System

Abstract

An object of a system according to an embodiment is to appropriately support eating habits of an elderly person living alone, a mother who cannot give birth on a homecoming trip, and the like.SOLUTION: A system includes a health data collection part, a nutrient specification part, a menu search part, and a delivery cooperation part. The health data collection unit collects health data of a user. The nutrient specification unit specifies a necessary nutrient based on the health data. The menu search unit searches for and proposes a menu based on nutrients. The delivery cooperation unit provides the menu in cooperation with the delivery service.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 technology has had the problem of making it difficult to adequately support the dietary habits of elderly people living alone or mothers who are unable to return to their hometowns to give birth.

[0005] The system according to the embodiment aims to appropriately support the eating habits of elderly people living alone and mothers who cannot return to their hometowns to give birth. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data collection unit, a nutrient identification unit, a menu search unit, and a delivery collaboration unit. The health data collection unit collects health data of a user. The nutrient identification unit identifies necessary nutrients based on the health data. The menu search unit searches for and suggests menus based on the nutrients. The delivery collaboration unit provides the menus in collaboration with a delivery service. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately support the dietary habits of elderly people living alone and mothers who cannot return to their hometowns to give birth. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The dietary life support system according to an embodiment of the present invention collects a user's health data, uses a generation AI to identify necessary nutrients, and automatically searches for and suggests appropriate menus. This allows the dietary life support system to suggest menus containing appropriate nutrients based on the user's health condition and support the user's dietary life.

[0029] The dietary life support system according to the embodiment includes a health data collection unit, a nutrient identification unit, a menu search unit, and a delivery linkage unit. The health data collection unit collects a user's health data. For example, it collects data such as weight, blood pressure, heart rate, and food records. The health data collection unit can also collect periodic health checks and medical data. For example, it collects data using a blood pressure monitor or a weight scale. The nutrient identification unit identifies necessary nutrients based on the health data collected by the health data collection unit. For example, the generation AI analyzes the user's health data and identifies necessary nutrients such as calcium and vitamin D. The generation AI can also identify nutrients taking into account the user's allergy information. For example, it makes suggestions to avoid certain nutrients based on the allergy information. The menu search unit searches for and suggests menus based on the nutrients identified by the nutrient identification unit. For example, the generation AI searches for menus high in calcium and suggests dishes such as "calcium-rich salad" and "smoothie made with calcium-fortified milk." The generation AI can also suggest menus taking into account the user's preferences and food allergies. For example, it suggests spicy dishes to a user who likes spicy food. The delivery linking unit provides the menu suggested by the menu search unit in cooperation with a delivery service. For example, the delivery service prepares meals based on the menu suggested by the generation AI and delivers them to the user's home. This saves the user the trouble of preparing meals themselves. As a result, the dietary life support system according to the embodiment can suggest menus containing appropriate nutrients based on the user's health condition and support the user's dietary life. For example, the system can save the user the trouble of preparing meals themselves and ensure that they consume the necessary nutrients. Furthermore, continuous health management can maintain and improve the user's health condition.

[0030] The health data collection unit can collect lifestyle data and integrate it with health data for analysis. For example, the health data collection unit can analyze the user's sleep patterns and, if the quality of sleep is declining, suggest a diet to improve sleep. For example, it can suggest foods high in tryptophan. The health data collection unit can also monitor the user's exercise volume and, if the user continues to lack exercise, suggest a diet suitable for replenishing energy. For example, it can suggest foods high in carbohydrates. The health data collection unit can also integrate the user's lifestyle data and analyze it together with the health data to evaluate the user's overall health condition. For example, if the user is suffering from both lack of sleep and lack of exercise, it can suggest a balanced diet. In this way, by collecting the user's lifestyle data and integrating it with the health data for analysis, a more comprehensive health condition can be evaluated.

[0031] The health data collection unit can analyze genetic information and suggest nutrients taking into account genetic health risks. For example, the health data collection unit analyzes the user's genetic information and suggests a diet rich in specific nutrients as a preventative measure against genetically high-risk diseases. For example, if the user has a high risk of heart disease, foods rich in omega-3 fatty acids are suggested. The health data collection unit also suggests a nutrient balance appropriate for the user based on the genetic information. For example, if the user has genetically poor vitamin D absorption, foods rich in vitamin D are suggested. The health data collection unit also analyzes the user's genetic information and suggests a diet to avoid excessive intake of specific nutrients. For example, if the user has genetically excessive iron absorption, a diet with less iron is suggested. In this way, by analyzing the user's genetic information and suggesting nutrients taking into account genetic health risks, more personalized health support is possible.

[0032] The health data collection unit also collects health data of the pet and can suggest a healthy diet for both the user and the pet. The health data collection unit, for example, collects health data of the user's pet and suggests meals according to the pet's health condition. For example, if the pet is slightly overweight, it suggests low-calorie pet food. The health data collection unit also integrates the health data of the user and the pet and suggests menus for both to maintain a healthy diet. For example, it suggests healthy ingredients that the user and the pet can eat together. The health data collection unit also suggests meals to improve the pet's health condition based on the pet's health data. For example, if the pet has an allergy, it suggests allergy-friendly pet food. In this way, by collecting health data of the user and the pet and suggesting a healthy diet for both, the health of both the user and the pet can be supported.

[0033] The health data collection unit can collect health data of all family members and suggest menus that support the health of the entire family. For example, the health data collection unit collects health data of all family members of the user and evaluates the health status of the entire family. For example, it suggests menus that allow the entire family to eat a balanced diet. The health data collection unit also suggests meals suitable for family members with specific health risks based on the health data of all family members. For example, if there is a family member at high risk of diabetes, it suggests low-carbohydrate menus. The health data collection unit also integrates the health data of all family members and suggests a meal plan to support the health of the entire family. For example, it suggests menus using healthy ingredients that the whole family can enjoy together. In this way, by collecting health data of all family members of the user and suggesting menus that support the health of the entire family, it is possible to maintain and improve the health of the entire family.

[0034] The nutrient identification unit can identify nutrients needed for each season, taking into account seasonal and climate changes. For example, the nutrient identification unit considers seasonal climate changes and suggests nutrients that boost immunity in winter. For example, it suggests foods that are high in vitamin C and zinc. The nutrient identification unit also suggests nutrients that help prevent physical exhaustion due to the heat in summer. For example, it suggests foods that are high in potassium and magnesium to maintain electrolyte balance. The nutrient identification unit also suggests nutrients that address seasonal health risks based on seasonal climate changes. For example, it suggests nutrients with antioxidant properties to combat hay fever in spring. In this way, seasonal health risks can be addressed by identifying necessary nutrients taking into account seasonal and climate changes.

[0035] The nutrient identification unit can analyze past dietary history and make suggestions that take into consideration long-term nutritional balance. The nutrient identification unit, for example, analyzes the user's past dietary history and identifies nutrient imbalances. For example, if vitamin D intake has been insufficient in the past, foods rich in vitamin D are suggested. The nutrient identification unit also takes long-term nutritional balance into consideration and makes suggestions to supplement the deficient nutrients based on the past dietary history. For example, if calcium intake is low, foods rich in calcium are suggested. The nutrient identification unit also suggests nutrients to support long-term health maintenance based on the user's past dietary history. For example, if iron intake has been insufficient in the past, foods rich in iron are suggested. In this way, by analyzing the user's past dietary history and making suggestions that take long-term nutritional balance into consideration, it is possible to maintain sustainable health.

