System

Abstract

An object of a system according to an embodiment is to propose an optimal ingredient or substitute based on a user's preference or nutritional needs.SOLUTION: A system includes a preference collection part, a nutrition analysis part, and a substitute proposal part. The preference collection unit collects user's preferences and nutritional needs. The nutrition analysis unit analyzes a nutrition balance based on the information collected by the preference collection unit. The substitute proposal unit proposes an optimum food material or substitute based on the result analyzed by the nutrition analysis 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 do not adequately suggest optimal ingredients and substitutes based on a user's preferences and nutritional needs, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest optimal ingredients and substitutes based on the user's preferences and nutritional needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference collection unit, a nutritional analysis unit, and a substitute suggestion unit. The preference collection unit collects user preferences and nutritional needs. The nutritional analysis unit analyzes nutritional balance based on the information collected by the preference collection unit. The substitute suggestion unit suggests optimal ingredients and substitutes based on the results of the analysis by the nutritional analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal ingredients and substitutes based on the user's preferences and nutritional needs. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The ingredient suggestion app according to the embodiment of the present invention is a system that suggests optimal ingredient combinations and substitutes based on a user's preferences and nutritional needs, allowing the user to easily create a nutritionally balanced meal that is tailored to their needs.

[0029] The ingredient suggestion app according to the embodiment includes a preference collection unit, a nutritional analysis unit, and a substitute suggestion unit. The preference collection unit collects user preferences and nutritional needs. For example, the user inputs information such as favorite ingredients, ingredients to avoid, and preferences for specific nutrients. The preference collection unit can also automatically analyze the user's dietary history and predict preferences based on past preference patterns. For example, the preference collection unit can analyze the user's dietary history and identify frequently selected ingredients and dishes. The preference collection unit can also acquire the user's health status and exercise volume from a wearable device and dynamically adjust nutritional needs. For example, the preference collection unit can acquire the user's heart rate and step count data from the wearable device and provide nutritional suggestions based on the user's exercise volume. The nutritional analysis unit analyzes nutritional balance based on the information collected by the preference collection unit. For example, if the user wants to increase their vitamin C intake, the nutritional analysis unit can suggest ingredients rich in vitamin C. The nutritional analysis unit can also analyze the user's genetic information and suggest nutritional balance based on the user's individual genetic characteristics. For example, if the absorption efficiency of a particular vitamin or mineral is low, the substitute suggestion unit suggests ingredients that are rich in that nutrient. The substitute suggestion unit suggests optimal ingredients and substitutes based on the results of the analysis by the nutrition analysis unit. For example, if a user wants to avoid dairy products, the substitute suggestion unit suggests substitutes such as almond milk or soy milk. The substitute suggestion unit can also analyze the user's allergy information in detail and suggest substitutes that do not contain allergens. For example, almond milk or soy milk is suggested for a user with a dairy allergy. This allows the ingredient suggestion app according to the embodiment to suggest optimal ingredients and substitutes based on the user's preferences and nutritional needs. For example, users can easily create meals that suit their preferences and nutritional needs and consume a balanced amount of nutrients necessary to maintain their health.

[0030] The preference collection unit can automatically analyze the meal history and predict preferences based on preference patterns. For example, the preference collection unit analyzes the meal history entered by the user in the past to identify frequently selected ingredients and dishes. For example, it lists ingredients that the user eats three or more times a week to understand preference trends. The preference collection unit can also analyze preference patterns from the past meal history to predict the user's preferences. For example, if the user tends to like a particular ingredient, it will preferentially suggest that ingredient. This makes it possible to predict preferences based on the user's past preference patterns.

[0031] The preference collection unit can acquire the user's health condition and amount of exercise from the wearable device and dynamically adjust nutritional needs. For example, the preference collection unit acquires the user's heart rate and step count data from the wearable device and makes nutritional suggestions based on the amount of exercise. For example, on days when the user exercises a lot, it can suggest high-protein foods. The preference collection unit can also monitor the user's health condition and dynamically adjust nutritional needs. For example, it can adjust the nutritional balance based on the user's weight and blood pressure data. This makes it possible to dynamically adjust nutritional needs according to the user's health condition and amount of exercise.

[0032] The preference collection unit allows users to share their preferences and nutritional needs with family and friends and jointly create meal plans. The preference collection unit provides a function for sharing, for example, the preferences and nutritional needs entered by the user with family and friends and jointly creating meal plans. For example, it may suggest menus that reflect the preferences of all family members. The preference collection unit may also jointly create meal plans based on information shared with family and friends. For example, it may suggest meal plans that take into account the nutritional balance of all family members. This allows users to share their preferences and nutritional needs with family and friends and jointly create meal plans.

[0033] The preference collection unit can dynamically change preferences according to the season or local specialties. The preference collection unit provides a function to dynamically change the user's preferences based on, for example, seasonal specialty product data. For example, watermelon and tomatoes are suggested in summer, and pumpkins and radishes in winter. The preference collection unit can also dynamically change the user's preferences based on local specialty product data. For example, local agricultural products are suggested. This allows the user's preferences to be dynamically changed according to the season or local specialties.

