Closed Caption Repetition Analysis for Media Segment Classification
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Solution Overview
Problem
Existing systems struggle to accurately identify and classify segments of media content, such as advertisements and program segments, within a sequence of media content, which is crucial for understanding when and where advertisements are inserted, obtaining accurate copies of advertisements, and removing advertisements from media content for precise fingerprinting.
Innovation Solution
A computing system retrieves closed captioning repetition data, generates features from this data, and uses a classification model, such as a neural network, to predict whether a segment is a program or advertisement segment, storing the prediction in a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content distribution systems use traditional methods to identify media segments, then the system complexity remains low, but the classification accuracy of advertisement and program segments deteriorates
Solution Approach 1:
The patent introduces closed captioning data as an intermediary element to bridge the gap between simple system operation and accurate segment classification. By extracting and analyzing text from closed captions, the system achieves improved classification accuracy without requiring complex analysis of video or audio signals directly.
Solution Approach 2:
The patent replaces traditional mechanical/content-based analysis methods (such as analyzing video frames or audio signals) with a text-based processing approach using closed captioning data. This substitution simplifies the system while improving accuracy, as text analysis is inherently more straightforward than multimedia analysis.
2Reliability
If content distribution systems implement accurate segment classification, then the identification of advertisements improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary extraction and storage of closed captioning data during content distribution, making this text data readily available for later classification tasks. This preliminary action eliminates the need for real-time caption extraction during segment identification, thereby reducing processing time while maintaining high identification accuracy.
3Ease of operation
If content distribution systems remove advertisements from media content, then the quality of on-demand streaming improves, but the complexity of content manipulation increases
Solution Approach 1:
The patent extracts advertisement segments from the media content based on classification results from closed captioning analysis. By identifying and separating advertisement portions using text-based classification, the system simplifies the content manipulation process compared to traditional methods that would require complex video editing and rendering operations.
Data Source
AI summary
In one aspect, an example method includes (i) retrieving, from a text index, closed captioning repetition data for a segment of a sequence of media content; (ii) generating features using the closed captioning repetition data; (iii) providing the features as input to a classification model, wherein the classification model is configured to output classification data indicative of a likelihood of the features being characteristic of a program segment; (iv) obtaining the classification data output by the classification model; (v) determining a prediction of whether the segment is a program segment using the classification data; and (vi) storing the prediction for the segment in a database.