the model, a probability that at least one of the products or services corresponds to one of the classifications, determining, based at least in part on the probability, that at least one of the products or services corresponds to a classification; identifying the intellectual property asset, determining a characteristic of the intellectual property asset, and utilizing the model to determine, based at least in part on the characteristic corresponding to a word, that the intellectual property asset corresponds to a classification to perform actions that include

30. The system of item 29, wherein the probability comprises a first probability, the classification comprises a first classification, and wherein the actions are based at least in part on a word and utilizing the model, wherein the at least one of the product or service is classified into the classifications. further comprising: determining a second probability corresponding to the second classification, wherein the first probability is greater than the second probability; Determining that the at least one of the products or services corresponds to the first classification includes determining that the at least one of the products or services corresponds to the first classification based at least in part on the first probability being greater than the second probability. do.

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

32. The system of any one of items 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 a second number of words in the classification. do.

33. The system of any one of item 29, item 30, item 31, or item 32, wherein the actions are based, at least in part, on words included in the information corresponding to words included in the library of features: further comprising determining a characteristic of , wherein the classification is associated with the characteristics, and the probability is based at least in part on inclusion of the characteristic of the product in the characteristics of the classification.

34. The system of any one of item 29, item 30, item 31, item 32, or item 33, wherein the word comprises a first word, the classification is associated with the words, and the actions are: and determining that the words include a second word included in the information associated with the one, wherein the probability is based at least in part on inclusion of the second word in the words.

35. The system of any one of items 29, 30, 31, 32, or 33, the actions further comprising: determining a proximity associated with the first word and the second word, wherein the proximity comprises: the number of intervening words between the first word and the second word; the second word is in the same sentence as the first word; or the second word is in a different sentence than the first word, and the probability is based at least in part on proximity.

36. The method includes generating a model configured to determine classifications of individual intellectual property assets of the intellectual property assets; receiving intellectual property assets; determining the words contained in the intellectual property asset; determining, based at least in part on the word and utilizing the model, a probability that at least a portion of the intellectual property asset corresponds to one of the classifications; determining, based at least in part on the probability, a probability that at least a portion of the intellectual property asset corresponds to a classification; receiving information associated with at least one of a product or service; identifying characteristics of the information; and determining, utilizing the model, that at least one of the product or service corresponds to a classification based, at least in part, on the feature corresponding to a word.

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

38. The method of item 36 or item 37, wherein the model comprises a first model, the probability comprises a first probability, and the method generates a second model configured to identify products corresponding to the intellectual property assets. to do; identifying products included in the classification; determining, based at least in part on the second model, a second probability that the product corresponds to the intellectual property asset; and determining, based at least in part on the second probability, that the product corresponds to the intellectual property asset.

39. The method of any one of items 36, 37, or 38, wherein the intellectual property asset comprises a trademark document, the method comprising: identifying a description of goods and services in the trademark document; determining words included in descriptions of goods and services; determining that the first number of words included in the description of goods and services corresponds to a second number of words in the classification, wherein the probability is: based at least in part on the words corresponding to the second number of words of the classification.

40. The method of any one of items 36, 37, 38, or 39, further comprising: receiving input data indicating that at least a portion of the intellectual property asset is not associated with a classification; and training the model based at least in part on the input data.

41. The method includes: determining first parts of speech for first words included in first information associated with a product; determining second parts of speech for second words included in second information corresponding to the claims of the patent document; determining a portion of the first words corresponding to a characteristic of the product; determining, based at least in part on the first parts of speech, a first action to be performed with respect to the feature; generating, based at least in part on the first action, a first linguistic structure for the feature, the first linguistic structure comprising: one or more first linguistic constructs between the first action and one or more first nouns included in the first information. 1 indicates a relationship ―; determining, based at least in part on the second parts of speech, a second action included in the claim; generating, based at least in part on the second action, a second linguistic structure for the claim, the second linguistic structure comprising: one or more second relationships between the second action and one or more second nouns included in the claim indicates ―; determining a similarity metric between the first linguistic structure and the second linguistic structure; and determining, based at least in part on the similarity metric, that the claim corresponds to a product.

42. The method of item 41, wherein the first linguistic structure comprises a first tree structure having a first level comprising a first node corresponding to the first action, and a second level comprising a second node; ; The second linguistic structure includes a second tree structure having a third level including a third node corresponding to the second action, and a fourth level including a fourth node.

