Aon has filed a patent for systems and methods for document analysis architectures. The invention involves storing machine-learning classification models in a taxonomy, training models based on user input data, and recommending relevant models for document analysis based on search queries. GlobalData’s report on Aon gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Aon, was a key innovation area identified from patents. Aon's grant share as of January 2024 was 41%. Grant share is based on the ratio of number of grants to total number of patents.
Document analysis system with machine-learning classification models and taxonomy
The patent application (Publication Number: US20230409647A1) describes a system and method for storing a taxonomy of machine-learning classification models trained to predict document classifications using predictive analytical techniques. The system includes one or more processors and computer-readable media storing instructions for operations such as storing the taxonomy, generating a user interface for search queries, determining related machine-learning models, and displaying search results visually. The method involves receiving user input data, generating datasets, determining model similarity, and displaying search results based on the determined relationships between the search query and the taxonomy of machine-learning models.
Furthermore, the system and method involve additional operations such as storing reference keywords for classification models, determining similarity values between search queries and reference representations, displaying recommended models based on user input, and training new models based on user classifications. The system also includes features for displaying confidence values, ranking sample documents, and suggesting training new models not yet in the taxonomy. Overall, the patent application outlines a comprehensive system and method for efficiently classifying documents using machine-learning models and providing users with relevant search results based on the relationships between search queries and the taxonomy of models.
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