Cooking apparatus and control method therefor

Abstract

A cooking apparatus is disclosed. The cooking apparatus comprises: a thermal imaging camera; a memory for storing one or more instructions; and one or more processors, wherein the one or more processors execute the one or more instructions to: acquire a thermal image of food inside the cooking apparatus by using the thermal imaging camera; identify food temperature-change information and food weight information on the basis of a plurality of frames included in the thermal image; and input, into a trained artificial intelligence model, a food target-cooking temperature, the food weight information, the food temperature-change information, and output level information of the cooking apparatus so as to acquire an expected cooking completion time of the food.

Description

Cooking device and its control method

[0001] The present disclosure relates to a cooking device and a control method thereof, and more particularly, to a cooking device that cooks food using electromagnetic waves and a control method thereof.

[0002] Advances in electronic technology have led to the development and widespread adoption of various types of electronic devices. In particular, microwave ovens, used in a variety of settings, including homes and restaurants, have seen continuous advancements in recent years.

[0003] In general, a microwave oven is a device that cooks food by heating the moisture in the food stored inside the cooking chamber using electromagnetic waves (or microwaves) generated by a magnetron.

[0004] According to one or more embodiments, a cooking device for cooking food using electromagnetic waves includes: a thermographic camera; a memory storing one or more commands; and one or more processors, wherein the one or more processors, by executing the one or more commands, obtain a thermal image of a food item in the cooking device using the thermal imaging camera, identify temperature change information of the food item and weight information of the food item based on a plurality of frames included in the thermal image, and input a target cooking temperature of the food item, weight information of the food item, temperature change information of the food item, and output level information of the cooking device into a learned artificial intelligence model to obtain an expected cooking completion time of the food item.

[0005] According to one or more embodiments, the one or more processors may, by executing the one or more commands, acquire a thermal image of a turn table on which the food is placed using the thermal imaging camera, identify an area corresponding to the food in each of the plurality of frames, and identify weight information of the food based on the time at which each of the plurality of frames was acquired and the location of the area corresponding to the food.

[0006] According to one or more embodiments, the one or more processors can identify a rotation angle of the turntable based on a position of an area corresponding to the food at a time point when each of the plurality of frames is acquired by executing the one or more commands, identify a rotation speed of the turntable based on the rotation angle of the turntable, and identify weight information of the food based on the identified rotation speed.

[0007] According to one or more embodiments, the learned artificial intelligence model may be a model learned using a target cooking temperature of a learning target food, weight information, temperature change information, and output level information of the cooking device as input data, and a cooking completion time corresponding to the target cooking temperature as output data.

[0008] According to one or more embodiments, the temperature change information of the food may include temperature rise rate information in a temperature rise section of the food and temperature fall rate information in a temperature fall section of the food according to an output level of the cooking device.

[0009] According to one or more embodiments, the cooking device further includes a display; and the one or more processors can, by executing the one or more commands, provide a UI (User Interface) including an expected cooking completion time of the food through the display.

[0010] According to one or more embodiments, the one or more processors may, by executing the one or more commands, provide a first UI including a first expected cooking completion time identified at a first point in time after a preset time has elapsed since the start of cooking through the display, identify a second expected cooking completion time at a second point in time after the first point in time, identify a third expected cooking completion time based on the first expected cooking completion time and the second expected cooking completion time, and provide a second UI in which the first expected cooking completion time is updated to the third expected cooking completion time through the display.

[0011] According to one or more embodiments, the one or more processors, by executing the one or more commands, provide a first UI including a first expected cooking completion time identified at a first point in time after a preset time has elapsed after the start of cooking through the display, identify a second expected cooking completion time at a second point in time after the first point in time, and if a difference between the first expected cooking completion time and the second expected cooking completion time is greater than or equal to a threshold time, provide a second UI through the display in which the first expected cooking completion time is updated to the second expected cooking completion time.

[0012] According to one or more embodiments, the one or more processors, by executing the one or more commands, when a cooking mode is selected according to a user command, identify the target cooking temperature corresponding to the cooking mode, update the target cooking temperature based on the user's usage pattern information, and input the updated target cooking temperature, weight information of the food, temperature change information of the food, and output level information of the cooking device into the learned artificial intelligence model to obtain an expected cooking completion time of the food.

[0013] According to one or more embodiments, the user's usage pattern information may include at least one of additional cooking information, information on stopping cooking during operation, or information on changing the set temperature.

[0014] According to one or more embodiments, a method for controlling a cooking device that cooks food using electromagnetic waves includes the steps of: obtaining a thermal image of a food item in the cooking device using a thermal image camera; identifying temperature change information and weight information of the food item based on a plurality of frames included in the thermal image; and inputting a target cooking temperature of the food item, weight information of the food item, temperature change information of the food item, and output level information of the cooking device into a learned artificial intelligence model to obtain an expected cooking completion time of the food item.

[0015] According to one or more embodiments, a non-transitory computer-readable medium storing computer instructions that, when executed by a processor of a cooking device that cooks food using electromagnetic waves, cause the cooking device to perform an operation, the operation includes: obtaining a thermal image of a food item in the cooking device using a thermal image camera; identifying temperature change information and weight information of the food item based on a plurality of frames included in the thermal image; and inputting a target cooking temperature of the food item, weight information of the food item, temperature change information of the food item, and output level information of the cooking device into a learned artificial intelligence model to obtain an expected cooking completion time of the food item.

[0016] The above and other aspects, features and advantages of specific embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings.

[0017] FIGS. 1A to 1C are drawings for explaining the structure and operation of a microwave oven to help understand the present disclosure.

[0018] FIG. 2A is a block diagram showing the configuration of a cooking device according to one or more embodiments.

[0019] FIG. 2b is a block diagram showing a detailed configuration of a cooking device according to one or more embodiments.

[0020] FIG. 3 is a flowchart illustrating a method for controlling a cooking device according to one or more embodiments.

[0021] FIG. 4 is a flowchart illustrating a method for identifying weight information of a food according to one or more embodiments.

[0022] FIG. 5 is a drawing for explaining a method for identifying a cooking area according to one or more embodiments.

[0023] FIG. 6 is a drawing for explaining a method for calculating the rotation speed of a cooking material according to one or more embodiments.

[0024] FIGS. 7A and 7B are drawings for explaining a method for calculating the rotation speed of a cooking object according to one or more embodiments.

[0025] FIG. 8 is a diagram illustrating a method for obtaining temperature change information according to one or more embodiments.

[0026] FIGS. 9A and 9B are drawings for explaining a method for predicting cooking completion time using an artificial intelligence model according to one or more embodiments.

[0027] FIGS. 10A and 10B are drawings illustrating a cooking completion time prediction effect according to one or more embodiments.

[0028] FIG. 11 is a flowchart illustrating a method for providing a UI according to one or more embodiments.

[0029] FIG. 12 is a flowchart illustrating a method for providing a UI according to one or more embodiments.

[0030] FIGS. 13A to 13C are drawings for explaining types of UI screens according to one or more embodiments.

[0031] The terms used in this specification will be briefly explained, and the present disclosure will be described in detail.

