Using pattern recognition in real-time LBS applications

• US 8,600,674 B1
• Filed: 08/15/2008
• Issued: 12/03/2013
• Est. Priority Date: 08/15/2007
• Status: Active Grant

• Current Assignee: University of South Florida (State University System of Florida)
• Original Assignee: University of South Florida (State University System of Florida)
• Inventors: Barbeau, Sean J., Winters, Philip L., Perez, Rafael, Labrador, Miguel, Georggi, Nevine
• Primary Examiner(s): Khatib, Rami
• Application Number:
  US12/192,339
• Time in Patent Office: 1,936 Days
• Field of Search: 701/25, 701/27, 701/116, 701/207, 701/208, 701/225, 701/23, 701/36, 701/117, 701/400, 701/408, 701/409, 701/410, 701/411, 701/412, 701/414, 701/423, 701/424, 701/472, 701/481, 701/495, 701/520, 701/521, 701/524, 701/527, 701/300, 706/2, 706/5, 706/6, 706/15, 706 45- 62, 706/900, 340988-994, 340/995.25, 700/13, 700/48, 700/50, 700/56, 700/58, 700/64, 700/66, 700/69, 700/70, 700/113, 342/357.43, 342/457, 455/11.1, 455/95, 455/404.2, 455/414.2, 455/422, 455/422.1, 455/440, 455/446, 455/456, 455/456.1, 455/456.2, 455/456.5, 455/456.6, 455/457
• US Class Current: 701/524
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

A prediction method that estimates the real-time position of a mobile device based on previously observed data is provided. The present invention can be used in real-time navigation, including providing real-time alerts of an upcoming destination and notifications of emergency events in close geographic proximity. The prediction method utilizes neural networks and/or functions generated using genetic algorithms in estimating the mobile device'"'"'s real-time position. The prediction method provides reliable Location-Based Services (LBS) in events where traditional positioning technologies become unreliable. It is also seamless, as the user remains unaware of any interruption in accessing the positioning technology.

Claims

1. A method of estimating the current location of a mobile device, the method comprising:

acquiring, by at least one server, a plurality of historical location parameters associated with the mobile device, wherein at least one of the plurality of historical location parameters is data indicative of a prior latitude and longitude of the mobile device acquired by a wireless geographic positioning unit of the mobile device;

training, by the at least one server, a neural network using the plurality of historical location parameters to produce at least one trip model;

obtaining, by the at least one server, at least one last known location parameter for the mobile device;

estimating, by the at least one server, the current location of the mobile device using the at least one last known location parameter for the mobile device and the at least one trip model.

2. The method of claim 1, further comprising:

identifying at least one location of interest for the mobile device;

calculating at least one trigger area around the at least one location of interest;

comparing the estimated current location of the mobile device and the at least one trigger area; and

outputting an alert to the mobile device if the estimated current location of the mobile device is within the at least one trigger area.

3. The method of claim 2, wherein the location of interest is a location selected from the group consisting of a destination location of the mobile device, an emergency event location and a traffic incident location.

4. The method of claim 2, wherein the location of interest is a location selected from the group consisting of a destination location of the mobile device, an emergency event location and a traffic incident location.

5. The method of claim 1, further comprising outputting the estimated current location of the mobile device.

6. The method in claim 1, wherein generating at least one trip model using the plurality of historical location parameters comprises employing a genetic algorithm to determine a function that models the historical location parameters.

7. The method of claim 1, wherein the mobile device is any device capable of transmitting the location parameters.

8. The method of claim 1, wherein the mobile device is a mobile device chosen from the group comprising a GPS-enabled cell phone, a GPS-enabled laptop, and a GPS-enabled Personal Digital Assistant (PDA).

9. The method of claim 1, wherein the plurality of historical location parameters and the at least one last known location parameter are chosen from the group consisting of data indicative of latitude and longitude, data indicative of a timestamp, data indicative of a wireless signal strength, data indicative of speed, and data indicative of direction of travel.

10. The method of claim 1, wherein the historical location parameters are achieved location parameters.

11. The method of claim 1, wherein the estimated current location is given latitude and longitude coordinates.

12. The method of claim 1, further comprising storing the plurality of historical location parameters at the server.

13. The method of claim 1 further comprising, transferring the estimated current location from the server to the mobile device if the server estimated the current location.

14. The method of claim 1 further comprising, transferring the at least one trip model from the server to the mobile device if the server produced the at least one trip model.

15. The method of claim 1, further comprising storing the plurality of historical location parameters at the mobile device.

