System

Abstract

An object of a system according to an embodiment is to propose an appropriate nutrient or food ingredient for a physical disorder of a user.SOLUTION: A system according to an embodiment includes a malfunction selector, a nutrient analyzer, an ingredient selector, and a recipe suggester. The bad condition selection unit selects a bad condition of the body that the user wants to improve. The nutrient analyzer analyzes necessary nutrients based on the poor condition selected by the poor condition selector. The ingredient selection unit selects an ingredient that supplements the nutrient analyzed by the nutrient analysis unit. The recipe proposal unit proposes a recipe using the ingredients selected by the ingredient selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 make it difficult for users to find the right nutrients and ingredients for their physical ailments, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest nutrients and ingredients suitable for a user's physical ailments. [Means for solving the problem]

[0006] The system according to the embodiment includes an illness selection unit, a nutrient analysis unit, an ingredient selection unit, and a recipe suggestion unit. The illness selection unit selects a physical illness that the user wishes to improve. The nutrient analysis unit analyzes necessary nutrients based on the illness selected by the illness selection unit. The ingredient selection unit selects ingredients that supplement the nutrients analyzed by the nutrient analysis unit. The recipe suggestion unit suggests recipes using the ingredients selected by the ingredient selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest nutrients and ingredients suitable for the user's physical condition. [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) A health support system according to an embodiment of the present invention is a system that proposes optimal ingredients and recipes to resolve physical or skin problems experienced by a user. As a result, the health support system can provide ingredients and recipes appropriate for the user's condition and support the improvement of the user's health.

[0029] A health support system according to an embodiment includes a symptom selection unit, a nutrient analysis unit, an ingredient selection unit, and a recipe suggestion unit. The symptom selection unit selects a physical symptom the user wants to improve. For example, the user can select the symptom most closely matching their own condition from options such as "easily tired," "dry skin," and "constipated" via an app or website. The nutrient analysis unit analyzes the nutrients needed based on the symptom selected by the symptom selection unit. For example, for the symptom "easily tired," the analysis determines that B vitamins and iron are needed. The nutrient analysis unit uses a generative AI to identify the necessary nutrients based on prompts containing information about the symptom selected by the user. The ingredient selection unit selects ingredients to supplement the nutrients analyzed by the nutrient analysis unit. For example, if B vitamins are needed, pork and eggs are selected, and if iron is needed, spinach and liver are selected. The ingredient selection unit references a nutritional value database of ingredients to select the most appropriate ingredients. The recipe suggestion unit suggests recipes using the ingredients selected by the ingredient selection unit. For example, a stir-fry using spinach and pork, or an omelet using eggs and liver, may be suggested. The recipe suggestion unit references a database of cooking methods to generate easy and delicious recipes. This allows the health support system according to the embodiment to suggest ingredients and recipes that are optimal for the user's physical condition. For example, the user can refer to the suggested recipes and incorporate them into their daily meals. This allows the user to easily practice a diet that will improve their condition.

[0030] The ailment selection unit can support more accurate ailment selection by referring to the user's past selection history and health data. For example, the ailment selection unit stores the history of ailments previously selected by the user in a database and displays it as a reference the next time the user selects an ailment. For example, if a user previously selected "easily tired," the same ailment will be displayed again. The ailment selection unit also refers to the user's health data (e.g., heart rate and sleep data from a wearable device) and suggests ailment options based on the user's current health condition. For example, if recent sleep data is poor, "easily tired" will be displayed preferentially. The ailment selection unit also combines the user's past selection history and health data to automatically suggest the most relevant ailment. For example, if a user previously selected "slightly constipated," the ailment selection unit will suggest "slightly constipated" again. This allows for more accurate ailment selection by referring to the user's past selection history and health data.

[0031] The ailment selection unit can analyze the user's lifestyle habits and dietary history and automatically suggest the most relevant ailment. The ailment selection unit, for example, analyzes the user's dietary history and detects imbalances in nutritional balance. For example, if there is a lack of iron, it suggests "easily tired." The ailment selection unit also analyzes the user's lifestyle ailment data (for example, amount of exercise and sleep time) and suggests related ailments. For example, if there is a lack of exercise, it suggests "feels heavy." The ailment selection unit also combines the dietary history and lifestyle ailment data to automatically suggest the most relevant ailment. For example, if there is an unbalanced diet and a lack of exercise, it suggests "easily tired." In this way, by analyzing the user's lifestyle habits and dietary history, it is possible to automatically suggest the most relevant ailment.

[0032] The nutrient analysis unit can analyze the user's genetic information and identify necessary nutrients based on individual genetic factors. The nutrient analysis unit, for example, analyzes the user's genetic information and identifies nutrients that are genetically likely to be deficient. For example, for a user who is genetically prone to vitamin D deficiency, it suggests foods that are high in vitamin D. The nutrient analysis unit also analyzes the absorption efficiency of specific nutrients based on the genetic information. For example, for a user who is genetically prone to poor iron absorption, it suggests foods that are high in iron. The nutrient analysis unit also combines the user's genetic information with health data to identify optimal nutrients. For example, for a user who is genetically prone to B vitamin deficiency and who gets tired easily, it suggests foods that are high in B vitamins. In this way, by analyzing the user's genetic information, it is possible to identify necessary nutrients based on individual genetic factors.

[0033] The nutrient analysis unit can analyze the user's intestinal flora data and identify nutrients that are optimal for the intestinal environment. The nutrient analysis unit, for example, analyzes the user's intestinal flora data and identifies nutrients that are optimal for the intestinal environment. For example, for a user whose intestinal flora is unbalanced, it suggests foods that are rich in probiotics. The nutrient analysis unit also analyzes the need for specific nutrients based on the intestinal flora data. For example, for a user whose intestinal flora does not easily produce vitamin K, it suggests foods that are rich in vitamin K. The nutrient analysis unit also combines the user's intestinal flora with health data to identify optimal nutrients. For example, for a user whose intestinal flora is unbalanced and prone to constipation, it suggests foods that are rich in dietary fiber. In this way, by analyzing the user's intestinal flora data, it is possible to identify nutrients that are optimal for the intestinal environment.