[0036] The nutrient identification unit can identify nutrients related to a user's hobby by taking into account the user's hobbies and preferences. For example, if the user's hobby is sports, the nutrient identification unit will suggest nutrients that support recovery after exercise. For example, it will suggest foods that are high in protein and amino acids. Furthermore, if the user has a sweet tooth, the nutrient identification unit will suggest desserts that use healthy sweeteners. For example, it will suggest low-calorie sweets and fruits. Furthermore, if the user's hobby is gardening, the nutrient identification unit will suggest nutrients for maintaining physical strength. For example, it will suggest foods that are high in vitamins and minerals. This makes it possible to provide more personalized nutritional support by identifying nutrients by taking into account the user's hobbies and preferences.

[0037] The nutrient identification unit can identify nutrients for each occupation by taking into account the user's occupation and daily activity level. For example, if the user's occupation involves mainly desk work, the nutrient identification unit will suggest nutrients that support eye health. For example, it will suggest foods that are rich in vitamin A and lutein. Furthermore, if the user's occupation involves physical labor, the nutrient identification unit will suggest nutrients that are suitable for energy replenishment. For example, it will suggest foods that are rich in carbohydrates and protein. Furthermore, if the user's occupation involves night shifts, the nutrient identification unit will suggest nutrients for regulating the body clock. For example, it will suggest foods that are rich in melatonin. In this way, by identifying nutrients by taking into account the user's occupation and daily activity level, it is possible to provide occupation-specific health support.

[0038] The menu search unit can analyze food allergies and preferences in detail and suggest individually customized menus. For example, the menu search unit suggests menus that avoid allergies based on the user's food allergy information. For example, it suggests nut-free dishes to a user with a nut allergy. The menu search unit also analyzes the user's preferences in detail and suggests menus that suit the user's preferences. For example, it suggests spicy dishes to a user who likes spicy food. The menu search unit also integrates the user's allergy information and preferences to suggest individually customized menus. For example, it suggests dairy-free desserts to a user with a dairy allergy. In this way, by analyzing the user's food allergies and preferences in detail and suggesting individually customized menus, it is possible to improve the user's safety and satisfaction.

[0039] The menu search unit can suggest regional menus taking into account the food culture and traditions of the region. For example, the menu search unit can suggest menus using ingredients specific to the region taking into account the food culture of the user's region. For example, it can suggest dishes using local specialties. The menu search unit can also suggest menus that are familiar to the user based on traditional dishes of the region. For example, it can suggest dishes that are eaten at local festivals and events. The menu search unit can also suggest menus that suit the climate and season of the user's region. For example, it can suggest hot soups and hot pot dishes in cold regions. In this way, by suggesting menus taking into account the food culture and traditions of the user's region, it is possible to provide the user with meals that are familiar to them.

[0040] The menu search unit can suggest menus for each time period, taking into account the time periods and frequency of meals. For example, the menu search unit suggests menus suitable for breakfast, lunch, and dinner, taking into account the time periods of the user's meals. For example, it suggests a high-protein menu for breakfast to replenish energy. The menu search unit also suggests appropriate snacks and light meals based on the frequency of the user's meals. For example, it suggests fruits and nuts as snacks for a user who eats three meals a day. The menu search unit also suggests menus according to the time periods of the user's meals. For example, it suggests lighter menus that are easy to digest if the user eats late at night. In this way, by suggesting menus taking into account the time periods and frequency of the user's meals, it is possible to provide more appropriate meals.

[0041] The menu search unit can take the user's meal budget into consideration and suggest a cost-effective menu. The menu search unit, for example, suggests a cost-effective menu based on the user's meal budget. For example, it suggests dishes using inexpensive, nutritious ingredients. The menu search unit also suggests a menu according to the user's budget. For example, it suggests a menu that provides a balanced meal within a limited budget. The menu search unit also takes the user's meal budget into consideration and suggests a cost-effective yet nutritious menu. For example, it suggests cost-effective dishes using seasonal ingredients. In this way, by suggesting menus taking the user's meal budget into consideration, it is possible to provide a meal with high cost performance.

[0042] The delivery coordination unit can optimize delivery times to suit the user's lifestyle. For example, the delivery coordination unit analyzes the user's lifestyle and suggests the optimal delivery time. For example, delivery is made to coincide with the user's lunch time. The delivery coordination unit also sets delivery times according to the user's lifestyle. For example, nighttime delivery is suggested for users who work night shifts. The delivery coordination unit also flexibly adjusts delivery times taking into account the user's lifestyle. For example, delivery is made to avoid times when the user is not at home. This makes it possible to improve user convenience by optimizing delivery times to suit the user's lifestyle.

[0043] The delivery collaboration unit can monitor the user's health condition in real time during delivery and take emergency action as necessary. For example, the delivery collaboration unit can monitor the user's health condition in real time during delivery and take emergency action if an abnormality is detected. For example, if blood pressure suddenly rises, it will contact a medical institution. The delivery collaboration unit also checks the user's health condition during delivery and provides health advice as necessary. For example, if the body temperature is high, it will encourage the user to drink more fluids. The delivery collaboration unit also monitors the user's health condition during delivery and builds a system that responds quickly in the event of an emergency. For example, if the heart rate is abnormally high, it will dispatch an ambulance. In this way, the user's health condition can be monitored in real time during delivery and emergency action can be taken as necessary to ensure the user's safety.

[0044] The delivery collaboration unit collaborates with delivery services to deliver not only meals to the user, but also daily necessities and medicines. The delivery collaboration unit, for example, collaborates with delivery services to deliver daily necessities along with the user's meals. For example, daily necessities such as toilet paper and detergent are delivered together. The delivery collaboration unit also builds a system that also delivers the user's medicines. For example, prescription medicines and supplements are delivered together with meals. The delivery collaboration unit also collaborates with delivery services to deliver health-related products along with the user's meals. For example, vitamins and health foods are delivered together. In this way, by delivering not only the user's meals but also daily necessities and medicines, it is possible to support the user's overall lifestyle.

[0045] The delivery cooperation unit can provide a service that delivers meals for the user's pet together with the delivery. The delivery cooperation unit, for example, cooperates with a delivery service to deliver the pet's meal along with the user's meal. For example, pet food and treats may be delivered together. The delivery cooperation unit also considers the health condition of the user's pet and suggests and delivers meals suitable for the pet. For example, it may suggest food that is suitable for the pet's allergies. The delivery cooperation unit also cooperates with a delivery service to build a system that delivers the user's meal and the pet's meal together. For example, it may provide a regular delivery service for pet meals. This makes it possible to meet the needs of both the user and the pet by providing a service that delivers the user's pet's meal together.

