USER INTERACTION ANALYSIS MODULE

• US 20160094866A1
• Filed: 09/29/2014
• Published: 03/31/2016
• Est. Priority Date: 09/29/2014
• Status: Abandoned Application

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: FRAZZINI, MICHAEL ANTHONY, DAVIS, COLLIN CHARLES, PESCE, MICHAEL SCHLEIF, HEINZ, GERARD JOSEPH II
• Application Number:
  US14/500,451
• Publication Number:
  US 20160094866A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04N 21/23412 for generating or manipulating the scene composition of objects, e.g. MPEG-4 objects
• CPC Class Codes: H04N 21/23418 involving operations for analysing video streams, e.g. detecting features or characteristics television picture signal circuitry for scene change detection H04N5/147; filtering for image enhancement G06T5/00; methods or arrangements for recognising scenes G06V20/00; arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/23439 for generating different versions
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08
• 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 21/2668 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 21/44226 on social networks
• CPC Class Codes: H04N 21/47205 for manipulating displayed content, e.g. interacting with MPEG-4 objects, editing locally
• CPC Class Codes: H04N 21/4781 Games
• 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 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 21/8126 involving additional data, e.g. news, sports, stocks, weather forecasts
• CPC Class Codes: H04N 21/8173 End-user applications, e.g. Web browser, game

Abstract

An interaction analysis module may collect data about user interactions with video content in a real-time video exploration (RVE) system, analyze the collected data to determine correlations between users or groups of users and particular video content, and provide the analysis data to one or more systems, for example to the RVE system or to an online merchant. The RVE system may dynamically render and stream new video content targeted at particular users or groups based at least in part on the analysis data. Network-based computation resources and services may be leveraged by the RVE system to enable interactive exploration of video content by the users, as well as the real-time rendering and streaming of the new video content. Entities such as online merchants may target information such as advertising or recommendations to particular users or groups based at least in part on the analysis information.

Claims

1. A system, comprising:

one or more computing devices configured to implement a real-time video exploration (RVE) system configured to;

stream video to a plurality of client devices;

receive input from one or more of the client devices indicating user interactions exploring video content in the streamed video, wherein the video content includes graphical representations of objects rendered from graphics data according to one or more computer graphics techniques;

render new video content from the graphics data based at least in part on the user interactions with the video content; and

stream video including the new video content to respective ones of the one or more client devices;

one or more computing devices configured to implement an interaction analysis module configured to;

obtain interaction data from the RVE system indicating at least some of the user interactions exploring the video content in the streamed video;

analyze the interaction data to determine correlations between users or groups of users and the video content in the streamed video; and

provide analysis data indicating the determined correlations to one or more systems;

wherein the one or more systems are configured to provide additional content or information targeted at particular users or groups of users based at least in part on the determined correlations as indicated in the analysis data.

2. The system as recited in claim 1, wherein the one or more systems include the RVE system, and wherein the RVE system is further configured to render new video content targeted at the particular users or groups of users based at least in part on the determined correlations as indicated in the analysis data and stream video including the targeted new video content to respective ones of the client devices.

3. The system as recited in claim 1, wherein at least one of the one or more systems is configured to provide, via one or more communications channels, information, advertising or recommendations for particular products or services targeted at the particular users or groups of users based at least in part on the determined correlations as indicated in the analysis data.

4. The system as recited in claim 1, wherein the interaction analysis module is further configured to correlate client information from one or more sources with the interaction data to associate particular users'"'"' interaction data with the particular users'"'"' client information, wherein the client information includes client identity information and client profile information for a plurality of users, and wherein the analysis data further indicates associations between the client information and the interaction data.

5. The system as recited in claim 1, wherein the interaction analysis module is implemented as an interaction analysis service on a provider network, wherein the interaction data is obtained from the RVE system according to an application programming interface (API) of the service, and wherein the analysis data is provided to the one or more systems according to the API.

6. The system as recited in claim 5, wherein the interaction analysis service is configured to:

obtain interaction data from at least one other RVE system indicating user interactions with video content in videos streamed by the at least one other RVE system;

combine the interaction data from the RVE systems and analyze the combined interaction data to determine correlations between users or groups of users and the video content in the videos based on the analysis of the combined interaction data; and

provide analysis data indicating the correlations determined based on the combined interaction data to at least one of the one or more systems.

7. The system as recited in claim 1, wherein the interaction analysis module is a component of the RVE system.

8. The system as recited in claim 1, wherein the one or more computing devices that implement the RVE system are on a provider network, and wherein the RVE system is configured to leverage one or more computing resources of the provider network to perform said rendering new video content and said streaming video including the new video content to the one or more client devices in real time during playback of pre-recorded video to the plurality of client devices.

