SYSTEM AND METHOD FOR DETERMINING A SEAT LOCATION OF A MOBILE COMPUTING DEVICE IN A MULTI-SEAT ENVIRONMENT

• US 20160142877A1
• Filed: 11/17/2015
• Published: 05/19/2016
• Est. Priority Date: 11/18/2014
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Ben-Zur, Liat, Gujral, Sunvir, Jacobs, Paul, Chandhok, Ravinder, Sharma, Sandeep
• Granted Patent: US 9,980,090 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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: E05F 15/77 using wireless control
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]
• CPC Class Codes: H04W 4/48 for in-vehicle communication
• CPC Class Codes: H04W 4/70 Services for machine-to-machine communication [M2M] or machine type communication [MTC]

Abstract

A system and method for associating a mobile computing device with a particular seat in a seating environment. The system collects first sensor data from device sensors of a first mobile computing device based on activity detected within the seating environment. The system then determines, for each of a plurality of seats in the seating environment, a degree of correlation with the mobile computing device based at least in part on the first sensor data, and associates the mobile computing device with the seat, among the plurality of seats, having the highest degree of correlation with the first mobile computing device.

Claims

1. A method for associating a mobile computing device with a particular seat in a seating environment, the method comprising:

collecting first sensor data from device sensors of a first mobile computing device based on activity detected within the seating environment;

determining, for each of a plurality of seats in the seating environment, a degree of correlation with the first mobile computing device based at least in part on the first sensor data; and

associating the first mobile computing device with the seat, among the plurality of seats, having the highest degree of correlation with the first mobile computing device.

2. The method of claim 1, wherein the determining comprises:

receiving second sensor data from each of a plurality of seat sensors; and

comparing, for each of the plurality of seats, the first sensor data with the second sensor data received from a corresponding one of the plurality of seat sensors.

3. The method of claim 2, wherein the first sensor data includes accelerometer data based on a movement of the first mobile computing device, and each of the second sensor data includes accelerometer data based on a movement of a corresponding one of the plurality of seats.

4. The method of claim 3, wherein the determining further comprises:

determining a similarity between respective movements of the first mobile computing device and each of the plurality of seats.

5. The method of claim 1, wherein the first sensor data includes magnetometer data based on a magnetic field in the seating environment.

6. The method of claim 5, wherein the magnetometer data indicates at least a direction and strength of the magnetic field at a location of the first mobile computing device.

7. The method of claim 5, wherein the determining comprises:

determining a relative proximity of the first mobile computing device to a source of the magnetic field based at least in part on the magnetometer data;

identifying a location of the source relative to each of the plurality of seats; and

determining a closeness of the first mobile computing device to each of the plurality of seats based at least in part on the location of the source and the relative proximity of the first mobile computing device to the source.

8. The method of claim 1, further comprising:

collecting third sensor data from device sensors of a second mobile computing device in the seating environment.

9. The method of claim 8, wherein the determining comprises:

comparing the first sensor data with the third sensor data; and

determining the degree of correlation of the first mobile computing device to each of the plurality of seats in the seating environment based at least in part on a result of the comparison.

10. A seat association system, comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the system to;

collect first sensor data from device sensors of a first mobile computing device based on activity detected within a seating environment;

determine, for each of a plurality of seats in the seating environment, a degree of correlation with the first mobile computing device based at least in part on the first sensor data; and

associate the first mobile computing device with the seat, among the plurality of seats, having the highest degree of correlation with the first mobile computing device.

11. The system of claim 10, wherein execution of the instructions to determine the degree of correlation causes the system to:

receive second sensor data from each of a plurality of seat sensors; and

compare, for each of the plurality of seats, the first sensor data with the second sensor data received from a corresponding one of the plurality of seat sensors.

12. The system of claim 11, wherein the first sensor data includes accelerometer data based on a movement of the first mobile computing device, and each of the second sensor data includes accelerometer data based on a movement of a corresponding one of the plurality of seats.

13. The system of claim 12, wherein execution of the instructions to determine the degree of correlation further causes the system to:

determine a similarity between respective movements of the first mobile computing device and each of the plurality of seats.

14. The system of claim 10, wherein the first sensor data includes magnetometer data based on a magnetic field in the seating environment.

