System

Abstract

An object of a system according to an embodiment is to provide an optimal meal menu based on a health condition and a goal of a user and to assist a cooking method.SOLUTION: A system includes a menu design part, a shopping list generation part, a cooking assist part, and a recipe update part. The menu formulator formulates a menu based on the health condition or goal of the user. The shopping list generation unit generates a shopping list based on the menu devised by the menu devising unit. The cooking assist unit assists a cooking method based on the shopping list generated by the shopping list generation unit. The recipe update unit collects the user's evaluation based on the cooking method assisted by the cooking assist unit, and updates the recipe.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately assist in creating meal menus or cooking methods tailored to individual health conditions and goals, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal meal menu based on the user's health condition and goals, and to assist with cooking methods. [Means for solving the problem]

[0006] The system according to the embodiment includes a menu planning unit, a shopping list generation unit, a cooking assistance unit, and a recipe update unit. The menu planning unit plans a menu based on a user's health condition or goals. The shopping list generation unit generates a shopping list based on the menu planned by the menu planning unit. The cooking assistance unit assists with cooking methods based on the shopping list generated by the shopping list generation unit. The recipe update unit collects user ratings based on the cooking methods assisted by the cooking assistance unit and updates the recipe. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal meal menu based on the user's health condition and goals, and can assist with cooking methods. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The health management system according to an embodiment of the present invention is a system that creates individual menus based on the user's health status and goals, automatically generates and orders shopping lists, assists with cooking methods, and updates recipes based on the user's ratings. As a result, the health management system can provide optimal dietary management for the user and support a healthy lifestyle.

[0029] A health management system according to an embodiment includes a menu design unit, a shopping list generation unit, a cooking assistance unit, and a recipe update unit. The menu design unit designs menus based on a user's health status and goals. For example, the menu design unit suggests low-calorie, nutritionally balanced menus to a user on a diet. The menu design unit also suggests high-protein, energy-replenishing menus to athletes. The menu design unit also suggests allergy-friendly menus to users with dietary restrictions. The shopping list generation unit generates a shopping list based on the menu designed by the menu design unit. For example, the shopping list generation unit lists necessary ingredients and automatically orders them through an online shopping service. The shopping list generation unit can also automatically order the necessary ingredients using an online shopping service designated by the user. The shopping list generation unit also analyzes the user's past purchase history and prioritizes frequently purchased ingredients to the list. The cooking assistance unit assists with cooking methods based on the shopping list generated by the shopping list generation unit. For example, the cooking assistance unit suggests specific cooking procedures, cooking times, and necessary cooking utensils. The cooking assistance unit also suggests optimal cooking methods based on the user's cooking skills and available cooking utensils. The cooking assistance unit also provides real-time feedback during cooking and advice according to the cooking progress. The recipe update unit collects user ratings based on the cooking methods assisted by the cooking assistance unit and updates the recipe. For example, when a user rates a provided recipe, the recipe update unit improves the recipe based on the rating. The recipe update unit also reflects user feedback in real time and improves the recipe instantly. The recipe update unit also uses an emotion estimation function to improve the recipe based on the user's emotional response, thereby increasing emotional satisfaction. As a result, the health management system according to the embodiment can provide optimal dietary management to the user and support a healthy lifestyle.

[0030] The menu design unit can analyze a user's past dietary history and health data to generate a menu based on their long-term health goals. For example, the menu design unit stores the user's past dietary history in a database and compares it with their health data to generate a menu based on their long-term health goals. For example, the menu design unit analyzes past dietary history and weight fluctuations to suggest an appropriate calorie intake. The menu design unit also collects health data such as blood pressure, blood sugar levels, and cholesterol levels, and generates a menu based on that data. For example, it suggests a low-carbohydrate menu for a user with a high blood sugar level. The menu design unit also sets the user's health goals (e.g., weight loss, muscle building, lowering cholesterol levels) and generates a meal plan aimed at that goal. For example, it suggests a low-calorie menu for a user aiming to lose weight. In this way, by analyzing a user's past dietary history and health data and generating a menu based on their long-term health goals, more personalized dietary management can be provided.

[0031] The menu creation unit can learn a user's dietary preferences or allergy information in real time and provide an individually optimized menu. For example, the menu creation unit learns a user's dietary preferences from questionnaires or past selection history and generates a menu based on that. For example, it proposes a menu that prioritizes the user's favorite ingredients. The menu creation unit also registers allergy information in a database and generates a menu that avoids allergies based on that information. For example, it proposes a menu that does not contain nuts to a user with a nut allergy. The menu creation unit also updates a user's dietary preferences and allergy information in real time and optimizes the menu accordingly. For example, if a new allergy is discovered, that information is immediately reflected. In this way, the system can learn a user's dietary preferences and allergy information in real time and provide an individually optimized menu, thereby increasing user satisfaction.

[0032] When creating a menu, the menu creation department can consider ingredients that are appropriate for the season or weather and propose seasonal menus. The menu creation department, for example, registers seasonal ingredients in a database and generates menus based on that. For example, in spring, it proposes menus using fresh vegetables. The menu creation department also obtains weather data and proposes menus accordingly. For example, on cold days, it proposes hot soups or hot pot dishes. The menu creation department also considers the nutritional value of ingredients that are appropriate for the season and weather and generates balanced menus. For example, in summer it proposes menus that emphasize hydration. In this way, by considering ingredients that are appropriate for the season and weather and proposing seasonal menus, it is possible to provide users with a new dining experience.

[0033] The menu creation department can incorporate cuisine from different cultures to provide users with a new dining experience. For example, the menu creation department registers recipes from different cultures in a database and generates menus based on them. For example, it proposes menus that incorporate Italian cuisine or Indian cuisine. The menu creation department also customizes cuisine from different cultures according to the user's preferences. For example, it proposes spicy Indian cuisine for a user who likes spicy food. The menu creation department also uses ingredients from different cultures to provide users with a new dining experience. For example, it proposes menus that use ethnic ingredients. In this way, it is possible to provide users with a new dining experience by incorporating cuisine from different cultures.

[0034] The shopping list generation unit can analyze the user's past purchase history and add frequently purchased ingredients to the list with priority. The shopping list generation unit, for example, stores the user's past purchase history in a database and automatically adds frequently purchased ingredients to the list. For example, milk and bread purchased weekly are added with priority to the list. The shopping list generation unit also analyzes the purchase history and understands seasonal purchasing patterns. For example, ice cream and cold drinks are added to the list in summer. The shopping list generation unit also predicts ingredients needed based on the user's purchase history and adds them to the list. For example, ingredients needed for the weekend are predicted from past data and added to the list. In this way, shopping efficiency can be improved by analyzing the user's past purchase history and adding frequently purchased ingredients to the list with priority.

