System and Method for Filtering Targeted Advertisements for Video Content Delivery

• US 20110184807A1
• Filed: 12/01/2010
• Published: 07/28/2011
• Est. Priority Date: 01/28/2010
• Status: Abandoned Application

• Current Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Inventors: Huang, Yu, Zhang, Dong-Qing, Wang, Shu, Wang, Qi, Yu, Hong Heather
• Application Number:
  US12/957,972
• Publication Number:
  US 20110184807A1
• Time in Patent Office: Days
• US Class Current: 705/14.53
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: H04N 21/23424 involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
• 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 21/4756 for rating content, e.g. scoring a recommended movie
• 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 21/8456 by decomposing the content in the time domain, e.g. in time segments

Abstract

In accordance with an embodiment, a method of inserting advertisements into video content includes electronically filtering a first list of advertisements according to user preference data to determine a second list of advertisements. The video content has a plurality of segments, each segment of which is associated with a category from the plurality of categories. Furthermore, each advertisement in the first list of advertisements is associated with a video category from a plurality of categories, and electronically filtering includes filtering the first list of advertisements for the plurality of video segments on a segment by segment basis. The method further includes transmitting the second list of advertisements to a user device for insertion with the video content.

Claims

1. A method of inserting advertisements into video content, the method comprising:

electronically filtering a first list of advertisements according to user preference data to determine a second list of advertisements, wherein

each advertisement in the first list of advertisements is associated with a video category from a plurality of categories;

the video content comprises a plurality of video segments, and each video segment is associated with a category from the plurality of categories,

electronically filtering comprises filtering the first list of advertisements for the plurality of video segments on a segment by segment basis; and

transmitting the second list of advertisements to a user device for insertion with the video content.

2. The method of claim 1, wherein electronically filtering comprises

adjusting the user preference data according to user profile data;

calculating a preference value based on the adjusted user preference data; and

selecting an advertisement from the list of advertisements according to the calculated preference value.

3. The method of claim 2, further comprising

receiving user feedback info from the user device;

and

adjusting the user preference data based on the user feedback.

4. The method of claim 3, wherein adjusting the user preference data comprises adjusting the user preference data according to a history of user preference data.

5. The method of claim 4, further comprising applying a sliding window to the history of user preference data.

6. The method of claim 2, wherein adjusting the user preference data according to the user profile data comprise and calculating the preference value comprises using a preference and profile (PNP) based Bayesian engine.

7. The method of claim 2, wherein:

the user preference data comprises a user preference matrix comprising preference values according to rows indexed by video categories and columns indexed by advertising categories; and

the user profile data comprises a user profile matrix comprising rows indexed by user profile categories and columns indexed by advertising categories.

8. The method of claim 7, wherein:

adjusting the user preference data according to user profile data comprises;

selecting a row of the user preference matrix corresponding to a present video category,

selecting at least one row of the user profile matrix,

calculating weights from the at least one row of the user profile matrix,

applying the weights to the row of the user preference matrix to form an adjusted row of the user preference matrix; and

calculating the preference value comprises calculating preference values for the adjusted row of the user preference matrix.

9. The method of claim 8, wherein:

each element in the user profile matrix and the user preference matrix comprises an a_ij^l value denoting a positive user disposition and an a_ij ^d value denoting a negative user disposition; and

calculating weights comprises calculating a weight according to;

10. The method of claim 9, wherein calculating the preference value comprises applying the following expression to elements in the row of the user preference matrix:

11. The method of claim 10, wherein the weight and the preference value are between −

1 and 1.

12. The method of claim 10, wherein:

each element in the user profile matrix and the user preference matrix further comprises a history of a last N user values; and

the method further comprises calculating the a_ij^l and a_ij^d values according to the history of the last N user values.

13. The method of claim 7, wherein the user profile categories comprises a first set of categories, the first set of categories comprising the video categories.

14. The method of claim 7, wherein the user profile categories comprises:

a first set of categories comprising the video categories; and

a second set of categories comprising user profile data.

