Dynamic Video Schedule Detection for Automatic Channel Adjustment
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Solution Overview
Problem
Existing video content delivery systems struggle with variations from predetermined schedules, such as extended sports events or channel changes, leading to rescheduling or cancellation of subsequent content without automatic adjustment.
Innovation Solution
Implementing content recognition and monitoring systems to detect schedule variations, allowing for automatic adjustments in recording times, channel changes, and program listings, and displaying relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video content delivery follows a predetermined schedule, then delivery stability is maintained, but adaptability to schedule variations (such as extended sports events) is poor
Solution Approach 1:
The system transitions from a static predetermined schedule to a dynamic schedule that automatically adjusts in response to detected variations. The content recognition system monitors actual content delivery and triggers rescheduling actions when deviations are detected, making the delivery system adaptive while maintaining reliability through automated responses.
Solution Approach 2:
The system implements feedback loops where content recognition continuously monitors actual content delivery against the predetermined schedule. When variations are detected (such as extended sports events), the system receives feedback and automatically adjusts subsequent content scheduling, resolving the contradiction between adaptability and reliability.
2Adaptability or versatility
If subsequent content is rescheduled or canceled when schedule variations occur, then adaptability is improved, but user disruption increases
Solution Approach 1:
The system performs self-service by automatically detecting schedule variations through content recognition and autonomously adjusting subsequent content scheduling without requiring user intervention. This minimizes user disruption while maintaining adaptability, as the system handles schedule adjustments independently.
Solution Approach 2:
The system takes preliminary action by pre-configuring automated rescheduling rules and content recognition parameters before schedule variations occur. When variations are detected, the system already has the framework in place to quickly adjust schedules, reducing user disruption through rapid automated responses.
3Measurement precision
If manual monitoring and adjustment of schedules is performed, then accuracy of schedule detection is improved, but system complexity and labor requirements increase
Solution Approach 1:
The system replaces manual monitoring mechanisms with automated content recognition technology. Computer vision and pattern recognition algorithms automatically detect schedule variations by analyzing visual and audio content characteristics, eliminating the need for manual monitoring while maintaining high detection accuracy and reducing system operational complexity.
Solution Approach 2:
The system creates a digital copy of the predetermined schedule and continuously compares actual content delivery against this digital model. Content recognition systems generate digital representations of actual content timing and characteristics, enabling automated detection of variations without manual intervention, thus reducing complexity while maintaining precision.
Data Source
AI summary
Various implementations described herein are directed to determining variations or changes to a predetermined programming schedule. In accordance with one method, a predicted end time of video content may be determined to be later than a scheduled end time of the video content. Also, it may be determined that video content scheduled to be displayed on a first stream or channel has been moved to a second stream or channel. A scheduled recording or transmission time of video content may be altered based on detected changes to the predetermined programming schedule. A program listing such as an electronic program guide may be revised based on detected changes to the predetermined programming schedule.