AI Appliance Lifetime Prediction for Unknown Age and Failure Risk
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Solution Overview
Problem
Homeowners often lack accurate prediction of appliance lifespan, leading to unexpected failures and potential damage, especially when moving into a new home where the age of appliances is unknown.
Innovation Solution
A system utilizing artificial intelligence models, such as large language trained generative AI models, aggregates data from various sources to predict the remaining lifetime of appliances and provides maintenance recommendations, integrating with user interfaces for timely actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to track appliance age, then the system is simple and easy to operate, but the measurement precision of appliance remaining lifetime is poor
Solution Approach 1:
The system integrates multiple data collection functions (appliance metadata, usage patterns, error logs, environmental conditions) into a single AI-based prediction platform that serves multiple purposes: predicting remaining lifetime, identifying failure patterns, and providing maintenance recommendations, thereby achieving high measurement precision through multi-functional data aggregation
Solution Approach 2:
The patent replaces simple mechanical tracking methods (manual registration, basic counters) with an AI-based predictive system that uses machine learning models to analyze complex patterns in appliance data, substituting straightforward mechanical approaches with intelligent computational systems to achieve superior prediction accuracy
2Measurement precision
If AI models with multiple data sources are used, then the prediction accuracy is improved, but the loss of information and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features and patterns from large volumes of appliance data using AI models, isolating key predictive indicators (usage patterns, error frequencies, environmental conditions) from raw data to maintain high prediction accuracy while minimizing unnecessary data processing and information loss
Solution Approach 2:
The system performs preliminary data processing, cleaning, and feature extraction before feeding data into prediction models, preparing data in advance to reduce processing requirements during actual prediction operations and minimize information loss through structured pre-processing
3Ease of operation
If the system provides detailed maintenance recommendations and timelines, then the usefulness to homeowners is improved, but the device complexity increases
Solution Approach 1:
The system automatically generates and delivers personalized maintenance recommendations, timelines, and alerts to homeowners without requiring manual intervention or complex user configuration, making the sophisticated AI system easy to use by handling complexity internally while providing simple, actionable outputs to users
Data Source
AI summary
A computer system is provided. The computer system may be programmed to: (a) receive appliance data relating to a first appliance; (b) compute, using an artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the received appliance data, wherein the artificial intelligence model is trained based upon historical appliance data including historical lifetimes of appliances; and/or (c) transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the predicted remaining lifetime.