Determining whether a mobile device user is substantially stationary within a geo-fence

• US 20170111765A1
• Filed: 08/26/2016
• Published: 04/20/2017
• Est. Priority Date: 06/13/2015
• Status: Abandoned Application

• Current Assignee: KeepTrax, Inc.
• Original Assignee: KeepTrax, Inc.
• Inventors: Benegal, Kedarnath, Thanickel, Ismail
• Application Number:
  US15/248,304
• Publication Number:
  US 20170111765A1
• Time in Patent Office: Days
• CPC Class Codes: G01S 19/34 Power consumption
• CPC Class Codes: G01S 5/017 Detecting state or type of motion
• CPC Class Codes: H04M 2250/10 including a GPS signal receiver
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 52/0254 detecting a user operation or a tactile contact or a motion of the device
• CPC Class Codes: H04W 52/0277 according to available power supply, e.g. switching off when a low battery condition is detected
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

A technique to determine whether a mobile device is in a “substantially stationary” state or a “substantially moving” state (e.g., with respect to a geo-fence in which the device may be within). These states are possible “motion” states associated with the mobile device. Preferably, the determination leverages a data table that includes a defined set of states (either “substantially stationary,” “substantially moving” or “no change”) associated with various transitions of the mobile device from, on the one hand, a “last reported activity” and, on the other hand, a currently-determined “probable activity.” Preferably, the new motion state is the substantially stationary state when the probable activity is relatively slower than last-reported activity, irrespective of whether the mobile device is in true motion. On the other hand, the new motion state is the substantially moving state when the probable activity is relatively faster than the last-reported activity.

Claims

1. A method to determine a motion state of a mobile device, comprising:

storing a data table that includes a defined set of motion states that include a substantially stationary state of the mobile device, a substantially moving state of the mobile device, and a no change state, the data table cross-referencing a last reported activity and a probable activity, the last reported activity having triggered a last reported motion state for the mobile device;

receiving data and determining a probable activity of the mobile device;

indexing into the data table using the last reported activity and the probable activity to determine a new motion state for the mobile device; and

comparing the new motion state to the last reported motion state to characterize the new motion state as substantially stationary or substantially moving or no change.

2. The method as described in claim 1 wherein the new motion state is characterized as substantially stationary when the probable activity is relatively slower than the last reported activity, irrespective of whether the mobile device is in true motion.

3. The method as described in claim 1 wherein the new motion state is characterized as substantially moving state when the probable activity is relatively faster than the last reported activity.

4. The method as described in claim 1 wherein the data is received from one or more low power sensors of the mobile device.

5. The method as described in claim 1 wherein the probable activity has a confidence value associated therewith.

6. A computer program product in a computer-readable storage media, the computer program product comprising a set of computer program instructions stored in memory and executed by a hardware processor to carry out operations to determine a motion state of a mobile device, the operations comprising:

storing a data table that includes a defined set of motion states that include a substantially stationary state of the mobile device, and a substantially moving state of the mobile device, the data table cross-referencing a last reported activity and a probable activity, the last reported activity having triggered a last reported motion state for the mobile device;

receiving data and determining a probable activity of the mobile device;

indexing into the data table using the last reported activity and the probable activity to determine a new motion state for the mobile device; and

comparing the new motion state to the last reported motion state to characterize the new motion state as substantially stationary or substantially moving or no change.

7. The computer program product as described in claim 6 wherein the new motion state is characterized as substantially stationary when the probable activity is relatively slower than the last reported activity, irrespective of whether the mobile device is in true motion.

8. The computer program product as described in claim 6 wherein the new motion state is characterized as substantially moving state when the probable activity is relatively faster than the last reported activity.

9. The computer program product as described in claim 6 wherein the data is received from one or more low power sensors of the mobile device.

10. The computer program product as described in claim 6 wherein the probable activity has a confidence value associated therewith.

11. A mobile device, comprising:

a hardware processor;

one or more low power sensors;

computer memory storing computer program instructions that perform operations to determine and report a motion state of the mobile device, the operations comprising;

storing a data table that includes a defined set of motion states that include a substantially stationary state of the mobile device, and a substantially moving state of the mobile device, the data table cross-referencing a last reported activity and a probable activity, the last reported activity having triggered a last reported motion state for the mobile device;

receiving data and determining a probable activity of the mobile device, the data received from the one or more low power sensors;

indexing into the data table using the last reported activity and the probable activity to determine a new motion state for the mobile device; and

comparing the new motion state to the last reported motion state to characterize the new motion state as substantially stationary or substantially moving or no change.

12. The mobile device as described in claim 11 wherein the new motion state is characterized as substantially stationary when the probable activity is relatively slower than the last reported activity, irrespective of whether the mobile device is in true motion.

13. The mobile device as described in claim 11 wherein the new motion state is characterized as substantially moving state when the probable activity is relatively faster than the last reported activity.

14. The mobile device as described in claim 11 wherein the data is received from one or more low power sensors of the mobile device.

15. The mobile device as described in claim 11 wherein the probable activity has a confidence value associated therewith.

16. The mobile device as described in claim 11 wherein the operations further include activating a GPS sensor and recording geo-location data.

17. The mobile device as described in claim 16 wherein the operations further include de-activating the GPS sensor following recording of the geo-location data.

18. The mobile device as described in claim 16 wherein the operations further include determining whether an identifying marker should be associated with the geo-location data.

19. The mobile device as described in claim 18 wherein a whitelist or a blacklist is used to determine whether the identifying marker should be associated with the geo-location data.

20. The mobile device as described in claim 16 wherein the operations further include collecting information associated with a geo-fence in which the geo-location data is determined to be within.