[0035] The shopping list generation unit can generate a shopping list that takes into account the shelf life of ingredients and minimizes waste. The shopping list generation unit, for example, registers the shelf life of ingredients in a database and generates a shopping list based on that. For example, it adds only the necessary amount of vegetables and fruits with short shelf lives to the list. The shopping list generation unit also makes a food consumption plan taking into account shelf lives. For example, it adjusts the menu so that ingredients with short shelf lives are consumed first. The shopping list generation unit also monitors the shelf life of ingredients in real time and generates a shopping list that minimizes waste. For example, it prioritizes adding ingredients whose shelf life is approaching the end of their shelf life to the list. In this way, food waste can be reduced by generating a shopping list that takes into account the shelf life of ingredients and minimizes waste.

[0036] The shopping list generation unit can prioritize adding environmentally friendly ingredients to the shopping list. For example, the shopping list generation unit registers eco-friendly ingredients in a database and generates a shopping list based on that. For example, organic vegetables and fair trade products are prioritized when added to the list. The shopping list generation unit also sets selection criteria for environmentally friendly ingredients and generates a shopping list based on those criteria. For example, locally produced ingredients and seasonal ingredients are prioritized when added to the list. The shopping list generation unit also develops an algorithm that prioritizes the selection of eco-friendly ingredients and generates a shopping list based on that algorithm. For example, ingredients with a low environmental impact are added to the list. This allows sustainable shopping to be promoted by prioritizing the addition of environmentally friendly ingredients.

[0037] The shopping list generation unit can support the local economy by adding ingredients that can be purchased directly from local farmers or markets to the list. For example, the shopping list generation unit registers ingredients that can be purchased directly from local farmers or markets in a database and generates a shopping list based on the ingredients. For example, fresh locally grown vegetables and fruits are added to the list. The shopping list generation unit also partners with local farmers and markets and prioritizes adding ingredients that can be purchased directly to the list. For example, ingredients that are shipped directly from local farmers are added to the list. The shopping list generation unit also develops an algorithm that prioritizes selecting ingredients from local farms and markets and generates a shopping list based on the algorithm. For example, locally grown ingredients are added to the list to support the local economy. By adding ingredients that can be purchased directly from local farmers and markets to the list, the local economy can be supported.

[0038] The cooking assist unit can learn the user's cooking skill level and suggest cooking methods according to the skill. For example, the cooking assist unit learns the user's cooking skill level from questionnaires and past cooking history, and suggests cooking methods based on that. For example, it suggests simple recipes to beginners. The cooking assist unit also registers cooking methods according to the cooking skill level in a database and makes suggestions based on that. For example, it suggests recipes that require advanced cooking techniques to advanced cooks. The cooking assist unit also updates the user's cooking skill level in real time and optimizes cooking methods accordingly. For example, it suggests recipes that reflect newly acquired skills. In this way, by learning the user's cooking skill level and suggesting cooking methods according to the skill, it is possible to increase cooking efficiency and satisfaction.

[0039] The cooking assist unit can provide real-time feedback during cooking and give advice according to the cooking progress. The cooking assist unit, for example, monitors the cooking progress using sensors or cameras and provides feedback in real time. For example, it gives advice on appropriately adjusting the heat or cooking time. The cooking assist unit also analyzes the user's actions during cooking and provides advice at the appropriate time. For example, it gives advice on how to cut and mix ingredients. The cooking assist unit also suggests the next step in real time according to the cooking progress. For example, it gives advice on when to add ingredients and how to use cooking utensils. In this way, by providing real-time feedback during cooking and giving advice according to the cooking progress, the accuracy and efficiency of cooking can be improved.

[0040] The cooking assist unit can take into account the cooking utensils owned by the user when proposing cooking methods and suggest ways to use the optimal utensils. For example, the cooking assist unit registers the cooking utensils owned by the user in a database and suggests cooking methods based on that. For example, it suggests recipes that use frying pans and pots owned by the user. The cooking assist unit also provides advice for optimizing the use of cooking utensils. For example, it suggests precautions and tips for using specific cooking utensils. The cooking assist unit also updates the cooking utensils owned by the user in real time and optimizes cooking methods accordingly. For example, it suggests recipes that reflect newly purchased cooking utensils. In this way, by taking into account the cooking utensils owned by the user and suggesting ways to use the optimal utensils, it is possible to increase cooking efficiency and satisfaction.

[0041] The cooking assist unit can provide cooking methods in video format to make them visually easier to understand. For example, the cooking assist unit provides cooking methods in video format to make them visually easier for users to understand. For example, cooking procedures are explained step by step in video. The cooking assist unit also registers cooking methods in video format in a database to make them easily accessible to users. For example, related videos are linked for each recipe. When providing cooking methods in video format, the cooking assist unit also provides content appropriate to the user's cooking skill level. For example, basic cooking methods for beginners and advanced techniques for advanced cooks are explained in video. In this way, providing cooking methods in video format makes them visually easier to understand, thereby increasing cooking efficiency and satisfaction.

[0042] The recipe update unit can analyze user evaluation data, extract highly rated elements, and reflect them in recipes. The recipe update unit, for example, stores user evaluation data in a database and extracts highly rated elements. For example, if a particular seasoning or cooking method is highly rated, it reflects that in other recipes. The recipe update unit also analyzes the evaluation data and identifies commonly shared highly rated elements. For example, it extracts ingredients and cooking procedures that are highly rated by multiple users. The recipe update unit also identifies and updates areas for improvement in recipes based on the user evaluation data. For example, it improves low-rated elements and strengthens highly rated elements. In this way, by analyzing user evaluation data, extracting highly rated elements, and reflecting them in recipes, it is possible to increase user satisfaction.

[0043] The recipe update unit can reflect user feedback in real time and improve recipes instantly. The recipe update unit, for example, builds a system that collects user feedback in real time and immediately improves recipes based on the results. For example, it uses online surveys and comment functions. The recipe update unit also analyzes the feedback in real time and identifies areas for improvement in the recipe. For example, it adjusts the amount of seasonings or cooking time based on user opinions. The recipe update unit also immediately improves recipes based on user feedback. For example, it immediately publishes a new recipe version after receiving feedback. In this way, user feedback can be reflected in real time and recipes can be immediately improved, thereby increasing user satisfaction.

[0044] The recipe update unit can also incorporate other users' evaluation data to improve recipes from a wider variety of perspectives. The recipe update unit, for example, stores other users' evaluation data in a database and improves recipes based on that data. For example, it incorporates elements that have been highly rated by multiple users. The recipe update unit also analyzes other users' evaluation data to identify commonly shared highly rated elements. For example, it extracts ingredients and cooking procedures that have been highly rated by multiple users. The recipe update unit also identifies areas for improvement in the recipe based on other users' evaluation data and updates the recipe. For example, it improves elements that have been poorly rated and strengthens elements that have been highly rated. In this way, it is possible to incorporate other users' evaluation data and improve recipes from a wider variety of perspectives, thereby increasing user satisfaction.

[0045] The recipe update unit updates recipes according to the season or trends, allowing the latest recipes to be provided at all times. The recipe update unit, for example, registers seasonal ingredients and trends in a database and updates recipes based on them. For example, in spring, it proposes recipes using fresh vegetables. The recipe update unit also obtains trend data and proposes recipes based on that. For example, it proposes recipes based on the latest diet trends. The recipe update unit also updates recipes in real time according to the season or trends. For example, it immediately publishes recipes that incorporate newly popular ingredients. In this way, by updating recipes according to the season or trends, the latest recipes can be provided at all times.

