Dynamic Multimedia Analysis System for Contextual Asset Matching
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Solution Overview
Problem
Contextual targeting in multimedia environments faces challenges in ensuring brand safety and relevancy across various multimedia types without relying on personal data, particularly in ensuring semantically understanding content in an automated and scalable manner.
Innovation Solution
A system and method for dynamic semantic targeting that extracts features from digital media, analyzes them semantically, and correlates assets with the media content based on identified topics, using natural language processing and emotional analysis to select and render relevant assets in real-time, ensuring contextual relevance and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contextual targeting is implemented using hierarchical taxonomies, then brand safety can be ensured, but relevancy and engagement rate performance are limited
Solution Approach 1:
The patent segments the content analysis process into multiple independent components: feature extraction, semantic analysis, topic extraction, and asset correlation. This segmentation allows each component to specialize in specific tasks, improving both brand safety through controlled analysis and engagement performance through sophisticated matching algorithms that go beyond hierarchical taxonomies.
Solution Approach 2:
The patent introduces an intermediary semantic analysis layer between the content and the hierarchical taxonomy. This intermediary layer extracts features and topics from the actual content, creating a bridge that allows for more precise matching while maintaining brand safety through controlled feature extraction and correlation processes.
2Productivity
If automated semantic analysis is performed on multimedia content, then relevancy can be improved, but system complexity increases
Solution Approach 1:
The patent divides the complex semantic analysis system into separate functional modules: feature extraction module, semantic analysis module, topic extraction module, and asset correlation module. This segmentation reduces overall system complexity by allowing each module to be developed, tested, and optimized independently while working together to achieve high relevancy.
Solution Approach 2:
The system employs self-service mechanisms where the semantic analysis and topic extraction processes automatically adapt to different multimedia content types without requiring manual reconfiguration. The feature extraction and correlation algorithms automatically adjust to the specific characteristics of audio, video, image, text, or HTML content, reducing operational complexity while maintaining high relevancy.
3Productivity
If personal information is used for targeting, then engagement rate improves, but consumer privacy concerns and data privacy regulations are violated
Solution Approach 1:
The patent extracts targeting signals from the content itself rather than from personal user data. By extracting features, topics, and semantic characteristics from the multimedia content being consumed, the system creates targeting capabilities that drive engagement without requiring personal information, thus eliminating privacy violations while maintaining high engagement rates.
Solution Approach 2:
The patent introduces content-based features and topics as intermediary targeting signals between the user's content consumption behavior and the advertising targeting decision. This intermediary approach allows the system to infer user interests and context from the content itself without directly accessing or storing personal user information, thereby avoiding privacy violations while maintaining effective targeting.
Data Source
AI summary
A system and method to dynamically analyze digital media and select multimedia assets or items to render with correlated IP-connected media and apps. Hierarchical Taxonomy, Engagement-based and Neural-based asset matching is rendered with rule-based and Diminishing Perspective decision-making. A user can listen, view and interact with the correlated and rendered material using an input device native to the computing device being used to access the IP-connected media. Embodiments extract features from the digital media. The extracted features are semantically analyzed for an understanding of characteristics associated with the respective features. Topics are extracted from the digital media based on the characteristics. Stored assets are correlated to the extracted topics to select an asset based on characteristics of the extracted topics correlating with the selected asset. The selected asset is rendered with the digital media.