Linear Transmission Program Segmentation with Dynamic Recording Extension
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Solution Overview
Problem
Existing video recording devices often fail to record entire programs due to fixed time frames, wasting space if programs are shorter or missing content if they run longer, while video-on-demand requires manual commercial segmentation, which is inefficient.
Innovation Solution
Automated video stream analysis using entropy, mutual information, and audio duplication detection to segment content into commercials, shows, and movies, with dynamic logo detection to adjust recording times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a video recording device records a specific time frame, then the recording process is simple and straightforward, but the device may not record the entire program if it runs long or wastes recording space if the program is shorter
Solution Approach 1:
The system performs preliminary actions by detecting program boundaries and estimating program durations in advance. The boundary detection module identifies transitions between programs, and the duration estimation module predicts how long programs will run, allowing the device to proactively adjust recording times before programs actually start or end
Solution Approach 2:
The system implements feedback mechanisms where recording decisions are continuously adjusted based on detected program boundaries and estimated durations. The device monitors program transitions and uses this feedback to dynamically modify recording schedules, ensuring complete program capture while optimizing storage utilization
2Productivity
If video-on-demand services create commercial-free versions by manual segmentation, then content can be delivered without commercials, but the process is inefficient and requires significant manual labor
Solution Approach 1:
The system enables self-service by implementing automated program boundary detection and commercial identification algorithms. The detection module automatically identifies program transitions and commercial segments without human intervention, while the classification module autonomously categorizes segments, eliminating the need for manual employee judgment and significantly improving processing efficiency
Solution Approach 2:
The patent replaces the mechanical manual segmentation process with automated electronic detection and classification systems. Instead of employees manually viewing and identifying commercials, the system uses algorithmic analysis of video streams to automatically detect boundaries and classify content, substituting human labor with computational processes
Data Source
AI summary
Content streams may be segmented to provide automatic extraction and storage of content items without intervening commercials or other unrelated content. These content items may then be stored in a database and made accessible to subscribers through, for example, an on-demand service. Automatic segmentation may include the identification of program boundaries, segmentation of a content stream based on the boundaries and the subsequent classification of the segments into content types. For example, audio and video duplication detection may be used to identify commercials since commercials tend to repeat frequently over a relatively short amount of time. A system may further identify an end of program indicator in a video stream to determine when a program ends. Accordingly, if a program ends after a scheduled end time, a recording device (e.g., the program is being recorded) may automatically extend the recording time to capture the entire program.