Automatic cooking equipment

Abstract

To provide an automatic cooking device capable of automatically preparing delicious dishes easily. [Solution] One aspect of the present invention includes a main body that houses a pot, a cooking utensil section that is inserted into the pot, a control section that controls cooking, a main body communication section provided in the main body, and a terminal device having an information input section, an information output section, and a terminal communication section, wherein the terminal device transmits an image of ingredients captured by the information input section to the main body communication section via the terminal communication section, and the control section transmits an ingredient calculation section that calculates the type and amount of ingredients from the ingredient image received by the main body communication section, and a cooking utensil calculation section that calculates the type and amount of ingredients based on preset teacher data. Artificial This automatic cooking device has a menu generation unit that uses intelligence to generate cooking candidate menus based on the types and amounts of ingredients, and a cooking control unit that controls the operation of the cooking utensil unit and the temperature of the pot unit based on the menu generated by the menu generation unit.

Description

Technical Field

[0001] The present invention relates to an automatic cooking apparatus that automatically cooks a desired menu by putting in food ingredients.

Background Art

[0002] Patent Document 1 discloses a cooking support system that supports proceeding with cooking according to a recipe. This cooking support system receives a designation of information indicating a cooking procedure including a first term representing the state inside a cooking appliance, receives data detected by a sensor that senses the state inside the cooking appliance, refers to a first storage area storing one or more pairs of the data detected by the sensor that senses the state inside the cooking appliance and the term representing the state inside the cooking appliance, and when it can be considered that the received data matches the data associated with the first term in the first storage area, outputs both information indicating that the current state inside the cooking appliance corresponds to the first term and information indicating that this is the timing to proceed to the next procedure of the cooking procedure, and has a control unit.

[0003] Patent Document 2 discloses a processing apparatus that, when processing an object by supplying energy, does not require a human to perform checking and that makes the state of the finished object uniform. This processing apparatus includes an energy supply means for supplying energy to the object, a motion detection means for detecting the dynamic state of the object, and an energy supply control means for controlling the amount of energy supplied by the energy supply means to the object. In this processing apparatus, the energy supply control means is characterized by controlling the amount of energy supplied by the energy supply means to the object according to the dynamic state of the object detected by the motion detection means.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] In an automatic cooking apparatus that inputs ingredients and automatically cooks a desired menu dish, it is desired that a favorite dish can be automatically completed with as little effort as possible. Furthermore, not only should the dish be automatically cooked, but also deliciousness is required.

[0006] An object of the present invention is to provide an automatic cooking apparatus that can automatically cook delicious dishes easily.

Means for Solving the Problems

[0007] One aspect of the present invention includes a main body unit that houses a pot unit, a cooking utensil unit inserted into the pot unit, a control unit that performs cooking-related control, a main body side communication unit provided in the main body unit, and a terminal device having an information input unit, an information output unit, and a terminal side communication unit. The terminal device transmits an image of the ingredients captured by the information input unit to the main body side communication unit via the terminal side communication unit. The control unit includes an ingredient calculation unit that calculates the type and quantity of the ingredients from the image of the ingredients received by the main body side communication unit, and an artificial intelligence-based Artificial menu generation unit that generates a menu that is a cooking candidate from the type and quantity of the ingredients, and a cooking control unit that controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit based on the menu generated by the menu generation unit. It is an automatic cooking apparatus.

[0008] According to such a configuration, the type and quantity of the ingredients are calculated by the ingredient calculation unit from the image of the ingredients captured by the information input unit of the terminal device, and a menu that is a cooking candidate is generated by artificial intelligence from the calculated type and quantity of the ingredients. Based on this menu, at least one of the operation of the cooking utensil unit and the temperature of the pot unit is controlled by the cooking control unit, so that automatic cooking is performed based on the menu derived from the ingredients.

[0009] In the above automatic cooking device, the menu generation unit may extract supplementary ingredients based on the difference between the types and quantities of ingredients required for cooking the menu candidates and the types and quantities of ingredients calculated from the image by the ingredient calculation unit, and the main body side communication unit may transmit the information of the supplementary ingredients to the terminal device. Thereby, the information on the shortage of the ingredients put in is sent to the terminal device, and the ingredients to be replenished are notified to the user.

[0010] In the above automatic cooking device, it may further include an image input unit provided in the main body unit, and the main body side communication unit may transmit the image of the cooking pot unit input by the image input unit to the terminal device. Thereby, the image of the cooking pot unit during cooking is sent to the terminal device, and the user can grasp the progress of cooking by referring to the image of the cooking pot unit on the terminal device.

[0011] In the above automatic cooking device, the terminal device may transmit the correction information regarding the operation of the cooking utensil unit and the temperature of the pot unit received from the user to the main body side communication unit from the terminal side communication unit, and the cooking control unit may correct the control of at least one of the operation of the cooking utensil unit and the temperature of the pot unit based on the correction information received by the main body side communication unit. Thereby, at least one of the operation of the cooking utensil unit and the temperature of the pot unit can be corrected by the user using the terminal device.

[0012] In the above automatic cooking device, the information input unit of the terminal device may receive the user's comments on the dish cooked in the pot unit based on the menu generated by the menu generation unit, the terminal side communication unit of the terminal device may transmit the comments to the main body side communication unit, and the menu generation unit of the control unit may reflect the comments received by the main body side communication unit in the teacher data. Thereby, the user's comments on the dish are reflected in the teacher data, Artificial and the degree of deliciousness when generating an intelligent menu will grow.

[0013] In the above automatic cooking device, the cooking control unit may control at least one of the temperature of the pot unit and the operation of the cooking utensil unit according to the color component based on the Maillard reaction obtained from the image of the food in the pot during cooking. Thereby, the way of applying heat to the food is grasped according to the color component based on the Maillard reaction, and control such as heat adjustment is performed.

[0014] In the above automatic cooking device, the cooking utensil unit may include a stirring portion rotatably provided in the pot unit, a lid portion disposed above the stirring portion in the pot unit and rotatably provided, and a clutch provided between the lid portion and the shaft portion for intermittently transmitting the rotational force of the shaft portion to the lid portion. Thereby, the stirring operation on the food can be changed to perform effective stirring.

[0015] In the above automatic cooking device, the main body unit or the terminal device is connected via a network to a database server that stores type and quantity data indicating the association between the image of the food and the type and quantity of the food, teacher data, and menu data that is menu data. The food calculation unit calculates the type and quantity of the food from the image of the food using the type and quantity data stored in the database server, and the menu generation unit may generate a menu using the menu data stored in the database server. Thereby, a wide variety of menus are generated using the vast amount of data stored in the database server connected via the network.

[0016] In the above automatic cooking device, the menu data includes data on the cooking procedure for cooking based on the menu, and the cooking control unit may control at least one of the operation of the cooking utensil unit and the temperature of the pot unit using the data on the cooking procedure stored in the database server. Thereby, since cooking is performed using the data on the cooking procedure stored in the database server, it is not necessary to store a vast amount of data in the main body unit or the terminal device.

[0017] In the above automatic cooking device, it further includes a delicious taste component detection unit that detects the amount of a delicious taste component, which is at least one of glutamic acid, guanylic acid, and inosinic acid, from the food being cooked. The database server stores food delicious taste component data indicating the association between the type and quantity of the food before cooking and the delicious taste component, and menu delicious taste data indicating the association between the menu and the delicious taste component. The cooking control unit controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit so as to bring the amount of the delicious taste component detected by the delicious taste component detection unit closer to the amount of the delicious taste component corresponding to the menu using the food delicious taste data and the menu delicious taste data stored in the database server.

[0018] Alternatively, the cooking control unit controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit so as to make the amount of the delicious taste component detected by the delicious taste component detection unit more or less than the amount of the delicious taste component corresponding to the menu using the food delicious taste data and the menu delicious taste data stored in the database server.

[0019] Alternatively, the cooking control unit, based on the teacher data of the preset delicious taste component, Artificial controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit so that the type and amount of the delicious taste component obtained by intelligence are achieved.

[0020] Thereby, the amount of the delicious taste component of the food during cooking is detected by the delicious taste component detection unit. Then, based on the detected amount of the delicious taste component, at least one of the operation of the cooking utensil unit and the temperature of the pot unit is controlled so as to bring it closer to the amount of the delicious taste component corresponding to the menu, or to increase or decrease the amount of the delicious taste component, or to make the type and amount of the delicious taste component obtained by artificial intelligence. A delicious dish based on the amount of the delicious taste component is automatically created.

[0021] Here, glutamic acid is L-glutamic acid, which is simply referred to as "glutamic acid" in this embodiment. Also, guanylic acid is guanosine monophosphate, GMP, or 5'-guanylic acid, which is simply referred to as "guanylic acid" in this embodiment. In addition, inosinic acid is inosine 5'-monophosphate, or IMP, which is simply referred to as "inosinic acid" in this embodiment.

Advantages of the Invention

[0022] According to the present invention, it becomes possible to provide an automatic cooking apparatus that can automatically cook delicious dishes easily.

Brief Description of the Drawings

[0023]

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Modes for Carrying Out the Invention

[0024] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following description, the same members are denoted by the same reference numerals, and the description of the members once described will be omitted as appropriate.

[0025] (Configuration of Automatic Cooking Device) FIG. 1 is a schematic diagram illustrating the configuration of an automatic cooking device according to the present embodiment. The automatic cooking device 1 according to the present embodiment is a device that can automatically create and cook a menu by putting in food M. The automatic cooking device 1 includes a main body unit 10, a cooking utensil unit 20, a control unit 30, a main body side communication unit 40, and a terminal device 50.

