SYSTEM AND METHOD FOR DETERMINING OFFERS BASED ON PREDICTIONS OF USER INTEREST

• US 20140006166A1
• Filed: 07/20/2012
• Published: 01/02/2014
• Est. Priority Date: 06/29/2012
• Status: Abandoned Application

• Current Assignee: Mobi Technologies Incorporated
• Original Assignee: Mobi Technologies Incorporated
• Inventors: Chiang, Chi-Hao, Csinger, Andrew
• Application Number:
  US13/554,431
• Publication Number:
  US 20140006166A1
• Time in Patent Office: Days
• US Class Current: 705/14.64
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Systems and methods for recommending offers to a user are implemented via one or more processors operating on one or more server systems. The systems and methods include receiving attribute data associated with one or more target users. An offer is determined for transmittal to the one or more target users. The offer is based at least on at least a portion of the attribute data analyzed by a predictive process including a decision tree combined with a clustering process. An offer is output that is configured to be received by the one or more targeted users.

Claims

1-20. -20. (canceled)

21. A computer-implemented method for recommending offers to a user, the method implemented via one or more processors operating on one or more server systems, the method comprising:

receiving attribute data associated with one or more target users at said one or more server systems;

analyzing at least a portion of said attribute data using a predictive process, said predictive process implemented using a decision tree combined with a clustering process using one or more clusters,

wherein said clustering process comprises assigning data points within said portion of attribute data to the one or more clusters,

said analyzing performed by said one or more processors;

determining an offer to transmit to said one or more target users, said offer based on at least a portion of said analyzed attribute data, said determining performed by said one or more processors;

determining an explanation associated with said offer;

said determining performed by said one or more processors and

outputting said offer and said explanation, said offer and said explanation being configured to be received by said one or more targeted users, said outputting performed by said one or more server systems.

22. The computer-implemented method of claim 21, wherein said offer is further based on at least a portion of received additional attribute data, said received additional attribute data associated with one or more first preselected offers to be distributed to said one or more target users.

23. The computer-implemented method of claim 22, further comprising receiving transactional data associated with one or more user-offer associations based on said attribute data and said received additional attribute data at said one or more server systems, said offer further based on at least a portion of said transactional data.

24. The computer-implemented method of claim 21, further comprising receiving a response transmitted by said one or more target users at said one or more server systems, said response including information relating to said one or more target users accepting or declining said offer.

25. The computer-implemented method of claim 21, further comprising receiving a response transmitted by said one or more target users at said one or more server systems, said response including explanation feedback from said one or more target users.

26. The computer-implemented method of claim 23, wherein said attribute data includes user demographic data and user description data, said received additional attribute data includes a plurality of second pre-selected offers, and said transaction data includes prior transactions including target users responses to prior offers.

27. The computer-implemented method of claim 21, further comprising:

receiving a response transmitted by said one or more target users at said one or more server systems, said response associated with said explanation;

receiving updated attribute and transactional data at said one or more server systems, wherein said updated transactional data includes said response;

analyzing, using said one or more processors, at least a portion of said updated attribute data using said predictive process; and

determining a second offer to transmit to said one or more target users, said determining performed by said one or more processors

28. The computer-implemented method of claim 21, wherein

said one or more clusters comprise a plurality of hidden clusters in data partitions generated by said decision tree;

said clustering process further comprises optimizing said assigning of data to said plurality of hidden clusters based on probability distributions.

29. The computer-implemented method of claim 21, wherein said one or more target users utilize one or more user devices, said one or more user devices communicatively coupled to said server systems by at least one network, further wherein said one or more devices includes a mobile device, and said at least one network includes at least in part a wireless network.