[0034] The nutritional analysis unit can analyze genetic information and suggest a nutritional balance based on genetic characteristics. The nutritional analysis unit, for example, analyzes a user's genetic information and builds a system that suggests a nutritional balance based on genetic characteristics. For example, if the absorption efficiency of a particular vitamin or mineral is low, ingredients that contain a lot of that nutrient are suggested. The nutritional analysis unit can also suggest an optimal nutritional balance for a user based on genetic information. For example, if a user is genetically prone to a deficiency in a particular nutrient, ingredients that supplement that nutrient are suggested. This makes it possible to suggest an optimal nutritional balance based on the user's genetic characteristics.

[0035] The nutritional analysis unit can analyze the state of the intestinal flora and suggest ingredients that are optimal for the intestinal environment. The nutritional analysis unit, for example, analyzes the state of the user's intestinal flora and builds a system that suggests ingredients that are optimal for the intestinal environment. For example, it can suggest yogurt or kimchi to increase beneficial bacteria. The nutritional analysis unit can also suggest ingredients that are optimal for the user based on the state of the intestinal flora. For example, it can suggest ingredients that will improve the intestinal environment. This makes it possible to suggest optimal ingredients based on the user's intestinal environment.

[0036] The nutritional analysis unit can analyze nutritional balance in an integrated manner with health data. The nutritional analysis unit, for example, builds a system that adjusts nutritional balance based on the user's blood pressure data. For example, if blood pressure is high, it can suggest low-salt ingredients. The nutritional analysis unit can also analyze nutritional balance based on the user's health data. For example, it can suggest low-carbohydrate ingredients based on blood sugar level data. This allows the user's nutritional balance to be analyzed in an integrated manner with other health data.

[0037] The nutritional analysis unit can adapt the nutritional balance to food cultures and cooking styles. For example, the nutritional analysis unit analyzes the user's nutritional balance and builds a system that adapts to different food cultures. For example, it makes nutritional suggestions that suit styles such as Japanese, Western, and Chinese cuisine. The nutritional analysis unit can also adjust the nutritional balance to suit the user's cooking style. For example, it can suggest a nutritional balance that is suitable for a specific cooking style. This allows the user's nutritional balance to adapt to different food cultures and cooking styles.

[0038] The substitution suggestion unit can analyze allergy information in detail and suggest substitutions that do not contain allergens. The substitution suggestion unit, for example, builds a system that analyzes a user's allergy information in detail and suggests substitutions that do not contain allergens. For example, almond milk or soy milk is suggested to a user who is allergic to dairy products. The substitution suggestion unit can also suggest the most suitable substitution for a user based on the allergy information. For example, gluten-free ingredients are suggested to a user who is allergic to gluten. This makes it possible to suggest substitutions that do not contain allergens based on the user's allergy information.

[0039] The substitute suggestion unit can analyze ingredient preferences and suggest substitutes with similar tastes and textures. The substitute suggestion unit, for example, builds a system that analyzes a user's ingredient preferences and suggests substitutes with similar tastes and textures. For example, soy meat is suggested instead of beef. The substitute suggestion unit can also suggest substitutes with similar tastes and textures based on the user's preferences. For example, if a user likes a particular ingredient, a substitute similar to that ingredient is suggested. This makes it possible to suggest substitutes with similar tastes and textures based on the user's preferences.

[0040] The substitute suggestion unit can change ingredient substitutes to environmentally friendly, sustainable options. The substitute suggestion unit, for example, builds a system that changes a user's ingredient substitutes to environmentally friendly, sustainable options. For example, it suggests plant-based foods instead of animal-based foods. The substitute suggestion unit can also suggest environmentally friendly substitutes based on the user's preferences. For example, if a user likes a particular ingredient, it suggests sustainable substitutes similar to that ingredient. This makes it possible to change the user's ingredient substitutes to environmentally friendly, sustainable options.

[0041] The substitute suggestion unit can change substitutes for ingredients to local specialties or seasonal ingredients. The substitute suggestion unit, for example, builds a system that changes substitutes for ingredients of a user to local specialties or seasonal ingredients. For example, it suggests local agricultural products. The substitute suggestion unit can also suggest local specialties or seasonal ingredients based on the user's preferences. For example, if a user likes a particular ingredient, it suggests local specialties or seasonal ingredients that are similar to that ingredient. This makes it possible to change substitutes for ingredients of a user to local specialties or seasonal ingredients.

[0042] The recipe providing unit can analyze cooking skills and suggest recipes according to the skill level. The recipe providing unit, for example, builds a system that analyzes the user's cooking skills and suggests recipes according to the skill level. For example, it suggests simple recipes for beginners and complex recipes for advanced cooks. The recipe providing unit can also suggest cooking methods according to the user's skill level. For example, it suggests simple cooking procedures for beginners and advanced cooking techniques for advanced cooks. This makes it possible to suggest recipes according to the user's cooking skills.

[0043] The recipe providing unit can analyze kitchen equipment and suggest cooking methods that suit the available equipment. The recipe providing unit, for example, builds a system that analyzes a user's kitchen equipment and suggests cooking methods that suit the available equipment. For example, it suggests recipes that use an oven or microwave. The recipe providing unit can also suggest cooking methods that suit the user's kitchen equipment. For example, it suggests recipes that use specific cooking equipment. This makes it possible to suggest cooking methods that suit the user's kitchen equipment.

