SYSTEM, METHOD AND APPARATUS FOR PREDICTIVE MODELING OF SPATIALLY DISTRIBUTED DATA FOR LOCATION BASED COMMERCIAL SERVICES

• US 20090024546A1
• Filed: 06/23/2008
• Published: 01/22/2009
• Est. Priority Date: 06/23/2007
• Status: Abandoned Application

• Current Assignee: Motivepath Incorporated
• Original Assignee: Motivepath Incorporated
• Inventors: Ficcaglia, Robert, Zapata, Daniel
• Application Number:
  US12/144,606
• Publication Number:
  US 20090024546A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A computer system implements a method to provide a class membership probability prediction based on collected usage data from a user device of a user. After device usage data, which contains location information, is collected from the user device, the collected usage data is processed to generate a predictive model by utilizing a machine learning algorithm. In response to a user input, a class membership probability estimation is produced by processing the user input through the probability predictive model. The resulted class membership probability estimation can then be used as a prediction of a demographic profile of the user.

Claims

1. A method, comprising:

processing usage data collected from a user device of a user, wherein the collected usage data contains location information;

generating a predictive model from the collected usage data by utilizing a machine learning algorithm;

in response to a user input, producing a class membership probability estimation by processing the user input through the predictive model, wherein the class membership probability estimation predicts a demographic profile of the user.

2. The method as recited in claim 1, further comprising:

initiating targeted commercial services to the user device based on the predicted demographic profile.

3. The method as recited in claim 1, further comprising:

producing a return on investment (ROI) estimation based on the predicted demographic profile.

4. The method as recited in claim 1, further comprising:

producing a user behavior simulation for marketing and advertising campaigns based on the predicted demographic profile.

5. The method as recited in claim 1, wherein the collected usage data further contains private demographic data of the user, the private demographic data being optionally cryptographically secured.

6. The method as recited in claim 1, wherein the predictive model does not contain the location information contained in the collected usage data, and the collected usage data can be optionally discarded upon the completion of the generating of the predictive model.

7. The method as recited in claim 1, wherein the class membership probability estimation classifies the user into one or more classes.

8. The method as recited in claim 1, wherein the class membership probability estimation provides a probability of the user being in one or more classes.

9. The method as recited in claim 1, wherein the user input contains a current location of the user device, the predicted demographic profile not containing the current location of the user device.

10. The method as recited in claim 1, wherein the user input contains a business location, the predicted demographic profile predicting a user preference with respect to the business location.

11. The method as recited in claim 1, wherein the user input does not contain location information, and the predicted demographic profile provides a geographic location relevant to the user.

12. The method as recited in claim 1, wherein the class membership probability estimation is associated with a predefined class category.

13. The method as recited in claim 1, wherein the processing of the usage data comprising:

optionally visualizing the usage data;

optionally performing comprehensive exploratory data analysis; and

optionally performing comprehensive exploratory structural analysis and modeling of anisotropic spatial correlation.

14. The method as recited in claim 1, wherein the processing of the usage data comprising:

splitting the usage data into training, testing and validation subsets;

utilizing the training subsets to train the predictive model;

utilizing the testing subsets to test the trained predictive model; and

utilizing the validation subset to validate the tested predictive model.

15. The method as recited in claim 1, wherein the machine learning algorithm is a Support Vector Machine (SVM).

16. The method as recited in claim 15, wherein the generating of the predictive model further comprising:

transforming the processed usage data to a SVM implementation format;

conducting scaling on the processed usage data;

testing multiple model parameters and kernel transformation functions;

using cross-validation to find optimal parameters for the multiple kernel transformation functions; and

using the optimal parameters to train the predictive model.

17. The method as recited in claim 1, wherein the machine learning algorithm is a probabilistic classification and decision making algorithm.

18. The method as recited in claim 1, wherein the method is embodied in a machine-readable medium as a set of instructions which, when executed by a processor, cause the processor to perform the method.

19. A method, comprising:

receiving a user input from a user device of a user;

retrieving a plurality of pre-generated predictive models, wherein the plurality of predictive models are related to the user input;

generating a plurality of class membership probability estimations by processing the user input through the plurality of pre-generated predictive models; and

selecting an optimal class membership probability estimation from the plurality of class membership probability estimations, wherein the optimal class membership probability estimation predicts a demographic profile of the user.

20. The method as recited in claim 19, further comprising:

providing targeted commercial services to the user device based on the predicted demographic profile.

21. The method as recited in claim 19, wherein the plurality of predictive models are generated based on usage data previously collected from one or more user devices, the usage data contains location information of the one or more user devices, the plurality of predictive models do not contain the location information, and the collected usage data can be optionally discarded upon the completion of the generating of the plurality of predictive models.

22. The method as recited in claim 19, wherein the user input contains location information obtained from the user device, and the predicted demographic profile does not contain the location information.

23. The method as recited in claim 19, wherein the user input does not contain location information, and the predicted demographic profile predicts a physical location for the user.

24. The method as recited in claim 19, wherein the user input contains location information obtained from the user device, and the predicted demographic profile predicts a future location for the user over a period of time.

25. The method as recited in claim 19, wherein the optimal class membership probability estimation is selected based on a probability of predicting a commercial location for the user.

26. The method as recited in claim 19, wherein the optimal class membership probability estimation is selected by ranking a probability value for each of the plurality of class membership probability estimations.

27. The method as recited in claim 19, wherein the method is embodied in a machine-readable medium as a set of instructions which, when executed by a processor, cause the processor to perform the method.

28. A device, comprising:

a location sensor to obtain location information of the device;

a class membership estimation engine coupled with the location sensor, wherein the class membership estimation engine generates a class membership probability estimation based on the location information and a predictive model, the predictive model being selected from a plurality of pre-generated predictive models; and

a commercial service engine coupled with the class membership estimation engine, to initiate targeted commercial services to the device based on the class membership probability estimation.

29. The device as recited in claim 28, wherein the location information is not transmitted out of the device.

30. A system, comprising:

a predictive modeling engine to generate a plurality of predictive models from collected device usage data, wherein the collected device usage data contains location information; and

a class membership estimation engine coupled with the predictive modeling engine, wherein the class membership estimation engine generates a class membership probability estimation based on a user device location information and a predictive model selected from the plurality of predictive models.

31. The system as recited in claim 30, wherein the user device location information and the location information contained in the collected device usage data are not transmitted out of the system.