Generative AI Contribution Attribution Using Meta-Feature Correlation
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Solution Overview
Problem
Existing generative AI models lack a systematic method to attribute contributions of copyright-protected assets to their training, leading to unclear recognition and compensation for asset owners.
Innovation Solution
A method and system to determine contribution distribution of assets to a generative AI model by extracting meta features from assets, processing these features to calculate contribution scores, and outputting data for attribution and compensation based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are trained using large datasets of copyrighted assets, then the model's generation capability and creativity are improved, but clear attribution and compensation mechanisms for asset owners are lacking
Solution Approach 1:
The patent applies preliminary action by embedding identification information (such as watermarks or metadata) into training assets before they are used to train the generative AI model. This allows the system to later trace and attribute generated content back to specific source assets and their owners, resolving the attribution loss problem while maintaining generation capability.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously tracks which training assets contribute to generated outputs and uses this information to provide attribution and compensation feedback to asset owners. This creates a closed-loop system that maintains both generation quality and proper credit distribution.
2Measurement precision
If contribution distribution of asset owners is determined through systematic analysis, then recognition and compensation for asset owners is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces intermediary components such as identification modules, feature extraction systems, and matching algorithms that serve as mediators between training assets and generated content. These intermediaries systematically track contributions without requiring complex direct analysis between all asset pairs, reducing overall system complexity while improving measurement precision.
Solution Approach 2:
The patent segments the contribution determination process into distinct modular components: asset identification, feature extraction, similarity matching, contribution calculation, and compensation distribution. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while achieving precise contribution measurement.
3Measurement precision
If meta features are extracted and processed to calculate contribution scores, then the accuracy of attribution is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by extracting and processing only the most relevant meta features from training assets rather than analyzing all possible features. This selective approach maintains high attribution accuracy while significantly reducing processing time and computational resource requirements compared to exhaustive feature analysis.
Solution Approach 2:
The patent changes parameters such as feature extraction depth, similarity threshold values, and processing batch sizes to optimize the balance between attribution accuracy and processing efficiency. By dynamically adjusting these parameters based on resource availability and precision requirements, the system achieves accurate attribution without excessive processing time.
Data Source
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AI summary
The present disclosure provides a solution for determining the extent of contribution of each copyright-protected assets owner to the training of a generative AI model. Furthermore, the present disclosure provides a solution for identifying the specific assets that contributed the most for the generation of a specific generated product being generated by the generative AI model. This is performed based on correlation of metrics related to meta features composing the assets and the generated product. By determining the contribution distribution of the owners to the generative AI model and a specific generated product, the owners can be attributed with recognition for their contribution. The recognition can be manifested in many ways, for example in attribution of copyright or an allocation of an income that is received for the use of the generative AI model according to a certain financial model. Therefore, by the solution of the present disclosure, the use of assets protected by copyrights in the training of generative AI models can be standardized.