Comparing Spatial-Temporal Trails In Location Analytics

• US 20100079336A1
• Filed: 09/30/2008
• Published: 04/01/2010
• Est. Priority Date: 09/30/2008
• Status: Active Grant

• Current Assignee: Verve Group, Inc. (Samarion SE)
• Original Assignee: Sense Networks, Inc. (Samarion SE)
• Inventors: Jebara, Tony, Rosenberg, David, Rao, Girish, Pentland, Alex (Sandy), Skibiski, Greg, Loecher, Markus, Uechi, Jason, Lemke, Christine, Mattiello, Joseph, Shaw, Blake
• Granted Patent: US 8,224,766 B2
• Time in Patent Office: Days
• US Class Current: 342/357.310
• 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 for determining similarities in one or more spatial-temporal trails, comprising;

receiving spatial-temporal flow data associated with one or more individuals;

assigning semantic coordinates to the flow data to create a first semantic trail; and

determining the 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 one or more individuals.

3. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises grid coordinates associated with one or more individuals.

4. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises GPS coordinates associated with one or more individuals.

5. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises time increments associated with one or more individuals.

6. The computer implemented method of claim 1 wherein the individual comprises a person, 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.

7. The computer implemented method of claim 1 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.

8. The computer implemented method of claim 1 wherein the spatial-temporal flow data comprises data indicating the time and place of one or more individuals'"'"' location.

9. The computer implemented method of claim 8 wherein the one or more individuals'"'"' location includes past locations.

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 assigning semantic coordinates further comprises:

replacing spatial-temporal coordinates with semantic coordinate values, wherein the semantic coordinate values identify a demographic characteristic.

13. The computer implemented method of claim 12 wherein the demographic characteristic 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.

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

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

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

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

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

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

20. 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.

21. The computer implemented method of claim 1 further comprising creating a time series of semantic coordinate to establish a first semantic trail.

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

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

determining the similarity of multiple first semantic trails associated with multiple different individuals with multiple second semantic trails associated with multiple different individuals; and

determining a similarity matrix between all semantic trails associated with all individuals.

24. A computer implemented method of conveying aggregate location-based information comprising:

receiving one or more location-based data flows associated with a first individual, wherein the location-based data flows comprise one or more spatial-temporal waypoints;

associating one or more demographics profiles with the spatial-temporal waypoints;

assigning a demographic-based data flow to the first individual; and

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

25. The computer implemented method of claim 24 wherein the first and second individual includes;

a person, 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.

26. The computer implemented method of claim 24 wherein the data flow is received in real time.

27. The computer implemented method of claim 24 wherein the data flow comprises historical data.

28. The computer implemented method of claim 24 wherein the spatial-temporal waypoint includes a latitude-longitude coordinate, a grid coordinate, a GPS coordinate, an address, a network cell, a time period, or a specific time.

29. The computer implemented method of claim 24 wherein the one or more demographic profiles 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.

30. The computer implemented method of claim 24 wherein a demographic profile is assigned to each spatial-temporal waypoint in a dataflow.

31. The computer implemented method of claim 24 wherein comparing demographic-based data flows comprises comparing similarity matrices of all demographic-based data flows for one or more individuals.

32. A computer implemented method comprising:

receiving one or more spatial-temporal data trails comprising one or more places, wherein each place is identified by a spatial temporal data point and wherein each spatial-temporal data trail is associated with an individual;

determining similarity between pairs of places to establish one or more groups of places or one or more groups of individuals.

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

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

35. The computer implemented method of claim 33 wherein the spatial-temporal data trail comprises grid coordinates associated with one or more individuals.

36. The computer implemented method of claim 33 wherein the spatial-temporal data trail comprises GPS coordinates associated with one or more individuals.

37. The computer implemented method of claim 33 wherein the spatial-temporal data trail comprises time increments associated with one or more individuals.

38. The computer implemented method of claim 33 wherein the individual comprises a person, 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 33 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.

40. The computer implemented method of claim 33 wherein determining the similarity further comprises assigning semantic coordinates to the spatial-temporal data points resulting in a semantic data point.

41. The computer implemented method of claim 40 wherein determining the similarity further comprises associating demographic information with the spatial-temporal data point or the semantic data point.

42. The computer implemented method of claim 40 wherein determining the similarity further comprises associating an industrial code with the spatial-temporal data point or the semantic data point.

43. The computer implemented method of claim 40 wherein determining the similarity further comprises associating an in-flow and out-flow data with the spatial-temporal data point or the semantic data point.

44. The computer implemented method of claim 40 wherein determining the similarity further comprises determining the probability of movement of the individual in semantic space.

45. The computer implemented method of claim 33 further comprising exporting established groups of individuals.

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

receiving one or more spatial-temporal data trails comprising one or more places, wherein each place is identified by a spatial temporal data point and wherein each spatial-temporal data trail is associated with an individual;

assigning semantic coordinates to the spatial-temporal data points resulting in a semantic data point.

determining similarity between pairs of places to establish one or more groups of places or one or more groups of individuals.

47. The computer readable medium of claim 46 further comprising determining the similarity between pairs of individuals to establish one or more groups of places or one or more groups of individuals.

48. A system for determining similarity between spatial-temporal trails comprising:

a network;

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

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

receives one or more spatial-temporal data trails comprising one or more places, wherein each place is identified by a spatial temporal data point and wherein each spatial-temporal data trail is associated with an individual;

assigns semantic coordinates to the spatial-temporal data points resulting in a semantic data point.

determines the similarity between pairs of places to establish one or more groups of places or one or more groups of individuals.

49. The computer implemented medium of claim 48 wherein the processor determine the similarity between pairs of individuals to establish one or more groups of places or one or more groups of individuals.

50. A system comprising:

a means for receiving one or more spatial-temporal data trails comprising one or more places, wherein each place is identified by a spatial temporal data point and wherein each spatial-temporal data trail is associated with an individual;

a means for assigning semantic coordinates to the spatial-temporal data points resulting in a semantic data point.

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

a means for determining the similarity between pairs of individuals from their corresponding spatial-temporal data trails to establish groups of individuals.