Media Attribution System Using Blockchain Consensus and Machine Learning
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Solution Overview
Problem
Digital media services face challenges in reliably attributing media works to their rightful owners, leading to piracy and monetization issues due to lack of verification mechanisms, resulting in significant time and money spent by rights management organizations in manual attribution.
Innovation Solution
A scalable computing system using machine learning and blockchain technology to generate attribution scores for media data items, ensuring accurate attribution through consensus mechanisms and exception handling processes, thereby establishing a definitive link between creators/owners and their works.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual attribution is used by rights management organizations, then attribution accuracy can be maintained, but time consumption and costs increase significantly
Solution Approach 1:
The system enables self-service attribution by automatically processing media data items through machine learning models and blockchain verification. The attribution system independently performs fingerprinting, similarity comparison, and consensus scoring without requiring manual intervention from rights management organizations, thereby maintaining accuracy while eliminating time-consuming manual processes
Solution Approach 2:
The patent replaces manual mechanical attribution processes with automated computational systems. Machine learning algorithms perform content analysis and similarity matching, while blockchain technology provides automated verification and consensus mechanisms. This substitution eliminates the need for human operators to manually review and verify attributions, significantly reducing time consumption while maintaining or improving accuracy
2Ease of operation
If blind-faith approach is used for user-submitted works, then ease of operation is improved, but reliability of attribution deteriorates due to piracy and unauthorized monetization
Solution Approach 1:
The system performs preliminary attribution verification before allowing media works to be published or monetized. By conducting automated fingerprinting, similarity comparison, and consensus scoring in advance, the system ensures reliable attribution is established prior to distribution, preventing piracy and unauthorized monetization while maintaining ease of operation through automated processing
Solution Approach 2:
The blockchain-based consensus mechanism provides feedback loops where multiple nodes verify and validate attribution claims. The system continuously monitors and verifies attribution reliability through distributed consensus, ensuring that only properly attributed works are allowed to proceed, thereby maintaining both ease of operation and high reliability
3Object-affected harmful factors
If notice and takedown approach is used, then harm to unauthorized uses is reduced, but attribution problem persists because correct identification at submission time is difficult
Solution Approach 1:
The system performs attribution identification and verification at the time of submission rather than relying on post-submission notice and takedown. By conducting automated fingerprinting, machine learning analysis, and blockchain verification in advance, the system correctly identifies and attributes works upfront, eliminating the need for later corrective actions and improving both accuracy and harm reduction
Data Source
AI summary
Systems and methods for providing reliable and authoritative attribution of digital media works at the time of submission to a media distribution service using distributed ledger and machine learning technology. In particular, the described systems and methods facilitate establishing a link between the creator or owner, and their work, in an authoritative and reliable manner.