US 12,437,008 B1
Resolving latent status from dense information using machine learning
Reynaldo Medina, III, San Antonio, TX (US); David Sokolowski, San Antonio, TX (US); James Warren Middleton, San Antonio, TX (US); and Michael Martin Amati, Geneseo, NY (US)
Assigned to United Services Automobile Association (USAA), San Antonio, TX (US)
Filed by UIPCO, LLC, San Antonio, TX (US)
Filed on Oct. 30, 2024, as Appl. No. 18/931,592.
Claims priority of provisional application 63/594,650, filed on Oct. 31, 2023.
Int. Cl. G06F 16/93 (2019.01); G06Q 40/03 (2023.01)
CPC G06F 16/93 (2019.01) [G06Q 40/03 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method for resolving a latent status of one or more documents, that contain dense information, using machine learning, the method comprising:
receiving one or more documents comprising a plurality of line items, wherein the one or more documents are organized into pages;
determining match instances between text from a subset of the line items and text descriptive of an issue category;
extracting, for each of the match instances, one or more distance and/or angle measurements between: a) a location of the matched text of the match instance on a corresponding page of the one or more documents, and b) a location of symbols associated with the matched text on the corresponding page of the one or more documents;
generating status predictions for the issue category by inputting, to one or more trained machine learning models, instances of input that correspond to the match instances, wherein each instance of input comprises at least (1) a portion of the matched text from the match instances, (2) distance and/or angle measurements extracted between the matched text and one or more of the associated symbols, and (3) one or more indicators representative of the associated symbols; and
analyzing the status predictions to generate an overall status prediction for the issue category.