Intelligent default weighting process for criteria utilized to score media content items

• US 8,874,574 B2
• Filed: 07/16/2012
• Issued: 10/28/2014
• Est. Priority Date: 11/26/2007
• Status: Active Grant

• Current Assignee: Jolly Seven, Series 70 of Allied Security Trust I (Allied Security Trust)
• Original Assignee: Abo Enterprises, LLC (Concert Technology Corporation)
• Inventors: Purdy, Sean
• Primary Examiner(s): TRAN, ANHTAI V
• Application Number:
  US13/549,598
• Publication Number:
  US 20120278342A1
• Time in Patent Office: 834 Days
• Field of Search: None
• US Class Current: 707/736
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 16/639 using playlists
• CPC Class Codes: G06F 16/68 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/735 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/738 Presentation of query results
• CPC Class Codes: G06F 16/78 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G11B 27/105 of operating discs
• CPC Class Codes: G11B 27/28 by using information signals recorded by the same method as the main recording G11B27/22 takes precedence
• CPC Class Codes: G11B 27/34 Indicating arrangements indicating means incorporated in magazine or cassette G11B23/046 and G11B23/0875; indicating measured values in general G01D
• CPC Class Codes: H04N 21/26258 for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list retrieval of multimedia data based on playlists G06F16/40
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• 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: Y10S 707/916 Audio

Abstract

A system and method are provided for intelligently, or programmatically, assigning weights for one or more criterion utilized to score media content items based on an analysis of a group of media content items. In general, scoring criteria to be used to score media content items for a user are defined. A group of media content items associated with the user is then analyzed with respect to the criteria to provide results such as a number or percentage of media content items from the group of media content items that satisfy each of the scoring criteria. Based on the results of the analysis, a weight is assigned to each of the scoring criteria. Thereafter, media content items are scored for the user as a function of the weights assigned to the scoring criteria.

Claims

1. A method performed on a computer, comprising:

defining a plurality of criteria;

analyzing, by the computer, a group of media content items with respect to the plurality of criteria to determine a number of media content items that satisfy each of the plurality of criteria to provide analysis results;

programmatically assigning weights to the plurality of criteria as a function of the analysis results, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items; and

scoring at least one media content item as a function of the weights assigned to the plurality of criteria.

2. The method of claim 1, wherein the group of media content items are associated with a profile, the weights are programmatically assigned to the plurality of criteria for the profile as a function of the analysis results, and the at least one media content item is scored as a function of the weights assigned to the plurality of criteria for the profile.

3. The method of claim 1, further comprising:

adjusting the weights assigned to the plurality of criteria before the scoring of the at least one media content item.

4. The method of claim 1, further comprising:

receiving a plurality of recommendations from a social recommendation system, wherein the recommendations identify the group of media content items;

wherein scoring the at least one media content item comprises scoring the group of media content items identified by the plurality of recommendations as a function of the weights assigned to the plurality of criteria to provide scores for the group of media content items.

5. The method of claim 1, wherein scoring the at least one media content item comprises scoring a plurality of media content items from a media distribution service to provide scores for the plurality of media content items, and the method further comprising:

identifying at least one media content item to recommend from the plurality of media content items available based on the scores; and

recommending the at least one media content item.

6. The method of claim 1, wherein the group of media content items comprises a plurality of media content items identified by at least one playlist.

7. The method of claim 1, wherein the group of media content items comprises a plurality of media content items previously obtained from a media distribution service.

8. The method of claim 1, wherein the group of media content items are associated with a profile and the analysis results are indicative of a relevancy of each of the plurality of criteria to the profile, and programmatically assigning the weights to the plurality of criteria as a function of the analysis results comprises:

assigning a maximum weight as the weight for a first criterion from the plurality of criteria having a greatest relevancy to the profile; and

determining the weights for at least one remaining criterion from the plurality of criteria as a function of the relevancy of the at least one remaining criterion to the profile as compared with the relevancy of the first criterion to the profile.