[0026] The main body unit 10 is provided in a box shape, for example, and a lid 15 that can be opened and closed is attached. The main body unit 10 houses a pot unit 11 that accommodates food M and performs cooking. The main body unit 10 has a heater 12 disposed around the pot unit 11. The heater 12 is provided on the outer periphery or the outside of the bottom of the pot unit 11. The heater 12 may be a heating wire type or an IH (Induction Heating) type. It is preferable that the heater 12 can perform heating control for the subdivided regions of the outer periphery and the bottom of the pot unit 11.

[0027] The cooking utensil unit 20 is a utensil for cooking that is inserted into the pot unit 11. The cooking utensil unit 20 has a rotating utensil 21 housed in the main body unit 10. The rotating utensil 21 has a shaft portion 211 and a spatula portion 212 provided on the tip side of the shaft portion 211. For example, the shaft portion 211 is rotated by a motor 25 provided on the lid 15, and thereby the spatula portion 212 rotates in the pot unit 11 to stir the food M. The rotating utensil 21 is detachably provided with respect to the motor 25 and can be exchanged according to the cooking content.

[0028] The control unit 30 is a part that performs control related to cooking. The control unit 30 is provided on the lid 15 or the main body unit 10, for example. The control unit 30 may be provided externally via a network. The functional configuration of the control unit 30 will be described later.

[0029] The main body side communication unit 40 is provided, for example, in the main body 10, and performs input / output of information with the terminal device 50.

[0030] The terminal device 50 is, for example, a smartphone or a tablet terminal. The terminal device 50 performs input / output of information with the main body 10. The terminal device 50 is operated by a user, and can send various settings and instructions related to cooking to the main body 10, and can receive and display information related to cooking transmitted from the main body 10. The functional configuration of the terminal device 50 will be described later. Note that the terminal device 50 may have a configuration separate from the main body 10 like a smartphone, or may have an integrated configuration incorporated in the main body 10.

[0031] (Functional configuration of the automatic cooking device) FIG. 2 is a functional block diagram illustrating the automatic cooking device according to the present embodiment. The automatic cooking device 1 includes an image input unit 110, a temperature detection unit 120, a umami component detection unit 130, and a main body side communication unit 40. The image input unit 110 is, for example, a camera, and has a function of acquiring an image of the food material M put into the pot part 11 or an image of the food material M during cooking. The image input unit 110 is provided, for example, inside the lid 15 (see FIG. 1). An anti-fog function may be provided for the lens of the camera. For example, means for removing fog on the lens with a wiper using the motor 25 that rotates the rotating device 21 as a drive source, or attaching an anti-fog film or applying an anti-fog film to the surface of the lens of the camera can be mentioned. The image input unit 110 may have a function of capturing an image from a database server SV or the like via the network N.

[0032] The temperature detection unit 120 has a function of detecting the temperature of the pot unit 11 and the temperature of the food M being cooked. Examples of the temperature detection unit 120 include an infrared sensor (infrared camera), a resistance temperature detector, and a thermocouple. The umami component detection unit 130 has a function of detecting the umami components contained in the food M being cooked. The umami component detection unit 130 detects the amounts of amino acids and nucleic acids that are umami components, for example, by ultraviolet absorption method or fluorescence analysis method. The method for detecting umami components by the umami component detection unit 130 will be described later.

[0033] The main body side communication unit 40 has a function of performing information communication with the terminal device 50 by wireless communication or wired communication. The main body side communication unit 40 also has a function of performing information communication with the database server SV via the network N.

[0034] The control unit 30 of the automatic cooking device 1 includes a foodstuff calculation unit 101, a menu generation unit 102, and a cooking control unit 103. The foodstuff calculation unit 101 has a function of calculating the type and quantity of the food M from the image of the food M received by the main body side communication unit 40. The menu generation unit 102 Artificial generates cooking candidate menus from the type and quantity of the food M by intelligence (hereinafter also referred to as "AI") based on the preset teacher data. The cooking control unit 103 controls at least one of the operation of the cooking utensil unit 20 and the temperature of the pot unit 11 based on the menu generated by the menu generation unit 102.

[0035] The terminal device 50 includes an information input unit 510, an information output unit 520, a terminal side communication unit 530, and a control unit 540. The information input unit 510 has a function of inputting information by text, image, voice, etc. Examples of the information input unit 510 include a touch panel, buttons, a camera, a microphone, etc. The information output unit 520 has a function of outputting information by text, image, voice, etc. Examples of the information output unit 520 include a display, a lamp, a speaker, etc.

[0036] The terminal-side communication unit 530 has a function of performing information communication with the main body unit 10 through wireless communication or wired communication. The terminal-side communication unit 530 also has a function of performing information communication with the database server SV via the network N. The control unit 540 controls each part of the terminal device 50. Further, the control unit 540 executes application software (hereinafter also referred to as "app") that performs various settings and instructions for automatic cooking.

[0037] (Cooking method by the automatic cooking device) FIG. 3 is a flowchart illustrating a cooking method by the automatic cooking device according to the present embodiment. In the automatic cooking device 1 according to the present embodiment, AI is used in various processes. The teacher data and AI engine of AI may be provided in the main body unit 10, but it is preferable to use those provided in the database server SV connected via the network N from the viewpoint of reducing the storage capacity on the main body unit 10 side.

[0038] First, as shown in step S101, the type and amount of the food material are calculated. For example, the user arranges the food material M on the table, captures an image with the information input unit 510 (for example, a camera) of the terminal device 50, and transmits it from the terminal-side communication unit 530 to the main body unit 10. The main body-side communication unit 40 receives the image transmitted from the terminal device 50 and sends it to the control unit 30. Note that the image of the food material M may be captured by the camera of the image input unit 110 as the image of the food material M put into the pot unit 11. The food material calculation unit 101 of the control unit 30 calculates the type and amount of the food material M based on the image of the food material M. For example, the type and amount of the food material M are automatically calculated by AI based on the teacher data indicating the correspondence between the image of the food material M and the type and amount.

[0039] Next, as shown in step S102, menu generation by AI is performed. The menu generation unit 102 of the control unit 30 generates a menu that is a cooking candidate from the type and amount of the food material M by AI based on the teacher data for generating a menu pattern based on the preset type and amount of the food material M. Since there may be a plurality of menus assumed from the type and amount of the food material M, a plurality of menu candidates may be generated.

[0040] Basically, cooking is a heat sequence (correlation between heating time and heating temperature). Therefore, by utilizing the self-learning function, various adjustments, and flexibility of AI, a wide range of candidate menus can be generated based on the type and quantity of ingredient M. For example, it is not limited to the menu that uses all of the currently available ingredient M, but menus that use a part of ingredient M or menus in the case where there is a shortage of ingredient M while using the available ingredient M can also be generated as candidates. In generating a menu by AI, menu generation considering the user's past selected menu and preference history may be performed.

[0041] Next, as shown in step S103, menu selection is performed. The menu candidates generated in the previous step S102 are transmitted from the main body side communication unit 40 to the terminal device 50. Information on the menu candidates transmitted from the main body side communication unit 40 is displayed on the information output unit 520 (for example, a display) of the terminal device 50. The user selects a preferred menu from the displayed menu candidates using the information input unit 510 (for example, a touch panel) of the terminal device 50. Also, the number of people (how many people to cook for) may be selected together with the menu selection. The information on the selected menu is transmitted from the terminal side communication unit 530 to the main body unit 10.

[0042] Next, as shown in step S104, it is determined whether there is a need for ingredient replenishment. The main body side communication unit 40 that has received the menu information transmitted from the terminal device 50 sends the menu information to the menu generation unit 102. The menu generation unit 102 extracts replenishment ingredients based on the difference between the type and quantity of ingredients required when cooking the menu selected by the user and the type and quantity of ingredient M calculated from the image of ingredient M by the ingredient calculation unit 101. For example, when there is not enough ingredient M for the number of people desired by the user or when a specific ingredient M is lacking, the type and quantity of the lacking ingredient M are extracted. If there are replenishment ingredients, the information on the replenishment ingredients is transmitted to the terminal device 50 as shown in step S105. The user can prepare the replenishment ingredients by referring to the information on the replenishment ingredients transmitted to the terminal device 50.

[0043] Next, as shown in step S106, the food material M is put into the pot part 11. If a supplementary food material is required, the supplementary food material is also put into the pot part 11 together. If the food material M has already been put into the pot part 11, in this step S106, the supplementary food material is put in, or if there is no supplementary food material, the process proceeds to the next step as it is.

[0044] Next, as shown in step S107, cooking processing is performed. The cooking control unit 103 of the control unit 30 controls the cooking appliance unit 20 according to the menu for the food material M put into the pot part 11. The cooking control unit 103 controls, for example, the motor 25 along the cooking procedure according to the menu. Thereby, the cooking appliance unit 20 rotates at a predetermined timing, and cooking such as kneading, stirring, and turning over the food material M in the pot part 11 is performed.

[0045] In addition, when it is necessary to replace the cooking appliance unit 20 during cooking, the timing of replacement and the information of the cooking appliance unit 20 to be replaced are transmitted to the terminal device 50. In cooking, the cooking control unit 103 controls energization to the heater 12 at a predetermined timing to apply heat to the food material M in the pot part 11. In the pot part 11, necessary cooking (stirring and heating) for the food material M is automatically performed along the cooking procedure according to the menu, and the dish according to the menu is automatically completed.

