IP Library Granted Patent US 12,399,966
Granted Patent B2
US 12,399,966 · App. 18/333,196 · Granted Aug 26, 2025

Modifying application function based on login attempt confidence score

Inventors: Amy Rose (Chapel Hill, NC); Joseph Aguayo (Fuquay Varina, NC); David Stone (Wake Forest, NC)
Assignee: Truist Bank
G06F21/316H04L63/0876H04L63/1483H04L63/20
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,399,966
App. No.
18/333,196
Granted
Aug 26, 2025
Kind
B2
Abstract

Account permissions and data accessibility can be modified based on level of confidence for a login attempt to the account. User activity observations corresponding to one or more login attempts to access a user account can be stored. A confidence score associated with a successful login attempt of the user account can be determined. The confidence score is based on the user activity observations. A level of access to an application with functions and data for the user account can be determined. The level of access is based on the confidence score. The level of access is associated with the functions and the data that are executable and accessible subsequent to the successful login attempt.

Claims (56)

1. A system comprising:

a processor; and

a non-transitory computer-readable memory comprising instructions that are executable by the processor for causing the processor to:

receive a first set of user activity observations associated with an authorized user of a user account;

generate, based on the first set of user activity observations, a user profile of user behavior for the authorized user associated with the user account;

validate a current login attempt as successful by comparing credentials received in relation to the current login attempt with stored account credentials; and

subsequent to validating the current login attempt as successful and prior to granting access to the user account:

determine a confidence score based on a second set of user activity observations associated with the current login attempt, the confidence score being determined from among a plurality of confidence scores associated with a plurality of access levels capable of being applied to the user account; and

determine a level of access among the plurality of access levels to grant to the user account based on a comparison between the user profile and the second set of user activity observations associated with the current login attempt and based on the confidence score, each access level of the plurality of access levels conferring a respective level of access to the user account that is different than other access levels of the plurality of access levels.

2. The system of claim 1 , wherein the memory further comprises instructions that are executable by the processor for causing the processor to, subsequent to granting access to the user account:

receive, from the authorized user, a modification to the plurality of access levels for subsequent login attempts by the authorized user; and

in response to receiving the modification, configure the plurality of access levels based on the modification.

3. The system of claim 1 , wherein the memory further comprises instructions that are executable by the processor for causing the processor to generate the user profile using a machine-learning system.

4. The system of claim 1 , wherein the plurality of confidence scores include a first confidence score associated with a first login attempt type involving a successful login attempt by the authorized user, a second confidence score associated with a second login attempt type involving an unsuccessful login attempt by the authorized user, a third confidence score associated with a third login attempt type involving a successful login attempt by the authorized user in an unsecure environment, and a fourth confidence score associated with a fourth login attempt type involving a hacking attempt by an unauthorized user.

5. The system of claim 1 , and wherein the memory further comprises instructions that are executable by the processor for causing the processor to generate the user profile by:

determining the user profile based on device characteristics associated with a device used by the authorized user to access the user account; and

storing the user behavior and the device characteristics in a database as the user profile.

6. The system of claim 5 , wherein the device characteristics comprise an IP address for the device, a MAC address for the device, a device identification for the device, browser settings for the device, a location for the device, or a network used by the device for a login attempt.

7. The system of claim 1 , wherein the memory further comprises instructions that are executable by the processor for causing the processor to:

generate a restriction rule for the user account based on the first set of user activity observations,

wherein the level of access to grant to the user account for the current login attempt is further determined based on the restriction rule.

8. The system of claim 7 , wherein the memory further comprises instructions that are executable by the processor for causing the processor to generate the restriction rule by:

determining a pattern of authorized user behavior based on the first set of user activity observations; and

generating the restriction rule based on the pattern of authorized user behavior.

9. The system of claim 7 , wherein the restriction rule corresponds to a particular confidence score of the plurality of confidence scores.

10. A method comprising:

receiving, by a processor, a first set of user activity observations associated with an authorized user of a user account;

generating, by the processor and based on the first set of user activity observations, a user profile of user behavior for the authorized user associated with the user account;

validating, by the processor, a current login attempt as successful by comparing credentials received in relation to the current login attempt with stored account credentials; and

subsequent to validating the current login attempt as successful and prior to granting access to the user account:

determining, by the processor, a confidence score based on a comparison between the user profile and a second set of user activity observations associated with the current login attempt, the confidence score being determined from among a plurality of confidence scores associated with a plurality of access levels capable of being applied to the user account; and

determining, by the processor, a level of access among the plurality of access levels to grant to the user account based on the confidence score, each access level of the plurality of access levels conferring a respective level of access to the user account that is different than other access levels of the plurality of access levels.

