Electronic device, method, and computer-readable storage medium for managing defrosting time point of refrigerator

Abstract

According to one embodiment, a method performed by an electronic device may comprise an operation of receiving, from a refrigerator connected to the electronic device through a network, information about the usage history of the refrigerator. The method may comprise an operation of inputting the information about the usage history of the refrigerator into a prediction model for setting a defrosting time point of the refrigerator. The method may comprise an operation of acquiring information about usage patterns of the refrigerator on the basis of an output of the prediction model. The method may comprise an operation of identifying, on the basis of the information about the usage patterns, a plurality of candidate time points for performing defrosting of the refrigerator. The method may comprise an operation of determining a time point for performing the defrosting of the refrigerator from among the plurality of candidate time points.

Description

Electronic device, method, and computer-readable storage medium for determining defrosting point of a refrigerator

[0001] The following descriptions relate to an electronic device, a method, and a computer-readable storage medium for determining a defrosting point in a refrigerator.

[0002] A refrigerator is a device that maintains a low temperature inside the refrigerator by lowering the temperature inside the refrigerator using an evaporator. As the temperature inside the refrigerator remains low, moisture around the evaporator can freeze on its surface, forming frost. Frost can reduce the efficiency of the refrigerator.

[0003] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art related to the present disclosure.

[0004] According to one embodiment, an electronic device may include a communication circuit, a memory storing instructions and including one or more storage media, and at least one processor including a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive information regarding a usage history of a refrigerator from a refrigerator connected to the electronic device via a network. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to input the information regarding the usage history of the refrigerator into a predictive model for setting a defrosting time of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain information regarding a usage pattern of the refrigerator based on an output of the predictive model. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a plurality of candidate points in time for performing defrosting of the refrigerator based on the information about the usage pattern. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine a point in time for performing defrosting of the refrigerator from among the plurality of candidate points in time.

[0005] According to one embodiment, a method performed by an electronic device may include receiving information regarding a usage history of a refrigerator from a refrigerator connected to the electronic device via a network. The method may include inputting the information regarding the usage history of the refrigerator into a prediction model for setting a defrosting time of the refrigerator. The method may include obtaining information regarding a usage pattern of the refrigerator based on an output of the prediction model. The method may include identifying a plurality of candidate times for performing defrosting of the refrigerator based on the information regarding the usage pattern. The method may include determining a time for performing defrosting of the refrigerator from among the plurality of candidate times.

[0006] According to one embodiment, a non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by at least one processor of an electronic device having communication circuitry, cause the electronic device to receive information regarding a usage history of a refrigerator from a refrigerator connected to the electronic device via a network. The one or more programs may include instructions that, when executed by at least one processor of the electronic device having communication circuitry, cause the electronic device to input the information regarding the usage history of the refrigerator into a predictive model for setting a defrosting time of the refrigerator. The one or more programs may include instructions that, when executed by at least one processor of the electronic device having communication circuitry, cause the electronic device to obtain information regarding a usage pattern of the refrigerator based on an output of the predictive model. The one or more programs may include instructions that, when executed by at least one processor of an electronic device having a communication circuit, cause the electronic device to identify a plurality of candidate points in time for performing defrosting of the refrigerator based on the information about the usage pattern. The one or more programs may include instructions that, when executed by at least one processor of an electronic device having a communication circuit, cause the electronic device to determine a point in time for performing defrosting of the refrigerator from among the plurality of candidate points in time.

[0007] According to one embodiment, a refrigerator may include a cooling system, a communication circuit, a memory storing instructions and including one or more storage media, and at least one processor including a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to obtain information regarding a usage history of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to input the information regarding the usage history of the refrigerator into a predictive model for setting a defrosting point for the cooling system of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to obtain information regarding a usage pattern of the refrigerator based on an output of the predictive model. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to identify a plurality of candidate points in time for performing the defrosting of the cooling system based on the information regarding the usage pattern of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to determine a time point for performing the defrosting of the cooling system among the plurality of candidate time points.

[0008] FIG. 1 illustrates an example environment including an electronic device, a refrigerator, and at least one external electronic device, according to one embodiment.

[0009] FIG. 2A is a simplified block diagram of an electronic device according to one embodiment.

[0010] FIG. 2b illustrates components of an electronic device according to one embodiment.

[0011] FIG. 3 illustrates an example of operation of an electronic device for identifying a usage pattern of a refrigerator, according to one embodiment.

[0012] FIG. 4 illustrates an example of operation of an electronic device for identifying a usage pattern of a refrigerator, according to one embodiment.

[0013] FIG. 5 illustrates an example of the operation of an electronic device for identifying a time to perform defrosting based on a usage pattern of a refrigerator, according to one embodiment.

[0014] FIG. 6 illustrates an example of the operation of an electronic device for identifying a time to perform defrosting based on a usage pattern of a refrigerator, according to one embodiment.

[0015] FIG. 7 illustrates an example of the operation of an electronic device for identifying a time to perform defrosting based on a usage pattern of a refrigerator, according to one embodiment.

[0016] FIG. 8 illustrates a flowchart of operations of an electronic device for identifying a point in time for performing a sacrifice, according to one embodiment.

[0017] FIG. 9 illustrates an example of the operation of an electronic device for identifying a point in time for performing a sacrifice, according to one embodiment.

[0018] FIG. 10 illustrates an example of the operation of an electronic device for identifying a point in time for performing a sacrifice, according to one embodiment.

[0019] FIG. 11 illustrates an example of operation of an electronic device, a refrigerator, and at least one external electronic device to identify a point in time to perform a freezing operation, according to one embodiment.

[0020] FIG. 12 is a block diagram of an electronic device within a network environment, according to one embodiment.

[0021] The following descriptions relate to electronic devices and methods for controlling them. For example, an electronic device and method for determining the defrosting point of a refrigerator using artificial intelligence (AI) (or an AI model) will be described.

[0022] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness.

[0023] The terms 'front', 'rear', 'top', 'bottom', 'side', 'left', 'right', 'upper', 'lower', etc. used in this disclosure are defined based on the drawings, and the shape and position of each component are not limited by these terms.

[0024] Hereinafter, terms such as “include” or “have” in the present disclosure are intended to specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the present disclosure, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0025] When a component is said to be “connected,” “coupled,” “supported,” or “in contact with” another component, this includes not only cases where the components are directly connected, coupled, supported, or in contact, but also cases where the components are indirectly connected, coupled, supported, or in contact through a third component.

[0026] When we say that a component is "on" another component, this includes not only cases where the component is in contact with the other component, but also cases where there is another component between the two components.

[0027] A refrigerator according to one embodiment may include a body.

[0028] The “body” may include an inner case, an outer case placed on the outside of the inner case, and an insulating material provided between the inner case and the outer case.

[0029] The "inner case" may include at least one of a case, plate, panel, or liner forming a storage compartment. The inner case may be formed as a single body, or may be formed by assembling a plurality of plates. The "outer case" may form the outer appearance of the body, and may be joined to the outer side of the inner case so that insulation is placed between the inner case and the outer case.

[0030] "Insulation" can insulate the interior and exterior of a storage room so that the temperature inside the storage room can be maintained at a set temperature without being affected by the external environment. In one embodiment, the insulation can include foam insulation. The foam insulation can be formed by injecting and foaming urethane foam, a mixture of polyurethane and a foaming agent, between the inner and outer layers.

[0031] In one embodiment, the insulation may include a vacuum insulation in addition to the foam insulation, or the insulation may consist solely of the vacuum insulation instead of the foam insulation. The vacuum insulation may include a core material and an outer shell material that accommodates the core material and seals the interior under a vacuum or near-vacuum pressure. However, the insulation is not limited to the foam insulation or vacuum insulation described above, and may include various materials that can be used for insulation.

[0032] A "storage room" may include a space defined by an interior wall. The storage room may further include an interior wall defining a corresponding space. The storage room may store various items, such as food, medicine, and cosmetics, and the storage room may be configured to be open on at least one side for the entry and exit of items.

[0033] A refrigerator may include one or more storage compartments. When a refrigerator includes two or more storage compartments, each compartment may have a different purpose and be maintained at different temperatures. To achieve this, each storage compartment may be separated from the others by a partition wall containing insulation.

[0034] The storage room may be provided to be maintained at an appropriate temperature range depending on the intended use, and may include a "refrigerator," a "freezer," or a "variable temperature room," which are distinguished according to the intended use and / or temperature range. The refrigerator room may be maintained at a temperature appropriate for refrigerating items, and the freezer room may be maintained at a temperature appropriate for freezing items. "Refrigeration" may mean cooling items to a temperature that does not freeze them, and for example, a refrigerator room may be maintained at a temperature ranging from 0 degrees Celsius to +7 degrees Celsius. "Freezing" may mean cooling items to freeze them or keep them in a frozen state, and for example, a freezer room may be maintained at a temperature ranging from -20 degrees Celsius to -1 degree Celsius. The variable temperature room may be used as either a refrigerator room or a freezer room, at the user's option or not.

[0035] In addition to names such as "refrigerator," "freezer," and "variable temperature room," a storage room may also be called by various other names such as "vegetable room," "fresh room," "cooling room," and "ice room." The terms "refrigerator," "freezer," and "variable temperature room" used hereinafter should be understood to encompass storage rooms having corresponding uses and temperature ranges.

[0036] According to one embodiment, the refrigerator may include at least one door configured to open and close an open side of a storage compartment. The door may be configured to open and close each of one or more storage compartments, or a single door may be configured to open and close multiple storage compartments. The door may be installed on the front of the main body in a pivotal or sliding manner.

[0037] The “door” may be configured to seal the storage compartment when the door is closed. The door may include insulation, similar to the body, to insulate the storage compartment when the door is closed.

[0038] According to one embodiment, the door may include a door outer panel forming the front of the door, a door inner panel forming the back of the door and facing the storage compartment, an upper cap, a lower cap, and door insulation provided on the inside of these.

[0039] The door inner panel may be provided with a gasket that seals the storage compartment by contacting the front of the body when the door is closed. The door inner panel may include a dyke that protrudes rearward to accommodate a door basket for storing items.

[0040] According to one embodiment, a door may include a door body and a front panel detachably coupled to a front side of the door body and forming a front surface of the door. The door body may include a door outer panel forming a front surface of the door body, a door inner panel forming a rear surface of the door body and facing a storage compartment, an upper cap, a lower cap, and door insulation provided inside these.

[0041] Depending on the arrangement of the door and storage compartment, refrigerators can be classified into French door type, side-by-side type, bottom mounted freezer (BMF), top mounted freezer (TMF), or single-door refrigerator.

[0042] According to one embodiment, the refrigerator may include a cold air supply device configured to supply cold air to the storage compartment.

[0043] A “cold air supply device” may include a system of machines, devices, electronic devices and / or combinations thereof that can generate cold air and guide the cold air to cool a storage room.

