System

Abstract

To provide a system that allows easy preparation of nutritionally balanced meals even in busy households.SOLUTION: A system according to an embodiment comprises: an input unit; a recommendation unit; an ordering unit; a delivery unit; a generation unit; and a cooking unit. The input unit inputs health data. The recommendation unit recommends necessary ingredients and meal plans on the basis of the health data input by the input unit. The ordering unit orders the ingredients recommended by the recommendation unit. The delivery unit delivers the ingredients ordered by the ordering unit. The generation unit generates recipes on the basis of the ingredients delivered by the delivery unit. The cooking unit performs cooking on the basis of the recipes generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques make it difficult for busy families to prepare nutritionally balanced meals, and there is room for improvement.

[0005] The system according to the embodiment aims to make it easy to prepare nutritionally balanced meals even in busy households. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a recommendation unit, an ordering unit, a delivery unit, a generation unit, and a cooking unit. The input unit inputs health data. The recommendation unit recommends necessary ingredients and a menu based on the health data input by the input unit. The ordering unit orders the ingredients recommended by the recommendation unit. The delivery unit delivers the ingredients ordered by the ordering unit. The generation unit generates a recipe based on the ingredients delivered by the delivery unit. The cooking unit cooks based on the recipe generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment allows busy families to easily prepare nutritionally balanced meals. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The help system according to the embodiment of the present invention is an innovative system for working mothers who are busy with work while having growing children and elderly people in their family. This help system is a system that recommends necessary ingredients and menus based on the user's health condition and preferences, automates the procurement of ingredients in cooperation with an order and delivery service, and uses recipes automatically generated by AI to automatically cook and serve the food using a cooking robot equipped with an automatic cooking function. For example, the system recommends necessary ingredients and menus based on the user's health condition and preferences. At this time, the user's health data and ingredient preferences are input. For example, ingredients rich in calcium and vitamin D are recommended for growing children, and ingredients that are easy to digest are recommended for elderly people. This allows a nutritionally balanced menu to be proposed. Next, the recommended ingredients are automatically procured in cooperation with an order and delivery service. For example, the necessary ingredients are ordered online based on the proposed menu, and delivered to the home using a delivery service. This allows the user to obtain the necessary ingredients without having to spend time shopping for ingredients. Furthermore, the cooking robot equipped with an automatic cooking function automatically cooks using recipes automatically generated by AI. For example, AI generates a recipe based on a proposed menu, and the cooking robot cooks according to that recipe. This allows users to save time and effort in cooking. This system can provide meals that take into account nutritional balance for growing children and the elderly, while reducing the burden on busy working mothers. For example, when a user returns home from work, a meal that has already been cooked can be provided immediately. This allows the whole family to eat healthy meals, improving the quality of the user's life. In this way, the assistance system reduces the burden on users and allows the whole family to eat healthy meals.

[0029] The help system according to the embodiment includes an input unit, a recommendation unit, an order unit, a delivery unit, a generation unit, and a cooking unit. The input unit inputs health data of a user. The health data includes, for example, weight, blood pressure, and diet history, but is not limited to such examples. The input unit allows, for example, a user to input health data using a smartphone or a personal computer. The input unit can also automatically acquire health data from a wearable device. The recommendation unit recommends necessary ingredients and a menu based on the health data input by the input unit. For example, the recommendation unit recommends ingredients rich in calcium and vitamin D for growing children and ingredients that are easy to digest for elderly people. The recommendation unit can analyze the user's health data using AI and suggest optimal ingredients and a menu. The order unit orders the ingredients recommended by the recommendation unit online. For example, the order unit can order the necessary ingredients based on the proposed menu at an online shopping site. The order unit can analyze the user's order history using AI and suggest an optimal ordering method. The delivery unit delivers the ingredients ordered by the order unit to the user's home using a delivery service. The delivery unit can deliver ingredients to a home using a delivery service such as a home delivery service or a drone delivery service. The delivery unit can use AI to calculate an optimal delivery route and deliver ingredients quickly and efficiently. The generation unit uses a recipe automatically generated by AI to generate a recipe based on a proposed menu. The generation unit can generate a recipe using, for example, a machine learning algorithm or a natural language processing technology. The generation unit can use AI to generate a recipe that takes into account the user's preferences and the characteristics of ingredients. The cooking unit uses a cooking robot to cook based on the recipe generated by the generation unit. The cooking unit can use, for example, a cooking robot of a specific manufacturer or model to cook. The cooking unit can optimize the operation of the cooking robot using AI to cook efficiently. This allows the help system according to the embodiment to reduce the burden on the user and allow the whole family to eat healthy meals. For example, the output unit notifies the user of a meal that has been cooked and provides the meal.The output unit can notify the user through, for example, a smartphone app or a voice assistant. The output unit can use AI to adjust the timing of meal provision according to the user's schedule.

[0030] The input unit inputs the health data of the user. The health data includes, for example, weight, blood pressure, and diet history, but is not limited to such examples. For example, the user can input the health data into the input unit using a smartphone or a personal computer. The input unit can also automatically acquire health data from a wearable device. Specifically, the user can manually input the daily weight, blood pressure, and dietary contents through a dedicated application on the smartphone or personal computer. This allows the user to record and manage his / her own health condition in detail. In addition, by using a wearable device, the user's health data is automatically collected, eliminating the need to input data. For example, a smart watch or fitness tracker can collect data such as heart rate, number of steps, and calories burned in real time and synchronize it with an application. In addition, these devices can also provide detailed health information such as sleep patterns and stress levels. This allows the input unit to comprehensively grasp the user's health condition and smoothly provide data to the recommendation unit, which is the next step.

[0031] The recommendation unit recommends necessary ingredients and menus based on the health data input by the input unit. For example, the recommendation unit recommends ingredients rich in calcium and vitamin D for growing children and ingredients that are easy to digest for the elderly. The recommendation unit can use AI to analyze the user's health data and suggest optimal ingredients and menus. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's past diet history and health data and suggests nutritionally balanced ingredients and menus. For example, if the user has had a calcium deficiency in the past, the AI ​​preferentially recommends ingredients that are rich in calcium. In addition, the recommendation unit can provide individually customized menus taking into account the user's allergy information and preferences. Furthermore, the recommendation unit can also suggest menus that incorporate seasonal ingredients and local specialties taking into account the characteristics of the season and region. This allows the user to enjoy healthy and delicious meals.

[0032] The ordering unit orders the ingredients recommended by the recommendation unit online. The ordering unit can, for example, order the necessary ingredients on an online shopping site based on the proposed menu. The ordering unit can use AI to analyze the user's order history and suggest the optimal ordering method. Specifically, the AI ​​analyzes the user's past order data and identifies frequently purchased ingredients and favorite brands. This helps the user efficiently order the ingredients they need at one time. In addition, the AI ​​has a price comparison function and can present the most cost-effective option from multiple online shopping sites. Furthermore, the ordering unit can provide a subscription service and automatically order the ingredients that the user needs on a regular basis. This allows the user to significantly reduce the effort required to order ingredients.

[0033] The delivery unit delivers the food ordered by the ordering unit to the home using a delivery service. The delivery unit can deliver the food to the home using a delivery service such as a courier or drone delivery. The delivery unit can use AI to calculate the optimal delivery route and deliver the food quickly and efficiently. Specifically, the AI ​​analyzes traffic conditions and weather information in real time and selects the most efficient delivery route. This shortens the delivery time and keeps the food fresh. The delivery unit can also adjust the delivery time to match the user's schedule. For example, if the user is not at home, the AI ​​suggests the optimal redelivery time, improving the user's convenience. In addition, the delivery unit also has a function to track the delivery status in real time and notify the user. This allows the user to understand the progress of the delivery and receive the food with peace of mind.

[0034] The generation unit uses recipes automatically generated by AI to generate recipes based on the proposed menu. The generation unit can generate recipes using, for example, machine learning algorithms and natural language processing technology. The generation unit can use AI to generate recipes that take into account the user's preferences and the characteristics of ingredients. Specifically, the AI ​​analyzes the user's past meal history and preference data to generate individually customized recipes. For example, if the user likes a particular ingredient, the AI ​​can suggest a new recipe that uses that ingredient. The AI ​​can also generate balanced and healthy recipes by taking into account the nutritional value and cooking method of the ingredients. Furthermore, the generation unit can also suggest appropriate recipes by taking into account the user's cooking skills and available cooking utensils. This allows users to easily find recipes that suit them and enjoy healthy meals.

[0035] In the cooking unit, the cooking robot cooks based on the recipe generated by the generation unit. For example, the cooking unit can use a cooking robot of a specific manufacturer or model to cook. The cooking unit can use AI to optimize the operation of the cooking robot and cook efficiently. Specifically, the AI ​​controls the operation of the cooking robot based on the recipe and accurately adjusts the cutting method of ingredients, heating time, amount of seasoning, etc. This makes it possible to maintain a consistent quality of cooking and provide delicious meals to users. In addition, the cooking unit has the function of cooking multiple dishes at the same time, which can save the user's time. Furthermore, the cooking unit can monitor the cooking situation in real time and make adjustments as necessary. For example, a sensor can detect the doneness of ingredients and stewed condition, and end cooking at the optimal timing. This allows the cooking unit to reduce the burden on the user and allow the whole family to eat healthy meals.

[0036] The output unit notifies the user that the meal has been cooked and serves the meal. The output unit can notify the user, for example, through a smartphone app or a voice assistant. The output unit can use AI to adjust the timing of meal serving to match the user's schedule. Specifically, the AI ​​works with the user's calendar and schedule app to calculate the optimal meal serving time. This allows the user to eat meals at the appropriate time even in their busy daily lives. The output unit also has a function of providing nutritional and calorie information about the meal, making it easier for the user to manage their health. Furthermore, the output unit can not only notify the user that the meal is ready, but also provide advice on how to store and reheat the meal. This allows the user to enjoy the meal in the optimal condition.

[0037] The input unit can input the user's health condition and preferences. The input unit can allow the user to input the health condition and preferences using, for example, a smartphone or a computer. The health condition includes, for example, medical history, current health condition, etc., but is not limited to such examples. The preferences include, for example, favorite ingredients, disliked ingredients, etc., but is not limited to such examples. The input unit can analyze the user's input data using AI and suggest an optimal input method. For example, the input unit can suggest an efficient input method based on data previously input by the user. In addition, the input unit can customize the input form based on the user's preferences. This makes it possible to recommend more appropriate ingredients and menus based on the user's health condition and preferences.

