Automated Media Rating System Using ML Mapping
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Solution Overview
Problem
Current systems for determining media content maturity ratings are inefficient and inaccurate, as they require separate analysis for each country's rating schema, leading to high computational costs and manual analysis challenges due to the large number of local rating schemas and nuances between global and local content descriptors.
Innovation Solution
A system that uses machine learning models and rule engines to automate the identification of mature content segments, mapping them to content descriptors and rating levels, allowing for a single system to determine maturity ratings for multiple countries and reuse data across different rating schemas, optimizing computing resources and network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate analysis is performed for each country's rating schema, then accuracy of local rating determination is improved, but computational cost and system complexity increase significantly
Solution Approach 1:
The patent implements a universal rating determination system that can handle multiple country-specific rating schemas through a single automated platform. The system uses machine learning models trained on global content descriptors that can be mapped to various local rating schemas (e.g., MPAA, BBFC, USK), allowing one system to perform multiple rating functions without requiring separate analysis systems for each country.
Solution Approach 2:
The patent segments the rating determination process into distinct modular components: content analysis module, machine learning model layer, mapping layer for different rating schemas, and output generation module. This segmentation allows the system to maintain high accuracy for each specific rating schema while managing overall system complexity through organized, reusable components.
2Measurement precision
If manual analysis is used for each local rating schema, then rating accuracy is maintained, but productivity and analysis speed decrease
Solution Approach 1:
The patent implements self-service through automated machine learning models that independently analyze media content and determine ratings without requiring manual human analysis for each local schema. The system automatically processes content, applies appropriate rating criteria, and generates ratings at scale, dramatically increasing productivity while maintaining consistency and accuracy through algorithmic precision.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated machine learning systems. Instead of human analysts manually reviewing content against each rating schema, the system uses trained ML models that automatically perform content analysis, feature extraction, and rating determination, substituting human labor with computational processes that are faster and more scalable.
3Reliability
If multiple separate systems are deployed for different rating schemas, then reliability of each schema is maintained, but loss of computational resources increases
Solution Approach 1:
The patent merges multiple rating schema processing capabilities into a single unified system. Instead of deploying separate systems for each rating schema, the consolidated system uses shared machine learning models, common content analysis infrastructure, and centralized mapping logic to handle all rating schemas, significantly reducing computational resource usage while maintaining the reliability needed for each specific schema through dedicated processing pipelines.
4Productivity
If automated machine learning models are used, then productivity and speed are improved, but measurement precision may decrease due to automation limitations
Solution Approach 1:
The patent applies preliminary action through extensive training of machine learning models on large datasets of media content and corresponding rating decisions. The models are pre-trained to recognize content patterns, features, and contextual nuances that correlate with different rating criteria. This preliminary preparation enables the automated system to achieve high measurement precision when processing new content, reducing the gap between automated speed and human-level accuracy.
Data Source
AI summary
A system can be utilized to retrieve media content and rating schemas, to determine maturity ratings for media content. The media content can be utilized to determine segments of data as building blocks associated with mature content. The building blocks can be mapped to content descriptors and rating levels associated with the rating schemas. The building blocks can be compared the media content to identify portions of the media content that have characteristics represented by the building blocks. The building blocks representing the characteristics in the portions of the media content can be utilized to select content descriptors and rating levels associated with the media content. The selected content descriptor and selected rating levels can be utilized to control how, and/or whether, the media content is made available for output to the consumers.