[0044] According to one embodiment, the cold air supply device can generate cold air through a refrigeration cycle that includes the processes of compression, condensation, expansion, and evaporation of a refrigerant. To this end, the cold air supply device can include a refrigeration cycle device having a compressor, a condenser, an expansion device, and an evaporator capable of driving the refrigeration cycle. According to one embodiment, the cold air supply device can include a semiconductor, such as a thermoelectric element. The thermoelectric element can cool a storage compartment through heat generation and cooling through the Peltier effect.

[0045] According to one embodiment, the refrigerator may include a machine room in which at least some components belonging to the cold air supply device are arranged.

[0046] The "machine room" may be designed to be partitioned and insulated from the storage room to prevent heat generated by components placed within the machine room from being transferred to the storage room. The interior of the machine room may be configured to be connected to the exterior of the main body to dissipate heat from components placed within the machine room.

[0047] According to one embodiment, the refrigerator may include a dispenser provided on the door to provide water and / or ice. The dispenser may be provided on the door so that it is accessible to a user without having to open the door.

[0048] According to one embodiment, a refrigerator may include an ice-making device configured to produce ice. The ice-making device may include an ice-making tray configured to store water, an ice-separating device configured to separate ice from the ice-making tray, and an ice bucket configured to store ice produced in the ice-making tray.

[0049] According to one embodiment, the refrigerator may include a control unit for controlling the refrigerator.

[0050] The “control unit” may include a memory that stores or memorizes a program and / or data for controlling the refrigerator, and a processor that outputs a control signal for controlling a cold air supply device, etc. according to the program and / or data memorized in the memory.

[0051] Memory stores or records various information, data, commands, programs, etc. necessary for the operation of the refrigerator. Memory can store temporary data generated during the generation of control signals for controlling components within the refrigerator. Memory may include at least one of volatile memory and non-volatile memory, or a combination thereof.

[0052] The processor controls the overall operation of the refrigerator. The processor can control the components of the refrigerator by executing programs stored in memory. The processor may include a separate NPU that performs the operations of an artificial intelligence model. The processor may also include a central processing unit (CPU), a graphics processing unit (GPU), or the like. The processor may generate control signals to control the operation of the cooling system. For example, the processor may receive temperature information about the storage compartment from a temperature sensor and generate a cooling control signal to control the operation of the cooling system based on the temperature information.

[0053] Additionally, the processor may process user input of the user interface and control the operation of the user interface based on programs and / or data stored / stored in the memory. The user interface may be provided using an input interface and an output interface. The processor may receive user input from the user interface. Additionally, the processor may transmit display control signals and image data to the user interface for displaying an image on the user interface in response to the user input.

[0054] The processor and memory may be provided as a single unit or separately. The processor may include one or more processors. For example, the processor may include a main processor and at least one subprocessor. The memory may include one or more memories.

[0055] According to one embodiment, a refrigerator may include a processor and memory that control all components included in the refrigerator, and may include multiple processors and multiple memories that individually control the components of the refrigerator. For example, the refrigerator may include a processor and memory that control the operation of a cooling air supply device based on the output of a temperature sensor. Additionally, the refrigerator may separately include a processor and memory that control the operation of a user interface based on user input.

[0056] The communication module can communicate with external devices, such as servers, mobile devices, and other home appliances, via a nearby access point (AP). The AP can connect the local area network (LAN) where the refrigerator or user device is connected to the wide area network (WAN) where the server is connected. The refrigerator or user device can then connect to the server via the WAN.

[0057] The input interface may include keys, a touchscreen, a microphone, etc. The input interface may receive user input and transmit it to the processor.

[0058] The output interface may include a display, a speaker, etc. The output interface may output various notifications, messages, information, etc. generated by the processor.

[0059] The artificial intelligence-related functions according to the present disclosure are operated via a processor and memory. The processor may be comprised of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU or a VPU (Vision Processing Unit), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0060] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is trained using a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0061] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks.

[0062] In a method for determining a defrosting time of a refrigerator in an electronic device according to the present disclosure, a method for recognizing a user's voice and interpreting an intention to determine a defrosting time of the refrigerator includes receiving a voice signal, which is an analog signal, and converting a portion of the voice into text readable by a computer using an Automatic Speech Recognition (ASR) model. The converted text can be interpreted using a Natural Language Understanding (NLU) model to obtain the user's speech intention. Here, the ASR model or the NLU model may be an artificial intelligence model. The artificial intelligence model may be processed by an artificial intelligence-dedicated processor designed with a hardware structure specialized for processing artificial intelligence models. The artificial intelligence model may be created through learning. Here, being created through learning means that a basic artificial intelligence model is learned using a plurality of learning data by a learning algorithm, thereby creating a predefined operation rule or artificial intelligence model set to perform a desired characteristic (or purpose). The artificial intelligence model may be composed of a plurality of neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weight values.

[0063] Linguistic understanding is the technology of recognizing, applying, and processing human language / characters, including natural language processing, machine translation, dialog systems, question answering, and speech recognition / synthesis.

[0064] In a method for determining a defrosting point of a refrigerator in an electronic device according to the present disclosure, an image or output data within an image may be obtained by using image data as input data of an artificial intelligence model to determine the defrosting point of the refrigerator. For example, the image data may include an image including an object corresponding to a user, an image regarding the external environment of the refrigerator, and / or an image representing the user's schedule. The artificial intelligence model may be created through learning. Here, being created through learning means that a basic artificial intelligence model is learned using a plurality of learning data by a learning algorithm, thereby creating a predefined operation rule or artificial intelligence model set to perform a desired characteristic (or purpose). The artificial intelligence model may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and performs a neural network operation through an operation between the operation result of the previous layer and the plurality of weight values.

[0065] Visual understanding is a technology that recognizes and processes objects like human vision, and includes object recognition, object tracking, image retrieval, human recognition, scene recognition, spatial understanding (3D reconstruction / localization), and image enhancement.

[0066] In a method for determining a defrosting time of a refrigerator in an electronic device according to the present disclosure, an artificial intelligence model may be used to recommend a defrosting time of the refrigerator using defrosting-related information as a method for inferring or predicting the defrosting time of the refrigerator. The processor of the electronic device may perform a preprocessing process on the data to convert it into a form suitable for use as input for the artificial intelligence model. The artificial intelligence model may be created through learning. Here, being created through learning means that a basic artificial intelligence model is learned using a plurality of learning data by a learning algorithm, thereby creating a predefined operation rule or artificial intelligence model set to perform a desired characteristic (or purpose). The artificial intelligence model may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and performs a neural network operation through an operation between the operation result of the previous layer and the plurality of weight values.

[0067] Inference prediction is a technology that logically infers and predicts by judging information, and includes knowledge-based reasoning, optimization prediction, preference-based planning, and recommendation.

[0068] With reference to the attached drawings below, an electronic device and method for determining the defrosting point of a refrigerator are specifically described.

[0069] FIG. 1 illustrates an example environment including an electronic device, a refrigerator, and at least one external electronic device, according to one embodiment.

[0070] Referring to FIG. 1, an electronic device (101) can establish a connection with a refrigerator (102) and at least one external electronic device (103). The electronic device (101) can establish a connection with the refrigerator (102). The electronic device (101) can establish a connection with at least one external electronic device (103).

[0071] According to one embodiment, the electronic device (101), the refrigerator (102), and / or at least one external electronic device (103) may be registered to a user account (or at least one user account) (e.g., a user's email address, a user's identifier, a user's phone number, a user's address). For example, the electronic device (101), the refrigerator (102), and / or the at least one external electronic device (103) may be managed based on the registered user account. For example, the device (101), the refrigerator (102), and / or the at least one external electronic device (103) may be connected through a network based on the registered user account.

[0072] In one embodiment, the electronic device (101) may be used to manage a plurality of refrigerators, including a refrigerator (102). Each of the plurality of refrigerators may be used (or owned) by a different user. For example, the electronic device (101) may be referred to as a server.

[0073] According to one embodiment, the electronic device (101) may be used to manage a refrigerator (102). Although FIG. 1 illustrates the electronic device (101) as being distinct from the refrigerator (102), this is not limiting. At least some or all of the electronic device (101) (or components of the electronic device (101)) may be included in the refrigerator (102).

[0074] According to one embodiment, the electronic device (101) may receive information about the refrigerator (102) from the refrigerator (102). The information about the refrigerator (102) may include information about the usage history of the refrigerator (102). For example, the electronic device (101) may determine the defrosting point of the refrigerator (102) based on the information about the usage history of the refrigerator (102).

[0075] According to one embodiment, the electronic device (101) may obtain information about at least one external electronic device (103) from at least one external electronic device (103) (e.g., information about the use of the at least one external electronic device (103). For example, the at least one external electronic device (103) may include a terminal (e.g., a smartphone or tablet) about a user of the refrigerator (102), a cloud server about a user of the refrigerator (102), home appliances other than the refrigerator (102) (e.g., a washing machine, a dryer, a television, an air conditioner, an induction stove, a gas range, a microwave oven, a door lock, a garage door, a light), or a server for controlling the refrigerator (102) and the home appliances. For example, the electronic device (101) may identify information about a user's schedule (e.g., an outing schedule or a travel schedule) based on the information about the at least one external electronic device (103). For example, the electronic device (101) can identify the time when the user is located in a space (e.g., home) where the refrigerator (102) is located based on information about at least one external electronic device (103).

[0076] According to one embodiment, the electronic device (101) can receive data from the refrigerator (102) or at least one external electronic device (103). The electronic device (101) can store information obtained based on the received data. For example, the electronic device (101) can obtain information about a user. The electronic device (101) can obtain information about a user based on information (or data) received from the refrigerator (102) or at least one external electronic device (103). The electronic device (101) can store information about the user. For example, the electronic device (101) can obtain and store information about the user's age, gender, and / or schedule.

[0077] The electronic device (101) can receive information about the usage history of the refrigerator (102) from the refrigerator (102). Based on the information about the usage history of the refrigerator (102), the electronic device (101) can determine a time for defrosting the refrigerator (102) and provide the determined time to the refrigerator (102).

[0078] According to one embodiment, the refrigerator (102) can generate cold air and maintain a low temperature inside the refrigerator (102) by repeating the compression process, condensation process, expansion process, and evaporation process of the refrigerant. The compressed liquid refrigerant can pass through the inside of the evaporator of the refrigerator (102). The refrigerant can vaporize according to the low pressure at the outlet side of the evaporator. As the refrigerant vaporizes, the heat energy around the evaporator is used to vaporize the refrigerant, and the temperature inside the refrigerator (102) can be lowered. Based on the decrease in the temperature of the evaporator, moisture around the evaporator can freeze on the surface of the evaporator. Therefore, frost can form on the surface of the evaporator.

[0079] For example, the formed frost can be removed within the refrigerator (102) through a defrosting operation. For example, the defrosting operation can be performed by increasing the temperature of the evaporator surface to remove frost on the evaporator surface. As the temperature of the evaporator surface increases, the frost melts, allowing the frost to be removed. After the removal of frost through the defrosting operation is completed, the refrigerator (102) can lower the temperature of the storage compartment again through the evaporator.