[0046] The health data collection unit can analyze health data over the long term, predict future health risks, and provide feedback. The health data collection unit, for example, analyzes a user's health data over the long term and predicts future health risks. For example, it predicts the risk of high blood pressure based on blood pressure fluctuations and suggests preventive measures. The health data collection unit also evaluates the user's health condition based on the long-term health data and provides feedback on future risks. For example, it predicts the risk of obesity based on weight gain or loss and suggests dietary improvements. The health data collection unit also analyzes a user's health data over the long term and builds a system that predicts future health risks. For example, it predicts the risk of diabetes based on blood sugar fluctuations and suggests preventive measures. This enables preventive health management by analyzing a user's health data over the long term and predicting future health risks and providing feedback.

[0047] The health data collection unit can anonymize and aggregate health data, analyze statistical health trends, and provide feedback. For example, the health data collection unit anonymizes and aggregates user health data to analyze statistical health trends. For example, it identifies common health risks among specific age groups and provides feedback. The health data collection unit also analyzes health trends by region based on the anonymized health data and provides feedback on health risks specific to the region. For example, it identifies the risk of high blood pressure in a specific region and suggests preventive measures. The health data collection unit also builds a system that anonymizes and aggregates user health data and analyzes statistical health trends. For example, it identifies common health risks among specific occupations and provides feedback. This makes it possible to identify and prevent a wide range of health risks by anonymizing and aggregating user health data and analyzing statistical health trends.

[0048] The health data collection unit can cooperate with personal trainers and nutritionists based on the health data to provide professional advice. The health data collection unit, for example, cooperates with personal trainers and nutritionists based on the user's health data to provide professional advice. For example, it may propose exercise plans and meal plans. The health data collection unit also shares the user's health data and manages health based on feedback from personal trainers and nutritionists. For example, it may evaluate the user's health status through regular counseling. The health data collection unit also strengthens cooperation with personal trainers and nutritionists based on the user's health data to provide individually customized health management plans. For example, it may provide advice on exercise and diet that addresses specific health risks. This enables more effective health management by coordinating with personal trainers and nutritionists and providing professional advice based on the user's health data.

[0049] The health data collection unit can provide health-related educational content based on the health data. The health data collection unit provides health-related educational content based on, for example, the user's health data. For example, it delivers videos about how to maintain a balanced diet and the importance of exercise. The health data collection unit also provides educational content according to the user's health condition. For example, it provides articles about diabetes prevention to users who are at high risk of diabetes. The health data collection unit also provides individually customized educational content based on the user's health data. For example, it delivers videos and articles that address specific health risks. In this way, by providing health-related educational content based on the user's health data, it is possible to raise the user's health awareness and promote preventive health management.

[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] The health data collection unit collects the user's health data. For example, it collects data such as weight, blood pressure, heart rate, and food records. The health data collection unit can also collect regular health checks and medical data. For example, it collects data using a blood pressure monitor or a scale. The nutrient identification unit identifies necessary nutrients based on the health data collected by the health data collection unit. For example, the generation AI analyzes the user's health data and identifies necessary nutrients such as calcium and vitamin D. The generation AI can also identify nutrients taking the user's allergy information into consideration. For example, it makes suggestions to avoid certain nutrients based on the allergy information. The menu search unit searches and suggests menus based on the nutrients identified by the nutrient identification unit. For example, the generation AI searches for menus high in calcium and suggests "calcium-rich salads" or "smoothies made with calcium-fortified milk." The generation AI can also suggest menus taking the user's preferences and food allergies into consideration. For example, it suggests spicy dishes for a user who likes spicy food. The delivery integration unit provides the menus suggested by the menu search unit in collaboration with a delivery service. For example, a delivery service prepares meals based on a menu proposed by the generation AI and delivers them to the user's home. This saves the user the trouble of preparing meals themselves. As a result, the dietary life support system according to the embodiment can suggest menus containing appropriate nutrients based on the user's health condition and support the user's dietary life. For example, the system can save the user the trouble of preparing meals themselves and ensure that they consume the necessary nutrients. Furthermore, continuous health management can maintain and improve the user's health condition.

[0052] The health data collection unit can collect lifestyle data and integrate it with health data for analysis. For example, the health data collection unit can analyze the user's sleep patterns and, if the quality of sleep is declining, suggest a diet to improve sleep. For example, it can suggest foods high in tryptophan. The health data collection unit can also monitor the user's exercise volume and, if the user continues to lack exercise, suggest a diet suitable for replenishing energy. For example, it can suggest foods high in carbohydrates. The health data collection unit can also integrate the user's lifestyle data and analyze it together with the health data to evaluate the user's overall health condition. For example, if the user is suffering from both lack of sleep and lack of exercise, it can suggest a balanced diet. In this way, by collecting the user's lifestyle data and integrating it with the health data for analysis, a more comprehensive health condition can be evaluated.

[0053] The health data collection unit can analyze genetic information and suggest nutrients taking into account genetic health risks. For example, the health data collection unit analyzes the user's genetic information and suggests a diet rich in specific nutrients as a preventative measure against genetically high-risk diseases. For example, if the user has a high risk of heart disease, foods rich in omega-3 fatty acids are suggested. The health data collection unit also suggests a nutrient balance appropriate for the user based on the genetic information. For example, if the user has genetically poor vitamin D absorption, foods rich in vitamin D are suggested. The health data collection unit also analyzes the user's genetic information and suggests a diet to avoid excessive intake of specific nutrients. For example, if the user has genetically excessive iron absorption, a diet with less iron is suggested. In this way, by analyzing the user's genetic information and suggesting nutrients taking into account genetic health risks, more personalized health support is possible.

[0054] The health data collection unit also collects health data of the pet and can suggest a healthy diet for both the user and the pet. The health data collection unit, for example, collects health data of the user's pet and suggests meals according to the pet's health condition. For example, if the pet is slightly overweight, it suggests low-calorie pet food. The health data collection unit also integrates the health data of the user and the pet and suggests menus for both to maintain a healthy diet. For example, it suggests healthy ingredients that the user and the pet can eat together. The health data collection unit also suggests meals to improve the pet's health condition based on the pet's health data. For example, if the pet has an allergy, it suggests allergy-friendly pet food. In this way, by collecting health data of the user and the pet and suggesting a healthy diet for both, the health of both the user and the pet can be supported.

[0055] The health data collection unit can collect health data of all family members and suggest menus that support the health of the entire family. For example, the health data collection unit collects health data of all family members of the user and evaluates the health status of the entire family. For example, it suggests menus that allow the entire family to eat a balanced diet. The health data collection unit also suggests meals suitable for family members with specific health risks based on the health data of all family members. For example, if there is a family member at high risk of diabetes, it suggests low-carbohydrate menus. The health data collection unit also integrates the health data of all family members and suggests a meal plan to support the health of the entire family. For example, it suggests menus using healthy ingredients that the whole family can enjoy together. In this way, by collecting health data of all family members of the user and suggesting menus that support the health of the entire family, it is possible to maintain and improve the health of the entire family.

[0056] The nutrient identification unit can identify nutrients needed for each season, taking into account seasonal and climate changes. For example, the nutrient identification unit considers seasonal climate changes and suggests nutrients that boost immunity in winter. For example, it suggests foods that are high in vitamin C and zinc. The nutrient identification unit also suggests nutrients that help prevent physical exhaustion due to the heat in summer. For example, it suggests foods that are high in potassium and magnesium to maintain electrolyte balance. The nutrient identification unit also suggests nutrients that address seasonal health risks based on seasonal climate changes. For example, it suggests nutrients with antioxidant properties to combat hay fever in spring. In this way, seasonal health risks can be addressed by identifying necessary nutrients taking into account seasonal and climate changes.

