Automated usage driven engineering

Patent metadata

Field	Value
Publication number	US-10545751-B2
Application number	US-201816041287-A
Country	US
Kind code	B2
Filing date	Jul 20, 2018
Priority date	Oct 3, 2017
Publication date	Jan 28, 2020
Grant date	Jan 28, 2020

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.

First claim

What is claimed is:

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;
aggregating, at a data aggregator component, collected vehicle data for other vehicles;
determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on (i) the gap analysis difference between the actual usage of the first vehicle and the expected usage of the first vehicle, and (ii) the collected vehicle data for other vehicles;
assigning a weight value to each of the plurality of feature updates based at least in part on the gap analysis;
determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle based at least in part on a weight value assigned to the selected feature update meeting a threshold; 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 and external information collected by the onboard sensors.

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

8. One or more non-transitory computer-readable storage media coupled to 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;
aggregating, at a data aggregate component, collected vehicle data for other vehicles;
determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on (i) the gap analysis difference between the actual usage of the first vehicle and the expected usage of the first vehicle, and (ii) the collected vehicle data for other vehicles;
assigning a weight value to each of the plurality of feature updates based at least in part on the gap analysis;
determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle based at least in part on a weight value assigned to the selected feature update meeting a threshold; 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 non-transitory computer-readable storage media of claim 8 , wherein the selected feature update is a modification to a software component of the vehicle.

10. The non-transitory computer-readable storage media of claim 9 , wherein the software component is a display feature shown on an adaptable display in the vehicle.

11. The non-transitory computer-readable storage media of claim 8 , wherein the operations further comprise:
assigning a weight value to the plurality of feature updates based at least in part on a machine learning algorithm, and wherein determining the selected feature at least in part based on the assigned weight value meeting a threshold.

12. The non-transitory computer-readable storage media of claim 8 , wherein the telematics data is collected by a plurality of onboard sensors associated with the vehicle.

13. The non-transitory computer-readable storage media of claim 12 , wherein the telematics data includes internal and external information collected by the onboard sensors.

14. 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;
aggregating, at a data aggregator component, collected vehicle data for other vehicles;
determining, using a configuration adjustment component, a plurality of feature updates that are implementable as software updates based at least in part on (i) the gap analysis difference between the actual usage of the first vehicle and the expected usage of the first vehicle, and (ii) the collected vehicle data for other vehicles;
assigning a weight value to each of the plurality of feature updates based at least in part on the gap analysis;
determining a selected feature update from the plurality of feature updates to provide to an onboard computer system of an active vehicle based at least in part on a weight value assigned to the selected feature update meeting a threshold; 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.

15. The system of claim 14 , wherein the selected feature update is a modification to a software component of the vehicle.

16. The system of claim 15 , wherein the software component is a display feature shown on an adaptable display in the vehicle.

17. The system of claim 14 , wherein a product engineering module validates the selected feature update.

18. The system of claim 17 , wherein validating the selected feature updates includes employing virtual reality, digital simulation, or micro-factories within a respective region.

19. The system of claim 18 , wherein the selected feature update is integrated into a next version of a model of the vehicle once validated by the product engineering module.

20. The system of claim 17 , wherein the selected feature update is a modification to a physical aspect of the vehicl

Assignees

• Accenture Global Solutions Ltd

Inventors

• Short Matthew Thomas
• Webb Sunny
• Schaeffer Eric Philippe Henri
• Krueger Robert
• Lio Teodoro
• Ellsworth Scott Anthony

Classifications

• G06Q10/00 Primary

  Administration; Management · CPC title

• G06F8/65 Primary

  Updates (security arrangements therefor G06F21/57) · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title