[0046] (Cooking processing) In the automatic cooking apparatus 1 according to the present embodiment, the state of the food material M during cooking can be grasped and the cooking processing can be automatically controlled. FIG. 4 is a flowchart exemplifying the cooking processing. The steps shown in FIG. 4 are an example of the cooking processing of step S107 shown in FIG. 3.

[0047] First, as shown in step S201, the state of the food ingredient M is detected. For example, at a predetermined timing during cooking, an image of the food ingredient M in the pot section 11 is acquired by an image input section 110 (e.g., a camera) provided in the main body section 10. Also, the temperature of the food ingredient M during cooking is detected by a temperature detection section 120. Further, the amount of umami components in the food ingredient M during cooking may be detected by an umami component detection section 130. The information on the state of the food ingredient M detected during cooking may be transmitted to the terminal device 50. Thereby, the user can refer to information such as an image and temperature of the food ingredient M during cooking on an information output section 520 (e.g., a display) of the terminal device 50.

[0048] Next, as shown in step S202, cooking check by AI is performed. The control section 30 makes a determination by AI as to whether the cooking is proceeding correctly based on the information on the state of the food ingredient M detected in the previous step S201. For example, a cooking control section 103 of the control section 30 recognizes the cooking situation according to the color component based on the Maillard reaction obtained from the image of the food ingredient M in the pot section 11 during cooking. Also, the cooking situation may be recognized by the amount of umami components detected by the umami component detection section 130.

[0049] Next, as shown in step S203, a determination is made as to whether cooking correction is necessary. The control section 30 determines whether cooking correction is necessary according to the check result of the cooking situation by the cooking control section 103. For example, according to the color component based on the Maillard reaction obtained from the image of the food ingredient M, the way of heating the food ingredient M is grasped, and it is determined whether adjustment such as heat control is necessary. When it is determined that cooking correction is necessary (Yes in the determination of step S203), cooking correction is performed as shown in step S204. For example, the cooking control section 103 controls the amount of power supplied to the heater 12 to adjust the heat for the food ingredient M. Also, the heat and stirring condition for the food ingredient M may be adjusted according to the amount of umami components detected by the umami component detection section 130.

[0050] The following methods can be used to perform the cooking correction shown in this step S204. (Method 1) Control at least one of the operation of the cooking appliance unit 20 and the temperature of the pot unit 11 so that the amount of umami component detected by the umami component detection unit 130 approaches the amount of umami component corresponding to the menu using the food umami data and the menu umami data stored in the database server SV. (Method 2) Control at least one of the operation of the cooking appliance unit 20 and the temperature of the pot unit 11 so that the amount of umami component detected by the umami component detection unit 130 is an amount greater than or less than the amount of umami component corresponding to the menu using the food umami data and the menu umami data stored in the database server SV. (Method 3) Control at least one of the operation of the cooking appliance unit 20 and the temperature of the pot unit 11 so that the amount of umami component detected by the umami component detection unit 130 becomes the type and amount of umami component obtained by AI based on the preset teacher data of umami component.

[0051] On the other hand, when cooking correction is not necessary (No in the determination of step S203), proceed to step S205 without performing cooking correction.

[0052] Next, as shown in step S205, determine whether cooking is completed. If cooking is not completed (No in the determination of step S205), return to step S201 and repeat the subsequent processing. If cooking is completed (Yes in the determination of step S205), cooking ends.

[0053] As described above, in this embodiment, since AI is used to perform automatic cooking from ingredients to menu candidates, the more it is used, the more teacher data is accumulated, and the range of menu candidates expands and the cooking control for making delicious dishes improves through learning.

[0054] (Example of a rotating device) Figs. 5 to 7 are schematic diagrams showing an example of a rotating device. FIG. 5 shows an example of a rotating device 21 suitable for stirring foodstuff M. This rotating device 21 includes a shaft portion 211 and a spatula portion 212 provided on the tip side of the shaft portion 211. A turning-back portion 213 is provided at the tip of the spatula portion 212. In this rotating device 21, by rotating the spatula portion 212 together with the shaft portion 211, the foodstuff M is stirred, and the stirred foodstuff M can be turned over by the turning-back surface 213a of the turning-back portion 213 and mixed. The rotation direction of the spatula portion 212 may be in one direction, both directions, or may be randomly switched.

[0055] FIG. 6 shows an example of a rotating device 21 suitable for kneading dough. As shown in the side view of FIG. 6(a), this rotating device 21 includes a shaft portion 211, a spatula portion 212 provided on the tip side of the shaft portion 211, and a lid portion 214 provided at the central portion of the shaft portion 211 (a position above the spatula portion 212). The surface 214a of the lid portion 214 on the spatula portion 212 side is in a circular concave shape. In this rotating device 21, by rotating the spatula portion 212 together with the shaft portion 211, while stirring the foodstuff M, the foodstuff M scooped upward can be rotated by the circular concave surface 214a of the lid portion 214. Thereby, it becomes easier to increase the kneading degree of the foodstuff M such as dough.

[0056] Also, as shown in the plan view of FIG. 6(b), the shaft portion 211 of the rotating device 21 may be rotated about the axis, and the entire rotating device 21 may be rotationally moved within the pot portion 11. Thereby, the spatula portion 212 can be rotated about the axis of the shaft portion 211 and revolved within the pot portion 11, so that the foodstuff M such as dough can be effectively kneaded.

[0057] FIG. 7 shows an example of a rotating device 21 having a clutch function. FIGS. 8(a) to (c) are schematic plan views illustrating the clutch operation. For convenience of explanation, FIGS. 8(a) to (c) schematically show the positional relationship between the shaft portion 211 and the elliptical hole 215a as viewed in the axial direction, and the lid portion 214 shown in FIG. 7 is omitted. This rotating device 21 is provided with a clutch 215 between the lid portion 214 and the shaft portion 211. In this rotating device 21, the shaft portion 212a of the spatula portion 212 is attached at a position offset from the center of the lid portion 214, and the spatula portion 212 can rotate by the rotation of the lid portion 214 to stir the food M. When stirring the food M with the spatula portion 212, the food M scooped upward hits the circular concave surface 214a of the lid portion 214, causing the lid portion 214 and the food M to rotate.

[0058] The lid portion 214 rotates intermittently by the clutch 215. As an example, the clutch 215 has an elliptical hole 215a provided at the central portion of the lid portion 214. The shaft portion 211 penetrates through the elliptical hole 215a, and stoppers 211a are provided at the upper and lower positions of the elliptical hole 215a on the shaft portion 211, and the vertical positioning of the lid portion 214 with respect to the shaft portion 211 is performed.

[0059] As shown in FIGS. 8(a) to (c), protrusions, teeth, or irregularities for increasing the frictional force may be provided on at least one of the shaft portion 211 and the elliptical hole 215a. In the example shown in FIG. 8, protrusions 211b are provided on the outer peripheral surface of the shaft portion 211, and protrusions 215c are provided on the inner wall surface of the elliptical hole 215a. Further, a clutch shoe 215b may be provided on the inner wall surface on the major axis of the elliptical hole 215a. Examples of the material of the clutch shoe 215b include elastic bodies such as rubber and elastomer, and engineering plastics such as nylon and polyacetal. The relative positional relationship between the shaft portion 211 and the elliptical hole 215a can be changed within the tolerance range between the shaft portion 211 and the elliptical hole 215a.

[0060] As shown in FIG. 8(a), when the shaft portion 211 is not in contact with the elliptical hole 215a, the rotation of the shaft portion 211 is not transmitted to the lid portion 214. Even in this case, when the food M comes into contact with the lid portion 214, the movement of the food M may be transmitted to the lid portion 214 and cause it to rotate.

[0061] As shown in FIGS. 8(b) and 8(c), when the food ingredient M contacts the lid portion 214 and the position of the lid portion 214 changes, and the shaft portion 211 contacts the elliptical hole 215a, the rotation of the shaft portion 211 is transmitted to the lid portion 214, and the rotational force of the shaft portion 211 is applied to the lid portion 214.

[0062] For example, as shown in FIG. 8(b), when the shaft portion 211 contacts the curved surface (the curved surface with a small R) on the major axis of the elliptical hole 215a, the lid portion 214 rotates (eccentric rotation) so as to be swung around the position of the curved surface as the approximate center. At this time, the provision of the clutch shoe 215b increases the frictional force, and more reliable transmission of the rotational force is performed.

[0063] Also, as shown in FIG. 8(c), when the shaft portion 211 contacts the curved surface (the curved surface with a large R) on the minor axis of the elliptical hole 215a, the lid portion 214 rotates (oscillatory rotation) so as to have many moving components in the major axis direction. At this time, the provision of the protrusions 211b and 215c increases the frictional force, and more reliable transmission of the force is performed.

[0064] Since the states shown in FIGS. 8(a) to 8(c) occur randomly depending on the way the food ingredient M hits the lid portion 214, the lid portion 214 and the spatula portion 212 connected thereto also move in a complicated manner, changing the stirring operation on the food ingredient M so that the food ingredient M can be efficiently scraped and mixed.

[0065] Note that since the shaft portion 212a of the spatula portion 212 is offset with respect to the center of the lid portion 214, in a state where no external force is applied to the lid portion 214 (initial state), the lid portion 214 is obliquely arranged with respect to the shaft portion 211 so that the side where the spatula portion 212 is provided goes down. Therefore, by aligning the offset direction of the shaft portion 212a with the major axis direction of the elliptical hole 215a, the contact state between the shaft portion 211 and the elliptical hole 215a shown in FIG. 8(b) can be achieved in the initial state, and the rotation of the lid portion 214 can always be started by the start of the rotation of the shaft portion 211. Also, in the above example, the elliptical hole 215a is shown, but instead of the elliptical hole 215a, a circular, oval, or polygonal (for example, the triangular loulo) hole may be used.