[0044] The recipe providing unit can adapt recipes to cooking styles and food cultures. For example, the recipe providing unit builds a system that adapts a user's recipes to different cooking styles and food cultures. For example, it proposes recipes that suit styles such as Japanese cuisine, Western cuisine, and Chinese cuisine. The recipe providing unit can also adjust recipes to suit the user's cooking style. For example, it proposes recipes that are suitable for a specific cooking style. This allows the user's recipes to be adapted to different cooking styles and food cultures.

[0045] The recipe providing unit can propose versions of recipes in which ingredients are substituted with healthier ingredients. The recipe providing unit, for example, builds a system that proposes versions of a user's recipes in which ingredients are substituted with healthier ingredients. For example, it proposes recipes that use brown rice instead of white rice. The recipe providing unit can also propose versions of a user's recipes in which ingredients are substituted with healthier ingredients. For example, it proposes recipes that use low-calorie ingredients. This makes it possible to propose versions of a user's recipes in which ingredients are substituted with healthier ingredients.

[0046] The nutritional balance monitoring unit can analyze the dietary history in detail and provide feedback on long-term nutritional balance trends. The nutritional balance monitoring unit, for example, builds a system that analyzes the user's dietary history in detail and provides feedback on long-term nutritional balance trends. For example, it displays the nutritional balance trend in a graph based on dietary data from the past year. The nutritional balance monitoring unit can also suggest areas for improving nutritional balance based on the user's dietary history. For example, if a specific nutrient is lacking, it will suggest ingredients that contain a large amount of that nutrient. This makes it possible to analyze the user's dietary history in detail and provide feedback on long-term nutritional balance trends.

[0047] The nutritional balance monitoring unit can analyze health data and suggest areas for improving nutritional balance. The nutritional balance monitoring unit can build a system that suggests areas for improving nutritional balance based on, for example, the user's weight data. For example, if the user's weight is increasing, low-calorie ingredients can be suggested. The nutritional balance monitoring unit can also adjust nutritional balance based on the user's blood pressure data. For example, if the user's blood pressure is high, low-salt ingredients can be suggested. This makes it possible to suggest areas for improving nutritional balance based on the user's health data.

[0048] The nutritional balance monitoring unit can compare the nutritional balance with other users and provide a benchmark. The nutritional balance monitoring unit, for example, builds a system that compares the user's nutritional balance with other users and provides a benchmark. For example, it shows the tendency of nutritional balance compared with users of the same age or gender. The nutritional balance monitoring unit can also provide the results of comparison with other users based on the user's nutritional balance. For example, it shows the degree to which the user's nutritional balance matches the average value. This makes it possible to compare the user's nutritional balance with other users and provide a benchmark.

[0049] The nutritional balance monitoring unit can adapt the nutritional balance to food cultures and cooking styles. For example, the nutritional balance monitoring unit analyzes the user's nutritional balance and builds a system that adapts to different food cultures. For example, it makes nutritional suggestions that match styles such as Japanese, Western, and Chinese cuisine. The nutritional balance monitoring unit can also adjust the nutritional balance to suit the user's cooking style. For example, it can suggest a nutritional balance that is suitable for a specific cooking style. This allows the user's nutritional balance to adapt to different food cultures and cooking styles.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Ingredient recommendation apps can incorporate game elements to make ingredient selection more fun for users. For example, users can earn points every time they try a new ingredient and use those points to receive in-app rewards. Users can also share their ingredient selection methods and recipes with each other and compete for rankings. Furthermore, a system can be introduced where users are challenged to cook dishes using specific ingredients and earn badges upon completion. This allows users to enjoy choosing healthy ingredients.

[0052] Ingredient recommendation apps can add a voice assistant function to help users select ingredients. For example, users can input their ingredient preferences and nutritional needs by voice. The voice assistant can also suggest ingredients and explain recipes to users. Furthermore, the voice assistant can answer users' questions in real time. This allows users to easily learn how to select ingredients and cook them.

[0053] Ingredient recommendation apps can add an augmented reality (AR) function to assist users in selecting ingredients. For example, when a user scans an ingredient using a smartphone camera, information about the ingredient and a recipe are displayed. The AR function can also be used to display an image of the finished dish using the ingredients selected by the user. Furthermore, the AR function can be used to provide navigation when the user is selecting ingredients. This allows users to visually understand how to select ingredients and how to cook them.

[0054] Ingredient recommendation apps can suggest food storage methods to help users choose ingredients. For example, users can check how to store ingredients they have purchased within the app. They can also provide information on the shelf life and storage location of ingredients. Furthermore, an alert function for food storage methods can be added to notify users when ingredients have passed their shelf life. This allows users to store ingredients in the best possible condition without wasting them.

