Diagnostic Code Association Model for Troubleshooting
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Solution Overview
Problem
Existing diagnostic systems for machines like automotive engines and welding stations face challenges in associating diagnostic codes with relevant problem-solution descriptions, as these descriptions are often not organized by codes, requiring users to manually review numerous descriptions to determine relevance, which is time-consuming and resource-intensive.
Innovation Solution
A method and system that utilize a processor to receive training data pairs of diagnostic codes and problem-solution descriptions, generate additional associations using a model, and train a system to automatically associate diagnostic codes with problem-solution descriptions, leveraging gold-standard and semi-gold-standard training data to improve the efficiency of code-description matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If problem-solution descriptions are organized by symptoms or component names instead of diagnostic codes, then descriptions can be more easily created and collected from various sources, but users must manually review numerous descriptions to find relevant ones, increasing time and resource consumption
Solution Approach 1:
The system performs preliminary association between diagnostic codes and problem-solution descriptions using a trained model before user inquiry. The model pre-processes the large set of descriptions and codes, creating ready-to-use associations that eliminate the need for manual user review, thus resolving the contradiction between ease of data collection and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated machine learning model. The model automatically associates diagnostic codes with relevant problem-solution descriptions by analyzing semantic relationships, substituting the manual mechanical process of users reading and categorizing descriptions with an automated intelligent system.
2Measurement precision
If users manually associate diagnostic codes with problem-solution descriptions using their expertise, then accurate associations can be achieved, but this requires significant effort and resources
Solution Approach 1:
The system enables self-service association where the machine learning model automatically performs the association task without requiring user expertise or manual intervention. The model learns from training data and independently associates codes with descriptions, eliminating the need for users to invest significant effort and resources while maintaining high accuracy.
Solution Approach 2:
The patent introduces a trained machine learning model as an intermediary between diagnostic codes and problem-solution descriptions. This intermediary automatically analyzes and matches codes with relevant descriptions based on learned patterns, replacing the need for manual user expertise while achieving accurate associations.
3Quantity of substance
If all problem-solution descriptions are made available without pre-association, then complete information is provided to users, but users cannot quickly identify relevant descriptions among the large volume of unorganized data
Solution Approach 1:
The system performs preliminary filtering and association of problem-solution descriptions with diagnostic codes before users need the information. The model pre-organizes the complete set of descriptions by associating them with relevant codes, so when users inquire, only relevant information is quickly retrieved, maintaining completeness while dramatically improving access speed.
Solution Approach 2:
The patent segments the large volume of unorganized problem-solution descriptions by associating them with specific diagnostic codes through the trained model. This segmentation organizes the complete information set into code-specific groups, allowing users to quickly access only the relevant segment corresponding to their diagnostic code without wading through all descriptions.
Data Source
AI summary
A method for associating diagnostic codes with problem-solution descriptions is disclosed. The method comprises receiving a first subset of a plurality of training data pairs. Each training data pair in the first plurality of training data pairs includes (i) a respective diagnostic code and (ii) a respective problem-solution description associated with the respective diagnostic code. The method further comprises receiving a plurality of problem-solution descriptions that are not yet associated with any diagnostic codes. The method further comprises generating a second subset of the plurality of training data pairs by associating the plurality of problem-solution descriptions with respective diagnostic codes, using the first subset of the plurality of training data pairs. The method further comprises training a model using on the plurality of training data pairs. The at least one model is configured to associate diagnostic codes with problem-solution descriptions.