Component configuration based on sensor data

Patent metadata

Field	Value
Publication number	US-10507774-B2
Application number	US-201715679680-A
Country	US
Kind code	B2
Filing date	Aug 17, 2017
Priority date	Aug 17, 2017
Publication date	Dec 17, 2019
Grant date	Dec 17, 2019

Abstract

Techniques are described for component configuration 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 the use of component(s) of the system by individual(s). The sensor data is analyzed (e.g., in real time) to determine an updated configuration for component(s) (e.g., an adjustment to the seat back, lumbar support of a car seat, etc.). The updated configuration may be communicated to the individual as a recommended configuration. In some implementations, the updated configuration may be communicated directly to the component which, on receiving and processing the configuration update, sends signals to various actuators to move the subcomponents of the component into the updated configuration. In some implementations, the sensor data is used to train, through machine learning, a model that provides configuration update(s) for component(s) based on the input sensor data.

First claim

The invention claimed is:

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 first sensor data describing at least one adjustment to a vehicle component that a user is using, that the user has made to improve the user's comfort in using the vehicle component;
after the user has made the adjustment to the vehicle component, receiving second sensor data that is other than data that reflects an explicit input, by the user, that the adjustment to the vehicle component failed to adequately improve the user's comfort in using the vehicle component, then automatically determining, based on the second sensor data, that the adjustment to the vehicle component likely failed to adequately improve the user's comfort in using the vehicle component;
in response to automatically determining that the adjustment to the vehicle component likely failed to adequately improve the user's comfort in using the vehicle component, providing, to a machine learning-trained model that is trained based on adjustments to similar vehicle components by other users, to output additional adjustments to the vehicle component that can be automatically made to improve users' comfort in using the vehicle component, (i) the first sensor data and (ii) data indicating a current state of the adjusted vehicle component;
receiving, from the machine learning-trained model, an additional adjustment that can be automatically made to improve the user's comfort in using the vehicle component; and
communicating the additional adjustment to the vehicle component.

2. The system of claim 1 , wherein determining that the adjustment failed to adequately improve the user's comfort comprises determining that the user is fidgeting while using the vehicle component.

3. The system of claim 1 , wherein determining that the adjustment failed to adequately improve the user's comfort comprises determining that the user has made one or more additional adjustments to the vehicle component.

4. The system of claim 1 , wherein the first sensor data is generated by one or more sensors inside of the vehicle component, and the second sensor data is generated by one or more sensors that are external to the vehicle component.

5. The system of claim 1 , wherein the operations comprise prompting the user with information regarding the additional adjustment contemporaneously with communicating the additional adjustment to the vehicle component.

6. The system of claim 1 , wherein the operations comprise determining an estimated height or weight of the user, wherein the estimated height or weight are provided to the machine learning-trained model.

7. The system of claim 1 , wherein the second sensor data comprise audio data comprising an utterance that was directed at another passenger of the vehicle.

8. A computer-implemented method comprising:
receiving, by one or more processors, first sensor data describing at least one adjustment to a vehicle component that a user is using, that the user has made to improve the user's comfort in using the vehicle component;
after the user has made the adjustment to the vehicle component, receiving, by the one or more processors, second sensor data that is other than data that reflects an explicit input, by the user, that the adjustment to the vehicle component failed to adequately improve the user's comfort in using the vehicle component, then automatically determining, based on the second sensor data, that the adjustment to the vehicle component likely failed to adequately improve the user's comfort in using the vehicle component;
in response to automatically determining that the adjustment to the vehicle component likely failed to adequately improve the user's comfort in using the vehicle component, providing, by the one or more processors and to a machine learning-trained model that is trained based on adjustments to similar vehicle components by other users, to output additional adjustments to the vehicle component that can be automatically made to improve users' comfort in using the vehicle component, (i) the first sensor data and (ii) data indicating a current state of the adjusted vehicle component;
receiving, by the one or more processors and from the machine learning-trained model, an additional adjustment that can be automatically made to improve the user's comfort in using the vehicle component; and
communicating, by the one or more processors, the additional adjustment to the vehicle component.

9. The method of claim 8 , wherein determining that the adjustment failed to adequately improve the user's comfort comprises determining that the user is fidgeting while using the vehicle component.

10. The method of claim 8 , wherein determining that the adjustment failed to adequately improve the user's comfort comprises determining that the user has made one or more additional adjustments to the vehicle component.

11. The method of claim 8 , wherein the first sensor data is generated by one or more sensors inside of the vehicle component, and the second sensor data is generated by one or more sensors that are external to the vehicle component.

12. The method of claim 8 , comprising prompting the user with information regarding the additional adjustment contemporaneously with communicating the additional adjustment to the vehicle component.

13. The method of claim 8 , comprising determining an estimated height or weight of the user, wherein the estimated height or weight are provided to the machine learning-trained model.

14. The method of claim 8 , wherein the second sensor data comprise audio data comprising an utterance that was directed at another passenger of the vehicle.

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 first sensor data describing at least one adjustment to a vehicle component that a user is using, that the user has made to improve the user's comfort in using the vehicle component;
after the user has made the adjustment to the vehicle component, receiving second sensor data that is other than data that reflects an explicit input, by the user, that the adjustment to the vehicle component failed to adequately improve the user's comfort in using the vehicle component, then automatically determining, based on the second sensor data, that the adjustment to the vehicle component likely failed to adequately improve the user's comfort in using the vehicle component;
in response to automatically determining that the adjustment to the vehicle component likely failed to adequately improve the user's comfort in using the vehicle component, providing, to a machine learning-trained model that is trained based on adjustments to similar vehicle components by other users, to output additional adjustments to the vehicle component that can be automatically made to improve users' comfort in using the vehicle component, (i) the first sensor data and (ii) data indicating a current state of the adjusted vehicle component;
receiving, from the machine learning-trained model, an additional adjustment that can be automatically made to improve the user's comfort in using the vehicle component; and
communicating the additional adjustment to the vehicle component.

16. The medium of claim 15 , wherein determining that the adjustment failed to adequately improve the

Assignees

• Accenture Global Solutions Ltd

Inventors

• Liongosari Edy S
• Kass Alex M
• Almquist Niclas

Classifications

• G16H10/20

  for electronic clinical trials or questionnaires · CPC title

• B60R16/037 Primary

  for occupant comfort {, e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel} · CPC title

• A61B5/6893

  Cars · CPC title

• B60R21/01552

  detecting position of specific human body parts, e.g. face, eyes or hands · CPC title

• B60W40/09

  Driving style or behaviour · CPC title