43. The method of item 41 or item 42 further comprises: determining a first amount of similarity between the first action and the second action; determining a second amount of similarity between the first level, the second level, the third level, and the fourth level; and determining a third similarity amount between the first node, the second node, the third node, and the fourth node, wherein the similarity metric includes: the first similarity amount, the second similarity amount, and the third similarity amount is determined based at least in part on the amount.

44. The method of any one of items 41, 42, or 43 further comprising determining an amount of similarity between a first word of a second node and a second word of a fifth node associated with the second node and the similarity metric is determined based at least in part on the similarity amount.

45. The method of any one of item 41, item 42, item 43, or item 44, wherein the claim comprises the elements, the action corresponds to one of the elements, and wherein the method is based at least in part on a similarity metric. and determining that the element corresponds to the feature.

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

47. The system comprises: one or more processors; and one or more computer readable media storing instructions executable by the one or more processors, which, when executed by the one or more processors, cause the one or more processors to receive information including claims in a patent document. thing; determining the words included in the claims; determining parts of speech for words; determining, based at least in part on the parts-of-speech, an action included in the claim, wherein the action is associated with a verb, wherein the part-of-speech includes a noun corresponding to the verb; and generating a linguistic structure for the claim, wherein the linguistic structure indicates one or more relationships between the verb and one or more additional words included in the claim.

48. The system of item 47, wherein the action comprises the first action, the verb comprises the first verb, the noun comprises the first noun, and the one or more additional words comprise the one or more first additional words. wherein the linguistic structure includes a first linguistic structure, the one or more relationships include a first one or more relationships, and wherein the actions determine, based at least in part on the parts-of-speech, a second action included in the claim. that the second action is associated with the second verb and the second noun included in the claim; and generating a second linguistic structure for the claim, wherein the second linguistic structure indicates one or more second relationships between the second verb and one or more second additional words included in the claim. do.

49. The system of item 47 or item 48, wherein the information comprises the first information, the action comprises the first action, the actions comprising: receiving second information corresponding to at least one of the product or service; 2 the information includes a second action performed in relation to a characteristic of at least one of the product or service; generating a third linguistic construct for at least one of the product or service, wherein the third linguistic construct establishes one or more third relationships between the second action and one or more third additional words included in the second information. marked ―; and determining, based at least in part on the first linguistic structure, the second linguistic structure, and the third linguistic structure, a similarity metric between the claim and the at least one of the product or service.

50. The system of any one of items 47, 48, or 49, wherein the actions are to determine that at least one of the product or service is not associated with a claim based at least in part on the similarity metric being less than a threshold similarity metric. to do; receiving input data indicating that at least one of the product or service corresponds to a claim; and increasing the value of the similarity metric based at least in part on the input data.

51. The system of any one of item 47, item 48, item 49, or item 50, wherein the actions are based at least in part that the similarity metric is at least a threshold similarity metric, wherein at least one of the product or service corresponds to a claim. deciding to do; receiving input data indicating that at least one of the product or service is not associated with a claim; and reducing the value of the similarity metric based at least in part on the input data.

52. The system of any one of item 47, item 48, item 49, item 50, or item 51, wherein the linguistic structure comprises a first linguistic structure, and wherein the actions include the first linguistic structure and the second language. performing a comparison between the linguistic constructs, wherein the second linguistic construct is associated with at least one of a product or 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 one of items 47, 48, 49, 50, 51, or 52, the actions comprising: determining a classification for a claim; determining that at least one of the products or services is associated with a classification; and determining that the claim relates to the at least one of the products or services based at least in part on the at least one of the products or services being associated with the classification.

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

55. The method includes: receiving information corresponding to at least one of a product or a service; determining words included in the information; determining parts of speech for words; determining portions of individual words corresponding to features of at least one of a product or service; determining, based at least in part on the parts-of-speech, an action to be performed with respect to the feature; and generating, based at least in part on the parts-of-speech, a linguistic structure for the at least one of the product or service, the linguistic structure indicating one or more relationships between the action and the words.

56. The method of item 55, wherein the linguistic structure comprises a tree structure having levels, wherein individual levels of the levels have one or more nodes.

