Multimedia Enrichment via Context Metadata Extraction
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Solution Overview
Problem
Existing digital content recommendation systems fail to accurately predict user interest in digital content due to lack of consideration for enriched metadata associated with the content, resulting in ineffective content prediction and consumption.
Innovation Solution
Implementing a system that uses multiple metadata enrichment services to extract and process context metadata from digital assets, such as videos, including visual recognition, natural language processing, and sentiment analysis, to enhance content recommendation based on brand safety policies and user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems are used that focus only on user interests, then the system is simple to implement, but the content prediction accuracy is low
Solution Approach 1:
The patent segments the recommendation system into multiple independent components: user interest analysis module, metadata enrichment module (with visual recognition, NLP, sentiment analysis), brand safety module, and recommendation generation module. Each module processes specific aspects separately, improving prediction accuracy through comprehensive analysis while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system implements multi-functional metadata enrichment services that perform visual recognition, natural language processing, sentiment analysis, and brand safety assessment simultaneously. This universal approach allows a single system to handle diverse content types (video, image, text) and multiple analysis dimensions, improving prediction accuracy without proportionally increasing complexity.
2Measurement precision
If enriched metadata is extracted and processed from digital assets, then content recommendation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary metadata enrichment and extraction in advance of the actual recommendation request. Visual recognition, NLP, and sentiment analysis are conducted during content ingestion and preprocessing phases, so that when a recommendation is needed, the enriched metadata is already available, reducing real-time processing delays while maintaining high recommendation accuracy.
3Loss of information
If multiple metadata enrichment services are used to extract context metadata, then predictive insights into viewing behavior improve, but system complexity and cost increase
Solution Approach 1:
The patent merges multiple metadata enrichment services (visual recognition, natural language processing, sentiment analysis, brand safety) into a unified recommendation system architecture. These services are integrated and coordinated to work together synergistically, improving predictive insights by combining multiple information sources while managing system complexity through centralized coordination and standardized interfaces.
Data Source
AI summary
Computer vision techniques are applied to a video segment to identify a specific media content category. A media context metadata portion including a specific media content indicator indicating the specific media content category is generated. When the video segment is to be played back, an enriching media content item is selected from among enriching media content items based on the specific media content indicator and played back before, after or during the video segment.