[0046] The health management system collaborates with recipe apps or ingredient procurement services to provide users with special discounts or promotions. For example, the health management system analyzes data obtained from recipe apps and preferentially suggests recipes that match the user's preferences. For example, suggestions are made based on recipes that the user frequently views. The health management system also analyzes data from recipe apps to identify common preference elements. For example, it extracts trends in preferences for specific ingredients or cooking methods. The health management system also suggests recipes that match the user's preferences in real time based on the data obtained from the recipe app. For example, it immediately suggests newly added recipes. In this way, by collaborating with recipe apps and ingredient procurement services, it is possible to provide users with special discounts and promotions, thereby increasing user satisfaction.

[0047] The health management system obtains inventory information from ingredient procurement services in real time and suggests the most suitable ingredients. The health management system, for example, obtains inventory information from ingredient procurement services in real time and suggests the most suitable ingredients based on that information. For example, ingredients with abundant stock may be suggested preferentially. The health management system also suggests ingredients that suit the user's preferences based on the inventory information. For example, ingredients that the user frequently purchases may be suggested by comparing them with inventory information. The health management system also obtains inventory information from ingredient procurement services in real time and suggests ingredients that are in line with the season or trends. For example, seasonal ingredients or trending ingredients may be suggested by comparing them with inventory information. In this way, by obtaining inventory information from ingredient procurement services in real time and suggesting the most suitable ingredients, user satisfaction can be increased.

[0048] The health management system integrates data from recipe apps or ingredient procurement services to provide users with a consistent dining experience. For example, the health management system integrates data from recipe apps and ingredient procurement services to build a system that provides users with a consistent dining experience. For example, an ingredient procurement service automatically places an order based on a recipe selected in a recipe app. The health management system also links data from the recipe app and ingredient procurement service to provide users with a seamless dining experience. For example, an ingredient procurement service automatically orders ingredients for a recipe selected in a recipe app. The health management system also integrates data from recipe apps and ingredient procurement services in real time to provide users with a consistent dining experience. For example, ingredients for a recipe selected in a recipe app are instantly ordered in an ingredient procurement service. In this way, by integrating data from recipe apps and ingredient procurement services to provide users with a consistent dining experience, user satisfaction can be increased.

[0049] The health management system analyzes delivery service menus and suggests menus that suit the user's preferences and nutritional balance. For example, the health management system registers delivery service menus in a database and suggests menus that suit the user's preferences and nutritional balance. For example, suggestions are made based on menus that the user frequently orders. The health management system also analyzes delivery service menus and identifies common preference elements. For example, it extracts trends in preference for specific ingredients and cooking methods. The health management system also suggests menus that suit the user's preferences and nutritional balance in real time based on the delivery service menu. For example, it immediately suggests newly added menu items. In this way, user satisfaction can be increased by analyzing delivery service menus and suggesting menus that suit the user's preferences and nutritional balance.

[0050] The health management system provides meals at optimal times, taking into account delivery times from delivery services. For example, the health management system obtains delivery times from delivery services in real time and provides meals at optimal times based on that information. For example, it adjusts orders to match the time desired by the user. The health management system also considers delivery times to suggest meals that fit the user's schedule. For example, if the user is eating a meal between work, it suggests a menu that can be delivered in a short time. The health management system also optimizes the user's meal schedule based on delivery times from delivery services. For example, it automatically adjusts orders to match meal timings. This allows for meals to be provided at optimal times, taking into account delivery times from delivery services, thereby increasing user satisfaction.

[0051] The health management system provides special discounts and promotions to users by collaborating with a delivery service. The health management system, for example, provides special discounts and promotions to users by collaborating with a delivery service. For example, a discount is provided when a specific menu is ordered. The health management system also provides special promotions to users by using the services of its partners. For example, points are awarded when a delivery service is used. The health management system also collaborates with a delivery service to implement special campaigns for users. For example, a gift is provided when a specific menu is ordered during a specific period. In this way, by collaborating with the delivery service, special discounts and promotions can be provided to users, thereby increasing user satisfaction.

[0052] The health management system integrates data from delivery services to provide users with a consistent dining experience. For example, the health management system integrates data from delivery services to build a system that provides users with a consistent dining experience. For example, the system suggests nutritional balance based on the menu ordered through the delivery service. The health management system also links data from delivery services to provide users with a seamless dining experience. For example, the health management system reflects nutritional information for the menu ordered through the delivery service in an app. The health management system also integrates data from delivery services in real time to provide users with a consistent dining experience. For example, nutritional information for the menu ordered through the delivery service is instantly reflected in the app. In this way, the health management system can integrate data from delivery services to provide users with a consistent dining experience, thereby increasing user satisfaction.

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

[0054] The health management system can further include an exercise assist unit. The exercise assist unit proposes an exercise plan based on the user's health condition and goals. For example, it proposes an exercise plan that emphasizes calorie consumption to a user who is on a diet. The exercise assist unit can also propose exercise plans for strength training and endurance improvement to athletes. The exercise assist unit can also analyze the user's exercise history and generate an optimal exercise plan based on past exercise data. This allows the health management system to support not only diet management but also exercise management.

[0055] The health management system may further include a sleep management unit. The sleep management unit collects and analyzes the user's sleep data to suggest an optimal sleeping environment. For example, the sleep management unit may analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. The sleep management unit may also monitor the user's sleeping environment (e.g., room temperature, humidity, lighting) and suggest an optimal environment. The sleep management unit may also provide advice to improve the user's sleep quality. This allows the health management system to support not only dietary management and exercise management, but also sleep management.

[0056] The health management system may further include a stress management unit. The stress management unit monitors the user's stress level and provides advice for stress reduction. For example, the stress management unit may analyze the user's heart rate and breathing patterns to estimate the stress level. The stress management unit may also suggest activities that have a relaxing effect (e.g., meditation, yoga, deep breathing) according to the user's stress level. The stress management unit may also monitor the user's stress level in real time and adjust advice according to changes in the stress level. This allows the health management system to support not only dietary management, exercise management, and sleep management, but also stress management.

[0057] The health management system may further include a hydration management unit. The hydration management unit monitors the user's water intake and suggests appropriate hydration. For example, the hydration management unit may calculate the amount of water the user needs per day based on the user's weight and activity level. The hydration management unit may also record the amount of water the user has drunk and provide reminders to achieve the required amount. The hydration management unit may also analyze the user's water intake history and suggest appropriate timing for hydration. This allows the health management system to support not only dietary management, exercise management, sleep management, and stress management, but also hydration management.