[0032] The terms used in the embodiments of this disclosure have been selected from widely used, current terms, taking into account the functions of this disclosure. However, these terms may vary depending on the intentions or cases of those skilled in the art, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the description of the relevant disclosure. Therefore, the terms used in this disclosure should not be defined simply as names of terms, but rather based on the meanings of the terms and the overall content of this disclosure.

[0033] In this specification, expressions such as “has,” “can have,” “includes,” or “may include” indicate the presence of a feature (e.g., a number, function, operation, or component such as a part), and do not exclude the presence of additional features.

[0034] In this disclosure, expressions such as “A or B,” “at least one of A and / or B,” or “one or more of A or / and B” can include all possible combinations of the listed items. For example, “A or B,” “at least one of A and B,” or “at least one of A or B” can all refer to cases where (1) only A is included, (2) only B is included, or (3) both A and B are included.

[0035] As used herein, the expressions “first,” “second,” “first,” or “second,” etc., may describe various components, regardless of order and / or importance, and are only used to distinguish one component from another, but do not limit the components.

[0036] When it is said that a component (e.g., a first component) is “operatively or communicatively coupled with / to” or “connected to” another component (e.g., a second component), it should be understood that the component may be directly coupled to the other component, or may be connected through another component (e.g., a third component).

[0037] The expression "configured to" as used in the present disclosure may be used interchangeably with, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" may not necessarily mean only "specifically designed to" in terms of hardware.

[0038] In some contexts, the phrase "a device configured to" may mean that the device, in conjunction with other devices or components, is "capable of" performing A, B, and C. For example, the phrase "a processor configured (or set) to perform A, B, and C" may refer to a dedicated processor (e.g., an embedded processor) for performing those operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform those operations by executing one or more software programs stored in a memory device.

[0039] Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as "comprise" or "consist of" are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but should be understood not to preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0040] In the embodiments, a "module" or "part" performs at least one function or operation and may be implemented as hardware or software, or as a combination of hardware and software. Furthermore, a plurality of "modules" or "parts" may be integrated into at least one module and implemented as at least one processor (not shown), excluding any "module" or "part" that needs to be implemented as specific hardware.

[0041] Meanwhile, the various elements and areas in the drawings are schematically drawn. Therefore, the technical concept of the present invention is not limited by the relative sizes or spacing depicted in the attached drawings.

[0042] The terms used in this specification will be briefly explained, and the present disclosure will be described in detail.

[0043] The terms used in the embodiments of this disclosure have been selected from widely used, current terms, taking into account the functions of this disclosure. However, these terms may vary depending on the intentions or cases of those skilled in the art, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the description of the relevant disclosure. Therefore, the terms used in this disclosure should not be defined simply as names of terms, but rather based on the meanings of the terms and the overall content of this disclosure.

[0044] In this specification, expressions such as “has,” “can have,” “includes,” or “may include” indicate the presence of a feature (e.g., a number, function, operation, or component such as a part), and do not exclude the presence of additional features.

[0045] In this disclosure, expressions such as “A or B,” “at least one of A and / or B,” or “one or more of A or / and B” can include all possible combinations of the listed items. For example, “A or B,” “at least one of A and B,” or “at least one of A or B” can all refer to cases where (1) only A is included, (2) only B is included, or (3) both A and B are included.

[0046] As used herein, the expressions “first,” “second,” “first,” or “second,” etc., may describe various components, regardless of order and / or importance, and are only used to distinguish one component from another, but do not limit the components.

[0047] When it is said that a component (e.g., a first component) is “operatively or communicatively coupled with / to” or “connected to” another component (e.g., a second component), it should be understood that the component may be directly coupled to the other component, or may be connected through another component (e.g., a third component).

[0048] The expression "configured to" as used in the present disclosure may be used interchangeably with, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" may not necessarily mean only "specifically designed to" in terms of hardware.

[0049] In some contexts, the phrase "a device configured to" may mean that the device, in conjunction with other devices or components, is "capable of" performing A, B, and C. For example, the phrase "a processor configured (or set) to perform A, B, and C" may refer to a dedicated processor (e.g., an embedded processor) for performing those operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform those operations by executing one or more software programs stored in a memory device.

[0050] Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as "comprise" or "consist of" are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but should be understood not to preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0051] In the embodiments, a "module" or "part" performs at least one function or operation and may be implemented as hardware or software, or as a combination of hardware and software. Furthermore, a plurality of "modules" or "parts" may be integrated into at least one module and implemented as at least one processor (not shown), excluding any "module" or "part" that needs to be implemented as specific hardware.

[0052] Meanwhile, the various elements and areas in the drawings are schematically drawn. Therefore, the technical concept of the present invention is not limited by the relative sizes or spacing depicted in the attached drawings.

[0053] An embodiment of the present disclosure will be described in more detail with reference to the attached drawings below.

[0054] FIGS. 1A to 1C are drawings for explaining the structure and operation of a microwave oven to help understand the present disclosure.

[0055] Referring to FIGS. 1A to 1C, the microwave oven (1) is formed in a roughly rectangular parallelepiped shape with a cooking chamber (22) inside, and the two side surfaces and the upper outer surface can be formed by a sub-case (10). The sub-case (10) is formed by bending a plate having a predetermined width and thickness, and can be combined with a rear plate (12) forming the rear outer surface of the microwave oven (1) and a lower plate (14) forming the lower outer surface. A cavity (20) forming a cooking chamber (22), which is a space for cooking food, can be provided on the upper side of the lower plate (14). The cavity (20) can be formed in a rectangular cylindrical shape with an open front having a size that can be accommodated inside the outer case (10). That is, the cavity (20) is formed in a cylindrical shape by joining the plates forming at least one side to each other, and a cooking chamber (22) can be formed in the opened interior.

[0056] A heater (40) may be provided on the ceiling of the cooking chamber (22) to heat food contained in the cooking chamber (22) by radiant heat. The heater (40) is generally a sheath heater, and various types of electric heaters such as a ceramic heater and a halogen heater may be used as needed. The heater (40) may be fixed to a top cavity (24) forming the upper surface of the cavity (20) so as to be exposed to the interior of the cooking chamber (22).

[0057] Meanwhile, a turntable (32) for supporting a container containing food may be provided on the floor of the cooking chamber (22). A turntable motor (33) may be provided on the lower side of the turntable (32). The turntable motor (33) provides driving force for the rotation of the turntable (32) when food is cooked inside the cooking chamber (22). The turntable motor (33) stops when food is kept warm inside the cooking chamber (22), so as to prevent unnecessary power consumption. A cooking chamber lamp (35) may be further provided on the side of the cooking chamber (22). The cooking chamber lamp (35) illuminates the inside of the cooking chamber (22) when food is being cooked inside the cooking chamber so that a user can know the cooking status of the food. The cooking chamber lamp (35) turns off when food is kept warm inside the cooking chamber, so as to prevent unnecessary power consumption.

[0058] In addition, a number of intake holes (28) may be perforated in the center of the back cavity (26) forming the rear of the cooking chamber (22) through which air inside the cooking chamber (22) is sucked in, and a number of discharge holes (29) may be perforated on the inside of the rim of the back cavity (26) through which heat generated by the convection heater (62) is discharged into the cooking chamber (22).

