AI-Generated Programmatic Workflows for Adaptive Execution
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Solution Overview
Problem
Conventional smart contract management systems struggle with ensuring ongoing compliance and adaptability to changing conditions due to their static and immutable nature, leading to inefficiencies and potential errors, as they require manual intervention and lack real-time adaptability.
Innovation Solution
A data generation platform utilizing decentralized AI models to dynamically generate and execute smart contracts, integrating AI-based agents to monitor and enforce compliance, adapt to real-time conditions, and maintain an audit trail on a blockchain ledger.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If smart contracts are deployed on blockchain with predefined rules, then automation and execution reliability are improved, but adaptability to changing conditions and compliance monitoring capability deteriorate
Solution Approach 1:
The patent implements dynamic smart contracts that can modify their execution parameters and rules during runtime based on changing conditions. The system allows contract terms to be updated and adjusted without complete redeployment, enabling adaptation to new scenarios while maintaining the automated execution framework.
Solution Approach 2:
The patent incorporates compliance monitoring mechanisms that continuously track smart contract executions and provide feedback to identify potential violations. This feedback loop enables the system to detect non-compliance issues and trigger appropriate responses, maintaining adaptability while preserving automated operation.
2Reliability
If smart contracts are made immutable for security, then execution reliability is improved, but compliance monitoring and error detection capability deteriorate
Solution Approach 1:
The patent segments the smart contract system into immutable execution code and mutable compliance monitoring layers. The core contract logic remains immutable for reliability, while separate monitoring components track compliance and can flag issues without requiring changes to the executed code, thus maintaining both reliability and monitorability.
Solution Approach 2:
The patent introduces intermediary compliance monitoring systems that observe smart contract executions without altering the immutable contract code. These intermediaries act as mediators between the immutable contracts and compliance requirements, detecting violations while preserving execution reliability.
3Reliability
If manual intervention is used for compliance checks, then compliance accuracy is improved, but operational efficiency and time consumption deteriorate
Solution Approach 1:
The patent implements self-monitoring smart contracts that automatically track their own compliance status through built-in counters and state variables. The contracts perform self-audits by comparing their execution state against compliance criteria, eliminating the need for external manual verification while maintaining high compliance accuracy.
Solution Approach 2:
The patent replaces manual compliance checking with automated computational verification systems. These systems use algorithmic approaches to monitor contract executions, substituting human reviewers with automated processes that maintain accuracy while dramatically improving operational efficiency.
4Difficulty of detecting and measuring
If comprehensive compliance monitoring is implemented, then compliance detection capability is improved, but system complexity and computational overhead deteriorate
Solution Approach 1:
The patent implements selective compliance monitoring that focuses on specific high-risk operations and critical compliance criteria rather than monitoring all contract executions comprehensively. This partial monitoring approach maintains adequate compliance detection capability while reducing system complexity and computational overhead.
Solution Approach 2:
The patent dynamically adjusts monitoring parameters and thresholds based on contract characteristics and risk levels. By changing monitoring intensity and scope parameters, the system maintains effective compliance detection for critical operations while reducing complexity for lower-risk scenarios.
Data Source
AI summary
Systems and methods disclosed herein automatically generate and executing computer programs using artificial intelligence (AI) models. The system obtains an output generation request (e.g., from a graphical user interface (GUI)) to generate an output using an unstructured dataset. The system determines a feature set of the unstructured dataset using a first AI model set, and dynamically generates a programmatic workflow set using a second AI model set (same as or different from the first AI model set) based on the feature set to be stored in a distributed database. The system automatically executes the programmatic workflow set using a third AI model set (same as or different from the first and/or second AI model sets) to generate an artifact responsive to the output generation request. The system displays (e.g., on the GUI) representations of the output generation request, programmatic workflow set, and/or generated artifact.