[0058] The health management system may further include a nutritional supplement suggestion unit. The nutritional supplement suggestion unit suggests appropriate nutritional supplements based on the user's health condition and goals. For example, the nutritional supplement suggestion unit may analyze the user's dietary content and suggest supplements to make up for missing nutrients. The nutritional supplement suggestion unit may also suggest supplements according to the user's health goals (e.g., muscle building, immune system improvement, beautiful skin). The nutritional supplement suggestion unit may also analyze the user's past supplement intake history and generate an optimal supplement plan. This allows the health management system to support not only dietary management, exercise management, sleep management, stress management, and hydration management, but also nutritional supplement suggestions.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The menu planning department creates a menu based on the user's health condition and goals. For example, they suggest low-calorie, nutritionally balanced menus for users on a diet, high-protein menus suitable for replenishing energy for athletes, and allergy-friendly menus for users with dietary restrictions. Step 2: The shopping list generation unit generates a shopping list based on the menu created by the menu creation unit. For example, it lists the necessary ingredients and automatically orders them through an online shopping service. Alternatively, the necessary ingredients can be automatically ordered using an online shopping service designated by the user. Furthermore, it analyzes the user's past purchase history and prioritizes frequently purchased ingredients to add them to the list. Step 3: The cooking assistance unit assists with cooking methods based on the shopping list generated by the shopping list generation unit. For example, it suggests specific cooking steps, cooking time, and necessary cooking utensils. It also suggests the optimal cooking method based on the user's cooking skills and available cooking utensils. It also provides real-time feedback during cooking and gives advice according to the cooking progress. Step 4: The recipe update unit collects user ratings based on the cooking methods assisted by the cooking assistance unit and updates the recipe. For example, when a user rates a provided recipe, the recipe is improved based on that rating. The system also reflects user feedback in real time and improves the recipe immediately. Furthermore, it uses an emotion estimation function to improve the recipe based on the user's emotional reactions, increasing emotional satisfaction.

[0061] (Example 2) The health management system according to an embodiment of the present invention is a system that creates individual menus based on the user's health status and goals, automatically generates and orders shopping lists, assists with cooking methods, and updates recipes based on the user's ratings. As a result, the health management system can provide optimal dietary management for the user and support a healthy lifestyle.

[0062] A health management system according to an embodiment includes a menu design unit, a shopping list generation unit, a cooking assistance unit, and a recipe update unit. The menu design unit designs menus based on a user's health status and goals. For example, the menu design unit suggests low-calorie, nutritionally balanced menus to a user on a diet. The menu design unit also suggests high-protein, energy-replenishing menus to athletes. The menu design unit also suggests allergy-friendly menus to users with dietary restrictions. The shopping list generation unit generates a shopping list based on the menu designed by the menu design unit. For example, the shopping list generation unit lists necessary ingredients and automatically orders them through an online shopping service. The shopping list generation unit can also automatically order the necessary ingredients using an online shopping service designated by the user. The shopping list generation unit also analyzes the user's past purchase history and prioritizes frequently purchased ingredients to the list. The cooking assistance unit assists with cooking methods based on the shopping list generated by the shopping list generation unit. For example, the cooking assistance unit suggests specific cooking procedures, cooking times, and necessary cooking utensils. The cooking assistance unit also suggests optimal cooking methods based on the user's cooking skills and available cooking utensils. The cooking assistance unit also provides real-time feedback during cooking and advice according to the cooking progress. The recipe update unit collects user ratings based on the cooking methods assisted by the cooking assistance unit and updates the recipe. For example, when a user rates a provided recipe, the recipe update unit improves the recipe based on the rating. The recipe update unit also reflects user feedback in real time and improves the recipe instantly. The recipe update unit also uses an emotion estimation function to improve the recipe based on the user's emotional response, thereby increasing emotional satisfaction. As a result, the health management system according to the embodiment can provide optimal dietary management to the user and support a healthy lifestyle.

[0063] The menu design unit can analyze a user's past dietary history and health data to generate a menu based on their long-term health goals. For example, the menu design unit stores the user's past dietary history in a database and compares it with their health data to generate a menu based on their long-term health goals. For example, the menu design unit analyzes past dietary history and weight fluctuations to suggest an appropriate calorie intake. The menu design unit also collects health data such as blood pressure, blood sugar levels, and cholesterol levels, and generates a menu based on that data. For example, it suggests a low-carbohydrate menu for a user with a high blood sugar level. The menu design unit also sets the user's health goals (e.g., weight loss, muscle building, lowering cholesterol levels) and generates a meal plan aimed at that goal. For example, it suggests a low-calorie menu for a user aiming to lose weight. In this way, by analyzing a user's past dietary history and health data and generating a menu based on their long-term health goals, more personalized dietary management can be provided.

[0064] The menu creation unit can learn a user's dietary preferences or allergy information in real time and provide an individually optimized menu. For example, the menu creation unit learns a user's dietary preferences from questionnaires or past selection history and generates a menu based on that. For example, it proposes a menu that prioritizes the user's favorite ingredients. The menu creation unit also registers allergy information in a database and generates a menu that avoids allergies based on that information. For example, it proposes a menu that does not contain nuts to a user with a nut allergy. The menu creation unit also updates a user's dietary preferences and allergy information in real time and optimizes the menu accordingly. For example, if a new allergy is discovered, that information is immediately reflected. In this way, the system can learn a user's dietary preferences and allergy information in real time and provide an individually optimized menu, thereby increasing user satisfaction.

[0065] The menu design unit uses the emotion estimation function to suggest a menu that corresponds to the user's current emotional state, thereby reducing stress and improving mood. The menu design unit, for example, estimates the user's emotional state using facial expression and voice analysis and suggests a menu based on the results. For example, if the user is highly stressed, it suggests a menu that uses ingredients that have a relaxing effect. The menu design unit also generates a menu to improve the user's mood based on the emotion estimation data. For example, if the user is feeling depressed, it suggests a menu that uses ingredients that have a mood-boosting effect. The menu design unit also monitors the user's emotional state in real time and adjusts the menu according to changes. For example, if the user's emotions are stable, it suggests a balanced menu. In this way, the emotion estimation function can be used to suggest a menu that corresponds to the user's current emotional state, thereby reducing stress and improving mood.

[0066] When creating a menu, the menu creation department can consider ingredients that are appropriate for the season or weather and propose seasonal menus. The menu creation department, for example, registers seasonal ingredients in a database and generates menus based on that. For example, in spring, it proposes menus using fresh vegetables. The menu creation department also obtains weather data and proposes menus accordingly. For example, on cold days, it proposes hot soups or hot pot dishes. The menu creation department also considers the nutritional value of ingredients that are appropriate for the season and weather and generates balanced menus. For example, in summer it proposes menus that emphasize hydration. In this way, by considering ingredients that are appropriate for the season and weather and proposing seasonal menus, it is possible to provide users with a new dining experience.

[0067] The menu creation department can incorporate cuisine from different cultures to provide users with a new dining experience. For example, the menu creation department registers recipes from different cultures in a database and generates menus based on them. For example, it proposes menus that incorporate Italian cuisine or Indian cuisine. The menu creation department also customizes cuisine from different cultures according to the user's preferences. For example, it proposes spicy Indian cuisine for a user who likes spicy food. The menu creation department also uses ingredients from different cultures to provide users with a new dining experience. For example, it proposes menus that use ethnic ingredients. In this way, it is possible to provide users with a new dining experience by incorporating cuisine from different cultures.

