Smart Contract Compliance Classification via Neural Network
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Solution Overview
Problem
The difficulty in quickly and efficiently determining whether smart contracts comply with regulations due to formatting and jargon disparities between smart contracts and regulations, leading to laborious and costly manual reviews.
Innovation Solution
A system using neural networks to classify smart contract sections by converting them into intermediate representation of code, allowing for automatic compliance determination and error identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review is used to determine smart contract compliance, then accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by converting smart contract code and regulatory text into intermediate representations before the actual compliance check. This preprocessing step creates standardized formats that enable faster automated comparison while maintaining accuracy, resolving the contradiction between thorough review and time efficiency
Solution Approach 2:
The patent introduces an intermediary mechanism - the intermediate representation layer - that bridges smart contract code and regulatory text. This intermediary format allows automated systems to accurately compare contractual terms with regulations without requiring manual interpretation, thus maintaining precision while reducing time loss
2Measurement precision
If manual review is used to determine smart contract compliance, then detailed analysis can be performed, but cost becomes prohibitive
Solution Approach 1:
The system enables self-service by allowing smart contracts to be automatically evaluated against regulations without requiring human reviewers. The automated classification and scoring mechanisms perform detailed compliance analysis independently, eliminating the need for expensive manual legal review while maintaining thorough analysis depth
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated computational system. The intermediate representation and machine learning classifiers substitute for human analysts, providing detailed compliance analysis at a fraction of the cost while eliminating the need for expensive professional review
3Reliability
If separate professionals review various sections of smart contracts, then comprehensive coverage is achieved, but costs increase even higher
Solution Approach 1:
The system merges multiple review functions into a single automated platform that simultaneously evaluates all sections of smart contracts against relevant regulations. The intermediate representation enables one system to perform what previously required multiple specialized professionals, achieving comprehensive coverage while eliminating the cost of hiring multiple reviewers
Solution Approach 2:
The patent creates a universal compliance review system that can handle various sections and types of smart contract provisions through a single platform. The intermediate representation and classification mechanisms provide multi-functional capability, allowing one system to perform the work of multiple specialized reviewers across different contract sections
Data Source
AI summary
A system for determining regulatory compliance of smart contracts is disclosed. The system may receive positive smart contracts that comply with regulations, convert positive section(s) of the positive smart contracts into a first set of intermediate representation of code, and train a neural network to classify smart contract sections. The system may then receive a first smart contract including first sections, convert the first sections into a second set of intermediate representation of code, classify the second set of intermediate representation of code as a first classification corresponding to the first set of intermediate representation of code or as a second classification not corresponding to the first set of intermediate representation of code, and generate for display a negative or positive indication based on the classification.