[0066] Further, the cross-sectional shape of the shaft portion 211 may be other than circular, for example, polygonal, asymmetric, or teardrop-shaped (such as a semi-circle + triangle). As a result, each time the shaft portion 211 rotates once, the shaft portion 211 hits the wall surface in the minor axis direction of the elliptical hole 215a at least once, making it easier to prevent the non-contact state (so-called idling) between the shaft portion 211 and the elliptical hole 215a. When the cross-sectional shape of the shaft portion 211 is teardrop-shaped, an elastic body may be provided at the apex portion of the triangle. Thereby, when the apex portion of the triangle of the shaft portion 211 hits the elliptical hole 215a, the elastic restoring force of the elastic body makes it easier for the shaft portion 211 to hit the other wall surface (for example, the wall surface in the major axis direction) of the elliptical hole 215a, enhancing the so-called idling prevention effect.

[0067] In the rotating device 21 using the clutch 215, by using the clutch shoe 215b as a brake to reverse the shaft rotation from clockwise to counterclockwise (or vice versa), the dough is twisted by the action of the shearing force at this time. Also, the lid may be made transparent and the state of kneading the dough may be monitored with a camera, and after confirming that the rotation of the rotating device 21 has stopped, the kneading of the dough may be controlled by reversing the shaft rotation or the like.

[0068] (Heat Quantity Calculation by Automatic Cooking Device) When performing automatic heating cooking in the automatic cooking device 1 according to the present embodiment, the control unit 30 performs the following heat quantity calculation. First, there are the following two formulas as heat quantity calculation formulas. (Formula 1) Heat quantity (Q) = mass (m) × specific heat capacity (c) × temperature change (ΔT) (Formula 2) W (watt) = J (joule) / sec (second), or J = W × sec For example, when continuously boiling 1000 cc of water at 100°C for 10 minutes, in Formula 1, · The specific heat capacity of water is approximately 4.18 J / g°C. · The temperature change is 100°C - 20°C (room temperature) = 80°C. Therefore, the energy = 1000 g × 4.18 J / g°C × 80°C = 334400 J.

[0069] As shown in (Formula 2), since 1 W (watt) is 1 J (joule) per second, when boiling for 10 minutes (600 seconds), the wattage is 334400 J ÷ 600 sec = approximately 557 W. In other words, it will be heated at approximately 557 W for 10 minutes (600 seconds).

[0070] Next, the calorie calculation for cooking 1 go of rice will be explained. In this case, · Assume that the specific heat capacity (c) is approximately 4.18 J / g℃ for water and 3.75 J / g℃ for rice. · The mass of 1 go (180 ml) of rice is generally about 150 g in the case of white rice. · The amount of water required to cook 1 go of rice is about 1.2 to 1.3 times the volume of 1 go of rice (180 ml). Therefore, the amount of water is about 220 g. · The temperature change is 100℃ - 20℃ (room temperature) = 80℃. Therefore, by simple calculation, the energy = 220 g × 4.18 J / g℃ × 80℃ + 150 g × 3.75 J / g℃ × 80℃ = 118568 J.

[0071] Regarding the detailed rice cooking, assuming it will be described later, as a calculation formula on the plane, the above calorie calculation formula can be applied to heating other foods in the same way as for 1 go of rice. For example, when there is a cooking method of boiling a certain food (such as meat), the calorie calculation is performed at 100 g × 100℃ × 15 minutes in the general cooking method, and J (joule) and W (watt) are calculated. It is also possible to vary the temperature and heating time within the range of that J (joule). These are generally referred to as low-temperature cooking, slow cooking, or time-saving cooking (which can also be understood as high-temperature cooking, and in the derivative, cooking in a pressure cooker), and they are also applicable to the automatic cooking device 1 according to the present embodiment.

[0072] The assumed conditions for the heating temperature of low-temperature cooking and slow cooking are shown below. · The heating temperature of slow cooking is usually between 50℃ and 85℃. As an overview, slow cooking involves setting a low heating temperature and heating for a long time to protect nutrients and flavors from heat while tenderizing ingredients (especially meat and fish).

[0073] · The heating temperature for low-temperature cooking (vacuum cooking) is approximately 50°C to 85°C for meats (e.g., beef, chicken), approximately 45°C to 65°C for seafood (e.g., salmon, white fish), and approximately 80°C to 85°C for vegetables. As an overview, low-temperature cooking (vacuum cooking) is similar to slow cooking in terms of temperature specifications. Generally, it involves cooking ingredients enclosed in a plastic casing by simmering. The resulting effects are generally the same as those of slow cooking.

[0074] (Temperature detection) Figure 9 is a schematic diagram for explaining an example of temperature detection by the temperature detection unit. Figure 9(a) shows a diagram illustrating the temperature distribution detected from above the pot part 11 by the temperature detection unit 120. Figure 9(b) shows an example in which the temperature sensor 121 is arranged on the bottom side of the pot part 11. As shown in Figure 1, a heater 12 is provided around the pot part 11 so that the entire pot part 11 can be heated.

[0075] When using, for example, an infrared sensor (infrared camera) as the temperature detection unit 120 provided on the lid 15 to detect the temperature, the distribution of the infrared image in the plan view of the pot part 11 as shown in Figure 9(a) can be obtained. Figure 9(a) shows an infrared image (thermograph) obtained by dividing the plan view of the pot part 11 into a mesh pattern. From the correspondence between the infrared amount in each mesh and the temperature detected by the temperature sensor 121, the temperature distribution in the plan view of the pot part 11 can be obtained.

[0076] Also, the temperature sensor 121 provided on the bottom side of the pot portion 11 shown in Fig. 9(b) can detect the temperature on the bottom side of the pot portion 11. Based on the thermograph from the upper side of the pot portion 11 and the detected temperature on the bottom side, the cooking control unit 103 precisely controls the energization of the heater 12 and the stirring of the food M by the rotating device 21. At this time, cooking checks can be performed by AI, leading to the completion of delicious dishes.

[0077] Next, specific examples of cooking and examples of state detection during cooking will be described.

[0078] (Example of baking bread) Figs. 10(a) to (c) are schematic diagrams showing an example of baking bread. Fig. 10(a) shows an example of baking white bread B, Fig. 10(b) shows an example of baking using a baking cup CP, and Fig. 10(c) shows an example of baking a roll pan RB. In any of the examples, the rotating device 21 (see Fig. 1) is shown in a removed state. In the baking of the white bread B shown in Fig. 10(a), a metal reflector 13 is arranged at the upper opening portion of the pot portion 11 to improve the thermal efficiency. By providing the reflector 13, the heat from the side surface of the pot portion 11 can be easily transmitted upward, and the escape of heat from the upper opening of the pot portion 11 can be suppressed. Note that the reflector 13 may be provided with an opening so as not to interfere with the operations of the image input unit 110 and the temperature detection unit 120. Instead of the metal reflector 13, a reflector 13 made of heat-resistant glass or heat-resistant plastic may be arranged.

[0079] As shown in Fig. 10(b), baking may be performed by accommodating the baking cup CP containing the dough in the pot portion 11. By capturing the state of the dough from above the baking cup CP with the image input unit 110 or detecting the temperature with the temperature detection unit 120, an optimal baking result can be obtained while recognizing the baking state.

[0080] As shown in Fig. 10(c), it is also possible to bake a roll pan RB by accommodating a roll-shaped dough in the pot section 11. In this case, by capturing the state of the dough from above the roll pan RB with the image input section 110 or detecting the temperature with the temperature detection section 120, it is possible to obtain an optimal baking result while recognizing the baking condition.

[0081] (Example of rice cooking) Next, an example of automatic rice cooking by the automatic cooking apparatus 1 according to the present embodiment will be described. There is an old saying "At the beginning, it bubbles gently, in the middle, it bubbles vigorously...". Using this as a reference, an explanation will be given. This is a rice cooking method using a kama (iron pot) or a stove, and it is still applicable today. That is, because even in an electric rice cooker, materials such as iron, SUS (stainless steel), aluminum, and copper are used.

[0082] Hereinafter, based on the saying "At the beginning, it bubbles gently, in the middle, it bubbles vigorously, and when the baby cries, don't lift the lid", the details of each stage will be described below. Here, the cooking condition is 1 go (150 g) of white rice.

[0083] (Stage 1: At the beginning, it bubbles gently) This means "At first, use a low heat", and it is a stage called pre-cooking or pre-boiling. · Let the rice absorb water. · Hydrolyze starch to produce sweetness (generate sugar). It takes about 10 to 15 minutes to bring to a boil, so it is calculated as 13 minutes (780 seconds). In the automatic cooking apparatus 1, energy calculation is performed in the same way as the above simple calculation. Energy = 220 g × 4.18 J / g°C × 80°C + 150 g × 3.75 J / g°C × 80°C = 118568 J 118568 J ÷ 780 seconds = 152 W (watts) That is, heating is performed at 152 W (watts) for 780 seconds.