[0080] For defrosting, the temperature of the evaporator surface increases, which may increase the temperature of the storage compartment (e.g., refrigerator, freezer, or variable temperature compartment). As the temperature around the evaporator increases, warm air may move toward the area where food is located. As the warm air moves toward the area where food is located, the temperature of the food may temporarily increase. This increase in the temperature of the food may cause discomfort to the user of the refrigerator (102).

[0081] Accordingly, the electronic device (101) can identify a time when the user of the refrigerator (102) is not using the refrigerator (102) as a time to perform defrosting of the refrigerator (102). For example, the electronic device (101) can obtain information about the usage pattern of the refrigerator (102) based on information about the usage history of the refrigerator (102). The electronic device (101) can determine a time to perform defrosting based on the information about the usage pattern of the refrigerator (102) and information obtained from at least one external electronic device (103) (e.g., information about the usage of at least one external electronic device (103). For example, the electronic device (101) can determine a time (or time period) when the refrigerator (102) is least likely to be used based on the information about the usage pattern of the refrigerator (102). The electronic device (101) can cause the refrigerator (102) to perform defrosting at the determined time.

[0082] In the following specification, specific operations of an electronic device (101) for identifying (or predicting) a plurality of candidate time points based on time intervals in which the refrigerator (102) is unlikely to be used, and determining a time point for performing defrosting in the refrigerator (102) among the plurality of candidate time points will be described.

[0083] FIG. 2A is a simplified block diagram of an electronic device according to one embodiment.

[0084] Referring to FIG. 2A, the electronic device (101) may include a processor (210), a communication circuit (220), and / or a memory (230). Depending on the embodiment, the electronic device (101) may include at least one of the processor (210), the communication circuit (220), and the memory (230). For example, at least some of the processor (210), the communication circuit (220), and the memory (230) may be omitted depending on the embodiment. The electronic device (101) may correspond to the electronic device (1201) or the server (1208) illustrated in FIG. 12. For example, the electronic device (101) may include at least some of the components of the electronic device (1201) of FIG. 12.

[0085] According to one embodiment, the electronic device (101) may include a processor (210). The processor (210) may be operatively or operably coupled with or connected to a communication circuit (220) and a memory (230). The processor (210) being operatively coupled with or connected to the communication circuit (220) and the memory (230) may mean that the processor (210) can control the communication circuit (220) and the memory (230). For example, the communication circuit (220) and the memory (230) may be controlled by the processor (210).

[0086] Although illustrated based on different blocks, the embodiment is not limited thereto, and some of the hardware of FIG. 2A (e.g., at least a portion of the processor (210), the communication circuit (220), and the memory (230)) may be included in a single integrated circuit, such as a system on a chip (SoC).

[0087] According to one embodiment, the processor (210) may include hardware components for processing data based on one or more instructions. The hardware components for processing data may include, for example, an arithmetic and logic unit (ALU), a field programmable gate array (FPGA), and / or a central processing unit (CPU). For example, the processor (210) may correspond to the processor (1220) of FIG. 12.

[0088] For example, the processor (210) may include an application processor, a supplementary processor (e.g., a sensor hub, a microcontroller unit (MCU)), a central processor unit (CPU), a neural processing unit (NPU), a graphic processing unit (GPU), and / or a processor for IoT (e.g., a processor integrated with a communication module).

[0089] According to one embodiment, the electronic device (101) may include a communication circuit (220). For example, the communication circuit (220) may be used for various radio access technologies (RATs). For example, the communication circuit (220) may be used to perform Bluetooth communication, wireless local area network (WLAN) communication, Zigbee communication, near field communication (NFC), ultra wide band (UWB) communication, radio-frequency identification (RFID) communication, or ANT+ communication. For example, the communication circuit (220) may be used to perform cellular communication (e.g., fourth generation (4G) communication, fifth generation (5G) communication, sixth generation (6G) communication, or narrow band IoT (NB-IoT)). For example, the processor (210) may establish a connection with the refrigerator (102) through the communication circuit (220). For example, the processor (210) may establish a connection with the refrigerator (102) via at least one of various radio access technologies (RATs). For example, the processor (210) may establish a connection with at least one external electronic device (103) via the communication circuit (220). For example, the processor (210) may establish a connection with at least one external electronic device (103) via at least one of various radio access technologies (RATs). For example, the communication circuit (220) may correspond to at least a part of the communication module (1290) of FIG. 12.

[0090] According to one embodiment, the electronic device (101) may include a memory (230). The memory (230) may be used to store information or data. For example, the memory (230) may be used to store data obtained from a user. For example, the memory (230) may be a volatile memory unit or units. For example, the memory (230) may be a non-volatile memory unit or units. As another example, the memory (230) may be another form of computer-readable media, such as a magnetic or optical disk. For example, the memory (230) may correspond to the memory (1230) of FIG. 12.

[0091] For example, the memory (230) may store data obtained based on operations performed by the processor (210). For example, the memory (230) may store information regarding the refrigerator (102). For example, the memory (230) may store information regarding the usage history of the refrigerator (102). For example, the memory (230) may store information for determining (or identifying) a duration for defrosting the refrigerator (102). For example, the memory (230) may store information regarding the usage of at least one external electronic device (103). For example, the memory (230) may store information regarding the user of the refrigerator (102). For example, the memory (230) may store information regarding the user's schedule.

[0092] FIG. 2b illustrates components of an electronic device according to one embodiment.

[0093] The components illustrated in FIG. 2b represent units that process at least one function or operation, and may be implemented using hardware, software, or a combination of hardware and software. Depending on the embodiment, at least some of the components illustrated in FIG. 2b may be omitted. Depending on the embodiment, at least some of the components illustrated in FIG. 2b may be included in and operated by a refrigerator (102) or other electronic device.

[0094] The terms '...bu', '...gi', etc., described below, mean a unit that processes at least one function or operation, and this can be implemented by hardware, software, or a combination of hardware and software.

[0095] Referring to FIG. 2B, the electronic device (101) may include a management device (250) and a prediction device (260). For example, the management device (250) and the prediction device (260) may be logically separated within the electronic device (101). Depending on the embodiment, the management device (250) and the prediction device (260) may also be physically separated. The management device (250) and the prediction device (260) may be controlled by the processor (210).

[0096] For example, the management device (250) may be used to obtain and store information (or data) about the refrigerator (102). The prediction device (260) may be used to predict the amount of frost in the refrigerator (102).

[0097] According to one embodiment, the management device (250) may process (or preprocess) data regarding the refrigerator (102) and store the processed data. For example, information related to defrosting performed in the refrigerator (102) may be summarized and stored. The defrosting-related information may be summarized based on the characteristics of the data regarding the refrigerator (102). For example, the number of times the door of the refrigerator (102) has been opened and closed and / or the amount of power consumed may be stored based on accumulated values ​​over a specified period of time.

[0098] For example, the management device (250) can manage (or set) the time for performing defrosting of the refrigerator (102). For example, the management device (250) can transmit a signal to the refrigerator (102) that causes the defrosting operation of the refrigerator (102) to be performed at the time for performing defrosting of the refrigerator (102).

[0099] For example, the management device (250) may include a data collection unit (270), a refrigerator control unit (280), and a database (290).

[0100] For example, the data collection unit (270) may include a refrigerator data acquisition unit (271), a refrigerator data processing unit (272), and / or a user data acquisition unit (273). The refrigerator data acquisition unit (271) may collect data (or information) regarding the refrigerator (102). The refrigerator data processing unit (272) may process (or process) the data (or information) regarding the refrigerator (102) and store the processed data in the database (290) (or memory (230)). The data regarding the refrigerator (102) may include data regarding the evaporator of the refrigerator (102) and data regarding the external environment of the refrigerator (102). The data regarding the refrigerator (102) may include information (or data) regarding the usage history of the refrigerator (102).

[0101] For example, information about the refrigerator (102) received from the refrigerator (102) may be in a compressed state. The refrigerator data processing unit (272) may process the compressed data and store the processed data in the database (290) (or memory (230)).

[0102] For example, information about the refrigerator (102) received from the refrigerator (102) (e.g., information about the usage history of the refrigerator (102)) may include information about the number of times the door of the refrigerator (102) has been opened and closed, information about the number of times the water purifier included in the refrigerator (102) has been used, and information about the number of times the ice maker included in the refrigerator (102) has been used. According to an embodiment, the information about the refrigerator (102) may be received in real time. According to an embodiment, the information about the refrigerator (102) may be received periodically.

[0103] For example, information about a refrigerator (102) can be processed in a refrigerator data processing unit (272). The refrigerator data processing unit (272) can process information about the refrigerator (102) into a form that is easy to use and store it in a database (290) (or memory (230)).

[0104] For example, the user data acquisition unit (273) may acquire information about the use of the user of the refrigerator (102) and / or at least one external electronic device (103) owned by the user, and store the acquired information in the database (290) (or memory (230)). For example, the information about the use of the at least one external electronic device (103) may include information about the user of the at least one external electronic device (103). As an example, the user data acquisition unit (273) may acquire information about the user from at least one of the user's terminal, the user's cloud server, or a server for managing the user's home appliances. The information about the user may include at least one of the user's gender, age, or schedule. According to an embodiment, the information about the user may also include at least one of the user's family's gender, age, and schedule.

[0105] According to one embodiment, the refrigerator control unit (280) may include a refrigerator control interface (281) and / or a communication interface (282). The refrigerator control interface (281) may be used to provide the refrigerator (102) with information regarding a timing for performing defrosting of the refrigerator (102). For example, the refrigerator control interface (281) may be used to transmit a signal to the refrigerator (102) that causes the refrigerator (102) to perform a defrosting operation at a timing for performing defrosting of the refrigerator (102). The communication interface (282) may be used to transmit and receive data (or information) to and from the prediction device (260). The communication interface (282) may be used to transmit information stored in the database (290) (e.g., information regarding the usage history of the refrigerator (102)) to the prediction device (260). The communication interface (282) can be used to receive (or obtain) information from the prediction device (260) regarding the timing for performing defrosting of the refrigerator (102).

[0106] According to one embodiment, the database (290) may be used to store information about the refrigerator (102) and / or information about the user. The database (290) may be configured based on at least a portion of the memory (230).

[0107] In one embodiment, the prediction device (260) can be used to obtain information about the usage pattern of the refrigerator (102). The prediction device (260) can be used to identify the predicted usage pattern of the refrigerator (102). In one embodiment, the prediction device (260) can be used to identify (or predict) the duration for defrosting the refrigerator (102).

[0108] For example, the prediction device (260) may include a prediction model (261) and a communication interface (264). The prediction model (261) may include a learning unit (262) and a prediction unit (263). The prediction model (261) may be configured based on artificial intelligence. The prediction model (261) may learn information about the refrigerator (102) through the learning unit (262). The prediction model (261) may predict a usage pattern of the refrigerator (102) through the prediction unit (263). For example, at least a portion of the information about the refrigerator (102) (e.g., information about the usage history of the refrigerator (102)) may be set as input data of the prediction model (261). The usage pattern of the refrigerator (102) may be set as output data of the prediction model (261).

