Linear Content Segmentation for Commercial-Free Program Recording
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Solution Overview
Problem
Video recording devices often fail to record entire programs due to fixed time frames, while video-on-demand requires manual segmentation of commercials, leading to inefficiencies and resource wastage.
Innovation Solution
Automated segmentation of linear content streams using image analysis and audio detection to identify program boundaries, classify content types, and adjust recording times based on program duration.
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 efficient, 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 self-service by automatically detecting program boundaries and adjusting recording parameters without user intervention. The recording device autonomously identifies when programs start and end, and automatically extends or truncates recording time frames based on detected program duration, eliminating the need for manual timing adjustments.
Solution Approach 2:
The system implements feedback by continuously monitoring the video stream for program boundary indicators and using this information to adjust recording parameters. The detection of program boundaries provides feedback that triggers automatic extension of recording time if a program runs long, or truncation if the program ends early, ensuring complete recording while maintaining simple operation.
2Adaptability or versatility
If a service provider manually segments video streams to create commercial-free versions, then video-on-demand content can be provided, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system replaces the mechanical manual segmentation process with automated electronic detection and processing. Instead of employees manually viewing and identifying commercial boundaries, the system uses automated algorithms to detect program boundaries and segment video streams, dramatically increasing processing speed while maintaining video-on-demand capability.
Solution Approach 2:
The segmentation process performs self-service by automatically detecting program boundaries and separating commercials without human intervention. The system autonomously identifies commercial start and end times, divides video feeds into segments, and creates commercial-free versions, eliminating the need for manual labor while providing video-on-demand content.
3Measurement precision
If manual judgment is used to identify commercial boundaries in video streams, then segmentation accuracy can be achieved, but the process requires significant human resources and time
Solution Approach 1:
The system substitutes manual visual inspection with automated electronic detection methods. The automated boundary detection algorithm analyzes video streams using technical indicators such as entropy changes, audio level transitions, and visual pattern recognition to identify commercial boundaries with high accuracy, eliminating the time required for manual review while maintaining precision.
Solution Approach 2:
The system introduces an intermediary automated detection mechanism between the video stream and the segmentation process. This intermediary layer uses algorithms to detect program boundaries by analyzing changes in video and audio characteristics, providing accurate boundary identification without requiring human time investment while maintaining measurement precision.
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.