Granted Patent B2

US 12,530,666 · App. 18/766,185 · Granted Jan 20, 2026

Inventors: Keegan Franklin (Tucson, AZ); Aeman Jamali (Indianapolis, IN); Jordan Arrieta (Wilmington, DE); Megan Obrien (New Orleans, LA); Danielle Wegrzyn (Bear, DE); Michael Croghan (Herndon, VA); Kai Si (Monterey Park, CA); Sasha Ahrestani (Philadelphia, PA); Jillian Peifer (Sterling, VA)

Assignee: Capital One Services, LLC

G06Q20/108

Abstract

Claims (39)

1 . A computer-implemented method for remote deposit process using a client device, comprising:

activating, on the client device, a remote deposit application;

activating, based on receiving a user request to initiate a remote deposit from the remote deposit application, an interactive user interface (UI), wherein the interactive UI provides at least a status indicator of the remote deposit;

predicting, by a first trained machine learning model, an occurrence of a remote deposit matter preventing advancement of the remote deposit process;

predicting, by a second trained machine learning model, one or more remote deposit remediation tasks to overcome the remote deposit matter;

modifying a weighting of metadata during training of the second trained machine learning model to trend towards greater success, wherein the greater success is recognized by an advancement or completion of the remote deposit process, and wherein the metadata includes any of: a time to complete a task, a time to complete the one or more remote deposit remediation tasks, a time for advancing the remote deposit process after encountering a matter, or a total time to complete the remote deposit process;

displaying, on the interactive UI, the one or more remote deposit remediation tasks; and

advancing, based on the user completing the one or more remote deposit remediation tasks, the remote deposit process; and

wherein the metadata further includes a user acceptance or denial, through the interactive UI, of the completion of the remote deposit process.

2 . The computer-implemented method of claim 1 , further comprising segmenting the remote deposit process into a plurality of stages and displaying a respective status indicator for each of the plurality of stages.

3 . The computer-implemented method of claim 2 , further comprising displaying one or more drop-down menus of one or more tasks or status indicators occurring during a respective one of the plurality of stages.

4 . The computer-implemented method of claim 1 , further comprising training the first trained machine learning model and the second trained machine learning model on historical remote deposit transactions comprising one or more of: remote deposit transactions, associated remote deposit matters, associated remote deposit remediation tasks, or associated remote deposit process outcomes.

5 . The computer-implemented method of claim 1 , wherein the first trained machine learning model and the second trained machine learning model are combined into a single ML model.

6 . The computer-implemented method of claim 1 , further comprising continuously modifying weighting of metadata during training of the first trained machine learning model to trend towards greater success, where success is recognized by identification of the matters that endanger the advancing of the remote deposit process.

7 . A system, comprising:

a memory; and

at least one processor coupled to the memory and configured to:

activate, based on receiving a user request to initiate a remote deposit from a remote deposit application, an interactive user interface (UI), wherein the interactive UI provides at least a status of the remote deposit;

predict, by a first trained machine learning model, an occurrence of a remote deposit matter preventing advancement of the remote deposit process;

predict, by a second trained machine learning model, one or more remote deposit remediation tasks to overcome the remote deposit matter;

modify a weighting of metadata during training of the second trained machine learning model to trend towards greater success, wherein the greater success is recognized by an advancement or completion of the remote deposit process, and wherein the metadata includes any of: a time to complete a task, a time to complete the one or more remote deposit remediation tasks, a time for advancing the remote deposit process after encountering a matter, or a total time to complete the remote deposit process;

display, on the interactive UI, the one or more remote deposit remediation tasks; and

advance, based on the user completing the one or more remote deposit remediation tasks, the remote deposit process; and

wherein the metadata further includes a user acceptance or denial, through the interactive UI, of the completion of the remote deposit process.

8 . The system of claim 7 , further configured to segment the remote deposit process into a plurality of stages and display a respective status indicator for each of the plurality of stages.

9 . The system of claim 8 , further configured to display one or more drop-down menus of one or more tasks or status indicators occurring during a respective one of the plurality of stages.

10 . The system of claim 7 , further configured to train the first trained machine learning model and the second trained machine learning model on historical remote deposit transactions comprising on one or more of: remote deposit transactions, associated remote deposit matters, associated remote deposit remediation tasks, or associated remote deposit process outcomes.

11 . The system of claim 7 , wherein the first trained machine learning model and the second trained machine learning model are combined into a single ML model.

12 . The system of claim 7 , further configured to continuously modify weighting of metadata during training of the first trained machine learning model to trend towards greater success, where success is recognized by identification of the matters that endanger the advancing of the remote deposit process.

13 . A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

activating a remote deposit application;

activating, based on receiving a user request to initiate a remote deposit from the remote deposit application, an interactive user interface (UI), wherein the interactive UI provides at least a status of the remote deposit;

predicting, by a first trained machine learning model, an occurrence of a remote deposit matter preventing advancement of the remote deposit process;

predicting, by a second trained machine learning model, one or more remote deposit remediation tasks to overcome the remote deposit matter;

modifying a weighting of metadata during training of the second trained machine learning model to trend towards greater success, wherein the greater success is recognized by an advancement or completion of the remote deposit process, and wherein the metadata includes any of: a time to complete a task, a time to complete the one or more remote deposit remediation tasks, a time for advancing the remote deposit process after encountering a matter, or a total time to complete the remote deposit process;

displaying, on the interactive UI, the one or more remote deposit remediation tasks; and

advancing, based on the user completing the one or more remote deposit remediation tasks, the remote deposit process; and

wherein the metadata further includes a user acceptance or denial, through the interactive UI, of the completion of the remote deposit process.

14 . The non-transitory computer-readable device of claim 13 , the operations further comprising training the first trained machine learning model and the second trained machine learning model on historical remote deposit transactions comprising on one or more of: remote deposit transactions, associated remote deposit matters, associated remote deposit remediation tasks, or associated remote deposit process outcomes.

Assignments (1)

Continuity (1)

References Cited (173)