[0059] Meanwhile, a power unit (30) may be provided in the top cavity (24) and the back cavity (26). The power unit (30) is a space where power components for the operation of the microwave oven (1) are provided. The power unit (30) formed in the back cavity (26) may be provided with a magnetron (34) that generates microwaves for heating food, a high-voltage transformer and a high-voltage capacitor for supplying high-voltage current to the magnetron (34), and a convection unit (60) that convectively heats the cooking chamber (22).

[0060] The convection unit (60) may be configured to include a convection heater (62) that generates heat by electrical resistance, and a convection fan (64) that forcibly convects the heat generated by the convection heater (62) into the cooking chamber (22). When the convection function of the microwave oven (1) is performed, power is supplied to the convection heater (62) to generate heat, and the heat generated by the convection heater (62) can circulate inside the cooking chamber (22) by the rotation of the convection fan (64). That is, food stored in the cooking chamber (22) can be cooked through a convection heating process in which the heat generated by the convection heater (62) is introduced into the cooking chamber (22) through the discharge hole (29) and then discharged to the outside of the cooking chamber (22) through the suction hole (28).

[0061] Meanwhile, the electrical room (30) formed on the upper surface of the top cavity (24) may be equipped with a control unit (or processor) (50) that controls the overall operation of the heater (40) and the microwave oven (1), and a fan assembly (70) that forces air flow to cool the electrical room (30).

[0062] The control unit (50) can form a predetermined circuit by a number of circuit components such as a resistor, a capacitor, an IC (Integrated Circuit) chip, and a microcomputer (MICOM).

[0063] The fan assembly (70) sucks in external air of the microwave oven (1) by means of rotational force to cool a number of electrical components provided in the electrical compartment (30), and a portion of this air is introduced into the cooking compartment (22) through an inlet (27) formed on the front side of the top cavity (24) and circulates within the cooking compartment (22). The air circulating within the cooking compartment (22) can be discharged to the outside of the cooking compartment (22) through an intake port (28).

[0064] Meanwhile, a front plate (16) may be formed at the front of the cavity (20). The front plate (16) forms the front outer shape of the cavity (20) and may be coupled to the front end of the outer case (10). A door (80) may be rotatably coupled to the front plate (16). The door (80) is hinge-coupled to the lower end of the front plate (16) to selectively open and close the open front of the cavity (20).

[0065] A viewing window (82) may be formed in the center of the door (80) so that the user can view the cooking status of food in the cooking chamber (22) without rotating the door (80). In addition, a door handle (84) that is gripped by the user may be provided on the front of the door (80) so that the door (80) can be easily opened and closed. Meanwhile, an operation unit (90) may be provided on the upper part of the door (80) so that the user inputs an operation command to cook food or keep food warm using the microwave oven (1). The operation unit (90) may be configured to include a plurality of buttons (92) for operating the microwave oven (1) and a display (94) that indicates the operating status. The display (94) may show the status of the microwave oven (1) set by the buttons (92).

[0066] Meanwhile, conventional microwave ovens set the time according to a time guide and operate based on the set time. Using a thermal imaging camera presents a problem: the current cooking time is displayed, making it difficult to determine how much time remains until cooking is complete. Therefore, the following describes various embodiments that use an artificial intelligence model to predict the cooking completion time.

[0067] FIG. 2A is a block diagram showing the configuration of a cooking device according to one or more embodiments.

[0068] According to FIG. 2a, the cooking device (100) includes a thermal imaging camera (110), a memory (120), and one or more processors (130). According to one example, the cooking device (100) can be implemented as a microwave oven as shown in FIG. 1.

[0069] A thermal imaging camera (110) is a device that tracks and detects heat and displays it at a glance on a screen. While the human eye or a general camera detects visible light, a thermal imaging camera (110) detects the radiant heat emitted by a hot object to create an image. The thermal imaging camera (110) displays the temperature in different colors so that we can identify the temperature with our eyes. In other words, the thermal imaging camera (110) is a camera that detects thermal radiation emitted from an object and visualizes it in various colors. It can be installed in a location where it can photograph the inside of a cooking chamber. For example, the thermal imaging camera (110) can be installed in a location where it can photograph a turntable from the front. Accordingly, the image acquired through the thermal imaging camera (110) can be a top-view image or an image taken at an angle close to a top-view image.

[0070] The memory (120) can store data required for various embodiments. The memory (120) may be implemented in the form of a memory embedded in the cooking device (100') or may be implemented in the form of a memory detachable from the cooking device (100) depending on the purpose of data storage. For example, data for driving the cooking device (100) may be stored in a memory embedded in the cooking device (100'), and data for expanding the functions of the cooking device (100) may be stored in a memory detachable from the cooking device (100). Meanwhile, in the case of the memory embedded in the cooking device (100), it may be implemented as at least one of volatile memory (e.g., dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM)), non-volatile memory (e.g., one time programmable ROM (OTPROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (e.g., NAND flash or NOR flash), hard drive, or solid state drive (SSD)). In addition, in the case of the memory that can be detachably attached to the cooking device (100'), it may be implemented as at least one of memory cards (e.g., compact flash (CF), secure digital (SD), micro secure digital (Micro-SD), mini secure digital (Mini-SD), extreme digital (xD), multi-media card (MMC), etc.), external memory that can be connected to a USB port (e.g., USB memory), etc. It can be implemented in the form of.

[0071] As an example, the memory (120) may store a computer program including at least one instruction or instructions for controlling the cooking device (100).

[0072] According to another example, the memory (120) may store an image received from an external device (e.g., a source device), an external storage medium (e.g., USB), an external server (e.g., web hard), etc., i.e., an input image. Alternatively, the memory (120) may store an image acquired through a thermal imaging camera (110) provided in the cooking device (100). Here, the image may be a 2D image, but is not limited thereto.

[0073] According to one embodiment, the memory (120) may be implemented as a single memory that stores data generated from various operations according to the present disclosure. However, according to another embodiment, the memory (120) may be implemented to include multiple memories that each store different types of data or each store data generated at different stages.

[0074] In the above-described embodiment, it has been described that various data are stored in the external memory (120) of the processor (130), but at least some of the above-described data may be stored in the internal memory of the processor (130) depending on the implementation example of at least one of the cooking device (100) or the processor (130).

[0075] One or more processors (130) control the overall operation of the cooking device (100). Specifically, one or more processors (130) may be connected to each component of the cooking device (100) to control the overall operation of the cooking device (100). For example, one or more processors (130) may be electrically connected to the display (110) and the memory (120) to control the overall operation of the cooking device (100). One or more processors (130) may be configured as one or more processors.

[0076] One or more processors (130) can perform operations of the cooking device (100) according to various embodiments by executing at least one instruction stored in the memory (120).

[0077] The one or more processors (130) may include one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Accelerated Processing Unit (APU), a Many Integrated Core (MIC), a Digital Signal Processor (DSP), a Neural Processing Unit (NPU), a hardware accelerator, or a machine learning accelerator. The one or more processors (130) may control one or any combination of other components of the cooking device, and may perform operations related to communication or data processing. The one or more processors (130) may execute one or more programs or instructions stored in a memory. For example, the one or more processors may perform a method according to one or more embodiments of the present disclosure by executing one or more instructions stored in a memory.

