US 12,481,664 B2
Machine learning model for recommending interaction parties
Dwipam Katariya, McLean, VA (US); Muhammad Uddin, San Bernardino, CA (US); Tania Cruz Morales, Washington, DC (US); Julian Duque, Arlington, VA (US); and Kimberly Stockley, Washington, DC (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Aug. 27, 2024, as Appl. No. 18/816,653.
Application 18/816,653 is a continuation of application No. 17/820,051, filed on Aug. 16, 2022, granted, now 12,093,266.
Prior Publication US 2024/0419672 A1, Dec. 19, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/00 (2019.01); G06F 16/2457 (2019.01); G06F 16/2458 (2019.01)
CPC G06F 16/24575 (2019.01) [G06F 16/2477 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
provide, to a machine learning model, historical interaction data associated with historical interactions of a user with a plurality of interaction parties;
receive an output, based on applying the machine learning model to the historical interaction data, that indicates one or more recommended interaction parties that are local entities to a geographic location associated with the user,
wherein the one or more recommended interaction parties are local entities based on having at least one location within a distance threshold of the geographic location and based on:
a comparison of a first threshold to a quantity of locations of the one or more recommended interaction parties, or
a comparison of a second threshold to a revenue of the one or more recommended interaction parties over a timeframe; and
transmit an indication of the one or more recommended interaction parties.