system

Abstract

The system according to this embodiment aims to suggest an optimal menu based on the user's health information, and to automatically order ingredients and provide recipes. [Solution] The system according to the embodiment comprises an acquisition unit, a proposal unit, an ordering unit, and a provision unit. The acquisition unit acquires the user's health information. The proposal unit proposes a menu based on the information acquired by the acquisition unit. The ordering unit automatically orders ingredients based on the menu proposed by the proposal unit. The provision unit provides recipes based on the ingredients ordered by the ordering unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to propose an optimal menu based on the user's health information and perform automatic ordering of food ingredients and provision of recipes, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu based on the user's health information and perform automatic ordering of food ingredients and provision of recipes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a suggestion unit, an ordering unit, and a provision unit. The acquisition unit acquires the user's health information. The suggestion unit suggests a menu based on the information acquired by the acquisition unit. The ordering unit automatically orders ingredients based on the menu suggested by the suggestion unit. The provision unit provides recipes based on the ingredients ordered by the ordering unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal menu based on the user's health information, and can also automatically order ingredients and provide recipes. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The nutrition management system according to an embodiment of the present invention is a system that uses AI to propose menus based on the user's health condition and preferences, and provides automatic ordering of ingredients and cooking support. The nutrition management system acquires the user's health information and dietary preferences, and the AI ​​proposes a menu with the optimal nutritional balance on a weekly basis based on that information. Furthermore, it automatically orders the ingredients necessary for the proposed menu from online supermarkets or meal kit services. Finally, it provides recipes tailored to the user's cooking skills and kitchen equipment, and supports the progress of cooking and time management. For example, the nutrition management system acquires the user's health information and dietary preferences. For example, it acquires health information such as the user's weight, blood pressure, and diet. Next, the nutrition management system uses AI to propose a menu with the optimal nutritional balance based on the acquired information. For example, the AI ​​analyzes the user's health information and generates a nutritionally balanced menu. Furthermore, the nutrition management system automatically orders the ingredients necessary for the proposed menu from online supermarkets or meal kit services. For example, it automatically orders the necessary ingredients based on the menu proposed by the AI. Finally, the nutrition management system provides recipes tailored to the user's cooking skills and kitchen equipment, and supports the progress of cooking and time management. For example, AI can analyze a user's cooking skills and provide appropriate recipes. This makes preparing and planning healthy meals more efficient and easier for health-conscious dual-income households. The nutrition management system can suggest menus based on the user's health information, automatically order ingredients, and provide recipes.

[0029] The nutrition management system according to this embodiment comprises an acquisition unit, a suggestion unit, an ordering unit, and a provision unit. The acquisition unit acquires the user's health information. The acquisition unit acquires health information such as the user's weight, blood pressure, and diet. The acquisition unit can also collect information through smart health devices and user feedback. The suggestion unit suggests menus based on the information acquired by the acquisition unit. The suggestion unit analyzes the acquired health information using AI, for example, and generates menus with an optimal nutritional balance. The suggestion unit can also suggest menus tailored to the season or specific events. The ordering unit automatically orders ingredients based on the menu suggested by the suggestion unit. The ordering unit automatically places orders with online supermarkets or meal kit services, for example. The ordering unit orders the necessary ingredients based on the service selected by the user. The provision unit provides recipes based on the ingredients ordered by the ordering unit. The provision unit provides recipes tailored to the user's cooking skills and kitchen equipment, for example. The provision unit can also support the progress of cooking and time management until completion. As a result, the nutrition management system according to this embodiment can suggest menus based on the user's health information, automatically order ingredients, and provide recipes.

[0030] The data acquisition unit acquires user health information. For example, it acquires health information such as the user's weight, blood pressure, and diet. Specifically, it uses a smart health device to periodically measure the user's weight and blood pressure and transmits the data to the cloud. This allows for real-time monitoring of the user's health status. In addition, the user can input their diet information through a dedicated application. The user takes a photo of their meal and uploads it to the application, and the AI ​​automatically analyzes the meal content and acquires information on calories and nutrients. Furthermore, information such as daily physical condition and exercise levels can be collected through user feedback. As a result, the data acquisition unit can centrally manage diverse user health information and create a detailed health profile. The data acquisition unit securely stores this data and can link with other departments and systems as needed. For example, the acquired data can be made accessible to the proposal and ordering departments to support optimal proposals and orders based on the user's health status. Also, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific health conditions and goals. As a result, the data acquisition unit can efficiently and effectively collect user health information and improve the overall system performance.

[0031] The suggestion unit proposes menus based on the information acquired by the acquisition unit. For example, the suggestion unit uses AI to analyze acquired health information and generate menus with optimal nutritional balance. Specifically, the AI ​​analyzes data such as the user's weight, blood pressure, diet, and exercise level, and proposes menus with optimal nutritional balance for each individual user. Based on past data and statistical information, the AI ​​generates menus tailored to the user's health condition and goals. For example, it suggests a low-calorie, high-protein menu for users aiming to lose weight, and a low-sodium menu for users with high blood pressure. The suggestion unit can also propose menus tailored to seasons and specific events. For example, it can suggest menus utilizing seasonal ingredients or special menus for specific events. Furthermore, the suggestion unit can propose optimal menus for each individual user, taking into account their preferences and allergy information. This allows the suggestion unit to propose optimal menus tailored to the user's health condition and preferences, supporting their health management. The suggestion unit notifies the user of the proposed menu, making it easy for them to review. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to propose optimal meal plans to users and support their health management.

[0032] The ordering department automatically places orders for ingredients based on menus proposed by the suggestion department. For example, the ordering department automatically places orders with online supermarkets and meal kit services. Specifically, it lists the ingredients needed for the proposed menu and automatically places orders with the online supermarket or meal kit service selected by the user. The ordering department manages user account information and delivery address information to support a smooth ordering process. For example, registering the account information of the online supermarket the user usually uses can simplify the ordering process. Furthermore, the ordering department can propose the optimal ordering plan considering the user's budget and ingredient inventory. For example, it can select the most cost-effective ingredients within the budget to avoid waste. In addition, the ordering department can monitor the ordering status in real time and notify the user. For example, it can notify the user when the order is completed, informing them of the expected delivery date and delivery status. This allows the ordering department to support users in ordering ingredients smoothly and efficiently, helping them realize their menus. The ordering department can collect user feedback and continuously improve the accuracy and efficiency of the ordering process. This allows the ordering department to support users in ordering the most suitable ingredients and improve the overall performance of the system.

[0033] The supply department provides recipes based on the ingredients ordered by the ordering department. For example, the supply department provides recipes tailored to the user's cooking skills and kitchen equipment. Specifically, it suggests easy-to-prepare recipes or recipes that require specific cooking utensils, taking into account the user's cooking skills and kitchen equipment. For example, it suggests recipes with simple steps for beginners and recipes requiring more advanced cooking techniques for experienced users. The supply department can also support the progress of cooking and time management until completion. For example, it can set timers for each step of cooking and notify the user to smoothly support the progress of cooking. Furthermore, the supply department can provide recipe videos and images to provide a visually easy-to-understand cooking guide. This makes it easier for users to intuitively understand the cooking procedure. The supply department can collect user feedback and continuously improve the accuracy and effectiveness of the recipe content and delivery method. For example, it can collect feedback on problems and areas for improvement that users experienced during cooking and reflect them in future recipe suggestions. In this way, the supply department can provide users with the best possible recipes and support their cooking. The service provider can support users' health management by calculating nutritional balance and calories, thereby promoting a healthy diet. This allows the service provider to offer users healthy and delicious meals and improve the overall system performance.

