IP Library Granted Patent US 12,450,667
Granted Patent B2
US 12,450,667 · App. 18/774,510 · Granted Oct 21, 2025

Automatically tracking driving activity

Inventors: Kristopher Keith Gaudin (Bloomington, IL); Andrew J. Zeglin (Normal, IL); Craig Benjamin Cope (Bloomington, IL); Joseph Harr (Bloomington, IL); Aaron Williams (Congerville, IL)
Assignee: State Farm Mutual Automobile Insurance Company
G06Q40/08G06Q30/0207G06Q30/0266
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Quick Facts
Patent No.
US 12,450,667
App. No.
18/774,510
Granted
Oct 21, 2025
Kind
B2
Abstract

A system and computer-implemented method detect and act upon deactivated vehicle components. The system and method include receiving measurements data associated with driving activity. The measurements data includes an indication that at least one feature of an Advanced Driver Assistance System (ADAS) of a vehicle has been deactivated for a driving activity. The system and method may include receiving historical driving data including a history of at least one driving activity aided by activation of the alert from the ADAS feature. The system and method may compare the measurements data to the historical driving data, determine a likelihood level that the feature of the ADAS would have provided the alert had the feature been activated based upon the comparing, and set, based at least upon the determining, at least a portion of an operator profile associated with an operator of the vehicle with the likelihood level.

Claims (81)

1. A computer-implemented method for configuring a vehicle component to assist with driving, the method comprising:

obtaining, by a processor and during a current trip of a vehicle, operating data indicating the vehicle component is deactivated;

obtaining, by the processor, historical driving data including historical driving activities performed while the vehicle component was activated;

determining, by the processor, and based on the operating data and the historical driving data, a likelihood value indicative of whether the vehicle component will be activated during the current trip;

generating, by the processor and based on the likelihood value, an alert to activate the vehicle component; and

transmitting, by the processor, the alert to a computing device associated with the vehicle, the alert causing the computing device to activate the vehicle component.

2. The computer-implemented method of claim 1 , wherein the historical driving data comprises:

historical driving behaviors of a plurality of operators that caused activation of the vehicle component, and

historical reaction data of the plurality of operators in response to the activation of the vehicle component.

3. The computer-implemented method of claim 2 , further comprising:

determining, by the processor, that the activation of the vehicle component caused at least one valid alert;

determining, by the processor and based on the historical reaction data, that the plurality of operators did not respond to the at least one valid alert; and

based on determining that the plurality of operators did not respond to the at least one valid alert, increasing, by the processor, the likelihood value.

4. The computer-implemented method of claim 2 , further comprising:

determining, by the processor, that the activation of the vehicle component caused at least one false alert;

determining, by the processor and based on the historical reaction data, that the plurality of operators did not respond to the at least one false alert; and

based on determining that the plurality of operators did not respond to the at least one false alert, decreasing, by the processor, the likelihood value.

5. The computer-implemented method of claim 1 , wherein the operating data indicates a steering pattern of the vehicle during the current trip, and the method further comprises:

determining, by the processor and based on the historical driving data, a historical steering pattern that has caused activation of the vehicle component;

determining, by the processor, that the steering pattern of the vehicle during the current trip matches the historical steering pattern; and

determining, by the processor, the likelihood value based on the steering pattern of the vehicle during the current trip matching the historical steering pattern.

6. The computer-implemented method of claim 1 , wherein the operating data indicates an acceleration pattern of the vehicle during the current trip, and the method further comprises:

determining, by the processor and based on the historical driving data, a historical acceleration pattern that has caused activation of the vehicle component;

determining, by the processor, that the acceleration pattern of the vehicle during the current trip matches the historical acceleration pattern; and

based on the acceleration pattern of the vehicle during the current trip matching the historical acceleration pattern, determining, by the processor, the likelihood value.

7. The computer-implemented method of claim 1 , further comprising:

associating, by the processor, the likelihood value with a profile associated with an operator operating the vehicle during the current trip; and

adjusting, by the processor and based on the profile, at least one of a price to risk model, a credit rating, an insurance rating, a permanent credit, or a temporary credit associated with the operator.

8. The computer-implemented method of claim 1 , further comprising:

determining, by the processor, that the likelihood value is equal to or greater than a threshold; and

based on the likelihood value being equal to or greater than the threshold, generating, by the processor, the alert to activate the vehicle component.

9. The computer-implemented method of claim 1 , further comprising:

switching, by the processor, an operation of the vehicle to be subject to an automated control by the vehicle component.

10. The computer-implemented method of claim 7 , further comprising:

transmitting, by the processor, the profile to an entity, wherein receiving the profile causes the entity to:

adjust a review associated with the operator, and

based at least in part on adjusting the review, automatically apply a rental discount to the operator.