[0078] When a method according to one or more embodiments of the present disclosure includes multiple operations, the multiple operations may be performed by one processor or by multiple processors. For example, when a first operation, a second operation, and a third operation are performed by a method according to one or more embodiments, the first operation, the second operation, and the third operation may all be performed by the first processor, or the first operation and the second operation may be performed by the first processor (e.g., a general-purpose processor) and the third operation may be performed by the second processor (e.g., an artificial intelligence-specific processor).

[0079] One or more processors (130) may be implemented as a single core processor including one core, or may be implemented as one or more multicore processors including multiple cores (e.g., homogeneous multicores or heterogeneous multicores). When one or more processors (130) are implemented as a multicore processor, each of the multiple cores included in the multicore processor may include an internal processor memory, such as a cache memory or an on-chip memory, and a common cache shared by the multiple cores may be included in the multicore processor. In addition, each of the multiple cores (or some of the multiple cores) included in the multicore processor may independently read and execute a program instruction for implementing a method according to one or more embodiments of the present disclosure, or all (or some) of the multiple cores may be linked to read and execute a program instruction for implementing a method according to one or more embodiments of the present disclosure.

[0080] When a method according to one or more embodiments of the present disclosure includes a plurality of operations, the plurality of operations may be performed by one core among the plurality of cores included in a multi-core processor, or may be performed by the plurality of cores. For example, when a first operation, a second operation, and a third operation are performed by a method according to one or more embodiments, the first operation, the second operation, and the third operation may all be performed by a first core included in the multi-core processor, or the first operation and the second operation may be performed by a first core included in the multi-core processor, and the third operation may be performed by a second core included in the multi-core processor.

[0081] In the embodiments of the present disclosure, a processor may mean a system on a chip (SoC) in which one or more processors and other electronic components are integrated, a single-core processor, a multi-core processor, or a core included in a single-core processor or a multi-core processor, wherein the core may be implemented as a CPU, a GPU, an APU, a MIC, a DSP, an NPU, a hardware accelerator, or a machine learning accelerator, but the embodiments of the present disclosure are not limited thereto. Hereinafter, for the convenience of explanation, one or more processors (130) will be referred to as a processor (130).

[0082] According to one embodiment, the processor (130) may acquire a thermal image of food within the cooking device (100) using a thermal imaging camera (110). For example, since the thermal image has different pixel values ​​depending on temperature, the processor (130) may acquire a temperature value for each pixel based on each pixel value. For example, the thermal image may include a plurality of frames.

[0083] According to one embodiment, the processor (130) may obtain a temperature value for each pixel area included in each of a plurality of frames. For example, a pixel area may be a single pixel unit, but is not limited thereto, and a plurality of pixels may be identified as a pixel area. When a plurality of pixels are identified as a pixel area, a representative value (e.g., an average value) of the plurality of pixels included in the pixel area may be obtained as the temperature value of the pixel area. However, for the convenience of explanation, it is assumed below that the temperature value is obtained on a pixel-by-pixel basis.

[0084] According to one embodiment, the processor (130) can identify temperature change information of the food and weight (or mass) information of the food based on a plurality of frames included in the thermal image.

[0085] According to one embodiment, the processor (130) can input the target cooking temperature of the food, weight information of the food, temperature change information of the food, and output level information of the cooking device (100) into the learned artificial intelligence model to obtain the expected cooking completion time of the food.

[0086] For example, the target cooking temperature may be determined to various values ​​based on the cooking type, the food type, and / or the user's temperature setting. For example, cooking types may be categorized based on the cooking method, such as defrosting, sous vide, or reheating. For example, food types may be categorized based on the characteristics of the food, such as meat, bread, rice, vegetables, milk, or water.

[0087] For example, the temperature change information of the food may include temperature rise rate information in a temperature rise section of the food and temperature drop rate information in a temperature drop section of the food according to the output level of the cooking device (100). For example, if the cooking device (100) is implemented in a form that periodically turns the output of the magnetron (34) ON / OFF, the temperature rise rate value when the output is ON and the temperature drop rate value when the output is OFF may be included.

[0088] For example, the output level information of the cooking device (100) may vary depending on at least one of the size, capacity, and usage function of the cooking device (100). For example, the output level of the cooking device (100) may be a value between 200 W and 1500 W. For example, the output level of the cooking device (100) may be, but is not limited to, five output levels such as 200 W, 400 W, 600 W, 800 W, and 1000 W.

[0089] According to one embodiment, the processor (130) may acquire a thermal image of a turntable on which food is placed using a thermal imaging camera (110), and identify an area corresponding to the food in each of a plurality of frames. Subsequently, the processor (130) may identify weight information of the food based on the time at which each of the plurality of frames was acquired and the location of the area corresponding to the food.

[0090] According to one embodiment, the processor (130) may identify a rotation angle of the turntable based on a position of an area corresponding to the food at a time point when each of the plurality of frames is acquired, identify a rotation speed of the turntable based on the rotation angle of the turntable, and identify weight information of the food based on the identified rotation speed. For example, information for calculating the weight of the food according to the rotation speed may be pre-stored in the memory (120). For example, the weight information of the food according to the rotation speed may be stored in the memory (120) in the form of a lookup table. For example, the weight information of the food according to the rotation speed may be pre-calculated through an experiment. Alternatively, a formula, an algorithm, a program, or the like for calculating the weight of the food according to the rotation speed may be stored in the memory (120).

[0091] According to one embodiment, the artificial intelligence model may be a model trained using the target cooking temperature, weight information, temperature change information, and output level information of the cooking device (100) of the learning target food as input data, and the cooking completion time corresponding to the target cooking temperature as output data. According to one example, the artificial intelligence model may be stored in the memory (120), but is not necessarily limited thereto. For example, the artificial intelligence model may be stored in an external server, and the processor (130) may transmit information to be input into the artificial intelligence model to the external server and then receive information on the expected cooking completion time.

[0092] Here, the learning of an artificial intelligence model means that a basic artificial intelligence model (e.g., an artificial intelligence model including any random parameters) is trained using a learning algorithm using a plurality of training data, thereby creating a predefined set of operation rules or an artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed through a separate server and / or system, but is not limited thereto, and may also be performed in a cooking device. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0093] Here, the artificial intelligence model can be implemented as, for example, 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), or a Deep Q-Network, but is not limited thereto.

[0094] According to one embodiment, when a cooking mode is selected according to a user command, the processor (130) can identify a target cooking temperature corresponding to the cooking mode and update the target cooking temperature based on the user's usage pattern information.

[0095] According to one embodiment, the processor (130) may input the updated target cooking temperature, weight information of the food, temperature change information of the food, and output level information of the cooking device (100) into the learned artificial intelligence model to obtain the expected cooking completion time of the food. For example, the user's usage pattern information may include at least one of additional cooking information, information on stopping cooking during operation, or information on changing the set temperature.

[0096] FIG. 2b is a block diagram showing a detailed configuration of a cooking device according to one or more embodiments.

[0097]

[0098] *According to FIG. 2a, the cooking device (100') includes a thermal imaging camera (110), a memory (120), one or more processors (130), a display (140), a communication interface (150), and a user interface (160). Among the configurations illustrated in FIG. 2b, a detailed description of configurations that overlap with those illustrated in FIG. 2a will be omitted.

