Patent ID: 11941714
Assignee: AON RISK SERVICES, INC. OF MARYLAND
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A system comprising:
one or more processors; and
one or more computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving financial data corresponding to at least one of a product or a service, the financial data indicating revenue associated with the at least one of the product or the service;
generating, by an intellectual-property mapping and learning system, a technology taxonomy including classifications of products;
determining, b the intellectual-property mapping and learning system, a classification of the at least one of the product or the service based at least partly on a feature of the product or service;
identifying an intellectual-property asset of an organization, the intellectual-property asset including a patent asset, a trademark asset, a copyright asset, or a trade-secret asset;
training a first machine learning model configured to determine products and services that correspond to sample intellectual-property assets such that a first trained machine learning model is generated;
utilizing one or more linguistic analysis techniques and the first trained machine learning model to determine that the at least one of the product or the service corresponds to the intellectual-property asset based at least partly on the intellectual-property asset being associated with the classification;
generating a second machine learning model configured to determine patent claim breadth;
training the second machine learning model using a training dataset such that a second trained machine learning model is generated;
determining a breadth of a patent claim of the intellectual-property asset utilizing one or more linguistic analysis techniques and the second trained machine learning model;
identifying, utilizing the intellectual-property mapping and learning system, a portion of a technology taxonomy generated by the intellectual-property mapping and learning system associated with the classification;
utilizing one or more linguistic analysis techniques and the second trained machine learning model to determine, by the intellectual-property mapping and learning system, a portion of the revenue to attribute to the intellectual-property asset with respect to other intellectual-property assets included in the portion of the technology taxonomy generated by the intellectual-property mapping and learning system; and
determining a measure of value of the intellectual-property asset based at least partly on the portion of the revenue attributed to the intellectual-property asset.