Targeting videos based on viewer similarity

• US 10,462,504 B2
• Filed: 03/29/2018
• Issued: 10/29/2019
• Est. Priority Date: 09/17/2008
• Status: Active Grant

• Current Assignee: Adobe Inc.
• Original Assignee: Adobe Inc.
• Inventors: Lopatecki, Jason, Rose, Adam, Hughes, John, Wilson, Brett
• Primary Examiner(s): Chae, Kyu
• Application Number:
  US15/940,882
• Publication Number:
  US 20180227615A1
• Time in Patent Office: 579 Days
• CPC Class Codes: G06F 15/16 Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs coordinating program control therefor G06F9/52; in regulating and control system G05B
• CPC Class Codes: G06F 16/738 Presentation of query results
• CPC Class Codes: G06F 16/743 a collection of video files or sequences
• CPC Class Codes: G06F 16/7867 using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/2662 Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/278 Content descriptor database or directory service for end-user access details of content or meta data based information retrieval of video data in video databases G06F16/739
• CPC Class Codes: H04N 21/44226 on social networks
• CPC Class Codes: H04N 21/4788 communicating with other users, e.g. chatting arrangements for providing for computer conferences, e.g. chat rooms, to substation in data switching networks H04L12/1813; distributed application using peer-to-peer [P2P] networks H04L67/104
• 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

Presentation of a video clip is made to persons having a high probability of viewing the clip. A database containing viewers of previously offered video clips is analyzed to determine similarities of preferences among viewers. When a new video clip has been watched by one or more viewers in the database, those viewers who have watched the new clip with positive results are compared with others in the database who have not yet seen it. Prospective viewers with similar preferences are identified as high likelihood candidates to watch the new clip when presented. Bids to offer the clip are based on the degree of likelihood. For one embodiment, a data collection agent (DCA) is loaded to a player and/or to a web page to collect viewing and behavior information to determine viewer preferences. Viewer behavior may be monitored passively by different disclosed methods.

Claims

1. A computer-implemented method for serving video clips comprising:

determining a subset of viewers of a video, from viewers of the video, that shared the video within a threshold period of time after viewing the video based on passive actions of the viewers of the video;

generating a viewer characteristic profile for the video based on characteristics of viewers in the subset of viewers of the video, the viewer characteristic profile for the video reflecting characteristics of a viewer most likely to have a positive interaction with the video;

determining a likelihood score of a prospective viewer based on a profile for the prospective viewer and the viewer characteristic profile for the video;

determining that the likelihood score of the prospective viewer is above a defined likelihood level; and

serving, over a network, the video to a client device associated with the prospective viewer based on the determination that the likelihood score of the prospective viewer is above the defined likelihood level.

2. The computer-implemented method of claim 1, further comprising inserting a data collection script into a player or webpage that displays videos, the data collection script programmed to monitor viewer activity relative to the videos.

3. The computer-implemented method of claim 2, further comprising:

determining the passive actions of the viewers of the video relative to the video by;

receiving HTTP requests from client devices having interacted with the video; and

extracting metadata from the HTTP requests indicating one or more passive actions relative to the video; and

determining the subset of viewers of the video based on the passive actions of the viewers of the video.

4. The computer-implemented method of claim 1, further comprising:

determining the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, prior to commencement of the video, one or more of;

viewing a pre-roll advertisement,

accessing the video by a particular method,

adjusting a bit rate for streaming or downloading the video prior to playing the video, or

clicking play if the video is not an auto-playing video; and

determining the subset of viewers of the video based on the passive actions of the viewers of the video.

5. The computer-implemented method of claim 1, further comprising:

determining the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, during play of the video, one or more of;

adjusting a volume level, muting, or un-muting the video,

pausing or un-pausing the video,

fast-forwarding or rewinding the video,

replaying the video,

viewing the video for a recorded duration or percent completion,

viewing the video in full-screen mode,

adjusting a bit rate for streaming or downloading the video,

clicking-through on an overlay image or on the video itself,

spending time viewing certain segments of the video, or

viewing a mid-roll advertisement; and

determining the subset of viewers of the video based on the passive actions of the viewers of the video.

6. The computer-implemented method of claim 1, further comprising:

determining the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, after completion of the video, one or more of;

sharing the video via an embedded code,

sharing the video via e-mail,

sharing the video via a social network,

sharing the video in multiple separate events,

bookmarking the video for later viewing,

downloading and saving the video for later viewing, or

subscribing to a channel or feed associated with the video; and

determining the subset of viewers of the video based on the passive actions of the viewers of the video.

7. The computer-implemented method of claim 1, further comprising determining the subset of viewers of the video that shared the video or purchased an item or service after watching the video based on the passive actions of the viewers of the video.

