AI Appliance Failure Prediction With Selective Abnormal Data Transfer
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Solution Overview
Problem
Current home appliance failure prediction systems lack efficiency in data transmission and customization for personalized diagnosis and treatment solutions, failing to leverage user profiles and appliance settings effectively.
Innovation Solution
A method and apparatus for selectively transmitting appliance operation data to a managing server, classifying it as normal or abnormal, and generating customized diagnosis treatment solutions based on user profiles, failure histories, and appliance settings using AI-driven data pattern detection routines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all appliance operation data is transmitted to the server for failure prediction, then the accuracy of failure prediction is improved, but the data transmission load and processing time increase
Solution Approach 1:
The patent extracts and transmits only abnormal operation data that deviates from normal patterns, rather than transmitting all operation data. The appliance controller compares operation data against stored normal patterns and selectively transmits only those data points that indicate potential failures, thereby maintaining prediction accuracy while reducing transmission load and time
Solution Approach 2:
The patent applies different quality standards to different data: normal data is filtered out while abnormal data is prioritized for transmission. The system identifies specific abnormal characteristics (such as temperature deviations, unusual operation sequences) and transmits only those with higher prediction value, creating a quality-based filtering mechanism that optimizes the signal-to-noise ratio in transmitted data
2Adaptability or versatility
If detailed operation data is transmitted for personalized diagnosis, then the customization accuracy is improved, but the data transmission volume and complexity increase
Solution Approach 1:
The patent extracts only the essential abnormal characteristics from operation data that are most relevant for personalized diagnosis. Instead of transmitting complete detailed operation logs, the system identifies and transmits specific abnormal parameters (such as temperature deviation magnitude, duration of abnormal state, sequence of abnormal events) that are sufficient for accurate failure prediction and personalized treatment planning
Solution Approach 2:
The patent performs preliminary data processing and classification at the appliance controller before transmission. The system pre-identifies abnormal data patterns and pre-categorizes them by type and severity, so that the server receives already-processed, structured abnormal data that requires minimal additional processing for personalized diagnosis, thereby reducing overall system complexity
Data Source
AI summary
A method performed by a managing server includes: receiving, from an electronic device, operation data of the electronic device; identifying, by using artificial intelligence (AI), a device usage pattern of the electronic device; identifying, by using the AI, information related to a failure or an abnormal operation of the electronic device and a solution to the failure or the abnormal operation based on the device usage pattern and the operation data received from the electronic device; and transmitting, to a user terminal, the information related to the failure or the abnormal operation of the electronic device and the solution to the failure or the abnormal operation.