[0099] The display (140) may be implemented as a display including a self-luminous element or a display including a non-luminous element and a backlight. For example, it may be implemented as various types of displays such as an LCD (Liquid Crystal Display), an OLED (Organic Light Emitting Diodes) display, an LED (Light Emitting Diodes), a micro LED, a Mini LED, a PDP (Plasma Display Panel), a QD (Quantum dot) display, a QLED (Quantum dot light-emitting diodes), etc. The display (140) may also include a driving circuit, a backlight unit, etc., which may be implemented in a form such as an a-si TFT, an LTPS (low temperature poly silicon) TFT, an OTFT (organic TFT), etc. According to an example, the display (140) may be implemented as a flat display, a curved display, a foldable or / and rollable flexible display, etc.

[0100] According to one embodiment, the processor (130) may provide a UI (User Interface) including an expected cooking completion time of the food through the display (140).

[0101] According to one embodiment, the processor (130) may provide a first UI including a first expected cooking completion time identified at a first point in time after a preset time has elapsed since the start of cooking, through the display (140).

[0102] According to one example, the processor (130) can identify a second expected cooking completion time at a second point in time after the first point in time, and identify a third expected cooking completion time based on the first expected cooking completion time and the second expected cooking completion time. The processor (130) can provide a second UI through the display (140) in which the first expected cooking completion time is updated to the third expected cooking completion time. For example, the processor (130) can identify an average value of the first expected cooking completion time and the second expected cooking completion time as the third expected cooking completion time. For example, the processor (130) can identify a value calculated by giving different weights to the first expected cooking completion time and the second expected cooking completion time as the third expected cooking completion time.

[0103] According to another example, the processor (130) may identify a second expected cooking completion time at a second point in time after the first point in time, and if the difference between the first expected cooking completion time and the second expected cooking completion time is greater than or equal to a threshold time, the processor (130) may provide a second UI through the display (140) in which the first expected cooking completion time is updated to the second expected cooking completion time.

[0104] The communication interface (150) includes a circuit and can communicate with an external device (server or user terminal). For example, the processor (130) can receive various data or information from an external device connected via the communication interface (150) and can also transmit various data or information to the external device.

[0105] The communication interface (150) may include at least one of a WiFi module, a Bluetooth module, a wireless communication module, an NFC module, and a UWB module (Ultra Wide Band). At this time, the wireless communication module may perform communication according to various communication standards such as IEEE, Zigbee, 3G (3rd Generation), 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), 5G (5th Generation), etc.

[0106] The user interface (160) may be implemented as a device such as a button, a touch pad, etc., or as a touch screen that can also perform the above-described display function and operation input function.

[0107] The speaker (170) can convert and amplify a digital audio signal processed by the processor (130) into an analog audio signal and output it. For example, the speaker (170) can include at least one speaker unit, a D / A converter, an audio amplifier, etc., which can output at least one channel. For example, the speaker can output various notifications, messages, information, etc. related to the guide information generated by the processor (130).

[0108] In addition, the cooking device (100') may further include a microphone and a sensor.

[0109] A microphone is a device that receives user voice or other sounds and converts them into audio data. In one example, the processor (130) may use audio received through the microphone as context data for the cooking device (100). However, in another embodiment, the cooking device (100) may receive user voice input through an external device through the communication interface (150).

[0110] The sensors may include various types of sensors, such as temperature sensors, light sensors, touch sensors, proximity sensors, humidity sensors, infrared sensors, biosensors, etc.

[0111] FIG. 3 is a flowchart illustrating a method for controlling a cooking device according to one or more embodiments.

[0112] According to FIG. 3, in operation 310, the cooking device (100) can acquire a thermal image of food inside the cooking device (100) using a thermal imaging camera (110). For example, the thermal image can include a plurality of thermal image frames (hereinafter, “frames”) acquired at preset time intervals.

[0113] In operation 320, the cooking device (100) can identify temperature change information and weight information of the food based on a plurality of frames included in the thermal image.

[0114] In operation 330, the cooking device (100) can input the target cooking temperature of the food, weight information of the food, temperature change information of the food, and output level information of the cooking device (100) into the learned artificial intelligence model to obtain the expected cooking completion time of the food.

[0115] Meanwhile, in Fig. 3, the order is mapped for all steps for convenience of explanation, but it is of course not necessarily limited to the order of steps that are not related to the order or can be performed in parallel.

[0116] FIG. 4 is a flowchart illustrating a method for identifying weight information of a food according to one or more embodiments.

[0117] According to FIG. 4, in operation 410, the cooking device (100) can obtain a thermal image of the turntable on which the food is placed using a thermal imaging camera (110).

[0118] In operation 420, the cooking device (100) can identify an area corresponding to the food in a plurality of frames included in the thermal image.

[0119] In operation 430, the cooking device (100) can identify the rotation angle of the turntable based on the position of the area corresponding to the food at the time when each of the plurality of frames is acquired.

[0120] In operation 440, the cooking device (100) can identify the rotation speed of the turntable based on the rotation angle of the turntable.

[0121] In operation 450, the cooking device (100) can identify weight information of the food based on the rotation speed of the turntable.

[0122] For example, the rotation speed of the turntable may vary depending on the weight of the food. For example, the heavier the food, the slower the rotation speed of the turntable. Accordingly, the cooking device (100) can estimate the weight of the food by calculating the rotation speed of the turntable, for example, the number of rotations per second.

[0123] Meanwhile, in Fig. 4, the order is mapped for all steps for convenience of explanation, but it is of course not necessarily limited to the order of steps that are not related to the order or can be performed in parallel.

[0124] FIG. 5 is a drawing for explaining a method for identifying a cooking area according to one or more embodiments.

[0125] According to one embodiment, the processor (130) may identify at least one cooking area in a thermal image by clustering pixels based on temperature values ​​per pixel included in each of a plurality of frames. According to one example, the processor (130) may analyze the correlation between the heat change amount (or temperature change amount) per pixel and the current temperature value in the plurality of frames to obtain a correlation graph, identify a plurality of pixel groups by clustering pixels in the correlation graph, and identify a pixel group corresponding to a preset condition among the plurality of pixel groups as a cooking area. For example, the correlation graph may be a statistical graph for analyzing the correlation (or correlation) between two variables, that is, the heat change amount per pixel and the current temperature value.

[0126] For example, the cooking device (100) can identify a group of pixels (511) including pixels with a heat change amount greater than a threshold value as a cooking area, as shown in FIG. 5, and identify the remaining group of pixels (512) as a background area (i.e., an area that is not a cooking area).

[0127] However, the example illustrated in FIG. 5 is merely an example, and pixel groups with different characteristics in the correlation graph may be identified as the food area and / or the background area depending on the cooking mode and / or the state of the food. For example, in defrosting mode, a pixel group with a relatively low current temperature and a relatively high heat change rate may be identified as the food area. As another example, if the food is frozen, a pixel group with a relatively high heat change rate as the temperature changes from low to high may be identified as the food area.

[0128] FIG. 6 is a drawing for explaining a method for calculating the rotation speed of a cooking material according to one or more embodiments.

[0129] According to one embodiment, the cooking device (100) can calculate the rotation speed of the turntable based on a plurality of frames included in a thermal image captured of the turntable on which the food is placed.

