Control method, information providing method, control system, information providing system, and program

Abstract

A control method comprises: acquiring target sensory information (step S110); acquiring initial cooking parameter information for cooking by the cooker on the basis of the acquired target sensory information (step S120); a step (step S150) of acquiring in-cooking sensory information of the object to be cooked while the cooker is cooking; changing the initial cooking parameter information to corrected cooking parameter information including one or more corrected cooking parameters on the basis of the target sensory information and the cooking sensory information (step S180); and outputting control information including the corrected cooking parameter information (step S190).

Description

Technical Field

[0001] The present disclosure relates to a control method related to cooking (cooking) or a method for providing information related to cooking, etc. Background Art

[0002] In the past, as a control method related to cooking, a cooking method for improving the convenience of cooking has been proposed (for example, see Patent Document 1). In this cooking method, increase and decrease information indicating the increase and decrease of cooking ingredients is obtained from a cooking device (appliance), the increase and decrease information is converted into heat, and the heating time of the cooking device is adjusted according to the heat. For example, the increase and decrease information is obtained by photographing with a camera. As a result, the user can save the effort of adjusting the heating time according to the increase and decrease of cooking ingredients.

[0003] In addition, a cooking assistance system for assisting cooking has been proposed (for example, refer to Patent Document 2). The cooking assistance system is configured as smart glasses. The smart glasses determine the size of the food slices generated by the cook cutting the food, and based on the size of the food slices, estimate the heating cooking time that is most suitable for the food slices and display it. That is, in a control method related to cooking using smart glasses, the size of the food slices is determined and the heating cooking time is estimated. As a result, even if the cook cuts the food into a size different from the recipe, the cook can easily cook with the heating cooking time suitable for the size of the food slices obtained by the cutting.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2020-159581

[0007] Patent Document 2: Japanese Patent No. 6692960 Summary of the invention

[0008] However, the control methods of the above-mentioned patent documents have a problem in that the sensory level of, for example, the level of flavor (aroma) of a dish obtained by cooking cannot be appropriately controlled.

[0009] The present disclosure provides a control method and the like capable of appropriately controlling the sensory level of a dish.

[0010] A control method involved in a technical solution of the present disclosure is a method executed by a computer to control a cooking device for cooking food, comprising: obtaining target sensory information, the target sensory information representing a target with respect to one or more numerical values ​​related to the sensory properties of a dish obtained by cooking the food; obtaining cooking parameter information including one or more cooking parameters for cooking performed by the cooking device based on the obtained target sensory information; obtaining cooking sensory information representing one or more numerical values ​​related to the sensory properties of the food when the cooking device is cooking; changing the cooking parameter information into modified cooking parameter information including one or more modified cooking parameters based on the target sensory information and the cooking sensory information; and outputting a control signal including the modified cooking parameter information.

[0011] In addition, the general or specific technical solution can be implemented by a device, a system, an integrated circuit or a computer-readable recording medium, or by any combination of a device, a system, a method, an integrated circuit, a computer program and a computer-readable recording medium. The computer-readable recording medium includes, for example, a non-volatile recording medium such as a CD-ROM (Compact Disc-Read Only Memory). The computer-readable recording medium can also be a non-transient recording medium.

[0012] According to the present disclosure, the sensory level of dishes can be appropriately controlled.

[0013] In addition, further advantages and effects in one technical solution of the present disclosure can be known from the specification and the drawings. The above advantages and / or effects are provided by several embodiments and the configurations described in the specification and the drawings, but not all configurations are necessarily required. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a diagram showing an example of the configuration of the information processing system in the embodiment.

[0015] Figure 2 This is a block diagram showing an example of the functional configuration of the operation terminal and the cooking device in the embodiment.

[0016] Figure 3 This is a block diagram showing an example of the functional structure of the server in the embodiment.

[0017] Figure 4 It is a diagram showing the details of the plurality of sensory information stored in the third storage unit in the embodiment.

[0018] Figure 5 This is a block diagram showing an example of a detailed functional structure of a server control unit in the embodiment.

[0019] Figure 6 This is a diagram showing an example of menu list information, recipe information, basic cooking parameter information, and basic sensory information in an embodiment.

[0020] Figure 7 This is a diagram showing an example of image data, chemical analysis data, and weight data included in cooking status information in the embodiment.

[0021] Figure 8 It is a diagram showing an example of a screen display of an operation terminal in the embodiment.

[0022] Fig. 9 It is a diagram showing another example of the screen display of the operation terminal in the embodiment.

[0023] Fig.10 This is a sequence diagram showing an example of processing operations of the information processing system according to the embodiment.

[0024] Fig.11 This is a flowchart showing an example of the processing operation of the server in the embodiment.

[0025] Fig.12 This is a flowchart showing in detail a part of the processing actions of the server in the embodiment.

[0026] Fig.13 This is a flowchart showing an example of constructing a first learning model in the embodiment.

[0027] Fig.14 This is a flowchart showing an example of constructing a second learning model in the embodiment.

[0028] Fig.15 This is a flowchart showing an example of constructing a third learning model in the embodiment.

[0029] Fig.16 This is a flowchart showing an example of constructing a fourth learning model in the embodiment.

[0030] Fig.17 An example of the first learning model is shown.

[0031] Fig.18 An example of the second learning model is shown.

[0032] Fig.19 An example of the third learning model is shown.

[0033] Fig. 20 This shows an example of the fourth learning model. DETAILED DESCRIPTION

[0034] (Insights from this disclosure)

[0035] When cooking, cooks consider the sensory level of the aroma of the dish obtained by cooking. Examples of dishes are bouillon, braised pork, etc. The sensory level is the degree of stimulation to organs such as the nose and tongue of a person, and can be, for example, the degree of aroma, sweetness (fragrance), sourness, bitterness, saltiness, etc. Taste, deliciousness, etc. are expressed by these sensory levels.

[0036] When cooking, a dish having a sensory level corresponding to the recipe can be prepared by preparing a dish including one or more ingredients and cooking the dish according to the recipe.

[0037] However, cooks may want a different sensory level from that described in a recipe. In addition, even if cooks want the sensory level described in a recipe, they may not be able to prepare the ingredients in the correct amount according to the recipe, or they may not be able to prepare the ingredients in the amount ratio that matches the recipe. Or, even if they prepare the ingredients in the amount ratio that matches the recipe, the sensory level may change during the cooking process. In this case, it is difficult to appropriately set the temperature, pressure, time, etc. of the cooker for cooking the food as desired by the cook.

[0038] Therefore, the control method involved in the first technical solution of the present disclosure is a method executed by a computer to control a cooking device for cooking food, comprising: obtaining target sensory information, the target sensory information indicating a target with respect to one or more numerical values ​​related to the sensory sense of a dish obtained by cooking the food; obtaining cooking parameter information including one or more cooking parameters for cooking by the cooking device based on the obtained target sensory information; obtaining cooking sensory information indicating one or more numerical values ​​related to the sensory sense of the food when the cooking device is cooking; based on the target sensory information and the cooking sensory information, changing the cooking parameter information to modified cooking parameter information including one or more modified cooking parameters; and outputting a control signal including the modified cooking parameter information. In addition, the one or more numerical values ​​related to the sense organs are also respectively referred to as sensory degrees or sensory evaluation values, and may be, for example, degrees of fragrance, sweetness, sourness, bitterness, saltiness, etc.

[0039] Thus, for example, the target sensory information desired by the cook is obtained, and the cooking parameter information is obtained based on the information, for example, as the initial cooking parameter information. Moreover, for example, by sending the cooking parameter information to the cooker, the cooker starts cooking according to the cooking parameter information. Thereafter, the cooking parameter information used for the cooker is changed according to one or more numerical values ​​(i.e., sensory levels) related to the sensory properties of the food being cooked during cooking. Therefore, by cooking according to the modified cooking parameter information, the sensory level of the dish finally made can be made close to the sensory level indicated by the target sensory information. In other words, the sensory level of the dish finally made can be made close to the sensory level desired by the cook. That is, even if the sensory level desired by the cook is different from the sensory level described as the basic recipe, or even if the amount of the food being cooked is not as in the recipe, the sensory level of the final dish can be made close to the sensory level desired by the cook. As a result, the sensory level of the dish can be appropriately controlled without the cook intentionally adjusting the cooking parameters.

[0040] In addition, in the control method involved in the second technical solution subordinate to the first technical solution, it is also possible that, in acquiring the sensory information during cooking, the sensory information during cooking at a time point after a first time has passed since the start of cooking with the cooker is acquired as the first sensory information, and the sensory information during cooking at a time point after a second time has passed after the first time has passed since the start of cooking with the cooker is acquired as the second sensory information, and in changing the cooking parameter information, the cooking parameter information is changed to the corrected cooking parameter information based on the difference between the first sensory information and the second sensory information and the target sensory information.

[0041] Thus, the cooking parameter information is changed to the modified cooking parameter information based on the amount of change in the sensory information during cooking and the target sensory information. Therefore, by considering the tendency of the change in the sensory level caused by cooking according to the cooking parameter information, the sensory level of the final dish can be effectively made close to the sensory level desired by the cook. In addition, the first time may also be 0 hours. In this case, the first sensory information represents one or more numerical values ​​related to the sensory senses of the cooked object at the time point when cooking starts.

[0042] In addition, in the control method involved in the third technical solution subordinate to the first technical solution or the second technical solution, it is also possible to obtain third sensory information, wherein the third sensory information represents one or more numerical values ​​related to the sensory sense of the dish obtained by cooking the dish according to the corrected cooking parameter information by the cooker.

[0043] Thus, information indicating the sensory level of the final dish is estimated as the third sensory information. Therefore, for example, by presenting the third sensory information to the cook, the cook can confirm whether the dish with the sensory level the cook wants can be made, or how close the sensory level of the final dish is to the expectation.

[0044] In addition, in the control method involved in the fourth technical solution belonging to any one of the first technical solution to the third technical solution, it is also possible that, in the change of the cooking parameter information, multiple candidates for the corrected cooking parameter information are obtained based on the target sensory information and the sensory information during cooking, and a candidate whose difference with the cooking parameter information is below a threshold or a candidate closest to the cooking parameter information is selected from the multiple candidates as the corrected cooking parameter information.

[0045] Thus, even when a plurality of new cooking parameter information are output as candidates by inputting target sensory information and sensory information during cooking into the learning model, for example, a candidate that is not much different from the original cooking parameter information (i.e., initial cooking parameter information) is used as the corrected cooking parameter information. Therefore, the processing burden of the cooking device accompanying the change of cooking parameter information can be reduced.

[0046] In addition, in the control method involved in the fifth technical solution subordinate to the third technical solution, it is also possible that, in the acquisition of the third sensory information, the third sensory information is acquired by at least inputting the corrected cooking parameter information into a learning model, and the learning model is machine-learned to output one or more numerical values ​​related to the sensory sense of the dish obtained by cooking the dish according to the one or more cooking parameters changed during cooking by the cooker, at least for the input of one or more cooking parameters changed during cooking by the cooker.

[0047] Thus, since the learning model is used to acquire the third sensory information, it is possible to acquire the third sensory information with high accuracy.

[0048] In addition, in the control method involved in the sixth technical solution belonging to any one of the first technical solution to the fifth technical solution, it is also possible that, in acquiring the sensory information during cooking, the sensory information during cooking is acquired by inputting at least one of an image of the food being cooked when the cooker is cooking, the weight of the food, and the amount of chemical components contained in the food into a learning model.

[0049] Thus, since the learning model is used to acquire the sensory information during cooking, it is possible to acquire the sensory information during cooking with high accuracy.

[0050] In addition, in the control method involved in the seventh technical solution subordinate to the sixth technical solution, the learning model may also be machine-learned to output one or more numerical values ​​related to the sensory organ of the one or more ingredients, based on at least one input of an image of one or more ingredients being cooked in the cooker, a weight of the one or more ingredients, and a quantity of chemical components contained in the one or more ingredients.

[0051] Thus, by inputting at least one of the image of the food to be cooked, the weight of the food to be cooked, and the amount of chemical components contained in the food to be cooked into the learning model obtained by machine learning, it is possible to obtain highly accurate sensory information during cooking from the learning model.