9. A method, comprising:

receiving, by a video system implemented on one or more computing devices, input from one or more of a plurality of client devices indicating user interactions with video content in video sent to the plurality of client devices by the video system, wherein the video content includes graphical representations of objects rendered from graphics data according to one or more computer graphics techniques;

rendering and sending new video content from the graphics data based at least in part on the user interactions with the video content

sending video including the new video content to respective ones of the one or more client devices;

analyzing, by an interaction analysis module, the user interactions with the video content in the video to determine correlations between at least one user and particular objects represented by the video content; and

providing additional content or information targeted at one or more particular users based at least in part on the determined correlations.

10. The method as recited in claim 9, wherein said providing additional content or information targeted at one or more particular users based at least in part on the determined correlations comprises rendering new video content targeted at the one or more particular users based at least in part on the determined correlations and sending video including the targeted new video content to respective client devices of the one or more particular users.

11. The method as recited in claim 9, wherein the video system is a real-time video exploration (RVE) system, and wherein said providing additional content or information targeted at one or more particular users based at least in part on the determined correlations comprises:

updating, by the interaction analysis module, profiles for one or more users maintained by the RVE system to indicate determined correlations between the users and particular objects represented by the video content;

rendering, by the RVE system, new video content targeted at the one or more particular users based at least in part on the correlations indicated in the particular users'"'"' profiles; and

sending video including the targeted new video content to respective client devices of the one or more particular users.

12. The method as recited in claim 9, wherein said providing additional content or information targeted at one or more particular users based at least in part on the determined correlations comprises providing information, advertising or recommendations for particular products or services to the particular users via one or more communications channels.

13. The method as recited in claim 9, further comprising correlating client information from one or more sources with the user interactions to associate particular users'"'"' interactions with the video content with the particular users'"'"' client information, wherein the client information includes client identity information and client profile information for a plurality of users.

14. The method as recited in claim 9, wherein the video system is a real-time video exploration (RVE) system or an online game system.

15. The method as recited in claim 9, wherein the interaction analysis module is implemented as an interaction analysis service, the method further comprising:

receiving, by the interaction analysis service from two or more video systems, interaction data indicating user interactions with video content in respective videos;

analyzing, by the interaction analysis module, the received interaction data from the two or more video systems to determine correlations between particular users or groups of users and particular objects represented by the video content in the respective videos; and

providing analysis data indicating the determined correlations to one or more systems.

16. A non-transitory computer-readable storage medium storing program instructions that when executed on one or more computers cause the one or more computers to implement a real-time video exploration (RVE) system configured to:

receive input from one or more client devices indicating user interactions with content of video streamed to the one or more client devices, wherein the video includes one or more scenes rendered at least in part according to one or more computer graphics techniques;

analyze the user interactions with the content of the streamed video to determine correlations between at least one user and particular video content in the streamed video;

render new video content targeted at one or more users based at least in part on the determined correlations, wherein the new video content includes graphical representations of objects rendered from graphics data according to one or more computer graphics techniques; and

stream video including the targeted new video content to respective client devices of the one or more users.

17. The non-transitory computer-readable storage medium as recited in claim 16, wherein the input is received from the one or more client devices according to an application programming interface (API) of the RVE system.

18. The non-transitory computer-readable storage medium as recited in claim 16, wherein the targeted new video content is different for at least two of the plurality of client devices.

19. The non-transitory computer-readable storage medium as recited in claim 16, wherein the targeted new video content for a particular user includes renderings of particular objects or types of objects selected for the user at least in part according to the user'"'"'s interactions with the video content in the streamed video.

20. The non-transitory computer-readable storage medium as recited in claim 16, wherein the RVE system is configured to perform said rendering new video content and said streaming video including the targeted new video content to respective client devices in real time during playback of pre-recorded video to the plurality of client devices.

21. The non-transitory computer-readable storage medium as recited in claim 16, wherein, to render new video content targeted at one or more users based at least in part on the determined correlations, the RVE system is configured to:

determine one or more groups of users at least in part according to the determined correlations; and

render the new video content targeted at the one or more users based at least in part on the determined one or more groups of users.

22. The non-transitory computer-readable storage medium as recited in claim 16, wherein, to render new video content targeted at one or more users based at least in part on the determined correlations, the RVE system is configured to render new video content targeted at one or more groups of users based at least in part on the determined correlations between a particular user and particular video content in the streamed video.