[0034] The suggestion department can propose menus tailored to the season and specific events. For example, it can suggest menus that incorporate seasonal ingredients, holidays, and specific events (such as birthdays or anniversaries). The suggestion department uses AI to collect information related to the season and events and generates menus based on that information. For example, the suggestion department can suggest menus using spring vegetables in the spring and cold dishes in the summer. It can also suggest a special dinner for a user's birthday. In this way, by suggesting menus tailored to the season and specific events, it can provide menus that meet the user's needs.

[0035] The service provider can offer recipes tailored to the user's cooking skills and kitchen equipment. For example, it can evaluate the user's cooking skills and provide recipes for beginners, intermediate users, and advanced users. The service provider uses AI to analyze the user's cooking skills and select appropriate recipes. For example, it can provide beginners with recipes that include simple cooking steps and intermediate users with slightly more complex recipes. The service provider can also customize recipes based on the user's kitchen equipment. For example, it can suggest the optimal recipe based on the cooking appliances the user owns (oven, blender, refrigerator, etc.). By providing recipes tailored to the user's cooking skills and kitchen equipment, the service can improve cooking efficiency.

[0036] The service unit can support the progress of cooking and time management until completion. For example, the service unit can manage each cooking step with a timer and notify the user. The service unit uses AI to monitor the progress of cooking in real time and instruct the next step at the appropriate time. For example, the service unit measures the time for stir-frying and notifies the user to add the next ingredient at the appropriate time. The service unit can also predict the time until cooking is complete and inform the user. In this way, by supporting the progress of cooking and time management until completion, cooking efficiency can be improved.

[0037] The data acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the data acquisition unit can select the most effective data acquisition method from the user's past health data. The data acquisition unit uses AI to analyze past health data and determine the optimal acquisition method. For example, the data acquisition unit analyzes the user's past weight records and blood pressure data to select the optimal device and data acquisition frequency. The data acquisition unit can also adjust the data acquisition frequency based on the user's past health data. For example, the data acquisition unit can reduce the data acquisition frequency when the user's health is stable and increase it when the health is fluctuating. The data acquisition unit can also focus on acquiring data for specific health indicators. For example, the data acquisition unit can focus on acquiring data for particularly important health indicators (e.g., blood glucose levels and cholesterol levels) from the user's past data. This allows the optimal data acquisition method to be selected by analyzing the user's past health data.

[0038] The data acquisition unit can filter health information based on the user's current lifestyle and activity level. For example, if the user is exercising, the unit prioritizes acquiring exercise-related health information. The data acquisition unit uses AI to analyze the user's lifestyle and activity level and filter the information accordingly. For example, if the user is exercising, the unit prioritizes acquiring exercise-related information such as heart rate and calorie consumption. The data acquisition unit can also acquire relaxation-related health information if the user is resting. For example, if the user is resting, the unit acquires information related to stress levels and sleep quality. The data acquisition unit can also acquire stress management-related health information if the user is working. For example, if the user is working, the unit acquires information related to stress levels and concentration. By filtering health information based on the user's lifestyle and activity level, more relevant information can be acquired.

[0039] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring health information. For example, if the user is in a specific region, the data acquisition unit will acquire information related to the health risks of that region. The data acquisition unit uses AI to analyze the user's geographical location and filter the information appropriately. For example, if the user is in a specific region, the data acquisition unit will prioritize the acquisition of information related to diseases and health risks prevalent in that region. The data acquisition unit can also prioritize the acquisition of health information related to the travel destination if the user is traveling. For example, if the user is traveling, the data acquisition unit will acquire health information related to the climate and diet of the travel destination. The data acquisition unit can also prioritize the acquisition of health information related to the area around the user's home if the user is at home. For example, if the user is at home, the data acquisition unit will acquire health information related to the environment and lifestyle around the user's home. In this way, by considering the user's geographical location, the data acquisition unit can prioritize the acquisition of highly relevant health information.

[0040] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring health information. For example, the data acquisition unit can acquire relevant information based on health information shared by the user on social media. The data acquisition unit uses AI to analyze the user's social media activity and filter appropriate information. For example, the data acquisition unit can acquire relevant information based on health information shared by the user on social media. The data acquisition unit can also identify health topics of interest from the user's social media activity and acquire information. For example, the data acquisition unit can acquire relevant information based on posts from health professionals followed by the user on social media. The data acquisition unit can also acquire relevant information based on health information shared by the user on social media. In this way, relevant health information can be acquired by analyzing the user's social media activity.

[0041] The suggestion function can adjust the level of detail in menu suggestions based on the importance of nutritional balance. For example, if a user prioritizes nutritional balance, the suggestion function will suggest a menu that includes detailed nutritional information. The suggestion function uses AI to analyze the importance of nutritional balance for the user and generate an appropriate menu. For example, if a user prioritizes nutritional balance, the suggestion function will suggest a menu that includes information related to vitamin and mineral intake. The suggestion function can also suggest a menu that emphasizes calorie information if the user is restricting calories. For example, if a user is restricting calories, the suggestion function will suggest a menu that emphasizes calorie information. The suggestion function can also suggest a menu that includes information related to a specific nutrient if the user prioritizes that nutrient. For example, if a user prioritizes a specific nutrient, the suggestion function will suggest a menu that includes information related to that nutrient. In this way, by adjusting the level of detail in suggestions based on the importance of nutritional balance, menus that meet the user's needs can be provided.

[0042] The suggestion function can apply different suggestion algorithms depending on the user's health goals when suggesting menus. For example, if the user is aiming to lose weight, the suggestion function will suggest a low-calorie menu. The suggestion function uses AI to analyze the user's health goals and apply the appropriate suggestion algorithm. For example, if the user is aiming to lose weight, the suggestion function will suggest a low-calorie menu. The suggestion function can also suggest a high-protein menu if the user is aiming to build muscle. For example, if the user is aiming to build muscle, the suggestion function will suggest a high-protein menu. The suggestion function can also suggest a balanced menu if the user is aiming to maintain their health. For example, if the suggestion function is aiming to maintain their health, the suggestion function will suggest a balanced menu. In this way, by applying different suggestion algorithms according to the user's health goals, it is possible to suggest more appropriate menus.

[0043] The suggestion function can prioritize menu suggestions based on the user's eating history. For example, it can prioritize dishes the user has enjoyed eating in the past. The suggestion function uses AI to analyze the user's eating history and make appropriate suggestions. For example, it can prioritize dishes the user has enjoyed eating in the past. The suggestion function can also exclude dishes the user has avoided in the past from its suggestions. For example, it can exclude dishes the user has avoided in the past from its suggestions. Furthermore, the suggestion function can prioritize suggestions while considering nutritional balance based on the user's eating history. For example, it can prioritize suggestions while considering nutritional balance based on the user's eating history. In this way, by prioritizing suggestions based on the user's eating history, it can suggest more appropriate menus.

[0044] The suggestion function can adjust the order of menu suggestions based on the user's dietary restrictions. For example, if the user has allergies, the suggestion function will prioritize suggesting menus that do not contain those allergens. The suggestion function uses AI to analyze the user's dietary restrictions and make appropriate suggestions. For example, if the user has allergies, the suggestion function will prioritize suggesting menus that do not contain those allergens. The suggestion function can also suggest menus that do not contain specific ingredients that the user is avoiding. For example, if the user is avoiding specific ingredients, the suggestion function will suggest menus that do not contain those ingredients. The suggestion function can also suggest menus that are suitable for a particular diet if the user is on one. For example, if the suggestion function is on one particular diet, the suggestion function will suggest menus that are suitable for that diet. By adjusting the order of suggestions based on the user's dietary restrictions, the system can suggest more appropriate menus.

