Blockchain Framework for Machine Learning Algorithm Update Transactions
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Solution Overview
Problem
The existing regulatory and quality control techniques for updating machine learning algorithms in medical software are inefficient, leading to time-consuming and costly processes for ensuring the safety and efficacy of updated medical software products, hindering the advancement of machine learning technology in medicine and other regulated industries.
Innovation Solution
A computer-implemented framework using blockchain technology to manage transactions of machine learning algorithm updates, ensuring secure, encrypted, and tamper-proof handling of transactions, maintaining security and privacy, and providing a secure mechanism for traceability and regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing regulatory and quality control techniques are used for updating machine learning algorithms, then safety and efficacy can be ensured, but the process becomes time-consuming and costly
Solution Approach 1:
The patent implements preliminary action by pre-establishing a blockchain-based framework that stores and verifies model development data (training data, test data, validation results) before updates are deployed. This preliminary structuring of quality control requirements on the blockchain enables automated verification during updates, eliminating time-consuming manual review processes while maintaining safety and efficacy standards.
Solution Approach 2:
The patent introduces blockchain technology as an intermediary between regulatory requirements and machine learning update processes. The blockchain system acts as a neutral mediator that automatically verifies update requests against pre-stored quality control criteria, replacing traditional manual regulatory review processes and significantly reducing update time while ensuring compliance with safety and efficacy standards.
2Reliability
If existing regulatory and quality control techniques are used for updating machine learning algorithms, then safety and efficacy can be ensured, but the process becomes costly
Solution Approach 1:
The patent implements self-service by enabling the blockchain system to automatically verify and process machine learning algorithm updates without requiring extensive manual regulatory review. The system uses smart contracts and automated validation mechanisms to check updates against pre-stored quality control criteria on the blockchain, dramatically reducing the labor costs and resource expenditure associated with traditional regulatory processes while maintaining safety and efficacy standards.
Solution Approach 2:
The blockchain intermediary automates and streamlines the regulatory review process, replacing costly manual procedures with efficient automated verification. This intermediary system reduces administrative overhead and operational costs while ensuring that safety and efficacy requirements are met through cryptographic verification of model development data.
3Reliability
If blockchain technology is used to manage transactions of machine learning algorithm updates, then security and traceability are improved, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a blockchain framework that serves multiple functions simultaneously: it stores model development data, verifies update requests, maintains audit trails, and enforces quality control criteria all within a single unified system. This multi-functional approach consolidates what would otherwise require multiple separate systems, reducing overall system complexity while enhancing security and traceability.
Solution Approach 2:
While blockchain introduces complexity, it acts as a specialized intermediary that handles security and traceability functions in a standardized way. The patent integrates this intermediary layer with existing machine learning update processes, allowing the blockchain to manage complex verification tasks while the rest of the system continues to operate with relatively simple update submission and deployment mechanisms.
4Adaptability or versatility
If blockchain technology is used to manage transactions of machine learning algorithm updates, then regulatory compliance is facilitated, but implementation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring the blockchain system with regulatory compliance requirements and quality control criteria before deployment. Model development data (training data, test data, validation results) is stored on the blockchain in advance with proper metadata and verification rules. This preliminary setup enables automated compliance verification during updates, making the system adaptable to regulatory requirements while reducing implementation complexity through upfront configuration rather than complex real-time processing.
Data Source
AI summary
Computer-implemented techniques for managing transactions of machine learning algorithm updates are described. In one embodiment, a computer-implemented is provided that comprises receiving, by a system operatively coupled to a processor, a request for an update to a machine learning model associated with a software program, wherein the request is received in accordance with a defined blockchain protocol, and wherein the request comprises model development data used in association with optimization of an instance of the machine learning model. The method further comprises, employing, by the system, a blockchain network to facilitate managing fulfillment of the request.