57. The method of item 55 or item 56, wherein the first of the levels comprises a first node, the first of the one or more nodes corresponds to a verb of words, the verb corresponds to an action, and the levels The second level includes a second node among the one or more nodes, and the second node indicates a noun corresponding to the verb.

58. The method of any one of items 55, 56, or 57, wherein the noun comprises a first noun, the second node indicates an adjective corresponding to the first noun, and wherein the second level is in the verb and a third node indicating a corresponding second noun, wherein a third of the levels includes a fourth node indicating a third noun associated with the first noun.

59. The method of any one of item 55, item 56, item 57, or item 58, wherein the linguistic structure comprises a first linguistic structure, the one or more relations comprises one or more second relations, and the verb is A method comprising a first verb, the method comprising: generating a second linguistic structure for a claim included in the patent document; performing a comparison between the first linguistic structure and the second linguistic structure; 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 one of item 55, item 56, item 57, item 58, or item 59, wherein the linguistic structure comprises: a first level having a first node and a second level having a second level having a second node 1 tree structure, wherein the linguistic structure comprises a first linguistic structure, the method comprising: generating a second linguistic structure for the intellectual property asset, the second linguistic structure having a third node comprising a second tree structure having a fourth level having a third level and a fourth node; performing a first comparison between the first word represented by the first node and the second word represented by the third node; performing a second comparison between the first number of nodes included in the second level and the second number of nodes included in the fourth level; and determining, based at least in part on the first comparison and the second comparison, that at least one of the product or service corresponds to an intellectual property asset.

61. A method comprising: receiving information indicative of revenue associated with a product; determining a classification of the product based at least in part on the technical characteristics of the product; identifying a patent claim corresponding to the product based at least in part on which the patent claim is associated with a classification; identifying words included in a patent claim; determining the scope of the patent claims; determining, based at least in part on the scope of the patent claims, a portion of the revenue to allocate to the patent claims; and determining, based at least in part on the portion of the revenue allocated to the patent claim, a measure of the value of the patent claim.

62. The method of item 61 further comprises applying a deduction factor to the value measure, the deduction factor comprising: a first exposure value corresponding to invalidation of the patent claim; and a second exposure value corresponding at least in part to a probability of litigation with respect to the patent claim.

63. The method of item 61 or item 62, wherein the classification comprises a first classification, and wherein the method relates to patents having a first classification for a second number of litigation cases occurring with respect to patents having a second classification determining a second exposure value based at least in part on the first number of litigation events occurring.

64. The method of any one of items 61, 62, or 63 further comprising determining the first exposure value based at least in part on a historical filing event related to the patent claim.

65. The method of any one of item 61, item 62, item 63, or item 64, wherein the method is associated with the first examiner for a second metric of a second examiner included in an examination room associated with the first examiner associated with the claim. determining, based at least in part on the first metric, a first exposure value, wherein at least one of the first metric or the second metric includes: a number of registration decision notifications generated over a period of time; at least one of the average number of interim cases prior to generating the notice, the number of notices of appeals filed over the period of time, or the number of overturns of appeals decisions over the period of time.

66. The method of any one of item 61, item 62, item 63, item 64, or item 65, wherein the patent claim is assigned to an organization, and revenues from the product are provided to the organization; Determining the deduction factor includes determining the deduction 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. The system may include one or more processors; and one or more computer-readable media storing instructions executable by the one or more processors, which, when executed by the one or more processors, cause the one or more processors to cause financial data corresponding to at least one of a product or service. receiving: the financial data indicative of revenue associated with at least one of a product or service; identifying an organization's intellectual property assets, which intellectual property assets include patent assets, trademark assets, copyright assets, or trade secret assets; determining that at least one of the products or services corresponds to an intellectual property asset; determining the portion of revenue attributable to intellectual property assets; and determining, based at least in part on the portion of revenue attributable to the intellectual property asset, a measure of a value of the intellectual property asset.

68. The system of item 67, wherein determining that at least one of the products or services corresponds to an intellectual property asset comprises: receiving input data indicating that at least one of the products or services corresponds to an intellectual property asset; or identifying information indicating that at least one of the products or services corresponds to an intellectual property asset, wherein the information is stored in the organization's data repository or accessible through the organization's website. made up of at least one of

69. The system of item 67 or item 68, wherein receiving the financial data comprises: generating a user interface comprising one or more user interface elements configured to receive the financial data; utilizing the organization's data repository to identify portions of financial data corresponding to at least one of a product or service; or utilizing information from one or more websites to identify a portion of the financial data corresponding to at least one of the product or service.