[0055] To help users select ingredients, food recommendation apps can add a function to compare the nutritional value of ingredients. For example, the app can compare the nutritional value of ingredients selected by the user and suggest the best options. It can also display the nutritional value of different ingredients in a graph for visual comparison. It can also suggest the best combination of ingredients based on the user's nutritional needs. This allows users to select the best ingredients while taking nutritional value into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The preference collection unit collects the user's preferences and nutritional needs. For example, the user inputs information such as favorite ingredients, ingredients to avoid, and preferences for specific nutrients. The preference collection unit can also automatically analyze the user's dietary history and predict preferences based on past preference patterns. Furthermore, the preference collection unit can obtain the user's health status and exercise volume from the wearable device and dynamically adjust nutritional needs. Step 2: The nutritional analysis unit analyzes the nutritional balance based on the information collected by the preference collection unit. For example, if the user wants to consume a lot of vitamin C, the nutritional analysis unit will suggest foods that are rich in vitamin C. The nutritional analysis unit can also analyze the user's genetic information and suggest nutritional balance based on individual genetic characteristics. Step 3: The substitution suggestion unit suggests optimal ingredients and substitutes based on the results of the analysis by the nutritional analysis unit. For example, if the user wants to avoid dairy products, the substitution suggestion unit suggests substitutes such as almond milk or soy milk. The substitution suggestion unit can also analyze the user's allergy information in detail and suggest substitutes that do not contain allergens.

[0058] (Example 2) The ingredient suggestion app according to the embodiment of the present invention is a system that suggests optimal ingredient combinations and substitutes based on a user's preferences and nutritional needs, allowing the user to easily create a nutritionally balanced meal that is tailored to their needs.

[0059] The ingredient suggestion app according to the embodiment includes a preference collection unit, a nutritional analysis unit, and a substitute suggestion unit. The preference collection unit collects user preferences and nutritional needs. For example, the user inputs information such as favorite ingredients, ingredients to avoid, and preferences for specific nutrients. The preference collection unit can also automatically analyze the user's dietary history and predict preferences based on past preference patterns. For example, the preference collection unit can analyze the user's dietary history and identify frequently selected ingredients and dishes. The preference collection unit can also acquire the user's health status and exercise volume from a wearable device and dynamically adjust nutritional needs. For example, the preference collection unit can acquire the user's heart rate and step count data from the wearable device and provide nutritional suggestions based on the user's exercise volume. The nutritional analysis unit analyzes nutritional balance based on the information collected by the preference collection unit. For example, if the user wants to increase their vitamin C intake, the nutritional analysis unit can suggest ingredients rich in vitamin C. The nutritional analysis unit can also analyze the user's genetic information and suggest nutritional balance based on the user's individual genetic characteristics. For example, if the absorption efficiency of a particular vitamin or mineral is low, the substitute suggestion unit suggests ingredients that are rich in that nutrient. The substitute suggestion unit suggests optimal ingredients and substitutes based on the results of the analysis by the nutrition analysis unit. For example, if a user wants to avoid dairy products, the substitute suggestion unit suggests substitutes such as almond milk or soy milk. The substitute suggestion unit can also analyze the user's allergy information in detail and suggest substitutes that do not contain allergens. For example, almond milk or soy milk is suggested for a user with a dairy allergy. This allows the ingredient suggestion app according to the embodiment to suggest optimal ingredients and substitutes based on the user's preferences and nutritional needs. For example, users can easily create meals that suit their preferences and nutritional needs and consume a balanced amount of nutrients necessary to maintain their health.

[0060] The preference collection unit can automatically analyze the meal history and predict preferences based on preference patterns. For example, the preference collection unit analyzes the meal history entered by the user in the past to identify frequently selected ingredients and dishes. For example, it lists ingredients that the user eats three or more times a week to understand preference trends. The preference collection unit can also analyze preference patterns from the past meal history to predict the user's preferences. For example, if the user tends to like a particular ingredient, it will preferentially suggest that ingredient. This makes it possible to predict preferences based on the user's past preference patterns.

[0061] The preference collection unit can acquire the user's health condition and amount of exercise from the wearable device and dynamically adjust nutritional needs. For example, the preference collection unit acquires the user's heart rate and step count data from the wearable device and makes nutritional suggestions based on the amount of exercise. For example, on days when the user exercises a lot, it can suggest high-protein foods. The preference collection unit can also monitor the user's health condition and dynamically adjust nutritional needs. For example, it can adjust the nutritional balance based on the user's weight and blood pressure data. This makes it possible to dynamically adjust nutritional needs according to the user's health condition and amount of exercise.

[0062] The preference collection unit uses the emotion estimation function to analyze the emotions of the user when selecting ingredients and can suggest ingredients that elicit positive emotions. The preference collection unit, for example, analyzes the user's facial expressions and voice when selecting ingredients and calculates an emotion score. For example, if a smile or an excited voice is detected, those ingredients are preferentially suggested. The preference collection unit can also analyze the user's emotions using the emotion estimation function and suggest ingredients that elicit positive emotions. For example, if the user prefers ingredients that have a relaxing effect, those ingredients are suggested. In this way, ingredients that elicit positive emotions can be suggested based on the user's emotions.

[0063] The preference collection unit allows users to share their preferences and nutritional needs with family and friends and jointly create meal plans. The preference collection unit provides a function for sharing, for example, the preferences and nutritional needs entered by the user with family and friends and jointly creating meal plans. For example, it may suggest menus that reflect the preferences of all family members. The preference collection unit may also jointly create meal plans based on information shared with family and friends. For example, it may suggest meal plans that take into account the nutritional balance of all family members. This allows users to share their preferences and nutritional needs with family and friends and jointly create meal plans.

[0064] The preference collection unit can dynamically change preferences according to the season or local specialties. The preference collection unit provides a function to dynamically change the user's preferences based on, for example, seasonal specialty product data. For example, watermelon and tomatoes are suggested in summer, and pumpkins and radishes in winter. The preference collection unit can also dynamically change the user's preferences based on local specialty product data. For example, local agricultural products are suggested. This allows the user's preferences to be dynamically changed according to the season or local specialties.

