SYSTEM AND METHOD FOR AGGREGATING AND ASSOCIATING MOBILE DEVICE LOCATION DATA

• US 20120108259A1
• Filed: 11/01/2010
• Published: 05/03/2012
• Est. Priority Date: 11/01/2010
• Status: Active Grant

• Current Assignee: Smith Micro Software Incorporated
• Original Assignee: Wavemarket Incorporated (Gen Digital Inc.)
• Inventors: Weiss, Andrew
• Granted Patent: US 8,504,062 B2
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0267 Wireless devices
• 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 4/025 using location based information parameters
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A computer-implemented method for processing mobile device location data is provided. Location data is filtered based on age and accuracy or precision of location coordinates. Location data is partitioned based on the location coordinates into a plurality of clusters including a plurality of location data instances. The plurality of clusters are filtered based on a number of data instances in a particular cluster and a period of time over which data instances of the particular data cluster are generated. A distance is determined from a location coordinate corresponding to the data instances of one or more of the plurality of clusters to a particular predetermined location, and the one or more of the plurality of clusters are correlated with the particular predetermined location in response to the distance being less than a predetermined distance. The invention further provides a location data processing system.

Claims

1. A computer-implemented method for processing mobile device location data, the method comprising:

collecting location data associated with a mobile device, the location data comprising location coordinates and time stamps respectively associated with the location coordinates;

filtering location data based on age and at least one of accuracy and precision of the location coordinates;

partitioning the location data based on the location coordinates into a plurality of clusters comprising a plurality of location data instances;

filtering the plurality of clusters based on a number of data instances in a particular cluster and a period of time over which data instances of the particular data cluster are generated as indicated by respective time stamps of the data instances;

determining a distance from a location coordinate corresponding to the data instances of at least a first one of the plurality of clusters to a particular predetermined location; and

correlating the at least the first one of the plurality of clusters with the particular predetermined location in response to the distance being less than a predetermined distance.

2. The method of claim 1, further comprising:

correlating the particular predetermined location with location-related data; and

transmitting the location-related data to the mobile device in response to correlating the at least the first one of the plurality of clusters with the particular predetermined location.

3. The method of claim 1, further comprising:

correlating the particular predetermined location with location-related data; and

transmitting the location-related data to a user associated with the mobile device in response to correlating the at least the first one of the plurality of clusters with the particular predetermined location.

4. The method of claim 1, further comprising:

correlating each of a plurality of predetermined locations with at least one category;

associating the mobile device with a user;

for each of the plurality of clusters, determining the distance from a location coordinate corresponding to the data instances of the cluster to each of the plurality of predetermined locations; and

correlating the user with the at least one category of those of the plurality of predetermined locations for which the distance is less than the predetermined distance.

5. The method of claim 4, further comprising for the mobile device user, assigning a frequency value to each of the at least one category correlated with the user based on the number of those of the plurality of predetermined locations for which the distance is less than the predetermined distance correlated with the at least one category.

6. The method of claim 5, further comprising:

for at least one of the plurality of predetermined locations, receiving location-related data in the form of at least one of an advertisement and a business name correlated with the at least one of the plurality of predetermined locations;

for the at least one of the plurality of predetermined locations, assigning a popularity value to each of the at least one category based on the number of the plurality of predetermined locations correlated with the at least one category;

determining a current position of the mobile device; and

transmitting to the user the location-related data corresponding to the at least one of the plurality of predetermined locations in response to the current position of the mobile device being within a predetermined proximity of the at least one of the plurality of predetermined locations and based on the popularity value and the frequency value.

7. The method of claim 6, further comprising:

for those of the plurality of predetermined locations within the predetermined proximity from the current position of the mobile device and correlated with location-related data, for each category compute a composite value based on the predetermined location'"'"'s popularity value for the category and the user'"'"'s frequency value for the category, and compute a total composite value for the predetermined location based on the composite value for each category;

compare the total composite values of the plurality of predetermined locations within the predetermined proximity from the current position of the mobile device; and

transmit the location-related data correlated with the at least one of the plurality of predetermined locations within the predetermined proximity from the current position of the mobile device based on the comparison of the total composite values of the plurality of predetermined locations within the predetermined proximity from the current position of the mobile device.

8. The method of claim 4, further comprising for the mobile device user, assigning a frequency value to each of the at least one category correlated with the user based on the number of those of the plurality of predetermined locations for which the distance is less than the predetermined distance correlated with the at least one category.

9. The method of claim 1, wherein correlating each of the plurality of predetermined locations with the at least one category comprises correlating each of the plurality of predetermined locations with a venue and correlating the venue with the at least one category.