[0068] The menu planning unit uses the emotion estimation function to select ingredients based on the user's emotions, thereby increasing emotional satisfaction. The menu planning unit, for example, uses the emotion estimation function to select ingredients according to the user's emotional state. For example, it may suggest a menu using herbs and spices that have a relaxing effect. The menu planning unit also selects ingredients that will increase emotional satisfaction based on the user's emotion data. For example, if the user is feeling depressed, it may suggest a menu using ingredients that have a mood-boosting effect. The menu planning unit also collects emotion estimation data in real time and selects ingredients based on the results. For example, if the user's emotions are stable, it may suggest a menu using balanced ingredients. In this way, it is possible to increase emotional satisfaction by using the emotion estimation function to select ingredients based on the user's emotions.

[0069] The shopping list generation unit can analyze the user's past purchase history and add frequently purchased ingredients to the list with priority. The shopping list generation unit, for example, stores the user's past purchase history in a database and automatically adds frequently purchased ingredients to the list. For example, milk and bread purchased weekly are added with priority to the list. The shopping list generation unit also analyzes the purchase history and understands seasonal purchasing patterns. For example, ice cream and cold drinks are added to the list in summer. The shopping list generation unit also predicts ingredients needed based on the user's purchase history and adds them to the list. For example, ingredients needed for the weekend are predicted from past data and added to the list. In this way, shopping efficiency can be improved by analyzing the user's past purchase history and adding frequently purchased ingredients to the list with priority.

[0070] The shopping list generation unit can generate a shopping list that takes into account the shelf life of ingredients and minimizes waste. The shopping list generation unit, for example, registers the shelf life of ingredients in a database and generates a shopping list based on that. For example, it adds only the necessary amount of vegetables and fruits with short shelf lives to the list. The shopping list generation unit also makes a food consumption plan taking into account shelf lives. For example, it adjusts the menu so that ingredients with short shelf lives are consumed first. The shopping list generation unit also monitors the shelf life of ingredients in real time and generates a shopping list that minimizes waste. For example, it prioritizes adding ingredients whose shelf life is approaching the end of their shelf life to the list. In this way, food waste can be reduced by generating a shopping list that takes into account the shelf life of ingredients and minimizes waste.

[0071] The shopping list generation unit uses the emotion estimation function to generate a shopping list that corresponds to the user's emotional state, thereby providing a less stressful shopping experience. The shopping list generation unit, for example, uses the emotion estimation function to generate a shopping list that corresponds to the user's emotional state. For example, if the user is highly stressed, ingredients that are easy to prepare are added to the list. The shopping list generation unit also adjusts the shopping list based on the user's emotional data. For example, if the user is feeling depressed, ingredients that have a mood-boosting effect are added to the list. The shopping list generation unit also collects emotion estimation data in real time and generates a shopping list based on the results. For example, if the user's emotions are stable, balanced ingredients are added to the list. In this way, by using the emotion estimation function to generate a shopping list that corresponds to the user's emotional state, a less stressful shopping experience can be provided.

[0072] The shopping list generation unit can prioritize adding environmentally friendly ingredients to the shopping list. For example, the shopping list generation unit registers eco-friendly ingredients in a database and generates a shopping list based on that. For example, organic vegetables and fair trade products are prioritized when added to the list. The shopping list generation unit also sets selection criteria for environmentally friendly ingredients and generates a shopping list based on those criteria. For example, locally produced ingredients and seasonal ingredients are prioritized when added to the list. The shopping list generation unit also develops an algorithm that prioritizes the selection of eco-friendly ingredients and generates a shopping list based on that algorithm. For example, ingredients with a low environmental impact are added to the list. This allows sustainable shopping to be promoted by prioritizing the addition of environmentally friendly ingredients.

[0073] The shopping list generation unit can support the local economy by adding ingredients that can be purchased directly from local farmers or markets to the list. For example, the shopping list generation unit registers ingredients that can be purchased directly from local farmers or markets in a database and generates a shopping list based on the ingredients. For example, fresh locally grown vegetables and fruits are added to the list. The shopping list generation unit also partners with local farmers and markets and prioritizes adding ingredients that can be purchased directly to the list. For example, ingredients that are shipped directly from local farmers are added to the list. The shopping list generation unit also develops an algorithm that prioritizes selecting ingredients from local farms and markets and generates a shopping list based on the algorithm. For example, locally grown ingredients are added to the list to support the local economy. By adding ingredients that can be purchased directly from local farmers and markets to the list, the local economy can be supported.

[0074] The shopping list generation unit can use the emotion estimation function to add special ingredients to the list based on the user's emotions (e.g., sweets to lift the mood). The shopping list generation unit, for example, uses the emotion estimation function to add special ingredients to the list according to the user's emotional state. For example, if the user is feeling depressed, sweets that have the effect of lifting the mood are added to the list. The shopping list generation unit also selects special ingredients based on the user's emotional data. For example, if the user is feeling high, herbal tea with a relaxing effect is added to the list. The shopping list generation unit also collects emotion estimation data in real time and adds special ingredients to the list based on the results. For example, if the user's emotions are stable, balanced ingredients are added to the list. In this way, by using the emotion estimation function to add special ingredients to the list based on the user's emotions, emotional satisfaction can be increased.

[0075] The cooking assist unit can learn the user's cooking skill level and suggest cooking methods according to the skill. For example, the cooking assist unit learns the user's cooking skill level from questionnaires and past cooking history, and suggests cooking methods based on that. For example, it suggests simple recipes to beginners. The cooking assist unit also registers cooking methods according to the cooking skill level in a database and makes suggestions based on that. For example, it suggests recipes that require advanced cooking techniques to advanced cooks. The cooking assist unit also updates the user's cooking skill level in real time and optimizes cooking methods accordingly. For example, it suggests recipes that reflect newly acquired skills. In this way, by learning the user's cooking skill level and suggesting cooking methods according to the skill, it is possible to increase cooking efficiency and satisfaction.

[0076] The cooking assist unit can provide real-time feedback during cooking and give advice according to the cooking progress. The cooking assist unit, for example, monitors the cooking progress using sensors or cameras and provides feedback in real time. For example, it gives advice on appropriately adjusting the heat or cooking time. The cooking assist unit also analyzes the user's actions during cooking and provides advice at the appropriate time. For example, it gives advice on how to cut and mix ingredients. The cooking assist unit also suggests the next step in real time according to the cooking progress. For example, it gives advice on when to add ingredients and how to use cooking utensils. In this way, by providing real-time feedback during cooking and giving advice according to the cooking progress, the accuracy and efficiency of cooking can be improved.

[0077] The cooking assist unit uses the emotion estimation function to suggest cooking methods that correspond to the user's emotional state, thereby reducing stress during cooking. The cooking assist unit, for example, uses the emotion estimation function to suggest cooking methods that correspond to the user's emotional state. For example, if the user is highly stressed, it suggests simple and time-saving recipes. The cooking assist unit also provides advice to reduce stress during cooking based on the user's emotional data. For example, it suggests music that has a relaxing effect. The cooking assist unit also collects emotion estimation data in real time and adjusts cooking methods based on the results. For example, if the user's emotions are stable, it suggests a challenging recipe. In this way, stress during cooking can be reduced by using the emotion estimation function to suggest cooking methods that correspond to the user's emotional state.

