AI Appliance Diagnostics Using Log Data for Remote Symptom Recognition
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Solution Overview
Problem
Conventional appliance diagnosis methods through call centers face challenges in accurately recognizing appliance symptoms, leading to reduced treatment efficiency and increased costs due to technician visits.
Innovation Solution
An AI-based appliance management device that acquires symptom information from users, analyzes log data using a machine learning model to diagnose symptoms, and provides diagnostic information remotely, allowing for setting adjustments and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If counselors listen to user descriptions or appliance sounds to diagnose symptoms, then the counseling process can be conducted remotely, but the accuracy of symptom recognition is insufficient
Solution Approach 1:
The patent introduces an AI-based appliance management device as an intermediary between the user and the counseling system. This device includes a processor that automatically analyzes appliance log data and generates diagnostic information, serving as a mediator that bridges the gap between remote counseling capabilities and accurate symptom recognition. The AI device processes appliance data objectively without human intervention, resolving the contradiction by providing both remote accessibility and high diagnostic accuracy simultaneously.
2Reliability
If technician visits are dispatched for simple symptoms, then comprehensive service can be provided, but efficiency is reduced and costs increase
Solution Approach 1:
The patent enables the appliance management system to perform self-diagnosis by automatically analyzing its own log data through the AI processor. The system can identify symptoms and generate diagnostic information independently without requiring technician intervention for simple issues. This self-service capability allows the system to handle routine diagnostics automatically, improving efficiency while maintaining reliable service for complex problems that truly require human technicians.
Solution Approach 2:
The patent applies partial action by having the AI system handle only the diagnostic portion of the service process, while reserving full technician deployment for cases requiring physical intervention. The AI performs sufficient analysis for simple symptoms, avoiding excessive technician deployment, while still providing complete service coverage for complex issues that exceed automated diagnostic capabilities.
3Measurement precision
If more log data is analyzed for accurate diagnosis, then diagnostic accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the relevant log data needed for diagnosis rather than processing all available data. The AI processor selectively identifies and analyzes specific data elements that are pertinent to the reported symptom, removing unnecessary data processing complexity while maintaining diagnostic accuracy. This extraction approach allows the system to focus computational resources on critical information only.
Solution Approach 2:
The patent changes the parameter of data processing from comprehensive analysis to targeted analysis based on symptom type. The AI system dynamically adjusts which log data parameters are analyzed based on the specific symptom reported, transforming the processing approach from static full-data analysis to dynamic selective analysis, thereby reducing complexity while preserving accuracy.
Data Source
AI summary
According to an embodiment, an appliance management device includes a communication device configured to acquire symptom information of a first appliance from a user's terminal, a memory including a database for accumulatively storing log data of each of appliances matched with the user, and a processor, wherein the processor may acquire log data of the first appliance among the log data of each of the appliances of the user based on the acquired symptom information, identify at least one piece of information related to the symptom information among a plurality of pieces of information included in the acquired log data, generate diagnostic information on the symptom information based on a result of identification, and transmit the generated diagnostic information to the terminal.