[0057] The nutrient identification unit can analyze past dietary history and make suggestions that take into consideration long-term nutritional balance. The nutrient identification unit, for example, analyzes the user's past dietary history and identifies nutrient imbalances. For example, if vitamin D intake has been insufficient in the past, foods rich in vitamin D are suggested. The nutrient identification unit also takes long-term nutritional balance into consideration and makes suggestions to supplement the deficient nutrients based on the past dietary history. For example, if calcium intake is low, foods rich in calcium are suggested. The nutrient identification unit also suggests nutrients to support long-term health maintenance based on the user's past dietary history. For example, if iron intake has been insufficient in the past, foods rich in iron are suggested. In this way, by analyzing the user's past dietary history and making suggestions that take long-term nutritional balance into consideration, it is possible to maintain sustainable health.

[0058] The nutrient identification unit can identify nutrients related to a user's hobby by taking into account the user's hobbies and preferences. For example, if the user's hobby is sports, the nutrient identification unit will suggest nutrients that support recovery after exercise. For example, it will suggest foods that are high in protein and amino acids. Furthermore, if the user has a sweet tooth, the nutrient identification unit will suggest desserts that use healthy sweeteners. For example, it will suggest low-calorie sweets and fruits. Furthermore, if the user's hobby is gardening, the nutrient identification unit will suggest nutrients for maintaining physical strength. For example, it will suggest foods that are high in vitamins and minerals. This makes it possible to provide more personalized nutritional support by identifying nutrients by taking into account the user's hobbies and preferences.

[0059] The nutrient identification unit can identify nutrients for each occupation by taking into account the user's occupation and daily activity level. For example, if the user's occupation involves mainly desk work, the nutrient identification unit will suggest nutrients that support eye health. For example, it will suggest foods that are rich in vitamin A and lutein. Furthermore, if the user's occupation involves physical labor, the nutrient identification unit will suggest nutrients that are suitable for energy replenishment. For example, it will suggest foods that are rich in carbohydrates and protein. Furthermore, if the user's occupation involves night shifts, the nutrient identification unit will suggest nutrients for regulating the body clock. For example, it will suggest foods that are rich in melatonin. In this way, by identifying nutrients by taking into account the user's occupation and daily activity level, it is possible to provide occupation-specific health support.

[0060] The menu search unit can analyze food allergies and preferences in detail and suggest individually customized menus. For example, the menu search unit suggests menus that avoid allergies based on the user's food allergy information. For example, it suggests nut-free dishes to a user with a nut allergy. The menu search unit also analyzes the user's preferences in detail and suggests menus that suit the user's preferences. For example, it suggests spicy dishes to a user who likes spicy food. The menu search unit also integrates the user's allergy information and preferences to suggest individually customized menus. For example, it suggests dairy-free desserts to a user with a dairy allergy. In this way, by analyzing the user's food allergies and preferences in detail and suggesting individually customized menus, it is possible to improve the user's safety and satisfaction.

[0061] The menu search unit can suggest regional menus taking into account the food culture and traditions of the region. For example, the menu search unit can suggest menus using ingredients specific to the region taking into account the food culture of the user's region. For example, it can suggest dishes using local specialties. The menu search unit can also suggest menus that are familiar to the user based on traditional dishes of the region. For example, it can suggest dishes that are eaten at local festivals and events. The menu search unit can also suggest menus that suit the climate and season of the user's region. For example, it can suggest hot soups and hot pot dishes in cold regions. In this way, by suggesting menus taking into account the food culture and traditions of the user's region, it is possible to provide the user with meals that are familiar to them.

[0062] The menu search unit can suggest menus for each time period, taking into account the time periods and frequency of meals. For example, the menu search unit suggests menus suitable for breakfast, lunch, and dinner, taking into account the time periods of the user's meals. For example, it suggests a high-protein menu for breakfast to replenish energy. The menu search unit also suggests appropriate snacks and light meals based on the frequency of the user's meals. For example, it suggests fruits and nuts as snacks for a user who eats three meals a day. The menu search unit also suggests menus according to the time periods of the user's meals. For example, it suggests lighter menus that are easy to digest if the user eats late at night. In this way, by suggesting menus taking into account the time periods and frequency of the user's meals, it is possible to provide more appropriate meals.

[0063] The menu search unit can take the user's meal budget into consideration and suggest a cost-effective menu. The menu search unit, for example, suggests a cost-effective menu based on the user's meal budget. For example, it suggests dishes using inexpensive, nutritious ingredients. The menu search unit also suggests a menu according to the user's budget. For example, it suggests a menu that provides a balanced meal within a limited budget. The menu search unit also takes the user's meal budget into consideration and suggests a cost-effective yet nutritious menu. For example, it suggests cost-effective dishes using seasonal ingredients. In this way, by suggesting menus taking the user's meal budget into consideration, it is possible to provide a meal with high cost performance.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The health data collection unit collects the user's health data. For example, it collects data such as weight, blood pressure, heart rate, and food records. It can also collect regular health checks and medical data. For example, it collects data using a blood pressure monitor or a weight scale. Step 2: The nutrient identification unit identifies necessary nutrients based on the health data collected by the health data collection unit. For example, the generation AI analyzes the user's health data and identifies necessary nutrients such as calcium and vitamin D. It can also identify nutrients taking into account the user's allergy information. For example, it makes suggestions to avoid certain nutrients based on the allergy information. Step 3: The menu search unit searches for and suggests menus based on the nutrients identified by the nutrient identification unit. For example, the generation AI searches for menus that are high in calcium and suggests dishes such as "calcium-rich salad" or "smoothie made with calcium-fortified milk." It can also suggest menus taking into account the user's preferences and food allergies. For example, it suggests spicy dishes to a user who likes spicy food. Step 4: The delivery integration unit provides the menu suggested by the menu search unit in cooperation with a delivery service. For example, the delivery service prepares meals based on the menu suggested by the generation AI and delivers them to the user's home. This saves the user the trouble of having to prepare meals themselves.

[0066] (Example 2) The dietary life support system according to an embodiment of the present invention collects a user's health data, uses a generation AI to identify necessary nutrients, and automatically searches for and suggests appropriate menus. This allows the dietary life support system to suggest menus containing appropriate nutrients based on the user's health condition and support the user's dietary life.

