Automated usage driven engineering

• US 10,768,924 B2
• Filed: 12/18/2019
• Issued: 09/08/2020
• Est. Priority Date: 10/03/2017
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Short, Matthew Thomas, Webb, Sunny, Schaeffer, Eric Philippe Henri, Krueger, Robert, Lio, Teodoro, Ellsworth, Scott Anthony
• Primary Examiner(s): Chen, Qing
• Assistant Examiner(s): Thatcher, Clint
• Application Number:
  US16/719,534
• Publication Number:
  US 20200125356A1
• Time in Patent Office: 265 Days
• Field of Search: 717172
• CPC Class Codes: G06F 8/65 Updates security arrangements therefor G06F21/57
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Implementations directed to providing a computer-implemented method for automating vehicle feature updates, the method being executed by one or more processors and comprising receiving telematics data identifying an actual usage of a vehicle; performing a gap analysis between the actual usage of the vehicle and an expected usage of the vehicle; determining a feature update based on the gap analysis; providing the feature update to a product engineering module when the feature cannot be implemented by a software update; and providing the feature update to an onboard computer system when the feature can be implemented by a software update.

Claims

1. A computer-implemented method for automating vehicle feature updates, the method being executed by one or more processors and comprising:

receiving telematics data identifying an actual usage of a first vehicle;

automatically performing a gap analysis, using a gap analysis component, to determine a difference between the actual usage of the first vehicle and an expected usage of the first vehicle based at least in part on the telematics data;

determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on the difference between the actual usage of the first vehicle and the expected usage of the first vehicle;

determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle; and

updating a selected feature on the onboard computer system of the active vehicle using the selected feature update when the selected feature update can be implemented by a software update.

2. The computer-implemented method of claim 1, wherein the selected feature update is a modification to a software component of the vehicle.

3. The computer-implemented method of claim 2, wherein the software component is a display feature shown on an adaptable display in the vehicle.

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

assigning a weight value to each of the plurality of feature updates based at least in part on a machine learning algorithm, and wherein determining the selected feature is determined at least in part based on the assigned weight value of a feature update meeting a threshold.

5. The computer-implemented method of claim 1, wherein the telematics data is collected by a plurality of onboard sensors associated with the vehicle.

6. The computer-implemented method of claim 5, wherein the telematics data includes internal vehicle and external vehicle information collected by the onboard sensors.

7. The computer-implemented method of claim 1, wherein the selected feature update is a corrective action.

8. A system, comprising:

one or more processors; and

a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for automating vehicle feature updates, the operations comprising;

receiving telematics data identifying an actual usage of a first vehicle;

automatically performing a gap analysis, using a gap analysis component, to determine a difference between the actual usage of the first vehicle and an expected usage of the first vehicle based at least in part on the telematics data;

determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on the difference between the actual usage of the first vehicle and the expected usage of the first vehicle;

determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle; and

updating a selected feature on the onboard computer system of the active vehicle using the selected feature update when the selected feature update can be implemented by a software update.

9. The system of claim 8, wherein the selected feature update is a modification to a software component of the vehicle.

10. The system of claim 9, wherein the software component is a display feature shown on an adaptable display in the vehicle.

11. The system of claim 8, wherein the operations further comprise:

assigning a weight value to each of the plurality of feature updates based at least in part on a machine learning algorithm, and wherein determining the selected feature is determined at least in part based on the assigned weight value of a feature update meeting a threshold.

12. The system of claim 8, wherein the telematics data is collected by a plurality of onboard sensors associated with the vehicle.

13. The system of claim 12, wherein the telematics data includes internal vehicle and external vehicle information collected by the onboard sensors.

14. The system of claim 8, wherein the selected feature update is a corrective action.

15. A method comprising:

receiving telematics data identifying an actual usage of a first vehicle;

automatically performing a gap analysis, using a gap analysis component, to determine a difference between the actual usage of the first vehicle and an expected usage of the first vehicle based at least in part on the telematics data;

determining, using a configuration adjustment component, a feature update that is implementable as software updates based at least in part on the difference between the actual usage of the first vehicle and the expected usage of the first vehicle; and

updating a feature on an onboard computer system of an active vehicle using the feature update when the feature update can be implemented by a software update.

16. The method of claim 15, wherein the feature update is a modification to a software component of the vehicle.

17. The method of claim 16, wherein the software component is a display feature shown on an adaptable display in the vehicle.

18. The method of claim 15, wherein the telematics data is collected by a plurality of onboard sensors associated with the vehicle.

19. The method of claim 18, wherein the telematics data includes internal vehicle and external vehicle information collected by the onboard sensors.

20. The method of claim 15, wherein the feature update is a corrective action.