Content aggregation and presentation

• US 8,898,713 B1
• Filed: 08/31/2010
• Issued: 11/25/2014
• Est. Priority Date: 08/31/2010
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Price, Roy F.
• Primary Examiner(s): Lonsberry, Hunter B
• Application Number:
  US12/872,883
• Time in Patent Office: 1,547 Days
• Field of Search: 725/21, 725/22, 725/28, 725/89, 725 93- 95
• US Class Current: 725/94
• CPC Class Codes: G06F 16/244 Grouping and aggregation
• CPC Class Codes: G06F 16/24573 using data annotations, e.g. user-defined metadata
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/738 Presentation of query results
• 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: 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 21/26258 for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list retrieval of multimedia data based on playlists G06F16/40
• 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 21/4622 Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies

Abstract

Techniques for providing personalized presentations of aggregated content items are described herein. These techniques may aggregate individual content items from multiple sources, filter the content items based on the preferences of a user, and create an ordered presentation of the content items. A different presentation may be available to the user at different times, for example, a new presentation may be available each day. The new presentation may include more recent or more relevant content items than previous presentations of content items. Each presentation may combine the multiple individual content items together in such a way that each individual content item appears to the user as part of a single content item. User interaction with the aggregation of content items may be analyzed and that analysis may be used to modify subsequent selection and ordering of content items in future presentations.

Claims

1. A computer-implemented method comprising:

under control of one or more computer systems configured with executable instructions,

aggregating videos from a plurality of content sources;

determining one or more keywords for the videos based at least in part on an analysis of content from the videos;

selecting at least a portion of the videos by filtering the videos based at least in part on the one or more keywords;

receiving user preferences regarding video consumption;

ranking the at least the portion of the videos based at least in part on the user preferences;

assembling a video program of content from the at least the portion of the videos based at least in part on the user preferences and the one or more keywords; and

providing a user access to the video program.

2. The method of claim 1, wherein the aggregating the videos comprises obtaining the videos and metadata associated with the videos from content sources including at least one of a free commercial content source, a paid commercial content source, or a content source provided by friends of the user that are identified in a contacts list associated with the user.

3. The method of claim 1, wherein the aggregating the videos includes obtaining metadata associated with the videos, the metadata comprising at least one of a genre, keywords, an identity of a vendor of a respective video, a name of a person appearing in the respective video, a time of publication of the respective video, a duration of the respective video, a geographic location associated with the respective video, a language of the respective video, a global popularity of the respective video, an importance rating of the respective video, or a popularity of the respective video among friends of the user.

4. The method of claim 1, wherein the aggregating the videos includes obtaining metadata associated with the videos, and wherein the ranking the videos based at least in part on the user preferences comprises:

comparing the metadata associated with the videos with the user preferences;

determining a degree of correlation between the metadata and the user preferences; and

ranking the videos in order of the degree of correlation.

5. The method of claim 1, wherein the method further comprises sequentially ordering the at least the portion of the videos based at least in part on the user preferences and the ranking of the at least the portion of the videos, the user preferences specifying a preferred consumption order based at least in part on one or more genres of the videos.

6. The method of claim 1, wherein individual ones of the ranked videos are associated with a respective decay rate used to generate a respective value for a respective video;

and further comprising;

excluding at least one of the individual ones of the ranked videos in response to respective value for the at least one of the individual ones of the ranked videos not satisfying a threshold value;

determining that a period of time corresponding to the decay rate has elapsed for at least one of the ranked videos that was not excluded; and

in response to the determining that the period of time has elapsed, decreasing, according to the respective decay rate, the respective value for the at least one of the ranked videos that was not excluded.

7. The method of claim 1, wherein the aggregating the videos further comprises filtering the videos based at least in part on global factors comprising at least one of a global popularity or a global importance of each respective video.

8. The method of claim 1, further comprising observing user interaction with the linear video program, and modifying a subsequent ranking based at least in part on the observations.

9. The method of claim 1, further comprising ordering the videos based at least in part on the user preferences and the ranking the videos, the user preferences specifying a preferred consumption order based at least in part on a duration of the video.

10. The method of claim 1, wherein assembling a linear video program of the ranked videos comprises including advertisements between the ranked videos.

11. A computing device comprising:

one or more processors; and

a memory coupled to the one or more processors, the memory storing computer readable instructions that, when executed by the one or more processors, causes the one or more processors to;

store preferences of a user in the memory, the preferences of the user comprising genres of interest to the user, identities of friends identified in a contacts list of the user, consumption behavior of the friends, and past consumption history of the user;

identify content items from a plurality of predetermined content sources;

assign a ranking to the identified content items at least in part according to the genres of interest, the consumption behavior of the friends, and the past consumption history;

determine that at least a portion of the identified content items have a ranking that satisfies a threshold rank; and

format the at least the portion of the identified content items as a single content item instead of separate content items.