[0067] The dietary life support system according to the embodiment includes a health data collection unit, a nutrient identification unit, a menu search unit, and a delivery linkage unit. The health data collection unit collects a user's health data. For example, it collects data such as weight, blood pressure, heart rate, and food records. The health data collection unit can also collect periodic health checks and medical data. For example, it collects data using a blood pressure monitor or a weight scale. The nutrient identification unit identifies necessary nutrients based on the health data collected by the health data collection unit. For example, the generation AI analyzes the user's health data and identifies necessary nutrients such as calcium and vitamin D. The generation AI can also identify nutrients taking into account the user's allergy information. For example, it makes suggestions to avoid certain nutrients based on the allergy information. The menu search unit searches for and suggests menus based on the nutrients identified by the nutrient identification unit. For example, the generation AI searches for menus high in calcium and suggests dishes such as "calcium-rich salad" and "smoothie made with calcium-fortified milk." The generation AI can also suggest menus taking into account the user's preferences and food allergies. For example, it suggests spicy dishes to a user who likes spicy food. The delivery linking unit provides the menu suggested by the menu search unit in cooperation with a delivery service. For example, the delivery service prepares meals based on the menu suggested by the generation AI and delivers them to the user's home. This saves the user the trouble of preparing meals themselves. As a result, the dietary life support system according to the embodiment can suggest menus containing appropriate nutrients based on the user's health condition and support the user's dietary life. For example, the system can save the user the trouble of preparing meals themselves and ensure that they consume the necessary nutrients. Furthermore, continuous health management can maintain and improve the user's health condition.

[0068] The health data collection unit can analyze the user's emotional state using an emotion estimation function to evaluate the impact of stress and anxiety on the diet. The health data collection unit, for example, analyzes the user's emotional state in real time and adjusts the contents of the diet when stress or anxiety is high. For example, when stress is high, it suggests ingredients that have a relaxing effect. The health data collection unit also uses the emotion estimation function to periodically monitor the user's emotional state and analyze long-term emotional fluctuations. This evaluates the impact of emotional fluctuations on the diet. The health data collection unit also analyzes the user's emotional state and adjusts the timing and amount of meals when stress or anxiety is high. For example, it suggests lighter meals when stress is high. In this way, by analyzing the user's emotional state and evaluating the impact of stress and anxiety on the diet, it is possible to suggest more appropriate menus.

[0069] The health data collection unit can collect lifestyle data and integrate it with health data for analysis. For example, the health data collection unit can analyze the user's sleep patterns and, if the quality of sleep is declining, suggest a diet to improve sleep. For example, it can suggest foods high in tryptophan. The health data collection unit can also monitor the user's exercise volume and, if the user continues to lack exercise, suggest a diet suitable for replenishing energy. For example, it can suggest foods high in carbohydrates. The health data collection unit can also integrate the user's lifestyle data and analyze it together with the health data to evaluate the user's overall health condition. For example, if the user is suffering from both lack of sleep and lack of exercise, it can suggest a balanced diet. In this way, by collecting the user's lifestyle data and integrating it with the health data for analysis, a more comprehensive health condition can be evaluated.

[0070] The health data collection unit can analyze genetic information and suggest nutrients taking into account genetic health risks. For example, the health data collection unit analyzes the user's genetic information and suggests a diet rich in specific nutrients as a preventative measure against genetically high-risk diseases. For example, if the user has a high risk of heart disease, foods rich in omega-3 fatty acids are suggested. The health data collection unit also suggests a nutrient balance appropriate for the user based on the genetic information. For example, if the user has genetically poor vitamin D absorption, foods rich in vitamin D are suggested. The health data collection unit also analyzes the user's genetic information and suggests a diet to avoid excessive intake of specific nutrients. For example, if the user has genetically excessive iron absorption, a diet with less iron is suggested. In this way, by analyzing the user's genetic information and suggesting nutrients taking into account genetic health risks, more personalized health support is possible.

[0071] The health data collection unit also collects health data of the pet and can suggest a healthy diet for both the user and the pet. The health data collection unit, for example, collects health data of the user's pet and suggests meals according to the pet's health condition. For example, if the pet is slightly overweight, it suggests low-calorie pet food. The health data collection unit also integrates the health data of the user and the pet and suggests menus for both to maintain a healthy diet. For example, it suggests healthy ingredients that the user and the pet can eat together. The health data collection unit also suggests meals to improve the pet's health condition based on the pet's health data. For example, if the pet has an allergy, it suggests allergy-friendly pet food. In this way, by collecting health data of the user and the pet and suggesting a healthy diet for both, the health of both the user and the pet can be supported.

[0072] The health data collection unit can collect health data of all family members and suggest menus that support the health of the entire family. For example, the health data collection unit collects health data of all family members of the user and evaluates the health status of the entire family. For example, it suggests menus that allow the entire family to eat a balanced diet. The health data collection unit also suggests meals suitable for family members with specific health risks based on the health data of all family members. For example, if there is a family member at high risk of diabetes, it suggests low-carbohydrate menus. The health data collection unit also integrates the health data of all family members and suggests a meal plan to support the health of the entire family. For example, it suggests menus using healthy ingredients that the whole family can enjoy together. In this way, by collecting health data of all family members of the user and suggesting menus that support the health of the entire family, it is possible to maintain and improve the health of the entire family.

[0073] The health data collection unit can use the emotion estimation function to suggest atmosphere creation for the user to enjoy a meal. For example, the health data collection unit uses the emotion estimation function to suggest music according to the user's emotional state. For example, it suggests calm music when the user wants to relax, and upbeat music when the user wants to cheer up. The health data collection unit also analyzes the user's emotional state and suggests lighting settings to enhance the meal atmosphere. For example, it suggests warm lighting when the user wants to relax, and white lighting when the user wants to concentrate. The health data collection unit also uses the emotion estimation function to suggest interior and decorations for the user to enjoy a meal. For example, it suggests decorations using plants and candles when the user wants to relax. In this way, it is possible to improve meal satisfaction by suggesting an atmosphere creation for the user to enjoy a meal.

[0074] The nutrient identification unit can analyze the emotional state using the emotion estimation function and identify the need for nutrients according to the emotion. For example, the nutrient identification unit analyzes the user's emotional state and, if stress is high, suggests nutrients that help reduce stress. For example, it suggests foods that are rich in magnesium and B vitamins. The nutrient identification unit also uses the emotion estimation function to identify the need for nutrients according to the user's emotional state. For example, if the user feels very tired, it suggests nutrients that are suitable for replenishing energy. The nutrient identification unit also analyzes the user's emotional state and suggests a balance of nutrients according to emotional fluctuations. For example, if the user's emotions are fluctuating greatly, it suggests nutrients that support a stable energy supply. In this way, by analyzing the user's emotional state and identifying the need for nutrients according to the emotion, it is possible to suggest more appropriate nutrients.

[0075] The nutrient identification unit can identify nutrients needed for each season, taking into account seasonal and climate changes. For example, the nutrient identification unit considers seasonal climate changes and suggests nutrients that boost immunity in winter. For example, it suggests foods that are high in vitamin C and zinc. The nutrient identification unit also suggests nutrients that help prevent physical exhaustion due to the heat in summer. For example, it suggests foods that are high in potassium and magnesium to maintain electrolyte balance. The nutrient identification unit also suggests nutrients that address seasonal health risks based on seasonal climate changes. For example, it suggests nutrients with antioxidant properties to combat hay fever in spring. In this way, seasonal health risks can be addressed by identifying necessary nutrients taking into account seasonal and climate changes.

[0076] The nutrient identification unit can analyze past dietary history and make suggestions that take into consideration long-term nutritional balance. The nutrient identification unit, for example, analyzes the user's past dietary history and identifies nutrient imbalances. For example, if vitamin D intake has been insufficient in the past, foods rich in vitamin D are suggested. The nutrient identification unit also takes long-term nutritional balance into consideration and makes suggestions to supplement the deficient nutrients based on the past dietary history. For example, if calcium intake is low, foods rich in calcium are suggested. The nutrient identification unit also suggests nutrients to support long-term health maintenance based on the user's past dietary history. For example, if iron intake has been insufficient in the past, foods rich in iron are suggested. In this way, by analyzing the user's past dietary history and making suggestions that take long-term nutritional balance into consideration, it is possible to maintain sustainable health.

