Transformer AI Metadata Matching for Royalty Tracking
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Solution Overview
Problem
Music rights holders face challenges in identifying and obtaining all owed royalties due to the complexity of the internet and existing systems' inefficiencies in managing digital rights.
Innovation Solution
A system and method that utilize processor-controlled operations to modify, transform, and deliver digital work metadata to digital service providers, employing transformer model-based artificial intelligence for matching and Dedupe library for entity resolution, to ensure accurate royalty tracking and payment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to identify and track digital works, then accuracy in royalty identification can be maintained, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical processes with transformer-based AI models that automatically process and match metadata. The system uses neural networks to analyze audio features, lyrics, and metadata automatically, eliminating the need for human operators to manually identify and track digital works while significantly improving processing speed and accuracy.
Solution Approach 2:
The system transforms metadata from unstructured or semi-structured formats into standardized structured formats that can be processed by AI models. The transformer model processes various parameter types (audio features, temporal information, spectral data) and outputs standardized matching results, enabling efficient automated processing while maintaining accuracy.
2Reliability
If comprehensive metadata collection is implemented to ensure all royalties are captured, then royalty completeness improves, but system complexity increases
Solution Approach 1:
The transformer-based system serves multiple functions within a unified architecture: it processes diverse metadata types (audio features, lyrics, metadata), performs entity resolution, matches digital works, and generates standardized outputs. This multi-functional approach ensures comprehensive royalty tracking while avoiding the complexity of separate specialized systems for each function.
Solution Approach 2:
The transformer model acts as an intermediary between diverse data sources and the royalty matching system. It receives various input formats, processes them through standardized attention mechanisms, and outputs unified structured data that can be directly used for matching and royalty calculation, simplifying the overall system architecture while maintaining comprehensive coverage.
3Measurement precision
If existing matching systems are used, then implementation is straightforward, but matching accuracy and handling of similar works is insufficient
Solution Approach 1:
The system segments the matching process into distinct stages: data collection, transformer-based analysis, entity resolution, and final matching. Each stage handles specific aspects of the matching task independently, improving overall accuracy while making the system more manageable and easier to implement through modular architecture.
Solution Approach 2:
The system incorporates feedback mechanisms where the transformer model continuously refines its predictions based on the input data and previous matching results. This iterative process improves accuracy by allowing the model to learn from initial mismatches and adjust its matching criteria, while the automated feedback loop simplifies implementation compared to manual verification processes.
Data Source
AI summary
A method and associated system for matching and delivering digital work metadata to one or more digital service providers including modifying one or more digital work metadata files, which includes removing non-critical data or segment-erroneous data or performing a language translation; reformatting the one or more digital work metadata files for compatibility with a transformer model-based AI matching operation; performing a block grouping operation on the one or more digital work metadata files, where data associated with the one or more digital work metadata files is grouped in blocks and analyzed for one or more pairs of data records; performing the transformer model-based AI matching operation to determine whether each pair of the one or more pairs of data records comprise a matching pair of data records; and transmitting output data from the transformer model-based artificial intelligence matching operation to the one or more digital service providers.