[0052] In addition, in the control method involved in the 8th technical solution belonging to any one of the 1st technical solution to the 7th technical solution, the one or more cooking parameters may include a parameter indicating the temperature used for cooking by the cooker and a parameter indicating the time used for cooking by the cooker.

[0053] Thereby, the sensory properties of a dish obtained by, for example, grilling can be appropriately controlled.

[0054] In the control method according to the ninth aspect dependent upon the eighth aspect, the one or more cooking parameters may further include a parameter indicating a pressure used for cooking by the cooking device.

[0055] This makes it possible to appropriately control the texture of a dish obtained by, for example, steaming or boiling with pressure applied.

[0056] In addition, a technical solution of the present disclosure involves an information providing method in which a computer provides information related to cooking of a cooked object by a cooker, comprising: accepting target sensory information based on a user's operation, the target sensory information representing a target of one or more numerical values ​​related to a sensory sense of a dish obtained by cooking the cooked object; and outputting final sensory information related to the sensory sense of the dish derived based on the target sensory information and sensory information during cooking, the sensory information during cooking being information representing one or more numerical values ​​related to the sensory sense of the cooked object when the cooker is cooking the cooked object.

[0057] Thus, for example, when dish information representing a dish desired by a cook as a computer user and target sensory information desired by the cook are received, the sensory perception (i.e., sensory level) of the dish finally made based on this information can be prompted to the cook. The sensory perception of the suggested dish can be prompted as a numerical value, as a radar chart, or as the number of stars. Therefore, the cook can properly grasp the sensory perception of the dish obtained by cooking. In other words, the cook can confirm whether a dish with the sensory level desired by the cook can be made, or how close the sensory level of the dish finally made is to the desired one.

[0058] Furthermore, the processing actions included in the above-mentioned control method and information providing method are executed by a computer.

[0059] Hereinafter, the embodiments of the present disclosure will be described with reference to the accompanying drawings. The embodiments described below all represent a specific example of the present disclosure. Therefore, the numerical values, shapes, materials, constituent elements, configuration positions and connection forms of the constituent elements, etc. shown in the following embodiments are only examples and are not intended to limit the present disclosure. Therefore, among the constituent elements in the following embodiments, the constituent elements that are not recorded in the independent claims representing the highest concept are described as arbitrary constituent elements.

[0060] In addition, each figure is a schematic diagram and is not necessarily a strictly illustrated figure. In addition, in each figure, substantially the same structure is assigned the same reference numeral, and repeated descriptions are omitted or simplified. The effects of the above-mentioned control method and information providing method are also realized in the system and program.

[0061] (Implementation Method 1)

[0062] [Overall structure of information processing system]

[0063] Figure 1 It is a diagram showing an example of the configuration of the information processing system 1000 in this embodiment.

[0064] The information processing system 1000 includes a server 100 , an operation terminal 200 , and a cooking device 300 , which are connected to each other via a communication network such as the Internet.

[0065] The cooker 300 is a device for preparing dishes by cooking food including one or more ingredients, and the cooking is performed by adjusting pressure, temperature, and time as cooking parameters. In the present embodiment, the cooker 300 is configured as a pressure cooker as an example.

[0066] In addition, the number of cooking parameters that can be adjusted by the cooker 300 is not limited to 3, and may be 1 or 2, or may be 4 or more. In addition, the cooker 300 may be a device for steaming or boiling the food to be cooked, an IH (Induction Heating) cooker, or a temperature-adjustable container. The temperature-adjustable container performs fermentation and brewing of soy sauce, miso (Japanese soybean paste), etc. as cooking by controlling the temperature and time.

[0067] In addition, the cooker 300 may be a cooker that uses a liquid or gas located in a region slightly before the critical point (a point where the temperature is 374°C and the pressure is 22MPa). This liquid (e.g., water) or gas has, for example, a pressure exceeding 0.2MPa and a temperature exceeding 120°C. The types of cookers include a pressure cooker type, a superheated steam type, a frying pan type, and the like. A pressure cooker type cooker can independently control the temperature and pressure in a closed system, and uses the liquid for boiling, stewing, and the like. A superheated steam type cooker sprays the gas (e.g., water vapor) onto the food to be cooked from a unit that can control at least the temperature of the temperature and the pressure in a closed system or an open system, and performs steaming, grilling, and the like. A frying pan type cooker includes an IH cooker and a frying pan placed on the IH cooker, and performs grilling by controlling the temperature and time. In this cooking method using liquid or gas, the hydrolysis reaction can be promoted.

[0068] The operation terminal 200 is configured as, for example, a smartphone or a tablet terminal. The operation terminal 200 is operated by a cook to cook with the cooking device 300. The cook may also be a user who uses the cooking device 300.

[0069] The server 100 controls the cooking device 300 according to the input operation performed by the cook to the operation terminal 200. That is, the server 100 is a computer that executes a control method for controlling the cooking device 300 that cooks the food. In other words, the server 100 is a control system that controls the cooking device 300 that cooks the food.

[0070] [Configuration of server, operation terminal, and cooking device]

[0071] Figure 2 This is a block diagram showing an example of the functional configuration of the operation terminal 200 and the cooking device 300 .

[0072] The operation terminal 200 is a computer that executes an information providing method for providing information related to cooking of a food by the cooking device 300, and includes an input unit 201, a display unit 202, a terminal control unit 203, a terminal storage unit 204, and a terminal communication unit 205. Such an operation terminal 200 can also be said to be an information providing system that provides information related to cooking of a food by the cooking device 300.

[0073] The display unit 202 is a device for displaying images, such as a liquid crystal display, an organic EL (Electro-Luminescence) display, etc. In addition, the display unit 202 is not limited to these, and can be any device as long as it can display images. In addition, the display unit 202 in the present embodiment can also be said to be an output unit that outputs information related to cooking. In addition, in the present embodiment, the display unit 202 is provided in the operation terminal 200 as an example of an output unit that outputs information related to cooking, but a voice output unit (such as a speaker, etc.) that outputs the information by voice can also be provided as the output unit. In addition, both the display unit 202 and the voice output unit can also be provided as the output unit.

[0074] The input unit 201 is configured as, for example, a touch sensor disposed on the display unit 202, which receives input operations corresponding to images such as icons displayed on the display unit 202 when the cook touches the images. Alternatively, the input unit 201 may include a button, and the cook presses the button to receive input operations corresponding to the button.

[0075] The terminal communication unit 205 communicates with the cooking device 300 wirelessly or by wire. Wireless communication can also be performed through Wi-Fi (registered trademark), Bluetooth (registered trademark), ZigBee (registered trademark) or a specific low-power wireless. Furthermore, the terminal communication unit 205 communicates with the server 100 via the above-mentioned communication network. In addition, the terminal communication unit 205 can communicate with the server 100 directly or communicate with the server 100 via the cooking device 300.

[0076] The terminal storage unit 204 is a recording medium for storing various information, data, programs, etc. Such a terminal storage unit 204 is a hard disk drive, RAM (Random Access Memory), ROM (Read Only Memory) or a semiconductor memory, etc. In addition, the terminal storage unit 204 can be volatile or non-volatile.

[0077] The terminal control unit 203 controls the input unit 201 , the display unit 202 , the terminal communication unit 205 , and the like by, for example, reading and executing a program stored in the terminal storage unit 204 .

[0078] The cooking device 300 includes a cooking control unit 303 , a cooking storage unit 304 , a cooking communication unit 305 , a cooking state acquisition unit 310 , and a cooking unit 320 .

[0079] The cooking unit 320 applies physical action to an object to be cooked including one or more ingredients according to cooking parameter information including three cooking parameters, namely, temperature, pressure and time, to cook the object to be cooked. The cooking unit 320 includes a pressure adjustment unit 321, a temperature adjustment unit 322 and a time adjustment unit 323.

[0080] The pressure adjusting unit 321 adjusts the pressure applied to the food to be cooked according to the cooking parameter indicating the pressure. The temperature adjusting unit 322 adjusts the temperature applied to the food to be cooked according to the cooking parameter indicating the temperature. The time adjusting unit 323 adjusts the duration of the pressure or temperature adjusted by the pressure adjusting unit 321 and the temperature adjusting unit 322 according to the cooking parameter indicating the time.

[0081] The cooking state acquisition unit 310 acquires cooking state information indicating the state of the food to be cooked. The cooking state acquisition unit 310 includes a weight measurement unit 311, a photographing unit 312, and a chemical analysis unit 313. The weight measurement unit 311 measures the weight of the food to be cooked placed in the cooking device 300. The weight measurement unit 311 outputs weight data indicating the measured weight. The photographing unit 312 is, for example, a camera, and photographs the food to be cooked placed in the cooking device 300. The photographing unit 312 outputs image data obtained by photographing the food to be cooked. The photographing unit 312 is, for example, an image sensor having water resistance and high dark performance. The chemical analysis unit 313 analyzes the chemical composition of the food to be cooked placed in the cooking device 300 by, for example, liquid chromatography, gas chromatography, etc. The chemical analysis unit 313 outputs chemical analysis data obtained by analyzing the chemical composition. In addition, the chemical analysis unit 313 is not limited to the above-mentioned chromatography, and may also analyze the chemical composition of the food to be cooked by other methods such as ether extraction. In addition, the chemical analysis unit 313 may also analyze the chemical composition of the food to be cooked arranged in the cooking device 300 based on the information detected by the odor sensor. The odor sensor includes a plurality of odor detection elements. Specifically, the odor sensor includes a first odor detection element to an nth odor detection element (n is an integer greater than or equal to 2). It is also possible to determine one or more chemical components contained in the food to be cooked and the amount of each of the one or more chemical components by machine learning / analyzing the output signals of the first odor detection element to the nth odor detection element.

[0082] The cooking status information includes the above-mentioned weight data, image data and chemical analysis data.

[0083] The cooking communication unit 305 communicates with the operation terminal 200 via wireless or wired communication. As described above, wireless communication may also be performed via Wi-Fi, Bluetooth, etc. Furthermore, the cooking communication unit 305 communicates with the server 100 via the above-mentioned communication network. In addition, the cooking communication unit 305 may communicate with the server 100 directly or via the operation terminal 200.

[0084] The recipe storage unit 304 is a recording medium for storing various information, data, programs, etc. The recipe storage unit 304 is a hard disk drive, RAM, ROM, etc., similar to the terminal storage unit 204. In addition, the recipe storage unit 304 can be volatile or non-volatile.

[0085] The cooking control unit 303 controls the cooking communication unit 305 , the cooking state acquisition unit 310 , the cooking unit 320 , and the like by, for example, reading and executing a program stored in the cooking storage unit 304 .

[0086] Furthermore, the cooker 300 in the present embodiment performs cooking according to three cooking parameters, but may also perform cooking according to one or two cooking parameters, or may further perform cooking according to four or more cooking parameters.

[0087] Figure 3 This is a block diagram showing an example of the functional configuration of the server 100 .

[0088] The server 100 is a computer that controls a cooking device 300 that cooks food, and includes a server control unit 103, a server storage unit 104, a server communication unit 105, a first storage unit 110, a second storage unit 120, a third storage unit 130, a fourth storage unit 140, and a model storage unit 150. The server storage unit 104, the first storage unit 110, the second storage unit 120, the third storage unit 130, the fourth storage unit 140, and the model storage unit 150 are recording media, which are hard disk drives, RAMs, ROMs, etc., similar to the terminal storage unit 204 and the cooking storage unit 304. In addition, these recording media may be volatile or non-volatile. Such a server 100 is also called a control system.

[0089] The first storage unit 110 stores dish list information 111, recipe information 112, basic cooking parameter information 113, and basic sensory information 114. The dish list information 111 indicates the names of a plurality of dishes (hereinafter referred to as dish names). The recipe information 112 indicates, for each dish name indicated by the dish list information 111, one or more materials (also referred to as ingredients) used in the dish of the dish name and the respective quantities of the one or more materials. The basic cooking parameter information 113 indicates, for each dish name indicated by the dish list information 111, one or more basic cooking parameters used in the cooking unit 320 of the cooker 300 to prepare the dish of the dish name. The basic sensory information 114 indicates, for each dish name indicated by the dish list information 111, one or more numerical values ​​related to the sensory properties of the dish made according to the recipe information 112 and the basic cooking parameter information 113 corresponding to the dish name.