[0065] The preference collection unit can use the emotion estimation function to monitor the user's emotions in real time when selecting ingredients and suggest ingredients that will reduce stress. The preference collection unit can, for example, use the emotion estimation function to monitor the user's emotions in real time when selecting ingredients and suggest ingredients that will reduce stress. For example, it can suggest herbal tea or chocolate that has a relaxing effect. The preference collection unit can also monitor the user's emotions in real time and suggest ingredients that will reduce stress. For example, if the user is feeling stressed, it can suggest ingredients that will reduce that emotion. In this way, it is possible to monitor the user's emotions in real time and suggest ingredients that will reduce stress.

[0066] The nutritional analysis unit can analyze genetic information and suggest a nutritional balance based on genetic characteristics. The nutritional analysis unit, for example, analyzes a user's genetic information and builds a system that suggests a nutritional balance based on genetic characteristics. For example, if the absorption efficiency of a particular vitamin or mineral is low, ingredients that contain a lot of that nutrient are suggested. The nutritional analysis unit can also suggest an optimal nutritional balance for a user based on genetic information. For example, if a user is genetically prone to a deficiency in a particular nutrient, ingredients that supplement that nutrient are suggested. This makes it possible to suggest an optimal nutritional balance based on the user's genetic characteristics.

[0067] The nutritional analysis unit can analyze the state of the intestinal flora and suggest ingredients that are optimal for the intestinal environment. The nutritional analysis unit, for example, analyzes the state of the user's intestinal flora and builds a system that suggests ingredients that are optimal for the intestinal environment. For example, it can suggest yogurt or kimchi to increase beneficial bacteria. The nutritional analysis unit can also suggest ingredients that are optimal for the user based on the state of the intestinal flora. For example, it can suggest ingredients that will improve the intestinal environment. This makes it possible to suggest optimal ingredients based on the user's intestinal environment.

[0068] The nutritional analysis unit uses the emotion estimation function to suggest a nutritional balance according to the user's emotional state and can select ingredients that reduce stress and anxiety. The nutritional analysis unit, for example, uses the emotion estimation function to build a system that suggests a nutritional balance according to the user's emotional state. For example, if the user is feeling stressed, ingredients that are high in magnesium are suggested. The nutritional analysis unit can also use the emotion estimation function to analyze the user's emotional state and select ingredients that reduce stress and anxiety. For example, ingredients that have a relaxing effect are suggested. This makes it possible to suggest ingredients that reduce stress and anxiety based on the user's emotional state.

[0069] The nutritional analysis unit can analyze nutritional balance in an integrated manner with health data. The nutritional analysis unit, for example, builds a system that adjusts nutritional balance based on the user's blood pressure data. For example, if blood pressure is high, it can suggest low-salt ingredients. The nutritional analysis unit can also analyze nutritional balance based on the user's health data. For example, it can suggest low-carbohydrate ingredients based on blood sugar level data. This allows the user's nutritional balance to be analyzed in an integrated manner with other health data.

[0070] The nutritional analysis unit can adapt the nutritional balance to food cultures and cooking styles. For example, the nutritional analysis unit analyzes the user's nutritional balance and builds a system that adapts to different food cultures. For example, it makes nutritional suggestions that suit styles such as Japanese, Western, and Chinese cuisine. The nutritional analysis unit can also adjust the nutritional balance to suit the user's cooking style. For example, it can suggest a nutritional balance that is suitable for a specific cooking style. This allows the user's nutritional balance to adapt to different food cultures and cooking styles.

[0071] The nutrition analysis unit can use the emotion estimation function to adjust the nutritional balance in real time according to the user's emotional state and suggest ingredients that will elicit positive emotions. The nutrition analysis unit, for example, uses the emotion estimation function to build a system that adjusts the nutritional balance in real time according to the user's emotional state. For example, if the user is feeling stressed, ingredients that have a relaxing effect are suggested. The nutrition analysis unit can also use the emotion estimation function to monitor the user's emotional state in real time and suggest ingredients that will elicit positive emotions. For example, ingredients that make the user feel happy are identified and suggested. This makes it possible to suggest ingredients that will elicit positive emotions based on the user's emotional state.

[0072] The substitution suggestion unit can analyze allergy information in detail and suggest substitutions that do not contain allergens. The substitution suggestion unit, for example, builds a system that analyzes a user's allergy information in detail and suggests substitutions that do not contain allergens. For example, almond milk or soy milk is suggested to a user who is allergic to dairy products. The substitution suggestion unit can also suggest the most suitable substitution for a user based on the allergy information. For example, gluten-free ingredients are suggested to a user who is allergic to gluten. This makes it possible to suggest substitutions that do not contain allergens based on the user's allergy information.

[0073] The substitute suggestion unit can analyze ingredient preferences and suggest substitutes with similar tastes and textures. The substitute suggestion unit, for example, builds a system that analyzes a user's ingredient preferences and suggests substitutes with similar tastes and textures. For example, soy meat is suggested instead of beef. The substitute suggestion unit can also suggest substitutes with similar tastes and textures based on the user's preferences. For example, if a user likes a particular ingredient, a substitute similar to that ingredient is suggested. This makes it possible to suggest substitutes with similar tastes and textures based on the user's preferences.

