Multi-Tiered Machine Learning for Content Rating Prediction
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Solution Overview
Problem
Content ratings for digital content vary significantly across different rating entities and jurisdictions due to differing guidelines, making it challenging for content creators to understand and apply consistent ratings across different audiences and regions.
Innovation Solution
The implementation of multi-tiered machine learning systems that analyze audio, video, and text components of digital content to predict content ratings, using modules for feature detection and machine learning models to determine ratings based on specific entity guidelines, with a feedback loop for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review processes are used for content rating determination, then accuracy can be maintained, but productivity is reduced and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical review processes with automated machine learning systems. Multiple ML models analyze different aspects of content (video, audio, text) and aggregate their predictions to determine content ratings, eliminating the need for human reviewers while maintaining or improving accuracy through systematic automated analysis.
Solution Approach 2:
The system creates virtual copies of rating guidelines from different entities and embeds them within ML models. These models learn to replicate the rating decisions that would be made by human reviewers following specific guidelines, allowing automated prediction of ratings without actual human involvement.
2Adaptability or versatility
If multiple rating entities with different guidelines are considered, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent segments the rating determination system into multiple specialized ML models, each trained on guidelines from specific rating entities. Each model handles a particular entity's guidelines independently, and their results are aggregated. This segmentation allows the system to adapt to multiple entities without creating a single overly complex model.
Solution Approach 2:
The system creates a universal rating determination framework that can handle multiple rating entities through a common architecture. The ML models share underlying structures and processing mechanisms but are configured to apply different entity-specific guidelines, achieving multi-functionality without proportionally increasing complexity.
3Measurement precision
If comprehensive feature analysis is performed across audio, video, and text components, then measurement precision improves, but computing efficiency deteriorates
Solution Approach 1:
The patent divides content analysis into separate specialized modules for audio, video, and text components. Each module processes its specific modality using optimized techniques, and results are aggregated. This segmentation allows comprehensive analysis without requiring one monolithic system to handle everything, improving computational efficiency through specialized processing.
Solution Approach 2:
The system performs preliminary extraction and preprocessing of features from audio, video, and text components before main analysis. By preparing and organizing feature data in advance, the system reduces the computational burden during the actual rating determination phase, improving overall computing efficiency while maintaining comprehensive analysis.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for systems and methods for intelligent content rating determination. Example methods include determining presence of a first feature in a first frame of a video using an object recognition algorithm, determining presence of a second feature in an audio file associated with the video using an audio processing algorithm, and determining presence of a third feature in a text file associated with the video using a natural language processing algorithm. Certain embodiments may include generating a predicted content rating for the video using a machine learning model, where the predicted content rating is based at least in part on the first feature, the second feature, and the third feature, and using feedback data for the predicted content rating to retrain the machine learning model.