The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 08, 2025
Filed:
Aug. 31, 2022
Wells Fargo Bank, N.a., San Francisco, CA (US);
Ashish B. Kurani, Hillsborough, CA (US);
James C. Noe, Charlotte, NC (US);
Imran Haider, San Ramon, CA (US);
Frank Fehrenbach, New York, NY (US);
Guruprasadh Ragothaman, San Francisco, CA (US);
Matthew C. Strader, San Francisco, CA (US);
Palani Munuswamy, San Francisco, CA (US);
Chandra Subramanian, San Francisco, CA (US);
George Atala, San Francisco, CA (US);
Mattie L. Morris, Chandler, AZ (US);
Braden More, San Francisco, CA (US);
Loftlon Worth, San Francisco, CA (US);
Nathan B. Coles, San Francisco, CA (US);
Wells Fargo Bank, N.A., San Francisco, CA (US);
Abstract
Systems and methods for receiving an enterprise resource dataset associated with a customer from an enterprise application associated with the customer, the enterprise resource dataset including a plurality of financial inputs captured on or before a first date, a plurality of customer recommendations, and a plurality of recommendation replies, receiving, from a first machine learning model, a predicted customer state including one or more predicted financial outputs corresponding with the predicted customer state at a second date subsequent to the first date, providing the plurality of financial inputs to a second machine learning model trained to predict a customer recommendations, determining, a customer recommendation corresponding to an action to be performed at a third date subsequent to the first date, wherein performing the action causes the first machine learning model to predict an updated customer state differing from the predicted customer state.