Granted Patent B2

US 12,254,724 · App. 16/940,215 · Granted Mar 18, 2025

Inventors: Callum Brook (Piedmont, CA); Theobolt N. Leung (San Francisco, CA)

Assignee: QUANATA, LLC

G07C5/0816G06F18/214G06N20/00G06V20/597G07C5/008H04W4/40

Abstract

Claims (82)

1. A computer-implemented method for providing feedback, the method comprising:

receiving, by one or more processors, one or more vehicle tire images for a vehicle;

analyzing, by the one or more processors, the one or more vehicle tire images to determine driving behavior data associated with a user, the driving behavior data indicative of an impact associated with the user on longevity of the vehicle;

determining, by the one or more processors, a driving performance metric associated with the user based at least in part upon the driving behavior data by at least:

applying, by the one or more processors, the driving behavior data associated with the user to a trained machine learning model for determining the driving performance metric,

wherein the trained machine learning model comprises a neural network and was trained:

to compare features identified in the one or more vehicle tire images with template features comprising tire dimensions, tire shapes, and tire thickness metrics at one or more tire positions,

using a plurality of sets of driving behavior data associated with a plurality of users with a plurality of vehicles each having known longevity, and

using a plurality of indications of respective known longevities of the plurality of vehicles associated with the plurality of users;

wherein the trained machine learning model creates numerical representations of the features identified in the one or more vehicle tire images to generate feature vectors that are compared to feature vectors of template objects using a vector distance metric to generate a tire wear score that is used to determine the driving performance metric associated with the user; and

wherein the features are extracted from the one or more vehicle tire images by (i) detecting stable regions within the one or more vehicle tire images using a scale-invariant feature transform, (ii) locating keypoints in the stable regions, and (iii) forming a bounding box around the keypoints representing the feature; and

generating, by the one or more processors, the feedback associated with the user based at least in part upon the driving performance metric and related to the impact on longevity of the vehicle; and

providing, by the one or more processors, the feedback to the user.

2. The method of claim 1 , further comprising:

obtaining, by the one or more processors, a plurality of driving performance metrics associated with a plurality of other users,

wherein generating the feedback associated with the user includes:

comparing, by the one or more processors, the driving performance metric associated with the user to the plurality of driving performance metrics associated with the plurality of other users; and

wherein providing, by the one or more processors, the feedback to the user includes:

providing, by the one or more processors, an indication of the driving performance metric associated with the user relative to the plurality of driving performance metrics associated with the plurality of other users to the user.

3. The method of claim 2 , wherein each other user of the plurality of other users is associated with a respective other vehicle that is at least one of a same make, a same model, or a same year as the vehicle associated with the user.

4. The method of claim 1 , wherein the driving performance metric includes a wear and tear score associated with wear and tear on the vehicle.

5. The method of claim 1 , wherein analyzing the one or more vehicle tire images comprises determining a thinning of tire material on a tire of the vehicle.

6. The method of claim 1 , further comprising:

providing, by the one or more processors, an indication of a condition of the vehicle based at least in part upon the driving performance metric.

7. The method of claim 1 , further comprising:

receiving additional driving behavior data from a mobile device of the user, sensors on the vehicle, or via user input on the mobile device,

wherein determining the driving performance metric is further based in part upon analyzing the additional driving behavior data.

8. The method of claim 1 , wherein providing the feedback to the user includes:

providing, by the one or more processors, a prompt to the user to change one or more driving behaviors based at least in part upon the driving performance metric.

9. A computing system for providing feedback, the computing system comprising:

one or more processors; and

a program memory storing executable instructions that, when executed by the one or more processors, cause the computing system to:

receive one or more vehicle tire images for a vehicle;

analyze the one or more vehicle tire images to determine driving behavior data associated with a user, the driving behavior data indicative of an impact associated with the user on longevity of the vehicle;

determine a driving performance metric associated with the user based at least in part upon the driving behavior data by at least:

applying the driving behavior data associated with the user to a trained machine learning model for determining the driving performance metric,

wherein the trained machine learning model comprises a neural network and was trained:

 to compare features identified in the one or more vehicle tire images with template features comprising tire dimensions, tire shapes, and tire thickness metrics at one or more tire positions,

 using a plurality of sets of driving behavior data associated with a plurality of users with a plurality of vehicles each having known longevity, and

 using a plurality of indications of respective known longevities of the plurality of vehicles associated with the plurality of users; and

wherein the trained machine learning model creates numerical representations of the features identified in the one or more vehicle tire images to generate feature vectors that are compared to feature vectors of template objects using a vector distance metric to generate a tire wear score that is used to determine the driving performance metric associated with the user; and

wherein the features are extracted from the one or more vehicle tire images by (i) detecting stable regions within the one or more vehicle tire images using a scale-invariant feature transform, (ii) locating keypoints in the stable regions, and (iii) forming a bounding box around the keypoints representing the feature; and

generate the feedback associated with the user based at least in part upon the driving performance metric and related to the impact on longevity of the vehicle; and

provide the feedback to the user.

