Comparing spatial-temporal trails in location analytics

• US 8,224,766 B2
• Filed: 09/30/2008
• Issued: 07/17/2012
• Est. Priority Date: 09/30/2008
• Status: Active Grant

• Current Assignee: Verve Group, Inc. (Samarion SE)
• Original Assignee: Sense Networks, Inc. (Samarion SE)
• Inventors: Skibiski, Greg, Pentland, Alex (Sandy), Jebara, Tony, Lemke, Christine, Loecher, Markus, Rao, Girish, Uechi, Jason, Shaw, Blake, Mattiello, Joseph, Rosenberg, David
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US12/241,227
• Publication Number:
  US 20100079336A1
• Time in Patent Office: 1,386 Days
• Field of Search: 706/55
• US Class Current: 706/55
• CPC Class Codes: G01S 2205/09 for tracking people
• CPC Class Codes: G01S 5/02 using radio waves using satellite radio beacon systems for determining position G01S19/00
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Systems and computer-implemented methods are provided for comparing, associating and deriving associations between two or more spatial temporal data trails. One or more spatial-temporal data trails comprising one or more places are received at a processor. Each place is identified by a spatial temporal data point. And each spatial-temporal data trail is associated with an individual. The similarity between pairs of places is determined to establish one or more groups of places or one or more groups of individuals. Similarity and/groups can be determined based on demographics associated with the place or individual.

Claims

1. A computer implemented method comprising:

receiving spatial-temporal flow data associated with a first individual, the spatial-temporal flow data comprising spatial-temporal data points;

replacing the spatial-temporal data points with semantic coordinate values, resulting in a first semantic trail being created, wherein the semantic coordinate values identify one or more demographic characteristics; and

determining a similarity of the first semantic trail with a second semantic trail.

2. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises geographic coordinates associated with the first individual.

3. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises grid coordinates associated with the first individual.

4. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises GPS coordinates associated with the first individual.

5. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises time increments associated with the first individual.

6. The computer implemented method of claim 1 wherein the first individual comprises a person, a Global Positioning System (GPS) enabled device, a location enabled device, a radio-frequency identification (RFID) enabled device, a cellular telephone, a personal data assistant, a networked computer, a communication device, or a vehicle.

7. The computer implemented method of claim 1 wherein the first individual is anonymously identified by use of a GPS enabled device, a location enabled device, an RFID enabled device, a cellular telephone, a personal data assistant, a networked computer, a communication device, or a vehicle.

8. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises data indicating the time and place of a location of the first individual.

9. The computer implemented method of claim 8 wherein the location is a past location.

10. The computer implemented method of claim 1 wherein the spatial-temporal flow data is received in real time.

11. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises historical data.

12. The computer implemented method of claim 1 wherein the one or more demographic characteristics comprise Census Bureau data, financial demographics, social demographics, marketing data, group demographics, organizational affiliation, social affiliation, sales data, historical demographical information, derived demographic information, gender, race, age, religion, ethnic affiliation, sexual preference, marital status, dependent status, educational level, historical geographic information, or user entered information/preferences.

13. The computer implemented method of claim 1 wherein the first semantic trail characterizes the first individual based on semantic data points comprising demographic information.

14. The computer implemented method of claim 1 wherein the similarity of the first semantic trail is determined using a similarity matrix.

15. The computer implemented method of claim 1 wherein the second semantic trail is a historical semantic trail.

16. The computer implemented method of claim 1 wherein the first and second semantic trails are associated with the same individual.

17. The computer implemented method of claim 1 wherein the first and second semantic trails are associated with different individuals.

18. The computer implemented method of claim 1 wherein the first semantic trail is compared with a plurality of second semantic trails.

19. The computer implemented method of claim 18 wherein the plurality of second semantic trails are associated with one or more individuals different than the individual associated with the first semantic trail.

20. The computer implemented method of claim 1 further comprising creating a time series of semantic coordinates to establish the first semantic trail.

21. The computer implemented method of claim 20 wherein the time series of semantic coordinates is determined using a hidden Markov model.

22. The computer implemented method of claim 1 further comprising:

determining a similarity of multiple first semantic trails associated with multiple different individuals with multiple second semantic trails associated with the multiple different individuals, including the first individual; and

determining a similarity matrix between all semantic trails associated with all individuals, including the first individual.

23. A computer implemented method comprising:

receiving a location-based data flow associated with a first individual, wherein the location-based data flow comprises spatial-temporal waypoints;

associating a demographic profile with the spatial-temporal waypoints;

assigning a first demographic-based data flow to the first individual, the first demographic-based data flow being based on the demographic profile; and

comparing the first demographic-based data flow assigned to the first individual to a second demographic-based data flow assigned to a second individual.

24. The computer implemented method of claim 23 wherein the first individual comprises a person, a GPS enabled device, a location enabled device, an RFID enabled device, a cellular telephone, a personal data assistant, a networked computer, a communication device, or a vehicle.