11. A computer system for configuring a vehicle component to assist with driving, comprising:

a processor, and

a non-transitory computer-readable memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform operations including:

obtaining, via a sensor and during a current trip of a vehicle, operating data indicating the vehicle component is deactivated;

obtaining historical driving data including historical driving activities performed while the vehicle component was activated;

determining, based on the operating data and the historical driving data, a likelihood value indicative of whether the vehicle component will be activated during the current trip;

generating, based on the likelihood value, an alert to activate the vehicle component; and

transmitting the alert to a computing device associated with the vehicle, the alert causing the computing device to activate the vehicle component.

12. The computer system of claim 11 , wherein the historical driving data comprises:

historical driving behaviors of a plurality of operators that caused activation of the vehicle component, and

historical reaction data of the plurality of operators in response to the activation of the vehicle component.

13. The computer system of claim 12 , wherein the operations further comprise:

determining that the activation of the vehicle component caused at least one valid alert;

determining, based on the historical reaction data, that the plurality of operators did not respond to the at least one valid alert; and

based on determining that the plurality of operators did not respond to the at least one valid alert, increasing the likelihood value.

14. The computer system of claim 12 , wherein the operations further comprise:

determining that the activation of the vehicle component caused at least one false alert;

determining, based on the historical reaction data, that the plurality of operators did not respond to the at least one false alert; and

based on determining that the plurality of operators did not respond to the at least one false alert, decreasing the likelihood value.

15. The computer system of claim 11 , wherein the operating data indicates a steering pattern of the vehicle during the current trip, and the operations further comprise:

determining, based on the historical driving data, a historical steering pattern that has caused activation of the vehicle component;

determining that the steering pattern of the vehicle during the current trip matches the historical steering pattern; and

based on the steering pattern of the vehicle during the current trip, matching the historical steering pattern, determining the likelihood value.

16. The computer system of claim 11 , wherein the operating data indicates an acceleration pattern of the vehicle during the current trip, and the operations further comprise:

determining, based on the historical driving data, a historical acceleration pattern that has caused activation of the vehicle component;

determining that the acceleration pattern of the vehicle during the current trip matches the historical acceleration pattern; and

based on the acceleration pattern of the vehicle during the current trip matching the historical acceleration pattern, determining the likelihood value.

17. A non-transitory computer-readable memory storing thereon instructions for configuring a vehicle component to assist with driving, that, when executed by a processor, cause the processor to perform operations including:

obtaining, via a sensor and during a current trip of a vehicle, operating data indicating the vehicle component is deactivated;

obtaining historical driving data including historical driving activities performed while the vehicle component was activated;

determining, based on the operating data and the historical driving data, a likelihood value indicative of whether the vehicle component will be activated during the current trip;

generating, based on the likelihood value, an alert to activate the vehicle component; and

transmitting the alert to a computing device associated with the vehicle, the alert causing the computing device to activate the vehicle component.

18. The non-transitory computer-readable memory of claim 17 , wherein the historical driving data comprises:

historical driving behaviors of a plurality of operators that caused activation of the vehicle component, and

historical reaction data of the plurality of operators in response to the activation of the vehicle component.

19. The non-transitory computer-readable memory of claim 18 , wherein the instructions, when executed by a processor, cause the processor to perform operations including:

determining that the activation of the vehicle component caused at least one valid alert;

determining, based on the historical reaction data, that the plurality of operators did not respond to the at least one valid alert; and

based on determining that the plurality of operators did not respond to the at least one valid alert, increasing the likelihood value.

20. The non-transitory computer-readable memory of claim 18 , wherein the instructions, when executed by a processor, cause the processor to perform operations including:

determining that the activation of the vehicle component caused at least one false alert;

determining, based on the historical reaction data, that the plurality of operators did not respond to the at least one false alert; and

based on determining that the plurality of operators did not respond to the at least one false alert, decreasing the likelihood value.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 16, 2024
From: GAUDIN, KRISTOPHER KEITH; ZEGLIN, ANDREW JOSEPH; COPE, CRAIG; HARR, JOSEPH; WILLIAMS, AARON
To: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Reel/Frame 068002/0200 →
Continuity (7)
Continuation 17092589 · Nov 9, 2020
Continuation 16053881 · Aug 3, 2018
Provisional Application 62563729 · Sep 27, 2017
Provisional Application 62563722 · Sep 27, 2017
Provisional Application 62563808 · Sep 27, 2017
Provisional Application 62563818 · Sep 27, 2017
Related Publication 20240371216A1 · Nov 7, 2024
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