[0038] The recommendation unit can recommend food ingredients rich in calcium and vitamin D to growing children and food ingredients easy to digest to elderly people based on the input health data. The recommendation unit, for example, recommends food ingredients rich in calcium and vitamin D to growing children. The growing children include, for example, age groups from 6 to 12 years old, but are not limited to such examples. The recommendation unit, for example, recommends food ingredients easy to digest to elderly people. The elderly include, for example, age groups 65 years old and older, but are not limited to such examples. The recommendation unit can use AI to analyze the user's health data and recommend optimal food ingredients. For example, the recommendation unit can recommend nutritionally balanced food ingredients based on the user's health data. In addition, the recommendation unit can select food ingredients based on the user's preferences. As a result, a nutritionally balanced meal can be provided by recommending food ingredients suitable for growing children and elderly people.

[0039] The ordering unit can order the necessary ingredients online based on the recommended menu. The ordering unit can, for example, order the necessary ingredients on an online shopping site based on the proposed menu. A specific website or app can be used when ordering online. The ordering unit can use AI to analyze the user's order history and suggest an optimal ordering method. For example, the ordering unit can suggest an efficient ordering method based on the user's past order data. In addition, the ordering unit can customize the order content based on the user's preferences. This can reduce the user's effort by automatically ordering ingredients based on the recommended menu.

[0040] The delivery unit can deliver the ordered ingredients to the home using a delivery service. The delivery unit can deliver the ingredients to the home using a delivery service such as a home delivery service or drone delivery. Delivery services include, but are not limited to, specific home delivery companies and drone delivery services. The delivery unit can use AI to calculate an optimal delivery route and deliver the ingredients quickly and efficiently. For example, the delivery unit can calculate an optimal delivery route based on the user's address information. In addition, the delivery unit can adjust the timing of delivery and deliver the ingredients according to the user's schedule. In this way, the ordered ingredients can be automatically delivered, thereby reducing the user's shopping effort.

[0041] The generation unit can generate a recipe based on the proposed menu using a recipe generated by AI. The generation unit can generate a recipe using, for example, a machine learning algorithm or a natural language processing technology. The recipe generated by AI includes, for example, ingredients, cooking steps, cooking time, etc., but is not limited to such examples. The generation unit can generate a recipe that takes into account the user's preferences and the characteristics of ingredients using AI. For example, the generation unit can generate a nutritionally balanced recipe based on the user's health data. In addition, the generation unit can customize the contents of the recipe based on the user's preferences. This allows the recipe to be efficiently generated by using a recipe automatically generated by AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health data into the generation AI and cause the generation AI to generate a recipe.

[0042] The cooking unit allows the cooking robot to cook according to the generated recipe. The cooking unit can, for example, use a cooking robot of a specific manufacturer or model to cook. The cooking robot has cooking functions such as frying, boiling, and baking, but is not limited to such examples. The cooking unit can optimize the operation of the cooking robot using AI to cook efficiently. For example, the cooking unit can control the operation of the cooking robot based on the generated recipe. In addition, the cooking unit can monitor the operation of the cooking robot in real time and make adjustments as necessary. This allows the cooking robot to automatically cook, thereby reducing the user's effort in cooking. Some or all of the above-mentioned processing in the cooking unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooking unit can input the generated recipe to the generation AI and have the generation AI control the operation of the cooking robot.

[0043] The input unit can analyze the user's past health data and select the optimal input method. The input unit can, for example, analyze the user's past health data and select the optimal input method. The past health data includes, for example, data from the past year, but is not limited to such an example. The input unit can analyze the user's past health data using AI and suggest the optimal input method. For example, the input unit can analyze the pattern of health data input by the user in the past and suggest the most efficient input method. In addition, the input unit can preferentially suggest the input method (voice, text, etc.) used by the user in the past. Furthermore, the input unit can analyze the tendency to input during a specific time period from the user's past health data and prompt the user to input during that time period. In this way, the optimal input method can be provided to the user by analyzing the past health data.

[0044] The input unit can filter the health data based on the user's current living situation and diet history when inputting the health data. The input unit can filter the health data based on the user's current living situation and diet history when inputting the health data, for example. The current living situation includes, for example, work situation, exercise habits, etc., but is not limited to such examples. The diet history includes, for example, diet history for the past week, but is not limited to such examples. The input unit can analyze the user's current living situation and diet history using AI, and preferentially input highly relevant data. For example, the input unit can filter the health data to be input in consideration of the user's current living situation. In addition, the input unit can preferentially input highly relevant health data based on the user's past diet history. Furthermore, the input unit can adjust the level of detail of the health data to be input according to the user's current living situation. As a result, more relevant data can be input by filtering the data based on the current living situation and diet history.

[0045] The input unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting health data. For example, the input unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting health data. The geographical location information includes, for example, a city, a region, a country, etc., but is not limited to such examples. The input unit can analyze the geographical location information of the user using AI and preferentially input highly relevant data. For example, when the user lives in a specific region, the input unit can preferentially input data related to the health risks of the region. In addition, when the user is traveling, the input unit can preferentially input data related to the health risks of the travel destination. Furthermore, when the user lives in a specific climate condition, the input unit can preferentially input health data related to the climate condition. Thereby, by considering the geographical location information, health data related to the region can be preferentially input.

[0046] The input unit can analyze the user's social media activity and input related data when inputting health data. The input unit can, for example, analyze the user's social media activity and input related data when inputting health data. Social media activity includes, for example, the content of posts, the number of likes, comments, etc., but is not limited to such examples. The input unit can use AI to analyze the user's social media activity and input related data. For example, the input unit can input related data based on health information shared by the user on social media. In addition, the input unit can analyze health-related interests from the user's social media activity and input related data. Furthermore, the input unit can input related data based on information on health experts the user follows on social media. In this way, health data based on the user's interests can be input by analyzing social media activity.

[0047] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the ingredient at the time of recommendation. For example, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the ingredient at the time of recommendation. The importance of the ingredient includes, for example, nutritional value, allergy information, etc., but is not limited to such examples. The recommendation unit can use AI to analyze the importance of the ingredient and adjust the level of detail of the recommendation. For example, the recommendation unit can provide detailed information for ingredients that contain important nutrients. In addition, the recommendation unit can provide concise information for common ingredients. Furthermore, the recommendation unit can make detailed recommendations for ingredients related to a specific health condition. As a result, important information can be provided preferentially by adjusting the level of detail of the recommendation based on the importance of the ingredient.

[0048] The recommendation unit can apply different recommendation algorithms depending on the category of ingredients when making a recommendation. For example, the recommendation unit can apply different recommendation algorithms depending on the category of ingredients when making a recommendation. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The recommendation unit can use AI to analyze the category of ingredients and apply the optimal recommendation algorithm. For example, the recommendation unit can make recommendations based on nutritional value for vegetables and fruits. In addition, the recommendation unit can make recommendations based on cooking methods for meat and fish. Furthermore, the recommendation unit can make recommendations based on ingredient labels for processed foods. This allows more appropriate recommendations to be made by applying recommendation algorithms according to the category of ingredients.

[0049] The recommendation unit can determine the priority of recommendation based on the time of submission of ingredients at the time of recommendation. The recommendation unit can, for example, determine the priority of recommendation based on the time of submission of ingredients at the time of recommendation. The time of submission of ingredients includes, for example, harvest time, expiration date, etc., but is not limited to such examples. The recommendation unit can use AI to analyze the time of submission of ingredients and determine the priority of recommendation. For example, the recommendation unit can give priority to recommending ingredients that need to be consumed quickly. In addition, the recommendation unit can postpone ingredients that can be stored for a long time. Furthermore, the recommendation unit can give priority to recommending seasonal ingredients. In this way, by determining the priority of recommendation based on the time of submission of ingredients, ingredients can be recommended at the appropriate time.

[0050] The recommendation unit can adjust the order of recommendation based on the relevance of ingredients at the time of recommendation. The recommendation unit can, for example, adjust the order of recommendation based on the relevance of ingredients at the time of recommendation. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The recommendation unit can use AI to analyze the relevance of ingredients and adjust the order of recommendation. For example, the recommendation unit can recommend ingredients of the same category together. Also, the recommendation unit can recommend ingredients with similar cooking methods together. Furthermore, the recommendation unit can recommend ingredients with similar nutritional values ​​together. This allows for more efficient recommendation by adjusting the order of recommendation based on the relevance of ingredients.

[0051] The ordering unit can adjust the level of detail of the order based on the importance of the ingredients when placing an order. The ordering unit can, for example, adjust the level of detail of the order based on the importance of the ingredients when placing an order. The importance of ingredients includes, for example, nutritional value, allergy information, and the like, but is not limited to such examples. The ordering unit can use AI to analyze the importance of ingredients and adjust the level of detail of the order. For example, the ordering unit can provide detailed information for ingredients that contain important nutrients. Also, the ordering unit can provide concise information for common ingredients. Furthermore, the ordering unit can provide detailed order information for ingredients related to a specific health condition. In this way, important information can be provided preferentially by adjusting the level of detail of the order based on the importance of the ingredients.

[0052] The ordering unit can apply different ordering algorithms depending on the category of ingredients when placing an order. The ordering unit can, for example, apply different ordering algorithms depending on the category of ingredients when placing an order. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The ordering unit can use AI to analyze the category of ingredients and apply the optimal ordering algorithm. For example, the ordering unit can place an order based on nutritional value for vegetables and fruits. Also, the ordering unit can place an order based on cooking method for meat and fish. Furthermore, the ordering unit can place an order based on ingredient labeling for processed foods. This allows for more appropriate ordering by applying an ordering algorithm according to the category of ingredients.

[0053] The ordering section can determine the priority of the order based on the time of submission of the ingredients at the time of ordering. The ordering section can, for example, determine the priority of the order based on the time of submission of the ingredients at the time of ordering. The time of submission of the ingredients includes, for example, the harvest season, the expiration date, and the like, but is not limited to such examples. The ordering section can use AI to analyze the time of submission of the ingredients and determine the priority of the order. For example, the ordering section can prioritize ordering ingredients that need to be consumed quickly. In addition, the ordering section can postpone ordering ingredients that can be stored for a long time. Furthermore, the ordering section can prioritize ordering ingredients that are limited to a certain season. In this way, by determining the priority of the order based on the time of submission of the ingredients, ingredients can be ordered at the appropriate time.