[0078] The cooking assist unit can take into account the cooking utensils owned by the user when proposing cooking methods and suggest ways to use the optimal utensils. For example, the cooking assist unit registers the cooking utensils owned by the user in a database and suggests cooking methods based on that. For example, it suggests recipes that use frying pans and pots owned by the user. The cooking assist unit also provides advice for optimizing the use of cooking utensils. For example, it suggests precautions and tips for using specific cooking utensils. The cooking assist unit also updates the cooking utensils owned by the user in real time and optimizes cooking methods accordingly. For example, it suggests recipes that reflect newly purchased cooking utensils. In this way, by taking into account the cooking utensils owned by the user and suggesting ways to use the optimal utensils, it is possible to increase cooking efficiency and satisfaction.

[0079] The cooking assist unit can provide cooking methods in video format to make them visually easier to understand. For example, the cooking assist unit provides cooking methods in video format to make them visually easier for users to understand. For example, cooking procedures are explained step by step in video. The cooking assist unit also registers cooking methods in video format in a database to make them easily accessible to users. For example, related videos are linked for each recipe. When providing cooking methods in video format, the cooking assist unit also provides content appropriate to the user's cooking skill level. For example, basic cooking methods for beginners and advanced techniques for advanced cooks are explained in video. In this way, providing cooking methods in video format makes them visually easier to understand, thereby increasing cooking efficiency and satisfaction.

[0080] The cooking assist unit can use the emotion estimation function to suggest cooking music or environmental sounds based on the user's emotions, thereby improving the cooking experience. For example, the cooking assist unit can use the emotion estimation function to suggest cooking music that corresponds to the user's emotional state. For example, it can suggest music that has a relaxing effect. The cooking assist unit can also suggest environmental sounds to play while cooking based on the user's emotional data. For example, it can play sounds of nature or the sounds of a cafe. The cooking assist unit can also collect emotion estimation data in real time and adjust the cooking music or environmental sounds based on the results. For example, if the user's emotions are stable, it can suggest lively music. This allows the emotion estimation function to suggest cooking music or environmental sounds based on the user's emotions, thereby improving the cooking experience.

[0081] The recipe update unit can analyze user evaluation data, extract highly rated elements, and reflect them in recipes. The recipe update unit, for example, stores user evaluation data in a database and extracts highly rated elements. For example, if a particular seasoning or cooking method is highly rated, it reflects that in other recipes. The recipe update unit also analyzes the evaluation data and identifies commonly shared highly rated elements. For example, it extracts ingredients and cooking procedures that are highly rated by multiple users. The recipe update unit also identifies and updates areas for improvement in recipes based on the user evaluation data. For example, it improves low-rated elements and strengthens highly rated elements. In this way, by analyzing user evaluation data, extracting highly rated elements, and reflecting them in recipes, it is possible to increase user satisfaction.

[0082] The recipe update unit can reflect user feedback in real time and improve recipes instantly. The recipe update unit, for example, builds a system that collects user feedback in real time and immediately improves recipes based on the results. For example, it uses online surveys and comment functions. The recipe update unit also analyzes the feedback in real time and identifies areas for improvement in the recipe. For example, it adjusts the amount of seasonings or cooking time based on user opinions. The recipe update unit also immediately improves recipes based on user feedback. For example, it immediately publishes a new recipe version after receiving feedback. In this way, user feedback can be reflected in real time and recipes can be immediately improved, thereby increasing user satisfaction.

[0083] The recipe update unit can use the emotion estimation function to improve recipes based on the user's emotional responses and increase emotional satisfaction. The recipe update unit, for example, uses the emotion estimation function to collect the user's emotional responses in real time and improve the recipe based on the results. For example, elements that have a high number of positive emotional responses are strengthened. The recipe update unit also identifies areas for improvement in the recipe based on the user's emotional data. For example, parts with low emotional scores are improved to increase emotional satisfaction. The recipe update unit also collects emotion estimation data in real time and improves the recipe based on the results. For example, if emotions are stable, a balanced recipe is suggested. In this way, emotional satisfaction can be increased by using the emotion estimation function to improve the recipe based on the user's emotional responses.

[0084] The recipe update unit can also incorporate other users' evaluation data to improve recipes from a wider variety of perspectives. The recipe update unit, for example, stores other users' evaluation data in a database and improves recipes based on that data. For example, it incorporates elements that have been highly rated by multiple users. The recipe update unit also analyzes other users' evaluation data to identify commonly shared highly rated elements. For example, it extracts ingredients and cooking procedures that have been highly rated by multiple users. The recipe update unit also identifies areas for improvement in the recipe based on other users' evaluation data and updates the recipe. For example, it improves elements that have been poorly rated and strengthens elements that have been highly rated. In this way, it is possible to incorporate other users' evaluation data and improve recipes from a wider variety of perspectives, thereby increasing user satisfaction.

[0085] The recipe update unit updates recipes according to the season or trends, allowing the latest recipes to be provided at all times. The recipe update unit, for example, registers seasonal ingredients and trends in a database and updates recipes based on them. For example, in spring, it proposes recipes using fresh vegetables. The recipe update unit also obtains trend data and proposes recipes based on that. For example, it proposes recipes based on the latest diet trends. The recipe update unit also updates recipes in real time according to the season or trends. For example, it immediately publishes recipes that incorporate newly popular ingredients. In this way, by updating recipes according to the season or trends, the latest recipes can be provided at all times.

[0086] The recipe update unit uses the emotion estimation function to improve the recipe based on the user's emotions, thereby gaining emotional empathy. The recipe update unit, for example, uses the emotion estimation function to collect the user's emotional reactions in real time and improve the recipe based on the results. For example, elements that have a high number of positive emotional reactions are strengthened. The recipe update unit also identifies areas for improvement in the recipe based on the user's emotional data. For example, parts with low emotional scores are improved to gain emotional empathy. The recipe update unit also collects emotion estimation data in real time and improves the recipe based on the results. For example, if the emotions are stable, a balanced recipe is suggested. In this way, emotional empathy can be gained by improving the recipe based on the user's emotions using the emotion estimation function.

[0087] The health management system collaborates with recipe apps or ingredient procurement services to provide users with special discounts or promotions. For example, the health management system analyzes data obtained from recipe apps and preferentially suggests recipes that match the user's preferences. For example, suggestions are made based on recipes that the user frequently views. The health management system also analyzes data from recipe apps to identify common preference elements. For example, it extracts trends in preferences for specific ingredients or cooking methods. The health management system also suggests recipes that match the user's preferences in real time based on the data obtained from the recipe app. For example, it immediately suggests newly added recipes. In this way, by collaborating with recipe apps and ingredient procurement services, it is possible to provide users with special discounts and promotions, thereby increasing user satisfaction.

