Vehicle Troubleshooting Using Natural Language Defect Classification
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Solution Overview
Problem
Conventional vehicle troubleshooting systems lack comprehensive sensor input, particularly missing human sensory inputs like smell, leading to incomplete defect diagnosis and increased complexity and time in identifying faulty parts.
Innovation Solution
A system utilizing a trained model to classify defect reports in natural language, identifying specific vehicle parts for repair by analyzing user observations, reducing dependency on experienced coordinators and streamlining troubleshooting through a network of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional sensor input is used for vehicle troubleshooting, then the system is simple and automated, but the diagnosis is incomplete and lacks human sensory inputs like smell
Solution Approach 1:
The patent merges automated sensor data with human user observations into a unified troubleshooting system. The natural language processing module combines structured sensor inputs with unstructured human descriptions, creating a comprehensive defect diagnosis that leverages both automated efficiency and human sensory capabilities.
Solution Approach 2:
The natural language processing system acts as an intermediary between human users and the automated troubleshooting system. It translates human observations into structured data that can be processed by the classification model, bridging the gap between human sensory input and automated analysis.
2Measurement precision
If manual troubleshooting by experienced coordinators is used, then diagnosis accuracy is high, but time consumption and dependency on personnel increase
Solution Approach 1:
The system enables self-service troubleshooting by automatically classifying defect reports and identifying faulty parts using trained classification models. The natural language processing and classification algorithms autonomously analyze defect descriptions and sensor data, reducing dependency on experienced coordinators while maintaining high accuracy.
Solution Approach 2:
The classification model is pre-trained on historical defect data to perform preliminary analysis of defect reports. This preliminary classification quickly narrows down potential faulty parts before detailed diagnosis, reducing both time and the need for extensive human expertise.
3Reliability
If comprehensive sensor input is implemented, then diagnosis completeness improves, but device complexity and cost increase
Solution Approach 1:
The natural language processing system serves multiple functions: it processes user observations, integrates sensor data, classifies defect types, and identifies faulty parts. This multi-functional approach achieves comprehensive diagnosis without requiring additional specialized sensors for each function.
Solution Approach 2:
The patent replaces physical sensors (like smell sensors) with electronic natural language processing that can analyze and interpret human descriptions of sensory experiences. This substitution achieves sensory capabilities through software rather than hardware, reducing physical system complexity.
Data Source
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AI summary
The disclosure relates to method performed by a system (100) configured to perform troubleshooting of a vehicle (223), the method comprising obtaining a defect report, related to the vehicle (223), in a natural language, selecting one or more classes from a plurality of classes by classifying the defect report using a trained model, wherein each class indicate a part of the vehicle (223), performing troubleshooting of the vehicle (223) by identifying one or more parts of the vehicle to be repaired using at least the one or more selected classes.