70. The system of any one of items 67, 68, or 69, determining that at least one of the products or services corresponds to an intellectual property asset is based at least in part on an intellectual property document associated with the intellectual property asset. , determining the first words of the intellectual property document; determining, based at least in part on the information related to at least one of the product or service, second words included in the information; determining a similarity metric between at least a portion of the first words and at least a portion of the second words; and determining that the similarity metric satisfies at least a threshold similarity metric.

71. The system of any one of items 67, 68, 69, or 70, wherein the actions are performed on at least one of the product or service based at least in part on information corresponding to the at least one of the product or service. determining the characteristics; determining a first similarity metric based at least in part on first criteria associated with the characteristic and the first classification; determining a second similarity metric based at least in part on second criteria associated with the characteristic and the second classification; and determining that the 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 value and the second similarity metric being less than the threshold value.

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

73. The system of any one of item 67, item 68, item 69, item 70, item 71, or item 72, wherein the operations are to determine a deduction factor to associate with the first classification, wherein the deduction factor is that of a patent claim. based at least in part on a first degree of exposure corresponding to invalidation and a second degree of exposure corresponding to a probability of litigation with respect to the patent claim; and determining, based at least in part on the deduction factor, an adjusted amount of revenue to use to determine the value measure.

74. The system of any one of item 67, item 68, item 69, item 70, item 71, item 72, or item 73, wherein the actions further comprise determining a scale measure of the intellectual property asset, The portion of the return attributable to the asset is based, at least in part, on the scope measure.

75. The method includes: receiving financial data corresponding to at least one of the product or service, the financial data indicative of revenue for the at least one of the product or service; identifying the organization's intellectual property assets; determining at least one first characteristic of the product or service, the first characteristics comprising at least one of a first physical characteristic of at least one of the product or service, or a first technical characteristic of at least one of the product or service —; determining a second characteristic of the intellectual property asset, wherein the second characteristics include at least one of a second physical characteristic of the intellectual property asset, or a second technical characteristic of the intellectual property asset; determining, based at least in part on the analysis of the first characteristics and the second characteristics, a similarity metric between the at least one of the product or service and the intellectual property asset; and determining, based at least in part on the similarity metric, that at least one of the product or service corresponds to an intellectual property asset.

76. The method of item 75 includes a user comprising one or more user interface elements configured to capture data related to at least one of a website, an organization's data store, or at least one of the products or services, the user interface element being configured to capture data related to at least one of the products or services. The method further comprises receiving, from at least one of the interfaces, information regarding at least one of the product or service, wherein at least one of the first characteristics is determined based at least in part on the information.

77. The method of item 75 or item 76 includes determining, based at least in part on the first characteristics, a classification to associate with at least one of a product or service of the plurality of classifications, wherein the classification comprises the first features or the first characteristics. associated with at least one of the 2 characteristics; and determining that the intellectual property asset corresponds to the classification based at least in part on the classification being associated with at least one of the first characteristics or the second characteristics.

78. The method of any one of items 75, 76, or 77, wherein the intellectual property asset comprises a first intellectual property asset, the method comprising: a second number of physical properties of a second intellectual property asset included in the classification determining, based at least in part on at least one of the first number of physical characteristics of the first intellectual property asset with respect to the characteristics, a first measure of scope of the first intellectual property asset; determine a second scale measure of the first intellectual property asset based at least in part on at least one of the first number of technical characteristics of the first intellectual property asset relating to the second number of technical characteristics of the second intellectual property asset to do; determining, based at least in part on the first scope measure and the second scope measure, a third scope measure of the first intellectual property asset; and determining, based at least in part on the third scope measure, a portion of the revenue of the product attributable to the intellectual property asset.

79. The method of any one of items 75, 76, 77, or 78, wherein the intellectual property asset comprises a trademark asset, wherein the method is based at least in part on a description of goods and services of a registered trademark. to determine the second characteristics.