[0090] The second storage unit 120 is a recording medium for storing the cooking state information 121 obtained by the cooking state acquisition unit 310 of the cooking device 300. The cooking state information 121 includes image data, chemical analysis data, and weight data obtained at substantially the same timing.

[0091] The third storage unit 130 is a recording medium for storing sensory information 131 indicating one or more numerical values ​​related to the sensory organs of a dish or a cooked food. For example, the sensory information 131 indicates a first sensory degree, a second sensory degree, etc. as one or more numerical values ​​related to the sensory organs. The first sensory degree may be the degree of fragrance, and the second sensory degree may be the degree of sweetness.

[0092] The fourth storage unit 140 is a recording medium for storing the initial cooking parameter information 141 and the modified cooking parameter information 142. The initial cooking parameter information 141 and the modified cooking parameter information 142 are each cooking parameter information indicating one or more cooking parameters used for cooking by the cooker 300. The initial cooking parameter information 141 indicates one or more cooking parameters used when cooking is started by the cooker 300. The modified cooking parameter information 142 is information obtained by modifying or changing the initial cooking parameter information 141. That is, the modified cooking parameter information 142 includes one or more modified cooking parameters obtained by modifying or changing one or more cooking parameters included in the initial cooking parameter information 141.

[0093] The model storage unit 150 stores a learning model set 150a corresponding to each dish name indicated by the dish list information 111. The learning model set 150a includes a first learning model 151, a second learning model 152, a third learning model 153, and a fourth learning model 154. These learning models are models obtained by machine learning, such as a neural network.

[0094] The first learning model 151 is a model for deriving the above-mentioned initial cooking parameter information 141. The second learning model 152 is a model for deriving the sensory information 131 of the food being cooked while the cooking device 300 is cooking as the sensory information during cooking. The third learning model 153 is a model for deriving the above-mentioned corrected cooking parameter information 142. The fourth learning model 154 is a model for deriving the final sensory information 131 of the dish obtained by the cooking device 300 cooking according to the initial cooking parameter information 141 and the corrected cooking parameter information 142 as the third sensory information.

[0095] The server communication unit 105 communicates with the operation terminal 200 and the cooking device 300 via the above-mentioned communication network. The server storage unit 104 is a recording medium for storing various information, data, programs, and the like.

[0096] The server control unit 103 controls the server communication unit 105 and the like by, for example, reading and executing a program stored in the server storage unit 104. For example, the server control unit 103 obtains the cooking state information 121 from the cooking device 300 via the server communication unit 105, and stores the cooking state information 121 in the second storage unit 120. In addition, the server control unit 103 obtains the sensory information 131, the initial cooking parameter information 141, and the corrected cooking parameter information 142 derived using the learning model set 150a. Furthermore, the server control unit 103 stores the obtained sensory information 131 in the third storage unit 130, and stores the obtained initial cooking parameter information 141 and the corrected cooking parameter information 142 in the fourth storage unit 140.

[0097] Figure 4 This is a diagram showing details of the plurality of sensory information 131 stored in the third storage unit 130 .

[0098] like Figure 4As shown, a plurality of sensory information 131 is stored in the third storage unit 130. The plurality of sensory information 131 is target sensory information 131a, first sensory information 131b, second sensory information 131c, and third sensory information 131d described later. The target sensory information 131a is sensory information 131 that is a target for the cook. The first sensory information 131b and the second sensory information 131c are sensory information 131 of the food being cooked, also referred to as sensory information during cooking. The third sensory information 131d is sensory information 131 of the dish made by cooking, also referred to as final sensory information.

[0099] Figure 5 This is a block diagram showing an example of a detailed functional configuration of the server control unit 103 .

[0100] The server control unit 103 includes an input acquisition unit 1031 , a parameter acquisition unit 1032 , a sensory information acquisition unit 1033 , a parameter correction unit 1034 , a processing unit 1035 , and a parameter output unit 1037 .

[0101] The input acquisition unit 1031 acquires input information from the operation terminal 200 via the server communication unit 105. The input information is information received by the terminal control unit 203 through an input operation performed by the cook on the input unit 201 of the operation terminal 200. The cook performs an input operation on the input unit 201 of the operation terminal 200, for example, inputting the name of a dish prepared by the cooking device 300 (i.e., the dish name) and a target value related to the senses. The value related to the senses is also called a sensory degree or a sensory evaluation value. As a result, the terminal control unit 203 receives input information including the dish information indicating the dish name and the target sensory information 131a indicating the target value related to the senses. The input acquisition unit 1031 acquires the input information from the terminal control unit 203 via the terminal communication unit 205 and the server communication unit 105.

[0102] That is, the input acquisition unit 1031 in this embodiment acquires dish information indicating a dish obtained by cooking the food to be cooked and (b) target sense information 131a indicating targets for one or more numerical values ​​related to the senses.

[0103] The parameter acquisition unit 1032 acquires cooking parameter information indicating one or more cooking parameters using the input information and the learning model set 150a stored in the model storage unit 150. That is, the parameter acquisition unit 1032 acquires cooking parameter information including one or more cooking parameters for cooking by the cooker 300 based on the above-mentioned dish information and the target sensory information 131a acquired by the input acquisition unit 1031 using the learning model set 150a. The cooking parameter information is the initial cooking parameter information 141. In addition, in the present embodiment, the one or more cooking parameters include a cooking parameter indicating the temperature used for cooking by the cooker 300 and a cooking parameter indicating the time used for cooking by the cooker 300. Furthermore, the one or more cooking parameters include a cooking parameter indicating the pressure used for cooking by the cooker 300.

[0104] The parameter output unit 1037 transmits the initial cooking parameter information 141 to the cooking device 300 via the server communication unit 105. When the cooking unit 320 of the cooking device 300 obtains the transmitted initial cooking parameter information 141 via the cooking communication unit 305, it starts cooking according to the initial cooking parameter information 141.

[0105] The sensory information acquisition unit 1033 acquires the sensory information 131, for example, using the cooking state information 121 stored in the second storage unit 120 and the learning model set 150a stored in the model storage unit 150. The sensory information 131 acquired in this way represents one or more numerical values ​​related to the sensory sense of the food being cooked when the cooking device 300 is cooking, and is also called the cooking sensory information (i.e., the first sensory information 131b or the second sensory information 131c). That is, the sensory information acquisition unit 1033 acquires the cooking sensory information representing one or more numerical values ​​related to the sensory sense of the food being cooked when the cooking device 300 is cooking the food according to the cooking parameter information.

[0106] The parameter correction unit 1034 changes the initial cooking parameter information 141 into the modified cooking parameter information 142 including one or more modified cooking parameters based on the target sensory information 131a and the sensory information during cooking. In other words, the parameter correction unit 1034 corrects the initial cooking parameter information 141 into the modified cooking parameter information 142. In the correction of the initial cooking parameter information 141, the learning model set 150a stored in the model storage unit 150 is used.

[0107] The parameter output unit 1037 outputs a control signal including the modified cooking parameter information 142 to the cooking device 300 via the server communication unit 105. In other words, the parameter output unit 1037 transmits a control signal including the modified cooking parameter information 142. When the cooking unit 320 of the cooking device 300 obtains the transmitted modified cooking parameter information 142 via the cooking communication unit 305, it interrupts cooking according to the initial cooking parameter information 141 and performs cooking according to the modified cooking parameter information 142.

[0108] In addition, the parameter output unit 1037 may transmit a control signal including the initial cooking parameter information 141 to the cooker 300 in the same manner as the modified cooking parameter information 142 when transmitting the initial cooking parameter information 141. Hereinafter, the transmission or output of the initial cooking parameter information 141 or the modified cooking parameter information 142 is performed in a state where these information are included in the control signal.

[0109] The processing unit 1035 performs processing different from the processing performed by each component other than the processing unit 1035 included in the server control unit 103. For example, the processing unit 1035 transmits the third sense information 131d stored in the third storage unit 130 to the operation terminal 200 via the server communication unit 105.

[0110] Figure 6 1 is a diagram showing an example of dish list information 111 , recipe information 112 , basic cooking parameter information 113 , and basic sensory information 114 .

[0111] For example Figure 6 As shown in (a), the dish list information 111 indicates the dish ID and dish name corresponding to each record number according to the record number. The dish ID is identification information for identifying the dish name and the dish. Specifically, for the record number "1", the dish ID "D001" and the dish name "gravy consomme" are shown in association with each other.

[0112] For example Figure 6 As shown in (b), the recipe information 112 is associated with the dish ID, indicating one or more ingredients used in the dish identified by the dish ID and the respective quantities of the one or more ingredients. Specifically, the recipe information 112 indicates the ingredients corresponding to the record numbers and the quantities of the ingredients according to the record numbers. More specifically, in the recipe information 112 associated with the dish ID "D0001", i.e., the dish name "gravy consomme", for the record number "1", the ingredient "carrot" and the quantity "200g" of the ingredient "carrot" are shown in association with each other.

[0113] For example Figure 6As shown in (c), the basic cooking parameter information 113 is associated with the dish ID, indicating one or more cooking parameters used in the cooker 300 to prepare the dish identified by the dish ID. Each of the one or more cooking parameters includes a cooking parameter name and a set value. Specifically, the basic cooking parameter information 113 indicates the cooking parameter name and the set value corresponding to each record number. More specifically, in the basic cooking parameter information 113 associated with the dish ID "D0001", that is, the dish name "gravy consomme", for the record number "1", the cooking parameter name "temperature" and the set value "100°C" are shown in association with each other. That is, for the record number "1", the temperature "100°C" is shown as the cooking parameter. In addition, the set value may also be expressed as a numerical range such as "90°C to 110°C". In addition, when a numerical value such as "100°C" is shown as the set value, the numerical value may also be an average value or a median value of the set values ​​(for example, temperature) from the start of cooking to the end of cooking in the cooker 300.

[0114] For example Figure 6 As shown in (d), the basic sensory information 114 is associated with the dish ID, indicating one or more numerical values ​​related to the basic senses of the dish represented by the dish ID. Specifically, the basic sensory information 114 indicates the sensory items and degrees corresponding to the record numbers according to each record number. More specifically, in the basic sensory information 114 associated with the dish ID "D0001", that is, the dish name "gravy clear soup", for the record number "1", the sensory item "aroma" and the degree "-1" are shown in association with each other. That is, for the record number "1", the aroma "-1" is indicated as a numerical value related to the senses, that is, the sensory degree. In addition, the sensory item indicates the category of the sensory degree. In addition, the sensory degree in the present embodiment is a numerical value within the numerical range of -3 to 3, but is not limited to this, and can be a numerical value within any numerical range. In addition, the sensory degree can be either an integer or a decimal. In addition, the degree can also be expressed as a numerical range, such as "-1 to 1".

[0115] Figure 7 The diagram shows an example of image data, chemical analysis data, and weight data included in the cooking status information 121.

[0116] For example Figure 7 As shown in (a) of FIG. 1 , the image data 121a is data showing an image of the food to be cooked placed in the cooking device 300. The image data 121a is obtained by imaging performed by the imaging unit 312 of the cooking device 300.

[0117] For example Figure 7As shown in (b), the chemical analysis data 121b is, for example, data representing a graph obtained using liquid chromatography. The horizontal axis of the graph represents the measurement time, and the vertical axis of the graph represents the number of counts measured during the measurement time. Such chemical analysis data 121b is obtained through analysis performed by the chemical analysis unit 313 of the cooking device 300. Such a graph can be said to represent the respective components of more than one chemical component. The graph can also be called a chromatogram. In addition, the chemical analysis unit 313 may not obtain data representing the graph itself, but may obtain data directly representing the respective components of several chemical components as the chemical analysis data 121b. In other words, the chemical analysis unit 313 may also derive the respective components of a plurality of predetermined chemical components from the graph, and output the chemical analysis data 121b directly representing the components of each of the derived chemical components.