[0054] The ordering unit can adjust the order order based on the relevance of ingredients when placing an order. The ordering unit can, for example, adjust the order order based on the relevance of ingredients when placing an order. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The ordering unit can use AI to analyze the relevance of ingredients and adjust the order order. For example, the ordering unit can order ingredients of the same category together. Also, the ordering unit can order ingredients that have similar cooking methods together. Furthermore, the ordering unit can order ingredients that have similar nutritional values ​​together. This allows for more efficient ordering by adjusting the order order based on the relevance of ingredients.

[0055] The delivery unit can adjust the level of detail of the delivery based on the importance of the ingredients at the time of delivery. The delivery unit can, for example, adjust the level of detail of the delivery based on the importance of the ingredients at the time of delivery. The importance of the ingredients includes, for example, nutritional value, allergy information, etc., but is not limited to such examples. The delivery unit can use AI to analyze the importance of the ingredients and adjust the level of detail of the delivery. For example, the delivery unit can provide detailed delivery information for ingredients that contain important nutrients. In addition, the delivery unit can provide concise delivery information for general ingredients. Furthermore, the delivery unit can provide detailed delivery information for ingredients related to a specific health condition. In this way, important information can be provided preferentially by adjusting the level of detail of the delivery based on the importance of the ingredients.

[0056] The delivery unit can apply different delivery algorithms depending on the category of ingredients at the time of delivery. The delivery unit can apply different delivery algorithms depending on the category of ingredients at the time of delivery. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The delivery unit can use AI to analyze the category of ingredients and apply the optimal delivery algorithm. For example, the delivery unit can apply a delivery method for preserving freshness to vegetables and fruits. In addition, the delivery unit can apply a refrigerated / frozen delivery method to meat and fish. Furthermore, the delivery unit can apply a normal delivery method to processed foods. This enables more appropriate delivery by applying a delivery algorithm according to the category of ingredients.

[0057] The delivery unit can determine the priority of delivery based on the time of submission of ingredients at the time of delivery. The delivery unit can, for example, determine the priority of delivery based on the time of submission of ingredients at the time of delivery. The time of submission of ingredients includes, for example, the harvest season, the expiration date, etc., but is not limited to such examples. The delivery unit can use AI to analyze the time of submission of ingredients and determine the priority of delivery. For example, the delivery unit can prioritize delivery of ingredients that need to be consumed quickly. In addition, the delivery unit can postpone delivery of ingredients that can be stored for a long time. Furthermore, the delivery unit can prioritize delivery of seasonal ingredients. In this way, by determining the priority of delivery based on the time of submission of ingredients, ingredients can be delivered at the appropriate time.

[0058] The delivery unit can adjust the order of delivery based on the relevance of ingredients at the time of delivery. The delivery unit can, for example, adjust the order of delivery based on the relevance of ingredients at the time of delivery. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The delivery unit can use AI to analyze the relevance of ingredients and adjust the order of delivery. For example, the delivery unit can deliver ingredients of the same category together. Also, the delivery unit can deliver ingredients that have similar cooking methods together. Furthermore, the delivery unit can deliver ingredients that have similar nutritional values ​​together. This allows for more efficient delivery by adjusting the order of delivery based on the relevance of ingredients.

[0059] The generation unit can adjust the level of detail of the recipe based on the importance of the ingredients when generating the recipe. The generation unit can adjust the level of detail of the recipe based on the importance of the ingredients when generating the recipe, for example. The importance of the ingredients includes, but is not limited to, for example, nutritional value and allergy information. The generation unit can use AI to analyze the importance of the ingredients and adjust the level of detail of the recipe. For example, the generation unit can generate a detailed recipe for ingredients that contain important nutrients. In addition, the generation unit can generate a concise recipe for common ingredients. Furthermore, the generation unit can generate a detailed recipe for ingredients related to a specific health condition. Thereby, important information can be preferentially provided by adjusting the level of detail of the recipe based on the importance of the ingredients.

[0060] The generation unit can apply different recipe generation algorithms depending on the category of ingredients when generating a recipe. The generation unit can apply different recipe generation algorithms depending on the category of ingredients when generating a recipe. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The generation unit can use AI to analyze the category of ingredients and apply an optimal recipe generation algorithm. For example, the generation unit can generate recipes based on nutritional value for vegetables and fruits. Furthermore, the generation unit can generate recipes based on cooking methods for meat and fish. Furthermore, the generation unit can generate recipes based on ingredient labels for processed foods. In this way, a more appropriate recipe is generated by applying a recipe generation algorithm according to the category of ingredients.

[0061] The generation unit can determine the priority of recipe generation based on the time of submission of ingredients when generating a recipe. The generation unit can determine the priority of recipe generation based on the time of submission of ingredients when generating a recipe, for example. The time of submission of ingredients includes, but is not limited to, the harvest season, the expiration date, and the like. The generation unit can use AI to analyze the time of submission of ingredients and determine the priority of recipe generation. For example, the generation unit can generate recipes with priority for ingredients that need to be consumed quickly. In addition, the generation unit can postpone the generation of ingredients that can be stored for a long time. Furthermore, the generation unit can generate recipes with priority for seasonal ingredients. In this way, by determining the priority of recipe generation based on the time of submission of ingredients, recipes can be generated at the appropriate time.

[0062] The generation unit can adjust the order of recipe generation based on the relevance of ingredients when generating a recipe. The generation unit can adjust the order of recipe generation based on the relevance of ingredients when generating a recipe, for example. The relevance of ingredients includes, but is not limited to, for example, nutrient combinations and commonalities in cooking methods. The generation unit can use AI to analyze the relevance of ingredients and adjust the order of recipe generation. For example, the generation unit can generate a recipe by grouping ingredients of the same category together. In addition, the generation unit can generate a recipe by grouping ingredients with similar cooking methods together. Furthermore, the generation unit can generate a recipe by grouping ingredients with similar nutritional values ​​together. This allows more efficient recipe generation by adjusting the order of recipe generation based on the relevance of ingredients.

[0063] The cooking unit can adjust the level of detail of cooking based on the importance of ingredients when cooking. The cooking unit can adjust the level of detail of cooking based on the importance of ingredients when cooking, for example. The importance of ingredients includes, but is not limited to, for example, nutritional value, allergy information, and the like. The cooking unit can use AI to analyze the importance of ingredients and adjust the level of detail of cooking. For example, the cooking unit can provide detailed cooking instructions for ingredients that contain important nutrients. In addition, the cooking unit can provide concise cooking instructions for common ingredients. Furthermore, the cooking unit can provide detailed cooking instructions for ingredients related to a specific health condition. Thereby, important information can be provided preferentially by adjusting the level of detail of cooking based on the importance of ingredients.

[0064] The cooking unit can apply different cooking algorithms depending on the category of ingredients when cooking. For example, the cooking unit can apply different cooking algorithms depending on the category of ingredients when cooking. The categories of ingredients include, but are not limited to, for example, vegetables, fruits, and meats. The cooking unit can use AI to analyze the category of ingredients and apply the optimal cooking algorithm. For example, the cooking unit can provide cooking instructions based on nutritional value for vegetables and fruits. In addition, the cooking unit can provide cooking instructions based on cooking methods for meat and fish. Furthermore, the cooking unit can provide cooking instructions based on ingredient labels for processed foods. This allows for more appropriate cooking by applying cooking algorithms according to the category of ingredients.

[0065] The cooking unit can determine the priority of cooking based on the time of submission of ingredients when cooking. The cooking unit can determine the priority of cooking based on the time of submission of ingredients when cooking, for example. The time of submission of ingredients includes, but is not limited to, the harvest season, the expiration date, and the like. The cooking unit can use AI to analyze the time of submission of ingredients and determine the priority of cooking. For example, the cooking unit can prioritize cooking of ingredients that need to be consumed quickly. In addition, the cooking unit can postpone cooking of ingredients that can be stored for a long time. Furthermore, the cooking unit can prioritize cooking of seasonal ingredients. In this way, by determining the priority of cooking based on the time of submission of ingredients, cooking can be done at the appropriate time.

[0066] The cooking unit can adjust the cooking order based on the relevance of ingredients when cooking. The cooking unit can adjust the cooking order based on the relevance of ingredients when cooking, for example. The relevance of ingredients includes, but is not limited to, for example, nutrient combinations and commonalities in cooking methods. The cooking unit can use AI to analyze the relevance of ingredients and adjust the cooking order. For example, the cooking unit can cook ingredients of the same category together. Also, the cooking unit can cook ingredients that have similar cooking methods together. Furthermore, the cooking unit can cook ingredients that have similar nutritional values ​​together. This allows for more efficient cooking by adjusting the cooking order based on the relevance of ingredients.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The assistance system can also analyze the user's life rhythm and adjust the timing of ordering ingredients based on the analyzed life rhythm. For example, if the user has a habit of shopping on a specific day of the week, ingredients can be ordered according to that day. Also, if the user is busy during a specific time period, orders can be placed avoiding that time period. Furthermore, if the user is traveling, ingredients can be delivered to the travel destination. This allows for flexible ingredient ordering to suit the user's life rhythm.

[0069] The input unit can also analyze the user's past meal history and complement the input of health data based on the analyzed meal history. For example, the current health data can be complemented based on ingredients and nutrients that the user has taken in the past. The input can also be complemented so that the user can avoid ingredients that the user has had an allergic reaction to in the past. Furthermore, ingredients that the user has liked to eat in the past can be preferentially suggested. This makes it possible to input health data with higher accuracy by utilizing the past meal history.

[0070] The ordering unit can also analyze the user's past order history and optimize the order contents based on the analyzed order history. For example, it can preferentially suggest ingredients that the user has frequently ordered in the past. It can also suggest ingredients that the user has ordered in the past that have given the user a particularly high level of satisfaction. It can also adjust the order contents to avoid ingredients that the user has ordered in the past that have caused allergic reactions. This makes it possible to make orders that provide a higher level of satisfaction by utilizing the past order history.

[0071] The generation unit can also analyze the user's past recipe usage history and optimize recipe generation based on the analyzed history. For example, it can preferentially suggest recipes that the user has used in the past with a preference. It can also suggest again recipes that the user has used in the past with a particularly high level of satisfaction. Furthermore, it can generate recipes that avoid recipes that the user has used in the past with which they have had an allergic reaction. This makes it possible to generate recipes with a higher level of satisfaction by utilizing the user's past recipe usage history.

