Remote Vehicle Diagnostics Using Customer Input and Symptom Prediction
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Solution Overview
Problem
Traditional vehicle service processes are inefficient due to reliance on onsite inspections and technician-dependent diagnoses, leading to variability in diagnosis results and increased customer inconvenience.
Innovation Solution
A vehicle diagnostic management service that analyzes customer input through a network-based system, utilizing machine learning to predict vehicle symptoms and automate diagnostic and repair actions, eliminating the need for physical presence and technician variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional onsite inspection and technician-dependent diagnosis are used, then vehicle service can be performed, but service efficiency is low and diagnosis results vary
Solution Approach 1:
The system enables self-service through automated diagnostic processes where the vehicle communication system automatically collects vehicle data, transmits it to the server, and receives diagnostic results without requiring continuous technician intervention or physical customer presence at the service center.
Solution Approach 2:
The patent replaces the mechanical system of manual technician inspection with an automated electronic diagnostic system that uses server-based processing, vehicle network communication, and automated data analysis to perform diagnoses remotely, eliminating the need for physical presence and manual assessment.
2Productivity
If remote automated diagnosis is implemented, then service efficiency improves and technician variability is eliminated, but customer input processing complexity increases
Solution Approach 1:
The server system performs multiple functions including receiving various types of customer inputs (natural language, structured data), processing diverse vehicle data from different protocols, generating diagnostic results, and providing recommendations - all through a single unified platform that handles multiple diagnostic scenarios.
Solution Approach 2:
The server acts as an intermediary between the customer input and the vehicle diagnostic system, translating customer descriptions into structured diagnostic queries, processing vehicle data through multiple protocols, and generating standardized results that can be presented to customers in various formats.
3Ease of operation
If traditional onsite service is required, then comprehensive vehicle inspection is possible, but customer convenience decreases
Solution Approach 1:
The system implements feedback loops where the server analyzes customer inputs, identifies additional diagnostic information needed, requests supplementary vehicle data or clarifying customer responses, and iteratively refines the diagnostic process until sufficient information is gathered for accurate diagnosis.
Solution Approach 2:
The system performs preliminary data collection by automatically gathering available vehicle diagnostic data before customer arrival, pre-processing sensor information, and preparing initial diagnostic assessments so that when customer input is received, the complete picture can be rapidly analyzed without requiring extensive onsite inspection.
Data Source
AI summary
Aspects of the present application correspond to vehicle diagnostic management service by utilizing a set of customer input from a customer. One aspect of the present application corresponds to the utilization of a set of customer inputs from the customer to predict vehicle symptoms associated with the customer input. In another aspect, the present application corresponds to determining action group for each predicted vehicle symptom. In another aspect, the present application. The customer input can be processed to predict vehicle symptoms. In another aspect, the present application corresponds to providing vehicle diagnostic and repair services for the customer.