AI Music Output Attribution Using Segment Embedding Distance
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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 is introduced to segment music-based output from generative AI into multiple segments, generate output embeddings, measure distances between these embeddings and training segment embeddings, correlate these measurements to content creators, and determine creator attributions, ultimately providing compensation based on these attributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI creates derivative content without tracking source content usage, then content creation speed and productivity are improved, but attribution precision and creator compensation accuracy deteriorate to zero
Solution Approach 1:
The system performs preliminary actions by embedding source content identifiers and metadata into the training data before the generative AI creates derivative content. This allows the AI to generate content at high speed while the attribution information is already prepared and embedded, enabling accurate tracking without slowing down the creation process.
Solution Approach 2:
The patent introduces an intermediary attribution tracking system that sits between the generative AI and the source content database. This intermediary layer captures and records usage information without interfering with the AI's rapid content generation, thus maintaining productivity while enabling precise measurement of source content influence.
2Measurement precision
If the system tracks and measures content usage proportionality accurately, then attribution precision is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The attribution tracking system is segmented into modular components: embedding insertion module, generation monitoring module, and attribution calculation module. Each segment handles a specific aspect of the tracking process, reducing overall system complexity while maintaining high measurement precision through specialized function distribution.
Solution Approach 2:
The system uses parameter changes in the embedding space to track content influence. By monitoring changes in embedding vectors before and after generation, the system can precisely measure source content usage without requiring complex structural modifications to the generative AI architecture.
3Measurement precision
If the system analyzes entire derivative content outputs, then measurement completeness is improved, but processing time and loss of time worsen
Solution Approach 1:
The system extracts only the critical attribution-relevant features from the complete derivative content output, rather than analyzing the entire content. By taking out and focusing on specific embedding vectors and metadata that indicate source content usage, the system achieves measurement completeness while significantly reducing processing time.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary portions of the derivative content that contain attribution information. This selective approach provides sufficient measurement completeness for attribution purposes without the time cost of comprehensive full-content analysis.
Data Source
AI summary
In some aspects, a music-based output produced by a generative artificial intelligence is segmented into multiple segments including multiple time segments having different lengths of time and multiple frequency segments using different frequency bands. An encoder generates multiple output embeddings, where individual 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 that are correlated to a plurality of content creators that created multiple content items that were used to train the generative artificial intelligence. One or more creator attributions are determined based on the correlating. A creator attribution vector that includes the one or more creator attributions is created and used to initiate providing compensation to one or more content creators of the plurality of content creators.