[0072] The input unit can also analyze the geographical location information of the user and complement the input of health data based on the analyzed location information. For example, if the user lives in a specific area, data related to health risks in that area can be input preferentially. Also, if the user is traveling, data related to health risks at the travel destination can be input preferentially. Furthermore, if the user lives in a specific climate condition, health data related to that climate condition can be input preferentially. This makes it possible to input more relevant health data by utilizing geographical location information.

[0073] The ordering unit can also analyze the user's past order history and adjust the frequency of orders based on the analyzed history. For example, it can periodically suggest ingredients that the user has frequently ordered in the past. It can also suggest ingredients that the user has ordered in the past that have given the user a particularly high level of satisfaction. It can also adjust the order content to avoid ingredients that the user has ordered in the past that have caused an allergic reaction. This makes it possible to make orders that provide a higher level of satisfaction by utilizing the past order history.

[0074] The process flow of the first embodiment will be briefly described below.

[0075] Step 1: The input unit inputs the user's health data. The health data includes, for example, weight, blood pressure, and diet history. The input unit allows the user to input the health data using a smartphone or a computer, and can also automatically obtain the health data from a wearable device. Step 2: The recommendation unit recommends necessary ingredients and menus based on the health data entered by the input unit. The recommendation unit recommends ingredients rich in calcium and vitamin D for growing children and easy-to-digest ingredients for the elderly. The recommendation unit uses AI to analyze the user's health data and suggest optimal ingredients and menus. Step 3: The ordering department orders the ingredients recommended by the recommendation department online. The ordering department can order the necessary ingredients based on the suggested menu at an online shopping site. The ordering department uses AI to analyze the user's order history and suggests the optimal ordering method. Step 4: The delivery department delivers the food ordered by the ordering department to the customer's home using a delivery service. The delivery department can deliver the food to the customer's home using a delivery service such as a courier or drone delivery. The delivery department uses AI to calculate the optimal delivery route and delivers the food quickly and efficiently. Step 5: The generation unit uses the recipes automatically generated by AI to generate recipes based on the proposed menu. The generation unit can generate recipes using machine learning algorithms and natural language processing technology. The generation unit generates recipes that take into account the user's preferences and the characteristics of ingredients. Step 6: The cooking unit uses the cooking robot to cook based on the recipe generated by the generation unit. The cooking unit can use a cooking robot of a specific manufacturer and model to cook. The cooking unit uses AI to optimize the operation of the cooking robot and cook efficiently.

[0076] (Example 2) The help system according to the embodiment of the present invention is an innovative system for working mothers who are busy with work while having growing children and elderly people in their family. This help system is a system that recommends necessary ingredients and menus based on the user's health condition and preferences, automates the procurement of ingredients in cooperation with an order and delivery service, and uses recipes automatically generated by AI to automatically cook and serve the food using a cooking robot equipped with an automatic cooking function. For example, the system recommends necessary ingredients and menus based on the user's health condition and preferences. At this time, the user's health data and ingredient preferences are input. For example, ingredients rich in calcium and vitamin D are recommended for growing children, and ingredients that are easy to digest are recommended for elderly people. This allows a nutritionally balanced menu to be proposed. Next, the recommended ingredients are automatically procured in cooperation with an order and delivery service. For example, the necessary ingredients are ordered online based on the proposed menu, and delivered to the home using a delivery service. This allows the user to obtain the necessary ingredients without having to spend time shopping for ingredients. Furthermore, the cooking robot equipped with an automatic cooking function automatically cooks using recipes automatically generated by AI. For example, AI generates a recipe based on a proposed menu, and the cooking robot cooks according to that recipe. This allows users to save time and effort in cooking. This system can provide meals that take into account nutritional balance for growing children and the elderly, while reducing the burden on busy working mothers. For example, when a user returns home from work, a meal that has already been cooked can be provided immediately. This allows the whole family to eat healthy meals, improving the quality of the user's life. In this way, the assistance system reduces the burden on users and allows the whole family to eat healthy meals.

[0077] The help system according to the embodiment includes an input unit, a recommendation unit, an order unit, a delivery unit, a generation unit, and a cooking unit. The input unit inputs health data of a user. The health data includes, for example, weight, blood pressure, and diet history, but is not limited to such examples. The input unit allows, for example, a user to input health data using a smartphone or a personal computer. The input unit can also automatically acquire health data from a wearable device. The recommendation unit recommends necessary ingredients and a menu based on the health data input by the input unit. For example, the recommendation unit recommends ingredients rich in calcium and vitamin D for growing children and ingredients that are easy to digest for elderly people. The recommendation unit can analyze the user's health data using AI and suggest optimal ingredients and a menu. The order unit orders the ingredients recommended by the recommendation unit online. For example, the order unit can order the necessary ingredients based on the proposed menu at an online shopping site. The order unit can analyze the user's order history using AI and suggest an optimal ordering method. The delivery unit delivers the ingredients ordered by the order unit to the user's home using a delivery service. The delivery unit can deliver ingredients to a home using a delivery service such as a home delivery service or a drone delivery service. The delivery unit can use AI to calculate an optimal delivery route and deliver ingredients quickly and efficiently. The generation unit uses a recipe automatically generated by AI to generate a recipe based on a proposed menu. The generation unit can generate a recipe using, for example, a machine learning algorithm or a natural language processing technology. The generation unit can use AI to generate a recipe that takes into account the user's preferences and the characteristics of ingredients. The cooking unit uses a cooking robot to cook based on the recipe generated by the generation unit. The cooking unit can use, for example, a cooking robot of a specific manufacturer or model to cook. The cooking unit can optimize the operation of the cooking robot using AI to cook efficiently. This allows the help system according to the embodiment to reduce the burden on the user and allow the whole family to eat healthy meals. For example, the output unit notifies the user of a meal that has been cooked and provides the meal.The output unit can notify the user through, for example, a smartphone app or a voice assistant. The output unit can use AI to adjust the timing of meal provision according to the user's schedule.

[0078] The input unit inputs the health data of the user. The health data includes, for example, weight, blood pressure, and diet history, but is not limited to such examples. For example, the user can input the health data into the input unit using a smartphone or a personal computer. The input unit can also automatically acquire health data from a wearable device. Specifically, the user can manually input the daily weight, blood pressure, and dietary contents through a dedicated application on the smartphone or personal computer. This allows the user to record and manage his / her own health condition in detail. In addition, by using a wearable device, the user's health data is automatically collected, eliminating the need to input data. For example, a smart watch or fitness tracker can collect data such as heart rate, number of steps, and calories burned in real time and synchronize it with an application. In addition, these devices can also provide detailed health information such as sleep patterns and stress levels. This allows the input unit to comprehensively grasp the user's health condition and smoothly provide data to the recommendation unit, which is the next step.

[0079] The recommendation unit recommends necessary ingredients and menus based on the health data input by the input unit. For example, the recommendation unit recommends ingredients rich in calcium and vitamin D for growing children and ingredients that are easy to digest for the elderly. The recommendation unit can use AI to analyze the user's health data and suggest optimal ingredients and menus. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's past diet history and health data and suggests nutritionally balanced ingredients and menus. For example, if the user has had a calcium deficiency in the past, the AI ​​preferentially recommends ingredients that are rich in calcium. In addition, the recommendation unit can provide individually customized menus taking into account the user's allergy information and preferences. Furthermore, the recommendation unit can also suggest menus that incorporate seasonal ingredients and local specialties taking into account the characteristics of the season and region. This allows the user to enjoy healthy and delicious meals.

[0080] The ordering unit orders the ingredients recommended by the recommendation unit online. The ordering unit can, for example, order the necessary ingredients on an online shopping site based on the proposed menu. The ordering unit can use AI to analyze the user's order history and suggest the optimal ordering method. Specifically, the AI ​​analyzes the user's past order data and identifies frequently purchased ingredients and favorite brands. This helps the user efficiently order the ingredients they need at one time. In addition, the AI ​​has a price comparison function and can present the most cost-effective option from multiple online shopping sites. Furthermore, the ordering unit can provide a subscription service and automatically order the ingredients that the user needs on a regular basis. This allows the user to significantly reduce the effort required to order ingredients.

[0081] The delivery unit delivers the food ordered by the ordering unit to the home using a delivery service. The delivery unit can deliver the food to the home using a delivery service such as a courier or drone delivery. The delivery unit can use AI to calculate the optimal delivery route and deliver the food quickly and efficiently. Specifically, the AI ​​analyzes traffic conditions and weather information in real time and selects the most efficient delivery route. This shortens the delivery time and keeps the food fresh. The delivery unit can also adjust the delivery time to match the user's schedule. For example, if the user is not at home, the AI ​​suggests the optimal redelivery time, improving the user's convenience. In addition, the delivery unit also has a function to track the delivery status in real time and notify the user. This allows the user to understand the progress of the delivery and receive the food with peace of mind.

[0082] The generation unit uses recipes automatically generated by AI to generate recipes based on the proposed menu. The generation unit can generate recipes using, for example, machine learning algorithms and natural language processing technology. The generation unit can use AI to generate recipes that take into account the user's preferences and the characteristics of ingredients. Specifically, the AI ​​analyzes the user's past meal history and preference data to generate individually customized recipes. For example, if the user likes a particular ingredient, the AI ​​can suggest a new recipe that uses that ingredient. The AI ​​can also generate balanced and healthy recipes by taking into account the nutritional value and cooking method of the ingredients. Furthermore, the generation unit can also suggest appropriate recipes by taking into account the user's cooking skills and available cooking utensils. This allows users to easily find recipes that suit them and enjoy healthy meals.

[0083] In the cooking unit, the cooking robot cooks based on the recipe generated by the generation unit. For example, the cooking unit can use a cooking robot of a specific manufacturer or model to cook. The cooking unit can use AI to optimize the operation of the cooking robot and cook efficiently. Specifically, the AI ​​controls the operation of the cooking robot based on the recipe and accurately adjusts the cutting method of ingredients, heating time, amount of seasoning, etc. This makes it possible to maintain a consistent quality of cooking and provide delicious meals to users. In addition, the cooking unit has the function of cooking multiple dishes at the same time, which can save the user's time. Furthermore, the cooking unit can monitor the cooking situation in real time and make adjustments as necessary. For example, a sensor can detect the doneness of ingredients and stewed condition, and end cooking at the optimal timing. This allows the cooking unit to reduce the burden on the user and allow the whole family to eat healthy meals.