[0034] The ingredient selection unit can analyze the user's allergy information and select the optimal ingredients that avoid allergies. For example, the ingredient selection unit registers the user's allergy information in a database and selects the optimal ingredients that avoid allergies. For example, for a user with a nut allergy, it suggests ingredients that do not contain nuts. The ingredient selection unit also builds a system that suggests alternative ingredients based on the allergy information. For example, it suggests soy milk or almond milk for a user with a dairy allergy. The ingredient selection unit also combines the user's allergy information with health data to select the optimal ingredients. For example, it suggests gluten-free ingredients for a user with a gluten allergy. In this way, by analyzing the user's allergy information, it is possible to select the optimal ingredients that avoid allergies.

[0035] The ingredient selection unit can analyze a user's taste preferences and select the optimal ingredients that match their preferences. For example, the ingredient selection unit registers the user's taste preferences in a database and selects the optimal ingredients that match their preferences. For example, for a user who likes spicy food, it suggests ingredients that contain chili peppers. The ingredient selection unit also builds a system that suggests recipes for dishes that the user likes based on the taste preferences. For example, for a user who likes sweet things, it suggests dessert recipes. The ingredient selection unit also combines the user's taste preferences with health data to select the optimal ingredients. For example, for a user who likes sour foods and is deficient in vitamin C, it suggests lemon. In this way, by analyzing the user's taste preferences, the optimal ingredients that match their preferences can be selected.

[0036] The recipe suggestion unit can analyze a user's cooking skills and suggest the optimal recipe based on the skills. For example, the recipe suggestion unit registers the user's cooking skills in a database and suggests the optimal recipe based on the skills. For example, it suggests a simple stir-fry recipe to a beginner. The recipe suggestion unit also builds a system that suggests recipes that users can make without difficulty based on their cooking skills. For example, it suggests recipes with fewer steps to a user with little cooking experience. The recipe suggestion unit also combines the user's cooking skills with health data to suggest the optimal recipe. For example, it suggests an easy-to-make recipe using lemon to a beginner who is deficient in vitamin C. This makes it possible to suggest the optimal recipe based on the user's cooking skills.

[0037] The recipe suggestion unit can analyze the user's mealtimes and suggest optimal recipes according to the time periods. For example, the recipe suggestion unit registers the user's mealtimes in a database and suggests optimal recipes according to the time periods. For example, it suggests recipes that can replenish energy for breakfast. The recipe suggestion unit also builds a system that suggests recipes that contain the nutrients the user needs during mealtimes, based on the mealtimes. For example, it suggests easy-to-digest recipes for dinner. The recipe suggestion unit also combines the user's mealtimes with health data to suggest optimal recipes. For example, it suggests light, easy-to-digest recipes for a user who eats late at night. This makes it possible to suggest optimal recipes according to the user's mealtimes.

[0038] The recipe suggestion unit can analyze the user's dietary goals and suggest the optimal recipe according to the goal. For example, the recipe suggestion unit registers the user's dietary goals in a database and suggests the optimal recipe according to the goal. For example, low-calorie recipes are suggested for a user who is dieting. The recipe suggestion unit also builds a system that suggests recipes containing the nutrients a user needs to achieve their dietary goals, based on the user's dietary goals. For example, high-protein recipes are suggested for a user who is trying to build muscle. The recipe suggestion unit also combines the user's dietary goals with health data to suggest the optimal recipe. For example, for a user who is dieting and has a vitamin C deficiency, low-calorie recipes that are high in vitamin C are suggested. This makes it possible to suggest the optimal recipe according to the user's dietary goals.

[0039] The recipe suggestion unit can analyze the user's meal frequency and suggest optimal recipes based on the frequency. For example, the recipe suggestion unit registers the user's meal frequency in a database and suggests optimal recipes based on the frequency. For example, it suggests balanced recipes to a user who eats three meals a day. The recipe suggestion unit also builds a system that suggests recipes that allow the user to consume nutrients based on the meal frequency. For example, it suggests nutritious recipes to a user who eats two meals a day. The recipe suggestion unit also combines the user's meal frequency with health data to suggest optimal recipes. For example, it suggests recipes that take nutritional balance into consideration to a user who eats one meal a day. This makes it possible to suggest optimal recipes based on the user's meal frequency.

[0040] The ailment selection unit can refer to the selection trends of other users and match users who share a common ailment with each other. The ailment selection unit, for example, analyzes ailment data selected by other users and matches users who share a common ailment with each other. For example, users who selected "get tired easily" are grouped together. The ailment selection unit also provides a function that allows matched users to exchange information with each other. For example, users with the same ailment can exchange advice with each other through a chat function. The ailment selection unit also suggests the optimal ailment selection based on data of users who share a common ailment. For example, if many users have selected "get tired easily," it will also suggest "get tired easily" to a new user. This allows users with a common ailment to be matched with each other, enabling information exchange and advice.

[0041] The ailment selection unit can propose an optimal ailment selection by taking into consideration the geographical environment of the user and seasonal variations. The ailment selection unit proposes an optimal ailment selection by taking into consideration, for example, the geographical environment of the user (e.g., urban or rural area). For example, it proposes "accumulating stress" for a user living in an urban area. The ailment selection unit also considers seasonal variations and proposes an ailment selection according to the season. For example, it proposes "dry skin" in winter. The ailment selection unit also combines the geographical environment and seasonal variations to propose an optimal ailment selection. For example, for a user living in an urban area and it is winter, it proposes "accumulating stress" and "dry skin." This makes it possible to select an ailment by taking into consideration the geographical environment of the user and seasonal variations.

[0042] The nutrient analysis unit can analyze the user's activity data and identify the nutrients required according to the amount of daily activity. The nutrient analysis unit, for example, analyzes the user's activity data and identifies the nutrients required according to the amount of daily activity. For example, for a user who exercises a lot, it suggests foods that are high in protein. The nutrient analysis unit also analyzes the need for specific nutrients based on the activity data. For example, for a user who exercises a lot and gets tired easily, it suggests foods that are high in B vitamins. The nutrient analysis unit also combines the user's activity data and health data to identify optimal nutrients. For example, for a user who exercises a lot and has muscle pain, it suggests foods that are high in protein and vitamin C. This makes it possible to identify the nutrients required according to the user's daily activity.

