US 11,893,630 B2
Systems and methods for debt management with spending recommendation
Austin Walters, Columbia, TN (US); Vincent Pham, Champaign, IL (US); and Jeremy Goodsitt, Champaign, IL (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Nov. 21, 2022, as Appl. No. 18/057,522.
Application 18/057,522 is a continuation of application No. 16/923,405, filed on Jul. 8, 2020, granted, now 11,532,041.
Prior Publication US 2023/0093371 A1, Mar. 23, 2023
Int. Cl. G06Q 40/02 (2023.01); G06Q 20/32 (2012.01); G06N 20/00 (2019.01)
CPC G06Q 40/02 (2013.01) [G06N 20/00 (2019.01); G06Q 20/3221 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for providing an adaptive goal management recommendation, the method comprising:
generating, by one or more processors, a user interface of a user device associated with a user, the user interface including a navigation bar, a chat bot feature, and a text bar;
receiving, by the one or more processors, account information regarding the user;
receiving, by the one or more processors, and via the chat bot feature of the user interface, a query from the user;
in response to the query, presenting, by the one or more processors, and via the chat bot feature of the user interface of the user device, a portion of the account information including categorized account information of the user;
receiving, by the one or more processors, and via the chat bot feature of the user interface of the user device, information regarding at least one goal preference of the user and at least one interests preference of the user;
determining, by the one or more processors, using a trained machine learning model, one or more activities available to the user based on the at least one goal preference and the at least one interests preference of the user received via the chat bot feature of the user interface of the user device, the trained machine learning model having been trained based on (i) training user data that includes information regarding goal preferences and interests preference data associated with persons other than the user; and (ii) training activities data that includes prior available activities data associated with persons other than the user, to learn relationships between the training user data and the training activities data, such that the trained machine learning model is configured to output one or more activities available to the user upon input of the at least one goal preference and the at least one interests preference of the user received via the chat bot feature of the user interface of the user device;
determining, by the one or more processors, for each of the one or more activities available to the user, an estimated influence on the at least one goal preference;
filtering, by the one or more processors, the one or more activities available to the user with a positive estimated influence on the at least one goal preference; and
presenting, by the one or more processors, and via the chat bot feature of the user interface, a recommendation of action relating to at least one activity available to the user of the filtered one or more activities available to the user and the determined estimated influence on the at least one goal preference for the at least one activity.