Caption Anomaly Detection with Sliding Time Windows
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Solution Overview
Problem
Existing systems face challenges in accurately monitoring and correcting anomalies in closed captioning and other content delivery systems, particularly due to network delays and the complexity of real-time detection, which can result in mis-timed, garbled, or missing captions, and the need for efficient two-way detection.
Innovation Solution
A system for anomaly detection in closed captioning and other content delivery systems that compares return captions with scheduled captions using sliding time windows and dynamic adjustments, employing forward and reverse detection methods to identify and correct anomalies, and includes monitoring other data types like XDS and SCTE-35 signaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time anomaly detection is performed in content delivery systems, then detection accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system segments the caption stream into discrete caption objects with associated timing information, allowing individual anomaly detection rather than processing the entire stream at once. This segmentation enables precise detection of timing deviations, missing captions, and garbled content while reducing overall system complexity through modular processing.
Solution Approach 2:
The system performs preliminary actions by establishing expected caption timing schedules before actual delivery occurs. By pre-defining when captions should appear based on content metadata, the system can efficiently detect anomalies during delivery without complex real-time analysis, thus improving detection accuracy while maintaining system simplicity.
2Measurement precision
If sliding time window comparison is used for caption detection, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system employs a dynamic sliding time window that adjusts its parameters based on content type, delivery conditions, and detected anomalies. This dynamic adjustment allows the window to optimize between processing speed and timing accuracy for different scenarios, preventing fixed window sizes from causing excessive processing delays while maintaining sufficient accuracy.
Solution Approach 2:
Instead of analyzing the entire caption stream, the system applies partial action by focusing the sliding time window on specific critical periods where anomalies are most likely to occur. This selective processing maintains detection accuracy for timing-sensitive captions while reducing overall processing time by ignoring less critical periods.
3Reliability
If two-way detection (forward and reverse) is implemented, then detection reliability is improved, but system complexity increases
Solution Approach 1:
The system merges forward detection (comparing scheduled vs. actual captions) and reverse detection (comparing actual vs. scheduled captions) into a unified processing framework. By combining both detection directions into a single integrated system rather than separate systems, the patent achieves enhanced reliability through comprehensive coverage while minimizing the increase in overall system complexity.
Solution Approach 2:
The detection system is designed with universal functionality that handles both forward and reverse detection using the same core mechanisms. The same caption object structure, timing comparison logic, and anomaly detection algorithms serve both detection directions, thereby improving reliability through multi-functional capability without proportionally increasing system complexity.
Data Source
AI summary
Systems, apparatuses, and methods are described for detecting anomalies in closed captioning or other video presentation systems. Anomaly detection may involve comparing detected captions that are delivered to one or more end devices (return captions) with corresponding scheduled captions. Other types of information may also be similarly compared between original scheduled instances of information to be delivered with the actual (return) delivered information. Such other types of information may include, for example, ratings information (such as V-chip ratings and/or flags) and/or content (e.g., advertisement) insertion information such as SCTE-35 signaling.