18206404. ELECTRONIC DEVICE AND CONTROLLING METHOD OF ELECTRONIC DEVICE simplified abstract (Samsung Electronics Co., Ltd.)
ELECTRONIC DEVICE AND CONTROLLING METHOD OF ELECTRONIC DEVICE
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ELECTRONIC DEVICE AND CONTROLLING METHOD OF ELECTRONIC DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18206404 titled 'ELECTRONIC DEVICE AND CONTROLLING METHOD OF ELECTRONIC DEVICE
Simplified Explanation
An electronic device is described in this patent application. The device includes a memory that stores information about two neural network models. The first neural network model is trained to predict the operation of a refrigerator, while the second neural network model is trained to obtain information related to the defrosting process of the refrigerator. The device also includes a processor that performs several tasks. It obtains data about the operation history of the refrigerator and inputs this data into the first neural network model. The processor then obtains the prediction result for a future operation of the refrigerator from the first neural network model. This result is then inputted into the second neural network model. The processor obtains third data from the second neural network model, which includes information about the degree of frost formation based on the refrigerator's operation and information about controlling the defrost operation of the refrigerator.
- The electronic device stores information about two neural network models: one for predicting the operation of a refrigerator and the other for obtaining information related to defrosting.
- The processor obtains data about the operation history of the refrigerator and inputs it into the first neural network model.
- The processor obtains a prediction result for a future operation of the refrigerator from the first neural network model.
- The prediction result is then inputted into the second neural network model.
- The processor obtains third data from the second neural network model, which includes information about the degree of frost formation and controlling the defrost operation of the refrigerator.
Potential Applications
- Smart refrigerators that can predict and optimize their own operation and defrosting process.
- Energy-efficient refrigerators that can adapt their defrosting operation based on frost formation.
Problems Solved
- Inefficient defrosting operations in refrigerators.
- Lack of predictive capabilities in refrigerators.
Benefits
- Improved energy efficiency in refrigerators.
- Reduced frost formation and maintenance requirements.
- Enhanced user experience with smart and optimized refrigerators.
Original Abstract Submitted
An electronic device includes: at least one memory configured to store information of a first neural network model trained to predict an operation of a refrigerator, and information of a second neural network model trained to obtain information associated with a defrosting of the refrigerator; and at least one processor configured to: obtain first data regarding an operation history of the refrigerator, input the first data to the first neural network model, and obtain, from the first neural network model, second data regarding a prediction result for a future operation of the refrigerator, and input the second data to the second neural network model, and obtain, from the second neural network model, third data including information regarding a degree of frost formation based on an operation of the refrigerator being performed according to the second data, and information regarding controlling a defrost operation of the refrigerator.
