METHOD AND SYSTEM FOR KEYWORD CORRELATION IN A MOBILE ENVIRONMENT

• US 20090125517A1
• 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: Bychkovsky, Vladimir, Krishnaswamy, Dilip, Verma, Nakul
• Application Number:
  US12/268,914
• Publication Number:
  US 20090125517A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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. A particular exemplary method includes receiving a plurality of sets of one or more first keywords on a mobile client, each set of first keywords associated with one or more respective first messages, monitoring user interaction of the respective first messages on the mobile client, determining a user selection rate for each unique first keyword of the plurality of sets of first keywords, receiving a set of target keywords associated with a target message, performing one or more matching operations between the set of target keywords and corresponding user selection rates to produce a set of one or more matching parameters, and displaying the target message on the mobile client dependent upon the matching parameters.

Claims

1. A method for determining a suitability for a mobile client to display information, comprising:

receiving a plurality of sets of one or more first keywords on a mobile client, each set of first keywords associated with one or more respective first messages;

monitoring user interaction of the respective first messages on the mobile client;

determining a user selection rate for each unique first keyword of the plurality of sets of first keywords;

receiving a set of target keywords associated with a target message;

performing one or more matching operations between the set of target keywords and corresponding user selection rates to produce a set of one or more matching parameters; and

displaying the target message on the mobile client dependent upon the matching parameters.

2. The method according to claim 1, further comprising receiving the target message over a wireless link if the one or more matching operations indicate a desirability of the target message.

3. The method according to claim 1, wherein determining a user selection rate includes monitoring interaction of a user of the mobile client and a plurality of the first messages.

4. The method according to claim 3, wherein determining a user selection rate includes monitoring a lack of user interaction, the lack of user interaction indicating a level of user interest.

5. The method according to claim 3, wherein determining a user selection rate for each keyword includes monitoring click-through-rates for each keyword from a plurality of the first messages.

6. The method according to claim 5, wherein the step of determining a rate of user selection is performed on the mobile client.

7. The method according to claim 6, wherein performing one or more matching operations is performed on the mobile client.

8. The method according to claim 1, wherein each target keyword of each set of one or more first keywords has a relative weight associated with its respective first message, and the one or more matching operations is based on the relative weights.

9. The method according to claim 8, wherein each relative weight of a keyword with a particular first message is 1/n, where n is the total number of keywords associated with the particular message.

10. The method according to claim 8, wherein at least one relative weight of a keyword with a particular first message is based on one or more current events.

11. The method according to claim 8, wherein the one or more matching operations is based on a baseline correlation metric.

12. The method according to claim 11, wherein the baseline correlation metric is determined based on at least one of a statistical method, fuzzy logic, neural techniques, vector mapping, vector space analysis, support vector machines, and principal components analysis.

13. The method according to claim 1, wherein the user selection rate for each keyword adapts to changes in user behavior

14. The method according to claim 1, wherein dependencies exist between target keywords.

15. The method according to claim 1, wherein the user selection rates determined do not exactly correspond to the true user preferences.

16. The method according to claim 1, wherein a rate of learning for the user selection rates is determined.

17. The method according to claim 16, wherein the rate of learning for the user selection rates is determined based on at least one of information-space sparseness, rate of presentation of information, value of initial seed, and aspects of a user profile.

18. The method according to claim 1, wherein a set of user selection rates are determined for a first context, and a second set of user selection rates are determined for a second context.

19. The method according to claim 1, wherein the keywords are specified from a specified hierarchical keyword dictionary, wherein the hierarchy has two or more levels.

20. The method according to claim 1, wherein the keywords are specified from a specified flat keyword dictionary.

21. The method according to claim 1, wherein the keywords are not associated with the semantics of a specific language.

22. The method according to claim 1, wherein one or more matching parameters are modified based on a random or pseudo-random probability.

23. The method according to claim 19, wherein a cardinality of the set of target keywords is sparse relative to the size of the keyword dictionary.

24. The method according to claim 20 wherein a cardinality of the set of target keywords is sparse relative to the size of the keyword dictionary.

25. The method according to claim 5, wherein a new user selection rate for a keyword is determined based on a weighted average between a previous user selection rate and user behavior associated with a plurality of the first messages.

26. The method according to claim 1, wherein the previous user selection rate is determined based on an initial seed profile

27. The method according to claim 26, wherein a user-provided preference indicator is used as the previous user selection rate.

28. The method according to claim 11, wherein the baseline correlation metric is required to exceed a threshold value to determine relevancy of the target message to the user.

29. The method according to claim 11, wherein the baseline correlation metric is based on using a subset of keywords relative estimates.

30. The method according to claim 4, wherein determining a user selection rate includes monitoring the type of user interaction, the type of user interaction indicating level of user interest.

31. The method according to claim 1, wherein a mobile client communicates user selection rates for a random sparse set of keywords to an external entity on a network to enable gathering of statistics across a population.

32. The method according to claim 31, wherein the random sparse sets are chosen differently across mobile clients to enable gathering of statistics on all keywords across a plurality of mobile clients.