[0109] For example, the prediction model (261) may be configured based on at least one of a linear regression model, a decision-tree model, a multi-layer-perception (MLP), a convolution network (CVNet), and / or a long short term memory (LSTM).

[0110] According to one embodiment, the prediction model (261) may include a first prediction model and a second prediction model. For example, the first prediction model may be configured to identify (or predict) a user's usage pattern during a workday. The second prediction model may be configured to identify (or predict) a user's usage pattern during a holiday. An operation for identifying a user's usage pattern during a workday and / or holiday using the first and second prediction models will be described below in FIG. 4.

[0111] For example, the communication interface (264) may be used to communicate with the management device (250). The communication interface (264) may be used to receive information about the refrigerator (102) from the management device (250), such as information about the usage history of the refrigerator (102). The communication interface (264) may be used to transmit information about a time for performing defrosting of the refrigerator (102) to the management device (250).

[0112] The components included in the above-described management device (250) and prediction device (260) may be configured based on instructions. The components configured based on instructions may be included in the memory (230) of the electronic device (101).

[0113] In FIG. 2B, the electronic device (101) is illustrated as including a management device (250) and a prediction device (260), but is not limited thereto. The management device (250) and the prediction device (260) may be composed of different electronic devices. For example, the management device (250) may be referred to as a first electronic device (or a first server, a management server). For example, the prediction device (260) may be referred to as a second electronic device (or a second server, a prediction server).

[0114] Depending on the embodiment, at least some or all of the components of the management device (250) may be included in the prediction device (260). Depending on the embodiment, at least some or all of the components of the prediction device (260) may be included in the management device (250).

[0115] According to an embodiment, at least some of the components included in the electronic device (101) (e.g., the management device (250) and the prediction device (260)) may be included in the refrigerator (102).

[0116] FIG. 3 illustrates an example of operation of an electronic device for identifying a usage pattern of a refrigerator, according to one embodiment.

[0117] Referring to FIG. 3, the processor (210) may receive information about the usage history of the refrigerator (102) from the refrigerator (102). For example, the processor (210) may receive information about the usage history of the refrigerator (102) for one week. The information about the usage history of the refrigerator (102) for one week may be represented as in graphs (301) to (307).

[0118] Each of the graphs (301) to (307) may represent the number of times the refrigerator (102) is used over time. For example, the number of times the refrigerator (102) is used may represent the number of times the door of the refrigerator (102) is opened and closed. For example, the number of times the refrigerator (102) is used may represent the time the door of the refrigerator (102) is opened. For example, the number of times the refrigerator (102) is used may represent the number of times the ice maker and / or the water purifier (or water cooler) included in the refrigerator (102) is used. For example, the number of times the refrigerator (102) is used may be identified based on at least one of the number of times the door of the refrigerator (102) is opened and closed, the time the door of the refrigerator (102) is opened, and / or the number of times the ice maker and / or the water purifier (or water cooler) included in the refrigerator (102) is used.

[0119] For example, graph (301) may represent the number of times a refrigerator (102) was used according to the time on Monday. Graph (302) may represent the number of times a refrigerator (102) was used according to the time on Tuesday. Graph (303) may represent the number of times a refrigerator (102) was used according to the time on Wednesday. Graph (304) may represent the number of times a refrigerator (102) was used according to the time on Thursday. Graph (305) may represent the number of times a refrigerator (102) was used according to the time on Friday. Graph (306) may represent the number of times a refrigerator (102) was used according to the time on Saturday. Graph (307) may represent the number of times a refrigerator (102) was used according to the time on Sunday.

[0120] According to one embodiment, the processor (210) of the electronic device (101) can obtain information about the usage pattern of the refrigerator (102) for one day based on information about the usage history of the refrigerator (102) for one week.

[0121] For example, the processor (210) may set information about the usage history of the refrigerator (102) for one week as input to the prediction model (261). The processor (210) may input information about the usage history of the refrigerator (102) for one week into the prediction model (261). Based on the output of the prediction model (261), the processor (210) may obtain information about the usage pattern of the refrigerator (102) for one day.

[0122] The processor (210) can input data (or information) regarding the graph (301) to the graph (307) into the prediction model (261). Based on the output of the prediction model (261), the processor (210) can obtain information regarding the usage pattern of the refrigerator (102) for a day. The information regarding the usage pattern of the refrigerator (102) for a day can be represented as a graph (310). For example, the graph (310) can represent the usage probability of the refrigerator (102) for a day. According to an embodiment, the graph (310) can represent the predicted number of times the refrigerator (102) is used for a day.

[0123] According to an embodiment, the processor (210) may identify the average usage history by time period based on the graphs (301) to (307) as information regarding the usage pattern of the refrigerator (102) for the day. For example, information regarding the usage pattern of the refrigerator (102) for the day may be represented as in the graph (310).

[0124] According to one embodiment, the processor (210) may identify a plurality of candidate points in time for performing defrosting of the refrigerator (102) based on information (or a graph (310)) regarding the usage pattern of the refrigerator (102) for a day. The processor (210) may determine one of the plurality of candidate points in time as a point in time for performing defrosting of the refrigerator (102).

[0125] For example, the processor (210) may identify a candidate time point with the lowest probability of use of the refrigerator (102) among a plurality of candidate time points as a time point for performing defrosting of the refrigerator (102). As an example, the processor (210) may identify a duration for defrosting of the refrigerator (102) as 3 hours. The processor (210) may identify the duration for defrosting as 3 hours based on the usage history of the refrigerator (102). The processor (210) may identify a plurality of candidate time points based on information about the usage pattern of the refrigerator (102) for a day. The processor (210) may identify 0 o'clock, 1 o'clock, and 2 o'clock as a plurality of candidate time points. The processor (210) may identify that the probability of use of the refrigerator (102) is the lowest from 1 o'clock to 4 o'clock. The processor (210) may identify 1 o'clock as a time point for performing defrosting of the refrigerator (102).

[0126] While Figure 3 illustrates an example of configuring information regarding daily usage patterns in one-hour increments, this is merely exemplary and not limiting. The information regarding daily usage patterns, as described below, can be structured in various time units, including 10 minutes or 30 minutes.

[0127] FIG. 4 illustrates an example of operation of an electronic device for identifying a usage pattern of a refrigerator, according to one embodiment.

[0128] Referring to FIG. 4, the processor (210) may receive information about the usage history of the refrigerator (102) from the refrigerator (102). For example, the processor (210) may receive information about the usage history of the refrigerator (102) for one week. The information about the usage history of the refrigerator (102) for one week may be represented as in graphs (401) to (407).

[0129] Each of the graphs (401) to (407) may represent the number of times the refrigerator (102) is used over time. For example, the number of times the refrigerator (102) is used may represent the number of times the door of the refrigerator (102) is opened and closed. For example, the number of times the refrigerator (102) is used may represent the time the door of the refrigerator (102) is opened. For example, the number of times the refrigerator (102) is used may represent the number of times the ice maker and / or the water purifier (or water cooler) included in the refrigerator (102) is used. For example, the number of times the refrigerator (102) is used may be identified based on at least one of the number of times the door of the refrigerator (102) is opened and closed, the time the door of the refrigerator (102) is opened, and / or the number of times the ice maker and / or the water purifier (or water cooler) included in the refrigerator (102) is used. Weights may be set for the number of times the door of a refrigerator (102) (e.g., a refrigerator or a freezer) is opened and closed, the time the door of the refrigerator (102) is opened, and / or the number of times the ice maker and / or water purifier (or water cooler) included in the refrigerator (102) is used. The processor (210) may identify the number of times the refrigerator (102) is used based on the set weights. For example, the weight for the number of times the door of the freezer is opened and closed may be set higher than the weight for the number of times the door of the refrigerator is opened and closed.

[0130] Graph (401) may represent the number of times a refrigerator (102) was used according to the time on Monday. Graph (402) may represent the number of times a refrigerator (102) was used according to the time on Tuesday. Graph (403) may represent the number of times a refrigerator (102) was used according to the time on Wednesday. Graph (404) may represent the number of times a refrigerator (102) was used according to the time on Thursday. Graph (405) may represent the number of times a refrigerator (102) was used according to the time on Friday. Graph (406) may represent the number of times a refrigerator (102) was used according to the time on Saturday. Graph (407) may represent the number of times a refrigerator (102) was used according to the time on Sunday.

[0131] In one embodiment, the processor (210) may identify Monday through Friday as the user's working days. The processor (210) may identify Saturday and Sunday as the user's holidays. The user's working days and holidays may be identified differently depending on the user's age, occupation, gender, nationality, and / or the country in which the refrigerator (102) is located.

[0132] For example, information regarding the usage history of a refrigerator (102) may include information regarding the usage history on working days and information regarding the usage history on holidays. The processor (210) may identify information (or data) according to graphs (401), (402), (403), (404), and (405) as information regarding the usage history on working days. The processor (210) may identify information (or data) according to graphs (406) and (407) as information regarding the usage history on holidays.

[0133] According to one embodiment, the prediction model (261) of FIG. 2B may include a first prediction model (410) and a second prediction model (420). The processor (210) may input information regarding usage history on a workday into the first prediction model (410) for identifying usage patterns on a workday. The processor (210) may obtain information regarding usage patterns on a workday based on the output of the first prediction model (410). The information regarding usage patterns on a workday may be represented as a graph (451). The processor (210) may input information regarding usage history on a holiday into the second prediction model (420) for identifying usage patterns on a holiday. The processor (210) may obtain information regarding usage patterns on a holiday based on the output of the second prediction model (420). The information regarding usage patterns on a holiday may be represented as a graph (452).

[0134] For example, the graph (451) may represent the probability of use of a refrigerator (102) on a workday. Depending on the embodiment, the graph (451) may represent the predicted number of uses of the refrigerator (102) on a workday. For example, the graph (452) may represent the probability of use of a refrigerator (102) on a holiday. Depending on the embodiment, the graph (452) may represent the predicted number of uses of the refrigerator (102) on a holiday.

[0135] According to one embodiment, the processor (210) may identify a plurality of candidate points in time for performing defrosting of the refrigerator (102) based on information about the usage pattern of the refrigerator (102) on working days and information about the usage pattern of the refrigerator (102) on holidays. The processor (210) may determine one of the plurality of candidate points in time as the point in time for performing defrosting of the refrigerator (102).

[0136] Although FIG. 4 illustrates an example in which weekdays (e.g., Monday through Friday) are workdays and weekends (e.g., Saturday and Sunday) are holidays, the present invention is not limited thereto. Workdays and holidays may be set differently based on the user's age, gender, occupation, and / or nationality. For example, holidays may vary by country or occupation. For example, if the processor (210) identifies the user as working on the weekend, the processor (210) may identify the weekend as a workday.

