Video Classification Using ML Feature Extraction
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Solution Overview
Problem
Administrators of websites with large collections of videos face the impractical task of manually classifying each video as standalone or non-standalone, as existing methods are not feasible for large quantities and fail to capture nonlinear correlations effectively.
Innovation Solution
A system and method that analyze video features using a machine-learned model to classify videos as standalone or non-standalone by extracting feature values from transcripts and metadata, employing co-viewing, embeddings, length, and time reference patterns, and updating the model regularly with new training data to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of each video is performed, then classification accuracy can be maintained, but the time and resources required become impractical for large quantities of videos
Solution Approach 1:
The patent replaces the mechanical manual classification system with an automated machine learning classification system. The system uses trained models to automatically analyze video features and determine standalone status, eliminating the need for human reviewers while maintaining classification accuracy through sophisticated algorithms that process multiple video attributes simultaneously.
Solution Approach 2:
The classification system performs self-service by automatically classifying videos without requiring manual intervention. The machine learning models are trained once and then autonomously process new videos, extracting features and making classification decisions independently, which enables the system to handle large volumes of videos efficiently and scale without additional human resources.
2Productivity
If simple classification rules are used, then processing speed increases, but the ability to capture nonlinear correlations between video features is lost
Solution Approach 1:
The patent transforms the classification approach by changing from simple linear rules to sophisticated machine learning models that can capture nonlinear relationships. The system extracts multiple video features and uses trained models to process these parameters in complex ways, enabling the detection of subtle patterns and correlations that simple rules would miss while maintaining high processing speed through automated computation.
Solution Approach 2:
The classification system combines multiple video features and metadata attributes into a composite analysis framework. By integrating diverse data sources including video content, metadata, and engagement metrics into a unified classification model, the system captures complex nonlinear correlations between different video characteristics that would be impossible to detect using single-feature or simple rule-based approaches.
Data Source
AI summary
Techniques for classifying videos as standalone or non-standalone are provided. Feature (or attribute) values associated with a particular video are identified. Feature values are extracted from metadata associated with the particular video and/or from within a transcript of the particular video. The extracted feature values of the particular video are input to a rule-based or a machine-learned model and the model scores the particular video. Once a determination pertaining to whether the particular video is standalone is made, information about the particular video being a standalone video is presented to one or more users within the network.