Rule Compliance Logic Generation for Multi-Environment Determination
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Solution Overview
Problem
Existing technologies require manual adjustment of determination logic for each environment, leading to decreased efficiency in determining compliance with rules at work sites or maintenance sites.
Innovation Solution
A computer program product that includes a non-transitory computer-readable medium with programmed instructions to acquire determination logic, perform determination processing, and output a determination result, utilizing learned large language models for automatic generation of determination logic based on input rule information and case collections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of determination logic is performed for each environment, then accuracy of rule compliance determination is improved, but determination efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically generating determination logic through large language models before actual rule compliance determination. The LLM generates determination logic based on rule information and case collections in advance, eliminating the need for manual adjustment while maintaining accuracy across different environments.
Solution Approach 2:
The system enables self-service by allowing the large language model to automatically generate and adapt determination logic without human intervention. The LLM processes rule information and case collections to produce environment-specific determination logic autonomously, improving efficiency while preserving accuracy.
2Adaptability or versatility
If manual adjustment of determination logic is performed for each environment, then adaptability to different environments is improved, but device complexity increases
Solution Approach 1:
The system replaces the mechanical process of manual determination logic adjustment with an intelligent system based on large language models. The LLM automatically generates determination logic by processing rule information and case collections, eliminating complex manual configuration while maintaining adaptability to different environments.
Solution Approach 2:
The system achieves adaptability through parameter changes by feeding different rule information and case collections to the large language model for each environment. The LLM generates appropriate determination logic by processing these input parameters, enabling environment-specific adaptation without increasing system complexity.
3Productivity
If automatic generation of determination logic using large language models is implemented, then determination efficiency is improved, but loss of information may increase
Solution Approach 1:
The system implements feedback by incorporating case collections into the determination logic generation process. The large language model uses historical cases as feedback to generate more accurate determination logic, ensuring that important information is preserved while maintaining high determination efficiency.
Solution Approach 2:
The system performs preliminary analysis by processing both rule information and case collections before generating determination logic. This preliminary action ensures that the LLM has access to all necessary information, reducing information loss while maintaining efficient automatic generation.
Data Source
AI summary
According to an embodiment, a computer program product includes a non-transitory computer-readable medium including programmed instructions. The instructions cause a computer to execute: acquiring determination logic that determines whether a state represented by a processing target complies with an input rule represented by input rule information; performing, on the processing target, determination processing according to the determination logic; and outputting, to an output device, output information including a determination result by the determination processing.