[0088] The health management system obtains inventory information from ingredient procurement services in real time and suggests the most suitable ingredients. The health management system, for example, obtains inventory information from ingredient procurement services in real time and suggests the most suitable ingredients based on that information. For example, ingredients with abundant stock may be suggested preferentially. The health management system also suggests ingredients that suit the user's preferences based on the inventory information. For example, ingredients that the user frequently purchases may be suggested by comparing them with inventory information. The health management system also obtains inventory information from ingredient procurement services in real time and suggests ingredients that are in line with the season or trends. For example, seasonal ingredients or trending ingredients may be suggested by comparing them with inventory information. In this way, by obtaining inventory information from ingredient procurement services in real time and suggesting the most suitable ingredients, user satisfaction can be increased.

[0089] The health management system uses an emotion estimation function to suggest recipes and ingredients based on the user's emotions, thereby increasing emotional satisfaction. For example, the health management system uses the emotion estimation function to suggest recipes and ingredients according to the user's emotional state. For example, it suggests recipes using ingredients that have a relaxing effect. The health management system also suggests recipes and ingredients that will increase emotional satisfaction based on the user's emotion data. For example, if the user is feeling depressed, it suggests recipes using ingredients that have a mood-boosting effect. The health management system also collects emotion estimation data in real time and suggests recipes and ingredients based on the results. For example, if the user's emotions are stable, it suggests balanced recipes and ingredients. In this way, it is possible to increase emotional satisfaction by using the emotion estimation function to suggest recipes and ingredients based on the user's emotions.

[0090] The health management system integrates data from recipe apps or ingredient procurement services to provide users with a consistent dining experience. For example, the health management system integrates data from recipe apps and ingredient procurement services to build a system that provides users with a consistent dining experience. For example, an ingredient procurement service automatically places an order based on a recipe selected in a recipe app. The health management system also links data from the recipe app and ingredient procurement service to provide users with a seamless dining experience. For example, an ingredient procurement service automatically orders ingredients for a recipe selected in a recipe app. The health management system also integrates data from recipe apps and ingredient procurement services in real time to provide users with a consistent dining experience. For example, ingredients for a recipe selected in a recipe app are instantly ordered in an ingredient procurement service. In this way, by integrating data from recipe apps and ingredient procurement services to provide users with a consistent dining experience, user satisfaction can be increased.

[0091] The health management system uses an emotion estimation function to suggest special recipes and ingredients based on the user's emotions, thereby gaining emotional empathy. For example, the health management system uses the emotion estimation function to suggest special recipes and ingredients according to the user's emotional state. For example, it suggests recipes using ingredients that have a relaxing effect. The health management system also suggests special recipes and ingredients to gain emotional empathy based on the user's emotional data. For example, if the user is feeling depressed, it suggests recipes using ingredients that have a mood-boosting effect. The health management system also collects emotion estimation data in real time and suggests special recipes and ingredients based on the results. For example, if the user's emotions are stable, it suggests balanced recipes and ingredients. In this way, the emotion estimation function can be used to suggest special recipes and ingredients based on the user's emotions, thereby gaining emotional empathy.

[0092] The health management system analyzes delivery service menus and suggests menus that suit the user's preferences and nutritional balance. For example, the health management system registers delivery service menus in a database and suggests menus that suit the user's preferences and nutritional balance. For example, suggestions are made based on menus that the user frequently orders. The health management system also analyzes delivery service menus and identifies common preference elements. For example, it extracts trends in preference for specific ingredients and cooking methods. The health management system also suggests menus that suit the user's preferences and nutritional balance in real time based on the delivery service menu. For example, it immediately suggests newly added menu items. In this way, user satisfaction can be increased by analyzing delivery service menus and suggesting menus that suit the user's preferences and nutritional balance.

[0093] The health management system provides meals at optimal times, taking into account delivery times from delivery services. For example, the health management system obtains delivery times from delivery services in real time and provides meals at optimal times based on that information. For example, it adjusts orders to match the time desired by the user. The health management system also considers delivery times to suggest meals that fit the user's schedule. For example, if the user is eating a meal between work, it suggests a menu that can be delivered in a short time. The health management system also optimizes the user's meal schedule based on delivery times from delivery services. For example, it automatically adjusts orders to match meal timings. This allows for meals to be provided at optimal times, taking into account delivery times from delivery services, thereby increasing user satisfaction.

[0094] The health management system uses an emotion estimation function to suggest a delivery menu based on the user's emotions, thereby increasing emotional satisfaction. For example, the health management system uses the emotion estimation function to suggest a delivery menu according to the user's emotional state. For example, a menu using ingredients that have a relaxing effect is suggested. The health management system also suggests a delivery menu that increases emotional satisfaction based on the user's emotion data. For example, if the user is feeling depressed, a menu using ingredients that have a mood-boosting effect is suggested. The health management system also collects emotion estimation data in real time and suggests a delivery menu based on the results. For example, if the user's emotions are stable, a balanced menu is suggested. In this way, emotional satisfaction can be increased by using the emotion estimation function to suggest a delivery menu based on the user's emotions.

[0095] The health management system provides special discounts and promotions to users by collaborating with a delivery service. The health management system, for example, provides special discounts and promotions to users by collaborating with a delivery service. For example, a discount is provided when a specific menu is ordered. The health management system also provides special promotions to users by using the services of its partners. For example, points are awarded when a delivery service is used. The health management system also collaborates with a delivery service to implement special campaigns for users. For example, a gift is provided when a specific menu is ordered during a specific period. In this way, by collaborating with the delivery service, special discounts and promotions can be provided to users, thereby increasing user satisfaction.

[0096] The health management system integrates data from delivery services to provide users with a consistent dining experience. For example, the health management system integrates data from delivery services to build a system that provides users with a consistent dining experience. For example, the system suggests nutritional balance based on the menu ordered through the delivery service. The health management system also links data from delivery services to provide users with a seamless dining experience. For example, the health management system reflects nutritional information for the menu ordered through the delivery service in an app. The health management system also integrates data from delivery services in real time to provide users with a consistent dining experience. For example, nutritional information for the menu ordered through the delivery service is instantly reflected in the app. In this way, the health management system can integrate data from delivery services to provide users with a consistent dining experience, thereby increasing user satisfaction.

[0097] The health management system uses an emotion estimation function to suggest a special delivery menu based on the user's emotions, thereby gaining emotional empathy. For example, the health management system uses the emotion estimation function to suggest a special delivery menu according to the user's emotional state. For example, a menu using ingredients that have a relaxing effect is suggested. The health management system also suggests a special delivery menu to gain emotional empathy based on the user's emotion data. For example, if the user is feeling depressed, a menu using ingredients that have a mood-boosting effect is suggested. The health management system also collects emotion estimation data in real time and suggests a special delivery menu based on the results. For example, if the user's emotions are stable, a balanced menu is suggested. In this way, emotional empathy can be gained by using the emotion estimation function to suggest a special delivery menu based on the user's emotions.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The health management system can further include an exercise assist unit. The exercise assist unit proposes an exercise plan based on the user's health condition and goals. For example, it proposes an exercise plan that emphasizes calorie consumption to a user who is on a diet. The exercise assist unit can also propose exercise plans for strength training and endurance improvement to athletes. The exercise assist unit can also analyze the user's exercise history and generate an optimal exercise plan based on past exercise data. This allows the health management system to support not only diet management but also exercise management.