[0084] The output unit notifies the user that the meal has been cooked and serves the meal. The output unit can notify the user, for example, through a smartphone app or a voice assistant. The output unit can use AI to adjust the timing of meal serving to match the user's schedule. Specifically, the AI ​​works with the user's calendar and schedule app to calculate the optimal meal serving time. This allows the user to eat meals at the appropriate time even in their busy daily lives. The output unit also has a function of providing nutritional and calorie information about the meal, making it easier for the user to manage their health. Furthermore, the output unit can not only notify the user that the meal is ready, but also provide advice on how to store and reheat the meal. This allows the user to enjoy the meal in the optimal condition.

[0085] The input unit can input the user's health condition and preferences. The input unit can allow the user to input the health condition and preferences using, for example, a smartphone or a computer. The health condition includes, for example, medical history, current health condition, etc., but is not limited to such examples. The preferences include, for example, favorite ingredients, disliked ingredients, etc., but is not limited to such examples. The input unit can analyze the user's input data using AI and suggest an optimal input method. For example, the input unit can suggest an efficient input method based on data previously input by the user. In addition, the input unit can customize the input form based on the user's preferences. This makes it possible to recommend more appropriate ingredients and menus based on the user's health condition and preferences.

[0086] The recommendation unit can recommend food ingredients rich in calcium and vitamin D to growing children and food ingredients easy to digest to elderly people based on the input health data. The recommendation unit, for example, recommends food ingredients rich in calcium and vitamin D to growing children. The growing children include, for example, age groups from 6 to 12 years old, but are not limited to such examples. The recommendation unit, for example, recommends food ingredients easy to digest to elderly people. The elderly include, for example, age groups 65 years old and older, but are not limited to such examples. The recommendation unit can use AI to analyze the user's health data and recommend optimal food ingredients. For example, the recommendation unit can recommend nutritionally balanced food ingredients based on the user's health data. In addition, the recommendation unit can select food ingredients based on the user's preferences. As a result, a nutritionally balanced meal can be provided by recommending food ingredients suitable for growing children and elderly people.

[0087] The ordering unit can order the necessary ingredients online based on the recommended menu. The ordering unit can, for example, order the necessary ingredients on an online shopping site based on the proposed menu. A specific website or app can be used when ordering online. The ordering unit can use AI to analyze the user's order history and suggest an optimal ordering method. For example, the ordering unit can suggest an efficient ordering method based on the user's past order data. In addition, the ordering unit can customize the order content based on the user's preferences. This can reduce the user's effort by automatically ordering ingredients based on the recommended menu.

[0088] The delivery unit can deliver the ordered ingredients to the home using a delivery service. The delivery unit can deliver the ingredients to the home using a delivery service such as a home delivery service or drone delivery. Delivery services include, but are not limited to, specific home delivery companies and drone delivery services. The delivery unit can use AI to calculate an optimal delivery route and deliver the ingredients quickly and efficiently. For example, the delivery unit can calculate an optimal delivery route based on the user's address information. In addition, the delivery unit can adjust the timing of delivery and deliver the ingredients according to the user's schedule. In this way, the ordered ingredients can be automatically delivered, thereby reducing the user's shopping effort.

[0089] The generation unit can generate a recipe based on the proposed menu using a recipe generated by AI. The generation unit can generate a recipe using, for example, a machine learning algorithm or a natural language processing technology. The recipe generated by AI includes, for example, ingredients, cooking steps, cooking time, etc., but is not limited to such examples. The generation unit can generate a recipe that takes into account the user's preferences and the characteristics of ingredients using AI. For example, the generation unit can generate a nutritionally balanced recipe based on the user's health data. In addition, the generation unit can customize the contents of the recipe based on the user's preferences. This allows the recipe to be efficiently generated by using a recipe automatically generated by AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health data into the generation AI and cause the generation AI to generate a recipe.

[0090] The cooking unit allows the cooking robot to cook according to the generated recipe. The cooking unit can, for example, use a cooking robot of a specific manufacturer or model to cook. The cooking robot has cooking functions such as frying, boiling, and baking, but is not limited to such examples. The cooking unit can optimize the operation of the cooking robot using AI to cook efficiently. For example, the cooking unit can control the operation of the cooking robot based on the generated recipe. In addition, the cooking unit can monitor the operation of the cooking robot in real time and make adjustments as necessary. This allows the cooking robot to automatically cook, thereby reducing the user's effort in cooking. Some or all of the above-mentioned processing in the cooking unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooking unit can input the generated recipe to the generation AI and have the generation AI control the operation of the cooking robot.

[0091] The input unit can analyze the user's emotions and adjust the timing of inputting the health data based on the analyzed user's emotions. The input unit can, for example, estimate the user's emotions and adjust the timing of inputting the health data based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, when a user is feeling stressed, the input of health data can be prompted during a time period when the user can relax. In addition, when the user is relaxed, the input of detailed health data can be prompted. Furthermore, when the user is busy, a simplified input form can be provided to allow the user to input health data quickly. This allows the data to be input at a more appropriate time by adjusting the timing of inputting the health data according to the user's emotions.

[0092] The input unit can analyze the user's past health data and select the optimal input method. The input unit can, for example, analyze the user's past health data and select the optimal input method. The past health data includes, for example, data from the past year, but is not limited to such an example. The input unit can analyze the user's past health data using AI and suggest the optimal input method. For example, the input unit can analyze the pattern of health data input by the user in the past and suggest the most efficient input method. In addition, the input unit can preferentially suggest the input method (voice, text, etc.) used by the user in the past. Furthermore, the input unit can analyze the tendency to input during a specific time period from the user's past health data and prompt the user to input during that time period. In this way, the optimal input method can be provided to the user by analyzing the past health data.

[0093] The input unit can filter the health data based on the user's current living situation and diet history when inputting the health data. The input unit can filter the health data based on the user's current living situation and diet history when inputting the health data, for example. The current living situation includes, for example, work situation, exercise habits, etc., but is not limited to such examples. The diet history includes, for example, diet history for the past week, but is not limited to such examples. The input unit can analyze the user's current living situation and diet history using AI, and preferentially input highly relevant data. For example, the input unit can filter the health data to be input in consideration of the user's current living situation. In addition, the input unit can preferentially input highly relevant health data based on the user's past diet history. Furthermore, the input unit can adjust the level of detail of the health data to be input according to the user's current living situation. As a result, more relevant data can be input by filtering the data based on the current living situation and diet history.

[0094] The input unit can analyze the user's emotions and determine the priority of the health data to be input based on the analyzed user's emotions. The input unit can, for example, estimate the user's emotions and determine the priority of the health data to be input based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, health data related to stress can be input preferentially. In addition, when the user is relaxed, detailed health data can be input preferentially. Furthermore, when the user is in a hurry, important health data can be input preferentially. Thereby, important data can be input preferentially by determining the priority of health data according to the user's emotions.

[0095] The input unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting health data. For example, the input unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting health data. The geographical location information includes, for example, a city, a region, a country, etc., but is not limited to such examples. The input unit can analyze the geographical location information of the user using AI and preferentially input highly relevant data. For example, when the user lives in a specific region, the input unit can preferentially input data related to the health risks of the region. In addition, when the user is traveling, the input unit can preferentially input data related to the health risks of the travel destination. Furthermore, when the user lives in a specific climate condition, the input unit can preferentially input health data related to the climate condition. Thereby, by considering the geographical location information, health data related to the region can be preferentially input.

[0096] The input unit can analyze the user's social media activity and input related data when inputting health data. The input unit can, for example, analyze the user's social media activity and input related data when inputting health data. Social media activity includes, for example, the content of posts, the number of likes, comments, etc., but is not limited to such examples. The input unit can use AI to analyze the user's social media activity and input related data. For example, the input unit can input related data based on health information shared by the user on social media. In addition, the input unit can analyze health-related interests from the user's social media activity and input related data. Furthermore, the input unit can input related data based on information on health experts the user follows on social media. In this way, health data based on the user's interests can be input by analyzing social media activity.

[0097] The recommendation unit can analyze the user's emotions and adjust the way of expressing the recommendation based on the analyzed user's emotions. The recommendation unit can, for example, estimate the user's emotions and adjust the way of expressing the recommendation based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, a simple and easy-to-understand recommendation can be made. Also, when the user is relaxed, a recommendation including detailed information can be made. Furthermore, when the user is in a hurry, a short recommendation that covers the main points can be made. As a result, a more appropriate recommendation can be made by adjusting the way of expressing the recommendation according to the user's emotions.

[0098] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the ingredient at the time of recommendation. For example, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the ingredient at the time of recommendation. The importance of the ingredient includes, for example, nutritional value, allergy information, etc., but is not limited to such examples. The recommendation unit can use AI to analyze the importance of the ingredient and adjust the level of detail of the recommendation. For example, the recommendation unit can provide detailed information for ingredients that contain important nutrients. In addition, the recommendation unit can provide concise information for common ingredients. Furthermore, the recommendation unit can make detailed recommendations for ingredients related to a specific health condition. As a result, important information can be provided preferentially by adjusting the level of detail of the recommendation based on the importance of the ingredient.

[0099] The recommendation unit can apply different recommendation algorithms depending on the category of ingredients when making a recommendation. For example, the recommendation unit can apply different recommendation algorithms depending on the category of ingredients when making a recommendation. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The recommendation unit can use AI to analyze the category of ingredients and apply the optimal recommendation algorithm. For example, the recommendation unit can make recommendations based on nutritional value for vegetables and fruits. In addition, the recommendation unit can make recommendations based on cooking methods for meat and fish. Furthermore, the recommendation unit can make recommendations based on ingredient labels for processed foods. This allows more appropriate recommendations to be made by applying recommendation algorithms according to the category of ingredients.

[0100] The recommendation unit can analyze the user's emotions and adjust the length of the recommendation based on the analyzed user's emotions. The recommendation unit can, for example, estimate the user's emotions and adjust the length of the recommendation based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is stressed, a short and to the point recommendation can be made. Also, if the user is relaxed, a longer recommendation including detailed information can be made. Furthermore, if the user is in a hurry, a short recommendation that can be quickly understood can be made. This allows more appropriate recommendations to be made by adjusting the length of the recommendation according to the user's emotions.

[0101] The recommendation unit can determine the priority of recommendation based on the time of submission of ingredients at the time of recommendation. The recommendation unit can, for example, determine the priority of recommendation based on the time of submission of ingredients at the time of recommendation. The time of submission of ingredients includes, for example, harvest time, expiration date, etc., but is not limited to such examples. The recommendation unit can use AI to analyze the time of submission of ingredients and determine the priority of recommendation. For example, the recommendation unit can give priority to recommending ingredients that need to be consumed quickly. In addition, the recommendation unit can postpone ingredients that can be stored for a long time. Furthermore, the recommendation unit can give priority to recommending seasonal ingredients. In this way, by determining the priority of recommendation based on the time of submission of ingredients, ingredients can be recommended at the appropriate time.