[0137] In FIG. 4, an example is described in which information regarding the usage pattern of a refrigerator (102) is obtained based on the usage history of the refrigerator (102) for one week, but this is for convenience of explanation and is not limited thereto. The information regarding the usage pattern of the refrigerator (102) described in the present disclosure may also be obtained based on a long-term usage history for three months (or six months).

[0138] FIG. 5 illustrates an example of the operation of an electronic device for identifying a time to perform defrosting based on a usage pattern of a refrigerator, according to one embodiment.

[0139] Referring to FIG. 5, the processor (210) of the electronic device (101) can identify information regarding the daily usage pattern of the refrigerator (102). For example, the processor (210) can identify information regarding the daily usage pattern of the refrigerator (102) based on information regarding the usage history of the refrigerator (102). The information regarding the daily usage pattern of the refrigerator (102) can be represented as a graph (510). For example, the graph (510) can correspond to the graph (310) of FIG. 3 or the graph (451) of FIG. 4.

[0140] According to one embodiment, the processor (210) can identify a usage pattern after a reference time period (535) (e.g., 24 hours) from the current point in time (521). For example, information regarding the usage pattern of the current day and information regarding the usage pattern of tomorrow can be identified.

[0141] Information about the current day's usage pattern can be represented as in graph (520). Information about tomorrow's usage pattern can be represented as in graph (530). Graph (520) can correspond to graph (510). Graph (530) can correspond to graph (510).

[0142] The processor (210) can identify a usage pattern during a reference time interval (535) from the current point in time (521) based on information about the usage pattern of the current date and information about the usage pattern of tomorrow. The processor (210) can identify a time interval (541) for performing defrosting within the usage pattern during the reference time interval (535) from the current point in time (521). The processor (210) can identify a time interval for performing defrosting during a duration (540) for defrosting from the time interval (541).

[0143] According to one embodiment, the processor (210) may identify the duration (540) for defrosting as 6 hours. The processor (210) may identify that the duration (540) required for defrosting the refrigerator (102) is 6 hours. For example, the processor (210) may identify that the duration (540) required for defrosting the refrigerator (102) is 6 hours based on information about the usage history of the refrigerator (102). The processor (210) may identify the time point (541) for performing defrosting based on information about the usage pattern of the current day, information about the usage pattern of tomorrow, and the duration (540).

[0144] FIG. 6 illustrates an example of the operation of an electronic device for identifying a time to perform defrosting based on a usage pattern of a refrigerator, according to one embodiment.

[0145] Referring to FIG. 6, the processor (210) can obtain information on the usage pattern of the refrigerator (102) based on information on the usage history of the refrigerator (102). For example, the processor (210) can obtain information on the usage pattern of the refrigerator (102) on a working day using a first prediction model (e.g., the first prediction model (410) of FIG. 4). The processor (210) can obtain information on the usage pattern of the refrigerator (102) on a holiday using a second prediction model (e.g., the second prediction model (420) of FIG. 4).

[0146] For example, information about the usage pattern of a refrigerator (102) on a workday may be represented as in graph (601). Graph (601) may correspond to graph (451) of FIG. 4. Information about the usage pattern of a refrigerator (102) on a holiday may be represented as in graph (602). Graph (602) may correspond to graph (452) of FIG. 4.

[0147] According to one embodiment, the date of the current point in time (621) may correspond to a work day, and tomorrow may correspond to a holiday. Information about the usage pattern of the current date may be represented as in the graph (610). Information about the usage pattern of tomorrow may be represented as in the graph (620). The graph (610) may correspond to the graph (601) representing information about the usage pattern of a work day. The graph (620) may correspond to the graph (602) representing information about the usage pattern of a holiday. The processor (210) may identify the usage pattern during a reference time interval (650) from the current point in time (621) based on the information about the usage pattern of the current date and the information about the usage pattern of tomorrow. The processor (210) may identify a time point (653) for performing the defrosting within the usage pattern during the reference time interval (650) from the current point in time (621). The processor (210) can identify the time period for performing the defrost from the time point (653) to the duration (651).

[0148] According to one embodiment, the date of the current point in time (621) may correspond to a holiday, and tomorrow may correspond to a work day. Information about the usage pattern of the current date may be represented as a graph (630). Information about the usage pattern of tomorrow may be represented as a graph (640). The graph (630) may correspond to the graph (602) representing information about the usage pattern of a holiday. The graph (640) may correspond to the graph (601) representing information about the usage pattern of a work day. The processor (210) may identify a usage pattern during a reference time interval (650) from the current point in time (621) based on the information about the usage pattern of the current date and the information about the usage pattern of tomorrow. The processor (210) may identify a time point (654) for performing the defrosting within the usage pattern during the reference time interval (650) from the current point in time (621). The processor (210) can identify the time period for performing the defrost from the point in time (654) to the duration (652).

[0149] According to one embodiment, each of the duration (651) and / or duration (652) described above may be identified (or predicted) based on information about the usage history of the refrigerator (102). The processor (210) may identify (or predict) each of the duration (651) and duration (652) based on information about the usage history of the refrigerator (102). For example, information about the usage history of the refrigerator (102) may include the number of times the door of the refrigerator (102) has been opened and closed. According to an embodiment, the processor (210) may identify (or predict) each of the duration (651) and duration (652) based on information about the usage history of the refrigerator (102) as well as information about the external environment (e.g., humidity, temperature, or weather).

[0150] FIG. 7 illustrates an example of the operation of an electronic device for identifying a time to perform defrosting based on a usage pattern of a refrigerator, according to one embodiment.

[0151] Referring to FIG. 7, the processor (210) can obtain information on the usage pattern of the refrigerator (102) based on information on the usage history of the refrigerator (102). For example, the processor (210) can obtain information on the usage pattern of the refrigerator (102) on a working day using a first prediction model (e.g., the first prediction model (410) of FIG. 4). The processor (210) can obtain information on the usage pattern of the refrigerator (102) on a holiday using a second prediction model (e.g., the second prediction model (420) of FIG. 4).

[0152] For example, information about the usage pattern of a refrigerator (102) on a workday may be represented as in graph (710). Graph (710) may correspond to graph (451) of FIG. 4. Information about the usage pattern of a refrigerator (102) on a holiday may be represented as in graph (720). Graph (720) may correspond to graph (452) of FIG. 4.

[0153] According to one embodiment, the date of the current point in time (721) corresponds to a holiday, and tomorrow may also correspond to a holiday. Information about the usage pattern of the current date may be represented as a graph (730). Information about the usage pattern of tomorrow may be represented as a graph (740). Graphs (730) and (740) may correspond to graph (720) representing information about the usage pattern of a holiday. The processor (210) may identify a usage pattern during a reference time interval (750) from the current point in time (721) based on the information about the usage pattern of the current date and the information about the usage pattern of tomorrow. The processor (210) may identify a plurality of candidate time intervals for performing the decomposition within the usage pattern during the reference time interval (750) from the current point in time (721).

[0154] For example, the processor (210) may identify the duration for defrosting as 4 hours. The processor (210) may identify time points (753) and (754) as multiple candidate time points based on information about the usage pattern of the current day, information about the usage pattern of tomorrow, and the duration for defrosting. For example, the time interval (751) during the duration for defrosting from time point (753) and the time interval (752) during the duration for defrosting from time point (754) may be identified as candidate time intervals for the defrosting operation.

[0155] According to one embodiment, the processor (210) may determine a time point for performing defrosting of the refrigerator (102) among a plurality of candidate time points based on obtaining information about the use of at least one external electronic device (103) from at least one external electronic device (103).

[0156] For example, the processor (210) may identify information about the user's schedule based on information about the use of at least one external electronic device (103). For example, the processor (210) may identify the user's schedule stored in the calendar application of the user's terminal. The processor (210) may identify that the user's schedule is from 5:00 PM to 8:00 PM outside the space where the refrigerator (102) is located.

[0157] The processor (210) may determine a time point for performing defrosting of the refrigerator (102) among a plurality of candidate time points based on information about the user's schedule. The processor (210) may identify that the user is outside the space where the refrigerator (102) is located during a time interval (751) for time point (753) among time points (753) and (754). The processor (210) may identify time point (753) as the time point for performing defrosting.

[0158] According to an embodiment, the processor (210) may determine the timing for performing defrosting based on information about not only the user but also other users in the space where the refrigerator (102) is located. For example, the processor (210) may identify users using the refrigerator (102) based on information obtained through a server for controlling home appliances. The processor (210) may determine the timing for performing defrosting based on information about the users (e.g., the users' schedules).

[0159] Figure 8 illustrates a flowchart of the operations of an electronic device for identifying a point in time for performing a task, according to one embodiment. In the following embodiments, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0160] Referring to FIG. 8, in operation 810, the processor (210) of the electronic device (101) may receive information about the usage history of the refrigerator (102) from the refrigerator (102). For example, the processor (210) may receive information about the usage history of the refrigerator (102) from the refrigerator (102) connected to the electronic device (101) via a network.

[0161] For example, information about the usage history of a refrigerator (102) may include information about the usage history of the refrigerator (102) on working days and information about the usage history of the refrigerator (102) on holidays. The processor (210) may identify information about the usage history of the refrigerator (102) on working days and information about the usage history of the refrigerator (102) on holidays based on the information about the usage history of the refrigerator (102).

[0162] The processor (210) may identify information about the usage history of the refrigerator (102) on a working day and information about the usage history of the refrigerator (102) on a holiday based on at least one of the age, gender, occupation, nationality, and / or the country in which the refrigerator (102) is located of the user of the refrigerator (102). For example, the processor (210) may identify that the working day of the user of the refrigerator (102) corresponds to a weekday and that the holiday corresponds to a weekend. The processor (210) may identify the usage history on a weekday as the usage history on a working day. The processor (210) may identify the usage history on a weekend as the usage history on a holiday. According to an embodiment, even if it is a weekday, if the date is a holiday, the usage history on that date may be identified as the usage history on a holiday.

[0163] In operation 820, the processor (210) may input information about the usage history of the refrigerator (102) into a prediction model for setting the defrosting time of the refrigerator (102).

[0164] In operation 830, the processor (210) can obtain information about the usage pattern of the refrigerator based on the output of the prediction model.

[0165] For example, the prediction model may include a first prediction model for identifying a usage pattern on a workday and a second prediction model for identifying a usage pattern on a holiday. The processor (210) may input information about the usage history on a workday into the first prediction model. The processor (210) may input information about the usage history on a holiday into the second prediction model. The processor (210) may obtain information about the usage pattern on a workday and information about the usage pattern on a holiday based on the output of the first prediction model and the output of the second prediction model. For example, the processor (210) may obtain information about the usage pattern on a workday based on the output of the first prediction model. The processor (210) may obtain information about the usage pattern on a holiday based on the output of the second prediction model.