[0043] The nutrient analysis unit can analyze the user's sleep data and identify necessary nutrients based on the quality of sleep. The nutrient analysis unit, for example, analyzes the user's sleep data and identifies necessary nutrients based on the quality of sleep. For example, for a user with poor sleep quality, it suggests foods that are high in melatonin. The nutrient analysis unit also analyzes the need for specific nutrients based on the sleep data. For example, for a user who is sleep deprived, it suggests foods that are high in vitamin B6. The nutrient analysis unit also combines the user's sleep data and health data to identify optimal nutrients. For example, for a user who has poor sleep quality and easily gets tired, it suggests foods that are high in melatonin and B vitamins. This makes it possible to identify necessary nutrients based on the user's sleep quality.

[0044] The ingredient selection unit can analyze ingredients in the user's region and select fresh local ingredients. For example, the ingredient selection unit selects fresh ingredients that are available locally based on the user's region information. For example, fresh seafood is suggested for a user who lives by the sea. The ingredient selection unit also references a regional ingredient database to build a system that suggests fresh ingredients for each season. For example, fresh locally grown vegetables are suggested in the spring. The ingredient selection unit also combines the user's region information with health data to select optimal ingredients. For example, fresh citrus fruits that are locally grown are suggested for a user who is deficient in vitamin C. This makes it possible to select fresh ingredients for the user's region.

[0045] The ingredient selection unit can analyze the availability of ingredients for a user and select the most suitable ingredients that are easily available. For example, the ingredient selection unit analyzes data from supermarkets and online stores in the user's area to select ingredients that are easily available. For example, it can suggest ingredients that are always on sale at a nearby supermarket. The ingredient selection unit also builds a system that suggests ingredients that the user can easily purchase based on ingredient availability. For example, it can suggest ingredients that can be purchased at an online store. The ingredient selection unit also combines the availability of ingredients for a user with health data to select the most suitable ingredients. For example, for a user who is iron deficient, it can suggest spinach that can be purchased at a nearby supermarket. This allows the user to select the most suitable ingredients that are easily available.

[0046] The recipe suggestion unit can analyze the user's dietary goals and suggest the optimal recipe according to the goal. For example, the recipe suggestion unit registers the user's dietary goals in a database and suggests the optimal recipe according to the goal. For example, low-calorie recipes are suggested for a user who is dieting. The recipe suggestion unit also builds a system that suggests recipes containing the nutrients a user needs to achieve their dietary goals, based on the user's dietary goals. For example, high-protein recipes are suggested for a user who is trying to build muscle. The recipe suggestion unit also combines the user's dietary goals with health data to suggest the optimal recipe. For example, for a user who is dieting and has a vitamin C deficiency, low-calorie recipes that are high in vitamin C are suggested. This makes it possible to suggest the optimal recipe according to the user's dietary goals.

[0047] The recipe suggestion unit can analyze the user's meal frequency and suggest optimal recipes based on the frequency. For example, the recipe suggestion unit registers the user's meal frequency in a database and suggests optimal recipes based on the frequency. For example, it suggests balanced recipes to a user who eats three meals a day. The recipe suggestion unit also builds a system that suggests recipes that allow the user to consume nutrients based on the meal frequency. For example, it suggests nutritious recipes to a user who eats two meals a day. The recipe suggestion unit also combines the user's meal frequency with health data to suggest optimal recipes. For example, it suggests recipes that take nutritional balance into consideration to a user who eats one meal a day. This makes it possible to suggest optimal recipes based on the user's meal frequency.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The health support system can also analyze the user's exercise history and suggest the best post-exercise ingredients and recipes. For example, it can identify the nutrients needed after exercise and suggest ingredients that are high in protein. It can also change the nutrients needed depending on the type of exercise. For example, it can suggest a protein shake recipe after strength training, and a recipe high in carbohydrates to replenish energy after aerobic exercise. It can also combine exercise history and health data to suggest the best ingredients and recipes. For example, it can suggest ingredients that are high in B vitamins for users who tend to get tired after exercise.

[0050] The health support system can also analyze the user's water intake and suggest ingredients and recipes that are ideal for hydration. For example, if a user's water intake is insufficient, it will suggest ingredients that are high in water. It can also suggest recipes for hydration. For example, it will suggest recipes for smoothies and soups. It can also combine water intake with health data to suggest optimal ingredients and recipes. For example, if a user is experiencing symptoms of dehydration, it will suggest ingredients that are high in electrolytes.

[0051] The health support system can also analyze the user's sleep data and suggest optimal ingredients and recipes to improve sleep quality. For example, for a user with poor sleep quality, it can suggest ingredients that are high in melatonin. It can also suggest recipes to promote sleep. For example, it can suggest recipes for chamomile tea or banana smoothies. It can also combine sleep data with health data to suggest optimal ingredients and recipes. For example, for a user who is sleep-deprived and easily fatigued, it can suggest ingredients that are high in B vitamins.

[0052] Health support systems can also analyze a user's mealtimes and suggest optimal exercise plans based on those times. For example, they can suggest light stretching or walking before breakfast, and relaxing yoga after dinner. It is also possible to build a system that suggests exercise plans appropriate for the user based on mealtimes. For example, they can suggest aerobic exercise to burn energy after lunch. Furthermore, they can combine mealtimes with health data to suggest optimal exercise plans. For example, they can suggest light exercise to aid digestion for a user who eats late at night.

[0053] The health support system can also propose optimal exercise plans by taking into account the user's geographical environment and seasonal variations. For example, it can propose an exercise plan that can be done indoors to a user living in an urban area, and an exercise plan that can be done in nature to a user living in a rural area. It can also propose exercise plans that are appropriate for the season by taking into account seasonal variations. For example, it can propose an exercise plan that can be done indoors in winter, and an exercise plan that can be done outdoors in summer. It can also propose optimal exercise plans by combining geographical environment and seasonal variations. For example, it can propose an indoor yoga and stretching plan for a user living in an urban area in winter.