[0077] The nutrient identification unit can identify nutrients related to a user's hobby by taking into account the user's hobbies and preferences. For example, if the user's hobby is sports, the nutrient identification unit will suggest nutrients that support recovery after exercise. For example, it will suggest foods that are high in protein and amino acids. Furthermore, if the user has a sweet tooth, the nutrient identification unit will suggest desserts that use healthy sweeteners. For example, it will suggest low-calorie sweets and fruits. Furthermore, if the user's hobby is gardening, the nutrient identification unit will suggest nutrients for maintaining physical strength. For example, it will suggest foods that are high in vitamins and minerals. This makes it possible to provide more personalized nutritional support by identifying nutrients by taking into account the user's hobbies and preferences.

[0078] The nutrient identification unit can identify nutrients for each occupation by taking into account the user's occupation and daily activity level. For example, if the user's occupation involves mainly desk work, the nutrient identification unit will suggest nutrients that support eye health. For example, it will suggest foods that are rich in vitamin A and lutein. Furthermore, if the user's occupation involves physical labor, the nutrient identification unit will suggest nutrients that are suitable for energy replenishment. For example, it will suggest foods that are rich in carbohydrates and protein. Furthermore, if the user's occupation involves night shifts, the nutrient identification unit will suggest nutrients for regulating the body clock. For example, it will suggest foods that are rich in melatonin. In this way, by identifying nutrients by taking into account the user's occupation and daily activity level, it is possible to provide occupation-specific health support.

[0079] The nutrient identification unit can use the emotion estimation function to make suggestions that will make the user feel positive about a specific nutrient. For example, the nutrient identification unit uses the emotion estimation function to suggest meals that will make the user feel positive about a specific nutrient. For example, when suggesting foods that are high in vitamin C, the nutrient identification unit suggests a menu that uses the user's favorite fruit. The nutrient identification unit also analyzes the user's emotional state and suggests nutrients that will elicit positive emotions. For example, the nutrient identification unit suggests foods that are high in magnesium, which is useful for reducing stress. The nutrient identification unit also uses the emotion estimation function to suggest recipes that will make the user feel positive about a specific nutrient. For example, when suggesting foods that are high in iron, the nutrient identification unit suggests recipes that use the user's favorite dishes. In this way, by making suggestions that will make the user feel positive about a specific nutrient, it is possible to promote nutrient intake.

[0080] The menu search unit can analyze the emotional state using the emotion estimation function and suggest a menu according to the emotion. For example, the menu search unit analyzes the user's emotional state and suggests a menu using ingredients that have a relaxing effect when stress is high. For example, it suggests dishes using chamomile tea or herbs. The menu search unit also uses the emotion estimation function to suggest a menu according to the user's emotional state. For example, it suggests a high-calorie menu to replenish energy when feeling low. The menu search unit also analyzes the user's emotional state and suggests a menu that will elicit positive emotions. For example, it suggests dishes using ingredients that the user likes. In this way, by analyzing the user's emotional state and suggesting menus according to the emotion, it is possible to improve user satisfaction.

[0081] The menu search unit can analyze food allergies and preferences in detail and suggest individually customized menus. For example, the menu search unit suggests menus that avoid allergies based on the user's food allergy information. For example, it suggests nut-free dishes to a user with a nut allergy. The menu search unit also analyzes the user's preferences in detail and suggests menus that suit the user's preferences. For example, it suggests spicy dishes to a user who likes spicy food. The menu search unit also integrates the user's allergy information and preferences to suggest individually customized menus. For example, it suggests dairy-free desserts to a user with a dairy allergy. In this way, by analyzing the user's food allergies and preferences in detail and suggesting individually customized menus, it is possible to improve the user's safety and satisfaction.

[0082] The menu search unit can suggest regional menus taking into account the food culture and traditions of the region. For example, the menu search unit can suggest menus using ingredients specific to the region taking into account the food culture of the user's region. For example, it can suggest dishes using local specialties. The menu search unit can also suggest menus that are familiar to the user based on traditional dishes of the region. For example, it can suggest dishes that are eaten at local festivals and events. The menu search unit can also suggest menus that suit the climate and season of the user's region. For example, it can suggest hot soups and hot pot dishes in cold regions. In this way, by suggesting menus taking into account the food culture and traditions of the user's region, it is possible to provide the user with meals that are familiar to them.

[0083] The menu search unit can suggest menus for each time period, taking into account the time periods and frequency of meals. For example, the menu search unit suggests menus suitable for breakfast, lunch, and dinner, taking into account the time periods of the user's meals. For example, it suggests a high-protein menu for breakfast to replenish energy. The menu search unit also suggests appropriate snacks and light meals based on the frequency of the user's meals. For example, it suggests fruits and nuts as snacks for a user who eats three meals a day. The menu search unit also suggests menus according to the time periods of the user's meals. For example, it suggests lighter menus that are easy to digest if the user eats late at night. In this way, by suggesting menus taking into account the time periods and frequency of the user's meals, it is possible to provide more appropriate meals.

[0084] The menu search unit can take the user's meal budget into consideration and suggest a cost-effective menu. The menu search unit, for example, suggests a cost-effective menu based on the user's meal budget. For example, it suggests dishes using inexpensive, nutritious ingredients. The menu search unit also suggests a menu according to the user's budget. For example, it suggests a menu that provides a balanced meal within a limited budget. The menu search unit also takes the user's meal budget into consideration and suggests a cost-effective yet nutritious menu. For example, it suggests cost-effective dishes using seasonal ingredients. In this way, by suggesting menus taking the user's meal budget into consideration, it is possible to provide a meal with high cost performance.

[0085] The menu search unit can use the emotion estimation function to suggest a presentation method that will allow the user to enjoy their meal. For example, the menu search unit uses the emotion estimation function to suggest a food presentation method that matches the user's emotional state. For example, when the user wants to relax, it suggests a simple and beautiful presentation. The menu search unit also analyzes the user's emotional state and suggests tableware to enjoy the meal. For example, when the user wants to cheer up, it suggests brightly colored tableware. The menu search unit also uses the emotion estimation function to suggest a table setting that will allow the user to enjoy their meal. For example, it suggests a gorgeous table setting for a special occasion. In this way, meal satisfaction can be improved by suggesting a presentation method that will allow the user to enjoy their meal.

[0086] The delivery coordination unit can use the emotion estimation function to analyze the user's emotional state at the time of delivery and provide services according to the emotion. For example, the delivery coordination unit analyzes the user's emotional state at the time of delivery and attaches a message card according to the emotion. For example, if the user is feeling down, it can attach an encouraging message. The delivery coordination unit also uses the emotion estimation function to provide services according to the user's emotional state. For example, if the user is under stress, it can serve herbal tea that has a relaxing effect. The delivery coordination unit also analyzes the user's emotional state and provides a surprise according to the emotion. For example, it can serve a special dessert on a birthday. In this way, by analyzing the user's emotional state and providing services according to the emotion, it is possible to improve user satisfaction.