[0118] For example Figure 7 As shown in (c) of FIG. 1 , the weight data 121c is data indicating the weight of the food to be cooked placed in the cooking device 300. The weight data 121c is obtained by measurement performed by the weight measuring unit 311 of the cooking device 300.

[0119] [Screen display]

[0120] Figure 8 2 is a diagram showing an example of a screen display of the operation terminal 200 .

[0121] For example Figure 8 As shown in (a), the terminal control unit 203 of the operation terminal 200 displays the search screen d1 on the display unit 202. The search screen d1 has an input field w1, and the input field w1 is used to accept the input of the dish name. The cook writes the dish name of the dish that the cook wants to make into the input field w1 by performing an input operation on the input unit 201 of the operation terminal 200. For example, the cook writes the dish name "gravy consomme" into the input field w1. As a result, the terminal control unit 203 obtains the input information indicating the dish name "gravy consomme", stores the input information in the terminal storage unit 204, and sends it to the server 100 via the terminal communication unit 205.

[0122] The input acquisition unit 1031 of the server 100 acquires the input information as the dish information from the operation terminal 200 via the server communication unit 105, and stores the dish information in, for example, the server storage unit 104. The processing unit 1035 searches for the dish name "gravy consomme" indicated by the dish information acquired by the input acquisition unit 1031 based on the dish list information 111 stored in the first storage unit 110. When the processing unit 1035 finds the dish name "gravy consomme" based on the dish list information 111, it determines the dish ID associated with the dish name "gravy consomme". Furthermore, the processing unit 1035 acquires the recipe information 112 and the basic sensory information 114 associated with the dish ID from the first storage unit 110.

[0123] The processing unit 1035 uses the recipe information 112 and the basic sensory information 114 to generate, for example, Figure 8 The sensory input screen d2 shown in (b) is sent to the operation terminal 200 via the server communication unit 105. When the terminal control unit 203 of the operation terminal 200 obtains the information from the server 100 via the terminal communication unit 205, the sensory input screen d2 represented by the information is displayed on the display unit 202. The sensory input screen d2 includes a recipe column w2, a basic sensory column w3, a sensory adjustment column w4, and a start button b2. In the recipe column w2, one or more ingredients for making the dish "gravy consomme" represented by the recipe information 112 and the respective amounts of the one or more ingredients are displayed. In the basic sensory column w3, multiple sensory levels of the dish "gravy consomme" represented by the basic sensory information 114 are displayed.

[0124] In the sense adjustment column w4, a plurality of operation buttons b1 for changing the plurality of sense levels displayed in the basic sense column w3 are displayed. The cook operates the operation button b1 corresponding to the sense level that the cook wishes to change among the plurality of operation buttons b1. When receiving input information corresponding to the input operation to the operation button b1, the terminal control unit 203 of the operation terminal 200 changes the sense level corresponding to the operation button b1.

[0125] For example, when the operation button b1 corresponding to the aroma is operated, the terminal control unit 203 changes the aroma "-1" displayed in the basic sensory column w3 to "+1". That is, the terminal control unit 203 accepts the aroma "0" as the sensory level desired by the cook, or the target of the sensory level. Similarly, when the operation button b1 corresponding to the sweet aroma is operated, the terminal control unit 203 changes the sweet aroma "3" displayed in the basic sensory column w3 to "-1". That is, the terminal control unit 203 accepts the sweet aroma "2" as the sensory level desired by the cook, or the target of the sensory level. If there is a sensory level that the cook wants to change in addition to the aroma and the sweet aroma, the cook repeatedly operates the operation button b1 corresponding to the sensory level. Then, the cook operates the start button b2 displayed on the sensory input screen d2. When the terminal control unit 203 accepts the input operation for the start button b2, it obtains the input information representing the target of the sensory level relative to the aroma, sweet aroma, sourness, bitterness, etc. as the target sensory information 131a. Then, the terminal control unit 203 transmits the target sense information 131 a to the server 100 via the terminal communication unit 205 .

[0126] Furthermore, the change range of the sense level may be limited to a predetermined range. Furthermore, when one of the plurality of sense levels is changed, the remaining sense levels may also be changed in conjunction with the change of the one sense level. Furthermore, in the sense adjustment column w4, not all the sense levels indicated in the basic sense column w3 may be displayed, but only one of them may be displayed as a changeable sense level.

[0127] The sensory information acquisition unit 1033 of the server 100 acquires the target sensory information 131a from the operation terminal 200 via the server communication unit 105, and stores the target sensory information 131a in the third storage unit 130. The server control unit 103 derives the initial cooking parameter information 141 using the target sensory information 131a. The server control unit 103 corrects the initial cooking parameter information 141 into the corrected cooking parameter information 142 by using the cooking state information 121 obtained by the cooking device 300. The server control unit 103 estimates one or more sensory levels of the dish "gravy consomme" obtained by the cooking device 300 according to the initial cooking parameter information 141 and the corrected cooking parameter information 142. That is, the server control unit 103 derives the third sensory information 131d as the final sensory information. The server control unit 103 stores the third sensory information 131d in the third storage unit 130, and transmits the third sensory information 131d to the operation terminal 200 via the server communication unit 105. In addition, the server control unit 103 may transmit the time correction cooking parameter included in the correction cooking parameter information 142 to the operation terminal 200 via the server communication unit 105.

[0128] The terminal control unit 203 of the operation terminal 200 obtains the third sensory information 131d and the modified cooking parameter of the time from the server 100 via the terminal communication unit 205. Figure 8 As shown in (c), the terminal control unit 203 displays the final sense screen d3 for displaying the third sense information 131d and the like on the display unit 202. The final sense screen d3 displays a plurality of sense levels represented by the third sense information 131d. Furthermore, in the final sense screen d3, the time represented by the above-mentioned modified cooking parameter is displayed as, for example, the cooking completion time "2 hours and 30 minutes".

[0129] By checking one or more sensory levels displayed on the final sensory screen d3 in response to the input of the target sensory information 131a, the cook can recognize that a dish having one or more sensory levels according to the target sensory information 131a will be prepared. Alternatively, the cook can recognize that even if the cook tries to cook so that the sensory level of the dish is like the target sensory information 131a, a dish having a sensory level different from the target sensory information 131a will be prepared. Alternatively, the cook can confirm how close the sensory level of the dish finally prepared is to the desired one. In addition, the cook can grasp the time when the cooking of such a dish is completed.

[0130] In addition, Figure 8 In the example shown in (c), the plurality of sensory levels are displayed as numerical values, but they may be displayed as radar charts, graphs, etc., or may be displayed as the number of stars.

[0131] In addition, when the sensory level is displayed as a numerical range in the basic sensory column w3, the central value of the numerical range can be changed, for example, by using the operating button b1 of the sensory adjustment column w4. In this case, the width of the numerical range from the minimum value to the maximum value can also be maintained constant. For example, the aroma "-1~1" is displayed in the basic sensory column w3, and is changed to "+1" according to the operation of the operating button b1. In this case, the terminal control unit 203 accepts the aroma "0~2" as the aroma desired by the cook, or the target of the aroma. In addition, the initial cooking parameter information 141 and the modified cooking parameter information 142 can also change the central value of the numerical range, for example, so that the aroma "-1~1" is close to "0~2".

[0132] Fig. 9 It is a diagram showing another example of the screen display of the operation terminal 200 .

[0133] exist Figure 8 In the example shown, the cook directly inputs the name of the dish, but the name of the material (i.e., ingredient) used in the dish can also be input. Fig. 9 As shown in (a), the terminal control unit 203 of the operation terminal 200 displays the ingredient input screen d11 on the display unit 202. The ingredient input screen d11 has an input field w11, which is used to accept the input of the name of the ingredient that the cook wants to use. The cook writes the name of the ingredient that the cook wants to use into the input field w11 by performing an input operation on the input unit 201 of the operation terminal 200. For example, the cook writes the name of the ingredient "carrot" into the input field w11. As a result, the terminal control unit 203 obtains input information indicating the name of the ingredient "carrot", stores the input information in the terminal storage unit 204, and sends it to the server 100 via the terminal communication unit 205.

[0134] The input acquisition unit 1031 of the server 100 acquires the input information from the operation terminal 200 via the server communication unit 105, and stores the input information in, for example, the server storage unit 104. The input acquisition unit 1031 retrieves one or more recipe information 112 indicating the name of the ingredient "carrot" based on the plurality of recipe information 112 stored in the first storage unit 110. Furthermore, the input acquisition unit 1031 determines the dish ID associated with each of the one or more recipe information 112. That is, the input acquisition unit 1031 determines the dish name identified by the dish ID. The input acquisition unit 1031 sends the search result information indicating the determined one or more dish names to the operation terminal 200 via the server communication unit 105.

[0135] When the terminal control unit 203 of the operation terminal 200 obtains the search result information from the server 100 via the terminal communication unit 205, for example, Fig. 9 As shown in (b), the search result screen d12 is displayed on the display unit 202. The search result screen d12 includes a dish name button b11 with the dish name recorded for each of the one or more dishes represented by the search result information. The cook selects the dish name button b11 corresponding to the dish the cook wants to make by performing an input operation on the input unit 201 of the operation terminal 200. For example, the cook selects the dish name button b11 with the dish name "gravy consomme" recorded. As a result, the terminal control unit 203 of the operation terminal 200 obtains the input information indicating the dish name "gravy consomme" written on the selected dish name button b11, stores the input information in the terminal storage unit 204, and sends it to the server 100 via the terminal communication unit 205.

[0136] The input acquisition unit 1031 of the server 100 acquires the input information as the dish information from the operation terminal 200 via the server communication unit 105, and stores the dish information in, for example, the server storage unit 104. The processing unit 1035 searches for the dish name "gravy consomme" indicated by the dish information acquired by the input acquisition unit 1031 based on the dish list information 111 stored in the first storage unit 110. When the processing unit 1035 finds the dish name "gravy consomme" from the dish list information 111, it determines the dish ID associated with the dish name "gravy consomme". In addition, the processing unit 1035 acquires the recipe information 112 and the basic sensory information 114 associated with the dish ID from the first storage unit 110.

[0137] Afterwards, if Fig. 9 As shown in (c) and (d), Figure 8 Similarly to (b) and (c), the operation terminal 200 displays the sense input screen d2 and the final sense screen d3 on the display unit 202 by communicating with the server 100 .

[0138] This operation terminal 200 is a computer that performs an information providing method for providing information related to the cooking device 300 that cooks the food. That is, the operation terminal 200 receives (a) dish information indicating a dish obtained by cooking the food and (b) target sensory information 131a indicating a target with respect to one or more numerical values ​​related to the senses, based on an operation performed by a user as a cook. Furthermore, the operation terminal 200 outputs final sensory information related to the senses of the dish derived from the dish information and the target sensory information 131a and the sensory information 131 of the food when the cooking device 300 is cooking the food, i.e., the sensory information during cooking. The final sensory information is the third sensory information 131d described above.

[0139] Thus, if dish information indicating the dish desired by the cook and the target sensory information 131a desired by the cook are received, one or more numerical values ​​(i.e. sensory levels) related to the sensory properties of the dish finally made based on this information can be presented to the cook. Therefore, the cook can properly grasp the sensory properties of the dish obtained by cooking. In other words, the cook can confirm whether a dish with the sensory level desired by the cook can be made, or how close the sensory level of the dish finally made is to the desired one.

[0140] In addition, in the above example, the input unit 201 of the operation terminal 200 receives text data such as the name of a dish or the name of an ingredient according to the input operation performed by the cook, but it can also receive a voice signal. In this case, the input unit 201 has a microphone and receives the voice signal output from the microphone. And the terminal control unit 203 obtains input information indicating the name of the dish or the name of the ingredient by performing voice recognition on the voice signal.

[0141] [Processing action]

[0142] Fig.10 This is a sequence diagram showing an example of the processing operation of the information processing system 1000 .

[0143] (Step S1)

[0144] First, the operation terminal 200 acquires the dish information and the target sense information 131 a based on the input operation performed by the cook on the input unit 201 .

[0145] (Step S2)

[0146] The operation terminal 200 transmits the dish information and the target sense information 131 a acquired in step S1 to the server 100 .

