INFERRING USER PROFILE PROPERTIES BASED UPON MOBILE DEVICE USAGE

• US 20100203876A1
• Filed: 02/11/2009
• Published: 08/12/2010
• Est. Priority Date: 02/11/2009
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Krishnaswamy, Dilip
• Application Number:
  US12/369,676
• Publication Number:
  US 20100203876A1
• Time in Patent Office: Days
• US Class Current: 455/418
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/025 for remote control or remote monitoring of applications
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Demographic information regarding a user of a mobile device is inferred by observing the user'"'"'s mobile device usage behavior. Bayesian probability principles are applied to the observed usage behavior in order to infer a most likely demographic category classification. Probabilities of the user being a member of various demographic category classifications may be obtained from population surveys. Conditional probabilities of the user being a member of a behavior category classification given a demographic category classification may also be obtained from population surveys. A most likely user demographic category can be determined by calculating the product of the probability of the user being a member of each of the demographic category classifications and the first conditional probability of the user being a member of the behavior category classification, and identifying the demographic category classification that yields a maximum relative likelihood. The user demographic category may alternatively be determined by a table look up using the determined behavior category classification.

Claims

10. A method for inferring a mobile device user profile property classification, comprising:

receiving a determined behavior category classification from the mobile device;

retrieving from a derived inference table a mobile device user profile property classification using the received determined behavior category classification as a look up value; and

transmitting the inferred mobile device user profile property classification to the mobile device.

12. A mobile device comprising:

means for logging at least one category of mobile device usage events; and

means for inferring the mobile device user profile property classification based upon the logged mobile device usage events and information derived from a population of users.

1. A method for inferring a mobile device user profile property classification, comprising:

logging at least one category of mobile device usage events; and

inferring the mobile device user profile property classification based upon the logged mobile device usage events and information derived from a population of users.

2. The method of claim 1, further comprising determining a behavior category classification based upon the logged mobile device usage events.

3. The method of claim 2 wherein inferring the mobile device user profile property classification based upon the logged mobile device usage events further comprises:

retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

retrieving a first conditional probability of the user being a member of the behavior category classification given the mobile device user profile property classification from a second table of derived information;

calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the first conditional probability of the user being a member of the behavior category classification; and

identifying the mobile device user profile property classification as one that yields a maximum relative likelihood.

4. The method of claim 3, further comprising determining a plurality of behavior category classifications based upon the logged mobile device usage events.

5. The method of claim 2, wherein inferring the mobile device user profile property classification based upon of the logged mobile device usage events further comprises:

determining a plurality of behavior category classifications based upon of the logged mobile device usage events;

retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

retrieving a plurality of conditional probability values of a user being a member of each of a plurality of mobile device user profile property classification given each of the plurality of behavior category classifications from a plurality of tables of derived information;

calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the conditional probability values of the user being a member of each of a plurality of mobile device user profile property classifications given each of the plurality of behavior category classifications; and

identifying the mobile device user profile property classification as one that yields a maximum relative likelihood value.

6. The method of claim 5, wherein said calculating the relative likelihood value of the user being a member of each of the plurality of mobile device user profile property classifications given the combination of the plurality of determined behavior category classifications comprises:

calculating a usage behavior product; and

multiplying the usage behavior product by a probability of the user being a member of the mobile device user profile property classification.

7. The method of claim 2, wherein inferring the mobile device user profile property classification based upon the logged mobile device usage events and information derived from the population of users comprises:

looking up the user demographic category classification in a derived inference table using the determined behavior category classification.

8. The method of claim 2, further comprising:

transmitting the determined behavior category classifications from the mobile device to a remote server configured to look up the inferred mobile device user profile property classification in a derived inference table using the received determined behavior category classification and transmit the inferred mobile device user profile property classification to the mobile device; and

receiving in the mobile device the inferred mobile device user profile property classification.

9. The method of claim 2, wherein determining the behavior category classification based upon the logged mobile device usage events further comprises:

determining whether sufficient mobile device usage events have been logged to accurately determine the behavior category classification; and

performing a statistical analysis of the logged mobile device usage events to determine the behavior category classification.

11. The method of claim 1, wherein said inferring the mobile device user profile property classification based upon the logged mobile device usage events and information derived from a population of users is performed by applying Bayesian probability principles.

13. The mobile device of claim 12, further comprising means for determining a behavior category classification based upon the logged mobile device usage events.

14. The mobile device of claim 13 wherein said means for inferring the mobile device user profile property classification based upon the logged mobile device usage events further comprises:

means for retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

means for retrieving a first conditional probability of the user being a member of the behavior category classification given the mobile device user profile property classification from a second table of derived information;

means for calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the first conditional probability of the user being a member of the behavior category classification; and

means for identifying the mobile device user profile property classification as one that yields a maximum relative likelihood.

15. The mobile device of claim 14, further comprising means for determining a plurality of behavior category classifications based upon the logged mobile device usage events.

