Abstract: The present disclosure relates to a management server for an ice maker that includes: a communicator configured to receive (i) operation information on extraction of ice stored in the ice maker and (ii) sensing information on an amount of the ice stored in the ice maker; and a controller configured, based on (i) the received operation information and sensing information in a first time section and (ii) the received operation information and sensing information in a second time section after the first time section, to determine whether the ice stored in the ice maker is clumped in the second time section.

Abstract: Provided herein are methods of diagnosing an electronic device having an ice making function, the method comprising: receiving, by a server, data related to water supply and ice making from the electronic device; based on receiving the data, checking, by the server, one or more of water pressure state, an ice making state, or an ice usage state of the electronic device; based on the checked state, diagnosing, by the server, the electronic device to be in a low water pressure state or an insufficient water supply state; and based on the diagnosis, activating, by the server, a repair-inspection service for the electronic device, or providing a notification service to a user terminal corresponding to the electronic device.

Abstract: Provided are a refrigerator and a cloud server that diagnose a cause of an abnormal state. According to an embodiment of the present disclosure, an abnormal state diagnosis unit included in the refrigerator or the cloud server generates information on the abnormal state of the refrigerator based on similarity between stored first group of information and a normal pattern, and a cause diagnosis unit generates, when the abnormal state diagnosis determines that a state of the refrigerator is an abnormal stte, information on cause of the abnormal state based on similarity between stored second group of information and a defect pattern.

Abstract: An artificial intelligent refrigerator is disclosed. The artificial intelligent refrigerator includes: one or more first temperature sensor that senses refrigerating compartment-internal temperature in a refrigerating compartment of the refrigerator; one or more second temperature sensor that senses freezing compartment-internal temperature in a freezing compartment of the refrigerator; and a refrigerator processor that calculates a load accumulation amount for food put in the refrigerator on the basis of the refrigerating compartment-internal temperature or the freezing compartment-internal temperature, and performs a load correspondence operation using the calculated load accumulation amount.

Abstract: An intelligent inspection device is disclosed. The intelligent inspection device comprising a sound collection collecting a first operation sound generated in a refrigerator and storing the collected first operating sound a reference sound set-up comparing the first operation sound stored in the sound collection with a preset normal sound of the refrigerator, learning a result of the comparison to set a reference sound, and setting a reference sound range based on the set reference sound and a processor collecting, through the sound collection, a second operation sound generated in the refrigerator after the reference sound range has been set, comparing the second operation sound with the reference sound range, and inspecting whether the refrigerator fails based on the result of the comparison.

Abstract: An intelligent inspection device is disclosed. The intelligent inspection device includes a camera configured to photograph a refrigerator that is installed and a processor configured to position a first refrigerator image photographed by the camera in a preset area of an inspection frame, compare the first refrigerator image with the inspection frame, inspect whether a step is present in the refrigerator based on a result of the comparison, and learn a solution corresponding to a result of the inspection to provide the solution to a user. The intelligent inspection device may be associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

Abstract: Provided are a refrigerator and a cloud server that diagnose a cause of an abnormal state. According to an embodiment of the present disclosure, an abnormal state diagnosis unit included in the refrigerator or the cloud server generates information on the abnormal state of the refrigerator based on similarity between stored first group of information and a normal pattern, and a cause diagnosis unit generates, when the abnormal state diagnosis determines that a state of the refrigerator is an abnormal stte, information on cause of the abnormal state based on similarity between stored second group of information and a defect pattern.

Abstract: An artificial intelligent food refrigerating device and a method for refrigerating food by using the same are disclosed. An artificial intelligent food refrigerating device according to the present disclosure identifies the size, volume, type, and quantity of food to be put into it and determines a target refrigeration temperature and refrigeration time for the food based on an identification result. The artificial intelligent food refrigerating device and method according to the present disclosure may be associated with an artificial intelligent module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a 5G service-related device, etc.

Abstract: Disclosed is an artificial refrigerator. The artificial refrigerator according to the present disclosure includes at least one sensor for sensing an operation state of the refrigerator and obtaining operation information about the operation state of the refrigerator and a processor that determines whether the operation state of the refrigerator is normal or abnormal using a deep-learning-based first diagnosis engine based on the operation information obtained using the at least one sensor and diagnoses, upon determination of the abnormality, a cause of the abnormality using a deep-learning-based second diagnosis engine. In the artificial refrigerator of the present invention, at least one of a user terminal or a server may be associated with an artificial intelligence module, a drone (Unmanned Aerial Vehicle, UAV) robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

Abstract: An intelligent inspection device is disclosed. The intelligent inspection device comprising a sound collection collecting a first operation sound generated in a refrigerator and storing the collected first operating sound a reference sound set-up comparing the first operation sound stored in the sound collection with a preset normal sound of the refrigerator, learning a result of the comparison to set a reference sound, and setting a reference sound range based on the set reference sound and a processor collecting, through the sound collection, a second operation sound generated in the refrigerator after the reference sound range has been set, comparing the second operation sound with the reference sound range, and inspecting whether the refrigerator fails based on the result of the comparison.

Abstract: An intelligent inspection device is disclosed. The intelligent inspection device includes a camera configured to photograph a refrigerator that is installed and a processor configured to position a first refrigerator image photographed by the camera in a preset area of an inspection frame, compare the first refrigerator image with the inspection frame, inspect whether a step is present in the refrigerator based on a result of the comparison, and learn a solution corresponding to a result of the inspection to provide the solution to a user. The intelligent inspection device may be associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

Abstract: An artificial intelligent refrigerator is disclosed. The artificial intelligent refrigerator includes: one or more first temperature sensor that senses refrigerating compartment-internal temperature in a refrigerating compartment of the refrigerator; one or more second temperature sensor that senses freezing compartment-internal temperature in a freezing compartment of the refrigerator; and a refrigerator processor that calculates a load accumulation amount for food put in the refrigerator on the basis of the refrigerating compartment-internal temperature or the freezing compartment-internal temperature, and performs a load correspondence operation using the calculated load accumulation amount.

Abstract: Disclosed is an artificial refrigerator. The artificial refrigerator according to the present disclosure includes at least one sensor for sensing an operation state of the refrigerator and obtaining operation information about the operation state of the refrigerator and a processor that determines whether the operation state of the refrigerator is normal or abnormal using a deep-learning-based first diagnosis engine based on the operation information obtained using the at least one sensor and diagnoses, upon determination of the abnormality, a cause of the abnormality using a deep-learning-based second diagnosis engine. In the artificial refrigerator of the present invention, at least one of a user terminal or a server may be associated with an artificial intelligence module, a drone (Unmanned Aerial Vehicle, UAV) robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.