[0166] According to one embodiment, the processor (210) may obtain information regarding the usage history of the refrigerator (102) for a specified time period (e.g., one month or six months) based on identifying that a predictive model is not stored. The processor (210) may identify the country in which the refrigerator (102) is located and the local time of the country. Based on the country and the local time of the country, the processor (210) may divide the information regarding the usage history of the refrigerator (102) for the specified time period (e.g., one month or six months) into information regarding the usage history on working days and information regarding the usage history on holidays. The processor (210) may generate a first predictive model based on the information regarding the usage history on working days. The processor (210) may generate a second predictive model based on the information regarding the usage history on holidays. For example, holidays (e.g., national holidays) may vary depending on the country. Additionally, working days may vary depending on the country. For example, in a first country, working days may be weekdays and holidays may be weekends. In a second country, working days may be Monday through Saturday and holidays may be Sundays. Accordingly, the processor (210) may generate the first prediction model and the second prediction model based on the country where the refrigerator (102) is located and the local time of the country. In an embodiment, if the country where the refrigerator (102) is located has multiple time zones, the processor (210) may identify one of the multiple time zones based on the specific location of the refrigerator (102). If the processor (210) cannot identify the specific location of the refrigerator (102), the processor (210) may identify the time zone of a densely populated area in the country where the refrigerator (102) is located as the local time.

[0167] In some embodiments, information regarding the usage history of the refrigerator (102) may not be sufficient to generate a predictive model. In this case, the processor (210) may generate a standard usage pattern for other refrigerators in the country where the refrigerator (102) is located, and identify the standard usage pattern as the usage pattern of the refrigerator (102).

[0168] According to one embodiment, the processor (210) may obtain information about a usage pattern of the refrigerator (102) through a predictive model based on information about the user, including at least one of the user's gender, age, nationality, and / or the country in which the refrigerator (102) is located.

[0169] In operation 840, the processor (210) may identify a plurality of candidate points of time for performing defrosting of the refrigerator (102). The processor (210) may identify a plurality of candidate points of time for performing defrosting of the refrigerator (102) based on information about the usage pattern of the refrigerator (102).

[0170] For example, the processor (210) may identify a duration for defrosting based on information about the usage history of the refrigerator (102). The processor (210) may identify multiple candidate points in time for defrosting the refrigerator (102) based on information about the usage pattern of the refrigerator (102) and the duration for defrosting.

[0171] For example, the processor (210) may identify a time period during which the user is expected not to use the refrigerator (102) based on information about the usage pattern of the refrigerator (102). The processor (210) may identify a plurality of candidate time periods for defrosting the refrigerator (102) based on the time period during which the user is expected not to use the refrigerator (102).

[0172] In operation 850, the processor (210) may determine a time point for performing defrosting of the refrigerator (102) from among a plurality of candidate time points based on obtaining information about the use of at least one external electronic device (103) from at least one external electronic device (103) around the refrigerator (102). For example, the processor (210) may determine a candidate time point having the lowest probability of use of the refrigerator (102) from among the plurality of candidate time points as a time point for performing defrosting of the refrigerator (102).

[0173] According to one embodiment, the processor (210) may obtain information regarding the use of at least one external electronic device (103) from at least one external electronic device (103) around the refrigerator (102). For example, the processor (210) may identify information regarding the user's schedule based on the information regarding the use of the at least one external electronic device (103).

[0174] For example, at least one external electronic device (103) may be located in a space (e.g., a user's home) where a refrigerator (102) is located. The processor (210) may identify a time at which the user is located within the space based on information about the user's schedule. The processor (210) may determine a time at which the refrigerator (102) is defrosted, among a plurality of candidate time points, based on the time at which the user is located within the space. For example, the processor (210) may identify at least one candidate time point corresponding to the time at which the user is located within the space. The processor (210) may determine a candidate time point having the lowest probability of use of the refrigerator (102), among the plurality of candidate time points from which at least one candidate time point has been excluded, as a time point at which the refrigerator (102) is defrosted.

[0175] According to one embodiment, the processor (210) may transmit a signal to the refrigerator (102) via the communication circuit (220) to cause the refrigerator (102) to perform defrosting at the determined time based on determining a time to perform defrosting of the refrigerator (102). The refrigerator (102) may perform the defrosting operation at the determined time based on the received signal.

[0176] According to one embodiment, the processor (210) may update predictive models (e.g., a first predictive model and a second predictive model). For example, the processor (210) may identify information about the usage history of the refrigerator during a reference time period (e.g., one week) among the information about the usage history. The processor (210) may generate another predictive model based on the information about the usage history of the refrigerator during the reference time period. The processor (210) may update the predictive model based on the other predictive model. For example, the processor (210) may update the existing predictive model based on combining the other predictive model with an existing predictive model. The processor (210) may combine the other predictive model with the existing predictive model based on applying a first weight to the existing predictive model and a second weight to the other predictive model. The first weight (e.g., 0.9) may be set higher than the second weight (e.g., 0.1). However, this is not limited to this, and the first weight may be set to be lower than or equal to the second weight.

[0177] In the above-described embodiment, an example in which information regarding the usage history of the refrigerator (102) is input into the prediction model and the usage pattern of the refrigerator (102) is output has been described, but is not limited thereto. Information regarding the usage history of the refrigerator (102) and information regarding the use of at least one external electronic device (103) may be input into the prediction model. The processor (210) may identify a time point for performing defrosting of the refrigerator (102) based on the output of the prediction model. The time point for performing defrosting of the refrigerator (102) may be set as the output of the prediction model.

[0178] FIG. 9 illustrates an example of the operation of an electronic device for identifying a point in time for performing a sacrifice, according to one embodiment.

[0179] Referring to FIG. 9, the processor (210) can obtain (or identify) information regarding a usage pattern of the refrigerator (102). The information regarding the usage pattern of the refrigerator (102) can be represented as a graph (951). Referring to the graph (951), the processor (210) can identify time intervals (961) and (962) as candidate time intervals for performing defrosting of the refrigerator (102). Based on the information regarding the usage history of the refrigerator (102), the processor (210) can identify the duration for defrosting as 4 hours. The processor (210) can identify time points (901), (902), (903), (904), (905), and (906) as a plurality of candidate time points.

[0180] According to one embodiment, the processor (210) may obtain information regarding the use of at least one external electronic device (103). For example, the processor (210) may receive information regarding the use of at least one external electronic device (103) from a server for controlling (or managing) the at least one external electronic device (103). The server may be configured for a smart home service. For example, the server may be configured to control (or manage) IoT (Internet of Things) devices, home appliances, and / or cooking appliances. For example, IoT devices may include lights, motion sensors, parking door sensors, and door sensors (or door locks). For example, home appliances may include air conditioners, washing machines, dryers, televisions, vacuum cleaners, and / or air dressers. Cooking appliances may include ovens, induction cookers, microwave ovens, cooktops, and / or electric rice cookers.

[0181] The processor (210) may identify a time period during which the user is located outside the space where the refrigerator (102) is located based on information regarding the use of at least one external electronic device (103) from the server. In some embodiments, the processor (210) may also receive information from the server regarding a time period during which the user is located outside the space where the refrigerator (102) is located.

[0182] For example, information about a time interval during which a user is located outside the space where a refrigerator (102) is located may be represented as in a graph (952). The time interval (971) may be a time interval during which the user is located outside the space where a refrigerator (102) is located.

[0183] The processor (210) may change information about the usage pattern of the refrigerator (102) based on acquiring information about the usage of at least one external electronic device (103). The information about the changed usage pattern may be represented as in the graph (953). For example, the processor (210) may identify a time section (981), which is a time section remaining after the time section (971) among the time sections (961). The processor (210) may identify that the user may be located within the space where the refrigerator (102) is located in the time section (981). The processor (210) may identify that the time section (981) is a section in which the user may use the refrigerator (102). The processor (210) may change the usage pattern (or usage probability) within the time section (981). The processor (210) may change information about the usage pattern to indicate that the refrigerator (102) may be used in a time interval (981).

[0184] The processor (210) may exclude time points (901), (904), and (905) from among a plurality of candidate time points based on information about the changed usage pattern. The processor (210) may determine one of time points (902), (903), and (906) as a time point for performing defrosting of the refrigerator (102). The processor (210) may identify that the probability of using the refrigerator (102) is the lowest at time point (903). For example, the processor (210) may identify that the probability of using the refrigerator (102) is the lowest during the identified duration (e.g., 4 hours) from time point (903). The processor (210) may determine time point (903) as the time point for performing defrosting.

[0185] FIG. 10 illustrates an example of the operation of an electronic device for identifying a point in time for performing a sacrifice, according to one embodiment.

[0186] Referring to FIG. 10, the processor (210) can change the timing for performing defrosting according to the duration for defrosting. According to one embodiment, the processor (210) can obtain (or identify) information regarding the usage pattern of the refrigerator (102). Information regarding the usage pattern of the refrigerator (102) can be represented as in the graph (1000).

[0187] Referring to graph (1000), the time interval (1010) is 5 hours, and the probability that the user will use the refrigerator (102) can be predicted to be within a reference range. The time interval (1020) is 4 hours, and the probability that the user will use the refrigerator (102) can be predicted to be 0.

[0188] According to one embodiment, the processor (210) can identify a duration for defrosting. Based on the duration for defrosting, the processor (210) can determine a time point for performing defrosting among a plurality of candidate time points.

[0189] For example, the processor (210) may identify the duration for defrosting as 4 hours. The processor (210) may identify time points (1011), (1012), and (1021) as multiple candidate time points for performing defrosting. The processor (210) may identify that the probability that a user will use the refrigerator (102) during the duration for defrosting is the lowest from time point (1021). The processor (210) may determine time point (1021) as the time point for performing defrosting.

[0190] For example, the processor (210) may identify the duration for defrosting as 5 hours. The processor (210) may identify that the probability that a user will use the refrigerator (102) during the duration for defrosting from time point (1011) is the lowest. The processor (210) may determine time point (1011) as the time point for performing defrosting.

[0191] FIG. 11 illustrates an example of operation of an electronic device, a refrigerator, and at least one external electronic device to identify a point in time to perform a freezing operation, according to one embodiment.

[0192] In operation 1101, the refrigerator (102) can obtain information regarding the usage history of the refrigerator (102). The information regarding the usage history of the refrigerator (102) may include information regarding the number of times the door of the refrigerator (102) has been opened and closed, information regarding the number of times the water purifier included in the refrigerator (102) has been used, and information regarding the number of times the ice maker included in the refrigerator (102) has been used. However, the present invention is not limited thereto.

[0193] In operation 1102, the refrigerator (102) can transmit information about the usage history of the refrigerator (102) to the management device (250) of the electronic device (101). The management device (250) can receive information about the usage history of the refrigerator (102) from the refrigerator (102).

[0194] For example, the management device (250) may identify information about the usage history of a workday and information about the usage history of a holiday based on information about the usage history of the refrigerator (102). For example, workdays and holidays may be identified based on the user's age, occupation, gender, nationality, and / or the country in which the refrigerator (102) is located.

