Blockchain-Verified Streaming Fraud Detection With Machine Learning
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Solution Overview
Problem
The music industry faces challenges in accurately tracking streaming media usage due to unlicensed activity, uncollected royalties, and the lack of an authoritative database, leading to fraudulent play count augmentation and inefficiencies in royalty distribution.
Innovation Solution
A system utilizing a blockchain-based platform for media file uploads, incorporating a machine learning model to analyze streaming data, including user engagement metrics and zero knowledge proofs, to detect fraudulent streaming activity and ensure accurate royalty distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional streaming platforms are used to distribute media, then access to songs is facilitated, but tracking accurate play counts and royalties becomes difficult due to unlicensed activity and lack of authoritative data
Solution Approach 1:
The patent introduces a blockchain as an intermediary system between streaming platforms and royalty distribution. The blockchain serves as a neutral, authoritative ledger that records all streaming transactions, providing a single source of truth for play counts that is independent of any single platform's internal tracking systems. This resolves the measurement precision issue by creating an external verification mechanism.
Solution Approach 2:
The patent replaces traditional mechanical tracking systems (platforms manually counting plays and maintaining databases) with a cryptographic blockchain system. The blockchain uses cryptographic hashing, consensus mechanisms, and immutable ledgers to automatically track and verify streaming transactions, eliminating the need for manual counting and providing tamper-proof records.
2Productivity
If bots are used to artificially augment play counts, then revenue for rights holders increases, but fraudulent activity increases and trust in the system decreases
Solution Approach 1:
The patent implements feedback mechanisms through machine learning models that continuously analyze streaming patterns and flag suspicious activity. The system compares detected play patterns against established norms and provides feedback to verify authenticity, creating a self-correcting system that can identify and report fraudulent bot activity while maintaining legitimate revenue streams.
Solution Approach 2:
The patent applies preliminary action by pre-establishing baseline streaming patterns and using machine learning models trained on legitimate user behavior before fraud occurs. The system proactively identifies anomalies by comparing real-time data against these pre-established norms, enabling early detection and prevention of fraudulent play count augmentation.
3Device complexity
If centralized databases are used to track streaming data, then data management is simplified, but single points of failure and lack of transparency occur
Solution Approach 1:
The patent segments the centralized database into distributed blockchain nodes across multiple participants. Instead of a single central authority holding all streaming data, the blockchain distributes the ledger across many nodes, each maintaining a copy. This segmentation eliminates single points of failure while maintaining data accessibility through the distributed network.
Solution Approach 2:
The patent merges the functions of data storage, verification, and transparency into a single blockchain system. By combining these functions into one unified distributed ledger, the system eliminates the need for separate centralized databases while providing both simplified data management and enhanced transparency through the shared, immutable record.
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.