[0084] (Stage 2: In the middle, it bubbles vigorously) This means "Strengthen the fire in the middle", and it is a stage called main cooking or cooking to completion. · Soften the rice and make it sticky. · Gelatinize (pasting) part of the starch with heat and water. Since the boiling state (= 100 °C) is kept for about 15 to 20 minutes, it is calculated in 18 minutes (1080 seconds). In the automatic cooking device 1, energy calculation is performed in the same way as the above simple calculation. Energy = 220g × 4.18J / g°C × 80°C + 150g × 3.75J / g°C × 80°C = 118568J 118568J ÷ 1080 seconds = 110W (watts) That is, heating is performed at 110W (watts) for 1080 seconds.

[0085] (Stage 3: Don't remove the lid when the baby cries) This means "Don't remove the lid no matter what happens", and it is the stage called steaming. · Further bring out the sweetness, stickiness, and aroma of the rice. Since 90 °C or higher is kept for about 10 to 15 minutes, it is calculated in 13 minutes (780 seconds). In the automatic cooking device 1, energy calculation is performed in the same way as the above simple calculation. 220g × 4.18J / g°C × 70°C + 150g × 3.75J / g°C × 70°C = 103747J 103747J ÷ 780 seconds = 133W (watts) That is, heating is performed at 133W (watts) for 780 seconds. Note that since it is cooked with preheating, 0J and 0W may also be used.

[0086] The total heating calorie amount from the above stage 1 to stage 3 is 340883J (when stage 3 is 0J and 0W, 237136J).

[0087] However, these are the on - paper calculated values. Therefore, in the automatic cooking device 1, heating cooking is started once with these on - paper calculated values, and the state is detected by the image input unit 110 (including a camera and an infrared camera) and the temperature sensor, and cooking is performed while correcting the heating amount. In addition, by machine learning of AI, the deviation between the on - paper calculated value and the actual heating amount is reduced.

[0088] (Role of the camera) In the automatic cooking device 1 according to the present embodiment, the camera, which is an example of the image input unit 110, plays various roles. By using an infrared camera, which is an example of the image input unit 110, an image as shown in Fig. 9(a) can be obtained in the same manner as the temperature detection unit 120. For example, by photographing the cooking of rice from above the pot unit 11 with an infrared camera, which is an example of the image input unit 110, an image as shown in Fig. 9(a) can be obtained. For example, a cylindrical pot is divided by a reticle, and each part is displayed by a thermograph (for example, color-coded). Naturally, the temperature decreases as the distance from the wall surface of the pot unit 11 increases. Since these are only surface temperatures, to grasp the total amount of heat, the temperature of the entire depth is estimated from the bottom surface temperature obtained from the temperature sensor set at the bottom and the temperature of each reticle obtained from the above thermograph. Then, the amount of heat Q per unit time (second) is calculated, and these are integrated to approach the on-board calculated value calculated earlier. As an example, when calculating the amount of heat Q when heating 1000 cc of water in a pot, with the bottom of the pot at 100°C, the water surface temperature at 80°C, and the room temperature at 20°C, it can be obtained by the following calculation formula. Q = m × c × ΔT Here, Q is the amount of heat (J: joule), m is the mass (g or kg), c is the specific heat capacity (J / g°C or J / kg°C), and ΔT is the temperature change (initial temperature - final temperature). Assuming that 1000 cc of water is in the pot and considering the density of water to be approximately 1 g / cc, the mass m is 1000 g. Also, the specific heat capacity c of water is generally about 4.18 J / g°C. The initial temperature is 100°C, which is the temperature of the bottom of the pot, and the final temperature is 80°C, which is the temperature of the water surface. Substituting these into the above calculation formula to calculate the amount of heat Q, Q = 1000 g × 4.18 J / g°C × 20°C = 83600 J Therefore, when heating 1000 cc of water poured into the pot, the amount of heat Q is approximately 83600 J. In this way, the amount of heat (J: joules) per unit time (seconds) is calculated by the above calculation formula and accumulated, approaching the initial calculated value. The upper surface temperature is the sum of the temperatures for each reticle, and the lower surface temperature is derived from the temperature sensor. At this time, convection is ignored. The above calculation is applicable when only the surface temperature is measured by an infrared sensor (thermograph), but this is not the case if the entire object can be measured by the infrared sensor.

[0089] Here, temperature sensors may be attached to the bottom and wall surfaces of the pot part 11 without gaps, and a method of estimating from the input power (W) can also be mentioned. However, if a thermograph is used, there are many advantages as it can detect temperature unevenness and stir immediately.

[0090] During rice cooking, stirring can make the heating more uniform. Furthermore, after cooking is complete, by rotating the stirrer to the left and right respectively, the trouble of putting in a rice spatula can be saved.

[0091] As another usage method during rice cooking, when performing water immersion cooking on rice (it is said that this method makes the rice more delicious as the water penetrates the rice), in the automatic cooking device 1, an image of the rice may be taken with a camera, and the degree of water immersion in the rice may be detected based on the captured image. Specifically, methods include observing the image of the rice at a fixed point and determining from the degree of swelling, and observing by image the fact that the rice turns white as it absorbs water (a method of managing whitening with a white index may also be used). Also, with the stirring function, the entire degree of water immersion may be detected by taking pictures of the parts other than the surface with a camera.

[0092] (Role of the camera as a safety mechanism) In the automatic cooking device 1, when an abnormality is detected by the camera, such as when boiling over or over-boiling occurs during cooking including stews other than rice cooking, when a large amount of white smoke is generated, or when black smoke is generated, the heating can be immediately stopped.

[0093] (Linkage between the image recognition function of the camera and the functions of the built-in camera and the terminal device camera) The automatic cooking device 1 can make the AI recognize the input raw materials (quantity and type) before cooking and set the preheating amount in advance from their specific heat capacities. For this purpose, there are various ways to make the AI recognize, and not only the camera of the terminal device 50 (such as a smartphone or a tablet terminal) used as the UI (user interface), but also the image input unit 110 (built-in camera) provided in the main body 10 can perform this function.

[0094] (Regarding the specification of ingredients other than the camera) The automatic cooking device 1 has a function of judging the type and quantity of the food material M from the image of the food material M captured by the information input unit 510 (camera) of the terminal device 50 or the camera of the image input unit 110 of the main body 10. However, even other than the camera (image), for example, the user can read out the name of the food material M and give it to the control unit 30 as voice data, or give the control unit 30 the code number associated with the menu of the recipe site or the recipe book (recipe book), the recipe data. In this case, it is preferable to inquire of the user whether the food material recognized from the given data matches the actually input food material by using the dialogue function by the AI or the like.

[0095] (Regarding the input of recipe data using the camera) In the automatic cooking device 1, the image captured by the image input unit 110 (camera) can be directly displayed on the information output unit 520 (display) of the terminal device 50 or via the network N. The user can view the image captured by the image input unit 110 (camera) through the information output unit 520 (display) of the terminal device 50.

[0096] Also, when photographing and reading the code number printed in the recipe book, the one-dimensional or two-dimensional code, etc. with the information input unit 510 (camera) of the terminal device 50, the read image can be confirmed on the information output unit 520 (display). Also, when photographing the food ingredients M (including water) scheduled to be input with the information input unit 510 (camera) and having the AI recognize the content and quantity thereof, the image and recognition result can be confirmed with the information output unit 520 (display). At this time, the type and quantity of the food ingredients may be recognized by the AI using image recognition technology from the captured image, and information for confirmation as to whether this content is acceptable may be output from the information output unit 520 (display or speaker).

[0097] (Function for recognizing color change of raw materials during cooking) The automatic cooking device 1 may be provided with a function of performing white indexation using an image photographed by a camera as a method for recognizing the degree of water immersion of the previous rice. Since rice becomes white when completely immersed in water, the value digitized by the solid-state imaging device (CCD image sensor, COMOS image sensor, etc.) of the camera is compared with the following white value, and completion is determined with an approximate value.

[0098] As the "white value", in the Lab value (CIELAB color space), the white value is such that the L value is 100. This value represents brightness and indicates that white is the brightest. It is common for the a value and the b value to be close to 0. Also, as the "white value", in the RGB color space using red (R), green (G), and blue (B) which are the three primary colors of light, values from 0 to 255 are used for each to represent the intensity of each color, and if all are 0, it is "black". If all are 255, it becomes "white". Note that the Lab value and the RGB value are convertible with each other.

[0099] Alternatively, separately from the above, the AI may be pre-trained using teacher image data of whitened rice to recognize an image of whitened rice, and the captured image may be compared with the AI.

[0100] (Maillard reaction) Next, an example of a method for measuring the color in the Maillard reaction of the food ingredient M will be illustrated. For color measurement, two methods, namely, the stimulus value direct reading method and the spectroscopic color measurement method, are used. The method of directly reading the stimulation value measures and calculates three colors, red, green, and blue, in the same way as the way humans recognize colors. On the other hand, the spectroscopic color measurement method measures the wavelength of light and performs color measurement. Therefore, it is used to measure a more advanced color tone than the method of directly reading the stimulation value.

[0101] In the automatic cooking device 1, for example, when cooking miso soup, it is possible to sandwich a cooking process of lightly stir-frying the ingredients in advance. Therefore, it is necessary to grasp the heating state of the food material M with a camera so as not to burn the food material M. For grasping the heating state, for example, the following methods can be mentioned. · Judgment by AI using teacher image data · Method of directly reading the stimulation value by a camera (solid-state imaging device) · Method of adding visible light (spectroscopic reflection light) to an ultraviolet spectrophotometer (reflecting transmitted light for spectroscopy) used for quantification of amino acids and nucleic acids (that is, spectroscopic color measurement method)

[0102] The color change of the food material M due to heating (boiling, roasting, frying, stir-frying, steaming) is greatly influenced by the Maillard reaction (browning). When the original color of the food material M is transparent or white-based (including cream color and yellow), it can be indexed by increasing the brown or black color. This method can make a typical color sample (for example, a color chart in which brown increases by 10% each) in advance for each reaction stage to promote the Maillard reaction due to the progress of heating, and can be indexed. For example, in the case of black, the lightness decreases at each stage and finally carbonizes to become black. Also, in the case of brown, since it becomes an image where brown covers the original color, it starts from zero with brown minus the original color, then brown increases at each stage, and in addition, the lightness decreases and finally becomes black. Then, the color change of the food material M obtained from the camera is compared with the color sample to grasp the reaction stage, and when the ideal (specified) state is reached, the heating is terminated.