[0054] The health support system can also analyze the user's meal frequency and suggest the optimal exercise plan based on that frequency. For example, a balanced exercise plan will be suggested for a user who eats three meals a day. It is also possible to build a system that suggests exercise plans that suit the user's meal frequency. For example, a user who eats two meals a day will be suggested an exercise plan that efficiently consumes energy. Furthermore, it is possible to combine meal frequency and health data to suggest the optimal exercise plan. For example, a user who eats one meal a day will be suggested an exercise plan that takes nutritional balance into consideration.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The discomfort selection section allows users to select the physical discomfort they wish to improve. For example, users can select the one that best suits their condition from options such as "easily tired," "dry skin," or "prone to constipation" via the app or website. Step 2: The nutrient analysis unit analyzes the necessary nutrients based on the symptom selected by the symptom selection unit. For example, for the symptom of "easily tired," it analyzes that B vitamins and iron are necessary. The nutrient analysis unit uses generative AI to identify the necessary nutrients based on prompts containing information about the symptom selected by the user. Step 3: The ingredient selection unit selects ingredients that supplement the nutrients analyzed by the nutrient analysis unit. For example, if B vitamins are needed, pork and eggs are selected, and if iron is needed, spinach and liver are selected. The ingredient selection unit refers to a database of the nutritional values ​​of ingredients and selects the most suitable ingredients. Step 4: The recipe suggestion unit suggests recipes using the ingredients selected by the ingredient selection unit. For example, a stir-fry using spinach and pork, or an omelet using eggs and liver may be suggested. The recipe suggestion unit refers to a database of cooking methods and generates easy and delicious recipes.

[0057] (Example 2) A health support system according to an embodiment of the present invention is a system that proposes optimal ingredients and recipes to resolve physical or skin problems experienced by a user. As a result, the health support system can provide ingredients and recipes appropriate for the user's condition and support the improvement of the user's health.

[0058] A health support system according to an embodiment includes a symptom selection unit, a nutrient analysis unit, an ingredient selection unit, and a recipe suggestion unit. The symptom selection unit selects a physical symptom the user wants to improve. For example, the user can select the symptom most closely matching their own condition from options such as "easily tired," "dry skin," and "constipated" via an app or website. The nutrient analysis unit analyzes the nutrients needed based on the symptom selected by the symptom selection unit. For example, for the symptom "easily tired," the analysis determines that B vitamins and iron are needed. The nutrient analysis unit uses a generative AI to identify the necessary nutrients based on prompts containing information about the symptom selected by the user. The ingredient selection unit selects ingredients to supplement the nutrients analyzed by the nutrient analysis unit. For example, if B vitamins are needed, pork and eggs are selected, and if iron is needed, spinach and liver are selected. The ingredient selection unit references a nutritional value database of ingredients to select the most appropriate ingredients. The recipe suggestion unit suggests recipes using the ingredients selected by the ingredient selection unit. For example, a stir-fry using spinach and pork, or an omelet using eggs and liver, may be suggested. The recipe suggestion unit references a database of cooking methods to generate easy and delicious recipes. This allows the health support system according to the embodiment to suggest ingredients and recipes that are optimal for the user's physical condition. For example, the user can refer to the suggested recipes and incorporate them into their daily meals. This allows the user to easily practice a diet that will improve their condition.

[0059] The ailment selection unit can support more accurate ailment selection by referring to the user's past selection history and health data. For example, the ailment selection unit stores the history of ailments previously selected by the user in a database and displays it as a reference the next time the user selects an ailment. For example, if a user previously selected "easily tired," the same ailment will be displayed again. The ailment selection unit also refers to the user's health data (e.g., heart rate and sleep data from a wearable device) and suggests ailment options based on the user's current health condition. For example, if recent sleep data is poor, "easily tired" will be displayed preferentially. The ailment selection unit also combines the user's past selection history and health data to automatically suggest the most relevant ailment. For example, if a user previously selected "slightly constipated," the ailment selection unit will suggest "slightly constipated" again. This allows for more accurate ailment selection by referring to the user's past selection history and health data.

[0060] The ailment selection unit can analyze the user's facial expression and tone of voice in real time and suggest an ailment selection that takes into account the user's emotional state. For example, when the user selects an ailment, the ailment selection unit uses a camera to analyze the facial expression and estimate the emotional state. For example, if the user has a tired expression, the ailment selection unit suggests "easily tired." The ailment selection unit also uses a microphone to analyze the user's tone of voice and estimate the emotional state. For example, if the user's voice sounds depressed, the ailment selection unit suggests "depressed." The ailment selection unit also combines facial expression analysis and voice analysis to comprehensively assess the user's emotional state and suggest the optimal ailment selection. For example, if the user has a tired expression and a low tone of voice, the ailment selection unit suggests "easily tired." This makes it possible to select an ailment that takes into account the user's emotional state.

[0061] The ailment selection unit can analyze the user's lifestyle habits and dietary history and automatically suggest the most relevant ailment. The ailment selection unit, for example, analyzes the user's dietary history and detects imbalances in nutritional balance. For example, if there is a lack of iron, it suggests "easily tired." The ailment selection unit also analyzes the user's lifestyle ailment data (for example, amount of exercise and sleep time) and suggests related ailments. For example, if there is a lack of exercise, it suggests "feels heavy." The ailment selection unit also combines the dietary history and lifestyle ailment data to automatically suggest the most relevant ailment. For example, if there is an unbalanced diet and a lack of exercise, it suggests "easily tired." In this way, by analyzing the user's lifestyle habits and dietary history, it is possible to automatically suggest the most relevant ailment.