9. The method of claim 1, wherein the group of media content items includes movies.

10. The method of claim 1, wherein the group of media content items includes songs.

11. The method of claim 1, wherein the plurality of criteria includes time periods.

12. The method of claim 1, wherein the plurality of criteria includes genre.

13. The method of claim 1, wherein the plurality of criteria includes recommendations received from a social network.

14. The method of claim 1, wherein scoring the at least one media content item comprises scoring a plurality of media content items in a playlist of the profile as a function of the weights assigned to the plurality of criteria to provide scores for the plurality of media content items in the playlist.

15. The method of claim 14, further comprising:

transmitting at least one media content item based on the scoring.

16. The method of claim 1, wherein the plurality of criteria includes a source of the media content items.

17. The method of claim 16, wherein the source of the media content items includes a subscription service.

18. A method performed on a computer comprising:

defining on the computer a plurality of criteria;

programmatically assigning weights to the plurality of criteria based on a degree of similarity between a plurality of profiles, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items; and

scoring at least one media content item as a function of the weights assigned to the plurality of criteria.

19. The method of claim 18, wherein the degree of similarity may be determined as a function of matching parameters between the plurality of profiles.

20. A method performed on a computer comprising:

defining on the computer a plurality of criteria;

programmatically assigning weights to the plurality of criteria as a function of a total number of media content items received from a recommender as compared to a number of recommended media content items that have been played, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items; and

scoring at least one media content item as a function of the weights assigned to the plurality of criteria.

21. A method performed on a computer comprising:

defining on the computer a plurality of criteria;

analyzing a group of media content items with respect to the plurality of criteria to determine a percentage of media content items that satisfy each of the plurality of criteria to provide analysis results;

programmatically assigning weights to the plurality of criteria as a function of the analysis results, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items; and

scoring at least one media content item as a function of the weights assigned to the plurality of criteria.

22. A method performed on a computer comprising:

defining on the computer a plurality of criteria;

analyzing a group of media content items with respect to the plurality of criteria to determine a number of media content items that satisfy each of the plurality of criteria to provide analysis results;

programmatically assigning weights to the plurality of criteria as a function of the analysis results, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items;

scoring at least one media content item as a function of the weights assigned to the plurality of criteria; and

recommending at least one media content item based on the scoring.

23. A method performed on a computer comprising:

defining on the computer a plurality of criteria, wherein the plurality of criteria include recommender and source;

analyzing a group of media content items with respect to the plurality of criteria to determine a number of media content items that satisfy each of the plurality of criteria to provide analysis results;

programmatically assigning weights to the plurality of criteria as a function of the analysis results, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items;

scoring at least one media content item as a function of the weights assigned to the plurality of criteria; and

sorting songs in a playlist based on the scoring of the at least one media item.

24. The method of claim 23, wherein the source is a social network.

25. A system comprising:

a processor in communication with a memory, the processor configured to execute a plurality of computer program modules, the memory including the plurality of computer program modules executable on the processor, the computer program modules including;

a weight assignment function adapted to;

analyze a group of media content items with respect to a plurality of criteria to determine a number of media content items that satisfy each of the plurality of criteria to provide analysis results;

programmatically assign weights to the plurality of criteria as a function of the analysis results, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items; and

score at least one media content item as a function of the weights assigned to the plurality of criteria.

26. The system of claim 25, wherein the system comprises a server, and the weight assignment and the scoring are implemented on the server.

27. The system of claim 25, wherein the system comprises a device, and the weight assignment and the scoring are implemented on the device.

28. A non-transitory computer readable medium comprising software with instructions to:

analyze a group of media content items with respect to a plurality of criteria to determine a number of media content items that satisfy each of the plurality of criteria to provide analysis results;

programmatically assign weights to the plurality of criteria as a function of the analysis results, wherein the weights are normalized according to the criteria having the greatest number or percentage of media content items; and

score at least one media content item as a function of the weights assigned to the plurality of criteria.