80. The method of any one of Item 75, Item 76, Item 77, Item 78, or Item 79 may include: the number of litigation cases relating to trademark assets included in the classification associated with trademark assets; the number of objections relating to trademark assets included in the classification; or based at least in part on at least one of the examiners' first metrics associated with the trademark assets relating to second metrics of the judges associated with the trademark assets included in the classification: It further comprises the step of determining.

81. A method comprising: receiving, from one or more data sources, first information regarding products provided for acquisition; receiving, from the one or more data sources, second information regarding the intellectual property assets; determining, based at least in part on the first information and the second information, that one of the intellectual property assets corresponds to a characteristic associated with the one of the products; receiving a first request to determine a value of an intellectual property asset; identifying, based at least in part on receiving the first request, economic data indicative of an organization's revenue associated with the product; determining a portion of the revenue attributable to the intellectual property asset; determining, based at least in part on the portion of the revenue, a measure of the value of the intellectual property asset; receiving a second request to determine at least one of a first exposure value representing a loss of coverage pertaining to the intellectual property asset, or a second exposure value representing a litigation case pertaining to the intellectual property asset; determining at least one of a first exposure value or a second exposure value based at least in part on receiving the second request; and displaying, via the one or more user interfaces, an indicator of the value measure of the product, and at least one of the first exposure value or the second exposure value.

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

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

84. The method of any one of items 81, 82, or 83 comprises: generating a classification system comprising classifications, wherein individual classifications of the classifications correspond to one or more technical groups; determining one or more first characteristics of the product based at least in part on a first linguistic analysis of the portion of the 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 linguistic analysis of the portion of the second information corresponding to the intellectual property asset; determining, based at least in part on the one or more first characteristics, that the product is included in one of the classifications; and determining, based at least in part on the one or more second characteristics, that the intellectual property asset is included in the classification.

85. The method of any one of item 81, item 82, item 83, or item 84 comprises generating a framework indicating relationships between first products associated with the classification and intellectual property assets associated with the classification. Further comprising, determining that the intellectual property asset corresponds to the product includes determining that the framework indicates a relationship between the product and the intellectual property asset.

86. The method of any one of items 81, 82, 83, 84, or 85 is based, at least in part, on a first noun associated with an intellectual property asset corresponding to a first action performed by the product. , determining a first one of the one or more first characteristics; determining a first linguistic structure for a first feature indicative of a first relationship between the first action and the first noun; determining, based at least in part on a second noun associated with the intellectual property asset corresponding to the second action, a second one of the one or more second characteristics; determining a second linguistic structure for a second feature indicative of a second relationship between the second action and the second noun; and determining a similarity metric indicating an amount of similarity between the first linguistic structure and the second linguistic structure, 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 one of item 81, item 82, item 83, item 84, item 85, or item 86, wherein the second exposure value associated with the litigation case is a litigation corresponding to intellectual property assets having that classification. is determined based, at least in part, on the number of events.

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

89. The system may include one or more processors; and one or more computer-readable media storing instructions executable by the one or more processors, which, when executed by the one or more processors, cause the one or more processors to cause the intellectual property asset to be attached to at least one of the product or service. deciding to respond; Receiving requests to determine the value of intellectual property assets; receiving economic data associated with at least one of the product or service, wherein the economic data includes revenue of an organization associated with the at least one of the product or service; determining the portion of revenue attributable to intellectual property assets; determining, based at least in part on the portion of the revenue, a first measure of the value of the intellectual property asset; receiving input data indicating that the first measure of value of the intellectual property asset should be modified; determining, based at least in part on the input data, a second measure of the value of the intellectual property asset; and displaying, via the user interface, the indication of the second value measure.

90. The system of item 89, wherein the intellectual property asset is a patent document, and the actions further include determining a measure of scope of the claims of the patent document, wherein the portion of the revenue is based at least in part on the measure of scope.

91. The system of item 89 or item 90, wherein the request comprises a first request, and the actions include: a second request to determine a first measure of value for individual intellectual property assets of the intellectual property assets associated with the loan to the organization to receive - the intellectual property assets associated with the organization; determining, based at least in part on the first criteria, a first measure of value; receiving a third request to determine a second measure of value for individual intellectual property assets of intellectual property assets associated with at least one of the sale of at least a portion of the organization or the merging of the organization with an additional organization; and determining, based at least in part on the second criteria, a second value measure, wherein the second value measure is different from the first value measure.

