Lg Electronics Inc.

Young Tag Kwon

Dongwoo Han

Yun Sik Park

Sung Mok Hwang

Jin-Hoon Jung

An air conditioner includes: a blower configured to discharge air, the blower being connected to an outdoor unit, a parameter generator configured to generate at least one parameter during a time period for which the air conditioner is operated with a first cooling capacity based on a set temperature, a learning unit configured to receive the generated at least one parameter as a learning factor and generate operation mode information, an operation mode controller configured to control at least one of the blower or the outdoor unit based on the generated operation mode information, and a central controller configured to control the parameter generator, the learning unit, and the operation mode controller. The air conditioner is operated with a second cooling capacity after the air conditioner is operated with the first cooling capacity for the time period, the second cooling capacity being different from the first cooling capacity.

Air-conditioner based on parameter learning using artificial intelligence, cloud server, and method of operating and controlling thereof

An air conditioner includes: an indoor unit including a blowing unit to discharge air; an outdoor unit supplying compressed refrigerant to the indoor unit, to exchange heat with the air before discharge. The air conditioner also includes at least one computer memory operably connectable to at least one processor and storing instructions that, when executed, perform operations that include: generating at least one parameter in a first operation mode, which is operated only within a preset time range; controlling, based on an operation mode information, at least one of (i) the blowing unit to change a wind direction and an air volume, or (ii) the outdoor unit; and switching to a second operation mode after a time period of operation in the first operation mode. The operation mode information includes at least one result factor obtained from at least one machine-learning network.

Disclosed herein is a method for generating schedule data based on action data, and a server and a refrigerator implementing the same. The method for generating schedule data based on action data according to an embodiment of the present invention includes, in a refrigerator including one or more divided storage spaces, a step of a storage unit of the refrigerator storing pattern base data including action data performed by the refrigerator and time data, a step of a communication unit of the refrigerator receiving a first learning data set from a server, a step of a schedule generation unit of the refrigerator generating first schedule data including predicted action data of the refrigerator and time data of the predicted action data by mapping a first learning data set to the pattern base data, and a step of a control unit of the refrigerator controlling an action of the refrigerator based on the first schedule data.

Growing community of inventors

Seoul, South Korea

Young Tag Kwon