8. A system for serving video clips comprising:

at least one server; and

at least one non-transitory computer readable storage medium storing instructions thereon, that, when executed by the at least one server, cause the system to;

determine a subset of viewers of a video that shared the video within a threshold period of time after viewing the video based on passive actions of the viewers of the video;

generate a viewer characteristic profile for the video based on characteristics of viewers in the subset of viewers of the video, the viewer characteristic profile for the video reflecting characteristics of a viewer most likely to have a positive interaction with the video;

determine a likelihood score of a prospective viewer based on a profile for the prospective viewer and the viewer characteristic profile for the video;

determine that the likelihood score of the prospective viewer is above a defined likelihood level; and

serve, over a network, the video to a client device associated with the prospective viewer based on the determination that the likelihood score of the prospective viewer is above the defined likelihood level.

9. The system of claim 8, further comprising instructions that, when executed by the at least one server, cause the system to:

insert a data collection script into a player or webpage that displays videos, the data collection script programmed to monitor viewer activity relative to the videos; and

determine the passive actions of the viewers of the video relative to the video by;

receiving HTTP requests from client devices having interacted with the video; and

extracting metadata from the HTTP requests indicating one or more passive actions relative to the video.

10. The system of claim 8, further comprising instructions that, when executed by the at least one server, cause the system to:

determine the passive actions of the viewers of the video relative to the video during one or more of a period of time prior to commencement of the video, during play of the video, or after completion of the video; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.

11. The system of claim 8, further comprising instructions that, when executed by the at least one server, cause the system to:

determine the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, prior to commencement of the video, one or more of;

viewing a pre-roll advertisement,

accessing the video by a particular method,

adjusting a bit rate for streaming or downloading the video prior to playing the video, or

clicking play if the video is not an auto-playing video; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.

12. The system of claim 8, further comprising instructions that, when executed by the at least one server, cause the system to:

determine the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, during play of the video, one or more of;

adjusting a volume level, muting, or un-muting the video,

pausing or un-pausing the video,

fast-forwarding or rewinding the video,

replaying the video,

viewing the video for a recorded duration or percent completion,

viewing the video in full-screen mode,

adjusting a bit rate for streaming or downloading the video,

clicking-through on an overlay image or on the video itself,

spending time viewing certain segments of the video, or

viewing a mid-roll advertisement; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.

13. The system of claim 8, further comprising instructions that, when executed by the at least one server, cause the system to:

determine the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, after completion of the video, one or more of;

sharing the video via an embedded code,

sharing the video via e-mail,

sharing the video via a social network,

sharing the video in multiple separate events,

bookmarking the video for later viewing,

downloading and saving the video for later viewing, or

subscribing to a channel or feed associated with the video; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.

14. The system of claim 8, further comprising instructions that, when executed by the at least one server, cause the system to determine the subset of viewers of the video that shared the video or purchased an item or service after watching the video based on the passive actions of the viewers of the video.

15. A non-transitory computer-readable medium storing instructions thereon that, when executed by at least one processor, cause a computing device to:

determine a subset of viewers of a video that shared the video within a threshold period of time after viewing the video based on passive actions of the viewers of the video;

generate a viewer characteristic profile for the video based on characteristics of viewers in the subset of viewers of the video, the viewer characteristic profile for the video reflecting characteristics of a viewer most likely to have a positive interaction with the video;

determine a likelihood score of a prospective viewer based on a profile for the prospective viewer and the viewer characteristic profile for the video;

determine that the likelihood score of the prospective viewer is above a defined likelihood level; and

serve, over a network, the video to a client device associated with the prospective viewer based on the determination that the likelihood score of the prospective viewer is above the defined likelihood level.

16. The non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computing device to:

insert a data collection script into a player or webpage that displays videos, the data collection script programmed to monitor viewer activity relative to the videos; and

determine the passive actions of the viewers of the video relative to the video by;

receiving HTTP requests from client devices having interacted with the video; and

extracting metadata from the HTTP requests indicating one or more passive actions relative to the video.

17. The non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computing device to:

determine the passive actions of the viewers of the video relative to the video during one or more of a period of time prior to commencement of the video, during play of the video, or after completion of the video; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.

18. The non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computing device to:

determine the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, prior to commencement of the video, one or more of;

viewing a pre-roll advertisement,

accessing the video by a particular method,

adjusting a bit rate for streaming or downloading the video prior to playing the video, or

clicking play if the video is not an auto-playing video; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.

19. The non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computing device to:

determine the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, during play of the video, one or more of;

adjusting a volume level, muting, or un-muting the video,

pausing or un-pausing the video,

fast-forwarding or rewinding the video,

replaying the video,

viewing the video for a recorded duration or percent completion,

viewing the video in full-screen mode,

adjusting a bit rate for streaming or downloading the video,

clicking-through on an overlay image or on the video itself,

spending time viewing certain segments of the video, or

viewing a mid-roll advertisement; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.

20. The non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computing device to:

determine the passive actions of the viewers of the video relative to the video by determining whether the viewers performed, after completion of the video, one or more of;

sharing the video via an embedded code,

sharing the video via e-mail,

sharing the video via a social network,

sharing the video in multiple separate events,

bookmarking the video for later viewing,

downloading and saving the video for later viewing, or

subscribing to a channel or feed associated with the video; and

determine the subset of viewers of the video based on the passive actions of the viewers of the video.