[0147] (Step S3)

[0148] When receiving the dish information and target sensory information 131a transmitted from the operation terminal 200 in step S2, the server 100 uses these information to derive the initial cooking parameter information 141. In deriving the initial cooking parameter information 141, the learning model set 150a corresponding to the dish indicated by the dish information is used among the plurality of learning model sets 150a stored in the model storage unit 150. Specifically, the first learning model 151 included in the learning model set 150a is used.

[0149] Fig.171 shows an example of the first learning model 151. The input to the first learning model 151 is the target sense information 131a. The output from the first learning model 151 is the initial cooking parameter information 141.

[0150] The target sensory information 131a includes the degree of aroma of the dish indicated by the dish information, the degree of sweetness of the dish indicated by the dish information, the degree of sourness of the dish indicated by the dish information, the degree of bitterness of the dish indicated by the dish information, and the degree of saltiness of the dish indicated by the dish information.

[0151] The degree of the aroma, the degree of the sweetness, the degree of the sourness, the degree of the bitterness, and the degree of the saltiness are respectively designated by the user.

[0152] The initial cooking parameter information 141 includes the temperature, pressure and time for the cooker to cook the food so that the food represented by the food information has the degree of fragrance, sweetness, sourness, bitterness, saltiness, etc. specified by the user. The food may include one or more materials.

[0153] (Step S4)

[0154] The server 100 transmits the derived initial cooking parameter information 141 to the cooker 300 .

[0155] (Step S5)

[0156] When the cooker 300 receives the initial cooking parameter information 141 from the server 100, it starts cooking the food placed in the cooker 300 by controlling the pressure, temperature and time according to the initial cooking parameter information 141. Furthermore, the cooker 300 obtains the cooking state information 121 at time t1. Time t1 is a time point when the first time has passed after the cooker 300 starts cooking. In addition, the first time may be a predetermined time or 0 hours. That is, time t1 may also be a time point when the cooker 300 starts cooking.

[0157] (Step S6)

[0158] The cooking device 300 transmits the cooking state information 121 at the time t1 to the server 100 .

[0159] (Step S7)

[0160] When the server 100 receives the cooking state information 121 at time t1 from the cooking device 300, the server 100 uses the cooking state information 121 to derive the sensory information 131 of the cooked object at time t1 as the first sensory information 131b. In addition, the first sensory information 131b is sensory information during cooking. In addition, in deriving the first sensory information 131b, the learning model set 150a corresponding to the dish represented by the above-mentioned dish information among the plurality of learning model sets 150a stored in the model storage unit 150 is used. Specifically, the second learning model 152 included in the learning model set 150a is used.

[0161] Fig.18 1 shows an example of the second learning model 152. The input to the second learning model 152 is the cooking state information 121. The output from the second learning model 152 is the sensory information 131.

[0162] The cooking status information 121 includes image data 121a, chemical analysis data 121b, and weight data 121c.

[0163] The image data 121a may also be an image obtained by the camera 312 at time t of the food being cooked by the cooking device 300 controlled based on the initial cooking parameter information 141 from the cooking start time to time t (for example, refer to Figure 7 (a)) has m×n pixel values. The m×n pixel values ​​can be I(1,1), ~, and I(m,n). I(1,1) is the pixel value of the pixel (1,1) of the image, ~, and I(m,n) is the pixel value of the pixel (m,n) of the image. The pixel (1,1) is located at (x,y)=(1,1) in the image, ~, and the pixel (m,n) is located at (x,y)=(m,n) in the image. m can be an integer greater than 2, and n can be an integer greater than 2. Examples of XY coordinate axes are shown in Figure 7 (a).

[0164] The chemical analysis data 121b may also be a chromatogram obtained by the chemical analysis unit 313 measuring the food cooked by the cooking device 300 controlled based on the initial cooking parameter information 141 from the cooking start time to the time t (for example, refer to Figure 7 The r intensity values ​​in (b)) of . The r intensity values ​​may be the first intensity value at the first measurement time in the chromatogram, , to, the rth intensity value at the rth measurement time in the chromatogram. r may be an integer greater than 1.

[0165] The chemical analysis data 121b may be s components of s chemical components. The s components of s chemical components may be the 1st component of the 1st chemical component, to the sth component of the sth chemical component. s may be an integer greater than 1.

[0166] The weight data 121c may be a weight value measured by the weight measuring unit 311 at time t of the food cooked by the cooking device 300 controlled based on the initial cooking parameter information 141 from the cooking start time to time t.

[0167] The sensory information 131 includes the degree of aroma, sweetness, sourness, bitterness, and saltiness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to time t at time t.

[0168] The above-mentioned time t corresponds to time t1 in step S7. The above-mentioned time t corresponds to time t2 in step S10 described later.

[0169] (Step S8)

[0170] The cooking device 300 acquires the cooking state information 121 at time t2. Time t2 is a time point when a second time has passed after a first time has passed since the cooking device 300 started cooking. The second time may be a predetermined time longer than the first time.

[0171] (Step S9)

[0172] The cooker 300 transmits the cooking state information 121 at the time t2 to the server 100 .

[0173] (Step S10)

[0174] When the server 100 receives the cooking state information 121 at time t2 from the cooking device 300, the server 100 uses the cooking state information 121 to derive the sensory information 131 of the cooked object at time t2 as the second sensory information 131c. In addition, the second sensory information 131c is the sensory information during cooking, similarly to the first sensory information 131b. In addition, in deriving the second sensory information 131c, the learning model set 150a corresponding to the dish represented by the above-mentioned dish information among the plurality of learning model sets 150a stored in the model storage unit 150 is used. Specifically, the second learning model 152 included in the learning model set 150a is used.

[0175] (Step S11)

[0176] The server 100 calculates the difference between the first sensory information 131b and the second sensory information 131c. That is, for each of the one or more sensory items, the server 100 calculates the difference between the sensory level of the sensory item represented by the first sensory information 131b and the sensory level of the sensory item represented by the second sensory information 131c.

[0177] The above-mentioned “the server 100 calculates the difference between the first sensory information 131b and the second sensory information 131c” can also be interpreted as “the server 100 calculates the difference information based on the first sensory information 131b and the second sensory information 131c”.

[0178] The above difference information can include

[0179] {(the degree of fragrance included in the second sensory information 131c)-(the degree of fragrance included in the first sensory information 131b)},

[0180] {(the degree of sweetness contained in the second sensory information 131c) - (the degree of sweetness contained in the first sensory information 131b)},

[0181] {(the degree of sourness contained in the second sensory information 131c)-(the degree of sourness contained in the first sensory information 131b)},

[0182] {(the degree of bitterness contained in the second sensory information 131c)-(the degree of bitterness contained in the first sensory information 131b)},

[0183] {(degree of saltiness contained in the second sensory information 131c)-(degree of saltiness contained in the first sensory information 131b)}.

[0184] The above difference information can also include

[0185] {(the degree of aroma of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to time t2 at time t2) - (the degree of aroma of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to time t2 at time t1)},

[0186] {(the sweetness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to time t2 at time t2) - (the sweetness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to time t2 at time t1)},

[0187] {(the sourness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to the time t2 at the time t2) - (the sourness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to the time t2 at the time t1)},

[0188] {(the degree of bitterness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to the time t2 at the time t2) - (the degree of bitterness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to the time t2 at the time t1)},

[0189] {(the saltiness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to time t2 at time t2)-(the saltiness of the food cooked by the cooker 300 controlled based on the initial cooking parameter information 141 from the start of cooking to time t2 at time t1)}.

[0190] (Step S12)

[0191] Next, the server 100 uses the difference calculated in step S11 to derive the corrected cooking parameter information 142. In deriving the corrected cooking parameter information 142, the learning model set 150a corresponding to the dish represented by the above-mentioned dish information is used among the plurality of learning model sets 150a stored in the model storage unit 150. Specifically, the third learning model 153 included in the learning model set 150a is used.

[0192] Fig.19 1 shows an example of the third learning model 153. The input to the third learning model 152 is the difference information calculated based on the first sensory information 131b and the second sensory information 131c, and the target sensory information 131a. The output from the third learning model 153 is the corrected cooking parameter information 142.

[0193] The modified cooking parameter information 142 includes the temperature, pressure, and time for cooking the food cooked by the cooker 300 controlled by the initial cooking parameter information 141 from the start time of cooking to the time t3, so that the dish represented by the dish information has the degree of aroma, sweetness, sourness, bitterness, saltiness, etc. specified by the user as shown in the target sensory information 131a.

[0194] Time t4 may be later than time t3 or the same as time t3. Time t3 may be later than time t1 and time t2. Time t1 and time t2 may be later than the cooking start time.

[0195] (Step S13)

[0196] Then, the server 100 transmits the corrected cooking parameter information 142 derived in step S12 to the cooker 300 .

[0197] (Step S14)

[0198] The cooker 300 receives the revised cooking parameter information 142 transmitted in step S13 and changes the initial cooking parameter information 141 to the revised cooking parameter information 142. That is, the cooker 300 interrupts cooking according to the initial cooking parameter information 141 and starts cooking according to the revised cooking parameter information 142.

[0199] (Step S15)

[0200] The server 100 further uses the modified cooking parameter information 142 derived in step S12 to derive the final sensory information 131 of the dish represented by the above-mentioned dish information when it is cooked as the third sensory information 131d. In other words, the third sensory information 131d is estimated. In deriving the third sensory information 131d, the learning model set 150a corresponding to the dish represented by the above-mentioned dish information is used among the plurality of learning model sets 150a stored in the model storage unit 150. Specifically, the fourth learning model 154 included in the learning model set 150a is used.

[0201] Fig. 20 14 shows an example of the fourth learning model 154. The input to the fourth learning model 154 is the modified cooking parameter information 142 and the sensory information during cooking. The output from the fourth learning model 153 is the sensory information of the prepared dish (i.e., the third sensory information 131d). The third sensory information 131d includes the degree of fragrance, sweetness, sourness, bitterness, and saltiness of the prepared dish.

[0202] (Step S16)

[0203] The server 100 transmits the corrected cooking parameter of time included in the corrected cooking parameter information 142 derived in step S12 and the third sensory information 131d derived in step S15 to the operation terminal 200. Alternatively, the server 100 may transmit all the corrected cooking parameters included in the corrected cooking parameter information 142.

[0204] (Step S17)

[0205] The operation terminal 200 receives the modified cooking parameter of the time included in the modified cooking parameter information 142 sent in step S16 and the third sensory information 131d. Figure 8 (c) or Fig. 9 As shown in (d) of FIG. 1 , the operation terminal 200 displays the corrected cooking parameter of the time and the third sensory information 131d on the display unit 202. The corrected cooking parameter of the time is displayed as, for example, the cooking completion time.

[0206] Thus, in this embodiment, the dish information indicating the dish desired by the cook and the target sensory information 131a desired by the cook are obtained, and the initial cooking parameter information 141 is obtained based on them. Then, by sending the initial cooking parameter information 141 to the cooker 300, the cooker 300 starts cooking according to the initial cooking parameter information 141. Thereafter, the initial cooking parameter information 141 used by the cooker 300 is corrected according to one or more numerical values ​​(i.e., sensory levels) related to the sensory properties of the food being cooked during cooking. Therefore, by cooking according to the corrected cooking parameter information 142, the sensory level of the dish finally prepared can be made close to the sensory level indicated by the target sensory information 131a. In other words, the sensory level of the dish finally prepared can be made close to the sensory level desired by the cook. That is, even if the sensory level desired by the cook is different from the sensory level described in the basic recipe, or even if the amount of the food being cooked is not as in the recipe, the sensory level of the dish finally prepared can be made close to the sensory level desired by the cook. As a result, the sensory experience of the dish can be properly controlled without the cook having to deliberately adjust the cooking parameters.

[0207] Fig.11 This is a flowchart showing an example of the processing operation of the server 100 .

[0208] (Step S110)

[0209] First, the input acquisition unit 1031 of the server 100 acquires the menu information and the target sense information 131 a from the operation terminal 200 via the server communication unit 105 .

