- Number
- 20240373073
- Published
- 2024-11-07
- Filed
- 2023-05-04
- Assignee
- Disney Enterprise, Inc.
- Inventors
- Nihei; Taryn et al.
- CPC
- H04N21/23418; G06T7/90; H04N5/147; H04N21/44008; H04N21/812; H04N21/8456
- Verdict
- Set aside ad-break prediction, business
- Source
- Google Patents · FreePatentsOnline
Abstract
A system includes a hardware processor and a memory storing software code. The software code is executed to receive media content including a video and an audio component, recognize the media content as ad-slugged or seamless content, and detect black video frames of the media content. For ad-slugged content, the software code further detects silent video frames, and identifies, using the black video frames and the silent video frames, candidate ad-insertion point(s) and a probability score associated with each, to provide ad-break prediction(s). For seamless content, the software code performs evaluations of blackness transitions between sequential video frames, and one or more evaluations of audio continuity across respective one or more sequences of the black video frames, and identifies, using the black video frames and the evaluations, candidate ad-insertion point(s) and a probability score associated with each to provide ad-break prediction(s). The ad-break prediction(s) are further provided as system outputs.
Background
BACKGROUND
Due to its nearly universal popularity as a content medium, ever more visual media content is being produced and made available to consumers. However, high-quality visual media content is expensive to produce, typically requiring the participation of numerous talented artists, performers, and technical professionals. Advertisements (ads) are features that enable high-quality, costly visual media content to be made available to consumers at a reduced price relative to its intrinsic artistic value.
Nevertheless, ads can be a double-edged sword for media content distributors and consumers alike. On the one hand, ads make subscribing to a content delivery service, for example, more affordable for consumers, thereby potentially growing a subscription base while delivery good value to the consumer. On the other hand, too many, or poorly placed ads can be significantly off-putting to the content consumption experience, thereby potentially driving existing subscribers away. Consequently, it is advantageous for both consumers and content distributors that ads inserted into content be in effect “content aware,” in the sense that those ads are presented so as to produce the least possible disruption to the media content consumption experience.
Significant challenges to identifying content aware ad-insertion points, or “ad-breaks,” include the size of the content libraries made available to consumers by most content delivery platforms, as well as the diver
Claims
1. An artificial intelligence (AI) system comprising: a computing platform having a hardware processor and a system memory storing a software code; the hardware processor configured to execute the software code to: receive media content including a video component and an audio component; recognize whether the media content is ad-slugged media content or seamless media content; detect a plurality of black video frames of the media content; detect, when the media content is the ad-slugged media content, a plurality of silent video frames of the ad-slugged media content; identify, using the plurality of black video frames and the plurality of silent video frames when the media content is ad-slugged media content, one or more candidate ad-insertion points for the ad-slugged media content and a respective probability score associated with each of the one or more candidate ad-insertion points to provide one or more ad-break predictions for the ad-slugged content; perform, when the media content is seamless media content, a first plurality of evaluations of blackness transitions between sequential video frames, and one or more second evaluations of audio continuity across respective one or more sequences of the plurality of black video frames; identify, using the plurality of black video frames, the first plurality of evaluations, and the one or more second evaluations when the media content is seamless media content, one or more candidate ad-insertion points for the seamless media content and a respective probability score associated with each of the one or more candidate ad-insertion points to provide one or more ad-break predictions for the seamless media content; and output the one or more ad-break predictions for the ad-slugged media content or the one or more ad-break predictions for the seamless media content. ||
11. A method for use by an artificial intelligence (AI) system including a computing platform having a hardware processor and a system memory storing a software code, the method comprising: receiving, by the software code executed by the hardware processor, media content including a video component and an audio component; recognizing, by the software code executed by the hardware processor, whether the media content is either ad-slugged media content or seamless media content; detecting, by the software code executed by the hardware processor, a plurality of black video frames of the media content; detecting, by the software code executed by the hardware processor when the media content is ad-slugged media content, a plurality of silent video frames of the ad-slugged media content; identifying, by the software code executed by the hardware processor and using the plurality of black video frames and the plurality of silent video frames, when the media content is ad-slugged media content, one or more candidate ad-insertion points for the ad-slugged media content and a respective probability score associated with each of the one or more candidate ad-insertion points to provide one or more ad-break predictions for the ad-slugged media content; performing, by the software code executed by the hardware processor when the media content is seamless media content, a first plurality of evaluations of blackness transitions between sequential video frames, and one or more second evaluations of audio continuity across respective one or more sequences of the plurality of black video frames; identifying, by the software code executed by the hardware processor and using the plurality of black video frames, the first plurality of evaluations, and the one or more second evaluations, when the media content is seamless media content, one or more candidate ad-insertion points for the seamless media content and a respective probability score associated with each of the one or more candidate ad-insertion points to provide one or more ad-break predictions for the seamless media content; and outputting, by the software code executed by the hardware processor, the one or more ad-break predictions for the ad-slugged media content or the one or more ad-break predictions for the seamless media content.