ASSIGNING MOBILE DEVICE DATA TO A VEHICLE

• US 20150154711A1
• Filed: 12/04/2013
• Published: 06/04/2015
• Est. Priority Date: 12/04/2013
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Christopulos, Nicholas U., Baker, Nicholas R., Bowne, Benjamin F., Bellas, Eric
• Granted Patent: US 11,182,859 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method for identifying a primary vehicle associated with a user of a mobile device includes receiving an indication of a vehicle entry event from a mobile device and retrieving sensor data from the mobile device. The method further includes receiving an indication of a vehicle exit event from the mobile device, generating a trip log including portions of the sensor data, and storing the trip log in a trip database. A server, or other suitable computing device, then analyzes the trip log and a plurality of previously stored trip logs in the trip database to determine a primary vehicle corresponding to the user of the mobile device. The method may allow a computing device to assign gathered mobile device data to a specific household vehicle.

Claims

1. A computer-implemented method comprising:

receiving, via a network interface, an indication of a vehicle entry event from a mobile computing device temporarily disposed inside the vehicle, wherein the vehicle entry event corresponds to a user of the mobile device entering a vehicle at a first point in time;

retrieving, with one or more particularly programmed computer processors and the network interface, sensor data from the mobile computing device;

receiving, via the network interface, an indication of a vehicle exit event from the mobile computing device, wherein the vehicle exit event corresponds to the user exiting the vehicle at a second point in time;

generating, with the one or more computer processors, a trip log including portions of the sensor data generated by the mobile computing device at times between the first point in time and the second point in time;

storing, with the one or more computer processors, the trip log in a trip database; and

analyzing, with the one or more computer processors, the trip log and a plurality of previously stored trip logs in the trip database to determine a first vehicle corresponding to the user of the mobile computing device.

2. The computer-implemented method of claim 1, further comprising receiving a geographic location of the vehicle entry event and the vehicle exit event in the indication of the vehicle entry event and the indication of the vehicle exit event, respectively.

3. The computer-implemented method of claim 1, further comprising receiving the indication of the vehicle entry event and the indication of the vehicle exit event based on an execution of a supervised classification algorithm on the mobile computing device.

4. The computer-implemented method of claim 1, further comprising continuously retrieving the sensor data from the mobile computing device.

5. The computer-implemented method of claim 1, further comprising retrieving sensor data that includes data from a geographic positioning system (GPS) receiver.

6. The computer-implemented method of claim 5, further comprising retrieving sensor data sensor data that includes at least one of accelerometer data, gyroscope data, microphone data, video data, barometer data, compass data, ambient light data, proximity data, and magnetometer data.

7. The computer-implemented method of claim 5, further comprising capturing in the trip log a vehicle operation route between an originating point and a destination point.

8. The computer-implemented method of claim 7, further comprising capturing in the trip log the originating point based on the indication of the vehicle entry event and the destination point based on the indication of the vehicle exit event.

9. The computer-implemented method of claim 1, wherein analyzing the trip log and a plurality of previously stored trip logs in the trip database includes:

detecting clusters of trip logs, wherein each trip log in the cluster of trip logs shares a similarity, and

labeling each of the clusters of trip logs as corresponding to at least the first vehicle and another vehicle, wherein the label indicates the identity of the at least the first and another vehicle.

10. The computer-implemented method of claim 9, wherein the similarity is related to at least one of vehicle operation routes or vehicle operation patterns.

11. The computer-implemented method of claim 9, wherein detecting clusters of trip logs includes executing an unsupervised clustering algorithm.

12. The computer-implemented method of claim 9, wherein labeling each of the clusters of trip logs includes:

labeling a first cluster of trip logs as corresponding to the first vehicle, wherein the label indicates the identity of the first vehicle, and

labeling a second cluster of trip logs as corresponding to a second vehicle, wherein the label indicates the identity of the second vehicle.

13. The computer-implemented method of claim 12, wherein labeling each of the clusters of trip logs further includes labeling a third cluster of trip logs as corresponding to a third vehicle.

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

determining, with the one or more computer processors, if a number of the plurality of previously stored trip logs exceeds a threshold number.

15. The computer-implemented method of claim 14, wherein the analyzing the trip log and a plurality of previously stored trip logs is dependent on determining if the number of the plurality of previously stored trip logs exceeds the threshold number.

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

retrieving, with the one or more processors, subsequent sensor data from the mobile computing device; and

labeling, with the one or more processors, the subsequent sensor data from the mobile computing device as data corresponding to the first vehicle, wherein the label indicates the identity of the first vehicle.

17. A computer device comprising:

one or more processors; and

one or more memories coupled to the one or more processors;

wherein the one or more memories include computer executable instructions stored therein that, when executed by the one or more processors, particularly program the one or more processors to;

receive, via a network interface, an indication of a vehicle entry event from a mobile computing device temporarily disposed inside the vehicle, wherein the vehicle entry event corresponds to a user of the mobile computing device entering a vehicle at a first point in time,

retrieve, with the network interface, sensor data from the mobile computing device,

receive, via the network interface, an indication of a vehicle exit event from the mobile computing device, wherein the vehicle exit event corresponds to the user exiting the vehicle at a second point in time,

generate a trip log including portions of the sensor data generated by the mobile computing device at times between the first point in time and the second point in time,

store the trip log in a trip database, and

analyze the trip log and a plurality of previously stored trip logs in the trip database to determine a first vehicle corresponding to the user of the mobile computing device.

18. The computer device of claim 17, wherein the indication of the vehicle entry event and the indication of the vehicle exit event are generated based on an execution of a supervised classification algorithm on the mobile computing device.

19. The computer device of claim 17, wherein the sensor data includes data from a geographic positioning system (GPS) receiver.

20. The computer device of claim 17, wherein the sensor data further includes at least one of accelerometer data, gyroscope data, microphone data, video data, barometer data, compass data, ambient light data, proximity data, and magnetometer data.

21. The computer device of claim 17, wherein analyzing the trip log and a plurality of previously stored trip logs in the trip database includes:

detecting clusters of trip logs, wherein each trip log in the cluster of trip logs shares a similarity, and

labeling each of the clusters of trip logs as corresponding to at least the first vehicle and another vehicle, wherein the label indicates the identity of the at least the first vehicle and another vehicle.

22. The computer device of claim 21, wherein the similarity is related to at least one of vehicle operation routes or vehicle operation patterns.

23. A computer readable storage medium comprising non-transitory computer readable instructions stored thereon, the instructions, when executed on one or more processors, particularly program the one or more processors to:

receive, via a network interface, an indication of a vehicle entry event from a mobile computing device temporarily disposed inside the vehicle, wherein the vehicle entry event corresponds to a user of the mobile device entering a vehicle at a first point in time,

retrieve, with the network interface, sensor data from the mobile computing device,

receive, via the network interface, an indication of a vehicle exit event from the mobile computing device, wherein the vehicle exit event corresponds to the user exiting the vehicle at a second point in time,

generate a trip log including portions of the sensor data generated by the mobile computing device at times between the first point in time and the second point in time,

store the trip log in a trip database, and

analyze the trip log and a plurality of previously stored trip logs in the trip database to determine a first vehicle corresponding to the user of the mobile computing device.