Trained Model Tokenization for Secure Access and Expert Incentives
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Solution Overview
Problem
Domain experts lack the necessary tools and incentives to effectively contribute to computer model development due to the specialized nature of machine learning processes, which are typically controlled by computer scientists, leading to a gap in integrating real-world knowledge into model development.
Innovation Solution
A method and system for developing trained computer models as tradeable assets, where domain experts can interact with a model preparation system, secure storage and access is managed through a blockchain, and tokens are used to facilitate ownership and reward contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain experts are excluded from model development due to lack of ML expertise, then model development is controlled by specialists, but real-world knowledge integration is reduced
Solution Approach 1:
The patent introduces an intermediary system that includes automated model development tools and blockchain-based incentive mechanisms. This intermediary enables domain experts to contribute their real-world knowledge through structured interfaces without needing to master complex ML algorithms, while still allowing specialists to maintain control over the technical aspects of model development.
2Reliability
If domain experts are involved in model development, then real-world knowledge is integrated, but incentive and reward mechanisms are lacking
Solution Approach 1:
The patent implements a feedback mechanism through blockchain-based token distribution. Domain experts receive automated rewards in the form of tokens when their contributed models are deployed and generate value. This creates a continuous feedback loop where experts can see the direct impact of their contributions and receive corresponding incentives, making the system adaptable to varying levels of expert involvement.
3Ease of operation
If trained models are distributed freely, then accessibility is improved, but model developer protection and reward are reduced
Solution Approach 1:
The patent segments the model distribution system into multiple layers: the model developer retains ownership and control rights, while users acquire specific usage rights through token-based mechanisms. The blockchain infrastructure divides the value chain into distinct segments including model creation, validation, deployment, and usage, with each segment having its own incentive and protection mechanisms.
4Productivity
If model development is automated, then efficiency is improved, but domain expert interaction is reduced
Solution Approach 1:
The patent creates a universal platform that serves multiple functions: it automates technical model development tasks while simultaneously providing interfaces for domain expert knowledge input, blockchain-based incentive distribution, and model deployment management. This multi-functional system allows automated processes and expert interaction to coexist and reinforce each other rather than compete.
Data Source
AI summary
A method of providing a computer model as a tradeable asset is described. A trained computer model is prepared—this comprises developing and training a computer model using training data and determining acceptance criteria for the computer model such that the computer model is determined to be a trained computer model when the acceptance criteria are met. The trained computer model is packaged for use by a third party and stored securely. A token corresponding to the trained computer model is established. The token, and transactions in the token, are posted to a blockchain such that the token is adapted for use as a tradeable asset. Access to the trained computer model is provided to a third party who has acquired rights to use the trained computer model through obtaining rights in the token. An associated system for developing of a computer model and for providing it as a tradeable asset is also described.