[0045] The ordering department can analyze a user's past purchase history to select the optimal ordering method when ordering ingredients. For example, the ordering department can select the most effective ordering method based on the user's past purchase history. The ordering department uses AI to analyze a user's past purchase history and determine the appropriate ordering method. For example, the ordering department can select the most effective ordering method based on the user's past purchase history. The ordering department can also adjust the frequency of orders based on the user's past purchase history. For example, the ordering department adjusts the frequency of orders based on the user's past purchase history. The ordering department can also analyze a user's past purchase history and focus orders on specific ingredients. For example, the ordering department analyzes a user's past purchase history and focuses orders on specific ingredients. This allows the ordering department to select the optimal ordering method by analyzing the user's past purchase history.

[0046] The ordering department can adjust the order quantity based on the user's current inventory status when ordering ingredients. For example, the ordering department can check the user's current inventory status and order only the necessary ingredients. The ordering department uses AI to analyze the user's inventory status and determine the appropriate order quantity. For example, the ordering department can check the user's current inventory status and order only the necessary ingredients. The ordering department can also reduce the order quantity if the user has a large inventory. For example, the ordering department can reduce the order quantity if the user has a large inventory. The ordering department can also increase the order quantity if the user has a small inventory. For example, the ordering department can increase the order quantity if the user has a small inventory. In this way, by adjusting the order quantity based on the user's inventory status, waste-free ordering becomes possible.

[0047] The ordering department can prioritize ordering ingredients that are highly relevant to the user's geographical location when placing an order. For example, if the user is in a specific region, the ordering department will prioritize ordering ingredients that are easily available in that region. The ordering department uses AI to analyze the user's geographical location and select appropriate ingredients. For example, if the user is in a specific region, the ordering department will prioritize ordering ingredients that are easily available in that region. The ordering department can also prioritize ordering ingredients that are easily available at the user's travel destination if the user is traveling. For example, if the user is traveling, the ordering department will prioritize ordering ingredients that are easily available at the user's travel destination if the user is traveling. The ordering department can also prioritize ordering ingredients that are easily available in the vicinity of the user's home if the user is at home. For example, if the user is at home, the ordering department will prioritize ordering ingredients that are easily available in the vicinity of the user's home. In this way, by considering the user's geographical location, the ordering department can prioritize ordering ingredients that are highly relevant to the user's location.

[0048] The ordering department can analyze users' social media activity when ordering ingredients and order relevant ingredients. For example, the ordering department can order relevant ingredients based on ingredient information shared by users on social media. The ordering department uses AI to analyze users' social media activity and select appropriate ingredients. For example, the ordering department can order relevant ingredients based on ingredient information shared by users on social media. The ordering department can also identify ingredients of interest from users' social media activity and order them. For example, the ordering department can order relevant ingredients based on posts from cooking experts that users follow on social media. The ordering department can also order relevant ingredients based on ingredient information shared by users on social media. In this way, relevant ingredients can be ordered by analyzing users' social media activity.

[0049] The service provider can select the optimal recipe by analyzing the user's past cooking history when providing recipes. For example, the service provider can suggest the optimal recipe based on dishes the user has enjoyed making in the past. The service provider uses AI to analyze the user's past cooking history and select an appropriate recipe. For example, the service provider can suggest the optimal recipe based on dishes the user has enjoyed making in the past. The service provider can also select a recipe considering cooking time based on the user's past cooking history. For example, the service provider can select a recipe considering cooking time based on the user's past cooking history. The service provider can also analyze the user's past cooking history and suggest a recipe that focuses on a specific cooking method. For example, the service provider analyzes the user's past cooking history and suggests a recipe that focuses on a specific cooking method. In this way, the service provider can select the optimal recipe by analyzing the user's past cooking history.

[0050] The service provider can customize recipes based on the user's current kitchen equipment when providing them. For example, the service provider can suggest the optimal recipe based on the cooking utensils the user owns. The service provider uses AI to analyze the user's kitchen equipment and select an appropriate recipe. For example, the service provider can suggest the optimal recipe based on the cooking utensils the user owns (oven, blender, refrigerator, etc.). The service provider can also adjust cooking methods considering the user's kitchen equipment. For example, the service provider can adjust cooking methods considering the user's kitchen equipment. The service provider can also optimize cooking time based on the user's kitchen equipment. For example, the service provider can optimize cooking time based on the user's kitchen equipment. This allows for improved cooking efficiency by customizing recipes based on the user's kitchen equipment.

[0051] The service provider can provide the most suitable recipe by considering the user's geographical location. For example, if the user is in a specific region, the service provider can provide a recipe using ingredients from that region. The service provider uses AI to analyze the user's geographical location and select an appropriate recipe. For example, if the user is in a specific region, the service provider can provide a recipe using ingredients from that region. For example, if the user is traveling, the service provider can provide a recipe using ingredients from their travel destination. For example, if the user is traveling, the service provider can provide a recipe using ingredients from their travel destination. For example, if the user is at home, the service provider can provide a recipe using ingredients that are easily available around their home. For example, if the user is at home, the service provider can provide a recipe using ingredients that are easily available around their home. In this way, the service provider can provide the most suitable recipe by considering the user's geographical location.

[0052] The service provider can analyze a user's social media activity when providing recipes and offer relevant recipes. For example, the service provider can offer relevant recipes based on cooking information shared by the user on social media. The service provider uses AI to analyze a user's social media activity and select appropriate recipes. For example, the service provider can offer relevant recipes based on cooking information shared by the user on social media. The service provider can also identify dishes of interest from a user's social media activity and offer recipes based on that. For example, the service provider can offer relevant recipes based on posts from cooking experts followed by the user on social media. The service provider can also offer relevant recipes based on cooking information shared by the user on social media. In this way, by analyzing a user's social media activity, relevant recipes can be offered.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] Nutrition management systems can further analyze users' eating history and increase the variety of menus they suggest based on past eating patterns. For example, the suggestion function can propose new dishes similar to those the user has enjoyed eating in the past. It can also prioritize menus that do not include ingredients the user has avoided in the past. Furthermore, the suggestion function can consider nutritional balance based on the user's eating history and suggest menus that supplement specific nutrients if there is a deficiency. This allows for the provision of more personalized menus based on the user's past eating history.

[0055] The nutrition management system can also take the user's geographical location into account and suggest menus using ingredients specific to that region. For example, if the user is in a particular area, the suggestion function can suggest menus using ingredients readily available in that area. If the user is traveling, it can suggest menus using ingredients from their travel destination. Furthermore, if the user is at home, it can suggest menus using ingredients readily available around their home. This allows the system to suggest more relevant menus by considering the user's geographical location.

[0056] The nutrition management system can further analyze users' social media activity and suggest relevant menus. For example, the suggestion function can suggest relevant menus based on cooking information shared by users on social media. It can also suggest relevant menus based on posts from cooking experts that users follow on social media. Furthermore, it can suggest relevant menus based on ingredient information shared by users on social media. This allows the system to analyze users' social media activity and suggest more relevant menus.