10. The method of claim 1, further comprising correlating the at least the first one of the plurality of clusters with the particular predetermined location further in response to the time stamp of at least one location data instance of the at least the first one of the plurality of clusters corresponding to a predetermined time range.

11. The method of claim 1, further comprising collecting the location data instances as tuples.

12. The method of claim 1, wherein filtering the location data comprises eliminating from consideration location data instances older than a predetermined length of time as indicated by respective time stamps of the location data instances.

13. The method of claim 1, wherein filtering the location data comprises eliminating from consideration location data instances comprising location coordinates corresponding to a measurement of at least one of accuracy and precision, wherein the measurement is outside a predetermined range.

14. The method of claim 1, further comprising defining at least one inhabitable area including at least one boundary, wherein filtering the location data comprises eliminating from consideration location data instances corresponding to geographic coordinates outside the inhabitable area.

15. The method of claim 1, further comprising defining at least one uninhabitable area including at least one boundary, wherein filtering the location data comprises eliminating from consideration location data instances corresponding to geographic coordinates inside the uninhabitable area.

16. The method of claim 1, further comprising determining a centroid from an average of at least two of the location coordinates of the data instances of each of the plurality of clusters, wherein determining the distance from the location coordinate corresponding to the data instances of the at least the first one of the plurality of clusters to the particular predetermined location comprises determining a distance from the centroid to the particular predetermined location.

17. The method of claim 1, further comprising defining at least one base location from data instances of at least a second one of the plurality of clusters in response to time stamps of the data instances of the at least the second one of the plurality of clusters corresponding to a particular time range on particular days of the week for a predetermined period of time.

18. The method of claim 17, further comprising:

correlating the at least one base location with location-related data; and

transmitting the location-related data to the mobile device in response to correlating the at least the second one of the plurality of clusters with the at least one base location.

19. The method of claim 17, further comprising:

correlating the at least one base location with demographic data; and

transmitting an advertisement corresponding to the demographic data to a user associated with the mobile device in response to correlating the at least the second one of the plurality of clusters with the at least one base location.

20. The method of claim 17, further comprising:

correlating the at least one base location with demographic data;

correlating the particular predetermined location with location-related data corresponding to the demographic data; and

transmitting the location-related data corresponding to the demographic data to a user of the mobile device in response to correlating the at least the first one of the plurality of clusters with the particular predetermined location and in response to correlating the at least the second one of the plurality of clusters with the at least one base location.

21. The method of claim 17, further comprising:

correlating a user of the mobile device with demographic data associated with the at least one base location; and

transmitting location-related data corresponding to the demographic data to the user of the mobile device in response to correlating the at least the second one of the plurality of clusters with the at least one base location.

22. The method of claim 17, further comprising:

correlating a user of the mobile device with demographic data associated with the at least one base location; and

transmitting location-related data corresponding to the demographic data to the user of the mobile device in response to correlating the at least the first one of the plurality of clusters with the particular predetermined location and in response to correlating the at least the second one of the plurality of clusters with the at least one base location.

23. The method of claim 17, further comprising:

correlating a user of the mobile device with demographic data associated with the at least one base location; and

transmitting an advertisement corresponding to the demographic data to the user of the mobile device in response to correlating the at least the first one of the plurality of clusters with the particular predetermined location and in response to correlating the at least the second one of the plurality of clusters with the at least one base location.

24. The method of claim 17, wherein determining at least one distance from at least one location coordinate of the data instances of at least the first one of the plurality of clusters to a particular predetermined location comprises eliminating from consideration the at least the second one of the plurality of clusters defining the at least one base location.

25. The method of claim 17, further comprising defining the at least one base location from the data instances of the at least the second one of the plurality of clusters in response to the time stamps of the data instances of the at least the second one of the plurality of clusters corresponding to a predetermined time range for a predetermined number of days.

26. The method of claim 1, wherein partitioning the location data into a plurality of clusters comprises generating a cluster wherein the location coordinates of any two data instances of the particular cluster are separated by less than a predetermined maximum distance.

27. The method of claim 1, wherein filtering the plurality of clusters comprises eliminating from consideration clusters in which at least one of:

a number of the data instances in a particular cluster are less than a predetermined number; and

a period of time over which the data instances of the particular data cluster are generated, as indicated by respective time stamps of the data instances, is less than a predetermined period of time.

28. The method of claim 1, wherein filtering the plurality of clusters comprises eliminating from consideration clusters in which:

a number of the data instances in a particular cluster are less than a predetermined number; and

a period of time over which the data instances of the particular data cluster are generated, as indicated by respective time stamps of the data instances, is less than a predetermined period of time.