15. A method of inserting advertisements into video content, the method comprising:

electronically filtering a first list of advertisements according to user preference data to determine a second list of advertisements, wherein

each advertisement in the first list of advertisements comprises a category from a plurality of categories,

the user preference data comprises a plurality of preference fields indexed by the plurality of categories,

electronically filtering comprises updating the user preference data according to a user profile matrix comprising a plurality of attributes according to a hierarchical user ontology; and

transmitting the second list of advertisements to a user device for insertion with the video content.

16. The method of claim 15, wherein:

video content comprises a plurality of segments;

each video segment is associated with a category from the plurality of categories; and

the electronically filtering further comprises filtering the advertisements for insertion on a segment by segment basis.

17. The method of claim 15, wherein the hierarchical user ontology comprises a first plurality of top level categories the first plurality of top level categories comprising:

a first category comprising subcategories relating to an individual'"'"'s internal attributes;

a second category comprising subcategories relating to an individual'"'"'s social status; and

a third comprising subcategories relating to an individual'"'"'s immediate situation.

18. The method of claim 17, wherein:

subcategories relating to the individual'"'"'s internal attributes comprise subcategories pertaining to age and gender;

subcategories relating to the individual'"'"'s social status comprises subcategories pertaining to occupation and education; and

subcategories relating to the individual'"'"'s immediate situation comprise subcategories pertaining to whether the individual is at home or whether the individual is on a trip.

19. A system for inserting advertisements into video content, the system comprising:

a filtering block configured to filter a first list of advertisements according to user preference data to determine a second list of advertisements, wherein

each advertisement in the first list of advertisements is associated with a video category from a plurality of categories,

the video content comprises a plurality of video segments, and each video segment is associated with a category from the plurality of categories, and

the filtering block filters the first list of advertisements on a segment by segment basis.

20. The system of claim 19, further comprising a communications adaptor transmitting the second list of advertisements to a user device for insertion with the video content.

21. The system of claim 19, wherein the filtering block is further configured to:

adjust the user preference data according to user profile data;

calculate a preference value based on the adjusted user preference data; and

select an advertisement from the list of advertisements according to the calculated preference value.

22. The system of claim 21, wherein:

the system further comprises a communications adaptor receiving user feedback info from a user device; and

the filtering block is further configured to adjust the user preference data based on the user feedback.

23. The system of claim 21, wherein the filtering block comprises a Bayesian engine configured to adjust the user preference data and calculate the preference value.

24. The system of claim 21, wherein:

the user preference data comprises a user preference matrix comprising preference values according to rows indexed by video categories and columns indexed by advertising categories; and

the user profile data comprises a user profile matrix comprising rows indexed by user profile categories and columns indexed by advertising categories.

25. The system of claim 19, wherein filtering block is further configured to update the user preference data according to a user profile matrix comprising a plurality of attributes according to a hierarchical user ontology.

26. A non-transitory computer readable medium with an executable program stored thereon, wherein the program instructs a microprocessor to perform the following steps:

filtering a first list of advertisements according to user preference data to determine a second list of advertisements, wherein

each advertisement in the first list of advertisements is associated with a video category from a plurality of categories,

the video content comprises a plurality of video segments, and each video segment is associated with a category from the plurality of categories, and

the filtering block filters the first list of advertisements on a segment by segment basis.

27. The non-transitory computer readable medium of claim 26, wherein the program further instructs the microprocessor to perform the following steps:

adjusting the user preference data according to user profile data;

calculating a preference value based on the adjusted user preference data; and

selecting an advertisement from the list of advertisements according to the calculated preference value.

28. The non-transitory computer readable medium of claim 26, wherein the program further instructs the microprocessor to transmit the second list of advertisements to a user device for insertion with the video content.

29. The non-transitory computer readable medium of claim 26, wherein the program further instructs the microprocessor to update the user preference data according to a user profile matrix comprising a plurality of attributes according to a hierarchical user ontology.