AI Content Attribution Vectors for Creator Compensation Tracking
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Solution Overview
Problem
Current systems lack the ability to determine the proportionality of content used by generative artificial intelligence (AI) to generate derivative content, preventing appropriate attribution and compensation to content creators.
Innovation Solution
A server determines the influence of content characteristics on AI-generated outputs using creator attribution vectors, enabling compensation based on the identified influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI creates derivative content based on training data, then content generation capability is improved, but inability to determine proportionality of used content prevents proper attribution
Solution Approach 1:
The system implements feedback by continuously monitoring and measuring the influence of training content on generated output through attribution vectors. The server receives feedback about content characteristics and their influence amounts, processes this information through embedding comparisons, and adjusts attribution calculations accordingly. This closed-loop feedback mechanism enables the system to track and quantify the proportionality of content usage throughout the generation process.
Solution Approach 2:
The patent introduces an intermediary attribution determination system that mediates between the generative AI and the training content creators. This intermediary server computes attribution vectors by comparing output embeddings with training content embeddings, acting as a bridge that quantifies the relationship between generated content and source material. The intermediary translates complex AI generation processes into measurable attribution data that can be used for compensation purposes.
2Device complexity
If no attribution mechanism exists for AI-generated content, then system complexity is reduced, but content creators cannot receive proper compensation
Solution Approach 1:
The attribution system is segmented into distinct functional modules: an embedding generation component that processes training content, an attribution determination component that compares embeddings and calculates influence proportions, and a compensation calculation component that translates attribution data into creator payments. This segmentation allows the complex attribution task to be broken down into manageable, independent operations that can be executed systematically without overwhelming system complexity.
3Measurement precision
If the system tracks detailed influence proportions for attribution, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The system transforms the complex problem of measuring content influence into a parameter-based solution using embedding vectors. By converting content characteristics into numerical embedding parameters and comparing these parameters through mathematical operations (cosine similarity, dot products), the system achieves precise measurement of influence proportions. This parameter transformation approach converts qualitative content analysis into quantitative computations that are more efficient and scalable.
Data Source
AI summary
In some aspects, a server determines that an AI has generated an initial output based on an initial input that identifies: a type of content, a first characteristic of the content, and a first amount of influence of the first characteristic. The server determines an initial output embedding associated with the initial output generated by the AI. The server determines that the AI has generated a subsequent output based on a subsequent input that identifies: a second characteristic and a second amount of influence of the second characteristic. The server determines a creator attribution vector based at least in part on: the first amount of influence of the first characteristic and the second amount of influence of the second characteristic. The server initiates providing compensation to one or more content creators of the plurality of content creators based on the creator attribution vector.