[0057] The nutrition management system can further analyze the user's past purchase history and select the optimal ordering method. For example, the ordering department can select the most effective ordering method based on the user's past purchase history. It can also adjust the frequency of orders based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history and focus orders on specific ingredients. This allows for the selection of the optimal ordering method by analyzing the user's past purchase history.

[0058] The nutrition management system can further adjust order quantities based on the user's current inventory status. For example, the ordering department checks the user's current inventory status and orders only the necessary ingredients. It can also reduce order quantities if the user has a large inventory, and increase them if the user has a small inventory. This allows for efficient ordering by adjusting order quantities based on the user's inventory status.

[0059] The nutrition management system can also prioritize ordering highly relevant ingredients by considering the user's geographical location. For example, if the user is in a specific region, the ordering system will prioritize ordering ingredients that are readily available in that region. If the user is traveling, it can also prioritize ordering ingredients that are readily available at their travel destination. Furthermore, if the user is at home, it can prioritize ordering ingredients that are readily available near their home. This allows the system to prioritize ordering highly relevant ingredients by considering the user's geographical location.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The acquisition unit acquires the user's health information. The acquisition unit acquires health information such as the user's weight, blood pressure, and diet. The acquisition unit can collect information through smart health devices and user feedback. Step 2: The suggestion unit proposes a menu based on the information acquired by the acquisition unit. For example, the suggestion unit uses AI to analyze the acquired health information and generate a menu with an optimal nutritional balance. The suggestion unit can also propose menus tailored to the season or specific events. Step 3: The ordering department automatically places orders for ingredients based on the menu proposed by the suggestion department. The ordering department automatically places orders with, for example, online supermarkets and meal kit services. The ordering department orders the necessary ingredients based on the service selected by the user. Step 4: The supply department provides recipes based on the ingredients ordered by the ordering department. The supply department provides recipes tailored to the user's cooking skills and kitchen equipment, for example. The supply department can also support the progress of cooking and time management until completion.

[0062] (Example of form 2) The nutrition management system according to an embodiment of the present invention is a system that uses AI to propose menus based on the user's health condition and preferences, and provides automatic ordering of ingredients and cooking support. The nutrition management system acquires the user's health information and dietary preferences, and the AI ​​proposes a menu with the optimal nutritional balance on a weekly basis based on that information. Furthermore, it automatically orders the ingredients necessary for the proposed menu from online supermarkets or meal kit services. Finally, it provides recipes tailored to the user's cooking skills and kitchen equipment, and supports the progress of cooking and time management. For example, the nutrition management system acquires the user's health information and dietary preferences. For example, it acquires health information such as the user's weight, blood pressure, and diet. Next, the nutrition management system uses AI to propose a menu with the optimal nutritional balance based on the acquired information. For example, the AI ​​analyzes the user's health information and generates a nutritionally balanced menu. Furthermore, the nutrition management system automatically orders the ingredients necessary for the proposed menu from online supermarkets or meal kit services. For example, it automatically orders the necessary ingredients based on the menu proposed by the AI. Finally, the nutrition management system provides recipes tailored to the user's cooking skills and kitchen equipment, and supports the progress of cooking and time management. For example, AI can analyze a user's cooking skills and provide appropriate recipes. This makes preparing and planning healthy meals more efficient and easier for health-conscious dual-income households. The nutrition management system can suggest menus based on the user's health information, automatically order ingredients, and provide recipes.

[0063] The nutrition management system according to this embodiment comprises an acquisition unit, a suggestion unit, an ordering unit, and a provision unit. The acquisition unit acquires the user's health information. The acquisition unit acquires health information such as the user's weight, blood pressure, and diet. The acquisition unit can also collect information through smart health devices and user feedback. The suggestion unit suggests menus based on the information acquired by the acquisition unit. The suggestion unit analyzes the acquired health information using AI, for example, and generates menus with an optimal nutritional balance. The suggestion unit can also suggest menus tailored to the season or specific events. The ordering unit automatically orders ingredients based on the menu suggested by the suggestion unit. The ordering unit automatically places orders with online supermarkets or meal kit services, for example. The ordering unit orders the necessary ingredients based on the service selected by the user. The provision unit provides recipes based on the ingredients ordered by the ordering unit. The provision unit provides recipes tailored to the user's cooking skills and kitchen equipment, for example. The provision unit can also support the progress of cooking and time management until completion. As a result, the nutrition management system according to this embodiment can suggest menus based on the user's health information, automatically order ingredients, and provide recipes.

[0064] The data acquisition unit acquires user health information. For example, it acquires health information such as the user's weight, blood pressure, and diet. Specifically, it uses a smart health device to periodically measure the user's weight and blood pressure and transmits the data to the cloud. This allows for real-time monitoring of the user's health status. In addition, the user can input their diet information through a dedicated application. The user takes a photo of their meal and uploads it to the application, and the AI ​​automatically analyzes the meal content and acquires information on calories and nutrients. Furthermore, information such as daily physical condition and exercise levels can be collected through user feedback. As a result, the data acquisition unit can centrally manage diverse user health information and create a detailed health profile. The data acquisition unit securely stores this data and can link with other departments and systems as needed. For example, the acquired data can be made accessible to the proposal and ordering departments to support optimal proposals and orders based on the user's health status. Also, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific health conditions and goals. As a result, the data acquisition unit can efficiently and effectively collect user health information and improve the overall system performance.

[0065] The suggestion unit proposes menus based on the information acquired by the acquisition unit. For example, the suggestion unit uses AI to analyze acquired health information and generate menus with optimal nutritional balance. Specifically, the AI ​​analyzes data such as the user's weight, blood pressure, diet, and exercise level, and proposes menus with optimal nutritional balance for each individual user. Based on past data and statistical information, the AI ​​generates menus tailored to the user's health condition and goals. For example, it suggests a low-calorie, high-protein menu for users aiming to lose weight, and a low-sodium menu for users with high blood pressure. The suggestion unit can also propose menus tailored to seasons and specific events. For example, it can suggest menus utilizing seasonal ingredients or special menus for specific events. Furthermore, the suggestion unit can propose optimal menus for each individual user, taking into account their preferences and allergy information. This allows the suggestion unit to propose optimal menus tailored to the user's health condition and preferences, supporting their health management. The suggestion unit notifies the user of the proposed menu, making it easy for them to review. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to propose optimal meal plans to users and support their health management.

[0066] The ordering department automatically places orders for ingredients based on menus proposed by the suggestion department. For example, the ordering department automatically places orders with online supermarkets and meal kit services. Specifically, it lists the ingredients needed for the proposed menu and automatically places orders with the online supermarket or meal kit service selected by the user. The ordering department manages user account information and delivery address information to support a smooth ordering process. For example, registering the account information of the online supermarket the user usually uses can simplify the ordering process. Furthermore, the ordering department can propose the optimal ordering plan considering the user's budget and ingredient inventory. For example, it can select the most cost-effective ingredients within the budget to avoid waste. In addition, the ordering department can monitor the ordering status in real time and notify the user. For example, it can notify the user when the order is completed, informing them of the expected delivery date and delivery status. This allows the ordering department to support users in ordering ingredients smoothly and efficiently, helping them realize their menus. The ordering department can collect user feedback and continuously improve the accuracy and efficiency of the ordering process. This allows the ordering department to support users in ordering the most suitable ingredients and improve the overall performance of the system.

