Methods and apparatus to generate relevance rankings for use by a program selector of a media presentation system

• US 9,277,287 B2
• Filed: 06/03/2015
• Issued: 03/01/2016
• Est. Priority Date: 05/14/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Chang, Hisao M.
• Primary Examiner(s): Chin, Ricky
• Application Number:
  US14/729,791
• Publication Number:
  US 20150271564A1
• Time in Patent Office: 272 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/42203 sound input device, e.g. microphone
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/47 End-user applications
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 21/64322 IP
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

Example methods and apparatus to generate relevance rankings for use by a program selector of a media presentation system are disclosed. An example method comprises tabulating a plurality of occurrence counts for respective ones of a plurality of terms of a program guide, each of the occurrence counts indicating a corresponding number of times each of the terms appears in the program guide, computing a plurality of relevance ranking scores for respective ones of a plurality of programs listed in the program guide based on the plurality of occurrence counts, selecting a list of programs from the program guide based on the relevance ranking scores, and outputting the list of programs

Claims

1. A system comprising:

a memory that stores instructions;

a processor that executes the instructions to perform operations, the operations comprising;

calculating an average of occurrence counts for a plurality of terms associated with a program in a program guide;

computing, for the program, a relevance ranking score, wherein the relevance ranking score is computed by multiplying the average of occurrence counts for the plurality of terms by a number of times the program is listed in the program guide;

adjusting the relevance ranking score for the program based on a user profile; and

generating, for display on a display device, a sorted list of programs including the program, wherein the sorted list is sorted based on the relevance ranking score for the program.

2. The system of claim 1, wherein the operations further comprise adjusting the relevance ranking score by applying a viewership rating to the relevance ranking score.

3. The system of claim 1, wherein the operations further comprise receiving search criteria for identifying a plurality of programs corresponding to the search criteria.

4. The system of claim 3, wherein the operations further comprise identifying the plurality of programs based on the search criteria and generating the sorted list of programs based on the search criteria.

5. The system of claim 1, wherein the operations further comprise providing the sorted list of programs in response to receiving search criteria.

6. The system of claim 5, wherein the operations further comprise receiving a request to receive additional choices of programs after providing the sorted list of programs.

7. The system of claim 6, wherein the operations further comprise providing an additional sorted list of programs in response to receiving the request.

8. The system of claim 1, wherein the operations further comprise monitoring media selections made by user to create the user profile.

9. The system of claim 1, wherein the operations further comprise adjusting the occurrence counts based on a boost table.

10. A method comprising:

determining, by utilizing instructions from memory that are executed by a processor, an average of occurrence counts for a plurality of terms associated with a program in a program guide;

calculating, for the program, a relevance ranking score, wherein the relevance ranking score is computed by multiplying the average of occurrence counts for the plurality of terms by a number of times the program is listed in the program guide;

adjusting the relevance ranking score for the program based on a user profile; and

generatin, for display on a display device, a sorted list of programs including the program, wherein the sorted list is sorted based on the relevance ranking score for the program.

11. The method of claim 10, further comprising adjusting the relevance ranking score by applying a viewership rating to the relevance ranking score.

12. The method of claim 10, further comprising receiving search criteria for identifying a plurality of programs corresponding to the search criteria.

13. The method of claim 12, further comprising identifying the plurality of programs based on the search criteria and generating the sorted list of programs based on the search criteria.

14. The method of claim 10, further comprising providing the sorted list of programs in response to receiving search criteria.

15. The method of claim 10, further comprising monitoring media selections made by a user to create the user profile.

16. The method of claim 10, further comprising adjusting the occurrence accounts based on a boost table.

17. A non-transitory medium including machine readable instructions which, when loaded and executed by a processor, cause the processor to perform operations, the operations comprising:

computing an average of occurrence counts for a plurality of terms associated with a program in a program guide;

computing, for the program, a relevance ranking score, wherein the relevance Ranking score is computed by multiplying the average of occurrence counts for the plurality of terms by a number of times the program is listed in the program guide;

adjusting the relevance ranking score for the program based on a user profile; and

generating, for display on a display device, a sorted list of programs including the program, wherein the sorted list is sorted based on the relevance ranking score for the program.

18. The non-transitory medium of claim 17, wherein the operations further comprise adjusting the occurrence accounts based on a boost table.

19. The non-transitory medium of claim 17, wherein the operations further comprise monitoring media selections made by a user to create the user profile.

20. The non-transitory medium of claim 17, wherein the operations further comprise selecting each of the programs in the sorted list of programs after computing the relevance ranking score.