Dynamic summaries for media content

• US 9,826,285 B1
• Filed: 03/24/2016
• Issued: 11/21/2017
• Est. Priority Date: 03/24/2016
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Mishra, Pragyana K., Goyal, Dushyant
• Primary Examiner(s): Lonsberry, Hunter B
• Application Number:
  US15/079,801
• Time in Patent Office: 607 Days
• Field of Search: 725 37
• CPC Class Codes: G06F 18/24155 Bayesian classification
• CPC Class Codes: G06F 18/29 Graphical models, e.g. Bayesian networks
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/84 using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/47 Detecting features for summarising video content
• CPC Class Codes: G06V 20/49 Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes
• 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 21/25891 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 21/44226 on social networks
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; 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 21/6582 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 21/8549 Creating video summaries, e.g. movie trailer retrieval in video databases by using presentations in form of a video summary G06F16/739

Abstract

Disclosed are various embodiments for providing dynamically generated summaries of media content, such as electronic books, audiobooks, audio series, video content series, and so on. A position in media content is determined based at least in part on input from a user. A summary of a portion of the media content is dynamically generated, where the portion of the media content begins or ends relative to the position. A summary of the portion of the media content is presented to the user via an output device.

Claims

1. A non-transitory computer-readable medium embodying a program executable in at least one computing device, wherein when executed the program causes the at least one computing device to at least:

receive a request from a user to resume viewing a video content series at a point of resumption;

determine an elapsed time since the user left off viewing the video content series at the point of resumption;

dynamically generate a video summary comprising scenes of significance in the video content series occurring prior to the point of resumption based at least in part on the elapsed time, wherein content of the video summary is selected according to a length of the elapsed time; and

cause the video summary to be presented upon a display before presentation of the video content series resumes on the display at the point of resumption.

2. The non-transitory computer-readable medium of claim 1, wherein the scenes of significance include a dynamically generated video summary of a previous season and a dynamically generated video summary of a previous episode.

3. The non-transitory computer-readable medium of claim 1, wherein when executed the program further causes the at least one computing device to at least automatically identify the scenes of significance based at least in part on a contextual comparison of metadata associated with each of a set of scenes of the video content series occurring prior to the point of resumption with metadata associated with a portion of the video content series beginning at the point of resumption.

4. The non-transitory computer-readable medium of claim 1, wherein the video summary is dynamically generated based at least in part on at least one characteristic of the user, the at least one characteristic comprising at least one of:

an age of the user, a gender of the user, a viewing history of the user, or a feedback history of the user regarding previous dynamically generated video summaries.

5. A system, comprising:

at least one computing device; and

at least one application executable in the at least one computing device, wherein when executed the at least one application causes the at least one computing device to at least;

determine a position in media content based at least in part on input from a user, wherein the media content is divided into a hierarchy of logical divisions including a first level division and a second level division;

dynamically generate a summary of a portion of the media content beginning or ending relative to the position; and

cause the summary of the portion of the media content to be presented to the user via an output device, wherein the summary includes a first summary at the first level division and a second summary at the second level division.

6. The system of claim 5, wherein the media content corresponds to at least one of:

an electronic book, an audio book, a video content series, a video program, an audio program, or an audio content series.

7. The system of claim 5, wherein the summary is of the portion of the media content ending relative to the position, and the summary is dynamically generated based at least in part on at least one characteristic of another portion of the media content beginning relative to the position.

8. The system of claim 5, wherein the position is at which the user commenced consumption of the media content, the summary is of the portion of the media content ending relative to the position, and the media content is real-time media content.

9. The system of claim 5, wherein the summary is dynamically generated based at least in part on metadata indicating which logical divisions of the portion of the media content are associated with a relative significance meeting a threshold.

10. The system of claim 5, wherein when executed the at least one application further causes the at least one computing device to at least:

analyze a plurality of logical divisions of the portion of the media content to determine a subset of the plurality of logical divisions that are associated with a relative significance meeting a threshold; and

wherein the summary is generated based at least in part on the subset of the logical divisions.

11. The system of claim 5, wherein the portion of the media content begins at the position in the media content at which the user ended consumption of the media content, and the portion of the media content corresponds to a balance of a logical division of the media content.

12. The system of claim 5, wherein when executed the at least one application further causes the at least one computing device to at least:

identify a target summary length based at least in part on a user preference or a length of time since the user ended consumption of the media content; and

wherein the summary is dynamically generated based at least in part on the target summary length.

13. A system, comprising:

at least one computing device; and

at least one application executable in the at least one computing device, wherein when executed the at least one application causes the at least one computing device to at least;

determine a position in media content based at least in part on input from a user;

dynamically generate a summary of a portion of the media content beginning or ending relative to the position; and

cause the summary of the portion of the media content to be presented to the user via an output device, wherein the summary is dynamically generated based at least in part on metadata indicating which logical divisions of the portion of the media content are associated with a relative significance meeting a threshold,

wherein the metadata is generated based at least in part on a popularity of dialogue searches relative to individual logical divisions of the portion of the media content.

14. A method, comprising:

receiving, by at least one computing device, a request from a user to resume consumption of media content at a point of resumption;

determining, by the at least one computing device, an elapsed time since the user ended consumption of the media content;

dynamically generating, by the at least one computing device, a summary of a portion of the media content occurring prior to the point of resumption, wherein the summary is dynamically generated based at least in part on the elapsed time, and content of the summary is selected according to a length of the elapsed time;

causing, by the at least one computing device, the summary to be presented to the user via an output device; and

causing, by the at least one computing device, the media content to be presented to the user at the point of resumption after presentation of the summary.

15. The method of claim 14, further comprising selecting, by the at least one computing device, a plurality of logical divisions of the portion of the media content to be included in the summary based at least in part on a machine learning model indicating automatically extracted logical divisions of significance from the portion of the media content.

16. The method of claim 14, further comprising selecting, by the at least one computing device, a plurality of logical divisions of the portion of the media content to be included in the summary based at least in part on metadata indicating at least one of a character, a plot, or a storyline featured in the media content after the point of resumption.

17. The method of claim 14, further comprising including, by the at least one computing device, in the summary a logical division of the portion of the media content at an above-normal speed.

18. The method of claim 14, wherein the summary is dynamically generated further based at least in part on a characteristic of the user.

19. The method of claim 14, wherein the media content corresponds to a video content series, and the point of resumption is within an episode of the video content series.

20. The method of claim 14, wherein the media content is divided into a hierarchy of logical divisions including a first level division and a second level division, and the summary includes a first summary at the first level division and a second summary at the second level division.