[0062] The nutrient analysis unit can analyze the user's genetic information and identify necessary nutrients based on individual genetic factors. The nutrient analysis unit, for example, analyzes the user's genetic information and identifies nutrients that are genetically likely to be deficient. For example, for a user who is genetically prone to vitamin D deficiency, it suggests foods that are high in vitamin D. The nutrient analysis unit also analyzes the absorption efficiency of specific nutrients based on the genetic information. For example, for a user who is genetically prone to poor iron absorption, it suggests foods that are high in iron. The nutrient analysis unit also combines the user's genetic information with health data to identify optimal nutrients. For example, for a user who is genetically prone to B vitamin deficiency and who gets tired easily, it suggests foods that are high in B vitamins. In this way, by analyzing the user's genetic information, it is possible to identify necessary nutrients based on individual genetic factors.

[0063] The nutrient analysis unit can analyze the user's intestinal flora data and identify nutrients that are optimal for the intestinal environment. The nutrient analysis unit, for example, analyzes the user's intestinal flora data and identifies nutrients that are optimal for the intestinal environment. For example, for a user whose intestinal flora is unbalanced, it suggests foods that are rich in probiotics. The nutrient analysis unit also analyzes the need for specific nutrients based on the intestinal flora data. For example, for a user whose intestinal flora does not easily produce vitamin K, it suggests foods that are rich in vitamin K. The nutrient analysis unit also combines the user's intestinal flora with health data to identify optimal nutrients. For example, for a user whose intestinal flora is unbalanced and prone to constipation, it suggests foods that are rich in dietary fiber. In this way, by analyzing the user's intestinal flora data, it is possible to identify nutrients that are optimal for the intestinal environment.

[0064] The nutrient analysis unit can use the user's emotion estimation function to identify necessary nutrients based on the user's emotional state. The nutrient analysis unit, for example, analyzes the user's emotional state and identifies necessary nutrients based on the emotion. For example, for a user who is feeling stressed, it suggests foods that are rich in magnesium. The nutrient analysis unit also analyzes the need for specific nutrients based on the emotion estimation data. For example, for a user who is feeling depressed, it suggests foods that are rich in vitamin B6, which helps produce serotonin. The nutrient analysis unit also combines the user's emotional state with health data to identify optimal nutrients. For example, for a user who is feeling stressed and easily fatigued, it suggests foods that are rich in magnesium and B vitamins. This makes it possible to identify necessary nutrients based on the user's emotional state.

[0065] The ingredient selection unit can analyze the user's allergy information and select the optimal ingredients that avoid allergies. For example, the ingredient selection unit registers the user's allergy information in a database and selects the optimal ingredients that avoid allergies. For example, for a user with a nut allergy, it suggests ingredients that do not contain nuts. The ingredient selection unit also builds a system that suggests alternative ingredients based on the allergy information. For example, it suggests soy milk or almond milk for a user with a dairy allergy. The ingredient selection unit also combines the user's allergy information with health data to select the optimal ingredients. For example, it suggests gluten-free ingredients for a user with a gluten allergy. In this way, by analyzing the user's allergy information, it is possible to select the optimal ingredients that avoid allergies.

[0066] The ingredient selection unit can analyze a user's taste preferences and select the optimal ingredients that match their preferences. For example, the ingredient selection unit registers the user's taste preferences in a database and selects the optimal ingredients that match their preferences. For example, for a user who likes spicy food, it suggests ingredients that contain chili peppers. The ingredient selection unit also builds a system that suggests recipes for dishes that the user likes based on the taste preferences. For example, for a user who likes sweet things, it suggests dessert recipes. The ingredient selection unit also combines the user's taste preferences with health data to select the optimal ingredients. For example, for a user who likes sour foods and is deficient in vitamin C, it suggests lemon. In this way, by analyzing the user's taste preferences, the optimal ingredients that match their preferences can be selected.

[0067] The ingredient selection unit can use the user's emotion estimation function to select optimal ingredients based on the user's emotional state. The ingredient selection unit, for example, analyzes the user's emotional state and selects optimal ingredients based on the emotion. For example, a user who is feeling stressed may be suggested chamomile tea, which has a relaxing effect. The ingredient selection unit also builds a system that suggests ingredients suitable for a specific emotional state based on the emotion estimation data. For example, a user who is feeling depressed may be suggested dark chocolate, which has a mood-boosting effect. The ingredient selection unit also combines the user's emotional state with health data to select optimal ingredients. For example, a user who is easily tired and stressed may be suggested almonds, which are rich in magnesium. This allows optimal ingredients to be selected based on the user's emotional state.

[0068] The recipe suggestion unit can analyze a user's cooking skills and suggest the optimal recipe based on the skills. For example, the recipe suggestion unit registers the user's cooking skills in a database and suggests the optimal recipe based on the skills. For example, it suggests a simple stir-fry recipe to a beginner. The recipe suggestion unit also builds a system that suggests recipes that users can make without difficulty based on their cooking skills. For example, it suggests recipes with fewer steps to a user with little cooking experience. The recipe suggestion unit also combines the user's cooking skills with health data to suggest the optimal recipe. For example, it suggests an easy-to-make recipe using lemon to a beginner who is deficient in vitamin C. This makes it possible to suggest the optimal recipe based on the user's cooking skills.

[0069] The recipe suggestion unit can analyze the user's mealtimes and suggest optimal recipes according to the time periods. For example, the recipe suggestion unit registers the user's mealtimes in a database and suggests optimal recipes according to the time periods. For example, it suggests recipes that can replenish energy for breakfast. The recipe suggestion unit also builds a system that suggests recipes that contain the nutrients the user needs during mealtimes, based on the mealtimes. For example, it suggests easy-to-digest recipes for dinner. The recipe suggestion unit also combines the user's mealtimes with health data to suggest optimal recipes. For example, it suggests light, easy-to-digest recipes for a user who eats late at night. This makes it possible to suggest optimal recipes according to the user's mealtimes.

[0070] The recipe suggestion unit can use the user's emotion estimation function to suggest optimal recipes based on the user's emotional state. The recipe suggestion unit, for example, analyzes the user's emotional state and suggests optimal recipes based on the emotion. For example, for a user who is feeling stressed, it suggests a recipe for herbal tea that has a relaxing effect. The recipe suggestion unit also builds a system that suggests recipes suitable for a specific emotional state based on the emotion estimation data. For example, for a user who is feeling depressed, it suggests a chocolate cake recipe that has a mood-boosting effect. The recipe suggestion unit also combines the user's emotional state with health data to suggest optimal recipes. For example, for a user who is easily tired and stressed, it suggests a recipe using almonds, which are rich in magnesium. In this way, it is possible to suggest optimal recipes based on the user's emotional state.