[0087] The delivery coordination unit can optimize delivery times to suit the user's lifestyle. For example, the delivery coordination unit analyzes the user's lifestyle and suggests the optimal delivery time. For example, delivery is made to coincide with the user's lunch time. The delivery coordination unit also sets delivery times according to the user's lifestyle. For example, nighttime delivery is suggested for users who work night shifts. The delivery coordination unit also flexibly adjusts delivery times taking into account the user's lifestyle. For example, delivery is made to avoid times when the user is not at home. This makes it possible to improve user convenience by optimizing delivery times to suit the user's lifestyle.

[0088] The delivery collaboration unit can monitor the user's health condition in real time during delivery and take emergency action as necessary. For example, the delivery collaboration unit can monitor the user's health condition in real time during delivery and take emergency action if an abnormality is detected. For example, if blood pressure suddenly rises, it will contact a medical institution. The delivery collaboration unit also checks the user's health condition during delivery and provides health advice as necessary. For example, if the body temperature is high, it will encourage the user to drink more fluids. The delivery collaboration unit also monitors the user's health condition during delivery and builds a system that responds quickly in the event of an emergency. For example, if the heart rate is abnormally high, it will dispatch an ambulance. In this way, the user's health condition can be monitored in real time during delivery and emergency action can be taken as necessary to ensure the user's safety.

[0089] The delivery collaboration unit collaborates with delivery services to deliver not only meals to the user, but also daily necessities and medicines. The delivery collaboration unit, for example, collaborates with delivery services to deliver daily necessities along with the user's meals. For example, daily necessities such as toilet paper and detergent are delivered together. The delivery collaboration unit also builds a system that also delivers the user's medicines. For example, prescription medicines and supplements are delivered together with meals. The delivery collaboration unit also collaborates with delivery services to deliver health-related products along with the user's meals. For example, vitamins and health foods are delivered together. In this way, by delivering not only the user's meals but also daily necessities and medicines, it is possible to support the user's overall lifestyle.

[0090] The delivery cooperation unit can provide a service that delivers meals for the user's pet together with the delivery. The delivery cooperation unit, for example, cooperates with a delivery service to deliver the pet's meal along with the user's meal. For example, pet food and treats may be delivered together. The delivery cooperation unit also considers the health condition of the user's pet and suggests and delivers meals suitable for the pet. For example, it may suggest food that is suitable for the pet's allergies. The delivery cooperation unit also cooperates with a delivery service to build a system that delivers the user's meal and the pet's meal together. For example, it may provide a regular delivery service for pet meals. This makes it possible to meet the needs of both the user and the pet by providing a service that delivers the user's pet's meal together.

[0091] The delivery coordination unit can use the emotion estimation function to provide a surprise that will delight the user at the time of delivery. For example, the delivery coordination unit uses the emotion estimation function to provide a surprise that matches the user's emotional state. For example, if the user is feeling down, it can include an encouraging message card. The delivery coordination unit can also provide a small gift that will delight the user at the time of delivery. For example, it can deliver seasonal flowers or sweets. The delivery coordination unit can also use the emotion estimation function to suggest a surprise that matches the user's emotional state. For example, it can provide a special message card or gift for a birthday. In this way, by providing a surprise that will delight the user at the time of delivery, it is possible to improve user satisfaction.

[0092] The health data collection unit can analyze the user's emotional state using an emotion estimation function and provide feedback according to the emotion. For example, the health data collection unit can analyze the user's emotional state and, if stress is high, suggest relaxation methods. For example, it can provide feedback on deep breathing or meditation techniques. The health data collection unit can also use the emotion estimation function to provide feedback according to the user's emotional state. For example, it can send an encouraging message when the user is feeling down. The health data collection unit can also analyze the user's emotional state and provide feedback according to emotional fluctuations. For example, it can suggest a stable lifestyle rhythm if the user's emotions are fluctuating greatly. In this way, by analyzing the user's emotional state and providing feedback according to the emotion, the user's psychological health can also be supported.

[0093] The health data collection unit can analyze health data over the long term, predict future health risks, and provide feedback. The health data collection unit, for example, analyzes a user's health data over the long term and predicts future health risks. For example, it predicts the risk of high blood pressure based on blood pressure fluctuations and suggests preventive measures. The health data collection unit also evaluates the user's health condition based on the long-term health data and provides feedback on future risks. For example, it predicts the risk of obesity based on weight gain or loss and suggests dietary improvements. The health data collection unit also analyzes a user's health data over the long term and builds a system that predicts future health risks. For example, it predicts the risk of diabetes based on blood sugar fluctuations and suggests preventive measures. This enables preventive health management by analyzing a user's health data over the long term and predicting future health risks and providing feedback.

[0094] The health data collection unit can anonymize and aggregate health data, analyze statistical health trends, and provide feedback. For example, the health data collection unit anonymizes and aggregates user health data to analyze statistical health trends. For example, it identifies common health risks among specific age groups and provides feedback. The health data collection unit also analyzes health trends by region based on the anonymized health data and provides feedback on health risks specific to the region. For example, it identifies the risk of high blood pressure in a specific region and suggests preventive measures. The health data collection unit also builds a system that anonymizes and aggregates user health data and analyzes statistical health trends. For example, it identifies common health risks among specific occupations and provides feedback. This makes it possible to identify and prevent a wide range of health risks by anonymizing and aggregating user health data and analyzing statistical health trends.

[0095] The health data collection unit can cooperate with personal trainers and nutritionists based on the health data to provide professional advice. The health data collection unit, for example, cooperates with personal trainers and nutritionists based on the user's health data to provide professional advice. For example, it may propose exercise plans and meal plans. The health data collection unit also shares the user's health data and manages health based on feedback from personal trainers and nutritionists. For example, it may evaluate the user's health status through regular counseling. The health data collection unit also strengthens cooperation with personal trainers and nutritionists based on the user's health data to provide individually customized health management plans. For example, it may provide advice on exercise and diet that addresses specific health risks. This enables more effective health management by coordinating with personal trainers and nutritionists and providing professional advice based on the user's health data.

[0096] The health data collection unit can provide health-related educational content based on the health data. The health data collection unit provides health-related educational content based on, for example, the user's health data. For example, it delivers videos about how to maintain a balanced diet and the importance of exercise. The health data collection unit also provides educational content according to the user's health condition. For example, it provides articles about diabetes prevention to users who are at high risk of diabetes. The health data collection unit also provides individually customized educational content based on the user's health data. For example, it delivers videos and articles that address specific health risks. In this way, by providing health-related educational content based on the user's health data, it is possible to raise the user's health awareness and promote preventive health management.

[0097] The health data collection unit can use the emotion estimation function to introduce gamification elements to allow users to enjoy health management. The health data collection unit, for example, uses the emotion estimation function to introduce gamification elements to allow users to enjoy health management. For example, a system is provided in which points can be earned each time a health goal is achieved. The health data collection unit also analyzes the user's emotional state and suggests game elements to elicit positive emotions. For example, a mini-game is added to a health management app. The health data collection unit also uses the emotion estimation function to provide incentives to allow users to enjoy health management. For example, a reward is provided each time a health goal is achieved. In this way, by introducing gamification elements to allow users to enjoy health management, it is possible to improve motivation for health management.

