AI Music Output Attribution Using Segment Embeddings
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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 the original content creators.
Innovation Solution
A method involving segmentation of AI-generated output into multiple segments, generating output embeddings, determining distance measurements between these embeddings and training segment embeddings, correlating these measurements to content creators, and providing creator attributions based on these correlations to facilitate compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI creates derivative content using training data, then content creation capability is improved, but ability to determine proportionality and provide attribution is worsened
Solution Approach 1:
The patent segments the AI-generated derivative content into multiple segments and creates embeddings for each segment. By analyzing the composition of these segments, the system can determine the proportionality of training data influence on different portions of the output, enabling precise attribution measurements that were previously impossible with holistic analysis.
Solution Approach 2:
The patent introduces embeddings as an intermediary representation layer between the raw training data and the derivative content. These embeddings capture the semantic and stylistic characteristics of training data, allowing the system to measure and attribute influence without directly comparing raw data, thus solving the proportionality determination problem.
2Adaptability or versatility
If AI uses multiple content items for training, then content quality and versatility are improved, but complexity of tracking and attributing individual creators is worsened
Solution Approach 1:
The patent merges the attribution information of multiple content creators into a unified attribution vector that represents the combined influence of all training data sources. This consolidation approach maintains track of individual creator contributions while simplifying the overall attribution structure, making it manageable even when many creators are involved.
Solution Approach 2:
The patent creates a universal embedding space that can represent and compare any training content item regardless of its source or type. This universal representation system allows the same attribution mechanism to handle diverse content from multiple creators, providing a scalable solution that works across different content types and creator numbers.
Data Source
AI summary
In some aspects, a music-based output produced by a generative artificial intelligence is segmented into multiple segments. An encoder is used to generate multiple output embedding. Individual output embeddings of the multiple output embeddings are derived from individual segments of the multiple segments. A distance measurement between individual output embeddings of the multiple embeddings and individual training segment embeddings of multiple training segment embeddings is determined to create a set of distance measurements. The plurality of distance measurements is correlated to a plurality of content creators that created multiple content items used to train the generative artificial intelligence. One or more creator attributions are determined based at least in part on the correlating. A creator attribution vector that includes the one or more creator attributions is determined and compensation is provided to one or more content creators of the plurality of content creators based on the creator attribution vector.