METHOD AND SYSTEM FOR USER PROFILE MATCH INDICATION IN A MOBILE ENVIRONMENT

• US 20090124241A1
• Filed: 11/11/2008
• Published: 05/14/2009
• Est. Priority Date: 11/14/2007
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Krishnaswamy, Dilip, Daley, Robert S., Lundqvist, Patrik, Renschler, Martin, Aggarwal, Pooja
• Application Number:
  US12/268,905
• Publication Number:
  US 20090124241A1
• Time in Patent Office: Days
• US Class Current: 455/414.200
• CPC Class Codes: G06F 40/10 Text processing natural language analysis G06F40/20; semantic analysis G06F40/30; processing or translation of natural language G06F40/40
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/004 Artificial life, i.e. computing arrangements simulating life
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 12/1859 adapted to provide push services, e.g. data channels
• CPC Class Codes: H04L 12/189 in combination with wireless systems selective distribution or broadcast in wireless communication networks H04W4/06
• CPC Class Codes: H04L 51/18 Commands or executable codes
• CPC Class Codes: H04L 51/212 using filtering or selective blocking
• CPC Class Codes: H04L 51/214 using selective forwarding
• CPC Class Codes: H04L 51/58 Message adaptation for wireless communication
• CPC Class Codes: H04L 67/04 specially adapted for terminals or networks with limited capabilities; specially adapted for terminal portability
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/53 using third party service providers
• 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: H04L 67/55 Push-based network services
• CPC Class Codes: H04L 67/61 taking into account QoS or priority requirements
• CPC Class Codes: H04L 69/08 Protocols for interworking; Protocol conversion
• CPC Class Codes: H04M 3/4878 Advertisement messages
• CPC Class Codes: H04W 4/12 Messaging; Mailboxes; Announcements
• CPC Class Codes: H04W 4/21 : for social networking applications
• CPC Class Codes: H04W 8/18 : Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data

Abstract

Methods and systems for determining a suitability for a mobile client to display information are disclosed. For example, a method for determining a suitability for a mobile client to receive a targeted content message includes generating user profile data by the mobile client, receiving a set of target profile data associated with the targeted content message, the set of target profile data being descriptive of the targeted content message, comparing the user profile data with the target set of profile data to produce a set of confidence-level data, a target set of profile data describing the content of a respective targeted-content message, and storing the targeted content message in the mobile client based upon the set of confidence-level data.

Claims

1. A method for determining a suitability for a mobile client to receive a targeted content message, comprising:

generating user profile data by the mobile client;

receiving a set of target profile data associated with the targeted content message, the set of target profile data being descriptive of the targeted content message;

comparing the user profile data with the target set of profile data to produce a set of confidence-level data, a target set of profile data describing the content of a respective targeted-content message; and

storing the targeted content message in the mobile client based upon the set of confidence-level data.

2. The method according to claim 1, wherein the comparing involves an implementation of a fuzzy logic algorithm.

3. The method according to claim 1, wherein the comparing involves an implementation of a neural net algorithm.

4. The method according to claim 1, wherein the comparing involves an implementation of a dot-product algorithm.

5. The method according to claim 1, wherein the comparing involves an implementation of a maximum-minimum qualification.

6. The method according to claim 1, wherein the comparing confidence-level data includes at least one of statistical averaging, curve fitting and regression analysis.

7. The method according to claim 1, wherein the comparing confidence-level data includes at least scalar manipulation of an n-dimensional vector

8. The method according to claim 1, wherein the confidence-level data includes a likely age range of a user of the mobile client.

9. The method according to claim 1, wherein the confidence-level data includes a likelihood for a location of interest of a user of the mobile client.

10. The method according to claim 9, wherein the confidence-level data includes a likelihood for a home location for the user of the mobile client.

11. The method according to claim 9, wherein the confidence-level data includes likelihood for a work location for the user of the mobile client.

12. The method according to claim 9, wherein the confidence-level data includes a likelihood for a recreational location for the user of the mobile client.

13. The method according to claim 1, wherein the confidence-level data includes a likely range of income for a user of the mobile client.

14. The method according to claim 1, wherein the set of target profile data include a set of words that are substantially non-overlapping conceptually to one another.

