Crowd rating media content based on micro-expressions of viewers
US-2021037271-A1 · Feb 4, 2021 · US
US11457249B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11457249-B2 |
| Application number | US-202017090137-A |
| Country | US |
| Kind code | B2 |
| Filing date | Nov 5, 2020 |
| Priority date | Nov 5, 2020 |
| Publication date | Sep 27, 2022 |
| Grant date | Sep 27, 2022 |
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Aspects of the subject disclosure may include obtaining a first media content item comprising a plurality of content segments. For each content segment of the plurality of content segments of the first media content item, comparing the content segment and a prior content segment to identify a content transition, analyzing the content segment to identify a content marker in the content segment, determining a viewing characteristic of the content segment according to the content transition and the content marker, determining if the content segment is unnecessary according to the viewing characteristic, and updating a set of skipping instructions associated with the first media content item responsive to the determining the content segment is unnecessary, and presenting the first media content item at a first device according to the set of skipping instructions associated with the first media content item. Other embodiments are disclosed.
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What is claimed is: 1. A method, comprising: obtaining, by a processing system including a processor, a first media content item comprising a plurality of content segments; accessing, by the processing system, limitation information and historic skipping data that includes segment characteristics associated with media content segments that have been previously skipped over during presentation; determining, by the processing system, genre information based on the segment characteristics included in the historic skipping data, and determining, by the processing system, a maximum rating to apply to the first media content item based on the limitation information; detecting, by the processing system, content transitions in the first media content item to cluster and categorize content data in the plurality of content segments of the first media content item, wherein the detecting the content transitions in the first media content item comprises performing an analysis of the first media content item to identify transitions between frames that have been edited by a video or image editing application and frames that have not been edited by a video or image editing application, transitions between frames that do not include any faces and frames that include a particular face or a group of faces, and transitions between frames associated with speech in a determined conversational tone and frames associated with speech in a determined angry tone; responsive to the detecting the content transitions, for each content segment of the plurality of content segments of the first media content item: analyzing, by the processing system, the content segment to identify one or more content markers in the content segment; determining, by the processing system, a plurality of viewing characteristics of the content segment according to the one or more content markers, wherein the plurality of viewing characteristics includes a content rating associated with the content segment and a genre associated with the content segment; determining, by the processing system, if the content segment is unnecessary according to a first comparison of the maximum rating and the content rating associated with the content segment, and according to a second comparison of the genre information and the genre associated with the content segment; and updating, by the processing system, a set of skipping instructions associated with the first media content item responsive to determining that the content segment is unnecessary; modifying, by the processing system, the first media content item based on the set of skipping instructions associated with the first media content item, resulting in a modified first media content item, wherein the modified first media content item excludes any content segment of the plurality of content segments that is associated with a content rating that exceeds the maximum rating, and wherein the modified first media content item further excludes any content segment of the plurality of content segments that is associated with any genre identified in the genre information such that the modified first media content item is of a first particular genre that is different from an overall intended genre of the first media content item; and causing, by the processing system, the modified first media content item to be streamed to a first device for presenting by the first device. 2. The method of claim 1 , wherein the overall intended genre of the first media content item comprises an action genre, and wherein the first particular genre of the modified first media content item comprises a romance genre. 3. The method of claim 1 , wherein the maximum rating comprises a Restricted (R) rating, a parents strongly cautioned (PG-13) rating, a parental guidance suggested (PG) rating, or a general audiences (G) rating. 4. The method of claim 1 , wherein the limitation information further includes a presentation runtime. 5. The method of claim 1 , wherein the historic skipping data is associated with a first user. 6. The method of claim 1 , wherein the historic skipping data is associated with a plurality of users. 7. The method of claim 1 , wherein the plurality of content segments comprise video frames, scenes, or any combination thereof. 8. The method of claim 1 , wherein one or more of the content transitions includes a transition of a video characteristic including color, sharpness, brightness, of any combination thereof. 9. The method of claim 1 , wherein one or more of the content transitions includes a transition of an audio characteristic including sound, music, voice, or any combination thereof. 10. The method of claim 1 , wherein the one or more content markers relate to faces, voice tone, mood, forms, scenery, or any combination thereof. 11. The method of claim 1 , wherein the detecting the content transitions is further in accordance with a machine learning algorithm that models transitions as differentials between frames, and wherein the machine learning algorithm models transitions based on differences in scenery between frames, differences in brightness between frames, differences in background music between frames, and differences in amounts of merged data between frames. 12. The method of claim 2 , further comprising modifying the first media content item based on a second set of skipping instructions that is derived according to second genre information and second limitation information that identifies a second maximum rating, resulting in a modified second media content item, and causing the modified second media content item to be streamed to a second device for presenting by the second device, wherein the modified second media content item excludes any content segment of the plurality of content segments that is associated with a content rating that exceeds the second maximum rating, and wherein the modified second media content item further excludes any content segment of the plurality of content segments that is associated with any genre identified in the second genre information such that the modified second media content item is of a second particular genre that is different from both the overall intended genre of the first media content item and the first particular genre of the modified first media content item. 13. A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining a first media content item comprising a plurality of content segments; accessing limitation information associated with a first user and historic skipping data associated with media content segments that the first user has previously skipped over during presentation; determining genre information based on the historic skipping data, and determining a maximum rating to apply to the first media content item based on the limitation information; detecting content transitions in the first media content item to cluster and categorize content data in the plurality of content segments of the first media content item, wherein the detecting the content transitions in the first media content item comprises performing an analysis of the first media content item to identify transitions between frames that have been edited by a video or image editing application and frames that have not been edited by a video or image editing application, transitions between frames that do not include any faces and frames that include a particular face or a group of faces, and transitions between frames associated with speech in a determined conversat
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