US 12,106,053 B2
Artificial intelligence (AI)-powered legacy scenarios for use within a mobile application
Ramakrishna R. Yannam, Plano, TX (US); Prejish Thomas, Plano, TX (US); Steven Zhao, Plano, TX (US); Saahithi Chillara, Plano, TX (US); Rajan Jhaveri, Plano, TX (US); Ryan Strug, Plano, TX (US); Kurt R. Schultz, Westlake Village, CA (US); and Priyank Shah, Plano, TX (US)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Apr. 18, 2022, as Appl. No. 17/722,995.
Prior Publication US 2023/0334251 A1, Oct. 19, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/30 (2020.01); G06F 3/0482 (2013.01); G06F 40/166 (2020.01); G06N 3/08 (2023.01)
CPC G06F 40/30 (2020.01) [G06F 3/0482 (2013.01); G06F 40/166 (2020.01); G06N 3/08 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for condensing user communications relating to a first topic, the method for use with a system, the system comprising a processor and a non-transitory memory with instructions stored thereon, wherein the instructions upon execution by the processor, cause the processor to execute the method, said method comprising:
designating a topic of user interest;
retrieving legacy communications regarding the topic;
determining whether duplicative communications are included among the legacy communications and, to the extent that duplicative communications are included in the legacy communications, removing said duplicative communications from the legacy communications;
retrieving legacy intelligence relating to historical user selections regarding the topic;
retrieving a plurality of outcomes based on the legacy intelligence;
with respect to the topic of interest, forming a topic-centric training set for a neural network, said topic-centric training set being based on the legacy communications, legacy intelligence, and the plurality of outcomes, said topic-centric training set being delimited by an analysis of the database, said neural network comprising a plurality of nodes;
synthesizing the neural network, said synthesizing using the topic-centric training set to assign individual weights to each of the plurality of nodes based on the legacy communications, legacy intelligence, and the plurality of outcomes;
in response to a selection of the topic of user interest, generating, for display to a user, and displaying a plurality of topic-related user options based on the neural network;
prompting the user to select a scenario indicator in which a plurality of artificial intelligence (AI)-based scenarios are displayed;
displaying the plurality of AI-based scenarios, each of the plurality of scenarios that corresponds to a single one of the plurality of user options;
prompting the user to select one of the scenarios; and
in response to the user selecting one of the scenarios triggering the option that corresponds to the selected scenario;
wherein each of said plurality of outcomes incorporates results of legacy user selections regarding the topic of user interest.