[0130] For example, the rotation speed of the turntable can be calculated based on the rotation angle of the first pixel (10) in the first frame (610) acquired at a specific point in time and the second pixel (20) in the second frame (620) acquired after a preset time (for example, 1 second later). For example, the first pixel (10) and the second pixel (20) may be pixels having the lowest temperature among the pixels included in the food area (611, 621). For example, since the pixel having the lowest temperature is very likely to be maintained in the lowest temperature state, the probability that they are the same pixel is very high. Accordingly, the first pixel (10) having the lowest temperature in the food area (611) included in the first frame (610) and the second pixel (20) having the lowest temperature in the food area (621) included in the second frame (620) are assumed to be the same pixel, and the rotation speed of the turntable can be calculated based on the rotation angle of the corresponding pixels. For example, the rotation angle can be calculated as the rotation angle of the first pixel (10) and the second pixel (20) based on the center point of the turntable.

[0131] According to one embodiment, the cooking device (100) can identify weight information of the food based on the rotation angle per second, i.e., the rotation speed. For example, information for calculating the weight of the food based on the rotation speed may be pre-stored in the memory (120). For example, the weight information of the food based on the rotation speed may be stored in the memory (120) in the form of a lookup table. For example, the weight information of the food based on the rotation speed may be pre-calculated through an experiment. Alternatively, a formula, an algorithm, a program, etc. for calculating the weight of the food based on the rotation speed may be stored in the memory (120).

[0132] FIGS. 7A and 7B are drawings for explaining a method for calculating the rotation speed of a cooking object according to one or more embodiments.

[0133] According to one embodiment, the cooking device (100) can obtain a plurality of rotation frames by rotating a frame acquired through a thermal imaging camera (110) by a preset angle, and compare a current frame acquired through the thermal imaging camera (110) with the plurality of rotation frames to identify a rotation angle corresponding to the currently acquired frame.

[0134] For example, the cooking device (100) can obtain rotating frames (712, 713, 714, 715) that are rotated by 5 to 25 degrees each by rotating the frame (711) obtained through the thermal imaging camera (110) by 1 degree as shown in FIG. 7a.

[0135] Next, when the cooking device (100) acquires the current frame (720) acquired through the thermal imaging camera (110), the rotation frames (712, 713, 714, 715) can be compared with the current frame (720) to identify the rotation angle corresponding to the currently acquired frame.

[0136] For example, the cooking device (100) may analyze the correlation between the rotation frames (712, 713, 714, 715) and the current frame (720) to identify the rotation frame (e.g., 713) having the largest correlation value (or similarity value) with the current frame (720) among the rotation frames (712, 713, 714, 715) and identify the rotation angle of the identified rotation frame (713) as the rotation angle of the current frame (720). For example, the correlation between the rotation frames (712, 713, 714, 715) and the current frame (720) may be analyzed using Pearson Correlation, but is not limited thereto.

[0137] FIG. 8 is a diagram illustrating a method for obtaining temperature change information according to one or more embodiments.

[0138] According to one embodiment, the cooking device (100) can obtain temperature change information of the food based on a thermal image acquired by the thermal imaging camera (110). For example, the cooking device (100) can obtain temperature rise rate information in a temperature rise section of the food and temperature decrease rate information in a temperature decrease section of the food according to the output level of the cooking device (100).

[0139] For example, if the cooking device (100) is implemented in a form that periodically turns ON / OFF the output of the magnetron (34), the temperature rises when the output is ON, and the temperature falls when the output is OFF, as illustrated in FIG. 8. For example, the cooking device (100) can obtain a temperature rise rate value corresponding to an output ON section and a temperature fall rate value corresponding to an output OFF section. For example, if the cooking device (100) is implemented in a form that turns ON the output of the magnetron (34) for A seconds and OFF for B seconds, the temperature rise rate value for a periodically repeated A second section and the temperature fall rate value for B seconds can be obtained. For example, the temperature rise rate value and the temperature fall rate value may be slope values ​​corresponding to each section in the graph illustrated in FIG. 8. For example, the temperature rise rate value and the temperature fall rate value for the initial N seconds (e.g., 20 seconds) may be used as temperature change information to input to an artificial intelligence model.

[0140] However, in the above-described embodiment, it is assumed and explained that the cooking device (100) operates in a manner of periodically turning the magnetron (34) output ON / OFF, but in the case where the cooking device (100) operates in a manner of maintaining the magnetron (34) output ON, the temperature rise rate value for each time interval may be used as temperature change information.

[0141] FIGS. 9A and 9B are drawings for explaining a method for predicting cooking completion time using an artificial intelligence model according to one or more embodiments.

[0142] According to an example, the cooking device (100) can acquire expected cooking completion time information by inputting target cooking temperature, weight information of the food, temperature change information of the food, and output level information into the learned artificial intelligence model as illustrated in FIG. 9A. For example, since the heat capacity (the amount of temperature increase per hour) is different for each food, the learned artificial intelligence model can be used to predict the cooking completion time corresponding to each food. For example, since the mathematical relationship of "device output * cooking time = specific heat of food * weight of food * change in temperature of food" is established, the relationship of "cooking time = (specific heat of food * weight of food * change in temperature of food) / output" can be established. Accordingly, in the present disclosure, the cooking time according to the specific heat information that is different for each substance based on the weight of the food, the temperature change, and the output of the cooking device (100) is learned through the artificial intelligence model, and the cooking completion time corresponding to each food can be predicted using the learned artificial intelligence model.

[0143] For example, an artificial intelligence model can be trained using the target cooking temperature, weight information, temperature change information, and output level information of a cooking device as input data and the cooking completion time corresponding to the target cooking temperature as output data.

[0144] According to an example, the cooking device (100) can obtain expected cooking completion time information by inputting target cooking temperature, weight information of food, temperature change information of food, output level information, and usage pattern information into a learned artificial intelligence model as shown in FIG. 9b.

[0145] For example, an AI model can be trained using the target cooking temperature, weight information, temperature change information, cooking device output level information, and usage pattern information of the learning target food as input data, and the cooking completion time corresponding to the target cooking temperature as output data. For example, the usage pattern information may include at least one of additional cooking information, information on interruption of cooking during operation, or information on change in the set temperature.

[0146] FIGS. 10A and 10B are drawings illustrating a cooking completion time prediction effect according to one or more embodiments.

[0147] Figure 10a is a diagram showing temperature change information when meat is defrosted at an output of 300 W according to an example.

[0148] Figure 10b is a diagram showing temperature change information when automatically cooking meat is defrosted with an output of 450W-0W-180W-100W according to an example.

[0149] For example, the cooking device (100) can obtain the temperature rise rate value and the temperature fall rate value for the initial N seconds (e.g., 20 seconds) and use them as temperature change information to input into an artificial intelligence model.

[0150] For example, the cooking device (100) may further train an artificial intelligence model based on information including newly acquired temperature change information to improve the accuracy of the artificial intelligence model. For example, the cooking device (100) may accumulate information every second and further train an artificial intelligence model based on the accumulated information. Alternatively, when the expected cooking completion time for the current food is acquired, the cooking device (100) may further train an artificial intelligence model based on information about the current food.