[0067] The supply department provides recipes based on the ingredients ordered by the ordering department. For example, the supply department provides recipes tailored to the user's cooking skills and kitchen equipment. Specifically, it suggests easy-to-prepare recipes or recipes that require specific cooking utensils, taking into account the user's cooking skills and kitchen equipment. For example, it suggests recipes with simple steps for beginners and recipes requiring more advanced cooking techniques for experienced users. The supply department can also support the progress of cooking and time management until completion. For example, it can set timers for each step of cooking and notify the user to smoothly support the progress of cooking. Furthermore, the supply department can provide recipe videos and images to provide a visually easy-to-understand cooking guide. This makes it easier for users to intuitively understand the cooking procedure. The supply department can collect user feedback and continuously improve the accuracy and effectiveness of the recipe content and delivery method. For example, it can collect feedback on problems and areas for improvement that users experienced during cooking and reflect them in future recipe suggestions. In this way, the supply department can provide users with the best possible recipes and support their cooking. The service provider can support users' health management by calculating nutritional balance and calories, thereby promoting a healthy diet. This allows the service provider to offer users healthy and delicious meals and improve the overall system performance.

[0068] The suggestion department can propose menus tailored to the season and specific events. For example, it can suggest menus that incorporate seasonal ingredients, holidays, and specific events (such as birthdays or anniversaries). The suggestion department uses AI to collect information related to the season and events and generates menus based on that information. For example, the suggestion department can suggest menus using spring vegetables in the spring and cold dishes in the summer. It can also suggest a special dinner for a user's birthday. In this way, by suggesting menus tailored to the season and specific events, it can provide menus that meet the user's needs.

[0069] The service provider can offer recipes tailored to the user's cooking skills and kitchen equipment. For example, it can evaluate the user's cooking skills and provide recipes for beginners, intermediate users, and advanced users. The service provider uses AI to analyze the user's cooking skills and select appropriate recipes. For example, it can provide beginners with recipes that include simple cooking steps and intermediate users with slightly more complex recipes. The service provider can also customize recipes based on the user's kitchen equipment. For example, it can suggest the optimal recipe based on the cooking appliances the user owns (oven, blender, refrigerator, etc.). By providing recipes tailored to the user's cooking skills and kitchen equipment, the service can improve cooking efficiency.

[0070] The service unit can support the progress of cooking and time management until completion. For example, the service unit can manage each cooking step with a timer and notify the user. The service unit uses AI to monitor the progress of cooking in real time and instruct the next step at the appropriate time. For example, the service unit measures the time for stir-frying and notifies the user to add the next ingredient at the appropriate time. The service unit can also predict the time until cooking is complete and inform the user. In this way, by supporting the progress of cooking and time management until completion, cooking efficiency can be improved.

[0071] The data acquisition unit can estimate the user's emotions and adjust the timing of health information acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will acquire health information during relaxed periods. The data acquisition unit uses AI to analyze the user's facial expressions and voice to estimate emotions. For example, the data acquisition unit uses facial recognition technology to detect the user's stress and relaxation states. The data acquisition unit can also acquire detailed health information when the user is relaxed. For example, the data acquisition unit will acquire detailed health information such as weight and blood pressure during relaxed periods. The data acquisition unit can also acquire simplified health information when the user is busy. For example, during busy periods, the data acquisition unit will prioritize simple questions and information that can be acquired quickly. By adjusting the timing of health information acquisition according to the user's emotions, information can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The data acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the data acquisition unit can select the most effective data acquisition method from the user's past health data. The data acquisition unit uses AI to analyze past health data and determine the optimal acquisition method. For example, the data acquisition unit analyzes the user's past weight records and blood pressure data to select the optimal device and data acquisition frequency. The data acquisition unit can also adjust the data acquisition frequency based on the user's past health data. For example, the data acquisition unit can reduce the data acquisition frequency when the user's health is stable and increase it when the health is fluctuating. The data acquisition unit can also focus on acquiring data for specific health indicators. For example, the data acquisition unit can focus on acquiring data for particularly important health indicators (e.g., blood glucose levels and cholesterol levels) from the user's past data. This allows the optimal data acquisition method to be selected by analyzing the user's past health data.

[0073] The data acquisition unit can filter health information based on the user's current lifestyle and activity level. For example, if the user is exercising, the unit prioritizes acquiring exercise-related health information. The data acquisition unit uses AI to analyze the user's lifestyle and activity level and filter the information accordingly. For example, if the user is exercising, the unit prioritizes acquiring exercise-related information such as heart rate and calorie consumption. The data acquisition unit can also acquire relaxation-related health information if the user is resting. For example, if the user is resting, the unit acquires information related to stress levels and sleep quality. The data acquisition unit can also acquire stress management-related health information if the user is working. For example, if the user is working, the unit acquires information related to stress levels and concentration. By filtering health information based on the user's lifestyle and activity level, more relevant information can be acquired.

[0074] The data acquisition unit can estimate the user's emotions and determine the priority of health information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring health information related to stress management. The data acquisition unit uses AI to analyze the user's facial expressions and voice to estimate emotions. For example, the data acquisition unit uses facial recognition technology to detect the user's stress and relaxation levels. The data acquisition unit can also prioritize acquiring health information related to nutritional balance if the user is relaxed. For example, if the user is relaxed, the data acquisition unit will prioritize acquiring information related to vitamin and mineral intake. The data acquisition unit can also prioritize acquiring health information related to rest if the user is tired. For example, if the user is tired, the data acquisition unit will prioritize acquiring information related to sleep quality and the effects of rest. This allows for the prioritization of more important information by determining the priority of health information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring health information. For example, if the user is in a specific region, the data acquisition unit will acquire information related to the health risks of that region. The data acquisition unit uses AI to analyze the user's geographical location and filter the information appropriately. For example, if the user is in a specific region, the data acquisition unit will prioritize the acquisition of information related to diseases and health risks prevalent in that region. The data acquisition unit can also prioritize the acquisition of health information related to the travel destination if the user is traveling. For example, if the user is traveling, the data acquisition unit will acquire health information related to the climate and diet of the travel destination. The data acquisition unit can also prioritize the acquisition of health information related to the area around the user's home if the user is at home. For example, if the user is at home, the data acquisition unit will acquire health information related to the environment and lifestyle around the user's home. In this way, by considering the user's geographical location, the data acquisition unit can prioritize the acquisition of highly relevant health information.

[0076] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring health information. For example, the data acquisition unit can acquire relevant information based on health information shared by the user on social media. The data acquisition unit uses AI to analyze the user's social media activity and filter appropriate information. For example, the data acquisition unit can acquire relevant information based on health information shared by the user on social media. The data acquisition unit can also identify health topics of interest from the user's social media activity and acquire information. For example, the data acquisition unit can acquire relevant information based on posts from health professionals followed by the user on social media. The data acquisition unit can also acquire relevant information based on health information shared by the user on social media. In this way, relevant health information can be acquired by analyzing the user's social media activity.

