Patent ID: 11868904
Assignee: UNIVERSITY-INDUSTRY COOPERATION GROUP OF KYUNG HEE UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. A method of training and managing a prediction model, the method comprising:
collecting, by at least one of a master apparatus including a hardware processor and a hardware storage, and a slave apparatus including a hardware processor and a hardware storage, data;
generating, by at least one of the master apparatus and the slave apparatus, a prediction model based on the data;
training, by at least one of the master apparatus and the slave apparatus, the prediction model and obtaining the trained prediction model; and
performing, by at least one of the master apparatus and the slave apparatus, a prediction based on at least one of the prediction model and the trained prediction model,
wherein the prediction model comprises:
a first prediction model configured to obtain a prediction result about a class corresponding to input data; and
a second prediction model configured to receive a result of the first prediction model and obtain a prediction result corresponding to the result of the first prediction model,

wherein the first prediction model comprises a first algorithm to which first data is input and a second algorithm to which an output result of the first algorithm is input,
wherein the hardware storage of the master apparatus stores feedback information that includes information on training accuracy and learning loss of a respective model, and
wherein:
the first algorithm is implemented using a convolutional neural network (CNN), and the second algorithm is implemented using a recurrent neural network (RNN), which is designed for temporal dynamics of input sequential data and includes a plurality of RNN cells;
the first data is inputted to and then convolution-processed by the CNN of the first algorithm, second data, which is convolution result data, is acquired, and the second data is input to the RNN of the second algorithm; and
third data, which is output data of the RNN, is transferred to the second prediction model.