GENERATING A GEOFENCE VIA AN ANALYSIS OF A GPS FIX UTILIZATION DISTRIBUTION

• US 20140258201A1
• Filed: 03/05/2013
• Published: 09/11/2014
• Est. Priority Date: 03/05/2013
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Finlow-Bates, Keir
• Application Number:
  US13/786,179
• Publication Number:
  US 20140258201A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• 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 4/029 Location-based management or tracking services

Abstract

Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for generating a geofence via an analysis of a GPS fix utilization distribution, such as for use in or with a mobile communication device. Briefly, in accordance with at least one implementation, a method may include obtaining multiple position fixes of one or more objects over an area or volume; determining a clustering of the multiple position fixes in a portion of the area or volume; and inferring a geofence boundary bounding the portion of the area or volume based, at least in part, on the clustering of the multiple position fixes, for example.

Claims

1. A method comprising:

obtaining multiple position fixes of one or more objects over an area or volume;

determining a clustering of said multiple position fixes in a portion of said area or said volume; and

inferring a geofence boundary bounding said portion of said area or said volume based, at least in part, on said clustering of said multiple position fixes.

2. The method of claim 1, and further comprising:

partitioning said area into one or more segments;

counting said multiple position fixes within said one or more segments; and

identifying at least one contiguous segment based, at least in part, on at least a threshold number of said multiple position fixes, wherein said inferring said geofence boundary comprises inferring said boundary to bound said at least one contiguous segment.

3. The method of claim 1, wherein said determining said clustering of said multiple position fixes further comprises:

identifying at least one attribute of said multiple position fixes; and

clustering said multiple position fixes based, at least in part, on said at least one attribute.

4. The method of claim 3, wherein said at least one attribute comprises at least one of the following:

latitude;

longitude;

altitude;

time;

or any combination thereof.

5. The method of claim 4, wherein said time is determined based, at least in part, on at least one of the following:

time of day;

day of week;

day of month;

day of year;

or any combination thereof.

6. The method of claim 3, wherein said multiple position fixes of said one or more objects are determined based, at least in part, on a mobile device co-located with a user of said mobile device.

7. The method of claim 3, wherein said at least one attribute comprises an attribute of a user of a mobile device co-located with said user.

8. The method of claim 1, wherein said geofence boundary is inferred based, at least in part, on a probability density function of said multiple position fixes of said one or more objects.

9. The method of claim 8, wherein said probability density function is determined based, at least in part, on a scatter graph of said multiple position fixes of said one or more objects.

10. The method of claim 9, wherein said scatter graph is plotted on a geographical map.

11. The method of claim 8, wherein said probability density function is determined based, at least in part, on at least one of the following:

a histogram-type distribution of said multiple position fixes;

a kernel density-type estimation of said multiple position fixes;

or any combination thereof.

12. The method of claim 8, wherein said geofence boundary is inferred to bound a peak of said probability density function defined by at least a threshold number of said multiple position fixes.

13. The method of claim 8, wherein said geofence boundary is inferred to bound a peak of said probability density function defined by at least a threshold number of a density of probability of said multiple position fixes.

14. The method of claim 1, wherein said geofence boundary is associated with at least one of the following:

a two-dimensional geofence;

a three-dimensional geofence;

or any combination thereof.

15. An apparatus comprising:

a communication interface; and

at least one processor programmed with instructions to;

obtain multiple position fixes of one or more objects over an area or volume;

determine a clustering of said multiple position fixes in a portion of said area or said volume; and

infer a geofence boundary bounding said portion of said area or said volume based, at least in part, on said clustering of said multiple position fixes.

16. The apparatus of claim 15, wherein said at least one processor further programmed with instructions to:

partition said area into one or more segments;

count said multiple position fixes within said one or more segments; and

identify at least one contiguous segment based, at least in part, on at least a threshold number of said multiple position fixes, wherein to said infer said geofence boundary comprises to infer said boundary to bound said at least one contiguous segment.

17. The apparatus of claim 15, wherein said at least one processor programmed with said instructions to said determine said clustering of said multiple position fixes further to:

identify at least one attribute of said multiple position fixes; and

cluster said multiple position fixes based, at least in part, on said at least one attribute.

18. The apparatus of claim 17, wherein said at least one attribute comprises at least one of the following:

latitude;

longitude;

altitude;

time;

or any combination thereof.

19. The apparatus of claim 15, wherein said at least one processor programmed with said instructions to said infer said geofence boundary based, at least in part, on a probability density function of said multiple position fixes of said one or more objects.

20. The apparatus of claim 19, wherein said at least one processor to said infer said geofence boundary further programmed with instructions to bound a peak of said probability density function defined by at least one of the following:

a threshold number of said multiple position fixes;

a threshold number of a density of probability of said multiple position fixes;

or any combination thereof.