[0210] (Step S120)

[0211] The parameter acquisition unit 1032 derives the initial cooking parameter information 141 using the dish information and the target sensory information 131 a .

[0212] (Step S130)

[0213] The parameter output unit 1037 transmits the initial cooking parameter information 141 acquired in step S120 to the cooking device 300 via the server communication unit 105 .

[0214] (Step S140)

[0215] The processing unit 1035 obtains the cooking status information 121 from the cooking device 300 via the server communication unit 105 , and stores the cooking status information 121 in the second storage unit 120 .

[0216] (Step S150)

[0217] The sensory information acquisition unit 1033 derives the cooking sensory information using the cooking status information 121 acquired in step S140 .

[0218] (Step S160)

[0219] After performing the processing of step S150, the parameter correction unit 1034 determines whether a plurality of sensory information during cooking has been derived. That is, the parameter correction unit 1034 determines whether the first sensory information 131b and the second sensory information 131c have been derived. Here, when it is determined in the processing of step S160 that a plurality of sensory information during cooking has not been derived (step S160: No), the processing unit 1035 and the sensory information acquisition unit 1033 repeatedly perform the processing of step S140 and step S150. That is, the sensory information acquisition unit 1033 acquires the first sensory information 131b and the second sensory information 131c by repeatedly performing the processing of step S150. Specifically, the sensory information acquisition unit 1033 acquires the sensory information during cooking at a time point after a first time has passed since the cooking device 300 started cooking as the first sensory information 131b. Furthermore, the sensory information acquisition unit 1033 acquires the cooking sensory information at a time point after a second time has passed since the cooking device 300 started cooking as the second sensory information 131c. Fig.10 At time t1, the time point after the second time is equivalent to Fig.10 moment t2.

[0220] (Step S170)

[0221] On the other hand, when the parameter correction unit 1034 determines that a plurality of cooking sensory information is derived in the process of step S160 (step S160: Yes), the parameter correction unit 1034 calculates the difference between the cooking sensory information 131b and the second sensory information 131c.

[0222] (Step S180)

[0223] Then, the parameter correction unit 1034 uses the target sensory information 131a obtained in step S110 and the difference calculated in step S170 to derive the corrected cooking parameter information 142. The corrected cooking parameter information 142 is information for bringing one or more numerical values ​​related to the sensory properties of the final dish closer to one or more numerical values ​​indicated by the target sensory information 131a. In this way, the parameter correction unit 1034 in this embodiment changes the initial cooking parameter information 141 to the corrected cooking parameter information 142 based on the difference between the first sensory information 131b and the second sensory information 131c and the target sensory information 131a.

[0224] Thus, in this embodiment, based on the amount of change in sensory information during cooking and the target sensory information 131a, the initial cooking parameter information 141 is corrected to the corrected cooking parameter information 142. Therefore, by considering the tendency of the change in the sensory level caused by cooking according to the initial cooking parameter information 141, the sensory level of the final dish can be effectively made close to the sensory level desired by the cook.

[0225] (Step S190)

[0226] The parameter output unit 1037 transmits the corrected cooking parameter information 142 derived in step S180 to the operation terminal 200 and the cooking device 300 via the server communication unit 105. The parameter output unit 1037 may transmit only the corrected cooking parameter for the time included in the corrected cooking parameter information 142 to the operation terminal 200.

[0227] (Step S200)

[0228] The sensory information acquisition unit 1033 derives the third sensory information 131d using the modified cooking parameter information 142 derived in step S180. That is, the sensory information acquisition unit 1033 acquires the third sensory information 131d indicating one or more numerical values ​​related to the sensory properties of the dish obtained by cooking with the cooking device 300 according to the modified cooking parameter information 142 based on the modified cooking parameter information 142.

[0229] (Step S210)

[0230] The parameter output unit 1037 transmits the third sense information 131 d derived in step S200 to the operation terminal 200 via the server communication unit 105 . The third sense information 131 d is displayed on the display unit 202 of the operation terminal 200 .

[0231] Thus, in this embodiment, information indicating the sensory level of the dish finally prepared is estimated as the third sensory information 131d. And the third sensory information 131d is presented to the cook. As a result, the cook can confirm whether the dish with the sensory level desired by the cook can be prepared, or how close the sensory level of the dish finally prepared is to the desired level.

[0232] Fig.12 Detailed description of a part of the processing operation of the server 100. Fig.12 (a) means Fig.11 The flowchart of the detailed processing of step S120 is as follows, Fig.12 (b) means Fig.11 Flowchart of the detailed processing of step S150. Fig.12 (c) means Fig.11A flowchart of the detailed processing of step S180 is shown in FIG. Fig.12 (d) means Fig.11 Flowchart of the detailed processing of step S200.

[0233] When the parameter acquisition unit 1032 derives the initial cooking parameter information 141, for example, Fig.12 The processing of steps S121 to S123 shown in (a).

[0234] (Step S121)

[0235] The parameter acquisition unit 1032 reads the first learning model 151 corresponding to the menu information from the model storage unit 150. That is, the parameter acquisition unit 1032 reads the first learning model 151 included in the learning model set 150a corresponding to the menu name indicated by the menu information from the model storage unit 150.

[0236] (Step S122)

[0237] The parameter acquisition unit 1032 inputs the target sense information 131 a to the first learning model 151 read out in step S121 .

[0238] (Step S123)

[0239] The parameter acquisition unit 1032 acquires the initial cooking parameter information 141 output from the first learning model 151 by the input in step S122. The initial cooking parameter information 141 is derived in this way.

[0240] When the sensory information acquisition unit 1033 derives the sensory information during cooking, such as the first sensory information 131b, for example, Fig.12 The processing of steps S151 to S153 shown in (b).

[0241] (Step S151)

[0242] The sensory information acquisition unit 1033 reads the second learning model 152 corresponding to the menu information from the model storage unit 150. That is, the sensory information acquisition unit 1033 reads the second learning model 152 included in the learning model set 150a corresponding to the menu name indicated by the menu information from the model storage unit 150.

[0243] (Step S152)

[0244] The sensory information acquisition unit 1033 inputs the latest cooking status information 121 to the read second learning model 152 .

[0245] (Step S153)

[0246] The sensory information acquisition unit 1033 acquires the sensory information 131 output from the second learning model 152 as the cooking sensory information. In this way, the cooking sensory information is derived.

[0247] In this way, the sensory information acquisition unit 1033 in this embodiment acquires the sensory information during cooking by inputting the image of the food being cooked, the weight of the food being cooked, and the amount of chemical components contained in the food being cooked into the second learning model 152. Thus, since the second learning model 152 is used to acquire the sensory information during cooking, it is possible to acquire the sensory information during cooking with high accuracy.

[0248] In addition, the image of the food to be cooked, the weight of the food to be cooked, and the amount of chemical components contained in the food to be cooked are included in the cooking state information 121 as image data 121a, weight data 121c, and chemical analysis data 121b, respectively. In addition, in the present embodiment, the image data 121a, weight data 121c, and chemical analysis data 121b are input to the second learning model 152, but only one of the image data 121a, chemical analysis data 121b, and weight data 121c, or only two of the image data 121a, chemical analysis data 121b, and weight data 121c may be input to the second learning model 152. In other words, at least one of the image data 121a, weight data 121c, and chemical analysis data 121b may be input to the second learning model 152. Even in this case, it is possible to obtain highly accurate sensory information during cooking.

[0249] When the parameter correction unit 1034 derives the corrected cooking parameter information 142, for example, Fig.12 The processing of steps S181 to S184 shown in (c).

[0250] (Step S181)

[0251] The parameter correction unit 1034 reads the third learning model 153 corresponding to the menu information from the model storage unit 150. That is, the parameter correction unit 1034 reads the third learning model 153 included in the learning model set 150a corresponding to the menu name indicated by the menu information from the model storage unit 150.

[0252] (Step S182)

[0253] The parameter correction unit 1034 inputs the difference in sensory information during cooking and the target sensory information 131 a to the read third learning model 153 .

[0254] (Step S183)

[0255] The parameter correction unit 1034 acquires a plurality of candidates outputted from the third learning model 153 by the input in step S182 . The plurality of candidates are candidates for correcting the cooking parameter information 142 .

[0256] (Step S184)

[0257] The parameter correction unit 1034 selects the candidate closest to the initial cooking parameter information 141 from among the plurality of candidates acquired in step S183 as the corrected cooking parameter information 142. Thus, the corrected cooking parameter information 142 is derived.

[0258] In this way, the parameter correction unit 1034 in this embodiment obtains multiple candidates for the corrected cooking parameter information 142 based on the target sensory information 131a and the sensory information during cooking. Moreover, the parameter correction unit 1034 selects the candidate closest to the initial cooking parameter information 141 from the multiple candidates as the corrected cooking parameter information 142. Alternatively, the parameter correction unit 1034 may select a candidate whose difference from the initial cooking parameter information 141 is less than a threshold value from the multiple candidates as the corrected cooking parameter information 142. For example, the multiple candidates are respectively represented as vectors including pressure cooking parameters, temperature cooking parameters, and time cooking parameters. Similarly, the initial cooking parameter information 141 is also represented as a vector including pressure cooking parameters, temperature cooking parameters, and time cooking parameters. Therefore, the parameter correction unit 1034 may also calculate the distance between the vectors of the multiple candidates and the vector of the initial cooking parameter information 141, and select the candidate whose distance is less than the threshold value or the smallest from the multiple candidates as the corrected cooking parameter information 142. In addition, among the multiple candidates, for example, one or two predetermined cooking parameters are the same as the initial cooking parameter information 141 and only the remaining two or one cooking parameter is different from the initial cooking parameter information 141, and the candidate may be selected as the corrected cooking parameter information 142. The predetermined one or two cooking parameters may also be a temperature cooking parameter, a pressure cooking parameter, or the like.

[0259] Thus, even when a plurality of candidates are output from the third learning model 153, a candidate that is not much different from the initial cooking parameter information 141 is adopted as the corrected cooking parameter information 142. Therefore, it is possible to reduce the processing load of the cooking device 300 associated with the correction of the initial cooking parameter information 141. In addition, it is possible to suppress a significant change in the taste of the dish.

[0260] When deriving the third sensory information 131d, the sensory information acquisition unit 1033 executes, for example, Fig.12 The processing of steps S201 to S203 shown in (d).

[0261] (Step S201)

[0262] The sensory information acquisition unit 1033 reads the fourth learning model 154 corresponding to the menu information from the model storage unit 150. That is, the sensory information acquisition unit 1033 reads the fourth learning model 154 included in the learning model set 150a corresponding to the menu name indicated by the menu information from the model storage unit 150.

[0263] (Step S202)

[0264] The sensory information acquisition unit 1033 inputs the second sensory information 131 c and the corrected cooking parameter information 142 to the read fourth learning model 154 .

[0265] (Step S203)

[0266] The sensory information acquisition unit 1033 acquires the sensory information 131 output from the fourth learning model 154 as the third sensory information 131d by the input in step S202. Thus, the third sensory information 131d is derived.

[0267] In this manner, the sensory information acquisition unit 1033 in the present embodiment acquires the third sensory information 131 d by inputting at least the corrected cooking parameter information 142 into the fourth learning model 154 .

[0268] [Example of building a learning model]

[0269] Fig.13 This is a flowchart showing an example of constructing the first learning model 151 .

[0270] (Step S301)

[0271] First, the model maker generates a plurality of types of cooking parameter information for a predetermined dish. The cooking parameter information indicates one or more cooking parameters used by the cooking unit 320 of the cooker 300.

[0272] (Step S302)

[0273] Next, the model maker creates a sample of a dish (also referred to as a dish sample) for each of the plurality of cooking parameter information by cooking according to the cooking parameter information by the cooker 300. The cooker 300 cooks one or more ingredients shown in the recipe information corresponding to the predetermined dish.

[0274] (Step S303)

[0275] The model maker evaluates the sensory state of each dish sample made in step S302. The sensory state includes one or more sensory levels of the dish sample. The evaluation of the sensory level can also be performed by multiple people. For example, multiple people taste the dish sample and evaluate the sensory level of the dish sample. In addition, the average value of the multiple sensory levels obtained by the evaluation of multiple people can also be used as the final sensory level for the dish sample.