15. A mobile client capable of determining a suitability for a mobile client to receive a targeted content message, comprising:

profile generation circuitry configured to generate user profile data;

receiving circuitry configured to receive a set of target profile data associated with the targeted content message, the set of target profile data being descriptive of the targeted content message;

comparing circuitry configured to compare the user profile data with the set of target profile data to produce a set of confidence-level data, a target set of profile data describing the content of a respective targeted-content message; and

cache controlling circuitry configured to store the targeted content message in the mobile client based upon the set of confidence-level data.

16. The mobile client according to claim 15, wherein the comparing circuitry implements of a fuzzy logic algorithm to compare user profile with target profile data.

17. The mobile client according to claim 15, wherein the comparing circuitry implements of a neural net algorithm to compare user profile with target profile data.

18. The mobile client according to claim 15, wherein the comparing circuitry implements of a dot-product algorithm to compare user profile with target profile data.

19. The mobile client according to claim 15, wherein the comparing circuitry implements of a maximum-minimum qualification to compare user profile with target profile data.

20. The mobile client according to claim 15, wherein the confidence-level data includes a likely age range of a user of the mobile client.

21. The mobile client according to claim 15, wherein the confidence-level data includes a likelihood for a location of interest of a user of the mobile client.

22. The mobile client according to claim 21, wherein the confidence-level data includes a likelihood for a home location for the user of the mobile client.

23. The mobile client according to claim 21, wherein the confidence-level data includes likelihood for a work location for the user of the mobile client.

24. The mobile client according to claim 21, wherein the confidence-level data includes a likelihood for a recreational location for the user of the mobile client.

25. The mobile client according to claim 15, wherein the confidence-level data includes a likely range of income for a user of the mobile client.

26. The mobile client according to claim 15, wherein the circuitry for comparing confidence-level data includes circuitry for performing at least one of statistical averaging, curve fitting, regression analysis, processing data through an artificial neural network and scalar manipulation.

27. The mobile client according to claim 26, wherein the circuitry for comparing confidence-level data includes circuitry for performing at least scalar manipulation of an n-dimensional vector.

28. The mobile client according to claim 15, wherein the set of target profile data include a set of words that are substantially non-overlapping conceptually to one another.

29. A mobile client capable of determining a suitability for a mobile client to receive a targeted content message, comprising:

means for generating user profile data by the mobile client;

means for receiving a set of target profile data associated with the targeted content message, the set of target profile data being descriptive of the targeted content message;

means for comparing the user profile data with the target set of profile data to produce a set of confidence-level data, a target set of profile data describing the content of a respective targeted-content message; and

means for storing the targeted content message in the mobile client based upon the set of confidence-level data.

30. The mobile client according to claim 29, wherein the means for comparing uses at least one of a fuzzy logic algorithm, a neural net algorithm, a dot-product algorithm, a maximum-minimum qualification, a statistical averaging algorithm, a curve fitting algorithm and a regression analysis algorithm.

31. The mobile client according to claim 29, wherein the means for comparing uses at least at least scalar manipulation of an n-dimensional vector

32. The mobile client according to claim 29, wherein the confidence-level data includes a likely age range of a user of the mobile client.

33. The mobile client according to claim 29, wherein the confidence-level data includes a likelihood for a location of interest of a user of the mobile client.

34. The mobile client according to claim 33, wherein the confidence-level data includes at least one of a likelihood for a home location for the user of the mobile client, a work location for the user of the mobile client, and a recreational location for the user of the mobile client.

35. The mobile client according to claim 29, wherein the confidence-level data includes a likely range of income for a user of the mobile client.

36. The mobile client according to claim 29, wherein the set of target profile data include a set of words that are substantially non-overlapping conceptually to one another.

37. A mobile client, comprising:

a memory;

a transceiver;

a processor coupled to the memory and operable to;

generate user profile data by the mobile client;

receive a set of target profile data associated with the targeted content message, the set of target profile data being descriptive of the targeted content message;

compare the user profile data with the target set of profile data to produce a set of confidence-level data, a target set of profile data describing the content of a respective targeted-content message; and

store the targeted content message in the memory based upon the set of confidence-level data.

38. A computer program product, comprising:

a computer-readable medium comprising;

a first instruction for generating user profile data by a mobile client;

a second instruction for receiving a set of target profile data associated with the targeted content message, the set of target profile data being descriptive of the targeted content message;

a third instruction for comparing the user profile data with the target set of profile data to produce a set of confidence-level data, a target set of profile data describing the content of a respective targeted-content message; and

a fourth instruction for storing the targeted content message in the mobile client based upon the set of confidence-level data.