[0103] However, color changes due to heating involve not only the Maillard reaction, but also a wide range of factors such as a decrease in lightness and chroma due to dehydration, an increase in lightness due to water absorption, elution of pigments, decomposition due to heating, carbonization of food material M, yellowing due to oxidation, and color changes due to absorption by sauces, etc. In addition, if the original color of the food material is not white or transparent, browning may not be noticeable even if it occurs. Therefore, it is preferable to use the combination of food material M input according to the menu to be cooked and the color change due to heating as teaching data, and to determine the cooking state by AI from the image of food material M captured during cooking.

[0104] (Cooking check by AI) The automatic cooking apparatus 1 according to the present embodiment may be provided with an AI-based cooking check function. FIG. 11 is a diagram showing an example of an interactive cooking check between a user and AI regarding the degree of burning of toast. In this example, the automatic cooking apparatus 1 detects the degree of burning of the finished toast and performs an AI-based cooking check. First, the control unit 30 checks the time change (thermograph history) of the burning state of the side surface of the toast. Based on this burning state history, the control unit 30 performs an AI-based check, and when it is determined that there is an abnormality, an AI-based response is transmitted to the terminal device 50. An example of the response is "There seems to have been overheating from ○○ minutes to △△ minutes during heating. There may be a heater failure, so in such cases in the future, please stop heating once and contact the user." When it is determined that there is no abnormality, a response such as "Check other possibilities." is transmitted to the terminal device 50 as an AI-based response.

[0105] Next, the control unit 30 checks the temporal change in the brown color due to the Maillard reaction on the upper surface of the sliced bread. Based on this temporal change in the brown color, the control unit 30 performs an AI-based check. If it is determined that there is an abnormality, an AI-generated response is transmitted to the terminal device 50. An example of the response is "The heating is strong overall, and especially on the sides, it looks burnt because it is close to or in contact with the heater. In the future, please request to weaken the heating during setting." If it is determined that there is no abnormality, a response such as "Check other possibilities." is transmitted to the terminal device 50 as an AI-generated response.

[0106] Next, the control unit checks the temporal change in the baking state (the history of the thermograph) of the upper surface portion of the sliced bread. Based on this history of the baking condition, the control unit 30 performs an AI-based check. If it is determined that there is an abnormality, an AI-generated response is transmitted to the terminal device 50. An example of the response is "The heating is strong overall, and especially on the sides, it looks burnt because it is close to or in contact with the heater. In the future, please request to weaken the heating during setting." If it is determined that there is no abnormality, when no heat reflector is used, a response such as "The upper surface is baked without problems, but the heating amount on the sides seems to be large. In the future, please review the heating correlation ratio between the upper surface and the sides. Also, it is recommended to use a heat reflector." is transmitted to the terminal device 50 as an AI-generated response. When a heat reflector is used, a response such as "Check other possibilities." is transmitted to the terminal device 50 as an AI-generated response.

[0107] Next, the control unit asks the user whether egg yolk or oil has been applied to the dough to prevent burning. If not, an AI-generated response is transmitted to the terminal device 50. An example of the response is "It is recommended to apply egg yolk or oil to the surface of the bread to prevent burning." If it has been applied, a response such as "Check other possibilities such as the temperature history." is transmitted to the terminal device 50 as an AI-generated response.

[0108] Also, the automatic cooking device 1 according to the present embodiment may be provided with a cooking check function through dialogue between the user and the AI. In the determination of whether the cooking correction shown in step S203 of FIG. 4 is necessary, an interactive determination of whether correction is necessary may be advanced by the user and the AI. For example, the control unit 30 transmits to the terminal device 50 an amendment to the cooking determined by the AI based on the detection results such as the image of the food M during cooking, the temperature distribution on the surface of the food M, and the amount of umami components. The user who refers to the amendment to the cooking by the AI transmitted to the terminal device 50 sends information indicating consent to the control unit 30 if following it, and the cooking amendment by the AI is executed. On the other hand, when the user refers to the cooking amendment by the AI and wants to make a cooking correction based on their own judgment, the user sends an amendment (which may be a fine-tuning of the AI amendment) to the control unit 30. Thereby, the cooking correction is performed according to the user's amendment. By incorporating such an exchange of cooking corrections with the user as teaching data, the accuracy of cooking correction by the AI can be improved.

[0109] (User review) The automatic cooking apparatus 1 according to the present embodiment may be provided with a function of inputting and reflecting user reviews. FIG. 12 is a diagram showing an example of input of a user review. As shown in FIG. 12, a screen for inputting a user review is displayed on the display (touch panel) of the terminal device 50. The user can input an evaluation (user review) of the menu cooked by the automatic cooking apparatus 1 from this screen display. The user can specify the evaluation of the cooking in association with a score. Also, the user's impressions can be input as text data in the comment column. Such a user review is received by the control unit 30 and reflected in the teaching data. In combination with the above-described function of checking cooking by the AI, the traceability, machine learning function, and self-diagnosis function in automatic cooking can be exhibited.

[0110] (Detection of umami components) Next, an example of detection of umami components in the automatic cooking apparatus 1 according to the present embodiment will be described.

[0111] (Amounts of glutamic acid and umami nucleotides (GMP·IMP) in each food) The production process of inosinic acid and guanylic acid, which are known as nucleic acid substances and are one of the umami components, is complex. Inosinic acid is often produced when ATP (adenosine triphosphate) releases the energy of phosphate due to cell death and is decomposed by an enzyme from AMP (adenylic acid) to inosinic acid (IMP), which is the original substance. One of the reasons why aged meat is delicious is that protein is decomposed to produce glutamic acid.

[0112] Guanylic acid is known to be abundant in dried shiitake mushrooms. This is often the case when the cell wall breaks due to cell death, the decomposing enzyme touches the nucleic base guanine, and it is decomposed into guanylic acid (GMP).

[0113] However, inosinic acid and guanylic acid originally exist in a free state in food. In addition, there are cases where they are produced by biodegradation through fermentation, and of course, they are also produced by "thermal decomposition". Therefore, when "thermal cooking" such as baking, steaming, smoking, stir-frying, boiling, or frying is performed, the umami of the ingredients increases. Therefore, it is considered theoretically possible to generate glutamic acid, guanylic acid, and inosinic acid by thermal decomposition through the "thermal cooking" of the automatic cooking device 1.

[0114] (Quantification of umami components) Next, an example of the detection of umami components by the umami component detection unit 130 of the automatic cooking device 1 will be described. First, umami components can be detected optically from the soup of stews and soups, and from the cooking liquid or dashi of simmered dishes. Regarding glutamic acid, which is an amino acid, it is possible to create a database of the amount of free glutamic acid in each ingredient. In addition, it is also possible to create a database of free "tyrosine, phenylalanine, tryptophan", which are aromatic amino acids.

[0115] Since the automatic cooking device 1 has already recognized in advance by AI how much of each ingredient has been put in, if the amount of free amino acids in each ingredient is made into a database, the amount of free amino acids can be calculated. Therefore, if any of the free aromatic amino acids is optically detected, it is considered possible to estimate the amount of free glutamic acid from the correlation ratio.

[0116] Regarding the amino acids newly generated by the thermal decomposition of proteins, it can be regarded as the amount exceeding the amount of free amino acids. That is, it is considered that the amount of glutamic acid (mg) newly generated by thermal decomposition ≒ the total amount of glutamic acid (mg) in the aqueous solution estimated by optical detection of aromatic amino acids in the aqueous solution - the total amount of free glutamic acid in each ingredient (mg).

[0117] Regarding the protein constituent amino acids (non-free amino acids), since the number of residues of the 20 types of constituent amino acids in each protein is known, if these are made into a database, especially if the three types of glutamic acid and aromatic amino acids are made into a database, the amount of free and non-free glutamic acid can be estimated from the correlation ratio with the amount of optically detected aromatic amino acids. Here, the amount of protein constituent amino acids is shown in the Japanese Food Standard Nutritional Composition Table. Regarding guanylic acid and inosinic acid, the free amounts of these are already known.

[0118] Here, regarding guanylic acid and inosinic acid, the amount of free aromatic amino acids is quantified and it is estimated that they eluted into the cooking liquid in association with the free aromatic amino acids in the same way as the free glutamic acid. In so doing, the content and content ratio of guanylic acid and inosinic acid in the above database are utilized. The elution of free amino acids and free guanylic acid / inosinic acid into the aqueous solution is different from thermal decomposition, so it is considered that each substance is almost proportional. Regarding the guanylic acid and inosinic acid generated by thermal decomposition, since the thermal decomposition temperature and the active temperature of the decomposing enzyme are different respectively, they are determined by the method shown below as an example.

