Generative AI Media Reconstruction With Semantic Metadata
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Solution Overview
Problem
Existing media transmission and reception systems are inadequate in accommodating diverse and complex user demands and evolving media environments, failing to provide personalized and seamless media experiences due to insufficient consideration of media semantics and user preferences.
Innovation Solution
A generative AI-based system that combines existing media with synthetic media, utilizing metadata to generate customized content in real-time, adjusting text prompts, and tracking user behavior for personalized content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional media transmission systems transmit all original media data to ensure content accuracy, then data transmission completeness is improved, but data transmission volume and network resource consumption increase
Solution Approach 1:
The patent segments media content into original media data and semantic metadata, transmitting only the essential semantic information rather than complete media data. This segmentation allows receivers to reconstruct or generate media content locally using the transmitted metadata, significantly reducing transmission volume while maintaining content integrity and accuracy.
Solution Approach 2:
Instead of transmitting complete media data, the system transmits semantic metadata that serves as a compact representation or 'copy' of the essential content information. Receivers use this metadata to regenerate or retrieve the full media content, achieving both data completeness and reduced transmission volume.
2Reliability
If media content is transmitted in complete form to ensure content accuracy, then content fidelity is improved, but user personalization capability deteriorates
Solution Approach 1:
The patent enables dynamic content generation at the receiver端 based on transmitted semantic metadata. The system transitions from static content transmission to dynamic content creation, allowing media content to be adaptively generated according to user preferences, device characteristics, and contextual information while maintaining fidelity to the original content's semantic meaning.
Solution Approach 2:
The patent applies local quality by allowing different receivers to generate personalized versions of media content based on their specific user profiles and preferences, while the transmitted semantic metadata ensures all versions maintain fidelity to the original content's essential meaning and structure.
3Adaptability or versatility
If traditional media systems transmit comprehensive content data to accommodate diverse user demands, then content completeness is improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent extracts essential semantic metadata from complete media content, separating the core informational elements from the full media data. This extraction process simplifies the transmission system by focusing only on essential content descriptors, while receivers handle the complexity of content reconstruction or generation locally based on user-specific requirements.
4Measurement precision
If all media data is transmitted to ensure content accuracy, then data precision is improved, but transmission time and synchronization speed deteriorate
Solution Approach 1:
By segmenting content into compact semantic metadata and separating transmission from generation, the system achieves rapid transmission of essential information while maintaining data precision. The metadata contains sufficient precise information for accurate content reconstruction, and the reduced data volume enables faster transmission and synchronization across distributed devices.
Data Source
AI summary
Disclosed herein is an apparatus and method for generating media content based on generative AI. The apparatus generates combined media by combining existing media with synthetic media generated using a generative AI, defines metadata required for the generative AI to generate media content for the combined media, and generates the media content using the generative AI by adjusting the text prompt to be input into the generative AI using the metadata.