92. The system of any one of items 89, 90, or 91, wherein the request comprises a first request, and wherein the actions include a frame indicating one or more relationships between the respective products or services and the respective patents. creating work; receiving a second request to identify one or more patents related to one or more of the products or services; utilizing the framework to determine one or more patents related to one or more of the products or services of the patents; and displaying, via the user interface, an indication of the one or more patents.

93. The system of any one of items 89, 90, 91, or 92, the operations comprising: determining a characteristic of a product or service of products or services; determining, based at least in part on the framework, claims of one or more patents related to the feature; and displaying, via the user interface, the representation of the claims.

94. The system of any one of item 89, item 90, item 91, item 92, or item 93, wherein the request comprises a first request, and the actions include generating a classification system comprising the classifications. individual classifications are associated with one or more technical groups; receiving a second request to identify one or more organizations associated with claims in patent documents associated with products or services included in one of the classifications; determining, based at least in part on the framework, organizations associated with claims corresponding to products or services included in the classification; and displaying, via the user interface, an indication of a number of claims associated with respective organizations of organizations corresponding to products or services included in the classification.

95. The system of any one of item 89, item 90, item 91, item 92, item 93, or item 94, wherein the economic data comprises the first economic data, the revenue comprises the first revenue, and the value measure includes a first measure of value, and the actions include: receiving second economic data associated with the product or service, wherein the second economic data includes a second revenue of an organization associated with the product or service; and determining, based at least in part on the second revenue, a second measure of the value of the intellectual property asset.

96. The method includes 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 a first revenue of an organization associated with the product or service; determining a portion of the first revenue attributable to the intellectual property asset; determining, based at least in part on the portion of the first revenue, a first measure of the value of the intellectual property asset; receiving second economic data comprising a second revenue of an organization associated with the product or service; determining, based at least in part on the second revenue, a second measure of the value of the intellectual property asset; and displaying, via the user interface, an indication of the second measure of value of the intellectual property asset.

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

98. The method of item 96 or item 97, wherein the intellectual property asset comprises a trade secret, the method further comprising: determining a probability of theft of the trade secret based at least in part on the portion of the information; determining a first exposure value based at least in part on a probability of theft of the trade secret; and displaying, via the user interface, the indication of the first exposure value.

99. The method of any one of items 96, 97, or 98 determines, based at least in part on at least one of the first exposure value or the second exposure value, an amount of the deduction for reducing the second amount of revenue. and wherein the second measure of value is based at least in part on a difference between the second amount of revenue and the amount of deduction.

100. The method of any one of item 96, item 97, item 98, or item 99 further comprises determining an intellectual property category associated with the intellectual property asset, wherein the intellectual property category includes: a patent category, a trademark category; at least one of a copyright category, or a trade secret category, wherein determining the second value measure includes determining the second value measure based at least in part on the intellectual property category associated with the intellectual property asset.

Moreover, the foregoing is merely illustrative of the principles of the present disclosure, and various modifications may be made by those skilled in the art without departing from the scope of the present disclosure. The examples described are presented for purposes of illustration and not limitation. The disclosure may also take many forms other than those expressly set forth herein. Accordingly, it is emphasized that this disclosure is not intended to be limited to the explicitly disclosed methods, systems, and apparatus, but is intended to cover variations and modifications that fall within the spirit of the following claims.

As a further example, variations in apparatus or process parameters (eg, dimensions, configurations, components, process step order, etc.) may further enhance the provided structures, devices, and methods as shown and described herein. can be made to optimize. In any case, the structures and devices described herein, as well as associated methods, have many applications. Accordingly, the disclosed subject matter should not be limited to any single example set forth herein, but rather should be construed in breadth and scope according to the appended claims.