[0074] The substitute suggestion unit can use the emotion estimation function to analyze the emotion of the user when selecting a substitute and suggest a substitute that elicits positive emotions. The substitute suggestion unit, for example, uses the emotion estimation function to analyze the emotion of the user when selecting a substitute and builds a system that suggests a substitute that elicits positive emotions. For example, it identifies a substitute that makes the user feel happy. The substitute suggestion unit can also use the emotion estimation function to analyze the user's emotions and suggest a substitute that elicits positive emotions. For example, if the user prefers a substitute that has a relaxing effect, it suggests that substitute. In this way, it is possible to suggest a substitute that elicits positive emotions based on the user's emotions.

[0075] The substitute suggestion unit can change ingredient substitutes to environmentally friendly, sustainable options. The substitute suggestion unit, for example, builds a system that changes a user's ingredient substitutes to environmentally friendly, sustainable options. For example, it suggests plant-based foods instead of animal-based foods. The substitute suggestion unit can also suggest environmentally friendly substitutes based on the user's preferences. For example, if a user likes a particular ingredient, it suggests sustainable substitutes similar to that ingredient. This makes it possible to change the user's ingredient substitutes to environmentally friendly, sustainable options.

[0076] The substitute suggestion unit can change substitutes for ingredients to local specialties or seasonal ingredients. The substitute suggestion unit, for example, builds a system that changes substitutes for ingredients of a user to local specialties or seasonal ingredients. For example, it suggests local agricultural products. The substitute suggestion unit can also suggest local specialties or seasonal ingredients based on the user's preferences. For example, if a user likes a particular ingredient, it suggests local specialties or seasonal ingredients that are similar to that ingredient. This makes it possible to change substitutes for ingredients of a user to local specialties or seasonal ingredients.

[0077] The substitute suggestion unit can use the emotion estimation function to monitor the emotions of the user when selecting a substitute in real time and suggest a substitute that reduces stress. The substitute suggestion unit, for example, uses the emotion estimation function to monitor the emotions of the user when selecting a substitute in real time and build a system that suggests a substitute that reduces stress. For example, a substitute that has a relaxing effect is suggested. The substitute suggestion unit can also monitor the user's emotions in real time and suggest a substitute that reduces stress. For example, if the user is feeling stressed, a substitute that reduces the emotion is suggested. In this way, the user's emotions can be monitored in real time and a substitute that reduces stress can be suggested.

[0078] The recipe providing unit can analyze cooking skills and suggest recipes according to the skill level. The recipe providing unit, for example, builds a system that analyzes the user's cooking skills and suggests recipes according to the skill level. For example, it suggests simple recipes for beginners and complex recipes for advanced cooks. The recipe providing unit can also suggest cooking methods according to the user's skill level. For example, it suggests simple cooking procedures for beginners and advanced cooking techniques for advanced cooks. This makes it possible to suggest recipes according to the user's cooking skills.

[0079] The recipe providing unit can analyze kitchen equipment and suggest cooking methods that suit the available equipment. The recipe providing unit, for example, builds a system that analyzes a user's kitchen equipment and suggests cooking methods that suit the available equipment. For example, it suggests recipes that use an oven or microwave. The recipe providing unit can also suggest cooking methods that suit the user's kitchen equipment. For example, it suggests recipes that use specific cooking equipment. This makes it possible to suggest cooking methods that suit the user's kitchen equipment.

[0080] The recipe providing unit can use the emotion estimation function to provide a guide to help the user reduce stress felt while cooking. The recipe providing unit, for example, uses the emotion estimation function to build a system that provides a guide to help the user reduce stress felt while cooking. For example, it can provide relaxing music or encouraging messages. The recipe providing unit can also analyze the user's emotions and suggest cooking procedures to reduce stress. For example, it can suggest simple cooking procedures. This makes it possible to provide a guide to help the user reduce stress felt while cooking.

[0081] The recipe providing unit can adapt recipes to cooking styles and food cultures. For example, the recipe providing unit builds a system that adapts a user's recipes to different cooking styles and food cultures. For example, it proposes recipes that suit styles such as Japanese cuisine, Western cuisine, and Chinese cuisine. The recipe providing unit can also adjust recipes to suit the user's cooking style. For example, it proposes recipes that are suitable for a specific cooking style. This allows the user's recipes to be adapted to different cooking styles and food cultures.

[0082] The recipe providing unit can propose versions of recipes in which ingredients are substituted with healthier ingredients. The recipe providing unit, for example, builds a system that proposes versions of a user's recipes in which ingredients are substituted with healthier ingredients. For example, it proposes recipes that use brown rice instead of white rice. The recipe providing unit can also propose versions of a user's recipes in which ingredients are substituted with healthier ingredients. For example, it proposes recipes that use low-calorie ingredients. This makes it possible to propose versions of a user's recipes in which ingredients are substituted with healthier ingredients.

