Refrigerator for providing information on item using artificial intelligence and method of operating the same
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Solution Overview
Problem
Conventional refrigerators lack the ability to recognize users and provide personalized item recommendations, leading to inefficient access to stored items.
Innovation Solution
A refrigerator equipped with an AI system that includes a sensing unit for user identification, a memory to store collaborative filtering and usage pattern models, and a processor to predict and recommend items based on user data, utilizing output units for easy item location guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional refrigerators are used without AI recognition systems, then the device complexity remains low, but the ability to provide personalized item recommendations and user recognition is poor
Solution Approach 1:
The refrigerator system automatically recognizes users and recommends items without requiring manual input or interaction from users. The sensing unit detects user presence and the processor automatically queries usage pattern models to generate recommendations, allowing the system to serve itself and users without additional complexity in user operation.
Solution Approach 2:
Usage pattern models stored in the memory act as intermediaries between the sensing unit (which detects user presence) and the output unit (which displays recommendations). These models process the raw sensor data and transform it into meaningful item recommendations, reducing the complexity burden on the processor while maintaining high adaptability.
2Ease of operation
If AI-based user recognition and item prediction systems are implemented, then user convenience and item access efficiency are improved, but the device complexity increases
Solution Approach 1:
Usage pattern models are pre-stored in the memory unit before runtime operation. These models contain pre-processed relationships between users, time periods, and item usage patterns, allowing the system to generate recommendations through simple model querying rather than complex real-time analysis, thus improving ease of operation while managing device complexity.
Solution Approach 2:
The patent replaces manual user input and mechanical search processes with automated sensing and AI-based prediction systems. The sensing unit automatically detects user presence and the processor automatically queries models and generates recommendations, substituting manual operations with automated intelligent systems that improve convenience despite increased complexity.
3Measurement precision
If collaborative filtering models are used to predict items, then recommendation accuracy is improved, but the loss of information required for model training increases
Solution Approach 1:
Instead of requiring extensive raw user usage data for each prediction query, the system creates compressed representations of usage patterns in the form of pre-trained models stored in memory. These model copies encapsulate the essential relationships between users, time periods, and items, maintaining high prediction accuracy while reducing the information burden during operation.
Data Source
AI summary
A refrigerator for providing an item guide service using artificial intelligence includes an output unit, a sensing unit configured to acquire user identification information for identifying a user, a memory configured to store a collaborative filtering model for determining an item to be used by the user, and a processor configured to recognize the user based on the acquired user identification information, predict an item to be used by the recognized user using the collaborative filtering model and output item identification information for identifying the predicted item through the output unit.