US 12,112,307 B1
Systems and methods for providing early payment recommendations
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); and Nathan B. Coles, San Francisco, CA (US)
Assigned to Wells Fargo Bank, N.A., San Francisco, CA (US)
Filed by Wells Fargo Bank, N.A., San Francisco, CA (US)
Filed on Jan. 6, 2023, as Appl. No. 18/094,062.
Application 18/094,062 is a continuation of application No. 17/900,705, filed on Aug. 31, 2022.
Application 17/900,705 is a continuation in part of application No. 17/720,117, filed on Apr. 13, 2022.
Claims priority of provisional application 63/287,426, filed on Dec. 8, 2021.
Claims priority of provisional application 63/208,908, filed on Jun. 9, 2021.
Claims priority of provisional application 63/189,513, filed on May 17, 2021.
Claims priority of provisional application 63/174,935, filed on Apr. 14, 2021.
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 20/22 (2012.01); G06Q 20/10 (2012.01)
CPC G06Q 20/227 (2013.01) [G06Q 20/102 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
training, by the one or more processors during a training phase, a machine-learning model to output scores corresponding to recommendations regarding payment of an invoice, the machine-learning model trained using a training set including a plurality of data entries for invoices from the first data source and account data for a plurality of accounts from the second data source;
deploying, by the one or more processors, the machine-learning model responsive to outputs from the machine-learning model satisfying one or more criteria during the training phase;
receiving, by an application programming interface (API) gateway of a computer system corresponding to the second data source, a first API call for a first API managed by the API gateway, the first API call including information corresponding to authentication of an enterprise application executing on a second server, the enterprise application being authenticated for accessing information via the API gateway based on the information corresponding to the authentication, the enterprise application linked to a common account holder of an account of the second data source, the enterprise application corresponding to the first data source;
establishing, responsive to receiving the first API call and authentication of the information, a connection between the computer system and the enterprise application;
detecting, by the one or more processors, a data entry for an invoice between the common account holder and a third party, the data entry detected via a webhook based on an indication of the data entry for the invoice at the first data source
extracting, by the one or more processors, a trade term from the invoice responsive to detecting the data entry;
applying, by the one or more processors, the trade term from the invoice to the machine-learning model as a first input;
applying, by the one or more processors, data of the account of the second data source to the machine-learning model as a second input;
receiving, by the one or more processors from the machine-learning model, an output comprising a score for early-payment of the data entry according to the trade term and the data of the account; and
transmitting, by the one or more processors, in response to determining that the score exceeds a predetermined threshold, a notification for the invoice to a computing device associated with the common account holder.