[0083] The recipe providing unit can use the emotion estimation function to monitor the emotions felt by the user while cooking in real time and suggest cooking methods that elicit positive emotions. The recipe providing unit, for example, uses the emotion estimation function to build a system that monitors the emotions felt by the user while cooking in real time and suggests cooking methods that elicit positive emotions. For example, it can suggest simple steps that allow the user to enjoy cooking. The recipe providing unit can also monitor the emotions felt by the user in real time and suggest cooking methods that elicit positive emotions. For example, it can suggest methods that allow the user to cook in a relaxed manner. In this way, it is possible to monitor the emotions felt by the user while cooking in real time and suggest cooking methods that elicit positive emotions.

[0084] The nutritional balance monitoring unit can analyze the dietary history in detail and provide feedback on long-term nutritional balance trends. The nutritional balance monitoring unit, for example, builds a system that analyzes the user's dietary history in detail and provides feedback on long-term nutritional balance trends. For example, it displays the nutritional balance trend in a graph based on dietary data from the past year. The nutritional balance monitoring unit can also suggest areas for improving nutritional balance based on the user's dietary history. For example, if a specific nutrient is lacking, it will suggest ingredients that contain a large amount of that nutrient. This makes it possible to analyze the user's dietary history in detail and provide feedback on long-term nutritional balance trends.

[0085] The nutritional balance monitoring unit can analyze health data and suggest areas for improving nutritional balance. The nutritional balance monitoring unit can build a system that suggests areas for improving nutritional balance based on, for example, the user's weight data. For example, if the user's weight is increasing, low-calorie ingredients can be suggested. The nutritional balance monitoring unit can also adjust nutritional balance based on the user's blood pressure data. For example, if the user's blood pressure is high, low-salt ingredients can be suggested. This makes it possible to suggest areas for improving nutritional balance based on the user's health data.

[0086] The nutritional balance monitoring unit can use the emotion estimation function to analyze the emotions felt by the user after a meal and provide feedback that elicits positive emotions. The nutritional balance monitoring unit, for example, uses the emotion estimation function to build a system that analyzes the emotions felt by the user after a meal and provides feedback that elicits positive emotions. For example, it identifies meals that make the user feel satisfied and recommends those meals. The nutritional balance monitoring unit can also analyze the user's emotions and provide feedback that elicits positive emotions. For example, it recommends meals that make the user feel happy. This makes it possible to analyze the emotions felt by the user after a meal and provide feedback that elicits positive emotions.

[0087] The nutritional balance monitoring unit can compare the nutritional balance with other users and provide a benchmark. The nutritional balance monitoring unit, for example, builds a system that compares the user's nutritional balance with other users and provides a benchmark. For example, it shows the tendency of nutritional balance compared with users of the same age or gender. The nutritional balance monitoring unit can also provide the results of comparison with other users based on the user's nutritional balance. For example, it shows the degree to which the user's nutritional balance matches the average value. This makes it possible to compare the user's nutritional balance with other users and provide a benchmark.

[0088] The nutritional balance monitoring unit can adapt the nutritional balance to food cultures and cooking styles. For example, the nutritional balance monitoring unit analyzes the user's nutritional balance and builds a system that adapts to different food cultures. For example, it makes nutritional suggestions that match styles such as Japanese, Western, and Chinese cuisine. The nutritional balance monitoring unit can also adjust the nutritional balance to suit the user's cooking style. For example, it can suggest a nutritional balance that is suitable for a specific cooking style. This allows the user's nutritional balance to adapt to different food cultures and cooking styles.

[0089] The nutritional balance monitoring unit can use the emotion estimation function to monitor the emotions a user feels after a meal in real time and provide feedback that elicits positive emotions. The nutritional balance monitoring unit, for example, uses the emotion estimation function to monitor the emotions a user feels after a meal in real time and builds a system that provides feedback that elicits positive emotions. For example, it identifies meals that make the user feel satisfied and recommends those meals. The nutritional balance monitoring unit can also monitor the user's emotions in real time and provide feedback that elicits positive emotions. For example, it recommends meals that make the user feel happy. This makes it possible to monitor the emotions a user feels after a meal in real time and provide feedback that elicits positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] Ingredient recommendation apps can incorporate game elements to make ingredient selection more fun for users. For example, users can earn points every time they try a new ingredient and use those points to receive in-app rewards. Users can also share their ingredient selection methods and recipes with each other and compete for rankings. Furthermore, a system can be introduced where users are challenged to cook dishes using specific ingredients and earn badges upon completion. This allows users to enjoy choosing healthy ingredients.

[0092] Ingredient recommendation apps can add a voice assistant function to help users select ingredients. For example, users can input their ingredient preferences and nutritional needs by voice. The voice assistant can also suggest ingredients and explain recipes to users. Furthermore, the voice assistant can answer users' questions in real time. This allows users to easily learn how to select ingredients and cook them.

[0093] Ingredient recommendation apps can add an augmented reality (AR) function to assist users in selecting ingredients. For example, when a user scans an ingredient using a smartphone camera, information about the ingredient and a recipe are displayed. The AR function can also be used to display an image of the finished dish using the ingredients selected by the user. Furthermore, the AR function can be used to provide navigation when the user is selecting ingredients. This allows users to visually understand how to select ingredients and how to cook them.

[0094] To help users select ingredients, the ingredient recommendation app can use an emotion estimation function to suggest ingredients based on the user's emotional state. For example, if a user is feeling stressed, it can suggest ingredients that have a relaxing effect. Or, if a user is feeling happy, it can suggest ingredients that will further enhance that emotion. Furthermore, it can use the emotion estimation function to monitor the user's emotions in real time when selecting ingredients and suggest ingredients that will elicit positive emotions. This allows users to select the best ingredients based on their emotions.