[0195] In operation 1103, the management device (250) may transmit (or transfer) information regarding the usage history of the refrigerator (102) to the prediction device (260) to identify the usage pattern of the refrigerator (102). The prediction device (260) may receive (or obtain) information regarding the usage history of the refrigerator (102) from the management device (250).

[0196] In operation 1104, the prediction device (260) may obtain information regarding the usage pattern of the refrigerator (102). The information regarding the usage pattern of the refrigerator (102) may include information regarding the usage pattern on working days and information regarding the usage pattern on holidays.

[0197] For example, the prediction device (260) may include a first prediction model and a second prediction model. The first prediction model may be configured to identify usage patterns on work days. The second prediction model may be configured to identify usage patterns on holidays.

[0198] The prediction device (260) can input information regarding the usage history of a workday into a first prediction model for identifying the usage pattern of a workday. Based on the output of the first prediction model, the prediction device (260) can obtain information regarding the usage pattern of a workday.

[0199] The prediction device (260) can input information regarding holiday usage history into a second prediction model for identifying holiday usage patterns. Based on the output of the second prediction model, the prediction device (260) can obtain information regarding holiday usage patterns.

[0200] In operation 1105, the prediction device (260) may transmit (or transfer) information regarding the usage pattern of the refrigerator (102) to the management device (250). The management device (250) may receive information regarding the usage pattern of the refrigerator (102) from the prediction device (260). The management device (250) may identify information regarding the usage pattern of a workday and information regarding the usage pattern of a holiday. Based on the information regarding the usage pattern of the refrigerator (102), the management device (250) may identify a plurality of candidate time points for performing defrosting of the refrigerator (102). For example, the management device (250) may identify a duration for defrosting based on information regarding the usage history of the refrigerator (102). The duration for defrosting may refer to a time interval required for performing a defrosting operation in the refrigerator (102). The management device (250) can identify multiple candidate points in time for performing the defrost based on information about the usage pattern and the duration for defrosting.

[0201] In operation 1106, the management device (250) may receive information regarding the use of at least one external electronic device (103) from at least one external electronic device (103).

[0202] In operation 1107, the management device (250) can determine a time point for performing defrosting of the refrigerator (102) among a plurality of candidate time points for performing defrosting of the refrigerator (102) based on information regarding the use of at least one external electronic device (103).

[0203] For example, the management device (250) may identify information about the user's schedule based on information about the use of at least one external electronic device (103). As an example, the management device (250) may identify the time at which the user is located in the space where the refrigerator (102) is located. Based on the time at which the user is located in the space, the management device (250) may determine a time at which to perform defrosting of the refrigerator (102) among a plurality of candidate time points.

[0204] In operation 1108, the management device (250) may transmit a signal to the refrigerator (102) causing the refrigerator (102) to perform defrosting at a determined time. The refrigerator (102) may receive the signal from the management device (250) causing the refrigerator (102) to perform defrosting at the determined time. For example, the signal may include information regarding a duration for defrosting and information regarding a time point for performing defrosting.

[0205] In operation 1109, the refrigerator (102) can perform defrosting (or defrosting operation) at a determined time based on the received signal. The refrigerator (102) can perform defrosting for a duration for defrosting from the determined time.

[0206] FIG. 12 is a block diagram of an electronic device within a network environment, according to one embodiment.

[0207] Referring to FIG. 12, in a network environment (1200), an electronic device (1201) may communicate with an electronic device (1202) via a first network (1298) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (1204) or a server (1208) via a second network (1299) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (1201) may communicate with the electronic device (1204) via the server (1208). According to one embodiment, the electronic device (1201) may include a processor (1220), a memory (1230), an input module (1250), an audio output module (1255), a display module (1260), an audio module (1270), a sensor module (1276), an interface (1277), a connection terminal (1278), a haptic module (1279), a camera module (1280), a power management module (1288), a battery (1289), a communication module (1290), a subscriber identification module (1296), or an antenna module (1297). In some embodiments, the electronic device (1201) may omit at least one of these components (e.g., the connection terminal (1278)), or may have one or more other components added. In some embodiments, some of these components (e.g., sensor module (1276), camera module (1280), or antenna module (1297)) may be integrated into one component (e.g., display module (1260)).

[0208] The processor (1220) may control at least one other component (e.g., hardware or software component) of the electronic device (1201) connected to the processor (1220) by executing, for example, software (e.g., program (1240)), and may perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (1220) may store commands or data received from other components (e.g., sensor module (1276) or communication module (1290)) in a volatile memory (1232), process the commands or data stored in the volatile memory (1232), and store result data in a non-volatile memory (1234). According to one embodiment, the processor (1220) may include a main processor (1221) (e.g., a central processing unit or an application processor) or an auxiliary processor (1223) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (1221). For example, when the electronic device (1201) includes the main processor (1221) and the auxiliary processor (1223), the auxiliary processor (1223) may be configured to use less power than the main processor (1221) or to be specialized for a given function. The auxiliary processor (1223) may be implemented separately from the main processor (1221) or as a part thereof.

[0209] The auxiliary processor (1223) may control at least a part of functions or states associated with at least one component (e.g., the display module (1260), the sensor module (1276), or the communication module (1290)) of the electronic device (1201), for example, on behalf of the main processor (1221) while the main processor (1221) is in an inactive (e.g., sleep) state, or together with the main processor (1221) while the main processor (1221) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (1223) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (1280) or a communication module (1290)). In one embodiment, the auxiliary processor (1223) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (1201) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (1208)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0210] The memory (1230) can store various data used by at least one component (e.g., the processor (1220) or the sensor module (1276)) of the electronic device (1201). The data can include, for example, software (e.g., the program (1240)) and input data or output data for commands related thereto. The memory (1230) can include a volatile memory (1232) or a non-volatile memory (1234).

[0211] The program (1240) may be stored as software in memory (1230) and may include, for example, an operating system (1242), middleware (1244), or an application (1246).

[0212] The input module (1250) can receive commands or data to be used in a component of the electronic device (1201) (e.g., a processor (1220)) from an external source (e.g., a user) of the electronic device (1201). The input module (1250) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0213] The audio output module (1255) can output audio signals to the outside of the electronic device (1201). The audio output module (1255) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0214] The display module (1260) can visually provide information to an external party (e.g., a user) of the electronic device (1201). The display module (1260) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (1260) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0215] The audio module (1270) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (1270) can acquire sound through the input module (1250), output sound through the sound output module (1255), or an external electronic device (e.g., electronic device (1202)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (1201).

[0216] The sensor module (1276) can detect the operating status (e.g., power or temperature) of the electronic device (1201) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (1276) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0217] The interface (1277) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (1201) with an external electronic device (e.g., the electronic device (1202)). In one embodiment, the interface (1277) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0218] The connection terminal (1278) may include a connector through which the electronic device (1201) may be physically connected to an external electronic device (e.g., the electronic device (1202)). According to one embodiment, the connection terminal (1278) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0219] The haptic module (1279) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (1279) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0220] The camera module (1280) can capture still images and videos. According to one embodiment, the camera module (1280) may include one or more lenses, image sensors, image signal processors, or flashes.

[0221] The power management module (1288) can manage power supplied to the electronic device (1201). According to one embodiment, the power management module (1288) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0222] A battery (1289) may power at least one component of the electronic device (1201). In one embodiment, the battery (1289) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0223] The communication module (1290) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (1201) and an external electronic device (e.g., electronic device (1202), electronic device (1204), or server (1208)), and the performance of communication through the established communication channel. The communication module (1290) may operate independently from the processor (1220) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (1290) may include a wireless communication module (1292) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (1294) (e.g., a local area network (LAN) communication module, or a power line communication module). Any of these communication modules may communicate with an external electronic device (1204) via a first network (1298) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (1299) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (1292) may use subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (1296) to verify or authenticate the electronic device (1201) within a communication network such as the first network (1298) or the second network (1299).

[0224] The wireless communication module (1292) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (1292) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (1292) may support various technologies for securing performance in high-frequency bands, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (1292) may support various requirements specified in the electronic device (1201), an external electronic device (e.g., the electronic device (1204)), or a network system (e.g., the second network (1299)). According to one embodiment, the wireless communication module (1292) may support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL) each, or 1 ms or less for round trip) for URLLC realization.

[0225] The antenna module (1297) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). According to one embodiment, the antenna module (1297) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (1297) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (1298) or the second network (1299), may be selected from the plurality of antennas, for example, by the communication module (1290). A signal or power may be transmitted or received between the communication module (1290) and an external electronic device via the selected at least one antenna. According to some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (1297).

[0226] According to various embodiments, the antenna module (1297) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band.

[0227] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0228] According to one embodiment, commands or data may be transmitted or received between the electronic device (1201) and an external electronic device (1204) via a server (1208) connected to a second network (1299). Each of the external electronic devices (1202 or 1204) may be the same or a different type of device as the electronic device (1201). According to one embodiment, all or part of the operations executed in the electronic device (1201) may be executed in one or more of the external electronic devices (1202, 1204, or 1208). For example, when the electronic device (1201) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (1201) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (1201). The electronic device (1201) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (1201) may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (1204) may include an Internet of Things (IoT) device. The server (1208) may be an intelligent server utilizing machine learning and / or a neural network.According to one embodiment, an external electronic device (1204) or server (1208) may be included in the second network (1299). The electronic device (1201) may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0229] The electronic device (1201) (or server (1208)) of FIG. 12 may be an example of the electronic device (101) described in FIGS. 1 to 8.

[0230] According to one embodiment, an electronic device may include a communication circuit, a memory storing instructions and including one or more storage media, and at least one processor including a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive information regarding a usage history of a refrigerator from a refrigerator connected to the electronic device via a network. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to input the information regarding the usage history of the refrigerator into a predictive model for setting a defrosting time of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain information regarding a usage pattern of the refrigerator based on an output of the predictive model. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a plurality of candidate points in time for performing defrosting of the refrigerator based on the information about the usage pattern. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine a point in time for performing defrosting of the refrigerator from among the plurality of candidate points in time.

[0231] For example, the information regarding the usage history may include information regarding the usage history on working days and information regarding the usage history on holidays. The prediction model may include a first prediction model for identifying the usage pattern on working days and a second prediction model for identifying the usage pattern on holidays.

[0232] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to input information about the usage history on a workday into the first predictive model and to input information about the usage history on a holiday into the second predictive model. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain information about the usage pattern on a workday and information about the usage pattern on a holiday based on an output of the first predictive model and an output of the second predictive model.

[0233] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a duration for the defrosting based on the information about the usage history. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify the plurality of candidate points in time for performing the defrosting based on the information about the usage pattern and the duration for the defrosting.

[0234] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify information about the user's schedule based on the information about the usage of the at least one external electronic device.

[0235] For example, the at least one external electronic device may be located within a space where the refrigerator is located. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a time at which the user is located within the space based on the information about the user's schedule. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine a time at which the refrigerator is to be defrosted, among the plurality of candidate time points, based on the time at which the user is located within the space.

