Automated identification of high/low value content based on social feedback

• US 8,176,191 B2
• Filed: 11/30/2006
• Issued: 05/08/2012
• Est. Priority Date: 11/30/2006
• Status: Active Grant

• Current Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Original Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Inventors: Fischer, Donald, Pennington, Havoc, Clark, Bryan
• Primary Examiner(s): BHATIA, AJAY M
• Application Number:
  US11/565,024
• Publication Number:
  US 20080133638A1
• Time in Patent Office: 1,986 Days
• Field of Search: 709/206, 709/218, 709/231, 705/319
• US Class Current: 709/231
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/44226 on social networks
• 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

Abstract

Embodiments of the present invention provide an automated scheme for identifying high/low value content. Playback data from users in a social network may be periodically submitted by users of the social network. The playback data indicates segments of content, such as audio or video that the user has skipped over or tagged as low value. The playback data may also indicate segments of content that the user has repeated or tagged as high value. The playback data is then analyzed in aggregate and various clips are identified. In addition, the playback data may be compiled and organized among the users for future use. The playback data may be used to indicate segments of high/low interest to peers in the social network or to arbitrary users.

Claims

1. A method comprising:

receiving one or more playback data that are based on playback behavior of a plurality of users who identify themselves as peers in a social network service;

determining start and end times of a segment of content based on the playback data; and

identifying, by a server, whether the segment is of low value or of high value based on one or both of;

(i) whether the playback data indicates that a user has skipped the segment during playback of the content, and

(ii) whether the playback data indicates that a user has repeated the segment during playback of the content.

2. The method of claim 1, wherein the identifying is also based on a user'"'"'s tagging of the segment.

3. An apparatus comprising:

a database to store one or more playback data that are based on playback behavior of a plurality of users who identify themselves as peers in a social network service; and

a server to;

determine start and end times of a segment of content based on the playback data, and

identify whether the segment is of low value or of high value based on one or both of;

(i) whether the playback data indicates that a user has skipped the segment during playback of the content, and

(ii) whether the playback data indicates that a user has repeated the segment during playback of the content.

4. The apparatus of claim 3 wherein the identifying is also based on a user'"'"'s tagging of the segment.

5. A non-transitory computer readable medium comprising executable program code to configure a computer to perform a method, the method comprising:

receiving one or more playback data that are based on playback behavior of a plurality of users who identify themselves as peers in a social network service;

determining start and end times of a segment of content based on the playback data; and

identifying whether the segment is of low value or of high value based on one or both of;

(i) whether the playback data indicates that a user has skipped the segment during playback of the content, and

(ii) whether the playback data indicates that a user has repeated the segment during playback of the content.

6. The non-transitory computer readable medium of claim 5 wherein the identifying is also based on a user'"'"'s tagging of the segment.