[0095] To assist users in selecting ingredients, the ingredient recommendation app can use an emotion estimation function to analyze the emotions the user feels when selecting ingredients and recommend ingredients that elicit positive emotions. For example, the app can analyze the user's facial expressions and voice when selecting ingredients and calculate an emotion score. If the user prefers ingredients that have a relaxing effect, the app can preferentially recommend those ingredients. Furthermore, the emotion estimation function can also be used to analyze the user's emotions and recommend ingredients that elicit positive emotions. This allows the user to select the best ingredients based on their emotions.

[0096] To assist users in selecting ingredients, the ingredient recommendation app uses an emotion estimation function to monitor the user's emotions in real time when selecting ingredients and can suggest ingredients that will reduce stress. For example, it can suggest herbal tea or chocolate that has a relaxing effect. If the user is feeling stressed, it can also suggest ingredients that will reduce that emotion. Furthermore, it can use the emotion estimation function to monitor the user's emotions in real time and suggest ingredients that will reduce stress. This allows the user to select the best ingredients based on their emotions.

[0097] To help users select ingredients, the ingredient suggestion app uses an emotion estimation function to suggest nutritional balance based on the user's emotional state and select ingredients that will reduce stress and anxiety. For example, if the user is feeling stressed, it can suggest ingredients that are rich in magnesium. If the user prefers ingredients that have a relaxing effect, it can also suggest those ingredients. Furthermore, it can use the emotion estimation function to analyze the user's emotional state and select ingredients that will reduce stress and anxiety. This allows the user to select the best ingredients based on their emotions.

[0098] To help users select ingredients, the ingredient recommendation app uses an emotion estimation function to adjust the nutritional balance in real time according to the user's emotional state and recommend ingredients that will elicit positive emotions. For example, if the user is feeling stressed, it can suggest ingredients that have a relaxing effect. It can also identify ingredients that bring joy to the user and recommend those ingredients. Furthermore, it can use the emotion estimation function to monitor the user's emotional state in real time and recommend ingredients that will elicit positive emotions. This allows users to select the best ingredients based on their emotions.

[0099] Ingredient recommendation apps can suggest food storage methods to help users choose ingredients. For example, users can check how to store ingredients they have purchased within the app. They can also provide information on the shelf life and storage location of ingredients. Furthermore, an alert function for food storage methods can be added to notify users when ingredients have passed their shelf life. This allows users to store ingredients in the best possible condition without wasting them.

[0100] To help users select ingredients, food recommendation apps can add a function to compare the nutritional value of ingredients. For example, the app can compare the nutritional value of ingredients selected by the user and suggest the best options. It can also display the nutritional value of different ingredients in a graph for visual comparison. It can also suggest the best combination of ingredients based on the user's nutritional needs. This allows users to select the best ingredients while taking nutritional value into consideration.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The preference collection unit collects the user's preferences and nutritional needs. For example, the user inputs information such as favorite ingredients, ingredients to avoid, and preferences for specific nutrients. The preference collection unit can also automatically analyze the user's dietary history and predict preferences based on past preference patterns. Furthermore, the preference collection unit can obtain the user's health status and exercise volume from the wearable device and dynamically adjust nutritional needs. Step 2: The nutritional analysis unit analyzes the nutritional balance based on the information collected by the preference collection unit. For example, if the user wants to consume a lot of vitamin C, the nutritional analysis unit will suggest foods that are rich in vitamin C. The nutritional analysis unit can also analyze the user's genetic information and suggest nutritional balance based on individual genetic characteristics. Step 3: The substitution suggestion unit suggests optimal ingredients and substitutes based on the results of the analysis by the nutritional analysis unit. For example, if the user wants to avoid dairy products, the substitution suggestion unit suggests substitutes such as almond milk or soy milk. The substitution suggestion unit can also analyze the user's allergy information in detail and suggest substitutes that do not contain allergens.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 preference collector for collecting user preferences and nutritional needs; a nutritional analysis unit that analyzes nutritional balance based on the information collected by the preference collection unit; and a substitute suggestion unit that suggests optimal ingredients and substitutes based on the results of the analysis by the nutritional analysis unit. A system characterized by:

2. The preference collection unit Health status and exercise volume are acquired from wearable devices, and the nutritional needs are dynamically adjusted.

2. The system of claim 1.

3. The nutritional analysis unit Analyze genetic information and propose the nutritional balance based on genetic characteristics 2. The system of claim 1.

4. The alternative product suggestion unit Analyze allergy information in detail and propose alternative products that do not contain allergens 2. The system of claim 1.

5. The recipe provider is Analyzes cooking skills and suggests recipes according to skill level 2. The system of claim 1.

6. Nutritional Balance Monitoring Department Analyzing the emotions felt by the user after eating and providing feedback that elicits positive emotions 2. The system of claim 1.

7. The preference collection unit Analyzing the emotions of the user when selecting the ingredients, and suggesting ingredients that evoke positive emotions 2. The system of claim 1.

8. The nutritional analysis unit Proposing the nutritional balance according to the emotional state of the user and selecting the ingredients that will reduce stress and anxiety 2. The system of claim 1.

Images