16. A method of estimating the current location of a mobile device, the method comprising:

acquiring, by at least one server, a plurality of historical location parameters associated with the mobile device, wherein at least one of the plurality of historical location parameters is data indicative of a prior latitude and longitude of the mobile device acquired by a wireless geographic positioning unit of the mobile device;

storing, by the at least one server, the historical location parameters;

training, by the at least one server, a neural network using the plurality of historical location parameters to produce at least one trip model;

obtaining, by the at least one server, at least one last known location parameter for the mobile device;

estimating, by the at least one server, the current location of the mobile device using the at least one last known location parameter for the mobile device and the at least one trip model.

17. The method of claim 16, wherein the plurality of historical location parameters include parameters for a transit system.

18. The method of claim 17, wherein the transit system'"'"'s plurality of historical location parameters are collected from an automatic vehicle location service.

19. The method of claim 17, wherein the transit system'"'"'s plurality of historical location parameters are collected from an archive of the mobile device'"'"'s previous trips on the transit system.

20. The method of claim 17, wherein the transit system'"'"'s plurality of historical location parameters are collected from an archive of historic trips of the transit system.

21. The method of claim 17, wherein the transit system'"'"'s plurality of historical location parameters are collected from the transit system'"'"'s schedule of arrival and departure times and locations.

22. The method of claim 16, wherein the plurality of historical location parameters include parameters for a mobile device and a transit system.

23. The method of claim 22, wherein the transit system'"'"'s plurality of historical location parameters are collected from an automatic vehicle location service.

24. The method of claim 22, wherein the transit system'"'"'s plurality of historical location parameters are collected from an archive of the mobile device'"'"'s previous trips on the transit system.

25. The method of claim 22, wherein the transit system'"'"'s plurality of historical location parameters are collected from an archive of historic trips of the transit system.

26. The method of claim 22, wherein the transit system'"'"'s plurality of historical location parameters are collected from the transit system'"'"'s schedule of arrival and departure times and locations.

27. The method of claim 22, wherein the plurality of historical location parameters for the mobile device include archived location parameters.

28. The method of claim 16, further comprising outputting the estimated current location of the mobile device.

29. The method of claim 16, further comprising:

identifying at least one location of interest for the mobile device;

calculating at least one trigger area around the at least one location of interest;

comparing the estimated current location of the mobile device and the at least one trigger area; and

outputting an alert to the mobile device if the estimated current location of the mobile device is within the at least one trigger area.

30. The method of claim 16, wherein generating at least one trip model using the plurality of historical location parameters comprises employing a genetic algorithm to determine a function that models the historical location parameters.

31. The method of claim 16, wherein the mobile device is any device capable of transmitting the location parameters.

32. The method of claim 16, wherein the mobile device is a mobile device chosen from the group comprising a GPS-enabled cell phone, a GPS-enabled laptop, and a GPS-enabled Personal Digital Assistant (PDA).

33. The method of claim 16, wherein the plurality of historical location parameters and the at least one last known location parameters are chosen from the group comprising data indicative of latitude and longitude, data indicative of a timestamp, data indicative of a wireless signal strength, data indicative of speed, and data indicative of direction of travel.

34. A system for estimating the current location of a mobile device, the system comprising:

a mobile device comprising a wireless geographic positioning unit, the wireless geographic unit to acquire a plurality of historical location parameters associated with the mobile device and at least one last known location parameter for the mobile device, wherein at least one of the plurality of historical location parameters is data indicative of a prior latitude and longitude of the mobile device acquired by a wireless geographic positioning unit of the mobile device;

at least one trip model, the at least one trip model produced by training a neural network using the plurality of historical location parameters associated with the mobile device, the at least one trip model to receive the at least one last known location parameter for the mobile device and to estimate a current location of the mobile device.

35. The system of claim 34, wherein the plurality of historical location parameters and the at least one last known location parameter are chosen from the group consisting of data indicative of latitude and longitude, data indicative of a timestamp, data indicative of a wireless signal strength, data indicative of speed, and data indicative of direction of travel.

36. The system of claim 34, wherein the at least one trip model is a function generated by a genetic algorithm.

37. The system of claim 34, further comprising a server wirelessly coupled to the mobile device.

38. The system of claim 37, wherein the at least one trip model is located at the server.

39. The system of claim 38, wherein estimate of the current location of the mobile device is transmitted from the server to the mobile device.

40. The system of claim 34, wherein the at least one trip model is located at the mobile device.

41. The system of claim 34, further comprising:

an alert generator coupled to the at least one trip model, the alert generator to output an alert to the mobile device if the estimate of the current location of the mobile device is within a trigger area associated with a location of interest for the mobile device.