OneTrust had six patents in artificial intelligence during Q4 2023. OneTrust LLC’s patents in Q4 2023 focus on managing machine-learning model implementation, determining clusters of similar digital documents using unique signatures, and protecting system software and data from unauthorized access. These patents involve generating common data objects to represent machine-learning models, identifying clusters of similar digital documents based on unique signatures, and mitigating risks associated with processing target data by software applications. The disclosed systems validate machine-learning models according to system requirements frameworks and take actions to secure data from destruction or unauthorized disclosure. GlobalData’s report on OneTrust gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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OneTrust grant share with artificial intelligence as a theme is 33% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Managing the development and usage of machine-learning models and datasets via common data objects (Patent ID: US20230376852A1)
The patent by OneTrust LLC discloses methods, systems, and computer-readable storage media for managing the implementation of machine-learning models within computing environments based on system requirements frameworks using common data objects. The system generates a common data object representing the machine-learning model's implementation with a data process and validates it against a digital representation of system requirements. This validation includes ensuring compliance with requirements for storing, handling, and processing specific data types within a computing environment, allowing for the implementation, suspension, or modification of the machine-learning model based on the validation results.
The method and system outlined in the patent involve determining a common data object that encapsulates attribute values representing the machine-learning model's implementation details and conducting data configuration validation against a system requirements framework. This validation process involves identifying configuration gaps relative to the requirements framework and generating recommendations for modifying the model, datasets, or data processes to address these gaps. The system also provides a graphical user interface for displaying validation results, recommended actions, and the status of implementing the machine-learning model, ensuring alignment with the specified system requirements for handling data within the computing environment.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