[0119] (Method for quantifying nucleic acids and nucleic acid-related substances by ultraviolet absorption method at 260 nm and estimating the elution amount and newly generated amount of guanylic acid and inosinic acid) First, nucleic acids and nucleic acid-related substances refer to nucleic acids (DNA and RNA) and nucleic acid-related substances (polynucleotides, deoxynucleotides, and polynucleotides, nucleotides), each of which is water-soluble. Incidentally, inosinic acid and guanylic acid are nucleotides.

[0120] Next, a method for quantifying guanylic acid and inosinic acid without using aromatic amino acids is shown below. First, the method of "slow cooking and low-temperature cooking" described above is shown again. · The heating temperature for slow cooking is usually between 50°C and 85°C. · The heating temperature for low-temperature cooking (vacuum cooking) is about 50°C to 85°C for meats (e.g., beef, chicken), about 45°C to 65°C for seafood (e.g., salmon, white fish), and about 80°C to 85°C for vegetables. In addition to the above, quantification is also added for the normal heating cooking temperature of 90°C to 100°C.

[0121] (1) Quantify the amount of free glutamic acid, free guanylic acid, and inosinic acid per unit weight (100 g) of each individual food ingredient and create a database. (2) Boil each individual food ingredient in water, analyze the amounts of nucleic acids, nucleic acid-related substances, glutamic acid, guanylic acid, and inosinic acid in the aqueous solution, and record the elution amounts as data. Note that the detection conditions include temperature conditions such as slow cooking, and are, for example, in 5°C increments up to 100°C.

[0122] The heating time is, for example, in 15-minute increments, which is 1 / 4 of 1 hour, and ranges from 0 to 12 hours. This is because slow cooking, low-temperature cooking, and long-term boiling are assumed. Under the above detection conditions, detect the amounts of nucleic acids and other umami substances in 5°C increments and 15-minute increments.

[0123] In addition, a heating heat cycle is also added to the above detection conditions. This is because, just as it is said that pot dishes taste better the second time and soups and stews taste better after the second time and later, it is said that they taste better when heated once and then left to stand (aged). For example, detection is performed on a sample that has been heated for 2 hours, left to stand for 4 hours, and finally heated for 30 minutes (the heating temperature is the same).

[0124] (3) Similar to the amount of glutamic acid described above, it is assumed that the amount exceeding the free amount shown in (1) above is the amount of umami substances newly generated by thermal decomposition. (4) It is found that the ratios (%) of the amounts of glutamic acid, inosinic acid, and guanylic acid to the amount of nucleic acids, etc. are the respective heating temperatures and heating times for each food material.

[0125] By integrating the above (1) to (4), theoretically, it is possible to estimate the amounts of glutamic acid, guanylic acid, and inosinic acid, which are umami components, from the amount of nucleic acids, etc. And it is also possible to obtain the correlation of the thermal decomposition temperatures of umami components from each food material from each heating temperature and each heating time. The mixing of glutamic acid, inosinic acid, and guanylic acid is also called the multiplication of taste or the synergistic effect of taste, and it is said to be up to 7 to 8 times at most. Here, in order to bring the umami components during cooking closer to the menu umami data, it is ideal to use the data of the umami components of the completed menu as a reference. In this case, a database and teacher data for storing the data of nucleic acids, nucleic acid-related substances, and glutamic acid, guanylic acid, and inosinic acid of the completed menu (completed menu umami data) are required.

[0126] (Quantification of Amino Acids and Nucleic Acids) Next, the quantification of amino acids and nucleic acids will be explained. (Quantitative Method Part 1) Quantitative method part 1 is a method of quantifying the increased amount of tryptophan (next candidate: tyrosine · phenylalanine) or all of the three aromatic amino acids by (a) ultraviolet absorption method (i) fluorescence analysis method, and estimating the amount of L-glutamic acid from the correlation ratio with them. At the start of quantification, since free amino acids are present, the starting measurement value is set to 0. The increased amount is measured from there. The above-mentioned correlation ratio is the ratio of the number of residues of tryptophan, phenylalanine and tyrosine in each protein to the number of residues of glutamic acid.

[0127] Next, the fluorescence wavelength of amino acids and the approximate content rate in proteins will be explained. 1. Tryptophan Tryptophan is an essential amino acid and is less than 1% in proteins. The fluorescence characteristics of tryptophan are fluorescence at 350 nm with excitation light at 280 nm. 2. Phenylalanine Phenylalanine is an essential amino acid and is less than 5% in proteins. Phenylalanine is also a raw material for tyrosine. The fluorescence characteristics of phenylalanine are fluorescence at 287 nm with excitation light at 257 nm. 3. Tyrosine Tyrosine is less than 1% in proteins. The fluorescence characteristics of tyrosine are fluorescence at 304 nm with excitation light at 275 nm. 4. L-Glutamic Acid L-Glutamic acid is 6 to 9% in proteins. The above four amino acids are the components of proteins.

[0128] (Quantitative Method Part 2) Quantitative method part 2 is a method of quantifying nucleic acid or nucleotide by ultraviolet absorption method at 260 nm, and estimating either (a) inosinic acid, (i) guanylic acid, (u) both inosinic acid and guanylic acid in the nucleic acid or nucleotide from the correlation ratio while quantifying the increased amount.

[0129] (Regarding the Effects of Quantitative Methods Part 1 and Part 2) By the quantitative methods 1 and 2, it is possible to quantify, although only as estimated values, the flavor substances (umami), namely L-glutamic acid, inosinic acid, and guanylic acid. In addition, it becomes possible to collect reviews from users regarding umami, score and text them to gather details, and create a database associated with the quantified values of umami components. Also, since the above cooking details are recorded in the database as heating time and heating temperature (heating sequence), analyzing these big data will lead to the elucidation of a more flavorful, that is, umami-rich cooking method. Thus, the cooking in the automatic cooking device 1 will evolve to be more delicious. Note that analyzing the heating sequence in the big data means obtaining the correlation of how many times and for how many minutes each raw material should be heated to promote the hydrolysis of proteins and nucleic acids and increase umami substances such as amino acids and flavorful nucleotides. Moreover, since it is best to simmer L-glutamic acid, inosinic acid, and guanylic acid in a pot for a long time, patterns such as preparing in the automatic cooking device 1 overnight and having it ready the next morning, or preparing it before going to work in the morning and having it ready when returning home are possible. Also, it is possible to confirm via the network N using the terminal device 50 the increased amount of amino acids, etc. during cooking and the cooking video.

[0130] (Ultraviolet Absorption Method) Next, an overview of the ultraviolet absorption method will be given. In the ultraviolet absorption method, the protein concentration (amino acid concentration) is calculated from Lambert-Beer's law using the "absorption coefficient". Since a protein is a polymer formed by the condensation polymerization of amino acids, a protein can be rephrased as an amino acid. Here, C (concentration mg / mL) = A (absorbance at 280 nm) / ε (absorption coefficient) × L (optical path length).

[0131] (When estimating the protein concentration by measuring the absorbance at 280 nm) The protein concentration is estimated by the following procedure. (1) Add the sample solution to a 1 cm optical path length cell (such as a quartz cell that can be used for ultraviolet region measurements). (2) Measure the absorbance (at 280 nm). (3) Estimate the concentration to be approximately 1 mg / mL when the absorbance is 1. (4) Apply the following formula for a single protein solution. Molar concentration of protein = Absorbance ÷ (Number of tyrosine residues in the protein × 1390 + Number of tryptophan residues × 5800)

[0132] Since the content of tyrosine and tryptophan varies depending on the protein, the value changes between proteins. However, for a crude protein solution containing various proteins, when the absorbance at 280 nm (using a 1 cm optical path length cell) is 1, the protein concentration of the solution is generally approximately 1 mg / mL.

[0133] Generally, protein solutions exhibit an ultraviolet absorption peak (around 200 nm to 215 nm) derived from peptide bonds and an absorption peak at 280 nm derived from the side chains of aromatic amino acids (tyrosine, tryptophan). Also, the extinction coefficients of nucleic acids, DNA, and RNA are often measured at a wavelength of 260 nm, and the values are approximately from 1.0 to 2.0.

[0134] For reference, the extinction coefficients are as follows. Tryptophan: Approximately 5500 to 5700 cm -1 / M (usually measured at a wavelength of approximately 280 nm in the ultraviolet region) Tyrosine: Approximately 1250 to 1300 cm -1 / M (usually measured at a wavelength of approximately 280 nm in the ultraviolet region) Here, to perform measurements by ultraviolet absorption method and fluorescence analysis method, methods such as providing the above cell and a reflector on a float and floating them in the pot part 11, or providing a part for arranging the cell on the side wall of the pot part 11 can be mentioned.

[0135] (Fluorescence analysis method) Next, an overview of the fluorescence analysis method will be given. Since tryptophan and some nucleic acids and nucleotides fluoresce, they are detected. Note that fluorescence analysis is characterized by high detection accuracy.

[0136] Since aromatic amino acids such as tryptophan fluoresce, fluorescence analysis is effective. L-glutamic acid does not fluoresce. Although the thermal decomposition rate from proteins is different from that of aromatic amino acids, if tryptophan etc. are quantified, an estimated value of L-glutamic acid can be obtained from the correlation ratio.

[0137] (Timer function) The automatic cooking device 1 has good compatibility with timer cooking. For example, a pattern can be considered where ingredients are loaded before going to bed at night, and one eats the just-finished cooked food in the morning, and then ingredients are loaded again before going out, and after returning home, one eats the just-finished cooked food in the same way. By performing cooking slowly during that time, it becomes possible to bring out the flavor of the food ingredients more.

