AI Usage Fee Distribution via Invisible Watermarking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The entertainment and creative industries face challenges due to generative artificial intelligence models that fail to provide proper royalties or compensation to original content creators when their content is used, leading to an unfair and inequitable framework for content usage and remuneration.
Innovation Solution
A system and method for distributing fees to content creators when their digital media files, including their likeness or property, are used by generative artificial intelligence, involving a processing device that receives digital media files, sets a share structure for usage fees, and distributes shares based on detected usage events such as training, creation, publication, or monetization of media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative artificial intelligence models are trained on existing content without compensation mechanisms, then the productivity and development of AI models is improved, but the fairness and equity for original content creators deteriorates
Solution Approach 1:
The patent introduces a watermarking system as an intermediary mechanism that embeds invisible identifiers in AI-generated content. This watermarking system acts as a mediator between AI models and content creators, enabling automatic tracking and attribution of content usage without hindering AI model training or development. The watermark allows the system to maintain productivity while ensuring fair compensation through automated royalty distribution.
Solution Approach 2:
The patent implements a feedback mechanism where usage information flows back from AI-generated content to content creators through the watermarking system. When AI models generate content using trained data, the embedded watermarks enable automatic detection and reporting of usage events. This feedback loop ensures that content creators receive compensation based on actual usage, maintaining fairness while allowing continuous AI development.
2Reliability
If a fee distribution system is implemented to compensate content creators, then the fairness and equity for creators is improved, but the system complexity and implementation difficulty increases
Solution Approach 1:
The patent employs self-service mechanisms where the watermarking system automatically performs tracking, detection, and royalty distribution without requiring complex manual intervention. The invisible watermarks embedded in AI-generated content enable automatic identification and attribution, reducing the need for complex administrative systems. This self-service approach maintains fairness while minimizing system complexity through automation.
Solution Approach 2:
The patent uses watermarking technology that creates invisible copies of usage information within the AI-generated content itself. Rather than requiring complex external tracking systems, the usage data is copied and embedded directly into the generated content through watermarks. This copying mechanism simplifies the fee distribution system by making usage information inherently available within the content, eliminating the need for complex monitoring infrastructure.
3Measurement precision
If watermarking technology is used to track usage, then the measurement precision of usage events is improved, but the detection difficulty and technical challenge increases
Solution Approach 1:
The patent replaces complex mechanical or manual detection systems with advanced signal processing and pattern recognition algorithms. Instead of relying on difficult-to-detect physical watermarks, the system uses digital signal processing techniques to identify and extract usage information from AI-generated content. This substitution of detection methods maintains high measurement precision while reducing the practical difficulty of detection through automated computational approaches.
Data Source
AI summary
A system to distribute fees to a distribution recipient for the use of the distribution recipient's digital media file by a generative artificial intelligence. The system may be configured to receive a digital media file, the digital media file including at least one of the likeness of a user associated with the distribution recipient or the likeness of a property associated with the distribution recipient, receive a share structure for the distribution of a usage fee, and distribute a share of the usage fee to the distribution recipient when a usage event is detected, where the share is set by the share structure.