[0102] The recommendation unit can adjust the order of recommendation based on the relevance of ingredients at the time of recommendation. The recommendation unit can, for example, adjust the order of recommendation based on the relevance of ingredients at the time of recommendation. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The recommendation unit can use AI to analyze the relevance of ingredients and adjust the order of recommendation. For example, the recommendation unit can recommend ingredients of the same category together. Also, the recommendation unit can recommend ingredients with similar cooking methods together. Furthermore, the recommendation unit can recommend ingredients with similar nutritional values ​​together. This allows for more efficient recommendation by adjusting the order of recommendation based on the relevance of ingredients.

[0103] The order unit can analyze the user's emotions and adjust the way of expressing the order based on the analyzed user's emotions. The order unit can, for example, estimate the user's emotions and adjust the way of expressing the order based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, a simple and easy-to-understand order screen can be provided. Also, when the user is relaxed, an order screen including detailed information can be provided. Furthermore, when the user is in a hurry, a simplified order screen that allows the user to quickly place an order can be provided. This allows for more appropriate ordering by adjusting the way of expressing the order according to the user's emotions.

[0104] The ordering unit can adjust the level of detail of the order based on the importance of the ingredients when placing an order. The ordering unit can, for example, adjust the level of detail of the order based on the importance of the ingredients when placing an order. The importance of ingredients includes, for example, nutritional value, allergy information, and the like, but is not limited to such examples. The ordering unit can use AI to analyze the importance of ingredients and adjust the level of detail of the order. For example, the ordering unit can provide detailed information for ingredients that contain important nutrients. Also, the ordering unit can provide concise information for common ingredients. Furthermore, the ordering unit can provide detailed order information for ingredients related to a specific health condition. In this way, important information can be provided preferentially by adjusting the level of detail of the order based on the importance of the ingredients.

[0105] The ordering unit can apply different ordering algorithms depending on the category of ingredients when placing an order. The ordering unit can, for example, apply different ordering algorithms depending on the category of ingredients when placing an order. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The ordering unit can use AI to analyze the category of ingredients and apply the optimal ordering algorithm. For example, the ordering unit can place an order based on nutritional value for vegetables and fruits. Also, the ordering unit can place an order based on cooking method for meat and fish. Furthermore, the ordering unit can place an order based on ingredient labeling for processed foods. This allows for more appropriate ordering by applying an ordering algorithm according to the category of ingredients.

[0106] The order unit can analyze the user's emotions and adjust the length of the order based on the analyzed user's emotions. The order unit can, for example, estimate the user's emotions and adjust the length of the order based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is stressed, the order can be short and to the point. Also, if the user is relaxed, the order can be longer with detailed information. Furthermore, if the user is in a hurry, the order can be short and quickly understood. This allows for more appropriate ordering by adjusting the length of the order according to the user's emotions.

[0107] The ordering section can determine the priority of the order based on the time of submission of the ingredients at the time of ordering. The ordering section can, for example, determine the priority of the order based on the time of submission of the ingredients at the time of ordering. The time of submission of the ingredients includes, for example, the harvest season, the expiration date, and the like, but is not limited to such examples. The ordering section can use AI to analyze the time of submission of the ingredients and determine the priority of the order. For example, the ordering section can prioritize ordering ingredients that need to be consumed quickly. In addition, the ordering section can postpone ordering ingredients that can be stored for a long time. Furthermore, the ordering section can prioritize ordering ingredients that are limited to a certain season. In this way, by determining the priority of the order based on the time of submission of the ingredients, ingredients can be ordered at the appropriate time.

[0108] The ordering unit can adjust the order order based on the relevance of ingredients when placing an order. The ordering unit can, for example, adjust the order order based on the relevance of ingredients when placing an order. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The ordering unit can use AI to analyze the relevance of ingredients and adjust the order order. For example, the ordering unit can order ingredients of the same category together. Also, the ordering unit can order ingredients that have similar cooking methods together. Furthermore, the ordering unit can order ingredients that have similar nutritional values ​​together. This allows for more efficient ordering by adjusting the order order based on the relevance of ingredients.

[0109] The delivery unit can analyze the user's emotions and adjust the delivery expression method based on the analyzed user's emotions. The delivery unit can, for example, estimate the user's emotions and adjust the delivery expression method based on the estimated user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, simple and easy-to-understand delivery information can be provided. Also, when the user is relaxed, delivery information including detailed information can be provided. Furthermore, when the user is in a hurry, simplified delivery information that can be quickly understood can be provided. This enables more appropriate delivery by adjusting the delivery expression method according to the user's emotions.

[0110] The delivery unit can adjust the level of detail of the delivery based on the importance of the ingredients at the time of delivery. The delivery unit can, for example, adjust the level of detail of the delivery based on the importance of the ingredients at the time of delivery. The importance of the ingredients includes, for example, nutritional value, allergy information, etc., but is not limited to such examples. The delivery unit can use AI to analyze the importance of the ingredients and adjust the level of detail of the delivery. For example, the delivery unit can provide detailed delivery information for ingredients that contain important nutrients. In addition, the delivery unit can provide concise delivery information for general ingredients. Furthermore, the delivery unit can provide detailed delivery information for ingredients related to a specific health condition. In this way, important information can be provided preferentially by adjusting the level of detail of the delivery based on the importance of the ingredients.

[0111] The delivery unit can apply different delivery algorithms depending on the category of ingredients at the time of delivery. The delivery unit can apply different delivery algorithms depending on the category of ingredients at the time of delivery. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The delivery unit can use AI to analyze the category of ingredients and apply the optimal delivery algorithm. For example, the delivery unit can apply a delivery method for preserving freshness to vegetables and fruits. In addition, the delivery unit can apply a refrigerated / frozen delivery method to meat and fish. Furthermore, the delivery unit can apply a normal delivery method to processed foods. This enables more appropriate delivery by applying a delivery algorithm according to the category of ingredients.

[0112] The delivery unit can analyze the user's emotions and adjust the length of delivery based on the analyzed user's emotions. The delivery unit can, for example, estimate the user's emotions and adjust the length of delivery based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, short and to the point delivery information can be provided. Also, when the user is relaxed, longer delivery information including detailed information can be provided. Furthermore, when the user is in a hurry, short delivery information that can be quickly understood can be provided. This allows more appropriate delivery by adjusting the length of delivery according to the user's emotions.

[0113] The delivery unit can determine the priority of delivery based on the time of submission of ingredients at the time of delivery. The delivery unit can, for example, determine the priority of delivery based on the time of submission of ingredients at the time of delivery. The time of submission of ingredients includes, for example, the harvest season, the expiration date, etc., but is not limited to such examples. The delivery unit can use AI to analyze the time of submission of ingredients and determine the priority of delivery. For example, the delivery unit can prioritize delivery of ingredients that need to be consumed quickly. In addition, the delivery unit can postpone delivery of ingredients that can be stored for a long time. Furthermore, the delivery unit can prioritize delivery of seasonal ingredients. In this way, by determining the priority of delivery based on the time of submission of ingredients, ingredients can be delivered at the appropriate time.

[0114] The delivery unit can adjust the order of delivery based on the relevance of ingredients at the time of delivery. The delivery unit can, for example, adjust the order of delivery based on the relevance of ingredients at the time of delivery. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The delivery unit can use AI to analyze the relevance of ingredients and adjust the order of delivery. For example, the delivery unit can deliver ingredients of the same category together. Also, the delivery unit can deliver ingredients that have similar cooking methods together. Furthermore, the delivery unit can deliver ingredients that have similar nutritional values ​​together. This allows for more efficient delivery by adjusting the order of delivery based on the relevance of ingredients.

[0115] The generation unit can analyze the user's emotions and adjust the expression method of recipe generation based on the analyzed user's emotions. The generation unit can, for example, estimate the user's emotions and adjust the expression method of recipe generation based on the estimated user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when a user is relaxed, a detailed recipe can be generated. Also, when a user is in a hurry, a concise recipe can be generated. Furthermore, when a user is excited, a visually attractive recipe can be generated. As a result, a more appropriate recipe can be generated by adjusting the expression method of recipe generation according to the user's emotions.

[0116] The generation unit can adjust the level of detail of the recipe based on the importance of the ingredients when generating the recipe. The generation unit can adjust the level of detail of the recipe based on the importance of the ingredients when generating the recipe, for example. The importance of the ingredients includes, but is not limited to, for example, nutritional value and allergy information. The generation unit can use AI to analyze the importance of the ingredients and adjust the level of detail of the recipe. For example, the generation unit can generate a detailed recipe for ingredients that contain important nutrients. In addition, the generation unit can generate a concise recipe for common ingredients. Furthermore, the generation unit can generate a detailed recipe for ingredients related to a specific health condition. Thereby, important information can be preferentially provided by adjusting the level of detail of the recipe based on the importance of the ingredients.

[0117] The generation unit can apply different recipe generation algorithms depending on the category of ingredients when generating a recipe. The generation unit can apply different recipe generation algorithms depending on the category of ingredients when generating a recipe. The categories of ingredients include, for example, vegetables, fruits, meats, etc., but are not limited to such examples. The generation unit can use AI to analyze the category of ingredients and apply an optimal recipe generation algorithm. For example, the generation unit can generate recipes based on nutritional value for vegetables and fruits. Furthermore, the generation unit can generate recipes based on cooking methods for meat and fish. Furthermore, the generation unit can generate recipes based on ingredient labels for processed foods. In this way, a more appropriate recipe is generated by applying a recipe generation algorithm according to the category of ingredients.

[0118] The generation unit can analyze the user's emotions and adjust the length of the recipe generation based on the analyzed user's emotions. The generation unit can, for example, estimate the user's emotions and adjust the length of the recipe generation based on the estimated user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, when the user is in a hurry, a short and to the point recipe can be generated. Also, when the user is relaxed, a longer recipe including detailed explanations can be generated. Furthermore, when the user is excited, a recipe with a visually stimulating effect can be generated. In this way, a more appropriate recipe can be generated by adjusting the length of the recipe generation according to the user's emotions.