[0276] (Step S304)

[0277] The model maker selects a machine learning algorithm for constructing the first learning model 151 for the above-mentioned predetermined dish.

[0278] (Step S305)

[0279] The model maker enables the learning model to learn the relationship between the sensory state and the cooking parameter information associated with the above-mentioned predetermined dish according to the machine learning algorithm selected in step S304.

[0280] (Step S306)

[0281] The model maker verifies the learning model that has been learned through the process of step S305. That is, the model maker verifies whether the correct cooking parameter information is output from the learning model as the initial cooking parameter information 141 in response to the input of the sensory information representing the sensory state to the learning model. The learning model that has been verified to have output the correct cooking parameter information is stored in the model storage unit 150 of the server 100 as the first learning model 151 corresponding to the predetermined dish. In addition, the correct cooking parameter information refers to information that a dish of the sensory state input to the learning model can actually be made by cooking according to the cooking parameter information.

[0282] Fig.14 This is a flowchart showing an example of constructing the second learning model 152 .

[0283] (Step S311)

[0284] First, the model maker generates multiple types of recipe information for a predetermined dish. In addition, the recipe information indicates more than one material used in the predetermined dish and the respective quantities of the more than one material.

[0285] (Step S312)

[0286] Next, the model maker creates a dish sample for each of the multiple types of recipe information by cooking according to the recipe information using the cooking device 300. The dish sample may also be a sample at a time point after the first time or the second time has passed since the start of cooking.

[0287] (Step S313)

[0288] The model maker determines the cooking state of each dish sample created in step S312. That is, the model maker obtains image data by photographing the dish sample using a camera, obtains chemical analysis data by analyzing the dish sample using a chemical analysis device, and obtains weight data of the dish sample using a weighing instrument. Thus, the image data, chemical analysis data, and weight data are determined as the cooking state of the dish sample.

[0289] (Step S314)

[0290] The model maker measures the sensory state of each dish sample created in step S312, that is, one or more sensory levels of the dish sample.

[0291] (Step S315)

[0292] The model maker selects a machine learning algorithm for constructing the second learning model 152 for the above-mentioned predetermined dish.

[0293] (Step S316)

[0294] The model maker enables the learning model to learn the relationship between the cooking state and the sensory state according to the machine learning algorithm selected in step S315.

[0295] (Step S317)

[0296] The model maker verifies the learning model that has been learned through the process of step S316. That is, the model maker verifies whether the sensory information indicating the correct sensory state is output from the learning model for the input of the cooking state information indicating the cooking state. The learning model that has been verified to output the sensory information indicating the correct sensory state is stored in the model storage unit 150 of the server 100 as the second learning model 152 corresponding to the predetermined dish. In addition, the correct sensory state refers to the actual sensory state of the dish sample of the cooking state input to the learning model.

[0297] In this way, the second learning model 152 in this embodiment is machine-learned to output one or more numerical values ​​related to the sensory senses of the one or more ingredients being cooked in the cooking device 300, the weight of one or more ingredients, and the amount of chemical components contained in one or more ingredients. In addition, the one or more numerical values ​​related to the sensory senses of the one or more ingredients are equivalent to the above-mentioned sensory state or sensory information 131. In addition, the image, weight, and amount of chemical components of the one or more ingredients are respectively equivalent to the above-mentioned image data 121a, weight data 121c, and chemical analysis data 121b, which represent the cooking state of the above-mentioned dish sample. In addition, in the machine learning in this embodiment, the image, weight, and amount of chemical components are input into the learning model, but only one of the image, weight, and amount of chemical components, or only two of the image, weight, and amount of chemical components can be input into the learning model. That is, the second learning model 152 can also be machine-learned to output sensory information representing the sensory state of the one or more ingredients for at least one of the input of the image, weight, and amount of chemical components of the one or more ingredients.

[0298] Thus, when at least one of the image data 121a, weight data 121c and chemical analysis data 121b of the cooked food is input into the second learning model 152 by the sensory information acquisition unit 1033, highly accurate cooking sensory information can be acquired from the second learning model 152.

[0299] Fig.15 This is a flowchart showing an example of constructing the third learning model 153 .

[0300] (Step S321)

[0301] First, the model maker generates cooking parameter information for a predetermined dish in various ways.

[0302] (Step S322)

[0303] Next, the model maker starts cooking the dish sample using the cooker 300 according to the cooking parameter information for each of the multiple types of cooking parameter information.

[0304] (Step S323)

[0305] After the model maker starts cooking in step S322, the model maker measures the sensory state of each dish sample at two time points. Moreover, the model maker calculates differential information based on the sensory state measured at the two time points. In addition, the two time points are the time points when the cooking device 300 is cooking. In other words, the measured sensory state is the sensory state during cooking. In addition, the two time points can also be the time point after the first time and the time point after the second time mentioned above.

[0306] (Step S324)

[0307] Then, the model maker modifies the cooking parameter information in a variety of ways during cooking. That is, the model maker interrupts cooking according to the cooking parameter information before modification and starts cooking according to the modified cooking parameter information.

[0308] (Step S325)

[0309] The model maker measures the sensory state of each dish sample made according to the corrected cooking parameter information, that is, one or more sensory levels of the dish sample. The sensory state measured at this time is the final sensory state after cooking.

[0310] (Step S326)

[0311] The model maker selects a machine learning algorithm for constructing the third learning model 153 for the above-mentioned predetermined dish.

[0312] (Step S327)

[0313] The model creator causes the learning model to learn the relationship between the difference in sensory state during cooking calculated in step S323 and the final sensory state measured in step S325 and the corrected cooking parameter information according to the machine learning algorithm selected in step S326 .

[0314] (Step S328)

[0315] The model maker verifies the learning model that has been learned through the process of step S327. That is, the model maker verifies whether the correct corrected cooking parameter information is output from the learning model as the corrected cooking parameter information 142 for the difference in the sensory state during cooking and the sensory information representing the final sensory state. The learning model that has been verified to output the correct corrected cooking parameter information is stored in the model storage unit 150 of the server 100 as the third learning model 153. In addition, the above-mentioned final sensory state is equivalent to the target sensory information 131a. In addition, the correct corrected cooking parameter information refers to the information actually required to prepare a dish of the final sensory state input to the learning model when the sensory state changes, such as the difference in the sensory state input to the learning model. Through the learning model constructed in this way, that is, the third learning model 153, it is possible to obtain the corrected cooking parameter information 142 that makes the sensory state during cooking close to the target sensory information 131a.

[0316] Fig.16 This is a flowchart showing an example of constructing the fourth learning model 154 .

[0317] (Step S331)

[0318] First, the model maker generates cooking parameter information for a predetermined dish in various ways.

[0319] (Step S332)

[0320] Next, the model maker starts cooking the dish sample using the cooker 300 according to the cooking parameter information for each of the multiple types of cooking parameter information.

[0321] (Step S333)

[0322] Then, after the model maker starts cooking in step S332, the model maker measures the sensory state of each dish sample. The sensory state measured at this time is the sensory state during cooking. Furthermore, the model maker modifies the cooking parameter information in a variety of ways during cooking. That is, the model maker interrupts cooking according to the cooking parameter information before modification and starts cooking according to the modified cooking parameter information. In addition, the processing of step S333 can also be performed at a time point after the above-mentioned second time has passed since the start of cooking. The processing of step S333 can also be started from a time point after the above-mentioned second time has passed since the start of cooking.

[0323] (Step S334)

[0324] The model maker measures the sensory state of each dish sample made according to the corrected cooking parameter information, that is, one or more sensory levels of the dish sample. The sensory state measured at this time is the final sensory state after cooking.

[0325] (Step S335)

[0326] The model maker selects a machine learning algorithm for constructing the fourth learning model 154 for the above-mentioned predetermined dish.

[0327] (Step S336)

[0328] The model creator causes the learning model to learn the relationship between the final sensory state measured in step S334, the sensory state during cooking measured in step S333, and the corrected cooking parameter information according to the machine learning algorithm selected in step S335.

[0329] (Step S337)

[0330] The model maker verifies the learning model that has been learned through the process of step S336. That is, the model maker verifies whether the correct final sensory state is output from the learning model as the third sensory information 131d for the sensory information representing the sensory state during cooking and the corrected cooking parameter information input to the learning model. The learning model that has been verified to output the sensory information representing the correct final sensory state is stored in the model storage unit 150 of the server 100 as the fourth learning model 154.

[0331] In this way, the fourth learning model 154 in the present embodiment is machine-learned to output one or more numerical values ​​related to the sensory properties of a dish obtained by cooking the dish according to the one or more cooking parameters that are changed during the cooking process of the cooker 300, at least for the input of one or more cooking parameters that are changed during the cooking process of the cooker 300. In addition, the one or more corrected cooking parameters correspond to the corrected cooking parameter information 142, and the one or more numerical values ​​related to the sensory properties of the dish are information indicating the final sensory state of the dish, which corresponds to the third sensory information 131d. In addition, the correct final sensory state refers to the actual sensory state of the final dish sample obtained by cooking the dish sample of the sensory state during cooking input into the learning model according to the corrected cooking parameter information input into the learning model.

[0332] Since the fourth learning model 154 obtained by such machine learning is used to acquire the third sensory information 131 d , it is possible to acquire the third sensory information 131 d with high accuracy.

[0333] As described above, in the information processing system 1000 of the present embodiment, the sensory properties of the dish can be appropriately controlled without the cook intentionally adjusting the cooking parameters. In addition, the cook can appropriately grasp the sensory properties of the dish obtained by cooking. In other words, the cook can confirm whether a dish with the sensory properties desired by the cook will be prepared, or how close the sensory properties of the dish finally prepared are to the desired properties.

[0334] <Others>

[0335] The above describes the information processing system, server, operation terminal, etc. involved in the present disclosure based on the implementation mode, but the present disclosure is not limited to the implementation mode. As long as it does not depart from the main purpose of the present disclosure, the various deformations that a person skilled in the art can think of to the implementation mode are also included in the scope of the present disclosure.

[0336] For example, the operation terminal 200 in the above embodiment is a device independent of the cooker 300, but it can also be assembled in the cooker 300. In other words, among the various functions of the operation terminal 200, the function for the information processing system 1000 can also be provided in the cooker 300. In addition, the operation terminal 200 can also be configured as a personal computer.

[0337] In addition, the cook in the above-mentioned embodiment may be a user who uses the information processing system 1000 , or may be an operator of the operation terminal 200 or the cooking device 300 .

[0338] In addition, the server 100 in the above embodiment includes the first storage unit 110, the second storage unit 120, the third storage unit 130, the fourth storage unit 140, and the model storage unit 150, but may not include these recording media. For example, the server 100 may utilize these recording media by communicating with a device that is external to the server 100 and has these recording media.

[0339] In addition, the server 100 in the above embodiment obtains cooking parameter information including one or more cooking parameters for cooking by the cooking device 300 based on the dish information and the target sensory information 131a. That is, the server 100 obtains the initial cooking parameter information 141. However, the server 100 may not obtain the initial cooking parameter information 141. In this case, the server 100 obtains sensory information of the object being cooked when the cooking device 300 cooks the object according to the basic cooking parameter information 113. Alternatively, the server 100 may obtain the initial cooking parameter information 141 generated by the cooking device 300.

[0340] In other words, the control method involved in the present disclosure may be a method executed by a computer to control a cooking device for cooking a food to be cooked, and the following processing actions are performed. That is, in the control method, dish information indicating a dish obtained by cooking the food to be cooked is obtained, and based on the obtained dish information, cooking parameter information including one or more cooking parameters used for cooking by the cooking device is obtained, and target sensory information indicating a numerical value that becomes a target with respect to one or more numerical values ​​related to a sensory sense is obtained. Furthermore, in the control method, cooking sensory information indicating one or more numerical values ​​related to the sensory sense of the food to be cooked when the cooking device is cooking the food to be cooked according to the cooking parameter information is obtained, and based on the target sensory information and the cooking sensory information, the cooking parameter information is changed to modified cooking parameter information including one or more modified cooking parameters, and a control signal including the modified cooking parameter information is output. In addition, the cooking parameter information is the basic cooking parameter information 113 or the initial cooking parameter information 141, which can be obtained from the cooking device 300. In addition, the control signal can be output or sent to the cooking device 300. This control method can also have the same effect as the control method in the above embodiment.