[0236] The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify at least one candidate time point corresponding to the time at which the user is located within the space. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine, among the plurality of candidate time points from which the at least one candidate time point has been excluded, a candidate time point having a lowest probability of use with respect to use of the refrigerator as the time point for performing the defrosting of the refrigerator.

[0237] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to generate another predictive model based on information about the usage history of the refrigerator during a reference time interval, among the information about the usage history. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to update the predictive model based on the other predictive model.

[0238] For example, the information about the usage pattern of the refrigerator can be obtained through the prediction model based on information about the user, including at least one of the gender, age, and nationality of the user of the refrigerator.

[0239] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to transmit, via the communication circuit, a signal to the refrigerator to cause the refrigerator to perform the defrosting at the time based on determining the time for performing the defrosting of the refrigerator.

[0240] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine, from among the plurality of candidate time points, the time point for performing the defrosting of the refrigerator based on obtaining information about the use of the at least one external electronic device from the at least one external electronic device surrounding the refrigerator.

[0241] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to transmit information about the plurality of candidate time points to the refrigerator.

[0242] According to one embodiment, a method performed by an electronic device may include receiving information regarding a usage history of a refrigerator from a refrigerator connected to the electronic device via a network. The method may include inputting the information regarding the usage history of the refrigerator into a prediction model for setting a defrosting time of the refrigerator. The method may include obtaining information regarding a usage pattern of the refrigerator based on an output of the prediction model. The method may include identifying a plurality of candidate times for performing defrosting of the refrigerator based on the information regarding the usage pattern. The method may include determining a time for performing defrosting of the refrigerator from among the plurality of candidate times.

[0243] For example, the information regarding the usage history may include information regarding the usage history on working days and information regarding the usage history on holidays. The prediction model may include a first prediction model for identifying the usage pattern on working days and a second prediction model for identifying the usage pattern on holidays.

[0244] For example, the method may include an operation of inputting the information regarding the usage history on a workday into the first prediction model and inputting the information regarding the usage history on a holiday into the second prediction model. The method may include an operation of obtaining information regarding the usage pattern on a workday and information regarding the usage pattern on a holiday based on the output of the first prediction model and the output of the second prediction model.

[0245] For example, the method may include an operation of identifying a duration for the defrosting based on the information regarding the usage history. The method may include an operation of identifying a plurality of candidate time points for performing the defrosting based on the information regarding the usage pattern and the duration for the defrosting.

[0246] For example, the method may include an action of identifying information about a schedule of the user based on the information about the usage of the at least one external electronic device.

[0247] For example, the at least one external electronic device may be located within a space where the refrigerator is located. The method may include an operation of identifying a time at which the user is located within the space based on the information regarding the user's schedule. The method may include an operation of determining a time at which the refrigerator is to be defrosted, from among the plurality of candidate time points, based on the time at which the user is located within the space.

[0248] For example, the method may include an operation of identifying at least one candidate time point corresponding to the time at which the user is located within the space. The method may include an operation of determining, among the plurality of candidate time points from which the at least one candidate time point has been excluded, a candidate time point having the lowest probability of use regarding the use of the refrigerator as the time point for performing the defrosting of the refrigerator.

[0249] For example, the method may include an operation of generating another predictive model based on information regarding the usage history of the refrigerator during a reference time period, among the information regarding the usage history. The method may include an operation of updating the predictive model based on the other predictive model.

[0250] For example, the method may include an operation of determining, among the plurality of candidate time points, the time point for performing the defrosting of the refrigerator based on obtaining information about the use of the at least one external electronic device from at least one external electronic device around the refrigerator.

[0251] For example, the method may include transmitting information about the plurality of candidate time points to the refrigerator.

[0252] According to one embodiment, a non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by at least one processor of an electronic device having communication circuitry, cause the electronic device to receive information regarding a usage history of a refrigerator from a refrigerator connected to the electronic device via a network. The one or more programs may include instructions that, when executed by at least one processor of the electronic device having communication circuitry, cause the electronic device to input the information regarding the usage history of the refrigerator into a predictive model for setting a defrosting time of the refrigerator. The one or more programs may include instructions that, when executed by at least one processor of the electronic device having communication circuitry, cause the electronic device to obtain information regarding a usage pattern of the refrigerator based on an output of the predictive model. The one or more programs may include instructions that, when executed by at least one processor of an electronic device having a communication circuit, cause the electronic device to identify a plurality of candidate points in time for performing defrosting of the refrigerator based on the information about the usage pattern. The one or more programs may include instructions that, when executed by at least one processor of an electronic device having a communication circuit, cause the electronic device to determine a point in time for performing defrosting of the refrigerator from among the plurality of candidate points in time.

[0253] According to one embodiment, a refrigerator may include a cooling system, a communication circuit, a memory storing instructions and including one or more storage media, and at least one processor including a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to obtain information regarding a usage history of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to input the information regarding the usage history of the refrigerator into a predictive model for setting a defrosting point for the cooling system of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to obtain information regarding a usage pattern of the refrigerator based on an output of the predictive model. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to identify a plurality of candidate points in time for performing the defrosting of the cooling system based on the information regarding the usage pattern of the refrigerator. The instructions, when individually or collectively executed by the at least one processor, may cause the refrigerator to determine a time point for performing the defrosting of the cooling system among the plurality of candidate time points.

[0254] According to the above-described embodiment, defrosting is essential to maintain the cooling performance of the refrigerator. During defrosting in the refrigerator, the temperature within the refrigerator (e.g., the freezer, refrigerator, or variable temperature room) may rise to a temperature that melts frost. Accordingly, this may cause inconvenience to the user if the user uses the refrigerator during defrosting. According to the above-described embodiment, since defrosting is performed during a time period when the user is determined not to use the refrigerator, this inconvenience can be alleviated.

[0255] Electronic devices according to embodiments disclosed herein may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments disclosed herein are not limited to the aforementioned devices.

[0256] The embodiments of this document and the terminology used herein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among the phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0257] In one embodiment of this document, the term "module" used may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0258] One embodiment of the present document may be implemented as software (e.g., a program) including one or more instructions stored in a storage medium (e.g., built-in memory or external memory) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., processor (210)) of the machine (e.g., electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0259] According to one embodiment, the method according to one embodiment disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0260] According to one embodiment, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and arranged in other components. According to one embodiment, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to one embodiment, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In electronic devices, communication circuit; A memory storing instructions and including one or more storage media; and At least one processor comprising a processing circuit, The above instructions, when individually or collectively executed by the at least one processor, Receive information about the usage history of the refrigerator from the refrigerator connected to the electronic device through the network, The above information about the above usage history of the above refrigerator is input into a prediction model for setting the defrosting time of the above refrigerator, Based on the output of the above prediction model, information about the usage pattern of the refrigerator is obtained, Based on the above information about the above usage pattern, a plurality of candidate points in time for performing defrosting of the refrigerator are identified, Causing the electronic device to determine a time point for performing the defrosting of the refrigerator among the plurality of candidate time points. Electronic devices.

2. In the first paragraph, the information regarding the usage history is: Includes information on usage history on working days and usage history on holidays. The above prediction model is, Including a first prediction model for identifying a usage pattern on the workday and a second prediction model for identifying a usage pattern on the holiday, Electronic devices.

3. In the second paragraph, when the instructions are individually or collectively executed by the at least one processor, The above information about the above usage history on working days is input into the above first prediction model, and the above information about the above usage history on holidays is input into the above second prediction model. Causing the electronic device to obtain information about the usage pattern on a workday and information about the usage pattern on a holiday based on the output of the first prediction model and the output of the second prediction model. Electronic devices.

4. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on the above information about the above usage history, identify the duration for the above-mentioned product, Causing the electronic device to identify the plurality of candidate time points for performing the defrost based on the information about the usage pattern and the duration for the defrost. Electronic devices.

5. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Causing said electronic device to identify information about said user's schedule based on said information about said use of said at least one external electronic device; Electronic devices.

6. In the fifth paragraph, the at least one external electronic device, Located within the space where the above refrigerator is located, The above instructions, when individually or collectively executed by the at least one processor, Based on the above information about the above user's schedule, identify the time at which the user is located within the space, Causing the electronic device to determine the time for performing the defrosting of the refrigerator among the plurality of candidate time points based on the time at which the user is located within the space. Electronic devices.

7. In the sixth paragraph, when the instructions are individually or collectively executed by the at least one processor, Identifying at least one candidate time point corresponding to the time at which the user is located within the space, Causing the electronic device to determine, among the plurality of candidate time points from which at least one candidate time point is excluded, a candidate time point having the lowest probability of use with respect to use of the refrigerator as the time point for performing the defrosting of the refrigerator. Electronic devices.

8. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Among the above information on the above usage history, another prediction model is created based on the information on the usage history of the refrigerator during the reference time period, Causing the electronic device to update the prediction model based on the other prediction model; Electronic devices.

9. In the first paragraph, the information regarding the usage pattern of the refrigerator is Based on information about the user including at least one of the gender, age, and nationality of the user of the refrigerator, obtained through the prediction model, Electronic devices.

10. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on determining the point in time for performing the defrosting of the refrigerator, causing the refrigerator to transmit, through the communication circuit, a signal causing the refrigerator to perform the defrosting at the point in time. Electronic devices.

11. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Causing the electronic device to determine the time point for performing the defrosting of the refrigerator among the plurality of candidate time points based on obtaining information about the use of the at least one external electronic device from at least one external electronic device around the refrigerator. Electronic devices.

12. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Causing the electronic device to transmit information about the plurality of candidate time points to the refrigerator, Electronic devices.

13. In a method performed by an electronic device, An action of receiving information about the usage history of a refrigerator from a refrigerator connected to the electronic device via a network; An action of inputting the information regarding the usage history of the refrigerator into a prediction model for setting the defrosting point of the refrigerator; An operation of obtaining information about the usage pattern of the refrigerator based on the output of the above prediction model; An operation of identifying a plurality of candidate points in time for performing defrosting of the refrigerator based on the information about the usage pattern; and An operation including determining a time point for performing the defrosting of the refrigerator among the plurality of candidate time points, method.

14. In the 13th paragraph, the information regarding the usage history is Includes information on usage history on working days and usage history on holidays. The above prediction model is, Including a first prediction model for identifying a usage pattern on the workday and a second prediction model for identifying a usage pattern on the holiday, method.

15. In a non-transitory computer-readable storage medium storing one or more programs, the one or more programs, when executed by at least one processor of an electronic device having a communication circuit, Receive information about the usage history of the refrigerator from the refrigerator connected to the electronic device through the network, The above information about the above usage history of the above refrigerator is input into a prediction model for setting the defrosting time of the above refrigerator, Based on the output of the above prediction model, information about the usage pattern of the refrigerator is obtained, Based on the above information about the above usage pattern, a plurality of candidate points in time for performing defrosting of the refrigerator are identified, Including instructions for causing the electronic device to determine a time point for performing the defrosting of the refrigerator among the plurality of candidate time points. Non-transitory computer-readable storage medium.

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