[0119] The generation unit can determine the priority of recipe generation based on the time of submission of ingredients when generating a recipe. The generation unit can determine the priority of recipe generation based on the time of submission of ingredients when generating a recipe, for example. The time of submission of ingredients includes, but is not limited to, the harvest season, the expiration date, and the like. The generation unit can use AI to analyze the time of submission of ingredients and determine the priority of recipe generation. For example, the generation unit can generate recipes with priority for ingredients that need to be consumed quickly. In addition, the generation unit can postpone the generation of ingredients that can be stored for a long time. Furthermore, the generation unit can generate recipes with priority for seasonal ingredients. In this way, by determining the priority of recipe generation based on the time of submission of ingredients, recipes can be generated at the appropriate time.

[0120] The generation unit can adjust the order of recipe generation based on the relevance of ingredients when generating a recipe. The generation unit can adjust the order of recipe generation based on the relevance of ingredients when generating a recipe, for example. The relevance of ingredients includes, but is not limited to, for example, nutrient combinations and commonalities in cooking methods. The generation unit can use AI to analyze the relevance of ingredients and adjust the order of recipe generation. For example, the generation unit can generate a recipe by grouping ingredients of the same category together. In addition, the generation unit can generate a recipe by grouping ingredients with similar cooking methods together. Furthermore, the generation unit can generate a recipe by grouping ingredients with similar nutritional values ​​together. This allows more efficient recipe generation by adjusting the order of recipe generation based on the relevance of ingredients.

[0121] The cooking unit can analyze the user's emotions and adjust the cooking expression method based on the analyzed user's emotions. The cooking unit can, for example, estimate the user's emotions and adjust the cooking expression method based on the estimated user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, detailed cooking instructions can be provided. Also, when the user is in a hurry, concise cooking instructions can be provided. Furthermore, when the user is excited, visually attractive cooking instructions can be provided. This allows more appropriate cooking by adjusting the cooking expression method according to the user's emotions.

[0122] The cooking unit can adjust the level of detail of cooking based on the importance of ingredients when cooking. The cooking unit can adjust the level of detail of cooking based on the importance of ingredients when cooking, for example. The importance of ingredients includes, but is not limited to, for example, nutritional value, allergy information, and the like. The cooking unit can use AI to analyze the importance of ingredients and adjust the level of detail of cooking. For example, the cooking unit can provide detailed cooking instructions for ingredients that contain important nutrients. In addition, the cooking unit can provide concise cooking instructions for common ingredients. Furthermore, the cooking unit can provide detailed cooking instructions for ingredients related to a specific health condition. Thereby, important information can be provided preferentially by adjusting the level of detail of cooking based on the importance of ingredients.

[0123] The cooking unit can apply different cooking algorithms depending on the category of ingredients when cooking. For example, the cooking unit can apply different cooking algorithms depending on the category of ingredients when cooking. The categories of ingredients include, but are not limited to, for example, vegetables, fruits, and meats. The cooking unit can use AI to analyze the category of ingredients and apply the optimal cooking algorithm. For example, the cooking unit can provide cooking instructions based on nutritional value for vegetables and fruits. In addition, the cooking unit can provide cooking instructions based on cooking methods for meat and fish. Furthermore, the cooking unit can provide cooking instructions based on ingredient labels for processed foods. This allows for more appropriate cooking by applying cooking algorithms according to the category of ingredients.

[0124] The cooking unit can estimate the user's emotion and adjust the cooking length based on the estimated user's emotion. The cooking unit can, for example, estimate the user's emotion and adjust the cooking length based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is in a hurry, short and to the point cooking instructions can be provided. Also, if the user is relaxed, longer cooking instructions including detailed explanations can be provided. Furthermore, if the user is excited, cooking instructions with visually stimulating effects can be provided. This allows more appropriate cooking by adjusting the cooking length according to the user's emotion.

[0125] The cooking unit can determine the priority of cooking based on the time of submission of ingredients when cooking. The cooking unit can determine the priority of cooking based on the time of submission of ingredients when cooking, for example. The time of submission of ingredients includes, but is not limited to, the harvest season, the expiration date, and the like. The cooking unit can use AI to analyze the time of submission of ingredients and determine the priority of cooking. For example, the cooking unit can prioritize cooking of ingredients that need to be consumed quickly. In addition, the cooking unit can postpone cooking of ingredients that can be stored for a long time. Furthermore, the cooking unit can prioritize cooking of seasonal ingredients. In this way, by determining the priority of cooking based on the time of submission of ingredients, cooking can be done at the appropriate time.

[0126] The cooking unit can adjust the cooking order based on the relevance of ingredients when cooking. The cooking unit can adjust the cooking order based on the relevance of ingredients when cooking, for example. The relevance of ingredients includes, but is not limited to, for example, nutrient combinations and commonalities in cooking methods. The cooking unit can use AI to analyze the relevance of ingredients and adjust the cooking order. For example, the cooking unit can cook ingredients of the same category together. Also, the cooking unit can cook ingredients that have similar cooking methods together. Furthermore, the cooking unit can cook ingredients that have similar nutritional values ​​together. This allows for more efficient cooking by adjusting the cooking order based on the relevance of ingredients.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The assistance system can also analyze the user's life rhythm and adjust the timing of ordering ingredients based on the analyzed life rhythm. For example, if the user has a habit of shopping on a specific day of the week, ingredients can be ordered according to that day. Also, if the user is busy during a specific time period, orders can be placed avoiding that time period. Furthermore, if the user is traveling, ingredients can be delivered to the travel destination. This allows for flexible ingredient ordering to suit the user's life rhythm.

[0129] The input unit can also analyze the user's past meal history and complement the input of health data based on the analyzed meal history. For example, the current health data can be complemented based on ingredients and nutrients that the user has taken in the past. The input can also be complemented so that the user can avoid ingredients that the user has had an allergic reaction to in the past. Furthermore, ingredients that the user has liked to eat in the past can be preferentially suggested. This makes it possible to input health data with higher accuracy by utilizing the past meal history.

[0130] The recommendation unit can also analyze the user's emotions and adjust the types of ingredients to be recommended based on the analyzed emotions. For example, if the user is stressed, ingredients with a relaxing effect can be recommended. If the user is tired, ingredients suitable for replenishing energy can be recommended. Furthermore, if the user is happy, ingredients that serve as a special dessert or a reward can be recommended. This makes it possible to recommend ingredients according to the user's emotions.

[0131] The ordering unit can also analyze the user's past order history and optimize the order contents based on the analyzed order history. For example, it can preferentially suggest ingredients that the user has frequently ordered in the past. It can also suggest ingredients that the user has ordered in the past that have given the user a particularly high level of satisfaction. It can also adjust the order contents to avoid ingredients that the user has ordered in the past that have caused allergic reactions. This makes it possible to make orders that provide a higher level of satisfaction by utilizing the past order history.

[0132] The delivery unit can also analyze the user's emotions and adjust the timing of delivery based on the analyzed emotions. For example, if the user is feeling stressed, delivery can be made during a time period when the user is able to relax. Also, if the user is busy, delivery can be made during a time period that is not appropriate. Furthermore, if the user is traveling, food can be delivered to the travel destination. This allows for flexible delivery according to the user's emotions.

[0133] The generation unit can also analyze the user's past recipe usage history and optimize recipe generation based on the analyzed history. For example, it can preferentially suggest recipes that the user has used in the past with a preference. It can also suggest again recipes that the user has used in the past with a particularly high level of satisfaction. Furthermore, it can generate recipes that avoid recipes that the user has used in the past with which they have had an allergic reaction. This makes it possible to generate recipes with a higher level of satisfaction by utilizing the user's past recipe usage history.

[0134] The cooking unit can also analyze the user's emotions and adjust the cooking progress based on the analyzed emotions. For example, if the user is stressed, the cooking progress can be slowed down to provide the user with time to relax. If the user is in a hurry, the cooking progress can be accelerated. Furthermore, if the user is excited, visually appealing cooking instructions can be provided. This allows the cooking progress to be tailored to the user's emotions.

[0135] The input unit can also analyze the geographical location information of the user and complement the input of health data based on the analyzed location information. For example, if the user lives in a specific area, data related to health risks in that area can be input preferentially. Also, if the user is traveling, data related to health risks at the travel destination can be input preferentially. Furthermore, if the user lives in a specific climate condition, health data related to that climate condition can be input preferentially. This makes it possible to input more relevant health data by utilizing geographical location information.

[0136] The recommendation unit can also analyze the user's emotions and adjust the amount of ingredients to be recommended based on the analyzed emotions. For example, if the user is stressed, a small amount of ingredients can be recommended to make meal preparation easier. If the user is relaxed, a normal amount of ingredients can be recommended. Furthermore, if the user is excited, a larger amount of ingredients can be recommended for special events. This allows the amount of ingredients to be recommended according to the user's emotions.

[0137] The ordering unit can also analyze the user's past order history and adjust the frequency of orders based on the analyzed history. For example, it can periodically suggest ingredients that the user has frequently ordered in the past. It can also suggest ingredients that the user has ordered in the past that have given the user a particularly high level of satisfaction. It can also adjust the order content to avoid ingredients that the user has ordered in the past that have caused an allergic reaction. This makes it possible to make orders that provide a higher level of satisfaction by utilizing the past order history.

[0138] The process flow of the second embodiment will be briefly described below.

