User-Centric Royalty Attribution System
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Solution Overview
Problem
Conventional media streaming services face inefficiencies in royalty distribution, often favoring popular artists and increasing network congestion due to complex transaction processes, which can lead to reduced diversity in media content and increased bandwidth usage for users seeking less popular artists.
Innovation Solution
Implementing a user-centric royalty model that determines artist payments based on user interaction data and intent values, allowing direct communication and payment between users and artists, reducing the need for intermediaries and streamlining transactions through a machine learning-driven attribution system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional media streaming services use complex transaction processes for royalty distribution, then they can manage payments through intermediaries, but network congestion increases and bandwidth usage increases
Solution Approach 1:
The patent extracts and eliminates intermediary entities from the royalty distribution chain, enabling direct payment between streaming services and rights holders. This reduces network congestion and bandwidth usage while maintaining payment accuracy through a streamlined attribution system that directly matches content consumption to rights holders without intermediate transaction layers.
Solution Approach 2:
The patent introduces a machine learning-based attribution system as a new intermediary that replaces traditional payment intermediaries. This attribution system analyzes user interaction data to determine intent values and directly attributes royalties, reducing the need for complex intermediary transaction processes while improving distribution accuracy through data-driven decision-making.
2Productivity
If conventional systems favor popular artists in royalty distribution, then transaction processes are simplified, but diversity in media content decreases
Solution Approach 1:
The patent applies local quality by differentiating royalty attribution based on specific user interaction characteristics rather than applying a uniform popular-artist-favoring model. The machine learning system analyzes individual user intent values for different content types and artists, enabling diverse content to receive appropriate attribution based on actual user engagement patterns rather than popularity alone.
Solution Approach 2:
The patent implements dynamics by using a machine learning-based attribution system that adaptively adjusts royalty distribution based on real-time user interaction data. The system continuously learns from user behavior patterns, allowing royalty allocation to dynamically respond to changing user preferences and content performance, thereby supporting media content diversity while maintaining distribution efficiency.
3Ease of operation
If conventional royalty models use intermediaries for payment, then transaction management is centralized, but bandwidth usage increases for users seeking less popular artists
Solution Approach 1:
The patent extracts unnecessary intermediary transaction layers from the payment process, enabling direct attribution and payment between streaming services and rights holders. This elimination of redundant intermediaries reduces bandwidth usage for users accessing less popular artists while maintaining centralized transaction management through the machine learning attribution system that coordinates payments efficiently.
4Measurement precision
If complex transaction processes are used for royalty distribution, then payment accuracy can be maintained, but network congestion increases
Solution Approach 1:
The patent replaces mechanical intermediary transaction processes with a machine learning-based attribution system that uses data analysis and algorithms to determine royalty distribution. This substitution maintains measurement precision through sophisticated intent value calculation while improving transaction processing speed by eliminating manual intermediary steps and enabling automated, direct attribution between content consumption and rights holder payment.
Data Source
AI summary
Disclosed are methods and systems to provide a streaming service platform that allows communication sessions and payments to an artist based on user-centric royalty models and additional payment requests by users including a method that comprises: generating first data indicating media content selected for output in association with a user account over a period of time; determining characteristics of the output of the media content, determining second data indicating an intent value associated with the user account in relation to the first data; generating third data representing an attribution schedule indicating a degree of attribution for the entity based on the intent value; determining that the period of time has lapsed; and facilitating a transfer of funds to an entity account associated with the entity using the third data and based on the period of time lapsing.