PERSONALIZED POSITION USING INFORMATION CORRELATION AND SELF-SOURCING

• US 20130267251A1
• Filed: 07/11/2012
• Published: 10/10/2013
• Est. Priority Date: 04/10/2012
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: KHORASHADI, Behrooz, NAGUIB, Ayman Fawzy, SRIDHARA, Vinay, DAS, Saumitra Mohan
• Application Number:
  US13/546,450
• Publication Number:
  US 20130267251A1
• Time in Patent Office: Days
• US Class Current: 455/456.3
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• 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

Abstract

Some aspects relate to developing a personalized location feasibility heatmap, representing motion information related to a particular device/user, and combining that personalized heatmap with general location feasibility information, pertinent to a number of users. The personalized locational feasibility heatmap may be formed using self-sourced motion data, static and dynamic data, such as contacts and appointments, context, and data derived from or available through social networks. Heatmaps may be associated with respective areas; within areas, regions may be defined that are to be considered for self-source data, which have random data, or which are especially relevant to personalized heatmaps. Personalized heatmaps may be shared among users, and used as a basis for further modification. Other aspects, such as using personalized heatmaps relevant to one area to produce a personalized heatmap for a different area are disclosed. Mobile devices, and servers may implement disclosed aspects, separately or together.

Claims

1. A method for position estimation, the method comprising:

accessing from a non-transitory medium, a general heatmap representing a generic position feasibility pattern for mobile devices within an area to which the general heatmap pertains;

accessing a personalized heatmap from the non-transitory medium, a personalized heatmap representing a position feasibility pattern for a user, within the area to which the general heatmap pertains;

combining data from the general heatmap and the personalized heatmap to form a combined heatmap; and

estimating a current position of the user using the combined heatmap.

2. The method of claim 1, wherein the combining comprises relatively weighting the general heatmap and the personalized heatmap to form a combined positional feasibility pattern.

3. The method of claim 1, further comprising generating the personalized heatmap based on position information gathered from one or more mobile devices associated with the user within the area to which the general heatmap pertains.

4. The method of claim 3, further comprising regenerating the personalized heatmap responsive to availability of any of new, additional, and updated information.

5. The method of claim 1, wherein generating of the personalized heatmap comprises:

obtaining position history information associated with the user;

weighting the position history information; and

combining the weighted position history information according to other location feasibility information to generate the personalized heatmap.

6. The method of claim 1, wherein the non-transitory medium storing the personalized heatmap is local to a mobile device associated with the user, and the combining is performed by the mobile device.

7. The method of claim 1, wherein the generating of the personalized heatmap for the area comprises predicting one or more high feasibility regions within the area, based on a different personalized heatmap for a different area and one or more characteristics shared between the area and the different area.

8. The method of claim 1, wherein the generating of the personalized heatmap for the area comprises predicting one or more high feasibility regions within the area, based on a contextual element shared between a mobile device associated with the user and a different mobile device determined to be in proximity to the mobile device associated with the user.

9. The method of claim 1, wherein the generating of the personalized heatmap for the area comprises predicting locational feasibility for one or more regions within the area, based on a contextual element shared between the user and a different mobile device user determined to be in proximity to the user.

10. The method of claim 1, wherein the generating of the personalized heatmap comprises initializing the personalized heatmap with information from a different personalized heatmap specific to a different user, a different mobile device selected based on a characteristic shared between a mobile device and the different mobile device or respective users of the mobile device and the different mobile device.

11. The method of claim 10, further comprising identifying of the characteristics using social network information from the users of the mobile device and the different mobile device.

12. The method of claim 1, wherein the general heatmap is based on crowd sourcing.

13. The method of claim 1, wherein a plurality of users are capable of using a mobile device, and the method further comprises determining the personalized heatmap, from a plurality of personalized heatmaps associated respectively with the plurality of users according to which of the plurality of users is currently using the mobile device.

14. The method of claim 1, further comprising collecting position data of a mobile device and periodically augmenting the personalized heatmap in one or more augmentation regions.

15. The method of claim 1, wherein the generating of the personalized heatmap further comprises determining a set of personalized augmentation regions and a set of general regions within the personalized heatmap.

16. The method of claim 1, wherein the personalized heatmap comprises location feasibility information resulting from processing one or more of position history of a mobile device, a calendar and a contact list on the mobile device.

17. The method of claim 1, wherein the personalized heatmap comprises personal information obtained from a network-accessible repository of information about a specific user.

18. The method of claim 17, wherein the network-accessible repository of information comprises information available from social networking services used by the specific user.

19. The method of claim 17, wherein the network-accessible repository of information comprises information pertaining to an organization to which the user is associated.

20. The method of claim 1, wherein the generating of the personalized heatmap comprises using information obtained from a message between the user of a mobile device and a third party.

21. The method of claim 1, wherein the generating of the personalized heatmap comprises using information obtained from peer to peer communications between a mobile device and a different mobile device, in which the different mobile device is determined according to commonality of location.

22. The method of claim 1, wherein the personalized heatmap comprises information obtained from a different mobile device, and the method further comprises selecting the different mobile device from a plurality of mobile devices according to a social network inferred by one or more characteristics shared between the respective users of a mobile device and the different mobile device.

23. The method of claim 22, further comprising adding to the social network and refining the personalized heatmap using the information derived from personalized heatmaps associated with additions to the social network.

24. The method of claim 1, wherein the combining data from the general heatmap with data from the personalized heatmap to generate the combined heatmap comprises:

weighting the general heatmap to form a weighted general heatmap;

weighting the personalized heatmap to form a weighted personalized heatmap; and

summing the weighted general heatmap and the weighted personalized heatmap to generate the combined heatmap.