[0139] Step 1: The input unit inputs the user's health data. The health data includes, for example, weight, blood pressure, and diet history. The input unit allows the user to input the health data using a smartphone or a computer, and can also automatically obtain the health data from a wearable device. Step 2: The recommendation unit recommends necessary ingredients and menus based on the health data entered by the input unit. The recommendation unit recommends ingredients rich in calcium and vitamin D for growing children and easy-to-digest ingredients for the elderly. The recommendation unit uses AI to analyze the user's health data and suggest optimal ingredients and menus. Step 3: The ordering department orders the ingredients recommended by the recommendation department online. The ordering department can order the necessary ingredients based on the suggested menu at an online shopping site. The ordering department uses AI to analyze the user's order history and suggests the optimal ordering method. Step 4: The delivery department delivers the food ordered by the ordering department to the customer's home using a delivery service. The delivery department can deliver the food to the customer's home using a delivery service such as a courier or drone delivery. The delivery department uses AI to calculate the optimal delivery route and delivers the food quickly and efficiently. Step 5: The generation unit uses the recipes automatically generated by AI to generate recipes based on the proposed menu. The generation unit can generate recipes using machine learning algorithms and natural language processing technology. The generation unit generates recipes that take into account the user's preferences and the characteristics of ingredients. Step 6: The cooking unit uses the cooking robot to cook based on the recipe generated by the generation unit. The cooking unit can use a cooking robot of a specific manufacturer and model to cook. The cooking unit uses AI to optimize the operation of the cooking robot and cook efficiently.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0142] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned input unit, recommendation unit, order unit, delivery unit, generation unit, cooking unit, and output unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14, and inputs the user's health data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends necessary ingredients and a menu based on the health data. The order unit is realized by the control unit 46A of the smart device 14, and orders the recommended ingredients online. The delivery unit is realized by the specific processing unit 290 of the data processing device 12, and delivers the ordered ingredients to the home using a delivery service. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a recipe using a recipe automatically generated by AI. The cooking unit is realized by the control unit 46A of the smart device 14, and the cooking robot cooks based on the generated recipe. The output unit is realized by the control unit 46A of the smart device 14, and notifies the user of the meal that has been cooked and provides the meal. The input unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the user's emotion, and adjusts the timing of inputting the health data based on the analyzed emotion. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0151] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements including the above-mentioned input unit, recommendation unit, order unit, delivery unit, generation unit, cooking unit, and output unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214, and inputs the user's health data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends necessary ingredients and a menu based on the health data. The order unit is realized by the control unit 46A of the smart glasses 214, and orders the recommended ingredients online. The delivery unit is realized by the specific processing unit 290 of the data processing device 12, and delivers the ordered ingredients to the home using a delivery service. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a recipe using a recipe automatically generated by AI. The cooking unit is realized by the control unit 46A of the smart glasses 214, and the cooking robot cooks based on the generated recipe. The output unit is realized by the control unit 46A of the smart glasses 214, and notifies the user of the meal that has been cooked and serves the meal. The input unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's emotions and adjusts the timing of inputting health data based on the analyzed user's emotions. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements including the above-mentioned input unit, recommendation unit, order unit, delivery unit, generation unit, cooking unit, and output unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset type terminal 314, and inputs the user's health data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends necessary ingredients and a menu based on the health data. The order unit is realized by the control unit 46A of the headset type terminal 314, and orders the recommended ingredients online. The delivery unit is realized by the specific processing unit 290 of the data processing device 12, and delivers the ordered ingredients to the home using a delivery service. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a recipe using a recipe automatically generated by AI. The cooking unit is realized by the control unit 46A of the headset type terminal 314, and the cooking robot cooks based on the generated recipe. The output unit is realized by the control unit 46A of the headset terminal 314, and notifies the user of the meal that has been cooked and serves the meal. The input unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's emotions and adjusts the timing of inputting health data based on the analyzed user's emotions. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0180] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0182] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0183] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0184] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0187] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] Each of the multiple elements including the above-mentioned input unit, recommendation unit, order unit, delivery unit, generation unit, cooking unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414 and inputs the user's health data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends necessary ingredients and a menu based on the health data. The order unit is realized by the control unit 46A of the robot 414 and orders the recommended ingredients online. The delivery unit is realized by the specific processing unit 290 of the data processing device 12 and delivers the ordered ingredients to the home using a delivery service. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a recipe using a recipe automatically generated by AI. The cooking unit is realized by the control unit 46A of the robot 414 and the cooking robot cooks based on the generated recipe. The output unit is realized by the control unit 46A of the robot 414 and notifies the user of the meal that has been cooked and provides the meal. The input unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the user's emotion, and adjusts the timing of inputting the health data based on the analyzed emotion. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0193] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0194] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0196] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0197] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0198] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0201] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0202] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0203] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0204] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0205] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0206] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0207] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0208] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0209] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0210] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0211] (Appendix 1) an input unit for inputting health data; A recommendation unit that recommends necessary ingredients and a menu based on the health data input by the input unit; an ordering unit for ordering the food ingredients recommended by the recommendation unit; a delivery unit that delivers the ingredients ordered by the ordering unit; A creation unit that creates a recipe based on the ingredients delivered by the delivery unit; a cooking unit that cooks based on the recipe generated by the generation unit. A system characterized by: (Appendix 2) The input unit is Input the user's health status and preferences 2. The system of claim 1, (Appendix 3) The recommendation unit is Based on the health data entered, the system recommends foods rich in calcium and vitamin D for growing children and foods that are easy to digest for the elderly. 2. The system of claim 1, (Appendix 4) The ordering unit, Order the ingredients you need online based on the recommended meals 2. The system of claim 1, (Appendix 5) The delivery unit includes: Ordered food delivered to your home using a delivery service 2. The system of claim 1, (Appendix 6) The generation unit is Generate recipes based on suggested menus using AI-generated recipes 2. The system of claim 1, (Appendix 7) The cooking section includes: The cooking robot cooks according to the generated recipe. 2. The system of claim 1, (Appendix 8) The input unit is Analyze user emotions and adjust the timing of health data input based on the analyzed user emotions 2. The system of claim 1, (Appendix 9) The input unit is Analyze the user's past health data and select the optimal input method 2. The system of claim 1, (Appendix 10) The input unit is When entering health data, filtering is performed based on the user's current living situation and dietary history. 2. The system of claim 1, (Appendix 11) The input unit is Analyze user emotions and determine the priority of health data to be input based on the analyzed user emotions. 2. The system of claim 1, (Appendix 12) The input unit is When entering health data, the system takes into account the user's geographic location to prioritize the most relevant data. 2. The system of claim 1, (Appendix 13) The input unit is Analyze users' social media activity and enter relevant data when entering health data 2. The system of claim 1, (Appendix 14) The recommendation unit is Analyze user sentiment and adjust the presentation of recommendations based on the analyzed user sentiment 2. The system of claim 1, (Appendix 15) The recommendation unit is When making recommendations, adjust the detail of the recommendations based on the importance of the ingredients 2. The system of claim 1, (Appendix 16) The recommendation unit is When making recommendations, apply different recommendation algorithms depending on the ingredient category 2. The system of claim 1, (Appendix 17) The recommendation unit is Analyze user sentiment and adjust the length of the recommendation based on the analyzed user sentiment 2. The system of claim 1, (Appendix 18) The recommendation unit is When making recommendations, prioritize recommendations based on when ingredients were submitted 2. The system of claim 1, (Appendix 19) The recommendation unit is When making recommendations, adjust the order of recommendations based on ingredient relevance 2. The system of claim 1, (Appendix 20) The ordering unit, Analyze user sentiment and adjust order wording based on the analyzed user sentiment 2. The system of claim 1, (Appendix 21) The ordering unit, When ordering, adjust the order detail based on the importance of each ingredient 2. The system of claim 1, (Appendix 22) The ordering unit, When ordering, apply different ordering algorithms depending on the food category 2. The system of claim 1, (Appendix 23) The ordering unit, Analyze user sentiment and adjust order length based on the analyzed user sentiment 2. The system of claim 1, (Appendix 24) The ordering unit, When placing an order, prioritize orders based on when ingredients are submitted 2. The system of claim 1, (Appendix 25) The ordering unit, When ordering, adjust the order sequence based on ingredient relevance 2. The system of claim 1, (Appendix 26) The delivery unit includes: Analyze user emotions and adjust delivery presentation based on the analyzed user emotions 2. The system of claim 1, (Appendix 27) The delivery unit includes: When delivering, adjust the delivery details based on the importance of the ingredients 2. The system of claim 1, (Appendix 28) The delivery unit includes: During delivery, different delivery algorithms are applied depending on the category of ingredients. 2. The system of claim 1, (Appendix 29) The delivery unit includes: Analyze user sentiment and adjust delivery length based on the analyzed user sentiment 2. The system of claim 1, (Appendix 30) The delivery unit includes: During delivery, prioritize deliveries based on when ingredients are submitted 2. The system of claim 1, (Appendix 31) The delivery unit includes: During delivery, adjust the delivery sequence based on the relevance of ingredients 2. The system of claim 1, (Appendix 32) The generation unit is Analyze user's emotions and adjust the way recipes are generated based on the analyzed user's emotions. 2. The system of claim 1, (Appendix 33) The generation unit is When generating recipes, adjust the level of detail in the recipe based on the importance of ingredients 2. The system of claim 1, (Appendix 34) The generation unit is When generating recipes, apply different recipe generation algorithms depending on the ingredient category. 2. The system of claim 1, (Appendix 35) The generation unit is Analyze user sentiment and adjust the length of recipe generation based on the analyzed user sentiment 2. The system of claim 1, (Appendix 36) The generation unit is During recipe generation, prioritize recipe generation based on when ingredients were submitted 2. The system of claim 1, (Appendix 37) The generation unit is During recipe generation, adjust the order of recipe generation based on ingredient associations 2. The system of claim 1, (Appendix 38) The cooking section includes: Analyze user's emotions and adjust cooking presentation based on the analyzed user's emotions 2. The system of claim 1, (Appendix 39) The cooking section includes: When cooking, adjust the cooking detail based on the importance of the ingredients 2. The system of claim 1, (Appendix 40) The cooking section includes: When cooking, apply different cooking algorithms depending on the food category 2. The system of claim 1, (Appendix 41) The cooking section includes: Estimating a user's emotion and adjusting the cooking length based on the estimated user's emotion 2. The system of claim 1, (Appendix 42) The cooking section includes: During cooking, prioritize cooking based on when ingredients were submitted 2. The system of claim 1, (Appendix 43) The cooking section includes: When cooking, adjust the cooking order based on the association of ingredients 2. The system of claim 1, [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting health data; A recommendation unit that recommends necessary ingredients and a menu based on the health data input by the input unit; an ordering unit for ordering the food ingredients recommended by the recommendation unit; a delivery unit that delivers the ingredients ordered by the ordering unit; A creation unit that creates a recipe based on the ingredients delivered by the delivery unit; a cooking unit that cooks based on the recipe generated by the generation unit. A system characterized by:

2. The input unit includes: Input the user's health status and preferences 2. The system of claim 1 .

3. The recommendation unit is Based on the health data entered, the system recommends foods rich in calcium and vitamin D for growing children and foods that are easy to digest for the elderly.

2. The system of claim 1 .

4. The ordering unit: Order the ingredients you need online based on the recommended meals 2. The system of claim 1 .

5. The delivery unit includes: Ordered food delivered to your home using a delivery service 2. The system of claim 1 .

6. The generation unit is Generate recipes based on suggested menus using AI-generated recipes 2. The system of claim 1 .

7. The cooking section includes: The cooking robot cooks according to the generated recipe.

2. The system of claim 1 .

8. The input unit includes: Analyze user emotions and adjust the timing of health data input based on the analyzed user emotions 2. The system of claim 1 .

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