[0151] FIG. 11 is a flowchart illustrating a method for providing a UI according to one or more embodiments.

[0152] According to FIG. 11, in operation 111O, the cooking device (100) can provide a first UI including a first expected cooking completion time identified at a first point in time after a preset time has elapsed since the start of cooking.

[0153] In operation 1120, the cooking device (100) may identify a second expected cooking completion time at a second point in time after the first point in time. For example, the second point in time may be determined based on a specific ratio of the time interval from the start of cooking to the first point in time. For example, the cooking device (100) may identify a time corresponding to 25% of the time interval from the start of cooking to the first point in time, and may identify the second expected cooking completion time at a second point in time after the identified time has elapsed from the first point in time. For example, the specific ratio identifying the second point in time may be updated over time.

[0154] In operation 1130, the cooking device (100) may identify a third expected cooking completion time based on the first expected cooking completion time and the second expected cooking completion time. For example, the cooking device (100) may identify an average time of the first expected cooking completion time and the second expected cooking completion time as the third expected cooking completion time. Alternatively, the cooking device (100) may identify an average time calculated by giving a relatively greater weight to the second expected cooking completion time than the first expected cooking completion time as the third expected cooking completion time. For example, the cooking device (100) may calculate the third expected cooking completion time by giving a relatively greater weight to the expected cooking completion time, assuming that the second expected cooking completion time predicted at a second point in time when cooking has progressed relatively further is more accurate than the first expected cooking completion time.

[0155] In operation 1140, the cooking device (100) may provide a second UI in which the first expected cooking completion time is updated to a third expected cooking completion time. This update of the expected cooking completion time may be maintained continuously until cooking is completed. For example, the cooking device (100) may identify a fifth expected cooking completion time based on the third expected cooking completion time and the fourth expected cooking completion time predicted at the third time point, and provide an updated UI including the fifth expected cooking completion time.

[0156] In one embodiment, the first expected cooking completion time may be obtained through an artificial intelligence model at a first time point, and the second expected cooking completion time may be obtained through the artificial intelligence model at a second time point. However, in some cases, the artificial intelligence model may output a third expected cooking time at the second time point. In this case, the artificial intelligence model may be trained to produce an average value (e.g., the third expected cooking time) between the expected cooking time predicted at a previous time point (e.g., the first expected cooking time) and the expected cooking time predicted at the current time point (e.g., the second expected cooking time).

[0157] Meanwhile, in Fig. 11, the order is mapped for all steps for convenience of explanation, but it is of course not necessarily limited to the order of steps that are not related to the order or can be performed in parallel.

[0158] FIG. 12 is a flowchart illustrating a method for providing a UI according to one or more embodiments.

[0159] According to FIG. 12, in operation 121O, the cooking device (100) may provide a first UI including a first expected cooking completion time identified at a first point in time after a preset time has elapsed since the start of cooking.

[0160] In operation 1220, the cooking device (100) can identify a second expected cooking completion time at a second time point after the first time point.

[0161] At step 1230, the cooking device (100) can determine whether the difference between the first expected cooking completion time and the second expected cooking completion time is greater than or equal to a threshold time. For example, the threshold time may be a fixed value, but may be updated over time. For example, the threshold time may be decreased as the cooking time elapses.

[0162] In operation 1230, if the cooking device (100) identifies that the difference between the first expected cooking completion time and the second expected cooking completion time is greater than a threshold time (S1230:Y), in operation 1240, the cooking device (100) may provide a second UI in which the first expected cooking completion time is updated to the second expected cooking completion time. For example, the cooking device (100) may provide a second UI in which the first expected cooking completion time is updated to the second expected cooking completion time, assuming that the second expected cooking completion time predicted at a second point in time when cooking has progressed relatively further is more accurate than the first expected cooking completion time.

[0163] According to one embodiment, the first expected cooking completion time can be obtained through the artificial intelligence model at a first point in time, and the second expected cooking completion time can be obtained through the artificial intelligence model at a second point in time.

[0164]

[0165] *Meanwhile, in Fig. 12, the order of all steps is mapped for convenience of explanation, but it is of course not necessarily limited to the order of steps that are not related to the order or can be performed in parallel.

[0166] FIGS. 13A to 13C are drawings for explaining types of UI screens according to one or more embodiments.

[0167] According to Fig. 13a, when the "AI warming menu" is selected without a separate time setting, the cooking completion time that can bring about the optimal warming result can be predicted and set using an artificial intelligence model. For example, as illustrated in Fig. 13a, when the AI ​​warming menu (1311) is selected, the cooking device (100) may provide a UI (1312) indicating that cooking is in progress while predicting the cooking completion time, and when the cooking completion time is predicted, a UI (1313) including information about the expected cooking completion time based on the predicted cooking completion time may be provided. Thereafter, when cooking is completed, the cooking device (100) may provide a UI (1314) indicating that cooking is completed.

[0168] According to FIG. 13b, when the AI ​​warming menu (1311) is selected, the cooking device (100) may provide UI menus (1322, 1323, 1324, 1325, 1326) for selecting a warming mode. For example, the cooking device (100) may provide UI menus (1322, 1323, 1324, 1325, 1326) for selecting a warming mode such as lukewarm / warm / hot / desired temperature. Thereafter, the cooking device (100) may provide UI (1327) including expected cooking completion time information acquired using the artificial intelligence model.

[0169] According to one embodiment, the target temperature for each heating mode is set to 40 / 60 / 80 degrees by default, but the optimal temperature for each mode may change depending on the user's usage pattern (additional cooking / stopping during operation / changing the set temperature, etc.).

[0170] For example, if the user selects the "Hot" mode but their usage pattern is such that cooking always stops at a temperature of around 70 degrees, the set temperature for the Hot mode may be lowered to 70 degrees.

[0171] For example, even if the user selects the "Warm" mode, if the user repeatedly cooks after the cooking is completed, raising the final food temperature to 70 degrees and then stops operating, the set temperature of the Warm mode may increase to 70 degrees.

[0172] According to Fig. 13c, when the "AI defrosting menu" is selected without setting the time when the AI ​​defrosting mode is in operation, the cooking completion time that can bring about the optimal defrosting result can be automatically predicted and set by the artificial intelligence model. For example, as illustrated in Fig. 13c, when the AI ​​defrosting menu (1331) is selected, the cooking device (100) can provide a UI (1332) indicating the defrosting stage. In this case, the cooking device (100) can provide a UI (1332) indicating the defrosting stage based on the expected cooking completion time obtained through the artificial intelligence model based on the weight of the food and the temperature change of the food. Thereafter, when the defrosting is completed, the cooking device (100) can provide a UI (1333) indicating that the defrosting is completed. Meanwhile, in Fig. 13c, a UI indicating the defrosting stage is provided instead of the expected defrosting completion time, but it is of course possible to provide a UI indicating the expected defrosting completion time.

[0173] In addition, a UI similar to the UI illustrated in FIGS. 13a to 13c may be provided for various cooking types, such as sous vide cooking.

[0174] In the above-described embodiments, the turntable type cooking device (100) is described as being limited, but it goes without saying that the embodiments applicable to a flatbed type cooking device among the above-described embodiments can also be applied to a flatbed type cooking device.