[0077] The suggestion unit can estimate the user's emotions and adjust the way the menu is presented based on those emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and visually relaxing menu. The suggestion unit uses AI to analyze the user's facial expressions and voice to estimate emotions. For example, the suggestion unit can use facial recognition technology to detect the user's stress or relaxation state. The suggestion unit can also suggest a menu with detailed nutritional information if the user is relaxed. For example, if the user is relaxed, the suggestion unit can suggest a menu that includes information related to vitamin and mineral intake. The suggestion unit can also suggest an easy-to-prepare menu if the user is in a hurry. For example, if the user is in a hurry, the suggestion unit can suggest a menu that can be prepared in a short time. By adjusting the way the menu is presented according to the user's emotions, a more appropriate menu can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The suggestion function can adjust the level of detail in menu suggestions based on the importance of nutritional balance. For example, if a user prioritizes nutritional balance, the suggestion function will suggest a menu that includes detailed nutritional information. The suggestion function uses AI to analyze the importance of nutritional balance for the user and generate an appropriate menu. For example, if a user prioritizes nutritional balance, the suggestion function will suggest a menu that includes information related to vitamin and mineral intake. The suggestion function can also suggest a menu that emphasizes calorie information if the user is restricting calories. For example, if a user is restricting calories, the suggestion function will suggest a menu that emphasizes calorie information. The suggestion function can also suggest a menu that includes information related to a specific nutrient if the user prioritizes that nutrient. For example, if a user prioritizes a specific nutrient, the suggestion function will suggest a menu that includes information related to that nutrient. In this way, by adjusting the level of detail in suggestions based on the importance of nutritional balance, menus that meet the user's needs can be provided.

[0079] The suggestion function can apply different suggestion algorithms depending on the user's health goals when suggesting menus. For example, if the user is aiming to lose weight, the suggestion function will suggest a low-calorie menu. The suggestion function uses AI to analyze the user's health goals and apply the appropriate suggestion algorithm. For example, if the user is aiming to lose weight, the suggestion function will suggest a low-calorie menu. The suggestion function can also suggest a high-protein menu if the user is aiming to build muscle. For example, if the user is aiming to build muscle, the suggestion function will suggest a high-protein menu. The suggestion function can also suggest a balanced menu if the user is aiming to maintain their health. For example, if the suggestion function is aiming to maintain their health, the suggestion function will suggest a balanced menu. In this way, by applying different suggestion algorithms according to the user's health goals, it is possible to suggest more appropriate menus.

[0080] The suggestion unit can estimate the user's emotions and adjust the length of the menu based on those emotions. For example, if the user is stressed, the suggestion unit will suggest a short and simple menu. The suggestion unit uses AI to analyze the user's facial expressions and voice to estimate emotions. For example, the suggestion unit uses facial recognition technology to detect the user's state of stress or relaxation. The suggestion unit can also suggest a longer menu with more detailed explanations if the user is relaxed. For example, if the user is relaxed, the suggestion unit will suggest a longer menu with more detailed explanations. The suggestion unit can also suggest a menu that can be prepared quickly if the user is in a hurry. For example, if the suggestion unit is in a hurry, the suggestion unit will suggest a menu that can be prepared quickly. In this way, by adjusting the length of the menu according to the user's emotions, a more appropriate menu can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The suggestion function can prioritize menu suggestions based on the user's eating history. For example, it can prioritize dishes the user has enjoyed eating in the past. The suggestion function uses AI to analyze the user's eating history and make appropriate suggestions. For example, it can prioritize dishes the user has enjoyed eating in the past. The suggestion function can also exclude dishes the user has avoided in the past from its suggestions. For example, it can exclude dishes the user has avoided in the past from its suggestions. Furthermore, the suggestion function can prioritize suggestions while considering nutritional balance based on the user's eating history. For example, it can prioritize suggestions while considering nutritional balance based on the user's eating history. In this way, by prioritizing suggestions based on the user's eating history, it can suggest more appropriate menus.

[0082] The suggestion function can adjust the order of menu suggestions based on the user's dietary restrictions. For example, if the user has allergies, the suggestion function will prioritize suggesting menus that do not contain those allergens. The suggestion function uses AI to analyze the user's dietary restrictions and make appropriate suggestions. For example, if the user has allergies, the suggestion function will prioritize suggesting menus that do not contain those allergens. The suggestion function can also suggest menus that do not contain specific ingredients that the user is avoiding. For example, if the user is avoiding specific ingredients, the suggestion function will suggest menus that do not contain those ingredients. The suggestion function can also suggest menus that are suitable for a particular diet if the user is on one. For example, if the suggestion function is on one particular diet, the suggestion function will suggest menus that are suitable for that diet. By adjusting the order of suggestions based on the user's dietary restrictions, the system can suggest more appropriate menus.

[0083] The ordering system can estimate the user's emotions and adjust the timing of ingredient orders based on those emotions. For example, if the user is stressed, the ordering system will order ingredients during a relaxed time. The ordering system uses AI to analyze the user's facial expressions and voice to estimate their emotions. For example, the ordering system uses facial recognition technology to detect the user's stress or relaxation state. The ordering system can also provide detailed ordering information if the user is relaxed. For example, the ordering system provides detailed ordering information if the user is relaxed. The ordering system can also provide simplified ordering information if the user is busy. For example, the ordering system provides simplified ordering information if the user is busy. By adjusting the timing of ingredient orders according to the user's emotions, ingredients can be ordered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The ordering department can analyze a user's past purchase history to select the optimal ordering method when ordering ingredients. For example, the ordering department can select the most effective ordering method based on the user's past purchase history. The ordering department uses AI to analyze a user's past purchase history and determine the appropriate ordering method. For example, the ordering department can select the most effective ordering method based on the user's past purchase history. The ordering department can also adjust the frequency of orders based on the user's past purchase history. For example, the ordering department adjusts the frequency of orders based on the user's past purchase history. The ordering department can also analyze a user's past purchase history and focus orders on specific ingredients. For example, the ordering department analyzes a user's past purchase history and focuses orders on specific ingredients. This allows the ordering department to select the optimal ordering method by analyzing the user's past purchase history.

[0085] The ordering department can adjust the order quantity based on the user's current inventory status when ordering ingredients. For example, the ordering department can check the user's current inventory status and order only the necessary ingredients. The ordering department uses AI to analyze the user's inventory status and determine the appropriate order quantity. For example, the ordering department can check the user's current inventory status and order only the necessary ingredients. The ordering department can also reduce the order quantity if the user has a large inventory. For example, the ordering department can reduce the order quantity if the user has a large inventory. The ordering department can also increase the order quantity if the user has a small inventory. For example, the ordering department can increase the order quantity if the user has a small inventory. In this way, by adjusting the order quantity based on the user's inventory status, waste-free ordering becomes possible.

[0086] The ordering system can estimate the user's emotions and prioritize the ingredients to order based on those emotions. For example, if the user is stressed, the ordering system will prioritize ordering ingredients that help reduce stress. The ordering system uses AI to analyze the user's facial expressions and voice to estimate emotions. For example, the ordering system uses facial recognition technology to detect the user's stress or relaxation state. The ordering system can also prioritize ordering ingredients that consider nutritional balance if the user is relaxed. For example, if the user is relaxed, the ordering system will prioritize ordering ingredients that consider nutritional balance. The ordering system can also prioritize ordering ingredients that help replenish energy if the user is tired. For example, if the user is tired, the ordering system will prioritize ordering ingredients that help replenish energy. By prioritizing ingredients according to the user's emotions, more appropriate ingredients can be ordered. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The ordering department can prioritize ordering ingredients that are highly relevant to the user's geographical location when placing an order. For example, if the user is in a specific region, the ordering department will prioritize ordering ingredients that are easily available in that region. The ordering department uses AI to analyze the user's geographical location and select appropriate ingredients. For example, if the user is in a specific region, the ordering department will prioritize ordering ingredients that are easily available in that region. The ordering department can also prioritize ordering ingredients that are easily available at the user's travel destination if the user is traveling. For example, if the user is traveling, the ordering department will prioritize ordering ingredients that are easily available at the user's travel destination if the user is traveling. The ordering department can also prioritize ordering ingredients that are easily available in the vicinity of the user's home if the user is at home. For example, if the user is at home, the ordering department will prioritize ordering ingredients that are easily available in the vicinity of the user's home. In this way, by considering the user's geographical location, the ordering department can prioritize ordering ingredients that are highly relevant to the user's location.