25. The computer implemented method of claim 23 wherein the location-based data flow is received in real time.

26. The computer implemented method of claim 23 wherein the location-based data flow comprises historical data.

27. The computer implemented method of claim 23 wherein the spatial-temporal waypoints comprise a latitude-longitude coordinate, a grid coordinate, a GPS coordinate, an address, a network cell, a time period, or a specific time.

28. The computer implemented method of claim 23 wherein the demographic profile comprises Census Bureau data, financial demographics, social demographics, marketing data, group demographics, organizational affiliation, social affiliation, sales data, historical demographical information, derived demographic information, gender, race, age, religion, ethnic affiliation, sexual preference, marital status, dependent status, educational level, historical geographic information, or user entered information/preferences.

29. The computer implemented method of claim 23 wherein an individual demographic profile is associated with each of the spatial-temporal waypoints.

30. The computer implemented method of claim 23 wherein the comparing of demographic-based data flows comprises comparing similarity matrices of all demographic-based data flows assigned to individuals.

31. A computer implemented method comprising:

receiving spatial-temporal data trails comprising spatial-temporal data points that identify places, wherein each of the places is identified by a corresponding spatial-temporal data point, and wherein each of the spatial-temporal data trails is associated with an individual; and

determining a similarity matrix between pairs of the places, to establish one or more groups of places or one or more groups of individuals.

32. The computer implemented method of claim 31 further comprising determining a similarity matrix between pairs of individuals, to establish one or more groups of places or one or more groups of individuals.

33. The computer implemented method of claim 31 wherein the spatial-temporal data trails comprise geographic coordinates associated with one or more individuals.

34. The computer implemented method of claim 31 wherein the spatial-temporal data trails comprise grid coordinates associated with one or more individuals.

35. The computer implemented method of claim 31 wherein the spatial-temporal data trails comprise GPS coordinates associated with one or more individuals.

36. The computer implemented method of claim 31 wherein the spatial-temporal data trails comprise time increments associated with one or more individuals.

37. The computer implemented method of claim 31 wherein the individual comprises a person, a GPS enabled device, a location enabled device, an RFID enabled device, a cellular telephone, a personal data assistant, a networked computer, a communication device, or a vehicle.

38. The computer implemented method of claim 31 wherein the individual is anonymously identified by use of a GPS enabled device, a location enabled device, an RFID enabled device, a cellular telephone, a personal data assistant, a networked computer, a communication device, or a vehicle.

39. The computer implemented method of claim 31 further comprising assigning a semantic coordinate to a spatial-temporal data point, resulting in a semantic data point.

40. The computer implemented method of claim 39 further comprising associating demographic information with the spatial-temporal data point or the semantic data point.

41. The computer implemented method of claim 39 further comprising associating an industrial code with the spatial-temporal data point or the semantic data point.

42. The computer implemented method of claim 39 further comprising associating an in-flow and out-flow data with the spatial-temporal data point or the semantic data point.

43. The computer implemented method of claim 39 further comprising determining the probability of movement of the individual in semantic space.

44. The computer implemented method of claim 31 further comprising exporting established groups of individuals.

45. A non-transitory computer readable medium having stored thereon a computer program that, when executed, causes a computer to perform the steps of:

receiving spatial-temporal data trails comprising spatial-temporal data points that identify places, wherein each of the places is identified by a corresponding spatial-temporal data point and wherein each of the spatial-temporal data trails is associated with an individual; and

determining a similarity matrix between pairs of the places, to establish one or more groups of places or one or more groups of individuals.

46. The non-transitory computer readable medium of claim 45 wherein the computer program, when executed, causes the computer also to perform the step of determining a similarity matrix between pairs of individuals to establish one or more groups of places or one or more groups of individuals.

47. A system comprising:

a network;

one or more user devices in communication with the network; and

a processor in communication with the network, wherein the processor is for;

receiving spatial-temporal data trails comprising spatial-temporal data points that identify places, wherein each of the places is identified by a corresponding spatial-temporal data point and wherein each of the spatial-temporal data trails is associated with an individual; and

determining a similarity matrix between pairs of the places, to establish one or more groups of places or one or more groups of individuals.

48. The system of claim 47 wherein the processor is also for determining a similarity matrix between pairs of individuals to establish one or more groups of places or one or more groups of individuals.

49. A system comprising:

means for receiving spatial-temporal data trails comprising spatial-temporal data points that identify places, wherein each of the places is identified by a corresponding spatial-temporal data point and wherein each of the spatial-temporal data trails is associated with an individual;

means for assigning a semantic coordinate to a spatial-temporal data point, resulting in a semantic data point;

means for determining a similarity matrix between pairs of the places, to establish one or more groups of places or one or more groups of individuals; and

means for determining a similarity matrix between pairs of individuals from their corresponding spatial-temporal data trails, to establish one or more groups of individuals.