Abstract: Provided is a home appliance of determining an operation command corresponding to an occupant through learning based on setting information of the home appliance according to the occupant to provide an operation satisfying all occupants. An air conditioner according to an embodiment of the disclosure includes: an outdoor unit; and an indoor unit including a heat exchanger, wherein the indoor unit includes: a communicator configured to communicate with an access point (AP); and a controller configured to receive information about a terminal connected to the access point through the communicator, and change at least one of operation temperature or an operation mode when a new terminal is connected to the access point or a terminal connected to the access point is disconnected from the access point.

Abstract: A smart home service which is capable of providing an environment in which calling a control command for a device is available via a user terminal protocol to control between the device and a user terminal based on different type of protocol and a control method for the same. The smart home service server connecting at least one device operated based on a first protocol to at least one user terminal operated based on a second protocol, includes an application programming interface (API) controller configured to allow a control command for the at least one device to be called via the second protocol of the at least one user terminal; a filter configured to convert the called control command according to the first protocol; and a control command transmitter configured to transmit the control command converted according to the first protocol, to the at least one device.

Abstract: An apparatus and a method for a data learning server is provided. The apparatus of the disclosure includes a communicator configured to communicate with an external device, at least one processor configured to acquire a set temperature set in an air conditioner and a current temperature of the air conditioner at the time of setting the temperature via the communicator, and a generate or renew a learning model using the set temperature and the current temperature, and a storage configured to store the generated or renewed learning model to provide a recommended temperature to be set in the air conditioner as a result of generating or renewing the learning model. For example, the data learning server of the disclosure may generate a learned learning model to provide a recommended temperature using a neural network algorithm, a deep learning algorithm, a linear regression algorithm, or the like as an artificial intelligence algorithm.

Abstract: An apparatus and a method for a data learning server is provided. The apparatus of the disclosure includes a communicator configured to communicate with an external device, at least one processor configured to acquire a set temperature set in an air conditioner and a current temperature of the air conditioner at the time of setting the temperature via the communicator, and a generate or renew a learning model using the set temperature and the current temperature, and a storage configured to store the generated or renewed learning model to provide a recommended temperature to be set in the air conditioner as a result of generating or renewing the learning model. For example, the data learning server of the disclosure may generate a learned learning model to provide a recommended temperature using a neural network algorithm, a deep learning algorithm, a linear regression algorithm, or the like as an artificial intelligence algorithm.

Abstract: Provided is a home appliance of determining an operation command corresponding to an occupant through learning based on setting information of the home appliance according to the occupant to provide an operation satisfying all occupants. An air conditioner according to an embodiment of the disclosure includes: an outdoor unit; and an indoor unit including a heat exchanger, wherein the indoor unit includes: a communicator configured to communicate with an access point (AP); and a controller configured to receive information about a terminal connected to the access point through the communicator, and change at least one of operation temperature or an operation mode when a new terminal is connected to the access point or a terminal connected to the access point is disconnected from the access point.

Abstract: An electronic device control system is provided.

Abstract: A smart home service which is capable of providing an environment in which calling a control command for a device is available via a user terminal protocol to control between the device and a user terminal based on different type of protocol and a control method for the same. The smart home service server connecting at least one device operated based on a first protocol to at least one user terminal operated based on a second protocol, includes an application programming interface (API) controller configured to allow a control command for the at least one device to be called via the second protocol of the at least one user terminal; a filter configured to convert the called control command according to the first protocol; and a control command transmitter configured to transmit the control command converted according to the first protocol, to the at least one device.

Abstract: An apparatus and a method for a data learning server is provided. The apparatus of the disclosure includes a communicator configured to communicate with an external device, at least one processor configured to acquire a set temperature set in an air conditioner and a current temperature of the air conditioner at the time of setting the temperature via the communicator, and a generate or renew a learning model using the set temperature and the current temperature, and a storage configured to store the generated or renewed learning model to provide a recommended temperature to be set in the air conditioner as a result of generating or renewing the learning model. For example, the data learning server of the disclosure may generate a learned learning model to provide a recommended temperature using a neural network algorithm, a deep learning algorithm, a linear regression algorithm, or the like as an artificial intelligence algorithm.