[0175] According to the various embodiments described above, user convenience can be improved because the cooking completion time can be predicted using a thermal imaging camera.

[0176] Meanwhile, the methods according to the various embodiments of the present disclosure described above can be implemented only with a software upgrade or a hardware upgrade for an existing cooking device.

[0177] Additionally, the various embodiments of the present disclosure described above can also be performed through an embedded server provided in the cooking device or an external server of the cooking device.

[0178] Meanwhile, according to a temporary example of the present disclosure, the various embodiments described above can be implemented as software including commands stored in a machine-readable storage medium that can be read by a machine (e.g., a computer). The machine is a device that can call commands stored from the storage medium and operate according to the called commands, and may include a cooking device (e.g., cooking device (A)) according to the disclosed embodiments. When a command is executed by a processor, the processor can perform a function corresponding to the command directly or by using other components under the control of the processor. The command may include code generated or executed by a compiler or interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' means that the storage medium does not contain a signal and is tangible, but does not distinguish between data being stored semi-permanently or temporarily in the storage medium.

[0179] Furthermore, according to one embodiment of the present disclosure, the method according to the various embodiments described above 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., compact disc read-only memory (CD-ROM)) or online through an application store (e.g., Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0180] In addition, each of the components (e.g., modules or programs) according to the various embodiments described above may be composed of a single or multiple entities, and some of the corresponding sub-components described above may be omitted, or other sub-components may be further included in various embodiments. Alternatively or additionally, some components (e.g., modules or programs) may be integrated into a single entity, which may perform the same or similar functions as those performed by each of the corresponding components prior to integration. Operations performed by modules, programs or other components according to various embodiments may be executed sequentially, in parallel, iteratively or heuristically, or at least some operations may be executed in a different order, omitted, or other operations may be added.

[0181] Although the preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above, and various modifications may be made by a person having ordinary skill in the art to which the present disclosure pertains without departing from the gist of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present disclosure.

Claims

1. In a cooking device that cooks food using electromagnetic waves, thermographic camera; A memory storing one or more instructions; and comprising one or more processors; The one or more processors, by executing the one or more instructions, Obtaining a thermal image of food inside the cooking device using the thermal imaging camera, Identifying temperature change information and weight information of the food based on a plurality of frames included in the thermal image, A cooking device that inputs the target cooking temperature of the food, weight information of the food, temperature change information of the food, and output level information of the cooking device into a learned artificial intelligence model to obtain the expected cooking completion time of the food.

2. In paragraph 1, The one or more processors, by executing the one or more instructions, The thermal imaging image is obtained by photographing the turn table on which the food is placed using the thermal imaging camera, Identifying an area corresponding to the food in each of the above multiple frames, A cooking device that identifies weight information of the food based on the time at which each of the plurality of frames is acquired and the location of the area corresponding to the food.

3. In paragraph 2, The one or more processors, by executing the one or more instructions, Identifying the rotation angle of the turntable based on the position of the area corresponding to the food at the time each of the plurality of frames is acquired, Identifying the rotation speed of the turn table based on the rotation angle of the turn table, A cooking device that identifies weight information of the food based on the identified rotation speed.

4. In paragraph 1, The above learned artificial intelligence model is, A cooking device, which is a model learned using target cooking temperature, weight information, temperature change information, and output level information of the cooking device as input data and the cooking completion time corresponding to the target cooking temperature as output data.

5. In paragraph 1, Information on temperature changes in the above food is: A cooking device including temperature rise rate information in a temperature rise section of the food and temperature decrease rate information in a temperature decrease section according to the output level of the cooking device.

6. In paragraph 1, including display; The one or more processors, by executing the one or more instructions, A cooking device that provides a UI (User Interface) including an expected cooking completion time of the food through the display.

7. In paragraph 6, The one or more processors, by executing the one or more instructions, Provide a first UI including a first expected cooking completion time identified at a first point in time after a preset time has elapsed since the start of cooking, through the display; Identifying a second expected cooking completion time at a second time point after the first time point, Identifying a third expected cooking completion time based on the first expected cooking completion time and the second expected cooking completion time; A cooking device, wherein the first expected cooking completion time is updated to the third expected cooking completion time through the display.

8. In paragraph 6, The one or more processors, by executing the one or more instructions, Provide a first UI including a first expected cooking completion time identified at a first point in time after a preset time has elapsed since the start of cooking, through the display; Identifying a second expected cooking completion time at a second time point after the first time point, A cooking device, wherein if the difference between the first expected cooking completion time and the second expected cooking completion time is greater than or equal to a threshold time, a second UI in which the first expected cooking completion time is updated to the second expected cooking completion time is provided through the display.

9. In paragraph 1, The one or more processors, by executing the one or more instructions, When a cooking mode is selected according to a user command, the target cooking temperature corresponding to the cooking mode is identified, Update the target cooking temperature based on the user's usage pattern information, A cooking device that inputs the updated target cooking temperature, weight information of the food, temperature change information of the food, and output level information of the cooking device into the learned artificial intelligence model to obtain the expected cooking completion time of the food.

10. In paragraph 9, The above user's usage pattern information is: A cooking device comprising at least one of additional cooking information, information for stopping cooking during operation, or information for changing the set temperature.

11. A method for controlling a cooking device that cooks food using electromagnetic waves, A step of obtaining a thermal image of food inside the cooking device using a thermal imaging camera; A step of identifying temperature change information of the food and weight information of the food based on a plurality of frames included in the thermal image; and A control method, comprising: a step of inputting a target cooking temperature of the food, weight information of the food, temperature change information of the food, and output level information of the cooking device into a learned artificial intelligence model to obtain an expected cooking completion time of the food.

12. In paragraph 11, The step of obtaining the above thermal image is as follows: A step of obtaining a thermal image of a turn table on which the food is placed using the thermal imaging camera; The step of identifying the weight information of the above food is: A step of identifying an area corresponding to the food in each of the plurality of frames; and A control method, comprising: a step of identifying weight information of the food based on the time at which each of the plurality of frames is acquired and the location of the area corresponding to the food.

13. In paragraph 12, The step of identifying the weight information of the above food is: A step of identifying a rotation angle of the turntable based on a position of an area corresponding to the food at a time when each of the plurality of frames is acquired; A step of identifying a rotation speed of the turn table based on a rotation angle of the turn table; and A control method, comprising: a step of identifying weight information of the food based on the identified rotation speed.

14. In paragraph 11, The above learned artificial intelligence model is, A control method, wherein the model is learned by using the target cooking temperature, weight information, temperature change information, and output level information of the cooking device as input data and the cooking completion time corresponding to the target cooking temperature as output data.

15. A non-transitory computer-readable medium storing computer commands that, when executed by a processor of a cooking device that cooks food using electromagnetic waves, cause the cooking device to perform an operation, The above actions are, A step of obtaining a thermal image of food inside the cooking device using a thermal imaging camera; A step of identifying temperature change information of the food and weight information of the food based on a plurality of frames included in the thermal image; and A non-transitory computer-readable medium comprising: a step of inputting target cooking temperature of the food, weight information of the food, temperature change information of the food, and output level information of the cooking device into a learned artificial intelligence model to obtain an expected cooking completion time of the food.

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