25. The method of claim 24, wherein the weighting of the personalized heatmap comprises weighting portions of location information from the personalized heatmap according to time of day and day of week information.

26. A mobile device for position estimation, the mobile device comprising:

a receiver operable for receiving data representative of a general heatmap, the general heatmap representing a generic position feasibility pattern for mobile devices within an area to which the general heatmap pertains; and

a processor configured to generate a personalized heatmap specific to the mobile device independently from the general heatmap, to combine the general heatmap and a personalized heatmap to produce a combined heatmap, and to determine an estimate of a position of the mobile device from the combined heatmap.

27. The mobile device of claim 26, wherein the processor is configured to regenerate the personalized heatmap responsive to availability of new or additional information pertaining to locational feasibility of the mobile device.

28. A mobile device for position estimation, the mobile device comprising:

means for receiving a general heatmap, the general heatmap representing a generic position feasibility pattern for mobile devices within an area to which the general heatmap pertains;

means for generating a personalized heatmap specific to the mobile device, a personalized heatmap generated independently from the general heatmap;

means for combining the personalized heatmap and the general heatmap to produce a combined heatmap; and

means for determining an estimate of a position of the mobile device from the combined heatmap.

29. The mobile device of claim 28, wherein the means for combining comprises a means for weighting the general heatmap to form a weighted general heatmap, for weighting the personalized heatmap to form a weighted personalized heatmap, and for summing the weighted general heatmap and the weighted personalized heatmap to generate the combined heatmap.

30. The mobile device of claim 28, further comprising:

means to collect position data; and

means to send the position data to a server for forming the general heatmap.

31. The mobile device of claim 28, wherein the means for generating comprises:

means for predicting locational feasibility for regions of the area, to form predicted locational feasibilities;

means for weighting the predicted locational feasibilities to form weighted predicted locational feasibilities; and

means for combining the weighted predicted locational feasibilities with other location feasibility information to generate the personalized heatmap.

32. A device comprising a processor and a non-transitory memory wherein the non-transitory memory includes instructions to:

determine a general heatmap for an area in which augmented location services are to be provided, the general heatmap representing a generic position feasibility pattern for mobile devices within the area;

generate a personalized heatmap of an area specific to a mobile device, the personalized heatmap generated independently from the general heatmap; and

provide the personalized heatmap for use in determining an estimate of a position of the mobile device using data from both the general heatmap and on the personalized heatmap.

33. The device of claim 32, wherein the software instructions further comprise software instructions to partition the area into:

regions in which crowd sourced mobility pattern information is collected, and in which the general heatmap dominates; and

regions in which personal mobility pattern data is separately used to generate the personalized heatmap.

34. The device of claim 33, wherein the software instructions further comprise software instructions to:

identify regions of apparently random motion to form random motion regions; and

marking the random motion regions as regions in which the general heatmap dominates.

35. The device of claim 33, wherein the software instructions further comprise software instructions to:

identify regions of apparently random motion to form random motion regions; and

identify those regions as regions in which the general heatmap dominates.

36. The device of claim 32, wherein the software instructions further comprise software instructions to initially generate the personalized heatmap based on point of interest information.

37. The device of claim 33, wherein the software instructions further comprise software instructions to use self-sourced historical position information from the mobile device.

38. The device of claim 37, wherein the software instructions further comprise software instructions to partition the area into one or more of regions and locations in which the personalized heatmap has associated one or more of specified mobility criteria and a specified feasibility description.

39. The device of claim 38, wherein the software instructions further comprise software instructions to apply respective transform functions to the one or more of regions and locations to produce the personalized heatmap.

40. The device of claim 39, wherein one or more of the transform functions are shared among two or more of the regions or locations to produce the personalized heatmap.

41. The device of claim 33, wherein the software instructions further comprise software instructions to use one or more of real time calendar information and social network information feeds associated with a user of the mobile device.

42. A non-transitory computer-readable storage medium including program code stored thereon, comprising program code to:

receive a general heatmap, the general heatmap representing a generic position feasibility pattern for mobile devices within an area to which the general heatmap pertains;

generate a personalized heatmap, wherein the personalized heatmap is specific to a mobile device, and the personalized heatmap generated independently from the general heatmap; and

determine an estimate of a position of the mobile device using data from both the general heatmap and the personalized heatmap.

43. The non-transitory computer-readable storage medium of claim 42, wherein the program code further comprises program code to weight data from each of the personalized heatmap and the general heatmap according to one or more of dynamic and static data.

44. The non-transitory computer-readable storage medium of claim 42, wherein the program code further comprises program code to use a contextual element shared between the mobile device and a different mobile device determined to be in proximity to the mobile device in combining data from both the general heatmap and the personalized heatmap.

45. The non-transitory computer-readable storage medium of claim 42, wherein the program code further comprises program code to identify one or more shared characteristics using social network information from a user of the mobile device and a different user of a different mobile device.

46. The non-transitory computer-readable storage medium of claim 42, wherein the program code further comprises program code to:

access points of interest data associated with the area to which the general heatmap pertains; and

identify one or more points of interest specific to a user of the mobile device and use the one or more points of interest specific to the user in producing the personalized heatmap.

47. The non-transitory computer-readable storage medium of claim 42, wherein the program code further comprises program code to obtain and use one or more position history of the mobile device, a calendar and a contact list on the mobile device, for forming the personalized heatmap.