15. The system of claim 14, wherein the magnetometer data indicates at least a direction and strength of the magnetic field at a location of the first mobile computing device.

16. The system of claim 14, wherein execution of the instructions to determine the degree of correlation causes the system to:

determine a relative proximity of the first mobile computing device to a source of the magnetic field based at least in part on the magnetometer data;

identify a location of the source relative to each of the plurality of seats; and

determine a closeness of the first mobile computing device to each of the plurality of seats based at least in part on the location of the source and the relative proximity of the first mobile computing device to the source.

17. The system of claim 10, wherein execution of the instructions further causes the system to:

collect third sensor data from device sensors of a second mobile computing device in the seating environment.

18. The system of claim 17, wherein execution of the instructions to determine the degree of correlation causes the system to:

compare the first sensor data with the third sensor data; and

determine the degree of correlation of the first mobile computing device to each of the plurality of seats in the seating environment based at least in part on a result of the comparison.

19. A seat association system, comprising:

means for collecting first sensor data from device sensors of a first mobile computing device based on activity detected within a seating environment;

means for determining, for each of a plurality of seats in the seating environment, a degree of correlation with the first mobile computing device based at least in part on the first sensor data; and

means for associating the first mobile computing device with the seat, among the plurality of seats, having the highest degree of correlation with the first mobile computing device.

20. The system of claim 19, wherein the means for determining the degree of correlation is to:

receive second sensor data from each of a plurality of seat sensors; and

compare, for each of the plurality of seats, the first sensor data with the second sensor data received from a corresponding one of the plurality of seat sensors.

21. The system of claim 20, wherein the first sensor data includes accelerometer data based on a movement of the first mobile computing device, and each of the second sensor data includes accelerometer data based on a movement of a corresponding one of the plurality of seats.

22. The system of claim 21, wherein the means for determining the degree of correlation is to further:

determine a similarity between respective movements of the first mobile computing device and each of the plurality of seats.

23. The system of claim 19, wherein the first sensor data includes magnetometer data based on a magnetic field in the seating environment.

24. The system of claim 23, wherein the means for determining the degree of correlation is to:

determine a relative proximity of the first mobile computing device to a source of the magnetic field based at least in part on the magnetometer data;

identify a location of the source relative to each of the plurality of seats; and

determine a closeness of the first mobile computing device to each of the plurality of seats based at least in part on the location of the source and the relative proximity of the first mobile computing device to the source.

25. A non-transitory computer-readable storage medium containing program instructions that, when executed by one or more processors of a seat association system, causes the system to:

collect first sensor data from device sensors of a first mobile computing device based on activity detected within a seating environment;

determine, for each of a plurality of seats in the seating environment, a degree of correlation with the first mobile computing device based at least in part on the first sensor data; and

associate the first mobile computing device with the seat, among the plurality of seats, having the highest degree of correlation with the first mobile computing device.

26. The non-transitory computer-readable storage medium of claim 25, wherein execution of the instructions to determine the degree of correlation causes the system to:

receive second sensor data from each of a plurality of seat sensors; and

compare, for each of the plurality of seats, the first sensor data with the second sensor data received from a corresponding one of the plurality of seat sensors.

27. The non-transitory computer-readable storage medium of claim 26, wherein the first sensor data includes accelerometer data based on a movement of the first mobile computing device, and each of the second sensor data includes accelerometer data based on a movement of a corresponding one of the plurality of seats.

28. The non-transitory computer-readable storage medium of claim 27, wherein execution of the instructions to determine the degree of correlation further causes the system to:

determine a similarity between respective movements of the mobile computing device and each of the plurality of seats.

29. The non-transitory computer-readable storage medium of claim 25, wherein the first sensor data includes magnetometer data based on a magnetic field in the seating environment.

30. The non-transitory computer-readable storage medium of claim 29, wherein execution of the instructions to determine the degree of correlation causes the system to:

determine a relative proximity of the mobile computing device to a source of the magnetic field based at least in part on the magnetometer data;

identify a location of the source relative to each of the plurality of seats; and

determine a closeness of the mobile computing device to each of the plurality of seats based at least in part on the location of the source and the relative proximity of the mobile computing device to the source.