US 12,394,283 B1
Generative artifical intelligence-based automated teller machine operation control
Paul Mattison, Charlotte, NC (US); Matthew Williams, Plano, TX (US); and Jennifer Raley, Mount Holly, NC (US)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on May 3, 2024, as Appl. No. 18/654,188.
Int. Cl. G07F 19/00 (2006.01)
CPC G07F 19/209 (2013.01) [G07F 19/206 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
train, using historical data related to at least automated teller machine (ATM) functionality, issues and anomalies, a first generative artificial intelligence model to identify correlations in subsequent data and output potential issue content based on the identified correlations, wherein the first generative artificial intelligence model includes a generative adversarial network;
train, using historical data related to at least ATM corrective actions associated with a plurality of types of issues, a plurality of second generative artificial intelligence models to identify correlations in subsequent data and output corrective action content based on the identified correlations;
receive, from a plurality of ATMs, current operation data;
execute the first generative artificial intelligence model, wherein executing the first generative artificial intelligence model includes inputting the current operation data to the first generative artificial intelligence model to output one or more potential issues;
analyze the output one or more potential issues, wherein analyzing the one or more potential issues includes:
for a first issue of the one or more potential issues:
retrieve, from an ATM of the plurality of ATMs at which the first issue was detected, additional data related to the first issue;
identify, based on a type of issue of the first issue and the retrieved additional data related to the first issue, a second generative artificial intelligence model of the plurality of second generative artificial intelligence models, wherein the second generative artificial intelligence model is associated with the type of issue;
execute the second generative artificial intelligence model, wherein executing the second generative artificial intelligence model includes inputting the additional data related to the first issue to output a corrective action;
execute the corrective action, wherein executing the corrective action includes transmitting a command to the ATM to at least modify an operation;
update the first generative artificial intelligence model based on the analyzing the first issue; and
update the second generative artificial intelligence model based on the executed corrective action.