10. The computing system of claim 9 , wherein the program memory further stores executable instructions that cause the computing system to:

obtain a plurality of driving performance metrics associated with a plurality of other users,

wherein to generate the feedback associated with the user includes:

comparing the driving performance metric associated with the user to the plurality of driving performance metrics associated with the plurality of other users; and

wherein to provide the feedback to the user includes:

providing an indication of the driving performance metric associated with the user relative to the plurality of driving performance metrics associated with the plurality of other users to the user.

11. The computing system of claim 10 , wherein each other user of the plurality of other users is associated with a respective other vehicle that is at least one of a same make, a same model, or a same year as the vehicle associated with the user.

12. The computing system of claim 9 , wherein the driving performance metric includes:

a wear and tear score associated with wear and tear on the vehicle.

13. The computing system of claim 9 , wherein to analyze the one or more vehicle tire images comprises to determine a thinning of tire material on a tire of the vehicle.

14. The computing system of claim 9 , wherein the program memory further stores executable instructions that cause the computing system to:

provide an indication of a condition of the vehicle based at least in part upon the driving performance metric.

15. The computing system of claim 9 , wherein the program memory further stores executable instructions that cause the computing system to:

receive additional driving behavior data from a mobile device of the user, sensors on the vehicle, or via user input on the mobile device,

wherein to determine the driving performance metric is further based at least in part upon analyzing the additional driving behavior data.

16. The computing system of claim 9 , wherein to provide the feedback to the user includes to provide a prompt to the user to change one or more driving behaviors based at least in part upon the driving performance metric.

17. One or more non-transitory computer-readable media storing instructions for providing feedback, that when executed by one or more processors of a computing device, cause the computing device to:

receive one or more vehicle tire images for a vehicle;

analyze the one or more vehicle tire images to determine driving behavior data associated with a user, the driving behavior data indicative of an impact associated with the user on longevity of the vehicle;

determine a driving performance metric associated with the user based at least in part upon the driving behavior data by at least:

applying the driving behavior data associated with the user to a trained machine learning model for determining driving performance metrics,

wherein the trained machine learning model comprises a neural network and was trained:

to compare features identified in the one or more vehicle tire images with template features comprising tire dimensions, tire shapes, and tire thickness metrics at one or more tire positions,

using a plurality of sets of driving behavior data associated with a plurality of users with a plurality of vehicles each having known longevity, and

using a plurality of indications of respective known longevities of the plurality of vehicles associated with the plurality of users; and

wherein the trained machine learning model creates numerical representations of the features identified in the one or more vehicle tire images to generate feature vectors that are compared to feature vectors of template objects using a vector distance metric to generate a tire wear score that is used to determine the driving performance metric associated with the user; and

wherein the features are extracted from the one or more vehicle tire images by (i) detecting stable regions within the one or more vehicle tire images using a scale-invariant feature transform, (ii) locating keypoints in the stable regions, and (iii) forming a bounding box around the keypoints representing the feature; and

generate the feedback associated with the user based at least in part upon the driving performance metric and related to the impact on longevity of the vehicle; and

provide the feedback to the user.

18. The one or more non-transitory computer-readable media of claim 17 , storing additional instructions, that when executed by the one or more processors of the computing device, further cause the computing device to:

obtain a plurality of driving performance metrics associated with a plurality of other users,

wherein to generate the feedback associated with the user includes:

comparing the driving performance metric associated with the user to the plurality of driving performance metrics associated with the plurality of other users; and

wherein to provide the feedback to the user includes:

providing an indication of the driving performance metric associated with the user relative to the plurality of driving performance metrics associated with the plurality of other users to the user.

19. The one or more non-transitory computer-readable media of claim 18 , wherein each other user of the plurality of other users is associated with a respective other vehicle that is at least one of a same make, a same model, or a same year as the vehicle associated with the user.

20. The one or more non-transitory computer-readable media of claim 17 , wherein the driving performance metric includes:

a wear and tear score associated with wear and tear on the vehicle.

Assignments (2)

Continuity (1)

References Cited (42)