Multiple stream tuning

• US 9,936,239 B2
• Filed: 06/28/2016
• Issued: 04/03/2018
• Est. Priority Date: 06/28/2016
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Pogorelik, Oleg, Wouhaybi, Rita H, Nayshtut, Alex
• Primary Examiner(s): MENGESHA, MULUGETA A
• Application Number:
  US15/195,203
• Publication Number:
  US 20170374402A1
• Time in Patent Office: 644 Days
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/738 Presentation of query results
• CPC Class Codes: G06F 16/78 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 65/60 Network streaming of media packets
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/306 User profiles
• 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: H04L 67/60 Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources admission control or resource allocation H04L47/70
• 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 21/4147 PVR [Personal Video Recorder] H04N5/76 takes precedence ; arrangements for broadcast with accumulation-type receivers H04H20/40
• CPC Class Codes: H04N 21/4382 Demodulation or channel decoding, e.g. QPSK demodulation analog front ends or means for connecting modulators, demodulators or transceivers to a transmission line H04L27/0002
• CPC Class Codes: H04N 21/44008 involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/44204 Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31
• 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 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 21/4784 receiving rewards
• CPC Class Codes: H04N 21/6125 involving transmission via Internet transmission by internet of broadcast information H04H60/82
• 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

Abstract

System and techniques for multiple stream tuning are described herein. A plurality of content streams may be received. The plurality of content streams may be ranked. A user attention level may be scored as the user observes at least one content stream of the plurality of content streams. The user attention level and a rank for the at least one content stream may be compared to remaining ranks of the plurality of content streams to produce a difference. A stream action may be performed on a set of content streams from the plurality of content streams based on the difference.

Claims

1. system for multiple stream tuning, the system comprising:

a decoder to receive a plurality of content streams;

a content sensor array to rank the plurality of content streams, wherein to rank the plurality of content streams includes, for each of the plurality of content streams, the content sensor is to apply a set of assays to a content stream to produce a set of artifacts;

a sensor to score a user attention level as the user observes at least one content stream of the plurality of content streams;

a comparator to compare the user attention level and a rank for the at least one content stream to remaining ranks of the plurality of content streams to produce a difference; and

a switch to;

perform a stream action on a set of content streams from the plurality of content streams based on the difference;

evaluate the set of artifacts in a user context for the user to produce artifact evaluations, wherein the user context includes a matching group to a set of users, and wherein to evaluate the set of artifacts in a user context includes the switch to use a portion of a second user context from a member of the set of users to produce the artifact evaluations; and

modify a rank for the content stream based on the artifact evaluations.

2. The system of claim 1, wherein a member of set of assays is automatic speech recognition (ASR), and wherein a member of the set of artifacts that correspond to the ASR assay is a string.

3. The system of claim 1, wherein a member of the set of assays is a visual classifier, and wherein a member of the set of artifacts include an object identification corresponding to the output of the visual classifier.

4. The system of claim 2, wherein to evaluate the set of artifacts in the user context includes the switch to perform a semantic comparison between text in the string and tokens in the user context, the tokens including a score corresponding to user action in regard to subject matter corresponding to the tokens.

5. The system of claim 4, wherein the sensor is to:

observe the user action in a sample period;

calculate an attention score for the user action; and

wherein the switch is to;

correlate the attention score to one or more tokens from a content stream during the sample period; and

update the user context to include a modified attention score for the one or more tokens.

6. The system of claim 1, wherein, given a second content stream in the plurality of content streams, the stream action is at least one of the switch to:

replace a current content stream on a screen with the second content stream when the second content stream has a higher rank than the current content stream;

replace a current audio stream with an audio stream from a second content stream that has a higher rank that the current audio stream;

place the second content stream in a picture-in-picture container on a screen when the second content;

record the second content stream;

modify the volume of an audio portion of the second content stream;

or

render a notification to the user regarding the second content stream.