12. The computing device of claim 11, wherein the preferences of the user further comprise at least one of keywords of interest, publication time, content item length, content source, person, geographical area, and language.

13. The computing device of claim 11, wherein the predetermined content sources include private sources of content sent from at least one of the friends.

14. The computing device of claim 11, wherein the memory storing the computer readable instructions further causes the one or more processors to rank the content items identified for the plurality of predetermined content sources at least in part according to a global popularity and a global importance.

15. The computing device of claim 11, wherein the memory storing the computer readable instructions further causes the one or more processors to apply a different weight to two or more factors used in the ranking.

16. The computing device of claim 11, wherein the memory storing the computer readable instructions further causes the one or more processors to include advertisements between the content items included in the single aggregate content item.

17. The computing device of claim 11, wherein the memory storing the computer readable instructions further causes the one or more processors to order the identified content items according to the ranking assigned to the identified content items.

18. The computing device of claim 17, wherein the memory storing the computer readable instructions further causes the one or more processors to order the identified content items based at least in part on the preferences of the user specifying an order based at least in part on the genres of interest.

19. The computing device of claim 11, wherein the memory storing the computer readable instructions further causes the one or more processors to:

format the single content item for a user computing device designated by the preferences of the user and send the single content item to the user computing device;

or

place the single content item in a data store accessible by a user, and

provide the user a link to the single content item.

20. The computing device of claim 11, wherein the memory storing the computer readable instructions further causes the one or more processors to:

analyze the content items; and

create metadata based at least in part on the analysis, the analysis comprising at least one of text recognition, speech recognition, and image recognition.

21. The computing device of claim 11, wherein the memory storing the computer-readable instructions further causes the one or more processors to modify a length of the single content item based at least in part on an observed user interaction with the single content item.

22. The computing device of claim 11, wherein the memory storing the computer-readable instructions further causes the one or more processors to filter the identified content items based at least in part on a comparison between the preferences of a user and metadata associated with one or more content items of the identified content items.

23. One or more computer-readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:

aggregating a plurality of content items from a plurality of predetermined content sources;

receiving preferences of a user;

determining metadata for at least a portion of the plurality of content items, wherein metadata for a content item of the plurality of content items includes a decay rate indicating a rate at which a value for the content item diminishes over time;

filtering the plurality of content items based at least in part on one or more of the preferences of the user and the metadata;

ordering the filtered plurality of content items based at least in part on a relevance ranking or an ordering explicitly designated by the user; and

delivering the ordered, filtered plurality of content items to the user.

24. The media of claim 23, wherein receiving the preferences of the user comprise receiving at least one of preferred genres, preferred topics, preferred keywords, preferred personalities, preferred geographical locations, preferred languages, preferred sources of content, preferred length of a content item, preferred publication time, preferred popularity level, or preferred importance rating.

25. The media of claim 23, further comprising aggregating additional content items from preferred sources of content items when the preferences of the user include preferred sources of content items.

26. The media of claim 23, wherein the determining the metadata comprises identifying information generated by at least one of, a vendor of a content item, other users, and an automated metadata extraction algorithm, and wherein the acts further comprise associating that information with a respective portion of the at least a portion of the plurality of content items.

27. The media of claim 23, wherein the filtering the plurality of content items comprises comparing the preferences of the user to the metadata associated with each of the at least a portion of the plurality of content items, and removing a particular content item for which the metadata associated with the particular content item does not match the preferences of the user.

28. The media of claim 27, wherein one or more of the preferences of the user include a plurality of user defined specifications;

and

the filtering the at least a portion of the plurality of content items further comprises assigning a relevance ranking to each content item of the at least a portion of the plurality of content items, the relevance ranking based at least in part on a degree of correspondence between the metadata associated with each content item of the at least a portion of the plurality of content items and a weighting applied to each user defined specification of the plurality of user defined specifications of the preferences of the user.

29. The media of claim 28, wherein the filtering the plurality of content items further comprises removing a content item when the content item does not satisfy a threshold relevance ranking.

30. The media of claim 23, wherein the acts further comprise:

receiving ratings from the user about previously consumed content items; and

modifying filter performance based at least in part on the ratings.

31. The media of claim 23, wherein the decay rate associated with the one content item is based at least in part on a genre of the one content item.

32. The media of claim 23, wherein the decay rate associated with the one content item is based at least in part on a publication source of the one content item.