33. The method according to claim 32, wherein a fraction of mobile clients in the plurality of mobile clients communicate user selection rates for at least one of the random sparse set of keywords to the external entity on a network.

34. A mobile client configured to display information, comprising:

a receiving circuit configured to receive a plurality of sets of one or more first keywords on a mobile client, each set of first keywords associated with one or more respective first messages, the receiving circuit further configured to receive a set of target keywords associated with a target message;

a monitoring agent configured to monitor user interaction of the respective first messages on the mobile client;

a determining agent configured to determine a user selection rate for each unique first keyword of the plurality of sets of first keywords;

a filtering agent configured to perform one or more matching operations between the set of target keywords and corresponding user selection rates to produce a set of one or more matching parameters; and

a display configured to display the target message on the mobile client dependent upon the matching parameters.

35. The mobile client according to claim 34, wherein the receiving circuit is further configured to receive the target message over a wireless link if the one or more matching operations indicate a desirability of the target message.

36. The mobile client according to claim 34, wherein each target keyword of each set of one or more first keywords has a relative weight associated with its respective first respective message, and the one or more matching operations is based on the relative weights.

37. The mobile client according to claim 34, wherein the determining agent is configured to determine the user selection rate by monitoring interaction of a user of the mobile client and a plurality of the first messages.

38. The mobile client according to claim 37, wherein the determining agent is configured to determine the user selection rate by monitoring click-through-rates of a plurality of the first messages.

39. The mobile client according to claim 37, wherein the determining agent is configured to determine the user selection rate by monitoring a lack of user interaction, the lack of user interaction indicating user interest.

40. The mobile client according to claim 36, wherein each relative weight of a keyword with a particular first message is 1/n, where n is the total number of keywords associated with the particular message.

41. The mobile client according to claim 36, wherein at least one relative weight of a keyword with a particular first message is based on one or more current events.

42. The mobile client according to claim 36, wherein the one or more matching operations is based on a baseline correlation metric.

43. The mobile client according to claim 34, wherein the baseline correlation metric is determined based on at least one of a statistical method, fuzzy logic, neural techniques, vector mapping, and principal components analysis.

44. A mobile client configured to display information, comprising:

means for receiving a plurality of sets of one or more first keywords on a mobile client, each set of first keywords associated with one or more respective first messages, the receiving circuit further configured to receive a set of target keywords associated with a target message;

means for monitoring user interaction of the respective first messages on the mobile client;

means for determining a user selection rate for each unique first keyword of the plurality of sets of first keywords;

means for performing one or more matching operations between the set of target keywords and corresponding user selection rates to produce a set of one or more matching parameters; and

a display configured to display the target message on the mobile client dependent upon the matching parameters.

45. The mobile client according to claim 44, wherein each target keyword of each set of one or more first keywords has a relative weight associated with its respective first respective message, and the one or more matching operations is based on the relative weights.

46. The mobile client according to claim 44, wherein the means for determining determines a user selection rate by monitoring interaction of a user of the mobile client and a plurality of the first messages.

47. The mobile client according to claim 46, wherein the means for determining determines a user selection rate by monitoring click-through-rates of a plurality of the first messages.

48. The mobile client according to claim 46, wherein the means for determining determines a user selection rate by monitoring a lack of user interaction, the lack of user interaction indicating user interest.

49. The mobile client according to claim 46, wherein each relative weight of a keyword with a particular first message is 1/n, where n is the total number of keywords associated with the particular message.

50. The mobile client according to claim 46, wherein at least one relative weight of a keyword with a particular first message is based on one or more current events.

51. The mobile client according to claim 46, wherein the one or more matching operations is based on a baseline correlation metric, and wherein the baseline correlation metric is determined based on at least one of a statistical method, fuzzy logic, neural techniques, vector mapping, and principal components analysis.

52. A mobile client for determining a suitability for displaying information, comprising:

a memory;

a transceiver;

a processor linked to the memory and operable to;

receive via the transceiver a plurality of sets of one or more first keywords on a mobile client, each set of first keywords associated with one or more respective first messages;

monitor user interaction of the respective first messages on the mobile client;

determine a user selection rate for each unique first keyword of the plurality of sets of first keywords;

receive a set of target keywords associated with a target message;

perform one or more matching operations between the set of target keywords and corresponding user selection rates to produce a set of one or more matching parameters; and

display the target message on the mobile client dependent upon the matching parameters.

53. A computer program product, comprising:

a computer-readable medium comprising;

instructions for receiving a plurality of sets of one or more first keywords on a mobile client, each set of first keywords associated with one or more respective first messages;

instructions for monitoring user interaction of the respective first messages on the mobile client;

instructions for determining a user selection rate for each unique first keyword of the plurality of sets of first keywords;

instructions for receiving a set of target keywords associated with a target message;

instructions for performing one or more matching operations between the set of target keywords and corresponding user selection rates to produce a set of one or more matching parameters; and

instructions for displaying the target message on the mobile client dependent upon the matching parameters.