[0138] An example of timer cooking is shown below. If the food ingredient M is loaded at 23:00 at night and is to be completed at 7:00 the next morning, there are 8 hours in between. This is sufficient time for stewed dishes such as stew, and it is possible to propose various cooking patterns to the user. (Proposal 1) Proposal for slow cooking In this proposal, meat etc. can be cooked tenderly and it is also possible to bring out the flavor of the ingredients themselves. (Proposal 2) Simmer gently This proposal is orthodox, but the food ingredient M becomes tender, and in addition, it is also possible to bring out the flavor of the food ingredient M itself. (Proposal 3) Heat once and stop heating, then "age", resume heating before getting up and complete In this proposal, damage to the food ingredient M can be avoided and it is also possible to age the food ingredient M.

[0139] In the cooking patterns of Proposals 1 to 3 above, the amount of umami components during cooking is detected by the umami component detector 130, and the increase in the amount of umami components is monitored. It has been found that the generation by hydrolysis of amino acids and nucleic acids is correlated in a complex manner with heating time, heating temperature, pH value, etc., and there are many parts that are not yet understood. In the automatic cooking device 1, it is possible to analyze the thermograph during cooking and the monitoring data (big data) of the increase in the amount of amino acids and nucleic acids, and search for an ideal heating pattern.

[0140] In addition, the automatic cooking device 1 according to the present embodiment is also advantageous for rice cooking that takes a long time to soak water, such as brown rice and mixed grain rice. By monitoring the degree of water immersion using a camera, it is possible to achieve an ideal cooking state.

[0141] Furthermore, the automatic cooking device 1 is also advantageous for bread fermentation. For example, by analyzing the image, thermograph, and monitoring data (big data) of the increase in the amount of amino acids and nucleic acids during cooking, it is possible to ferment and age slowly to increase the amount of amino acids, and it is also possible to control the fermentation temperature.

[0142] (Cooperation with cloud services) The automatic cooking device 1 according to the present embodiment can cooperate with a recipe site or a cooking app via the network N. Furthermore, as a specific example in other application software (app), it is also highly compatible with apps that handle body record data, such as a health management app. Since the automatic cooking device 1 recognizes the food material M by AI before cooking, it can perform independent nutrition calculation using a database such as the "Japanese Food Standard Nutritional Composition Table". It is also possible to transmit and share the nutritional component data for one meal to the body record data for management, and it is also possible to propose menus and recipes recommended from the insufficient nutrients found therefrom and the activity content and physical condition derived from the body record data.

[0143] As described above, according to the automatic cooking device 1 according to the present embodiment, delicious dishes can be easily made automatically.

[0144] In addition, although the above-described embodiments and their application examples (modifications, specific examples) have been explained, the present invention is not limited to these examples. For example, for each of the above-described embodiments or their application examples (modifications, specific examples), those obtained by appropriately adding, deleting, or changing the design of components by those skilled in the art, or those obtained by appropriately combining the features of each embodiment, are also included in the scope of the present invention as long as they have the gist of the present invention.

Description of Reference Numerals

[0145] 1... Automatic cooking device 10... Main body part 11... Pot part 12... Heater 13... Reflector 15... Lid 20... Cooking utensil part 21... Rotating utensil 25... Motor 30... Control part 40... Main body side communication part 50... Terminal device 101... Food ingredient calculation part 102... Menu generation part 103... Cooking control part 110... Image input part 120... Temperature detection part 121... Temperature sensor 130... Umami component detection part 211... Shaft part 211a... Stopper 211b... Projection 212... Spatula part 212a... Shaft portion 213... Return part 213a... Return surface 214... Lid portion 214a... Surface 215... Clutch 215a... Elliptical hole 215b... Clutch shoe 215c... Projection 510... Information input part 520... Information output part 530... Terminal side communication part 540... Control part B…Bread CP…Baking cup M…Ingredients N…Network RB…Roll pan SV…Database server

Claims

1. A main body that houses the pot; A cooking utensil part that is inserted into the pot part; A control unit that controls cooking; a main body side communication unit provided in the main body unit; a terminal device having an information input unit, an information output unit, and a terminal side communication unit; Equipped with the terminal device transmits the image of the ingredient captured by the information input unit to the main body communication unit via the terminal communication unit; The control unit is an ingredient calculation unit that calculates the type and amount of the ingredient from the image of the ingredient received by the main body side communication unit; A menu generation unit that generates a menu as a cooking candidate from the types and amounts of the ingredients using artificial intelligence based on preset training data for menu generation; a cooking control unit that controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit based on the menu generated by the menu generating unit, The cooking control unit uses artificial intelligence based on cooking teacher data to determine whether or not the heat of the ingredients needs to be adjusted, depending on the color components based on the Maillard reaction obtained from an image of the ingredients in the pot during cooking, and if it determines that the heat adjustment is necessary, uses the artificial intelligence to present the user with suggested adjustments regarding the heat, and controls the adjustment of at least one of the temperature of the pot and the operation of the cooking utensil unit based on the user's response, and processes to reflect in the cooking teacher data the relationship between the menu, the changes in color components based on the Maillard reaction, and the dialogue with the user regarding the heat adjustment and the suggested adjustments.

2. the menu generation unit extracts supplementary ingredients based on a difference between the types and amounts of ingredients required to prepare the candidate menu and the types and amounts of the ingredients calculated from the image by the ingredient calculation unit; The automatic cooking apparatus according to claim 1 , wherein the main body communication unit transmits information about the supplementary ingredients to the terminal device.

3. The image input unit is further provided in the main body. The automatic cooking apparatus according to claim 1 , wherein the main body side communication unit transmits the image of the pot during cooking inputted by the image input unit to the terminal device.

4. the terminal device transmits correction information regarding the operation of the cooking utensil unit and the cooking temperature of the pot unit, which is received from the user, from the terminal side communication unit to the main body side communication unit; The automatic cooking device according to claim 1 , wherein the cooking control unit corrects control of at least one of the operation of the cooking utensil unit and the temperature of the pot unit based on the correction information received by the main body side communication unit.

5. the information input unit of the terminal device accepts comments from a user regarding the food cooked in the pot unit based on the menu generated by the menu generation unit; The terminal communication unit of the terminal device transmits the comment to the main body communication unit, The automatic cooking device according to claim 1 , wherein the menu generation unit of the control unit associates the comment received by the main body side communication unit with a procedure for automatic cooking of the menu and reflects the comment in the teacher data for menu generation.

6. The information input unit of the terminal device receives a user's evaluation regarding the cooking controlled by the control unit, the terminal side communication unit of the terminal device transmits the evaluation to the main body side communication unit; The automatic cooking device according to claim 1 , wherein the control unit is configured to associate the evaluation received by the main body communication unit with a procedure for automatic cooking of the menu and to reflect the evaluation in the cooking teacher data.

7. The cooking utensil section includes: A stirring part rotatably provided within the pot part; A lid portion that is rotatably provided and disposed above the stirring portion in the pot portion; a clutch provided between the cover portion and the shaft portion for interrupting transmission of a rotational force of the shaft portion to the cover portion, The automatic cooking device of claim 1, wherein the cooking control unit monitors images of the ingredients in the pot portion during cooking, and after confirming that the rotation of the stirring portion has stopped due to the operation of the clutch, controls the rotation of the shaft portion to be reversed.

8. the main body or the terminal device is connected via a network to a database server that stores type and amount data indicating correspondence between the images of the ingredients and the types and amounts of the ingredients, the teacher data for menu generation, the teacher data for cooking, and menu data which is data on the menu; the ingredient calculation unit calculates the type and amount of the ingredient from the image of the ingredient using the type and amount data stored in the database server; The automatic cooking apparatus according to claim 1 , wherein the menu generating unit generates the menu using the menu data stored in the database server.

9. The menu data includes data on a cooking procedure for cooking based on the menu, The automatic cooking apparatus according to claim 8 , wherein the cooking control unit controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit using the cooking procedure data stored in the database server.

10. The cooking method further includes a umami component detection unit that detects an amount of at least one of umami components, which are glutamic acid, guanylic acid, and inosinic acid, from the food material being cooked. the database server stores ingredient umami component data indicating a correspondence between the type and amount of the ingredient before cooking and the umami component, and menu umami data correlating the menu with a quantified value of the umami component and a review from a user; The automatic cooking device of claim 8, wherein the cooking control unit controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit so as to bring the amount of the umami components detected by the umami component detection unit closer to the amount of the umami components corresponding to the menu using the ingredient umami data and the menu umami data stored in the database server.

11. The cooking method further includes a umami component detection unit that detects an amount of at least one of umami components, which are glutamic acid, guanylic acid, and inosinic acid, from the food material being cooked. the database server stores ingredient umami component data indicating a correspondence between the type and amount of the ingredient before cooking and the umami component, and menu umami data correlating the menu with a quantified value of the umami component and a review from a user; The automatic cooking device of claim 8, wherein the cooking control unit controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit so that the amount of the umami component detected by the umami component detection unit is more or less than the amount of the umami component corresponding to the menu using the ingredient umami data and the menu umami data stored in the database server.

12. The cooking method further includes a umami component detection unit that detects an amount of at least one of umami components, which are glutamic acid, guanylic acid, and inosinic acid, from the food material being cooked. The automatic cooking device of claim 8, wherein the cooking control unit controls at least one of the operation of the cooking utensil unit and the temperature of the pot unit so that the amount of the umami component detected by the umami component detection unit becomes the type and amount of the umami component determined by artificial intelligence based on preset umami component teacher data.

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