[0088] The ordering department can analyze users' social media activity when ordering ingredients and order relevant ingredients. For example, the ordering department can order relevant ingredients based on ingredient information shared by users on social media. The ordering department uses AI to analyze users' social media activity and select appropriate ingredients. For example, the ordering department can order relevant ingredients based on ingredient information shared by users on social media. The ordering department can also identify ingredients of interest from users' social media activity and order them. For example, the ordering department can order relevant ingredients based on posts from cooking experts that users follow on social media. The ordering department can also order relevant ingredients based on ingredient information shared by users on social media. In this way, relevant ingredients can be ordered by analyzing users' social media activity.

[0089] The service provider can estimate the user's emotions and adjust the way the recipe is presented based on those emotions. For example, if the user is stressed, the service provider can provide a simple, visually relaxing recipe. The service provider uses AI to analyze the user's facial expressions and voice to estimate their emotions. For example, the service provider can use facial recognition technology to detect the user's stress or relaxation state. The service provider can also provide a recipe with detailed cooking instructions if the user is relaxed. For example, if the user is relaxed, the service provider can provide a recipe with detailed cooking instructions. The service provider can also provide a recipe that can be prepared quickly if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide a recipe that can be prepared quickly. By adjusting the way the recipe is presented according to the user's emotions, the service provider can provide a more appropriate recipe. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The service provider can select the optimal recipe by analyzing the user's past cooking history when providing recipes. For example, the service provider can suggest the optimal recipe based on dishes the user has enjoyed making in the past. The service provider uses AI to analyze the user's past cooking history and select an appropriate recipe. For example, the service provider can suggest the optimal recipe based on dishes the user has enjoyed making in the past. The service provider can also select a recipe considering cooking time based on the user's past cooking history. For example, the service provider can select a recipe considering cooking time based on the user's past cooking history. The service provider can also analyze the user's past cooking history and suggest a recipe that focuses on a specific cooking method. For example, the service provider analyzes the user's past cooking history and suggests a recipe that focuses on a specific cooking method. In this way, the service provider can select the optimal recipe by analyzing the user's past cooking history.

[0091] The service provider can customize recipes based on the user's current kitchen equipment when providing them. For example, the service provider can suggest the optimal recipe based on the cooking utensils the user owns. The service provider uses AI to analyze the user's kitchen equipment and select an appropriate recipe. For example, the service provider can suggest the optimal recipe based on the cooking utensils the user owns (oven, blender, refrigerator, etc.). The service provider can also adjust cooking methods considering the user's kitchen equipment. For example, the service provider can adjust cooking methods considering the user's kitchen equipment. The service provider can also optimize cooking time based on the user's kitchen equipment. For example, the service provider can optimize cooking time based on the user's kitchen equipment. This allows for improved cooking efficiency by customizing recipes based on the user's kitchen equipment.

[0092] The service provider can estimate the user's emotions and prioritize recipes based on those emotions. For example, if the user is stressed, the service provider will prioritize providing recipes with a relaxing effect. The service provider uses AI to analyze the user's facial expressions and voice to estimate emotions. For example, the service provider can use facial recognition technology to detect the user's stress or relaxation state. The service provider can also prioritize providing nutritionally balanced recipes if the user is relaxed. For example, if the user is relaxed, the service provider will prioritize providing nutritionally balanced recipes. The service provider can also prioritize providing recipes that can be prepared quickly if the user is in a hurry. For example, if the service provider is in a hurry, the service provider will prioritize providing recipes that can be prepared quickly. In this way, by prioritizing recipes according to the user's emotions, more appropriate recipes can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The service provider can provide the most suitable recipe by considering the user's geographical location. For example, if the user is in a specific region, the service provider can provide a recipe using ingredients from that region. The service provider uses AI to analyze the user's geographical location and select an appropriate recipe. For example, if the user is in a specific region, the service provider can provide a recipe using ingredients from that region. For example, if the user is traveling, the service provider can provide a recipe using ingredients from their travel destination. For example, if the user is traveling, the service provider can provide a recipe using ingredients from their travel destination. For example, if the user is at home, the service provider can provide a recipe using ingredients that are easily available around their home. For example, if the user is at home, the service provider can provide a recipe using ingredients that are easily available around their home. In this way, the service provider can provide the most suitable recipe by considering the user's geographical location.

[0094] The service provider can analyze a user's social media activity when providing recipes and offer relevant recipes. For example, the service provider can offer relevant recipes based on cooking information shared by the user on social media. The service provider uses AI to analyze a user's social media activity and select appropriate recipes. For example, the service provider can offer relevant recipes based on cooking information shared by the user on social media. The service provider can also identify dishes of interest from a user's social media activity and offer recipes based on that. For example, the service provider can offer relevant recipes based on posts from cooking experts followed by the user on social media. The service provider can also offer relevant recipes based on cooking information shared by the user on social media. In this way, by analyzing a user's social media activity, relevant recipes can be offered.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] Nutrition management systems can further analyze users' eating history and increase the variety of menus they suggest based on past eating patterns. For example, the suggestion function can propose new dishes similar to those the user has enjoyed eating in the past. It can also prioritize menus that do not include ingredients the user has avoided in the past. Furthermore, the suggestion function can consider nutritional balance based on the user's eating history and suggest menus that supplement specific nutrients if there is a deficiency. This allows for the provision of more personalized menus based on the user's past eating history.

[0097] The nutrition management system can further estimate the user's emotions and adjust menu suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion system can suggest menus using ingredients that have a relaxing effect. If the user is relaxed, it can also suggest menus that prioritize nutritional balance. Furthermore, if the user is in a hurry, it can suggest simple menus that can be prepared in a short time. In this way, the system can suggest the most suitable menu according to the user's emotions.

[0098] The nutrition management system can also take the user's geographical location into account and suggest menus using ingredients specific to that region. For example, if the user is in a particular area, the suggestion function can suggest menus using ingredients readily available in that area. If the user is traveling, it can suggest menus using ingredients from their travel destination. Furthermore, if the user is at home, it can suggest menus using ingredients readily available around their home. This allows the system to suggest more relevant menus by considering the user's geographical location.

[0099] The nutrition management system can further analyze users' social media activity and suggest relevant menus. For example, the suggestion function can suggest relevant menus based on cooking information shared by users on social media. It can also suggest relevant menus based on posts from cooking experts that users follow on social media. Furthermore, it can suggest relevant menus based on ingredient information shared by users on social media. This allows the system to analyze users' social media activity and suggest more relevant menus.

[0100] The nutrition management system can further estimate the user's emotions and adjust the timing of food orders based on those emotions. For example, if the user is feeling stressed, the ordering system will order food during a time when the user is relaxed. It can also provide detailed ordering information when the user is relaxed. Furthermore, if the user is busy, it can provide simplified ordering information. This allows for more appropriate ordering times by adjusting the timing of food orders according to the user's emotions.

[0101] The nutrition management system can further analyze the user's past purchase history and select the optimal ordering method. For example, the ordering department can select the most effective ordering method based on the user's past purchase history. It can also adjust the frequency of orders based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history and focus orders on specific ingredients. This allows for the selection of the optimal ordering method by analyzing the user's past purchase history.