30. A system for recommending offers, the system comprising:

one or more non-transitory physical computer-readable storage media configured to store attribute and transaction data associated with one or more target users, said attribute and transaction data including target user identifications, offer attributes, and prior target user transaction data;

a recommender component including one or more communication interfaces for connecting with said storage media, said communication interfaces configured to send and receive data, said recommender component further including one or more processors, said one or more processors operative to generate a recommended offer for at least one of said one or more target users, said generation comprising

receiving said attribute and transaction data from at least one of said one or more storage media;

analyzing at least a portion of said attribute data using a predictive process, said predictive process implemented using a decision tree combined with a clustering process using one or more clusters,

wherein said clustering process comprises assigning data points within said portion of attribute data to the one or more clusters,

determining said recommended offer based on at least a portion of said analyzed attribute and transaction data;

determining an explanation associated with said offer; and

outputting said offer and said explanation, via at least one of said one or more communication interfaces, said outputting of said offer and said explanation configured to be received by said one or more target users.

31. The system of claim 30, wherein said generation further comprises receiving a response via at least one of said one or more communication interfaces, said response associated with said explanation.

32. The system of claim 31, wherein said response includes information relating to said one or more target users accepting or declining said offer.

33. The system of claim 31, wherein said response includes explanation feedback from said one or more target users.

34. The system of claim 30, further comprising:

receiving in said recommender component updated attribute and transaction data, wherein said updated transaction data includes data related to said response;

said receiving performed by said one or more communication interfaces and

analyzing, using said one or more processors, at least a portion of said updated attribute data using said predictive process; and

determining a second offer to transmit to said one or more target users, said offer based on at least a portion of said analyzed updated attribute data, said determining performed by said one or more processors.

35. The system of claim 30, wherein

said one or more clusters comprise a plurality of hidden clusters in data partitions generated by said decision tree;

said clustering process further comprises optimizing said assigning of data to said plurality of hidden clusters based on probability distributions.

36. One or more non-transitory physical machine-readable storage media including instructions which, when executed by a recommender system, said recommender system implemented using one or more processors operating on one or more server systems, and said recommender system comprising one or more communication interfaces coupled to a network, cause the one or more processors to perform operations comprising:

receiving in said recommender system attribute and transactional data from one or more memory devices, said attribute and transactional data being associated with one or more target users of said recommender system;

analyzing at least a portion of said attribute data using a predictive process, said predictive process implemented using a decision tree combined with a clustering process using one or more clusters,

wherein said clustering process comprises assigning data points within said portion of attribute data to the one or more clusters,

determining, by said one or more processors associated with said recommender system, an offer to transmit to said one or more target users, said offer based on at least a portion of said analyzed attribute and transaction data;

determining by said one or more processors associated with said recommender system an explanation associated with said offer; and

outputting said offer and said explanation, said outputting performed by the one or more communication interfaces over said network, said outputting of said offer and said explanation configured to be received by said one or more targeted users.

37. The one or more non-transitory physical machine-readable storage media of claim 36, further including instructions which cause the one or more processors to perform operations further comprising receiving a response via at least one of said one or more communication interfaces, said response associated with said explanation.

38. The one or more non-transitory physical machine-readable storage media of claim 37, wherein said response includes information relating to said one or more target users accepting or declining said offer.

39. The one or more non-transitory physical machine-readable storage media of claim 36, further including instructions which cause the one or more processors to perform operations further comprising:

receiving a response via at least one of said one or more communication interfaces;

receiving in said recommender system updated attribute and transactional data, wherein said updated transactional data includes said response data;

analyzing, using said one or more processors, at least a portion of said updated attribute data using a predictive process implemented using a decision tree combined with a clustering process using one or more clusters; and

determining a second offer to transmit to said one or more target users, said offer based on at least a portion of said analyzed updated attribute and transactional data.

40. The one or more non-transitory physical machine-readable storage media of claim 36, wherein

said one or more clusters comprise a plurality of hidden clusters in data partitions generated by said decision tree;

said clustering process further comprises optimizing said assigning of data to said plurality of hidden clusters based on probability distributions.