[0071] The recipe suggestion unit can analyze the user's dietary goals and suggest the optimal recipe according to the goal. For example, the recipe suggestion unit registers the user's dietary goals in a database and suggests the optimal recipe according to the goal. For example, low-calorie recipes are suggested for a user who is dieting. The recipe suggestion unit also builds a system that suggests recipes containing the nutrients a user needs to achieve their dietary goals, based on the user's dietary goals. For example, high-protein recipes are suggested for a user who is trying to build muscle. The recipe suggestion unit also combines the user's dietary goals with health data to suggest the optimal recipe. For example, for a user who is dieting and has a vitamin C deficiency, low-calorie recipes that are high in vitamin C are suggested. This makes it possible to suggest the optimal recipe according to the user's dietary goals.

[0072] The recipe suggestion unit can analyze the user's meal frequency and suggest optimal recipes based on the frequency. For example, the recipe suggestion unit registers the user's meal frequency in a database and suggests optimal recipes based on the frequency. For example, it suggests balanced recipes to a user who eats three meals a day. The recipe suggestion unit also builds a system that suggests recipes that allow the user to consume nutrients based on the meal frequency. For example, it suggests nutritious recipes to a user who eats two meals a day. The recipe suggestion unit also combines the user's meal frequency with health data to suggest optimal recipes. For example, it suggests recipes that take nutritional balance into consideration to a user who eats one meal a day. This makes it possible to suggest optimal recipes based on the user's meal frequency.

[0073] The recipe suggestion unit can use the user's emotion estimation function to suggest optimal recipes based on the user's emotional state. The recipe suggestion unit, for example, analyzes the user's emotional state and suggests optimal recipes based on the emotion. For example, for a user who is feeling stressed, it suggests a recipe for herbal tea that has a relaxing effect. The recipe suggestion unit also builds a system that suggests recipes suitable for a specific emotional state based on the emotion estimation data. For example, for a user who is feeling depressed, it suggests a chocolate cake recipe that has a mood-boosting effect. The recipe suggestion unit also combines the user's emotional state with health data to suggest optimal recipes. For example, for a user who is easily tired and stressed, it suggests a recipe using almonds, which are rich in magnesium. In this way, it is possible to suggest optimal recipes based on the user's emotional state.

[0074] The ailment selection unit can refer to the selection trends of other users and match users who share a common ailment with each other. The ailment selection unit, for example, analyzes ailment data selected by other users and matches users who share a common ailment with each other. For example, users who selected "get tired easily" are grouped together. The ailment selection unit also provides a function that allows matched users to exchange information with each other. For example, users with the same ailment can exchange advice with each other through a chat function. The ailment selection unit also suggests the optimal ailment selection based on data of users who share a common ailment. For example, if many users have selected "get tired easily," it will also suggest "get tired easily" to a new user. This allows users with a common ailment to be matched with each other, enabling information exchange and advice.

[0075] The illness selection unit can support illness selection based on emotions using the user's emotion estimation function. The illness selection unit, for example, analyzes the user's emotional state in real time and suggests illness selection based on emotions. For example, if the user is feeling stressed, it suggests "stressed." The illness selection unit also analyzes the association between the user's past emotional state and illness selection based on emotion estimation data. For example, if a user selected "headache" when feeling stressed in the past, it suggests "headache" again. The illness selection unit also uses the emotion estimation function to support illness selection taking into account the user's emotional state. For example, if the user is feeling depressed, it suggests "depressed." This makes it possible to support illness selection based on the user's emotions.

[0076] The ailment selection unit can propose an optimal ailment selection by taking into consideration the geographical environment of the user and seasonal variations. The ailment selection unit proposes an optimal ailment selection by taking into consideration, for example, the geographical environment of the user (e.g., urban or rural area). For example, it proposes "accumulating stress" for a user living in an urban area. The ailment selection unit also considers seasonal variations and proposes an ailment selection according to the season. For example, it proposes "dry skin" in winter. The ailment selection unit also combines the geographical environment and seasonal variations to propose an optimal ailment selection. For example, for a user living in an urban area and it is winter, it proposes "accumulating stress" and "dry skin." This makes it possible to select an ailment by taking into consideration the geographical environment of the user and seasonal variations.

[0077] The nutrient analysis unit can analyze the user's activity data and identify the nutrients required according to the amount of daily activity. The nutrient analysis unit, for example, analyzes the user's activity data and identifies the nutrients required according to the amount of daily activity. For example, for a user who exercises a lot, it suggests foods that are high in protein. The nutrient analysis unit also analyzes the need for specific nutrients based on the activity data. For example, for a user who exercises a lot and gets tired easily, it suggests foods that are high in B vitamins. The nutrient analysis unit also combines the user's activity data and health data to identify optimal nutrients. For example, for a user who exercises a lot and has muscle pain, it suggests foods that are high in protein and vitamin C. This makes it possible to identify the nutrients required according to the user's daily activity.

[0078] The nutrient analysis unit can analyze the user's sleep data and identify necessary nutrients based on the quality of sleep. The nutrient analysis unit, for example, analyzes the user's sleep data and identifies necessary nutrients based on the quality of sleep. For example, for a user with poor sleep quality, it suggests foods that are high in melatonin. The nutrient analysis unit also analyzes the need for specific nutrients based on the sleep data. For example, for a user who is sleep deprived, it suggests foods that are high in vitamin B6. The nutrient analysis unit also combines the user's sleep data and health data to identify optimal nutrients. For example, for a user who has poor sleep quality and easily gets tired, it suggests foods that are high in melatonin and B vitamins. This makes it possible to identify necessary nutrients based on the user's sleep quality.

