METHODS AND SYSTEMS FOR DYNAMICALLY PROVIDING CONTENT

• US 20180293627A1
• Filed: 06/15/2018
• Published: 10/11/2018
• Est. Priority Date: 12/05/2012
• Status: Active Grant

• Current Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Original Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Inventors: MURGAI, Nikhil
• Granted Patent: US 10,402,866 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

Systems and methods are disclosed for dynamically providing content to a user. The disclosed embodiments include generating a recommendation matrix based on event data received from a client. In certain embodiments, the client may be configured to generate event data associated with the client or a user associated with the client. The disclosed embodiments may also include generating an offer score matrix based on the recommendation matrix and the event data. In certain aspects, the offer score matrix may include score values associated with a set of offers to be provided to the user. The disclosed embodiments may identify a first offer from the set of offers based on the score values for the offers and provide an identification of the first offer. The disclosed embodiments may also receive content associated with the first offer for display on the client.

Claims

1-20. -20. (canceled)

21. A mobile device for dynamically providing content to a user, comprising:

a memory storing instructions; and

a processor configured to execute the instructions to perform operations including;

receiving a recommendation matrix associating potential offers with potential trigger events;

receiving a travel graph specific to a user, the travel graph generated using location information provided by the mobile device and reflecting travel movements of the mobile device between designated geographical locations over a prior period of time;

receiving a trigger event associated with a geographical location determined from the travel graph;

generating feature data based on the received trigger event;

identifying a first offer using the feature data and the recommendation matrix;

providing an indication of the first offer; and

receiving content associated with the first offer for display on the mobile device.

22. The mobile device of claim 21, wherein identifying the first offer includes calculating score values for the potential offers using the feature data and the recommendation matrix.

23. The mobile device of claim 22, wherein calculating the score values includes:

generating a feature vector based on the feature data, and

multiplying the recommendation matrix with the feature vector to generate the score values.

24. The mobile device of claim 21, wherein the recommendation matrix includes weight values for combinations of the potential offers and the potential trigger events.

25. The mobile device of claim 21, wherein the operations further include sending the feature data to a remote computer.

26. The mobile device of claim 25, wherein the operations further include:

receiving a second recommendation matrix from the remote computer, the second recommendation matrix updated using the feature data; and

identifying a second offer using the feature data and the second recommendation matrix.

27. The mobile device of claim 26, wherein the operations further include:

providing an indication of the second offer; and

receiving second content associated with the second offer for display on the mobile device.

28. The mobile device of claim 21, wherein the feature data indicates at least one of:

a geographic location of the mobile device;

an update to a social network parameter of the user;

that the mobile device is traveling;

that the mobile device was used to generate a search query; and

that the mobile device is located within a determined distance from a specified geographic location.

29. The mobile device of claim 21, wherein the operations further include generating at least one feature data event based on the travel graph.

30. The mobile device of claim 21, wherein the content includes content associated with at least one of an advertisement, a promotional offer, a discount, a coupon, and a sale associated with an item or service.

31. A computer-implemented method performed by a mobile device for dynamically providing content to a user, comprising:

storing a recommendation matrix associating potential offers with potential trigger events;

receiving a travel graph specific to a user, the travel graph generated using location information provided by the mobile device and reflecting travel movements of the mobile device between designated geographical locations over a prior period of time;

identifying a trigger event associated with a geographical location, the geographical location determined from the travel graph;

generating feature data based on the received trigger event;

identifying a first offer using the feature data and the recommendation matrix;

providing an identification of the first offer; and

receiving content associated with the first offer for display on the mobile device.

32. The method of claim 31, wherein the identification of the first offer is provided to an advertisement server and the content associated with the first offer is received from the advertisement server.

33. The method of claim 31, the method further comprising decrypting an encrypted recommendation matrix to generate the recommendation matrix.

34. The method of claim 31, wherein the recommendation matrix comprises rows correlating to potential offers and columns correlating to potential trigger event data.

35. The method of claim 34, wherein the recommendation matrix comprises model coefficients associated with a potential trigger event and a potential offer.

36. The method of claim 31, wherein the feature data comprises a feature vector, and generating the feature data based on the received trigger event comprises applying current event data to rules, the rules corresponding to components of the feature vector.

37. The method of claim 36, wherein identifying the first offer comprises:

multiplying the recommendation matrix by the feature vector to generate a score matrix;

determining a highest-score value for the potential offers in the score matrix;

selecting an offer of the potential offers corresponding to the highest-score value for the potential offers in the score matrix as the first offer.

38. The method of claim 36, wherein the components of the feature vector are binary-valued.

39. The method of claim 31, wherein the feature data indicates at least one of:

a geographic location of the mobile device;

an update to a social network parameter of the user;

that the mobile device is traveling;

that the mobile device was used to generate a search query; and

that the mobile device is located within a determined distance from a specified geographic location.

40. A system for recommendation data associated with offers to be presented to a user, comprising:

a memory storing instructions; and

a processor configured to execute the instructions to perform operations including;

generating a travel graph specific to the user, the travel graph based on location information provided by a mobile device and reflecting travel movements of the mobile device between designated geographical locations over a prior period of time;

generating a recommendation matrix based on the feature data provided by the mobile device, consumer data for the user provided by a third party, and offer data for potential offers, the recommendation matrix including model coefficient values associating trigger events and the potential offers, at least one trigger event based on the travel graph; and

sending the recommendation matrix to the mobile device for use in generating a score matrix for identifying a first offer of the potential offers to display on the mobile device.