[0100] The health management system may further include a sleep management unit. The sleep management unit collects and analyzes the user's sleep data to suggest an optimal sleeping environment. For example, the sleep management unit may analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. The sleep management unit may also monitor the user's sleeping environment (e.g., room temperature, humidity, lighting) and suggest an optimal environment. The sleep management unit may also provide advice to improve the user's sleep quality. This allows the health management system to support not only dietary management and exercise management, but also sleep management.

[0101] The health management system may further include a stress management unit. The stress management unit monitors the user's stress level and provides advice for stress reduction. For example, the stress management unit may analyze the user's heart rate and breathing patterns to estimate the stress level. The stress management unit may also suggest activities that have a relaxing effect (e.g., meditation, yoga, deep breathing) according to the user's stress level. The stress management unit may also monitor the user's stress level in real time and adjust advice according to changes in the stress level. This allows the health management system to support not only dietary management, exercise management, and sleep management, but also stress management.

[0102] The health management system may further include a hydration management unit. The hydration management unit monitors the user's water intake and suggests appropriate hydration. For example, the hydration management unit may calculate the amount of water the user needs per day based on the user's weight and activity level. The hydration management unit may also record the amount of water the user has drunk and provide reminders to achieve the required amount. The hydration management unit may also analyze the user's water intake history and suggest appropriate timing for hydration. This allows the health management system to support not only dietary management, exercise management, sleep management, and stress management, but also hydration management.

[0103] The health management system may further include a nutritional supplement suggestion unit. The nutritional supplement suggestion unit suggests appropriate nutritional supplements based on the user's health condition and goals. For example, the nutritional supplement suggestion unit may analyze the user's dietary content and suggest supplements to make up for missing nutrients. The nutritional supplement suggestion unit may also suggest supplements according to the user's health goals (e.g., muscle building, immune system improvement, beautiful skin). The nutritional supplement suggestion unit may also analyze the user's past supplement intake history and generate an optimal supplement plan. This allows the health management system to support not only dietary management, exercise management, sleep management, stress management, and hydration management, but also nutritional supplement suggestions.

[0104] The health management system can use the emotion estimation function to suggest an exercise plan based on the user's emotions. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching. If the user is feeling energetic, it can suggest high-intensity interval training. The emotion estimation function can also be used to suggest exercise timing based on the user's emotional state. For example, if the user is feeling depressed, it can suggest exercise to lift their spirits. In this way, by using the emotion estimation function to suggest an exercise plan based on the user's emotions, it is possible to increase emotional satisfaction.

[0105] The health management system can use the emotion estimation function to suggest a sleeping environment based on the user's emotions. For example, if the user is feeling stressed, relaxing music or aromas can be suggested. If the user is feeling anxious, lighting and temperature settings that give a sense of security can be suggested. The emotion estimation function can also be used to suggest a pre-bedtime routine based on the user's emotional state. For example, if the user is excited, relaxation meditation or deep breathing can be suggested. In this way, by using the emotion estimation function to suggest a sleeping environment based on the user's emotions, emotional satisfaction can be increased.

[0106] The health management system can use the emotion estimation function to suggest stress reduction methods based on the user's emotions. For example, if the user is feeling stressed, relaxing music or aromas can be suggested. If the user is feeling anxious, activities that will give a sense of security (e.g., meditation, yoga, deep breathing) can be suggested. The emotion estimation function can also be used to suggest timing for stress reduction based on the user's emotional state. For example, if the user is feeling depressed, activities to lift the user's mood can be suggested. In this way, by using the emotion estimation function to suggest stress reduction methods based on the user's emotions, emotional satisfaction can be increased.

[0107] The health management system can use the emotion estimation function to suggest the timing of hydration based on the user's emotions. For example, if the user is feeling stressed, a relaxing herbal tea can be suggested. If the user is feeling energetic, a sports drink can be suggested. The emotion estimation function can also be used to suggest the timing of hydration based on the user's emotional state. For example, if the user is feeling depressed, a drink to lift the user's spirits can be suggested. In this way, by using the emotion estimation function to suggest the timing of hydration based on the user's emotions, it is possible to increase emotional satisfaction.

[0108] The health management system can use the emotion estimation function to suggest nutritional supplements based on the user's emotions. For example, if the user is feeling stressed, it can suggest supplements with a relaxing effect (e.g., magnesium or B vitamins). If the user is feeling energetic, it can suggest supplements suitable for energy replenishment (e.g., caffeine or amino acids). The emotion estimation function can also be used to suggest the timing of supplement intake based on the user's emotional state. For example, if the user is feeling depressed, it can suggest supplements to lift the user's mood. In this way, the emotion estimation function can be used to suggest nutritional supplements based on the user's emotions, thereby increasing emotional satisfaction.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The menu planning department creates a menu based on the user's health condition and goals. For example, they suggest low-calorie, nutritionally balanced menus for users on a diet, high-protein menus suitable for replenishing energy for athletes, and allergy-friendly menus for users with dietary restrictions. Step 2: The shopping list generation unit generates a shopping list based on the menu created by the menu creation unit. For example, it lists the necessary ingredients and automatically orders them through an online shopping service. Alternatively, the necessary ingredients can be automatically ordered using an online shopping service designated by the user. Furthermore, it analyzes the user's past purchase history and prioritizes frequently purchased ingredients to add them to the list. Step 3: The cooking assistance unit assists with cooking methods based on the shopping list generated by the shopping list generation unit. For example, it suggests specific cooking steps, cooking time, and necessary cooking utensils. It also suggests the optimal cooking method based on the user's cooking skills and available cooking utensils. It also provides real-time feedback during cooking and gives advice according to the cooking progress. Step 4: The recipe update unit collects user ratings based on the cooking methods assisted by the cooking assistance unit and updates the recipe. For example, when a user rates a provided recipe, the recipe is improved based on that rating. The system also reflects user feedback in real time and improves the recipe immediately. Furthermore, it uses an emotion estimation function to improve the recipe based on the user's emotional reactions, increasing emotional satisfaction.

[0111] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0151] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a menu planning unit that plans a menu based on the user's health status or goals; a shopping list creation unit that creates a shopping list based on the menu created by the menu creation unit; a cooking assist unit that assists with cooking methods based on the shopping list generated by the shopping list generating unit; a recipe update unit that collects user evaluations based on the cooking methods assisted by the cooking assist unit and updates the recipes. A system characterized by:

2. The menu planning department Analyzing the user's past dietary history and health data to generate the menu based on long-term health goals 2. The system of claim 1.

3. The menu planning department Learn the user's dietary preferences or allergy information in real time and provide the user with an individually optimized menu 2. The system of claim 1.

4. The menu planning department The menu is proposed according to the user's current emotional state, thereby reducing stress and improving mood.

2. The system of claim 1.

5. The menu planning department When creating a menu, consider ingredients that are appropriate for the season or weather, and propose a menu with a seasonal feel.

2. The system of claim 1.

6. The menu planning department Incorporating cuisine from different cultures to provide users with new dining experiences 2. The system of claim 1.

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