Dynamic Content Rating Assistant Using Machine Learning
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Solution Overview
Problem
Current media content rating systems are inconsistent and subjective, as they rely on human judgment and lack clear guidelines, making it difficult to determine appropriate ratings for diverse audiences across different countries and regions, especially when content is distributed globally.
Innovation Solution
A machine learning-based dynamic content rating engine that extracts linguistic, visual, and audio features from media content using cognitive analytics tools to predict ratings across various content rating systems, allowing for automatic editing to achieve target maturity levels and adapt to viewer feedback in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human judgment is used for content rating, then subjectivity and cultural context can be considered, but consistency and objectivity across different rating systems deteriorate
Solution Approach 1:
The patent replaces human judgment with an automated machine learning system that uses computational models to analyze content features. The system extracts linguistic, visual, and audio features using cognitive analytics tools, then applies trained classification models to predict ratings objectively, eliminating the variability inherent in human subjectivity while maintaining cultural adaptability through region-specific model training
Solution Approach 2:
The system changes the parameters for rating determination from subjective human interpretation to objective feature extraction metrics. By transforming content into quantifiable features (linguistic patterns, visual characteristics, audio properties) and using these as input parameters for machine learning models, the system achieves consistent, reproducible ratings across different rating systems while adapting to cultural differences through targeted model training
2Adaptability or versatility
If multiple content versions are created for different regions, then adaptability to local rating systems improves, but device complexity and production cost increase
Solution Approach 1:
The system implements dynamic content adaptation where the same base content is automatically modified based on the target region's rating requirements. The machine learning model analyzes the desired rating and dynamically generates appropriate content versions by selectively modifying specific features, allowing the system to adapt to different rating systems without manually creating each version
Solution Approach 2:
The system performs self-service by automatically generating region-specific content versions without requiring manual intervention. The machine learning model independently analyzes rating requirements, extracts relevant content features, and produces adapted versions autonomously, reducing the complexity of content version management while maintaining high adaptability across different regions
3Adaptability or versatility
If subjective human rating guidelines are used, then cultural context is captured, but ease of operation and automation deteriorate
Solution Approach 1:
The patent substitutes manual rating operations with an automated machine learning system that handles the entire rating process. The system extracts features from content, applies trained classification models, and generates ratings automatically without human intervention, making the process easy to operate while maintaining cultural sensitivity through region-specific model training on localized rating criteria
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: training of a machine learning model for predicting a rating in certain content rating systems based on training data. The machine learning model includes a plurality of maturity classifiers corresponding to individual features of the previously rated contents. An input content is obtained and features of the input content are extracted by use of respective content analysis tools and subsequently classified by the maturity classifiers of the machine learning model.