16. The mobile device of claim 13, wherein said means for inferring the mobile device user profile property classification based upon the logged mobile device usage events further comprises:

means for determining a plurality of behavior category classifications based upon the logged mobile device usage events;

means for retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

means for retrieving a plurality of conditional probability values of a user being a member of each of a plurality of mobile device user profile property classifications given each of the plurality of behavior category classifications from a plurality of tables of derived information;

means for calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the conditional probability values of the user being a member of each of a plurality of mobile device user profile property classifications given each of the plurality of behavior category classifications; and

means for identifying the mobile device user profile property classification as one that yields a maximum relative likelihood value.

17. The mobile device of claim 16, wherein said means for calculating the relative likelihood value of the user being a member of each of the plurality of mobile device user profile property classifications given the combination of the plurality of determined behavior category classifications comprises:

means for calculating a usage behavior product; and

means for multiplying the usage behavior product by a probability of the user being a member of the mobile device user profile property classification.

18. The mobile device of claim 13, wherein said means for inferring the user mobile device user profile property classification based upon the logged mobile device usage events comprises:

means for looking up the mobile device user profile property classification in a derived inference table using the determined behavior category classification.

19. The mobile device of claim 13, further comprising:

means for transmitting the determined behavior category classifications from the mobile device to a remote server; and

means for receiving the inferred mobile device user profile property classification from the remote server.

20. The mobile device of claim 13, wherein said means for determining the behavior category classification based upon the logged mobile device usage events further comprises:

means for determining whether sufficient mobile device usage events have been logged to accurately determine the behavior category classification; and

means for performing a statistical analysis of the logged mobile device usage events to determine the behavior category classification.

22. The mobile device of claim 12, wherein said means for inferring the mobile device user profile property classification based upon the logged mobile device usage events and information derived from a population of users further comprises means for inferring the mobile device user profile property classification through the application of Bayesian probability principles.

28. The mobile device of claim 27, wherein the processor is configured with software instructions to perform further steps comprising:

calculating a usage behavior product; and

multiplying the usage behavior product by a probability of the user being a member of the mobile device user profile property classification.

21. A remote server comprising:

means for receiving a determined behavior category classification from a mobile device;

means for retrieving from a derived inference table a mobile device user profile property classification using the received determined behavior category classification as a look up value; and

means for transmitting the inferred mobile device profile property classification to the mobile device.

23. A mobile device, comprising:

a memory unit; and

a processor coupled to the memory unit, wherein the processor is configured with software instructions to perform steps comprising;

logging at least one category of mobile device usage events in the memory unit; and

inferring a mobile device profile property classification based upon the logged mobile device usage events and information derived from a population of users.

24. The mobile device of claim 23, wherein the processor is configured with software instructions to perform further steps comprising:

determining a behavior category classification based upon the logged mobile device usage events.

25. The mobile device of claim 24, wherein the processor is configured with software instructions to perform further steps comprising:

retrieving a probability of the user being a member of each of a plurality of user profile property classifications from a first table of derived information;

retrieving a first conditional probability of the user being a member of the behavior category classification given the mobile device user profile property classification from a second table of derived information;

calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the first conditional probability of the user being a member of the behavior category classification; and

identifying the mobile device user profile property classification as one that yields a maximum relative likelihood.

26. The mobile device of claim 25, wherein the processor is configured with software instructions to perform further steps comprising:

determining a plurality of behavior category classifications based upon the logged mobile device usage events.

27. The mobile device of claim 24, wherein the processor is configured with software instructions to perform further steps comprising:

determining a plurality of behavior category classifications based upon of the logged mobile device usage events;

retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

retrieving a plurality of conditional probability values of a user being a member of each of a plurality of mobile device user profile property classifications given each of the plurality of behavior category classifications from a plurality of tables of derived information;

calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the conditional probability values of the user being a member of each of a plurality of mobile device user profile property classifications given each of the plurality of behavior category classifications; and

identifying the mobile device user profile property classification as one that yields a maximum relative likelihood value.

29. The mobile device of claim 24, wherein the processor is configured with software instructions to perform further steps comprising:

looking up the mobile device user profile property classification in a derived inference table using the determined behavior category classification.

31. The mobile device of claim 24, wherein the processor is configured with software instructions to perform further steps comprising:

determining whether sufficient mobile device usage events have been logged to accurately determine the behavior category classification; and

performing a statistical analysis of the logged mobile device usage events to determine the behavior category classification.

32. The mobile device of claim 23, wherein the processor is configured with software instructions to perform further steps comprising:

applying Bayesian probability principles to infer the mobile device user profile property classification based upon the logged mobile device usage events and information derived from a population of users.

28-1. The mobile device of claim 22, wherein the processor is configured with software instructions to perform further steps comprising:

transmitting the determined behavior category classifications from the mobile device to a remote server configured to look up the inferred mobile device user profile property classification in a derived inference table using the received determined behavior category classification and transmit the inferred mobile device user profile property classification to the mobile device; and

receiving in the mobile device the inferred mobile device profile property classification.

