Blockchain Streaming Data Verification for Fraud Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The music industry faces challenges in accurately tracking streaming media due to unlicensed activity, uncollected royalties, and the lack of an authoritative database for music rights, leading to potential fraudulent play count inflation.
Innovation Solution
A system utilizing blockchain technology to extract streaming data, transform it for machine learning models, and determine potentially fraudulent streams by analyzing user engagement data and employing zero-knowledge proofs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional streaming platforms are used to track play counts, then access to music is facilitated, but accurate tracking of licensed vs unlicensed activity becomes difficult
Solution Approach 1:
The patent introduces blockchain as an intermediary system between streaming platforms and rights holders. The blockchain records cryptographic hashes of audio content and verifies licensing status independently of the streaming platform's internal tracking systems. This intermediary verification mechanism enables accurate distinction between licensed and unlicensed activity without interfering with user access to music.
Solution Approach 2:
The patent replaces traditional mechanical tracking methods (platform-based counters and databases) with cryptographic verification mechanisms. Instead of relying on streaming platforms to accurately count and categorize plays, the system uses blockchain-based cryptographic hashing and verification to independently determine licensing status and play count authenticity.
2Ease of manufacture
If no authoritative database exists for music rights, then registration and identification becomes simpler, but fraud detection capability deteriorates
Solution Approach 1:
The patent enables rights holders to self-register their music content on the blockchain by providing cryptographic hashes of their audio files. The blockchain automatically creates verifiable records of ownership and licensing terms without requiring manual verification or maintenance of an authoritative database. This self-service registration maintains simplicity while enabling reliable fraud detection through immutable cryptographic records.
Solution Approach 2:
The patent changes the fundamental parameter of data storage from traditional databases to blockchain cryptography. Instead of storing and querying music rights information in conventional databases, the system uses cryptographic hashes and blockchain chains to create tamper-proof records. This parameter change enables both simple registration (via hash submission) and reliable fraud detection (via cryptographic verification).
3Productivity
If bots are used to increase play counts, then revenue for rights holders increases, but system integrity deteriorates
Solution Approach 1:
The patent implements a feedback verification system where each play count claim is independently verified against blockchain records. The system continuously compares streaming platform data with blockchain-verified licensing status and cryptographic hashes. When bots generate fake play counts, the feedback mechanism detects discrepancies between reported statistics and verified blockchain records, automatically flagging or rejecting fraudulent revenue claims.
Solution Approach 2:
The patent replaces traditional mechanical revenue tracking with cryptographic verification mechanisms. Instead of trusting platform-reported statistics, the system uses blockchain-based cryptographic hashing and verification to independently validate each play count and licensing status, making it impossible for bots to manipulate revenue without detection.
Data Source
AI summary
Systems and methods for detecting fraudulent streaming activity. Streaming activity is posted to a blockchain by one or more DSPs. Blockchain streaming data is extracted from the blockchain and used as input in a machine learning model. The machine learning model takes the extracted blockchain data, along with additional inputs such as DSP trend pool and social pool inputs, and makes a determination regarding potentially fraudulent streaming activity.