Patent ID: 11914688
Assignee: SAMSUNG ELECTRONICS CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 17:
18. An electronic device for identifying transition suitability of a neural network model included in an external device, the electronic device comprising:
a communicator;
a memory configured to store internal model information on one or more neural network models included in the electronic device and a model suitability identifier that identifies neural network models suitable for replacing the neural network models included in the electronic device; and
a processor configured to:
control the communicator to transmit a first signal for requesting information related to one or more neural network models included in one or more external devices, based on a user input being received,
receive a second signal comprising external model information on one or more neural network models included in a first external device from the first external device among the one or more external devices through the communicator, as a response to the first signal,
identify whether each of the one or more neural network models included in the first external device is suitable for replacing the one or more neural network models included in the electronic device by inputting the internal model information and the external model information into the model suitability identifier,
control the communicator to transmit a third signal comprising a request for installation data of the one or more neural network models identified as suitable for replacing the neural network models included in the electronic device to the first external device, and
receive a fourth signal comprising the installation data of the one or more identified neural network models from the first external device through the communicator, as a response to the third signal, wherein the processor is further configured to:
compare service types of the one or more neural network models included in the electronic device with service types of the one or more neural network models included in the first external device, based on information on a service type included in each of the internal model information and the external model information,
compare a personalization level of a first neural network model and a personalization level of a second neural network model based on the information on the personalization level included in each of the internal model information and the external model information, based on the service type of the first neural network model among the one or more neural network models included in the first external device being the same as the service type of the second neural network model among the one or more neural network models included in the electronic device, and
identify the first neural network model as suitable for replacing the second neural network model, based on the personalization level of the first neural network model being higher than the personalization level of the second neural network model.