Closed Captioning Text Analysis for Video Scene Importance Ranking
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Solution Overview
Problem
Existing video content segmentation methods fail to accurately identify important scenes or moments within media content, as they rely solely on chapter data without considering the relative importance of scenes, leading to uneven and uninformative segmentation.
Innovation Solution
A system that utilizes textual analysis of closed captioning data, including sentiment analysis and user feedback, to rank the importance of scenes and moments within video content features, employing data models to determine and update importance levels based on dialog, non-dialog content, and external metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video content is segmented using only chapter data, then segmentation can be implemented, but the segmentation is uneven and fails to identify important scenes accurately
Solution Approach 1:
The patent introduces closed captioning data as an intermediary element to bridge the gap between existing chapter data and scene importance determination. By analyzing text from closed captions (dialogue, narration, sound effects) and comparing it against plot summaries and external metadata, the system derives importance scores for scenes without requiring direct modification of the video content or chapter structure.
Solution Approach 2:
The patent replaces the mechanical/manual approach to scene importance identification (relying solely on pre-defined chapter data) with an automated textual analysis system. Natural language processing techniques analyze closed captioning data, sentiment analysis determines emotional intensity, and machine learning models calculate importance metrics, substituting human judgment with computational analysis.
2Measurement precision
If textual analysis of closed captioning data is performed to determine scene importance, then scene importance can be accurately identified, but processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by selectively analyzing only the most informative portions of closed captioning data. Rather than processing every single caption with full sentiment analysis and comparison against all external metadata sources, the system prioritizes scenes with higher potential importance based on initial filtering criteria, applying more intensive analysis only where needed to achieve accurate importance ranking.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and indexing closed captioning data, plot summaries, and external metadata before actual importance determination. Sentiment analysis models are pre-trained, and text databases are pre-indexed for rapid querying, allowing the system to quickly analyze scenes during playback or preview without performing all computational heavy lifting in real-time.
3Reliability
If user feedback is incorporated to verify and train importance determination models, then model accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where user interactions (such as pausing, rewinding, or skipping scenes) are collected and used to verify and refine importance determination accuracy. User feedback loops allow the system to learn from actual viewing behavior, adjusting importance scores and retraining models based on whether users actually engage with predicted important scenes or bypass them.
Solution Approach 2:
The patent applies self-service by enabling the system to automatically collect, process, and utilize user feedback without requiring manual intervention for model training. The system autonomously aggregates feedback data, identifies patterns in user behavior, and retrains importance determination models using machine learning algorithms, making the improvement process self-sustaining without additional human resources.
Data Source
AI summary
Disclosed are various embodiments for identifying importance of scenes or moments in video content relative to one another. Closed captioning data is extracted from a video content feature. A textual analysis of the closed captioning data is performed. The importance level of scenes can be ranked with respect to one another.