Automatically associating relevant advertising with video content

• US 20080120646A1
• Filed: 11/20/2006
• Published: 05/22/2008
• Est. Priority Date: 11/20/2006
• Status: Abandoned Application

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Stern, Benjamin J., Gilbert, Mazin, Gupta, Narendra
• Application Number:
  US11/601,993
• Publication Number:
  US 20080120646A1
• Time in Patent Office: Days
• US Class Current: 725/34
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04N 21/23892 involving embedding information at multiplex stream level, e.g. embedding a watermark at packet level
• CPC Class Codes: H04N 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/47202 for requesting content on demand, e.g. video on demand
• CPC Class Codes: H04N 21/8133 specifically related to the content, e.g. biography of the actors in a movie, detailed information about an article seen in a video program
• CPC Class Codes: H04N 21/816 involving special video data, e.g 3D video
• CPC Class Codes: H04N 21/8456 by decomposing the content in the time domain, e.g. in time segments

Abstract

A method and system are provided for automatically selecting advertisements for placement in media content segments such as video segments. The method utilizes a classification engine to analyze values of a feature set extracted from the video segment, and to select one or more categories of advertisements to place in the segment. The classification engine is trainable using training data such as historical video segments in which advertisements were placed manually, and using performance data measuring the effectiveness of past advertisement placement in particular segments.

Claims

1. A method for associating advertisements with a video segment, the method comprising the steps of:

for a training content set including a plurality of video segments in which a first set of advertisements has previously been placed;

categorizing each of the first set of advertisements into advertisement categories based on characteristics of the advertisements;

extracting values of a feature set from each segment of the training content set;

training a classifier to associate the feature set values extracted from each segment of the training content set with advertisement categories in which advertisements placed in each segment were categorized;

extracting new values of the feature set from a new video segment;

using the trained classifier to select advertisement categories from the plurality of advertisement categories, based on the new values of the feature set; and

placing advertisements categorized in the selected advertisement categories into the new video segment.

2. The method of claim 1, wherein the advertisement characteristics include a type of product sold.

3. The method of claim 1, wherein the advertisement characteristics include an income of a target audience.

4. The method of claim 1, wherein the feature set includes a transcript of audio content.

5. The method of claim 1, wherein the feature set includes a length of a show.

6. The method of claim 1, wherein the feature set includes dates that content was created.

7. The method of claim 1, wherein the feature set includes reviews of content.

8. The method of claim 1, wherein the feature set includes descriptions of the content.

9. The method of claim 1, wherein the feature set includes viewer demographics.

10. The method of claim 1, wherein the training content set comprises a broadcast programming block.

11. The method of claim 1, wherein the training content set is video content.

12. The method of claim 1, wherein the training content includes metadata.

13. A system for selecting categories of advertisements for placement in media content segments, comprising:

a feature set extractor for extracting values of a feature set relating to a segment, the feature set characterizing the media content segments;

an advertisement category database containing a list of advertisement categories based on characteristics of the advertisements;

a classification engine in communication with the feature set extractor and the advertisement category database, the classification engine including;

a classifier model for selecting at least one of the advertising categories based on extracted values of the feature set; and

a training module for receiving training data relating historical values of the feature set to advertisement categories, and for updating the classifier model based on the training data.

14. The system of claim 13, wherein the training data comprises historical media content programming including content segments and advertisements placed in the segments.

15. The system of claim 14, wherein the advertisements were manually placed in the segments.

16. The system of claim 13, wherein the training data comprises performance data relating to advertisements placed in segments.

17. The system of claim 16, wherein the performance data comprises sales data.

18. The system of claim 16, wherein the performance data comprises a quantity of network accesses responding to the advertisements.

19. The system of claim 13, wherein the feature set extractor extracts information from a transcript of audio material in the segment.

20. The system of claim 13, wherein the feature set extractor extracts information from metadata included in the segment.