Expert System Error Detection via Diagnostic Code Encapsulation
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Solution Overview
Problem
Existing systems lack an efficient method for detecting errors in specific domains using expert systems, which are limited in adapting to different knowledge bases and require manual encoding of human expertise.
Innovation Solution
A method and apparatus utilizing an expert system with a knowledge base and inference engine to detect errors by applying diagnostic codes to data snippets, allowing for automatic error identification and encapsulation, enabling scalable error detection across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert systems are used to detect errors in specific domains, then measurement precision and reliability improve, but device complexity and difficulty of detecting and measuring increase due to manual encoding requirements
Solution Approach 1:
The patent creates a simplified copy or representation of expert knowledge through structured diagnostic codes and standardized error patterns. Instead of manually encoding entire expert systems, the invention captures essential error detection logic in compact diagnostic code formats that can be automatically applied, reducing the complexity burden while maintaining detection precision.
Solution Approach 2:
The patent transforms complex expert knowledge into parameterized diagnostic codes with standardized structures. By changing the representation parameters from unstructured expert rules to structured codes with defined formats, the system achieves both precision in error detection and reduced complexity in implementation and maintenance.
2Adaptability or versatility
If expert systems are adapted to different domains, then adaptability improves, but device complexity increases due to manual encoding requirements
Solution Approach 1:
The patent creates a universal diagnostic code framework that can be applied across multiple domains. The standardized code structure serves as a multi-functional template that adapts to different domains through parameter configuration rather than complete re-encoding, enabling domain versatility while maintaining system simplicity.
Solution Approach 2:
The patent implements a dynamic adaptation mechanism where diagnostic codes can be configured and adjusted for different domains without restructuring the entire system. The code framework allows dynamic insertion and customization of domain-specific error patterns, enabling flexible adaptation across accounting, healthcare, manufacturing and other domains.
3Reliability
If manual encoding of human expertise is performed, then knowledge base accuracy improves, but loss of time and productivity decrease
Solution Approach 1:
The patent performs preliminary structuring of expert knowledge into standardized diagnostic code templates before actual error detection. By pre-defining code structures, formats, and validation rules, the system captures essential accuracy requirements upfront, allowing rapid deployment across domains without repeated manual encoding while maintaining knowledge base reliability.
4Measurement precision
If comprehensive error detection is implemented, then measurement precision improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments comprehensive error detection into modular diagnostic codes, each handling specific error types or patterns. This segmentation allows the system to achieve complete error detection coverage by combining multiple specialized codes rather than implementing one complex monolithic detection mechanism, reducing processing complexity while maintaining detection completeness.
Data Source
AI summary
A method for detecting errors includes obtaining input data, applying a knowledge base to the input data, identifying diagnostics associated with errors in the input data, encapsulating data snippets corresponding to errors with associated diagnostic codes to obtain encapsulated data snippets, and outputting encapsulated data snippets.