Predictive Vehicle Diagnostic System Using Historical Defect Data
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Solution Overview
Problem
Automotive diagnostics face challenges in predicting failures due to vehicle-specific vulnerabilities and the lack of a universal template, leading to uncertainty for vehicle owners and potential overcharging by professionals, with anecdotal information providing limited reliability.
Innovation Solution
A predictive diagnostic system and method that uses a historical database to compare vehicle characteristic data with similar vehicles, filtering and sorting defects by mileage, and adjusting for climatic and geographic factors to generate a predictive report on likely component failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a universal template or formula is applied to all vehicles for predicting failure, then the diagnostic process becomes simpler and more standardized, but the prediction accuracy decreases because different vehicles exhibit different vulnerabilities
Solution Approach 1:
The patent segments the vehicle population into distinct groups based on shared characteristics such as make, model, year, and component type. By creating segmented cohorts of similar vehicles, the system maintains prediction accuracy through targeted analysis while preserving operational simplicity through automated classification algorithms that handle the segmentation process.
Solution Approach 2:
The system dynamically adjusts prediction parameters based on the specific characteristics of each vehicle cohort. Instead of using fixed universal thresholds, the system modifies failure probability calculations, mileage intervals, and risk factors to match the specific vulnerability patterns of each segmented vehicle group, thereby maintaining both accuracy and simplicity.
2Reliability
If automotive professionals use their experience and knowledge to diagnose vehicle problems, then diagnostic expertise is applied, but costs increase and consumer distrust arises due to potential overcharging and unnecessary repairs
Solution Approach 1:
The patent introduces an intermediary predictive diagnostic system that acts as a mediator between the vehicle owner and the automotive professional. This system processes historical data and vehicle characteristics to generate evidence-based failure predictions, which then guide the professional's diagnosis. This intermediary layer ensures that expert knowledge is applied only when truly necessary, reducing unnecessary repairs and costs while maintaining diagnostic reliability.
Solution Approach 2:
The system performs preliminary diagnostic analysis before the vehicle reaches the professional. By pre-processing vehicle data, identifying potential issues, and generating a predictive assessment, the system prepares a roadmap that guides the professional's examination. This preliminary action reduces the scope of required professional intervention, lowering costs while maintaining diagnostic quality through targeted expert review of pre-identified issues.
3Loss of information
If anecdotal information from experienced individuals is used to gauge vehicle diagnostic future, then some measure of assistance is provided, but the information reliability decreases because failures may not represent a reliable pattern
Solution Approach 1:
The patent implements a feedback mechanism that continuously collects actual failure data from the vehicle cohort and compares it against predicted failures. This feedback loop validates and refines the predictive models over time, ensuring that only statistically significant patterns are retained. The system automatically adjusts based on real-world outcomes, converting anecdotal observations into validated predictive rules through continuous data feedback.
Solution Approach 2:
The system replaces the mechanical process of gathering and evaluating anecdotal information with an automated computational system. Instead of manually collecting and assessing individual vehicle owner experiences, the system uses algorithms to process large datasets, identify statistical patterns, and generate reliable predictions. This substitution transforms unreliable anecdotal data into dependable predictive insights through systematic computational analysis.
Data Source
AI summary
There is provided a method of predicting defects likely to occur in a vehicle over a predetermined period. The method includes receiving vehicle characteristic data regarding a vehicle under consideration, and comparing the received vehicle characteristic data with a defect database. The defect database includes information related to defects that have occurred in different vehicles and the mileage at which such defects occurred. The method additionally includes identifying defects that occurred in vehicles corresponding to the vehicle under consideration, and the mileage at which such defects occurred. Defects which fail to satisfy minimum count requirements are then filtered out, and the defects are then sorted in order of the highest defect count.