User-Centric Royalty Attribution via Intent-Based Scheduling
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Solution Overview
Problem
Conventional media streaming services face inefficiencies in royalty distribution, often transferring majority funds to labels, which reduces payments to less popular artists, leading to reduced media content diversity and increased bandwidth usage as users search for new artists.
Innovation Solution
Implementing a user-centric royalty model that directly pays artists based on their individual contributions to user subscription retention, using metadata and machine learning to determine user intent and calculate royalties, reducing transactions and network congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional royalty distribution models are used that transfer majority funds to labels, then label revenue is improved, but artist payment and media content diversity deteriorate
Solution Approach 1:
The patent segments the royalty distribution system into multiple independent attribution schedules: one for label revenue and another for direct artist payments. This segmentation allows the system to simultaneously optimize for both label revenue and artist compensation without the zero-sum tradeoff inherent in conventional models. The machine learning model divides user interaction data into distinct features that can be independently weighted for different royalty calculations.
Solution Approach 2:
The system changes the parameters of royalty distribution by introducing intent-based attribution that dynamically adjusts payment allocation based on user interaction quality rather than fixed formulas. The machine learning model continuously updates attribution weights based on changing user behavior patterns, allowing the system to adapt royalty distribution parameters in real-time to balance label and artist compensation while promoting content diversity.
2Device complexity
If conventional royalty distribution is used, then transaction simplicity is improved, but network bandwidth usage increases due to user searches for new artists
Solution Approach 1:
The system implements self-service through automated machine learning models that independently process user interaction data and generate attribution schedules without requiring manual intervention. The model automatically updates royalty allocations based on real-time user behavior, eliminating the need for complex manual transaction processing and reducing the bandwidth overhead associated with traditional royalty management systems.
3Measurement precision
If user-centric royalty model with machine learning is implemented, then royalty attribution accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge user interaction data and royalty attribution decisions. These intermediary models process complex user behavior patterns and translate them into precise attribution schedules, achieving high measurement precision while encapsulating system complexity within the ML layer rather than requiring complex changes to the core royalty distribution infrastructure.
Data Source
AI summary
Methods and systems to facilitate communication and payment to an artist associated with a streaming service platform based on user-centric royalty models are disclosed herein. In one example, a method include receiving a selection of media content to output to a user device associated with a user account and providing the media content to the user device to be presented. The method further determines characteristics of the output of the media content related to how the media content was output by the user device and determine an intent value associated with the media content based on the characteristics. The method further updates the attribution schedule indicating at least a change of a degree of attribution for one or more entities based on the intent value and facilitates a transfer of funds from the user account to an account associated with the one or more entities based on the attribution schedule.