21. The apparatus of claim 15, wherein said geofence boundary is associated with at least one of the following:

a two-dimensional geofence;

a three-dimensional geofence;

or any combination thereof.

22. An apparatus comprising:

means for obtaining multiple position fixes of one or more objects over an area or volume;

means for determining a clustering of said multiple position fixes in a portion of said area or said volume; and

means for inferring a geofence boundary bounding said portion of said area or said volume based, at least in part, on said clustering of said multiple position fixes.

23. The apparatus of claim 22, and further comprising:

means for partitioning said area into one or more segments;

means for counting said multiple position fixes within said one or more segments; and

means for identifying at least one contiguous segment based, at least in part, on at least a threshold number of said multiple position fixes, wherein said means for inferring said geofence boundary comprises means for inferring said boundary to bound said at least one contiguous segment.

24. The apparatus of claim 22, wherein said means for determining said clustering of said multiple position fixes further comprises:

means for identifying at least one attribute of said multiple position fixes; and

means for clustering said multiple position fixes based, at least in part, on said at least one attribute.

25. The apparatus of claim 24, wherein said at least one attribute comprises at least one of the following:

latitude;

longitude;

altitude;

time;

or any combination thereof.

26. The apparatus of claim 25, wherein said time is determined based, at least in part, on at least one of the following:

time of day;

day of week;

day of month;

day of year;

or any combination thereof.

27. The apparatus of claim 24, wherein said multiple position fixes of said one or more objects are determined based, at least in part, on a mobile device co-located with a user of said mobile device.

28. The apparatus of claim 24, wherein said at least one attribute comprises an attribute of a user of a mobile device co-located with said user.

29. The apparatus of claim 22, wherein said means for inferring said geofence boundary further comprise means for inferring said geofence boundary based, at least in part, on a probability density function of said multiple position fixes of said one or more objects.

30. The apparatus of claim 29, wherein said probability density function is determined based, at least in part, on a scatter graph of said multiple position fixes of said one or more objects.

31. The apparatus of claim 30, wherein said scatter graph is plotted on a geographical map.

32. The apparatus of claim 29, wherein said probability density function is determined based, at least in part, on at least one of the following:

a histogram-type distribution of said multiple position fixes;

a kernel density-type estimation of said multiple position fixes;

or any combination thereof.

33. The apparatus of claim 29, wherein said means for inferring said geofence boundary further comprise means for inferring said geofence boundary to bound a peak of said probability density function defined by at least a threshold number of said multiple position fixes.

34. The apparatus of claim 29, wherein said means for inferring said geofence boundary further comprise means for inferring said geofence boundary to bound a peak of said probability density function defined by at least a threshold number of a density of probability of said multiple position fixes.

35. The apparatus of claim 22, wherein said geofence boundary is associated with at least one of the following:

a two-dimensional geofence;

a three-dimensional geofence;

or any combination thereof.

36. An article comprising:

a non-transitory storage medium having instructions stored thereon executable by a special purpose computing platform to;

obtain multiple position fixes of one or more objects over an area or volume;

determine a clustering of said multiple position fixes in a portion of said area or said volume; and

infer a geofence boundary bounding said portion of said area or said volume based, at least in part, on said clustering of said multiple position fixes.

37. The article of claim 36, wherein said storage medium further comprises instructions to:

partition said area into one or more segments;

count said multiple position fixes within said one or more segments; and

identify at least one contiguous segment based, at least in part, on at least a threshold number of said multiple position fixes, wherein to said infer said geofence boundary comprises to infer said boundary to bound said at least one contiguous segment.

38. The article of claim 36, wherein said storage medium having said instructions to said determine said clustering of said multiple position fixes further comprises instructions to:

identify at least one attribute of said multiple position fixes; and

cluster said multiple position fixes based, at least in part, on said at least one attribute.

39. The article of claim 38, wherein said at least one attribute comprises at least one of the following:

latitude;

longitude;

altitude;

time;

or any combination thereof.

40. The article of claim 36, wherein said storage medium having said instructions to said infer said geofence boundary further comprises instructions to infer said geofence boundary based, at least in part, on a probability density function of said multiple position fixes of said one or more objects.

41. The article of claim 40, wherein said storage medium having said instructions to said infer said geofence boundary further comprises instructions to bound a peak of said probability density function defined by at least one of the following:

a threshold number of said multiple position fixes;

a threshold number of a density of probability of said multiple position fixes;

or any combination thereof.

42. The article of claim 36, wherein said geofence boundary is associated with at least one of the following:

a two-dimensional geofence;

a three-dimensional geofence;

or any combination thereof.