Vehicle Query Response Using Sensor-Linked Diagnostic Context
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Solution Overview
Problem
Vehicles equipped with sensors often provide vague alerts, such as the 'check engine' light, leaving users uncertain about the nature of the issue, necessitating manual consultation of manuals or service stations for diagnosis.
Innovation Solution
A system utilizing vehicle sensors and machine learning to dynamically receive user queries, determine the vehicle's status, and provide targeted responses by linking the query with sensor data and manuals, enhancing confidence in the response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dashboard lights are used to alert users of vehicle issues, then users are notified of problems, but users are left uncertain about the nature of the issue
Solution Approach 1:
The patent introduces an intermediary system comprising sensors, processors, and communication interfaces that act as a mediator between the vehicle's diagnostic systems and the user. This intermediary translates complex vehicle status data into comprehensible information, providing both notification and diagnostic clarity without requiring direct user interpretation of raw sensor data.
Solution Approach 2:
The patent replaces the traditional mechanical/dashboard-based alert system with an electronic information processing system. Instead of relying solely on physical dashboard lights, the system uses electronic sensors, digital processing, and electronic communication to deliver detailed diagnostic information, substituting mechanical notification with electronic information delivery.
2Measurement precision
If users schedule appointments with service stations for diagnosis, then accurate diagnosis can be obtained, but time is lost and convenience is reduced
Solution Approach 1:
The patent performs preliminary diagnostic actions by continuously monitoring vehicle parameters through sensors and processing data in real-time. The system proactively identifies issues, retrieves relevant manual information, and prepares diagnostic results before the user even requests information, eliminating the need for time-consuming service station visits for routine diagnostics.
Solution Approach 2:
The patent enables the vehicle system to serve itself by automatically monitoring its own status, detecting anomalies, retrieving diagnostic information from manuals, and communicating results to the user. This self-service capability allows the vehicle to perform its own preliminary diagnosis without external intervention, saving time and improving convenience.
3Adaptability or versatility
If general vehicle sensors are installed, then basic monitoring is provided, but targeted and confident responses to user queries cannot be provided
Solution Approach 1:
The patent implements a dynamic system that adapts its information retrieval and processing based on the specific user query. When a user asks a question, the system dynamically determines which sensors are relevant, retrieves only the necessary manual sections, and processes information specifically tailored to the query, rather than providing static or generic responses.
Solution Approach 2:
The patent applies local quality by retrieving and processing only the specific manual sections and sensor data relevant to the user's particular query. Instead of providing general information or processing all available data uniformly, the system focuses computational resources on the specific local area of information needed to answer the user's question accurately.
Data Source
AI summary
A computer-implemented method for a query response system that provides targeted query response results related to a vehicle. The computer-implemented method receives a query from a user related to the vehicle. The computer-implemented method further determines a current status of one or more vehicle systems and one or more subsystems via a plurality of sensors associated with the vehicle. The computer-implemented method further links the received query together with the determined current status of the one or more vehicle systems and the one or more subsystems, and provides targeted query response results based on the linking. The computer-implemented method further includes training a machine learning system on digital twin content for the vehicle, wherein the trained machine learning system for the digital twin content is shared with the query response system.