AI-Generated Derivative Content Approval With Watermark Access Control
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Solution Overview
Problem
AI-generated derivative works often infringe on the rights of content creators or copyright holders without permission, and existing systems lack effective mechanisms for controlling and managing derivative content transformations.
Innovation Solution
A system and method for transforming predetermined content into derivative works using generative artificial intelligence, with approval based on machine learning models or predefined rule sets, and applying digital watermarks to govern use, ensuring compliance with content owner preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI transforms predetermined content into derivative works, then creative freedom and content transformation capability are improved, but copyright infringement risk and lack of control over derivative content increase
Solution Approach 1:
The system performs preliminary actions by obtaining content transformation preferences from content owners before generating derivative works. Preference data is collected and stored in advance, including permissible transformation types, prohibited transformations, and content owner contacts. This preliminary preparation enables automated compliance checking during derivative work generation, allowing creative freedom while maintaining copyright compliance through pre-established guidelines.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring generated derivative works against stored content transformation preferences. The system provides feedback loops where transformation requests are evaluated against preference data, and results are communicated to users. This feedback ensures that derivative works align with content owner preferences, resolving the contradiction between creative freedom and copyright compliance.
2Productivity
If automated AI systems generate derivative works without human review, then productivity and processing speed are improved, but ability to ensure compliance with content owner preferences deteriorates
Solution Approach 1:
The system implements self-service by enabling automated compliance checking where the AI system independently evaluates transformation requests against stored preference data without requiring manual human review for each derivative work. The system uses machine learning models to assess compliance accuracy, allowing high-speed automated generation while maintaining precision through self-monitoring mechanisms that compare generated content against content owner preferences.
Solution Approach 2:
The system applies parameter changes by using machine learning models with adjustable parameters to dynamically assess compliance accuracy. The models can be trained and fine-tuned to improve their evaluation precision over time, allowing the system to maintain high compliance accuracy while operating at automated speeds. Parameter adjustments enable the system to adapt to different content types and preference complexities.
3Reliability
If strict control mechanisms are applied to all derivative work transformations, then copyright compliance is improved, but creative freedom and flexibility in content transformation deteriorate
Solution Approach 1:
The system applies local quality by implementing differentiated control mechanisms based on specific transformation types and content contexts. Rather than uniform strict control, the system tailors compliance checking to local characteristics of each transformation request and content owner preference. This allows flexible transformations where permitted while maintaining strict control where required, resolving the contradiction between compliance and creativity.
Solution Approach 2:
The system implements dynamics by making control mechanisms adaptable and flexible rather than rigid. The machine learning models dynamically adjust compliance assessment based on the specific transformation request, content type, and stored preferences. This dynamic approach allows the system to be strict when necessary for compliance while being flexible when creativity is permitted, enabling both copyright protection and creative freedom.
Data Source
AI summary
Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.