Repair Order Clustering for Specific Component Identification
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Solution Overview
Problem
Manufacturers face challenges in automatically generating repair information for repair technicians, as existing methods require significant human and financial resources to create repair manuals and technical service bulletins, and there is a need for more efficient processing of repair orders to identify specific vehicle components and symptoms.
Innovation Solution
A method and system that analyze vehicle repair orders (ROs) to determine if they correspond to existing clusters based on symptoms and components, using a processor to identify specific components and add ROs to appropriate clusters for better information management and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to generate repair information, then repair manuals and technical service bulletins can be created, but significant human and financial resources are consumed
Solution Approach 1:
The system enables automatic generation of repair information by having the computing device autonomously analyze repair order data, extract symptoms and components, and generate repair manuals and technical service bulletins without requiring manual human intervention for each repair case
Solution Approach 2:
The patent replaces the manual mechanical process of creating repair information with an automated computing system that uses data processing and pattern recognition to generate the same repair manuals and technical service bulletins
2Loss of information
If repair orders are processed without clustering, then all repair information is available, but it is difficult to efficiently identify specific vehicle components and symptoms
Solution Approach 1:
The patent segments the large set of repair orders into distinct clusters based on shared symptoms and vehicle components, making it easier to identify and analyze specific repair patterns without losing access to the complete repair information across all clusters
3Ease of manufacture
If repair information is manually organized, then repair manuals can be created, but the process requires significant time and resources
Solution Approach 1:
The system performs preliminary analysis of repair order data by automatically extracting symptoms, components, and repair actions, and pre-organizing this information into clusters before repair manuals need to be generated, significantly reducing the time required for manual organization
Data Source
AI summary
A processor may determine that a particular computer-readable vehicle repair order (RO) (e.g., including first and second RO portions) corresponds to an existing cluster of ROs due to the particular RO including RO data that refers to a particular vehicle symptom. The processor may determine that the first RO portion includes first data representative of a non-specific vehicle component and may then responsively also determine that the second RO portion includes second data that the at least one processor can use to determine a specific vehicle component associated with the particular RO. Responsively, the processor may determine the specific vehicle component based on the first and second data and may then add the particular RO to a different cluster of ROs that is arranged to contain ROs that correspond to the particular vehicle symptom and to the specific vehicle component.