[0079] The ingredient selection unit can analyze ingredients in the user's region and select fresh local ingredients. For example, the ingredient selection unit selects fresh ingredients that are available locally based on the user's region information. For example, fresh seafood is suggested for a user who lives by the sea. The ingredient selection unit also references a regional ingredient database to build a system that suggests fresh ingredients for each season. For example, fresh locally grown vegetables are suggested in the spring. The ingredient selection unit also combines the user's region information with health data to select optimal ingredients. For example, fresh citrus fruits that are locally grown are suggested for a user who is deficient in vitamin C. This makes it possible to select fresh ingredients for the user's region.

[0080] The ingredient selection unit can analyze the availability of ingredients for a user and select the most suitable ingredients that are easily available. For example, the ingredient selection unit analyzes data from supermarkets and online stores in the user's area to select ingredients that are easily available. For example, it can suggest ingredients that are always on sale at a nearby supermarket. The ingredient selection unit also builds a system that suggests ingredients that the user can easily purchase based on ingredient availability. For example, it can suggest ingredients that can be purchased at an online store. The ingredient selection unit also combines the availability of ingredients for a user with health data to select the most suitable ingredients. For example, for a user who is iron deficient, it can suggest spinach that can be purchased at a nearby supermarket. This allows the user to select the most suitable ingredients that are easily available.

[0081] The ingredient selection unit can use the user's emotion estimation function to select optimal ingredients based on the user's emotional state. The ingredient selection unit, for example, analyzes the user's emotional state and selects optimal ingredients based on the emotion. For example, a user who is feeling stressed may be suggested chamomile tea, which has a relaxing effect. The ingredient selection unit also builds a system that suggests ingredients suitable for a specific emotional state based on the emotion estimation data. For example, a user who is feeling depressed may be suggested dark chocolate, which has a mood-boosting effect. The ingredient selection unit also combines the user's emotional state with health data to select optimal ingredients. For example, a user who is easily tired and stressed may be suggested almonds, which are rich in magnesium. This allows optimal ingredients to be selected based on the user's emotional state.

[0082] The nutrient analysis unit can use the user's emotion estimation function to identify necessary nutrients based on the user's emotional state. The nutrient analysis unit, for example, analyzes the user's emotional state and identifies necessary nutrients based on the emotion. For example, for a user who is feeling stressed, it suggests foods that are rich in magnesium. The nutrient analysis unit also analyzes the need for specific nutrients based on the emotion estimation data. For example, for a user who is feeling depressed, it suggests foods that are rich in vitamin B6, which helps produce serotonin. The nutrient analysis unit also combines the user's emotional state with health data to identify optimal nutrients. For example, for a user who is feeling stressed and easily fatigued, it suggests foods that are rich in magnesium and B vitamins. This makes it possible to identify necessary nutrients based on the user's emotional state.

[0083] The recipe suggestion unit can analyze the user's dietary goals and suggest the optimal recipe according to the goal. For example, the recipe suggestion unit registers the user's dietary goals in a database and suggests the optimal recipe according to the goal. For example, low-calorie recipes are suggested for a user who is dieting. The recipe suggestion unit also builds a system that suggests recipes containing the nutrients a user needs to achieve their dietary goals, based on the user's dietary goals. For example, high-protein recipes are suggested for a user who is trying to build muscle. The recipe suggestion unit also combines the user's dietary goals with health data to suggest the optimal recipe. For example, for a user who is dieting and has a vitamin C deficiency, low-calorie recipes that are high in vitamin C are suggested. This makes it possible to suggest the optimal recipe according to the user's dietary goals.

[0084] The recipe suggestion unit can analyze the user's meal frequency and suggest optimal recipes based on the frequency. For example, the recipe suggestion unit registers the user's meal frequency in a database and suggests optimal recipes based on the frequency. For example, it suggests balanced recipes to a user who eats three meals a day. The recipe suggestion unit also builds a system that suggests recipes that allow the user to consume nutrients based on the meal frequency. For example, it suggests nutritious recipes to a user who eats two meals a day. The recipe suggestion unit also combines the user's meal frequency with health data to suggest optimal recipes. For example, it suggests recipes that take nutritional balance into consideration to a user who eats one meal a day. This makes it possible to suggest optimal recipes based on the user's meal frequency.

[0085] The recipe suggestion unit can use the user's emotion estimation function to suggest optimal recipes based on the user's emotional state. The recipe suggestion unit, for example, analyzes the user's emotional state and suggests optimal recipes based on the emotion. For example, for a user who is feeling stressed, it suggests a recipe for herbal tea that has a relaxing effect. The recipe suggestion unit also builds a system that suggests recipes suitable for a specific emotional state based on the emotion estimation data. For example, for a user who is feeling depressed, it suggests a chocolate cake recipe that has a mood-boosting effect. The recipe suggestion unit also combines the user's emotional state with health data to suggest optimal recipes. For example, for a user who is easily tired and stressed, it suggests a recipe using almonds, which are rich in magnesium. In this way, it is possible to suggest optimal recipes based on the user's emotional state.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The health support system can also analyze the user's exercise history and suggest the best post-exercise ingredients and recipes. For example, it can identify the nutrients needed after exercise and suggest ingredients that are high in protein. It can also change the nutrients needed depending on the type of exercise. For example, it can suggest a protein shake recipe after strength training, and a recipe high in carbohydrates to replenish energy after aerobic exercise. It can also combine exercise history and health data to suggest the best ingredients and recipes. For example, it can suggest ingredients that are high in B vitamins for users who tend to get tired after exercise.

[0088] The health support system can also analyze the user's water intake and suggest ingredients and recipes that are ideal for hydration. For example, if a user's water intake is insufficient, it will suggest ingredients that are high in water. It can also suggest recipes for hydration. For example, it will suggest recipes for smoothies and soups. It can also combine water intake with health data to suggest optimal ingredients and recipes. For example, if a user is experiencing symptoms of dehydration, it will suggest ingredients that are high in electrolytes.

[0089] The health support system can also analyze the user's sleep data and suggest optimal ingredients and recipes to improve sleep quality. For example, for a user with poor sleep quality, it can suggest ingredients that are high in melatonin. It can also suggest recipes to promote sleep. For example, it can suggest recipes for chamomile tea or banana smoothies. It can also combine sleep data with health data to suggest optimal ingredients and recipes. For example, for a user who is sleep-deprived and easily fatigued, it can suggest ingredients that are high in B vitamins.

