Office of Technology Transfer – University of Michigan

Diagnostic Text Search Engine

Technology #3770

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Yi Lu Murphey
Managed By
Drew Bennett
Associate Director - Software Licensing 734-615-4004


Automotive on-board diagnostics systems enable vehicle self-diagnostic and reporting capabilities. They provide the vehicle owner or a repair technician access to the information related to the performance of various vehicle sub-systems. Since the introduction of on-board vehicle computers, the amount of diagnostic information available via such on-board diagnostics systems has increased dramatically, reaching in some cases thousands of possible codes per vehicle. In addition to standardized diagnostic trouble codes, these systems provide various real-time vehicle performance data, which allow the technicians to rapidly identify and remedy malfunctions within the vehicle. Rapid expansion of codes and other information available through on-board diagnostics systems creates a need for fast identification and classification of the associated problems.


Researchers at the University of Michigan have developed a machine learning technology that generates a knowledge base for vehicle diagnostic classification. This knowledge base relies on a training set of vehicle diagnostic text descriptions associated with the appropriate diagnostic code. In addition, they also developed a vehicle diagnostic text classification technology that takes any vehicle problem description and accurately assigns it a diagnostic code.

Applications and Advantages


  • Correlation of vehicle diagnostic codes the vehicle malfunction/performance problems


  • Easy correlation between diagnostic code and related vehicle problem
  • Ability to derive a diagnostic code based on vehicle performance/problem description
  • Could enable end-users to independently determine the severity of the problem indicated by the on-board diagnostic system.