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

Claim 13:
14. A method comprising:
receiving financial data corresponding to at least one of a product or a service, the financial data indicating revenue for the at least one of the product or the service;
identifying an intellectual-property asset of an organization;
training a first machine learning model configured to determine features of products and services 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 first features of the at least one of the product or the service, the first features including at least one of:
a first physical feature of the at least one of the product or the service; or
a first technical feature of the at least one of the product or the service;

utilizing one or more linguistic analysis techniques and the first trained machine learning model to determine second features of the intellectual-property asset, independent of the first features of the at least one of the product or the service, the second features including at least one of:
a second physical feature of the intellectual-property asset; or
a second technical feature of the intellectual-property asset;

determining a similarity metric between the at least one of the product or the service and the intellectual-property asset based at least partly on an analysis of the first features and the second features;
determining, based at least partly on the similarity metric, that the at least one of the product or the service corresponds to the intellectual-property asset;
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 first measure of breadth of the first intellectual-property asset utilizing the second trained machine learning model and based at least partly on a first number of physical features of the first intellectual-property asset in relation to a second number of physical features of a second intellectual-property asset;
identifying, utilizing an intellectual-property mapping and learning system, a portion of a technology taxonomy generated by the intellectual-property mapping and learning system associated with the first intellectual-property asset;
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 revenue to attribute to the first 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.