[0090] The health support system can also analyze a user's stress level and suggest ingredients and recipes that are ideal for reducing stress. For example, for a user with a high stress level, it can suggest ingredients that are rich in magnesium. It can also suggest recipes that have a relaxing effect. For example, it can suggest recipes for herbal tea or dark chocolate. It can also combine stress levels with health data to suggest optimal ingredients and recipes. For example, for a user who is stressed and easily fatigued, it can suggest ingredients that are rich in magnesium and B vitamins.

[0091] The health support system can also use the user's emotion estimation function to suggest an optimal exercise plan based on the user's emotional state. For example, if the user is feeling stressed, a plan of yoga or stretching that has a relaxing effect can be suggested. It is also possible to build a system that suggests an exercise plan suited to a specific emotional state based on emotion estimation data. For example, a depressed user can be suggested an aerobic exercise plan that has a mood-boosting effect. Furthermore, the optimal exercise plan can be suggested by combining the emotional state and health data. For example, a user who easily gets tired and feels stressed can be suggested an exercise plan with a relaxing effect.

[0092] Health support systems can also analyze a user's mealtimes and suggest optimal exercise plans based on those times. For example, they can suggest light stretching or walking before breakfast, and relaxing yoga after dinner. It is also possible to build a system that suggests exercise plans appropriate for the user based on mealtimes. For example, they can suggest aerobic exercise to burn energy after lunch. Furthermore, they can combine mealtimes with health data to suggest optimal exercise plans. For example, they can suggest light exercise to aid digestion for a user who eats late at night.

[0093] The health support system can also use the user's emotion estimation function to suggest optimal relaxation methods based on the user's emotional state. For example, if the user is feeling stressed, aromatherapy or meditation can be suggested. It is also possible to build a system that suggests relaxation methods suited to specific emotional states based on emotion estimation data. For example, for a user who is feeling depressed, music therapy, which has a mood-boosting effect, can be suggested. Furthermore, the emotional state can be combined with health data to suggest optimal relaxation methods. For example, for a user who is easily fatigued and feeling stressed, a relaxation method with a relaxing effect can be suggested.

[0094] The health support system can also propose optimal exercise plans by taking into account the user's geographical environment and seasonal variations. For example, it can propose an exercise plan that can be done indoors to a user living in an urban area, and an exercise plan that can be done in nature to a user living in a rural area. It can also propose exercise plans that are appropriate for the season by taking into account seasonal variations. For example, it can propose an exercise plan that can be done indoors in winter, and an exercise plan that can be done outdoors in summer. It can also propose optimal exercise plans by combining geographical environment and seasonal variations. For example, it can propose an indoor yoga and stretching plan for a user living in an urban area in winter.

[0095] The health support system can also use the user's emotion estimation function to suggest the optimal sleeping environment based on the user's emotional state. For example, if the user is feeling stressed, it can suggest aromas or music with a relaxing effect. It can also build a system based on emotion estimation data to suggest sleeping environments suited to specific emotional states. For example, for a user who is feeling depressed, it can suggest lighting and bedding that will lift their mood. Furthermore, it can combine emotional states and health data to suggest the optimal sleeping environment. For example, it can suggest a sleeping environment with a relaxing effect to a user who is easily tired and stressed.

[0096] The health support system can also analyze the user's meal frequency and suggest the optimal exercise plan based on that frequency. For example, a balanced exercise plan will be suggested for a user who eats three meals a day. It is also possible to build a system that suggests exercise plans that suit the user's meal frequency. For example, a user who eats two meals a day will be suggested an exercise plan that efficiently consumes energy. Furthermore, it is possible to combine meal frequency and health data to suggest the optimal exercise plan. For example, a user who eats one meal a day will be suggested an exercise plan that takes nutritional balance into consideration.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The discomfort selection section allows users to select the physical discomfort they wish to improve. For example, users can select the one that best suits their condition from options such as "easily tired," "dry skin," or "prone to constipation" via the app or website. Step 2: The nutrient analysis unit analyzes the necessary nutrients based on the symptom selected by the symptom selection unit. For example, for the symptom of "easily tired," it analyzes that B vitamins and iron are necessary. The nutrient analysis unit uses generative AI to identify the necessary nutrients based on prompts containing information about the symptom selected by the user. Step 3: The ingredient selection unit selects ingredients that supplement the nutrients analyzed by the nutrient analysis unit. For example, if B vitamins are needed, pork and eggs are selected, and if iron is needed, spinach and liver are selected. The ingredient selection unit refers to a database of the nutritional values ​​of ingredients and selects the most suitable ingredients. Step 4: The recipe suggestion unit suggests recipes using the ingredients selected by the ingredient selection unit. For example, a stir-fry using spinach and pork, or an omelet using eggs and liver may be suggested. The recipe suggestion unit refers to a database of cooking methods and generates easy and delicious recipes.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 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.

[0144] 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.

[0145] 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.

[0146] 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 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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, to avoid confusion and 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.

[0165] 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]

[0166] 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 malfunction selection unit for selecting a physical malfunction that the user wants to improve; a nutrient analysis unit that analyzes necessary nutrients based on the illness selected by the illness selection unit; an ingredient selection unit that selects ingredients that supplement the nutrients analyzed by the nutrient analysis unit; a recipe suggestion unit that suggests recipes using the ingredients selected by the ingredient selection unit. A system characterized by:

2. The malfunction selection unit Refer to the user's past selection history and health data to support more accurate selection of symptoms.

2. The system of claim 1.

3. The malfunction selection unit Analyze the user's facial expressions and tone of voice in real time and suggest symptom selection that takes into account their emotional state.

2. The system of claim 1.

4. The malfunction selection unit Analyze the user's lifestyle and dietary history and automatically suggest the most relevant symptoms.

2. The system of claim 1.

5. The nutrient analysis unit Analyzing the user's genetic information and identifying necessary nutrients based on individual genetic factors 2. The system of claim 1.

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