7. A method for multiple stream tuning, the method comprising:

receiving a plurality of content streams;

ranking the plurality of content streams, wherein ranking the plurality of content streams includes, for each of the plurality of content streams;

applying a set of assays to a content stream to produce a set of artifacts;

evaluating the set of artifacts in a user context for the user to produce artifact evaluations, wherein the user context includes a matching group to a set of users, and wherein evaluating the set of artifacts in a user context includes using a portion of a second user context from a member of the set of users to produce the artifact evaluations; and

modifying a rank for the content stream based on the artifact evaluations;

scoring a user attention level as the user observes at least one content stream of the plurality of content streams;

comparing the user attention level and a rank for the at least one content stream to remaining ranks of the plurality of content streams to produce a difference; and

performing a stream action on a set of content streams from the plurality of content streams based on the difference.

8. The method of claim 7, wherein a member of set of assays is automatic speech recognition (ASR), and wherein a member of the set of artifacts that correspond to the ASR assay is a string.

9. The method of claim 7, wherein a member of the set of assays is a visual classifier, and wherein a member of the set of artifacts include an object identification corresponding to the output of the visual classifier.

10. The method of claim 8, wherein evaluating the set of artifacts in the user context includes performing a semantic comparison between text in the string and tokens in the user context, the tokens including a score corresponding to user action in regard to subject matter corresponding to the tokens.

11. The method of claim 10, comprising:

observing the user action in a sample period;

calculating an attention score for the user action;

correlating the attention score to one or more tokens from a content stream during the sample period; and

updating the user context to include a modified attention score for the one or more tokens.

12. The method of claim 7, wherein, given a second content stream in the plurality of content streams, the stream action is at least one of:

replacing a current content stream on a screen with the second content stream when the second content stream has a higher rank than the current content stream;

replacing a current audio stream with an audio stream from a second content stream that has a higher rank that the current audio stream;

placing the second content stream in a picture-in-picture container on a screen when the second content;

recording the second content stream;

modifying the volume of an audio portion of the second content stream;

or

rendering a notification to the user regarding the second content stream.

13. At least one non-transitory machine readable medium including instructions for multiple stream tuning, the instructions, when executed by a machine, causing the machine to:

receive a plurality of content streams;

rank the plurality of content streams, wherein, to rank the plurality of content streams includes, for each of the plurality of content streams, includes the instructions to cause the machine to;

apply a set of assays to a content stream to produce a set of artifacts;

evaluate the set of artifacts in a user context the user to produce artifact evaluations, wherein the user context includes a matching group to a set of users, and wherein, evaluate the set of artifacts in a user context, the machine is to us a portion of a second user context from a member of the set of users to produce the artifact evaluations; and

modify a rank for the content stream based on the artifact evaluations;

score a user attention level as the user observes at least one content stream of the plurality of content streams;

compare the user attention level and a rank for the at least one content stream to remaining ranks of the plurality of content streams to produce a difference; and

perform a stream action on a set of content streams from the plurality of content streams based on the difference.

14. The at least one machine readable medium of claim 13, wherein a member of set of assays is automatic speech recognition (ASR), and wherein a member of the set of artifacts that correspond to the ASR assay is a string.

15. The at least one machine readable medium of claim 13, wherein a member of the set of assays is a visual classifier, and wherein a member of the set of artifacts include an object identification corresponding to the output of the visual classifier.

16. The at least one machine readable medium of claim 14, wherein to evaluate the set of artifacts in the user context includes the machine to perform a semantic comparison between text in the string and tokens in the user context, the tokens including a score corresponding to user action in regard to subject matter corresponding to the tokens.

17. The at least one machine readable medium of claim 16, wherein the instructions further cause the machine to:

observe the user action in a sample period;

calculate an attention score for the user action; and

wherein the switch is to;

correlate the attention score to one or more tokens from a content stream during the sample period; and

update the user context to include a modified attention for the one or more tokens.

18. The at least one machine readable medium of claim 13, wherein, given a second content stream in the plurality of content streams, the stream action is at least one of the machine to:

replace a current content stream on a screen with the second content stream when the second content stream has a higher rank than the current content stream;

replace a current audio stream with an audio stream from a second content stream that has a higher rank that the current audio stream;

place the second content stream in a picture-in-picture container on a screen when the second content;

record the second content stream;

modify the volume of an audio portion of the second content stream;

or

render a notification to the user regarding the second content stream.