29. A computer-implemented method for processing mobile device location data, the method comprising:

partitioning a collection of tuples including time stamps and location coordinates, generated via a particular mobile device associated with a user, into a plurality of clusters wherein distances between location coordinates of the tuples in each of the plurality of clusters are less than a predetermined distance;

deriving from the plurality of clusters size-based clusters, wherein the number of tuples in each size-based cluster is greater than a predetermined number;

deriving from the plurality of clusters time-based clusters, wherein a time period from a substantially earliest tuple to a substantially latest tuple in each of the time-based clusters, as indicated by respective time stamps, is greater than a predetermined length of time; and

designating at least one base location cluster from at least one of the size-based clusters and the time-base clusters wherein at least a predetermined percentage of the tuples correspond to at least one particular time window.

30. The method of claim 29, further comprising designating the at least one base location cluster as the largest size-based cluster wherein at least a predetermined percentage of the tuples correspond to the at least one particular time window.

31. The method of claim 29, further comprising for each particular one of the size-based clusters and the time-based clusters different from the at least one base location cluster, determining a centroid of the particular cluster and designating the particular cluster as a venue cluster if the centroid of the particular cluster is within a predetermined distance from a location coordinate of a known venue.

32. The method of claim 31, further comprising transmitting location-related data corresponding to the known venue to the user.

33. The method of claim 29, further comprising deriving the time-based clusters from those of the plurality of clusters not derived as size-based clusters.

34. The method of claim 29, further comprising partitioning the collection of tuples wherein the distances between location coordinates of each of the tuples in each of the plurality of clusters are less than a predetermined distance

35. The method of claim 29, further comprising partitioning the collection of tuples wherein the distances between location coordinates of a majority of the tuples in each of the plurality of clusters are less than a predetermined distance

36. A location data processing system comprising at least one computing device including at least one memory comprising instructions operable to enable the computing device to perform a procedure comprising:

collecting location data associated with a mobile device, the location data comprising location coordinates and time stamps respectively associated with the location coordinates;

filtering location data based on age and at least one of accuracy and precision of the location coordinates;

partitioning the location data based on the location coordinates into a plurality of clusters comprising a plurality of location data instances;

filtering the plurality of clusters based on a number of data instances in a particular cluster and a period of time over which data instances of the particular data cluster are generated as indicated by respective time stamps of the data instances;

determining a distance from a location coordinate corresponding to the data instances of at least a first one of the plurality of clusters to a particular predetermined location; and

correlating the at least the first one of the plurality of clusters with the particular predetermined location in response to the distance being less than a predetermined distance.

37. Non-transitory computer-readable media tangibly embodying a program of instructions executable by a computing device to implement a method, the computing device being capable of interfacing with a communications network, the method comprising:

collecting location data associated with a mobile device, the location data comprising location coordinates and time stamps respectively associated with the location coordinates;

filtering location data based on age and at least one of accuracy and precision of the location coordinates;

partitioning the location data based on the location coordinates into a plurality of clusters comprising a plurality of location data instances;

filtering the plurality of clusters based on a number of data instances in a particular cluster and a period of time over which data instances of the particular data cluster are generated as indicated by respective time stamps of the data instances;

determining a distance from a location coordinate corresponding to the data instances of at least a first one of the plurality of clusters to a particular predetermined location; and

correlating the at least the first one of the plurality of clusters with the particular predetermined location in response to the distance being less than a predetermined distance.

38. A computer-implemented method for processing mobile device location data, the method comprising:

correlating each of a plurality of predetermined locations with at least one category;

determining for at least one data instance of a mobile device the distance from a location coordinate corresponding to the at least one data instance to each of the plurality of predetermined locations;

correlating a user of the mobile device with the at least one category of those of the plurality of predetermined locations for which the distance is less than a predetermined distance;

assigning a frequency value to each of the at least one category correlated with the user based on the number of those of the plurality of predetermined locations correlated with the at least category for which the distance is less than the predetermined distance;

correlating location-related data with at least one of the plurality of predetermined locations;

for the one or more of the plurality of predetermined locations, assigning a popularity value to each of the at least one categories based on the number of the plurality of predetermined locations correlated with each of the one or more categories;

determining a current position of the mobile device; and

transmitting location-related data corresponding to the at least one of the plurality of predetermined locations is transmitted to the user in response to the current position of the mobile device being within a predetermined proximity of the at least one of the plurality of predetermined locations and based on the popularity value and the frequency value.

39. The method of claim 38, wherein correlating each of the plurality of predetermined locations with the at least one category comprises correlating each of the plurality of predetermined locations with a venue and correlating the venue with the at least one category.