US 11,893,065 B2
Document analysis architecture
Samuel Cameron Fleming, Spokane, WA (US); John E. Bradley, III, Duvall, WA (US); Lewis C. Lee, Atherton, CA (US); Jared Dirk Sol, Spokane, WA (US); Scott Buzan, Spokane Valley, WA (US); and Timothy Seegan, Spokane, WA (US)
Assigned to AON RISK SERVICES, INC. OF MARYLAND, New York, NY (US)
Filed by AON RISK SERVICES, INC. OF MARYLAND, New York, NY (US)
Filed on Jun. 10, 2020, as Appl. No. 16/897,637.
Prior Publication US 2023/0409647 A1, Dec. 21, 2023
Int. Cl. G06F 16/93 (2019.01); G06F 16/9038 (2019.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)
CPC G06F 16/93 (2019.01) [G06F 16/9038 (2019.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
one or more processors; and
non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
storing a taxonomy of machine-learning classification models, individual ones of the machine-learning classification models configured to receive documents and predict a classification of documents in a document set utilizing predictive analytical techniques, the individual ones of the machine-learning classification models trained based at least in part on a document dataset indicated to be in class from first user input data, the documents comprising patents and patent applications, wherein the individual ones of the machine-learning classification models differ from the documents that the individual ones of the machine-learning classification models are configured to receive and analyze;
generating a user interface configured to accept second user input data representing a search query, the search query including keywords from the second user input data;
receiving the second user input data representing the search query;
generating, utilizing the keywords, a first dataset based at least in part on the search query;
determining, utilizing the first dataset, that at least one machine-learning classification model of the taxonomy of machine-learning classification models was trained by a second dataset similar to the first dataset;
generating, based at least in part on the determining, a recommended machine-learning classification model of the machine-learning classification models determined to be most related to the search query; and
based at least in part on the determining, causing display, via the user interface, of search results for the search query, the search results displaying a visual representation of the taxonomy of machine-learning models along with an emphasized portion of the visual representation associated with the at least one machine-learning classification model of the taxonomy of machine-learning models trained by the second dataset, the search results also indicating the recommended machine-learning classification model.