[0102] The nutrition management system can further adjust order quantities based on the user's current inventory status. For example, the ordering department checks the user's current inventory status and orders only the necessary ingredients. It can also reduce order quantities if the user has a large inventory, and increase them if the user has a small inventory. This allows for efficient ordering by adjusting order quantities based on the user's inventory status.

[0103] The nutrition management system can further estimate the user's emotions and prioritize the ingredients to order based on those emotions. For example, if the user is stressed, the ordering system will prioritize ordering ingredients that help reduce stress. If the user is relaxed, it can also prioritize ordering ingredients that provide a balanced diet. Furthermore, if the user is tired, it can prioritize ordering ingredients that help replenish energy. By prioritizing ingredients according to the user's emotions, the system can order more appropriate ingredients.

[0104] The nutrition management system can also prioritize ordering highly relevant ingredients by considering the user's geographical location. For example, if the user is in a specific region, the ordering system will prioritize ordering ingredients that are readily available in that region. If the user is traveling, it can also prioritize ordering ingredients that are readily available at their travel destination. Furthermore, if the user is at home, it can prioritize ordering ingredients that are readily available near their home. This allows the system to prioritize ordering highly relevant ingredients by considering the user's geographical location.

[0105] The nutrition management system can further estimate the user's emotions and adjust the way recipes are presented based on those emotions. For example, if the user is stressed, the system can provide simple, visually relaxing recipes. If the user is relaxed, it can provide recipes with detailed cooking instructions. Furthermore, if the user is in a hurry, it can provide recipes that can be prepared quickly. In this way, by adjusting the way recipes are presented according to the user's emotions, more appropriate recipes can be provided.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The acquisition unit acquires the user's health information. The acquisition unit acquires health information such as the user's weight, blood pressure, and diet. The acquisition unit can collect information through smart health devices and user feedback. Step 2: The suggestion unit proposes a menu based on the information acquired by the acquisition unit. For example, the suggestion unit uses AI to analyze the acquired health information and generate a menu with an optimal nutritional balance. The suggestion unit can also propose menus tailored to the season or specific events. Step 3: The ordering department automatically places orders for ingredients based on the menu proposed by the suggestion department. The ordering department automatically places orders with, for example, online supermarkets and meal kit services. The ordering department orders the necessary ingredients based on the service selected by the user. Step 4: The supply department provides recipes based on the ingredients ordered by the ordering department. The supply department provides recipes tailored to the user's cooking skills and kitchen equipment, for example. The supply department can also support the progress of cooking and time management until completion.

[0108] 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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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 audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the acquisition unit, proposal unit, ordering unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the user's health information using the camera 42 and microphone 38B of the smart device 14 and transmits that information to the data processing unit 12 via the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired health information to generate an optimal menu. The ordering unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically orders the necessary ingredients from online supermarkets or meal kit services based on the proposed menu. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides recipes tailored to the user's cooking skills and kitchen equipment, supporting the progress of cooking and time management. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] 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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a 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. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the acquisition unit, proposal unit, ordering unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the user's health information using the camera 42 and microphone 238 of the smart glasses 214 and transmits that information to the data processing unit 12 via the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the acquired health information to generate an optimal menu. The ordering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically orders the necessary ingredients from online supermarkets or meal kit services based on the proposed menu. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides recipes tailored to the user's cooking skills and kitchen equipment, and supports the progress and time management of cooking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the acquisition unit, proposal unit, ordering unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the user's health information using the camera 42 and microphone 238 of the headset terminal 314 and transmits that information to the data processing unit 12 via the control unit 46A. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the acquired health information to generate an optimal menu. The ordering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically orders the necessary ingredients from online supermarkets or meal kit services based on the proposed menu. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides recipes tailored to the user's cooking skills and kitchen equipment, and supports the progress and time management of cooking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the 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.

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

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the acquisition unit, proposal unit, ordering unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the user's health information using the camera 42 and microphone 238 of the robot 414 and transmits that information to the data processing unit 12 via the control unit 46A. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the acquired health information to generate an optimal menu. The ordering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically orders the necessary ingredients from online supermarkets or meal kit services based on the proposed menu. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides recipes tailored to the user's cooking skills and kitchen equipment, and supports the progress and time management of cooking. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] Alternatively, 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 in response to a request from the data processing device 12.

[0171] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) An acquisition unit that acquires user health information, A proposal unit that proposes a menu based on the information acquired by the acquisition unit, An ordering unit that automatically orders ingredients based on the menu proposed by the aforementioned proposal unit, The system includes a supply unit that provides recipes based on ingredients ordered by the ordering unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose menus tailored to the season and specific events. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provides recipes tailored to the user's cooking skills and kitchen equipment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Supports the progress of cooking and time management until completion. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of health information acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, Analyze the user's past health data and select the optimal method of data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring health information, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, It estimates the user's emotions and determines the priority of health information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring health information, the system prioritizes the acquisition of highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring health information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way the menu is presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When suggesting menus, adjust the level of detail based on the importance of nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When suggesting menus, different suggestion algorithms are applied depending on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the length of the menu based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When suggesting menus, the system prioritizes suggestions based on the user's meal history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When suggesting menus, the order of suggestions is adjusted based on the user's dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The ordering department said, The system estimates the user's emotions and adjusts the timing of ingredient orders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The ordering department said, When ordering ingredients, the system analyzes the user's past purchase history to select the optimal ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The ordering department said, When ordering ingredients, adjust the order quantity based on the user's current inventory status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The ordering department said, The system estimates the user's emotions and determines the priority of ingredients to order based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The ordering department said, When ordering ingredients, the system prioritizes ordering ingredients that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The ordering department said, When ordering ingredients, the system analyzes the user's social media activity and orders ingredients that are relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing recipes, the system analyzes the user's past cooking history to select the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing recipes, customize them based on the user's current kitchen equipment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and prioritizes recipes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing recipes, we take the user's geographical location into consideration to provide the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing recipes, we analyze users' social media activity and provide relevant recipes. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires user health information, A proposal unit that proposes a menu based on the information acquired by the acquisition unit, An ordering unit that automatically orders ingredients based on the menu proposed by the aforementioned proposal unit, The system includes a supply unit that provides recipes based on ingredients ordered by the ordering unit. A system characterized by the following features.

2. The aforementioned proposal section is, We propose menus tailored to the season and specific events. The system according to feature 1.

3. The aforementioned supply unit is, Provides recipes tailored to the user's cooking skills and kitchen equipment. The system according to feature 1.

4. The aforementioned supply unit is, Supports the progress of cooking and time management until completion. The system according to feature 1.

5. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of health information acquisition based on those estimated emotions. The system according to feature 1.

6. The acquisition unit is, Analyze the user's past health data and select the optimal method of data acquisition. The system according to feature 1.

7. The acquisition unit is, When acquiring health information, filtering is performed based on the user's current lifestyle and activity level. The system according to feature 1.

8. The acquisition unit is, It estimates the user's emotions and determines the priority of health information to acquire based on the estimated user emotions. The system according to feature 1.

9. The acquisition unit is, When acquiring health information, the system prioritizes the acquisition of highly relevant information by considering the user's geographical location. The system according to feature 1.

10. The acquisition unit is, When acquiring health information, the system analyzes the user's social media activity and retrieves relevant information. The system according to feature 1.

Images