Systems and methods for using spatial and temporal analysis to associate data sources with mobile devices

• US 9,439,033 B2
• Filed: 06/04/2015
• Issued: 09/06/2016
• Est. Priority Date: 10/09/2013
• Status: Active Grant

• Current Assignee: Mobile Design Technologies Incorporated
• Original Assignee: Mobile Technology Corporation, LLC
• Inventors: Hartzell, Dale, Welton, Mark, Perri, Michael
• Primary Examiner(s): DOAN, KIET M
• Application Number:
  US14/731,281
• Publication Number:
  US 20150271635A1
• Time in Patent Office: 460 Days
• Field of Search: 455/456.1, 455/456.3, 455/456.5, 455/456.2, 455/236, 705/14.58, 709/204
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0277 Online advertisement
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 12/02 Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
• CPC Class Codes: H04W 12/50 Secure pairing of devices
• CPC Class Codes: H04W 4/02 Services making use of location information
• 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
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audience with greater accuracy.

Claims

1. A non-transitory computer-implemented method comprising:

organizing, using a processor associated with a data delivery platform, a plurality of latitude-longitude pairs into clusters corresponding to geographic regions visited by a mobile device during a time period;

calculating, using the processor associated with the data delivery platform, a score for each cluster, the score representing a probability that a user of the mobile device resides in a household within the cluster;

identifying, using the processor associated with the data delivery platform, a location of the household of the user, the location corresponding to the cluster having a highest score; and

associating, using the processor associated with the data delivery platform, the mobile device with the household of the user;

wherein calculating the score comprises applying a weight based on at least one of a time and a location associated with a longitude-latitude pair.

2. The non-transitory computer-implemented method of claim 1, wherein the latitude-longitude pairs are obtained using a technique selected from the group consisting of global positioning with a global positioning system, cellular triangulation, and/or WiFi identification.

3. The non-transitory computer-implemented method of claim 1, wherein the latitude-longitude pairs do not originate from a mobile carrier.

4. The non-transitory computer-implemented method of claim 1, wherein the latitude-longitude pairs are associated with an ad request sent by the mobile device.

5. The non-transitory computer-implemented method of claim 1, wherein the weight is based on the time associated with the latitude-longitude pair, according to a probability that the mobile device was in the household at the time.

6. The non-transitory computer-implemented method of claim 1, wherein the weight is based on the location associated with the latitude-longitude pair, according to a probability that the location corresponds to a residential location.

7. The non-transitory computer-implemented method of claim 1, further comprising rejecting a latitude-longitude pair based on an indication that the latitude-longitude pair is erroneous.

8. The non-transitory computer-implemented method of claim 1, further comprising:

receiving at a communications port, during a second time period, a plurality of second latitude-longitude pairs for the mobile device, the second latitude-longitude pairs corresponding to a location of the mobile device during the second time period;

organizing the plurality of second latitude-longitude pairs into a plurality of second clusters, the second clusters corresponding to geographic regions visited by the mobile device during the second time period;

calculating, using the processor associated with the data delivery platform, a second score for each second cluster, the second score representing a likelihood that the user of the mobile device resides in a household within the second cluster; and

identifying, using the processor associated with the data delivery platform, a second location of the household of the user, the second location corresponding to the second cluster having the highest second score.

9. The non-transitory computer-implemented method of claim 8, further comprising performing a state update comprising:

determining whether the location of the household of the user and the second location of the household of the user are consistent;

if the location and the second location are consistent, determining a revised location of the household of the user as a function of the location and the second location;

if (i) the location and the second location are not consistent, and (ii) the highest second score is less than or equal to the highest score, associating the mobile device with the location; and

if (i) the location and the second location are not consistent, and (ii) the highest second score is greater than the highest score, associating the mobile device with the second location of the household of the user.

10. The non-transitory computer-implemented method of claim 1, further comprising assigning statistical metrics to measure fitness of the device ID and household ID association.

