Episodic Content Cue Point Discovery Using Unsupervised Learning
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Solution Overview
Problem
Existing methods for cue point discovery in episodic content, such as TV shows, are inefficient and lack automation, leading to suboptimal user experiences.
Innovation Solution
Employ unsupervised machine learning to classify cue points in episodic content by dividing videos into sections, determining representations for each, comparing similarities across episodes, and using temporal positions to identify types of sections like opening or end credits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to identify cue points, then accuracy can be maintained, but time consumption and labor requirements increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing video content and classifying cue points without requiring manual annotation. The unsupervised machine learning model independently processes video sections, extracts features, and identifies cue point types (opening credits, end credits, recaps, etc.), eliminating the need for human annotators to manually review and label each segment.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated machine learning-based classification. Instead of humans visually inspecting and annotating video frames, the system uses computational algorithms that automatically detect patterns, extract temporal and visual features, and classify cue points based on learned representations from video data.
2Productivity
If unsupervised machine learning is used to automatically classify cue points, then productivity increases, but system complexity increases
Solution Approach 1:
The system segments the video into smaller temporal sections and analyzes each segment independently. By dividing the video stream into manageable chunks and processing them separately through the machine learning model, the system reduces computational complexity at any given moment while maintaining overall processing efficiency across the entire video.
Solution Approach 2:
The system performs preliminary feature extraction and representation learning during an initial processing phase. By pre-computing visual and temporal features, extracting key frames, and preparing data representations before final classification, the system reduces the complexity of subsequent cue point identification operations.
3Measurement precision
If all video sections are analyzed in detail, then classification accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system applies local quality analysis by examining different sections of the video with appropriate detail levels. Rather than uniformly processing every frame at maximum resolution, the system identifies and analyzes only the most relevant temporal segments (such as potential cue point sections) with higher computational precision, while using lighter processing for transitional or clearly non-cue sections.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for cue point discovery for content. For example, system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof are provided for using unsupervised machine learning to automatically classify cue points for episodic content. The cue points can be associates with an opening credits section, an end credits section, a recap section, or a behind-the-scenes section.