Component design based on sensor data

• US 10,507,775 B2
• Filed: 09/05/2017
• Issued: 12/17/2019
• Est. Priority Date: 08/17/2017
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Liongosari, Edy S., Kass, Alex M., Almquist, Niclas
• Primary Examiner(s): Lonsberry, Hunter B
• Assistant Examiner(s): Martinez Borrero, Luis A
• Application Number:
  US15/695,796
• Publication Number:
  US 20190057166A1
• Time in Patent Office: 833 Days
• CPC Class Codes: A61B 5/6893 Cars
• CPC Class Codes: B60N 2/0244 with logic circuits
• CPC Class Codes: B60N 2/0268 using sensors or detectors for adapting the seat or seat part, e.g. to the position of an occupant
• CPC Class Codes: B60N 2/0273 taking into account user data, e.g. knee height or physical state
• CPC Class Codes: B60N 2/0278 using sensors external to the seat for measurements in relation to the seat adjustment, e.g. for identifying the presence of obstacles or the appropriateness of the occupants position
• CPC Class Codes: B60N 2220/10 using a database
• CPC Class Codes: B60N 2220/20 using a deterministic algorithm
• CPC Class Codes: B60N 2220/30 using a neural network
• CPC Class Codes: B60R 16/037 for occupant comfort , e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel
• CPC Class Codes: B60R 21/01552 detecting position of specific human body parts, e.g. face, eyes or hands
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/085 Changing the parameters of the control units, e.g. changing limit values, working points by control input
• CPC Class Codes: G05B 19/0426 Programming the control sequence
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Abstract

Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Claims

1. A system comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising;

receiving, from each of a plurality of instances of a vehicle component, sensor data describing at least one adjustment to the vehicle component that a respective user made while the user was using the vehicle component to improve the user'"'"'s comfort in using the vehicle component, and data indicating that the at least one adjustment to the vehicle component likely failed to adequately improve the user'"'"'s comfort in using the vehicle component;

in response to receiving the sensor data and the data indicating that the at least one adjustment to the vehicle component likely failed to adequately improve the user'"'"'s comfort in using the vehicle component, generating design information that describes at least one modification to be made to a subsequent version of the component to improve users'"'"' comfort in using the vehicle component, comprising aggregating the sensor data, then providing the aggregated sensor data to a machine learning-trained module that, for given sensor data, outputs one or more proposed modifications for improving the users'"'"' comfort in using vehicle components; and

providing the design information for output through a user interface.

2. The system of claim 1, wherein one or more of the proposed modifications comprises proposed modifications to a design of the vehicle component.

3. The system of claim 1, wherein one or more of the proposed modifications comprises a recommendation to remove a feature from the vehicle component.

4. The system of claim 1, wherein one or more of the proposed modifications comprises a recommendation to add a feature to the vehicle component.

5. The system of claim 1, wherein the aggregated sensor data is provided to the machine learning-trained module in batches.

6. The system of claim 1, wherein the design information is provided for output in a computer-aided design (CAD) or a computer-aided manufacturing (CAM) interface.

7. The system of claim 1, wherein one or more of the proposed modifications comprises a recommendation to extend a range of motion of the vehicle component.

8. A computer-implemented method comprising:

receiving, by one or more processors and from each of a plurality of instances of a vehicle component, sensor data describing at least one adjustment to the vehicle component that a respective user made while the user was using the vehicle component to improve the user'"'"'s comfort in using the vehicle component, and data indicating that the at least one adjustment to the vehicle component likely failed to adequately improve the user'"'"'s comfort in using the vehicle component;

in response to receiving the sensor data and the data indicating that the at least one adjustment to the vehicle component likely failed to adequately improve the user'"'"'s comfort in using the vehicle component, generating, by the one or more processors, design information that describes at least one modification to be made to a subsequent version of the component to improve users'"'"' comfort in using the vehicle component, comprising aggregating the sensor data, then providing the aggregated sensor data to a machine learning-trained module that, for given sensor data, outputs one or more proposed modifications for improving the users'"'"' comfort in using vehicle components; and

providing, by the one or more processors, the design information for output through a user interface.

9. The method of claim 8, wherein one or more of the proposed modifications comprises proposed modifications to a design of the vehicle component.

10. The method of claim 8, wherein one or more of the proposed modifications comprises a recommendation to remove a feature from the vehicle component.

11. The method of claim 8, wherein one or more of the proposed modifications comprises a recommendation to add a feature to the vehicle component.

12. The method of claim 8, wherein the aggregated sensor data is provided to the machine learning-trained module in batches.

13. The method of claim 8, wherein the design information is provided for output in a computer-aided design (CAD) or a computer-aided manufacturing (CAM) interface.

14. The method of claim 8, wherein one or more of the proposed modifications comprises a recommendation to extend a range of motion of the vehicle component.

15. A non-transitory computer-readable storage medium storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

receiving, from each of a plurality of instances of a vehicle component, sensor data describing at least one adjustment to the vehicle component that a respective user made while the user was using the vehicle component to improve the user'"'"'s comfort in using the vehicle component, and data indicating that the at least one adjustment to the vehicle component likely failed to adequately improve the user'"'"'s comfort in using the vehicle component;

in response to receiving the sensor data and the data indicating that the at least one adjustment to the vehicle component likely failed to adequately improve the user'"'"'s comfort in using the vehicle component, generating design information that describes at least one modification to be made to a subsequent version of the component to improve users'"'"' comfort in using the vehicle component, comprising aggregating the sensor data, then providing the aggregated sensor data to a machine learning-trained module that, for given sensor data, outputs one or more proposed modifications for improving the users'"'"' comfort in using vehicle components; and

providing the design information for output through a user interface.

16. The medium of claim 15, wherein one or more of the proposed modifications comprises proposed modifications to a design of the vehicle component.

17. The medium of claim 15, wherein one or more of the proposed modifications comprises a recommendation to remove a feature from the vehicle component.

18. The medium of claim 15, wherein one or more of the proposed modifications comprises a recommendation to add a feature to the vehicle component.

19. The medium of claim 15, wherein the aggregated sensor data is provided to the machine learning-trained module in batches.

20. The medium of claim 15, wherein the design information is provided for output in a computer-aided design (CAD) or a computer-aided manufacturing (CAM) interface.