Claims (15)

As a method,
receiving information about products from one or more data sources;
identifying intellectual property assets;
determining one or more relationships between individual products of the products and individual intellectual property assets of the intellectual property assets;
generating association data indicative of the one or more relationships between respective products of the products and respective intellectual property assets 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 associated data; and
generating a response to the request, the response indicating that the intellectual property asset is associated with the product. According to claim 1,
the data source comprises a publicly accessible data source;
The method is
determining a keyword associated with the product;
identifying, based at least in part on the publicly accessible data source, data corresponding to the keyword; and
and extracting 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 acquisition;
The method is
determining, by a second organization, keywords associated with the product;
identifying data corresponding to a keyword by the second organization and from a data store of the first organization; and
and extracting, by the second organization, data corresponding to the keyword. 4. The method of claim 3,
utilizing the data repository to identify data indicative of a relationship between the intellectual property asset and the product;
wherein generating the association data comprises generating the association data based at least in part on data indicative of a relationship between the intellectual property asset and the product. 5. The method according to any one of claims 1 to 4,
the request comprises a first request;
The method is
a second request for information about the product;
causing the computing devices to be posted on an accessible website, or
causing it to be transmitted to the computing devices; and
In response to the second request, the method further comprising: receiving data indicative of at least one of a source of the information or the information. 6. The method according to any one of claims 1 to 5,
generating a user interface comprising a user interface element configured to receive input representing information relating to the intellectual property asset; and
receiving the input utilizing the user interface element;
wherein generating the relevant data comprises generating the relevant data based at least in part on the input. 7. The method according to any one of claims 1 to 6,
determining a metric associated with the intellectual property asset, the metric comprising:
a measure of the extent of at least a portion of the intellectual property assets;
an exposure measure associated with at least a portion of said intellectual property asset; or
comprising at least one of the coverage measures of at least a portion of the intellectual property asset;
determining a revenue associated with the product over a period of time; and
determining, based at least in part on the metric, an amount of revenue attributable to at least a portion of the intellectual property asset. As a system,
one or more processors; and
one or more computer-readable media storing instructions executable by the one or more processors;
The instructions, when executed by the one or more processors, cause the one or more processors to:
receiving information regarding at least one of the products or services, wherein at least a portion of the information comprises economic data;
determining a relationship between one of said products or a service of said services and an intellectual property asset;
generating associative data indicative of a relationship between the product or service and the intellectual property asset; and
and identify the intellectual property asset corresponding to the product or service from among a plurality of intellectual property assets based at least in part on the associated data. 9. The method of claim 8,
The information regarding at least one of the products or services comprises a description of at least one of the products or services, and wherein the actions are based at least in part on the description of at least one of the products or services. further comprising determining a characteristic for 10. The method according to claim 8 or 9,
The feature comprises a first feature,
The actions are
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 service; and
determining, based at least in part on the information about the intellectual property asset, a second characteristic of the intellectual property asset;
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 method according to any one of claims 8 to 10,
said intellectual property assets include patent documents;
The actions are
receiving a description of at least one of the product or the service, the description including words related to at least one of the product or the service; and
further comprising determining that at least a portion of said words are included in a claim of said patent document;
and the associative data indicates that the claim corresponds to at least one of the product or the service based at least in part on inclusion of at least a portion of the words in the claim. 12. The method according to any one of claims 8 to 11,
The operations further include generating a user interface comprising one or more user interface elements configured to capture information regarding the plurality of intellectual property assets;
The one or more user interface elements include:
a first element configured to receive first information associated with trade secret documents;
a second element configured to receive second information associated with the trademark documents; or
at least one of a third element configured to receive third information associated with copyright documents. 13. The method according to any one of claims 8 to 12,
The actions are
a request for information regarding at least one of said products or said services;
causing the computing devices to be posted on an accessible website, or
to be transmitted to the computing devices; and
and receiving, in response to the request, data indicative of at least one of a source of the information or the information. 14. The method according to any one of claims 8 to 13,
The actions are
determining, based at least in part on the economic data, an amount of revenue associated with at least one of the product or the service obtained over a period of time;
determining the portion of the amount of revenue attributable to the intellectual property asset; and
and determining the value of the intellectual property asset based at least in part on the portion of the amount of revenue. As a method,
receiving information regarding at least one of the products or services, wherein at least a portion of the information includes economic data associated with at least one of the products or services;
determining relationships between individual products or individual services of at least one of the products or services and individual intellectual property assets of intellectual property assets;
generating association data indicative of relationships between individual products of the products and individual intellectual property assets of the intellectual property assets;
identifying, based at least in part on the associated data, an intellectual property asset corresponding to at least one of the at least one of the products or services; and
and generating data indicating that the intellectual property asset is associated with at least one of the product or the service.

Images