US 12,136,122 B2
Systems and methods for using machine learning models to automatically identify and compensate for recurring charges
Christopher Wallace, Frisco, TX (US); Grant Eden, San Francisco, CA (US); Brian Barr, Schenectady, NY (US); Samuel Sharpe, Cambridge, MA (US); Anh Truong, Champaign, IL (US); and Austin Walters, Columbia, TN (US)
Assigned to CAPITAL ONE SERVICES, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Sep. 19, 2022, as Appl. No. 17/947,216.
Application 17/947,216 is a continuation of application No. PCT/CN2021/121724, filed on Sep. 29, 2021.
Claims priority of application No. 202011119603.5 (CN), filed on Oct. 19, 2020.
Prior Publication US 2023/0013086 A1, Jan. 19, 2023
Int. Cl. G06Q 40/02 (2023.01); G06F 9/451 (2018.01); G06F 16/21 (2019.01); G06F 16/22 (2019.01); G06F 16/2453 (2019.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01)
CPC G06Q 40/02 (2013.01) [G06F 9/451 (2018.02); G06F 16/211 (2019.01); G06F 16/2282 (2019.01); G06F 16/2453 (2019.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An incremental data designation system comprising:
one or more processors; and
memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:
receive historical data associated with a user;
train, using training data comprising historical data associated with other users, a first machine learning model to predict charges for the user;
identify, using the first machine learning model, a set of recurring charges from the historical data associated with the user;
monitor, by the first machine learning model, an account associated with the user to update the set of recurring charges in response to identifying new or changing recurring charges;
calculate an incremental amount based on an incremental period and the set of recurring charges, wherein the incremental period is a predetermined period of time comprising a predetermined number of discrete time increments and the incremental amount multiplied by the incremental period is an expected amount;
assign, at each successive time increment of the predetermined number of discrete time increments of the incremental period, the incremental amount to a savings bucket comprising a value;
generate a graphical user interface for displaying a reduced balance equal to an actual balance minus the value of the savings bucket;
transmit the graphical user interface to a user device;
receive current data;
extract a second amount from the current data;
determine, using a second machine learning model, that the second amount corresponds to the set of recurring charges; and
reduce the value of the savings bucket by the second amount.