AI Troubleshooting Tree Construction from Unstructured Manuals
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Solution Overview
Problem
Constructing troubleshooting trees for complex systems is a tedious and time-consuming process that requires multidisciplinary collaboration, often taking several months to ensure reliability and accuracy.
Innovation Solution
A method using a pre-trained language model to automatically extract and structure information from unstructured text sources, such as service manuals, to generate troubleshooting trees by segmenting text into sections, embedding metadata, and generating nodes based on fault, cause, and repair recommendations, with a grounding process to verify accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a pre-trained language model is used to automatically extract and structure information from unstructured text sources, then the time required to construct troubleshooting trees is significantly reduced, but the reliability and accuracy of the extracted information may be compromised
Solution Approach 1:
The system implements an iterative feedback mechanism where the language model generates initial troubleshooting tree structures, which are then validated against the original unstructured text sources. The grounding process provides feedback by cross-referencing extracted information with source materials, allowing the system to identify and correct inaccuracies while maintaining high construction speed.
Solution Approach 2:
The patent introduces a grounding process as an intermediary layer between the language model's extracted information and the final troubleshooting tree. This intermediary validates and verifies the extracted content against original sources, ensuring accuracy without significantly impacting the overall construction speed achieved through automated processing.
2Loss of time
If automated information extraction is used to rapidly generate troubleshooting trees, then construction time is reduced, but the depth and completeness of the tree structure may be insufficient
Solution Approach 1:
The system performs preliminary segmentation of unstructured text into relevant sections before feeding them to the language model. This preliminary organization ensures that all necessary information is captured and structured appropriately, allowing the automated process to maintain completeness while achieving rapid construction speeds.
Solution Approach 2:
The patent segments the unstructured text source into distinct sections and processes them systematically. This segmentation allows the language model to focus on specific portions of information, ensuring thorough extraction of details while maintaining efficient processing overall.
3Measurement precision
If manual verification processes are implemented to ensure accuracy of automated extraction, then information reliability is improved, but the time savings from automation are reduced
Solution Approach 1:
The system implements partial verification by focusing grounding efforts on critical sections of the troubleshooting tree rather than uniformly verifying all content. This approach maintains high accuracy for essential diagnostic information while preserving overall construction efficiency through selective rather than exhaustive verification.
Data Source
AI summary
Systems and methods for constructing an equipment troubleshooting tree involve receiving an unstructured text source and using a parsing tool to segment it into sections that are associated with equipment components, units, or processes. If the parsing tool fails, a pre-trained language model is used to identify the sections using the extracted listing as content. Metadata, including section-specific text and page ranges, are obtained for each section. A non-static process enhances similarity matching, resulting in segmented data. An indexing process embeds this data, along with the metadata, into index nodes to improve retrieval efficiency. The pre-trained language model generates a catalog of false, causes, or repair recommendations, which are structured as index notes in the troubleshooting tree. In response to a user query, the constructed troubleshooting tree is generated and output after a grounding process cross-references it with the unstructured text to ensure accuracy.