11. A system comprising:

a data storage device operating on a server computer, the data storage device storing a plurality of latitude-longitude pairs for a mobile device, the latitude-longitude pairs corresponding to a location of a mobile device during a time period;

a memory having stored thereon non-transitory computer readable instructions; and

a processor to execute the non-transitory computer-readable instructions, wherein when executed cause the system to;

organize the plurality of latitude-longitude pairs into one or more clusters corresponding to geographic regions or locations visited by the mobile device during the time period;

calculate, for each of the one or more clusters, a score representing a probability that a user of the mobile device resides in a household within the cluster;

identify a household of the user based on a residential location of a cluster with a highest score;

associate the mobile device with the household of the user;

anonymize mobile device identifiers to provide security and anonymity of users; and

associate data on the household of the user with the mobile device identifiers in the data storage device;

wherein calculating the score comprises applying a weight based on at least one of a time and a location associated with a longitude-latitude pair.

12. The system of claim 11, wherein the non-transitory computer-readable instructions when executed by the processor cause the system to filter the plurality of latitude-longitude pairs.

13. The system of claim 11, wherein the non-transitory computer-readable instructions when executed by the processor cause the system to associate and append each cluster with additional data elements to provide more detailed information about the locations in the cluster for use in additional analytics.

14. The system of claim 11, wherein the non-transitory computer-readable instructions when executed by the processor cause the system to:

compare data on each individual within the household against the appended data to identify an individual within the household associated with the device;

associate the mobile device with an individual; and

link data on an individual with the mobile device.

15. The system of claim 11, wherein the non-transitory computer-readable instructions when executed by the processor cause the system to:

receive, during a second time period, a plurality of second latitude-longitude pairs for the mobile device, the second latitude-longitude pairs corresponding to a location of the mobile device during the second time period;

filter and organize the plurality of second latitude-longitude pairs into a plurality of second clusters, the second clusters corresponding to geographic regions or locations visited by the mobile device during the second time period;

calculate a second score for each second cluster, the second score representing a likelihood that the user of the mobile device resides in a household within the second cluster; and

identifies a second location of the household of the user, the second location corresponding to the second cluster having the highest second score.

16. The system of claim 11, wherein the latitude-longitude pairs are obtained using a technique selected from the group consisting of global positioning with a global positioning system, cellular triangulation, Bluetooth, and/or W-Fi identification.

17. The system of claim 11, wherein the latitude-longitude pairs are associated with any activity of a mobile device, such as an ad request sent by the mobile device, in-app location collection, device network registration or communication, or data exchange.

18. The system of claim 11, wherein the weight is based on the time associated with the latitude-longitude pair, according to a probability that the mobile device was in the household at the time.

19. The system of claim 11, wherein the weight is based on the location associated with the latitude-longitude pair, according to a probability that the location corresponds to a residential location.

20. The system of claim 11, wherein the weight is based on the location of the latitude-longitude pair, according to a likelihood that the location corresponds to a specific residential address based on analysis.

21. The system of claim 11, further comprising filtering and rejecting a latitude-longitude pair based on analysis of the latitude-longitude pair.

22. The system of claim 11, wherein the data associated with a cluster, household, or individual includes data from commercial sources, enterprise databases, retailer databases, government databases.

23. The system of claim 22, wherein the data includes point of interest information, business information, or retail information.

24. The system of claim 11, further comprising performing a state update comprising:

determining whether the location of the household of the user and the second location of the household of the user are consistent;

if the location and the second location are consistent, determining a revised location of the household of the user as a function of the location and the second location;

if (i) the location and the second location are not consistent, and (ii) the highest second score is less than or equal to the highest score, associating the mobile device with the location; and

if (i) the location and the second location are not consistent, and (ii) the highest second score is greater than the highest score, associating the mobile device with the second location of the household of the user.

25. The system of claim 11, further comprising assigning statistical metrics to measure fitness of the device ID and the household association.