11. The method of claim 10 , wherein the method further comprises, subsequent to granting access to the user account:

receiving, from the authorized user, a modification to the plurality of access levels for subsequent login attempts by the authorized user; and

in response to receiving the modification, configuring the plurality of access levels based on the modification.

12. The method of claim 10 , further comprising generating the user profile using a machine-learning system.

13. The method of claim 10 , wherein the plurality of confidence scores include a first confidence score associated with a first login attempt type involving a successful login attempt by the authorized user, a second confidence score associated with a second login attempt type involving an unsuccessful login attempt by the authorized user, and a third confidence score associated with a third login attempt type involving a successful login attempt by the authorized user via an unsecured network.

14. The method of claim 10 , wherein generating the user profile further comprises:

determining the user profile based on device characteristics associated with a device used by the authorized user to access the user account; and

storing the user behavior and the device characteristics in a database as the user profile.

15. The method of claim 14 , wherein the device characteristics includes an IP address for the device, a MAC address for the device, a device identification for the device, browser settings for the device, a location for the device, or a network used by the device for a login attempt.

16. A non-transitory computer-readable medium having program code that is executable by a processor for causing the processor to perform operations, the operations comprising:

receiving a first set of user activity observations associated with an authorized user of a user account;

generating, based on the first set of user activity observations, a user profile of user behavior for the authorized user associated with the user account;

validating a current login attempt as successful by comparing credentials received in relation to the current login attempt with stored account credentials; and

subsequent to validating the current login attempt as successful and prior to granting access to the user account:

determining a confidence score based on a comparison between the user profile and a second set of user activity observations associated with the current login attempt, the confidence score being determined from among a plurality of confidence scores associated with a plurality of access levels capable of being applied to the user account; and

determining a level of access among the plurality of access levels to grant to the user account based on the confidence score, each access level of the plurality of access levels conferring a respective level of access to the user account that is different than other access levels of the plurality of access levels.

17. The non-transitory computer-readable medium of claim 16 , further comprising program code that is executable by the processor for causing the processor to, subsequent to granting access to the user account:

receive, from the authorized user, a modification to the plurality of access levels for subsequent login attempts by the authorized user; and

in response to receiving the modification, configure the plurality of access levels based on the modification.

18. The non-transitory computer-readable medium of claim 16 , further comprising program code that is executable by the processor for causing the processor to generate the user profile using a machine-learning system.

19. The non-transitory computer-readable medium of claim 18 , wherein the plurality of confidence scores include a first confidence score associated with a first login attempt type involving a successful login attempt by the authorized user, and a second confidence score associated with a second login attempt type involving an unsuccessful login attempt by the authorized user.

20. The non-transitory computer-readable medium of claim 15 , further comprising program code that is executable by the processor for causing the processor to generate the user profile further by:

determining the user profile based on device characteristics associated with a device used by the authorized user to access the user account; and

storing the user behavior and the device characteristics in a database as the user profile.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 1, 2024
From: ROSE, AMY; AGUAYO, JOSEPH; STONE, DAVID
To: BRANCH BANKING AND TRUST COMPANY
Reel/Frame 067882/0814 →
CHANGE OF NAME Recorded Jul 1, 2024
From: BRANCH BANKING AND TRUST COMPANY
To: TRUIST BANK
Reel/Frame 068097/0544 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 16, 2023
From: HUANG, KUANTA; LEE, YUHAO; ZHANG, DACHENG; TONG, HUI; SHEN, XIAOBIN; LI, SHIPENG; YANG, CHAO; LI, DONGSHENG
To: BOE TECHNOLOGY GROUP CO., LTD.
Reel/Frame 063971/0880 →
Continuity (3)
Continuation 17826804 · May 27, 2022
Continuation 16353419 · Mar 14, 2019
Related Publication 20230325479A1 · Oct 12, 2023
References Cited (12)
US 9349014B1 · Hubing et al. · 2016 [cited by applicant]
US 10237298B1 · Nguyen · 2019 [cited by examiner]
US 11921856B1 · Salour · 2024 [cited by examiner]
US 20100281059A1 · Lynch · 2010 [cited by examiner]
US 20160163320A1 · Scheffer et al. · 2016 [cited by applicant]
US 20180046796A1 · Wright et al. · 2018 [cited by applicant]
US 20180077174A1 · Hebert · 2018 [cited by examiner]
US 20190019363A1 · Zellner · 2019 [cited by applicant]
US 20190220583A1 · Douglas et al. · 2019 [cited by applicant]
US 20200026830A1 · Alameh et al. · 2020 [cited by applicant]
US 20200110870A1 · Girdhar · 2020 [cited by examiner]
US 20200280573A1 · Johnson · 2020 [cited by examiner]