[0341] In addition, in the above-mentioned embodiments, each component may be formed by dedicated hardware or implemented by executing a software program suitable for each component. Each component may also be implemented by a program execution unit such as a CPU or a processor reading a software program recorded on a recording medium such as a hard disk or a semiconductor memory and executing it. Here, the program for implementing the system such as the server 100 and the operation terminal 200 in the above-mentioned embodiments may also cause the processor to execute Fig.10 In addition, the program for implementing the server 100 in the above-mentioned embodiment may also cause the processor to execute Fig.11 as well as Fig.12 The steps included in each flowchart.

[0342] (Hardware Structure)

[0343] The server 100 or the operation terminal 200 may also be composed of a computer system including a microprocessor, a ROM, a RAM, a hard disk drive, a display unit, a keyboard, a mouse, etc. A program is stored in the RAM or the hard disk drive. The microprocessor works according to the program, and the server 100 or the operation terminal 200 realizes its function. Here, the program is composed of a plurality of command codes representing instructions to the computer in order to realize a predetermined function.

[0344] Furthermore, part or all of the components constituting the above-mentioned server 100 or operation terminal 200 may also be constituted by a system LSI (Large Scale Integration). System LSI is a super multifunctional LSI manufactured by integrating multiple components into one chip, specifically, a computer system consisting of a microprocessor, ROM, RAM, etc. Computer programs are stored in RAM. The microprocessor works according to the computer program, and the system LSI realizes its functions.

[0345] Furthermore, part or all of the components constituting the above-mentioned server 100 or operation terminal 200 may also be constituted by an IC card or a single module that can be loaded and unloaded on a computer. The IC card or module is a computer system composed of a microprocessor, ROM, and RAM. The IC card or module may also include the above-mentioned super-multifunctional LSI. The IC card or module realizes its function by the microprocessor working according to the computer program. The IC card or the module may also have anti-tampering performance.

[0346] In addition, the present disclosure may also be a control method or an information providing method executed by the above-mentioned server 100 or operation terminal 200. In addition, these methods may be implemented by a computer executing a program or by a digital signal generated by the program.

[0347] Furthermore, the present disclosure may also be constituted by a non-transient recording medium of a computer-readable program or digital signal. Examples of the recording medium include a floppy disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, a DVD-RAM, a BD (Blu-ray (registered trademark) Disc), a semiconductor memory, and the like. In addition, the program may also be constituted by the above-mentioned digital signal recorded on a non-transient recording medium.

[0348] Furthermore, the present disclosure may be configured by transmitting the above-mentioned program or digital signal via a telecommunication line, a wireless or wired communication line, a network represented by the Internet, or data broadcasting.

[0349] Furthermore, the present disclosure may be a computer system including a microprocessor and a memory, wherein the memory stores a program and the microprocessor operates according to the program.

[0350] Furthermore, the program or digital signal may be recorded on the above-mentioned non-transitory recording medium and transferred, or the program or digital signal may be transferred via the above-mentioned network or the like, so that it can be implemented by another independent computer system.

[0351] (other)

[0352] Modifications of the embodiments of the present disclosure may also be the following methods.

[0353] A computer-implemented method comprising:

[0354] (a) determining information including a first temperature based on information including a first sourness degree specified by a user and information indicating one or more ingredients, whereby the cooking device starts cooking the one or more ingredients from a first moment under a first condition in order to produce a dish having the first sourness degree, the first condition including that the cooking device cooks the one or more ingredients at the first temperature,

[0355] (b) determining a second sourness degree of the one or more ingredients cooked under the first condition from the first moment to the second moment based on a first image of the one or more ingredients cooked under the first condition from the first moment to the second moment captured by a camera at a second moment,

[0356] (c) determining a third sourness degree of the one or more ingredients cooked under the first condition from the first moment to the third moment based on a second image of the one or more ingredients cooked under the first condition from the first moment to the third moment captured by the camera at a third moment,

[0357] (d) determining information including a second temperature based on the first sourness level, the second sourness level, and the third sourness level, whereby the cooking device cooks the one or more ingredients cooked from the first to the fourth time under the second condition from the fifth time in order to produce the dish having the first sourness level,

[0358] The second condition includes the situation that the cooker cooks the one or more materials cooked from the first moment to the fourth moment at the second temperature, the fifth moment is later than the fourth moment or the same as the fourth moment, the fourth moment is later than the second moment and the third moment, and the second moment and the third moment are later than the first moment.

[0359] The above (a) is supported by, for example, the first learning model and its related description.

[0360] The above (b) and (c) are supported by, for example, the second learning model and its related description.

[0361] The above (d) is supported by, for example, the third learning model and its related description.

[0362] Industrial Applicability

[0363] The present disclosure has the effect of being able to appropriately control the sensory level of dishes, and is useful for a system or device that performs control or information provision related to cooking.

[0364] Description of symbols

[0365] 100 server; 103 server control unit; 104 server storage unit; 105 server communication unit; 110 first storage unit; 111 dish list information; 112 recipe information; 113 basic cooking parameter information; 114 basic sensory information; 120 second storage unit; 121 cooking status information; 121a image data; 121b chemical analysis data; 121c weight data; 130 third storage unit; 131 sensory information; 131a target sensory information; 131b first sensory information; 131c second sensory information; 131d third sensory information; 140 fourth storage unit; 141 initial cooking parameter information; 142 modified cooking parameter information; 150 model storage unit; 150a learning model set; 151 first learning model set type; 152 the second learning model; 153 the third learning model; 154 the fourth learning model; 200 the operation terminal; 201 the input unit; 202 the display unit; 203 the terminal control unit; 204 the terminal storage unit; 205 the terminal communication unit; 300 the cooker; 303 the cooking control unit; 304 the cooking storage unit; 305 the cooking communication unit; 310 the cooking status acquisition unit; 311 the weight measurement unit; 312 the photographing unit; 313 the chemical analysis unit; 320 the cooking unit; 321 the pressure adjustment unit; 322 the temperature adjustment unit; 323 the time adjustment unit; 1000 the information processing system; 1031 the input acquisition unit; 1032 the parameter acquisition unit; 1033 the sensory information acquisition unit; 1034 the parameter correction unit; 1035 the processing unit; 1037 the parameter output unit.

Claims

1. A control method is a method executed by a computer to control a cooking device for cooking food, comprising: acquiring target sensory information, the target sensory information indicating targets with respect to one or more numerical values ​​related to the sensory properties of a dish obtained by cooking the food to be cooked; acquiring cooking parameter information including one or more cooking parameters for cooking performed by the cooking device based on the acquired target sensory information; acquiring cooking sensory information indicating one or more numerical values ​​related to the sensory perception of the food being cooked when the cooking device is cooking; Based on the target sensory information and the sensory information during cooking, changing the cooking parameter information into modified cooking parameter information including one or more modified cooking parameters; A control signal including the modified cooking parameter information is output.

2. The control method according to claim 1, In the acquisition of sensory information in cooking, acquiring the cooking sensory information at a time point after a first time has passed since the cooking device started cooking as first sensory information, acquiring the cooking sensory information at a time point when a second time has passed after the first time has passed since the start of cooking by the cooking device as second sensory information, In the change of the cooking parameter information, The cooking parameter information is changed to the corrected cooking parameter information based on the difference between the first sensory information and the second sensory information and the target sensory information.

3. The control method according to claim 1, The third sensory information is also acquired, the third sensory information indicating one or more numerical values ​​related to the sensory sense of the dish obtained by cooking the dish according to the corrected cooking parameter information using the cooking device.

4. The control method according to claim 1, In the change of the cooking parameter information, Based on the target sensory information and the sensory information during cooking, a plurality of candidates for the modified cooking parameter information are obtained; A candidate whose difference from the cooking parameter information is less than a threshold or a candidate closest to the cooking parameter information is selected from the plurality of candidates as the corrected cooking parameter information.

5. The control method according to claim 3, In acquiring the third sensory information, the third sensory information is acquired by inputting at least the corrected cooking parameter information into a learning model, The learning model is machine-learned to output, at least for an input of one or more cooking parameters that are changed during cooking by the cooker, one or more numerical values ​​related to the sensory perception of the dish obtained by cooking with the cooker according to the one or more cooking parameters.

6. The control method according to claim 1, In acquiring the sensory information during cooking, the sensory information during cooking is acquired by inputting at least one of an image of the food being cooked, a weight of the food being cooked, and an amount of chemical components contained in the food being cooked into a learning model.

7. The control method according to claim 6, The learning model is machine-learned to output one or more numerical values ​​related to the sensory perception of the one or more ingredients based on at least one input of an image of one or more ingredients being cooked in the cooker, a weight of the one or more ingredients, and a quantity of chemical components contained in the one or more ingredients.

8. The control method according to any one of claims 1 to 7, The one or more cooking parameters include a parameter indicating a temperature used by the cooker for cooking and a parameter indicating a time used by the cooker for cooking.

9. The control method according to claim 8, The one or more cooking parameters further include a parameter indicating a pressure used for cooking by the cooking device.

10. A method for providing information, wherein a computer provides information related to cooking of a food by a cooking device, comprising: receiving target sensory information in accordance with a user operation, the target sensory information indicating a target with respect to one or more numerical values ​​related to a sensory feeling of a dish obtained by cooking the food to be cooked; Outputting final sensory information related to the sensory sense of the dish derived based on the target sensory information and the sensory information during cooking, wherein the sensory information during cooking is information representing one or more numerical values ​​related to the sensory sense of the food being cooked when the cooker is cooking the food.

11. A control system is a system for controlling a cooking device for cooking food, comprising: an input acquisition unit that acquires target sensory information indicating a target with respect to one or more numerical values ​​related to a sensory feeling of a dish obtained by cooking the food to be cooked; a parameter acquisition unit that acquires cooking parameter information including one or more cooking parameters for cooking performed by the cooking device based on the acquired target sensory information; a sensory information acquisition unit configured to acquire cooking sensory information indicating one or more numerical values ​​related to the sensory sense of the food being cooked when the cooking device is cooking; a parameter correction unit, which corrects the cooking parameter information into corrected cooking parameter information including one or more corrected cooking parameters based on the target sensory information and the sensory information during cooking; as well as A parameter output unit outputs a control signal including the modified cooking parameter information to the cooking device.

12. An information providing system is a system for providing information related to cooking of a food by a cooking device, comprising: an input unit that receives target sensory information in response to a user's operation, the target sensory information indicating a target with respect to one or more numerical values ​​related to a sensory feeling of a dish obtained by cooking the food to be cooked; and An output unit outputs final sensory information related to the sensory organ of the dish derived based on the target sensory information and sensory information during cooking, wherein the sensory information during cooking is information representing one or more numerical values ​​related to the sensory organ of the food being cooked when the cooker is cooking the food.

13. A program for controlling a cooking device for cooking food, the program causing a computer to execute: acquiring target sensory information, the target sensory information indicating targets with respect to one or more numerical values ​​related to the sensory properties of a dish obtained by cooking the food to be cooked; acquiring cooking parameter information including one or more cooking parameters for cooking performed by the cooking device based on the acquired target sensory information; acquiring cooking sensory information indicating one or more numerical values ​​related to the sensory perception of the food being cooked when the cooking device is cooking; Based on the target sensory information and the sensory information during cooking, changing the cooking parameter information into modified cooking parameter information including one or more modified cooking parameters; A control signal including the modified cooking parameter information is output.

14. A program for providing information related to cooking of food by a cooking device, causing a computer to execute: receiving target sensory information in accordance with a user operation, the target sensory information indicating a target with respect to one or more numerical values ​​related to a sensory feeling of a dish obtained by cooking the food to be cooked; Outputting final sensory information related to the sensory sense of the dish derived based on the target sensory information and the sensory information during cooking, wherein the sensory information during cooking is information representing one or more numerical values ​​related to the sensory sense of the food being cooked when the cooker is cooking the food.

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