33. A remote server comprising:

a remote server memory unit;

a remote server processing unit coupled to the remote server memory unit, wherein the remote server processor is configured with software instructions to perform steps comprising

receiving a determined behavior category classification from a mobile device;

retrieving from a derived inference table the mobile device user profile property classification using the received determined behavior category classification as a look up value; and

transmitting the inferred mobile device user profile property classification to the mobile device.

34. A tangible storage medium having stored thereon processor-executable software instructions configured to cause a processor to perform steps comprising:

logging at least one category of mobile device usage events; and

inferring a mobile device user profile property classification based upon the logged mobile device usage events and information derived from a population of users.

35. The tangible storage medium of claim 34, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

determining a behavior category classification based upon the logged mobile device usage events.

36. The tangible storage medium of claim 35, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

retrieving a first conditional probability of the user being a member of the behavior category classification given the mobile device user profile property classification from a second table of derived information;

calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the first conditional probability of the user being a member of the behavior category classification; and

identifying the mobile device user profile property classification as one that yields a maximum relative likelihood.

37. The tangible storage medium of claim 35, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

determining a plurality of behavior category classifications based upon the logged mobile device usage events.

38. The tangible storage medium of claim 35, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

determining a plurality of behavior category classifications based upon of the logged mobile device usage events;

retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

retrieving a plurality of conditional probability values of a user being a member of each of a plurality of mobile device user profile property classifications given each of the plurality of behavior category classifications from a plurality of tables of derived information;

calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the conditional probability values of the user being a member of each of a plurality of mobile device user profile property classifications given each of the plurality of behavior category classifications; and

identifying the demographic category classification as one that yields a maximum relative likelihood value.

39. The tangible storage medium of claim 38, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

calculating a usage behavior product; and

multiplying the usage behavior product by a probability of the user being a member of the mobile device user profile property classification.

40. The tangible storage medium of claim 35, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

looking up the mobile device user profile property classification in a derived inference table using the determined behavior category classification.

41. The tangible storage medium of claim 35, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

transmitting the determined behavior category classifications from the mobile device to a remote server configured to look up the inferred mobile device user profile property classification in a derived inference table using the received determined behavior category classification and transmit the inferred mobile device user profile property classification to the mobile device; and

receiving in the mobile device the inferred mobile device user profile property classification.

42. The tangible storage medium of claim 35, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

determining whether sufficient mobile device usage events have been logged to accurately determine the behavior category classification; and

performing a statistical analysis of the logged mobile device usage events to determine the behavior category classification.

43. The tangible storage medium of claim 35, wherein the tangible storage medium has processor-executable software instructions configured to cause a processor to perform further steps comprising:

applying Bayesian probability principles to infer the mobile device user profile property classification based upon the logged mobile device usage events and information derived from a population of users.

44. A tangible storage medium having stored thereon processor-executable software instructions configured to cause a processor to perform steps comprising:

receiving a determined behavior category classification from a mobile device;

retrieving from an inference table a mobile device user profile property classification using the received determined behavior category classification as a look up value; and

transmitting the inferred mobile device user profile property classification to the mobile device.

45. A system for inferring mobile device user profile property classification comprising:

at least one mobile device configured to log at least one category of mobile device usage events occurring on the mobile device and determine at least one behavior category classification based upon the logged mobile device usage events;

a remote server; and

a communication network connecting the at least one mobile device with the remote server,

wherein;

the at least one mobile device is further configured to transmit the determined at least one behavior category classification to the remote server via the communication network;

the remote server is configured to;

receive the determined at least one behavior category classification;

infer the mobile device user profile property classification based upon the logged mobile device usage events; and

transmit the inferred mobile device user profile property classification to the mobile device via the communication network; and

the at least one mobile device is further configured to receive the inferred mobile device user profile property classification.

46. The system of claim 45, wherein said remote server is configured to infer the mobile device user profile property classification by looking up the inferred mobile device user profile property classification in a derived inference table based upon the received determined at least one behavior category classification

47. The system of claim 45, wherein said remote server is configured to infer the mobile device user profile property classification by

retrieving a probability of the user being a member of each of a plurality of mobile device user profile property classifications from a first table of derived information;

retrieving a conditional probability values of a user being a member of each of a plurality of mobile device user profile property classifications given each of the at least one behavior category classifications from at least one table of derived information;

calculating a plurality of relative likelihood values as a product of the probability of the user being a member of each of the mobile device user profile property classifications and the conditional probability values of the user being a member of each of a plurality of mobile device user profile property classifications given each of the at least one behavior category classifications; and

identifying the mobile device user profile property classification as one that yields a maximum relative likelihood value.

48. The system of claim 45, wherein said at least one mobile device is further configured to:

determine whether sufficient mobile device usage events have been logged to accurately determine the behavior category classification; and

perform a statistical analysis of the logged mobile device usage events to determine the behavior category classification.