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

[0099] The health data collection unit collects the user's health data. For example, it collects data such as weight, blood pressure, heart rate, and food records. The health data collection unit can also collect regular health checks and medical data. For example, it collects data using a blood pressure monitor or a scale. The nutrient identification unit identifies necessary nutrients based on the health data collected by the health data collection unit. For example, the generation AI analyzes the user's health data and identifies necessary nutrients such as calcium and vitamin D. The generation AI can also identify nutrients taking the user's allergy information into consideration. For example, it makes suggestions to avoid certain nutrients based on the allergy information. The menu search unit searches and suggests menus based on the nutrients identified by the nutrient identification unit. For example, the generation AI searches for menus high in calcium and suggests "calcium-rich salads" or "smoothies made with calcium-fortified milk." The generation AI can also suggest menus taking the user's preferences and food allergies into consideration. For example, it suggests spicy dishes for a user who likes spicy food. The delivery integration unit provides the menus suggested by the menu search unit in collaboration with a delivery service. For example, a delivery service prepares meals based on a menu proposed by the generation AI and delivers them to the user's home. This saves the user the trouble of preparing meals themselves. As a result, the dietary life support system according to the embodiment can suggest menus containing appropriate nutrients based on the user's health condition and support the user's dietary life. For example, the system can save the user the trouble of preparing meals themselves and ensure that they consume the necessary nutrients. Furthermore, continuous health management can maintain and improve the user's health condition.

[0100] The health data collection unit can analyze the user's emotional state using an emotion estimation function to evaluate the impact of stress and anxiety on the diet. The health data collection unit, for example, analyzes the user's emotional state in real time and adjusts the contents of the diet when stress or anxiety is high. For example, when stress is high, it suggests ingredients that have a relaxing effect. The health data collection unit also uses the emotion estimation function to periodically monitor the user's emotional state and analyze long-term emotional fluctuations. This evaluates the impact of emotional fluctuations on the diet. The health data collection unit also analyzes the user's emotional state and adjusts the timing and amount of meals when stress or anxiety is high. For example, it suggests lighter meals when stress is high. In this way, by analyzing the user's emotional state and evaluating the impact of stress and anxiety on the diet, it is possible to suggest more appropriate menus.

[0101] The health data collection unit can collect lifestyle data and integrate it with health data for analysis. For example, the health data collection unit can analyze the user's sleep patterns and, if the quality of sleep is declining, suggest a diet to improve sleep. For example, it can suggest foods high in tryptophan. The health data collection unit can also monitor the user's exercise volume and, if the user continues to lack exercise, suggest a diet suitable for replenishing energy. For example, it can suggest foods high in carbohydrates. The health data collection unit can also integrate the user's lifestyle data and analyze it together with the health data to evaluate the user's overall health condition. For example, if the user is suffering from both lack of sleep and lack of exercise, it can suggest a balanced diet. In this way, by collecting the user's lifestyle data and integrating it with the health data for analysis, a more comprehensive health condition can be evaluated.

[0102] The health data collection unit can analyze genetic information and suggest nutrients taking into account genetic health risks. For example, the health data collection unit analyzes the user's genetic information and suggests a diet rich in specific nutrients as a preventative measure against genetically high-risk diseases. For example, if the user has a high risk of heart disease, foods rich in omega-3 fatty acids are suggested. The health data collection unit also suggests a nutrient balance appropriate for the user based on the genetic information. For example, if the user has genetically poor vitamin D absorption, foods rich in vitamin D are suggested. The health data collection unit also analyzes the user's genetic information and suggests a diet to avoid excessive intake of specific nutrients. For example, if the user has genetically excessive iron absorption, a diet with less iron is suggested. In this way, by analyzing the user's genetic information and suggesting nutrients taking into account genetic health risks, more personalized health support is possible.

[0103] The health data collection unit also collects health data of the pet and can suggest a healthy diet for both the user and the pet. The health data collection unit, for example, collects health data of the user's pet and suggests meals according to the pet's health condition. For example, if the pet is slightly overweight, it suggests low-calorie pet food. The health data collection unit also integrates the health data of the user and the pet and suggests menus for both to maintain a healthy diet. For example, it suggests healthy ingredients that the user and the pet can eat together. The health data collection unit also suggests meals to improve the pet's health condition based on the pet's health data. For example, if the pet has an allergy, it suggests allergy-friendly pet food. In this way, by collecting health data of the user and the pet and suggesting a healthy diet for both, the health of both the user and the pet can be supported.

[0104] The health data collection unit can collect health data of all family members and suggest menus that support the health of the entire family. For example, the health data collection unit collects health data of all family members of the user and evaluates the health status of the entire family. For example, it suggests menus that allow the entire family to eat a balanced diet. The health data collection unit also suggests meals suitable for family members with specific health risks based on the health data of all family members. For example, if there is a family member at high risk of diabetes, it suggests low-carbohydrate menus. The health data collection unit also integrates the health data of all family members and suggests a meal plan to support the health of the entire family. For example, it suggests menus using healthy ingredients that the whole family can enjoy together. In this way, by collecting health data of all family members of the user and suggesting menus that support the health of the entire family, it is possible to maintain and improve the health of the entire family.

[0105] The health data collection unit can use the emotion estimation function to suggest atmosphere creation for the user to enjoy a meal. For example, the health data collection unit uses the emotion estimation function to suggest music according to the user's emotional state. For example, it suggests calm music when the user wants to relax, and upbeat music when the user wants to cheer up. The health data collection unit also analyzes the user's emotional state and suggests lighting settings to enhance the meal atmosphere. For example, it suggests warm lighting when the user wants to relax, and white lighting when the user wants to concentrate. The health data collection unit also uses the emotion estimation function to suggest interior and decorations for the user to enjoy a meal. For example, it suggests decorations using plants and candles when the user wants to relax. In this way, it is possible to improve meal satisfaction by suggesting an atmosphere creation for the user to enjoy a meal.

[0106] The nutrient identification unit can analyze the emotional state using the emotion estimation function and identify the need for nutrients according to the emotion. For example, the nutrient identification unit analyzes the user's emotional state and, if stress is high, suggests nutrients that help reduce stress. For example, it suggests foods that are rich in magnesium and B vitamins. The nutrient identification unit also uses the emotion estimation function to identify the need for nutrients according to the user's emotional state. For example, if the user feels very tired, it suggests nutrients that are suitable for replenishing energy. The nutrient identification unit also analyzes the user's emotional state and suggests a balance of nutrients according to emotional fluctuations. For example, if the user's emotions are fluctuating greatly, it suggests nutrients that support a stable energy supply. In this way, by analyzing the user's emotional state and identifying the need for nutrients according to the emotion, it is possible to suggest more appropriate nutrients.

[0107] The nutrient identification unit can identify nutrients needed for each season, taking into account seasonal and climate changes. For example, the nutrient identification unit considers seasonal climate changes and suggests nutrients that boost immunity in winter. For example, it suggests foods that are high in vitamin C and zinc. The nutrient identification unit also suggests nutrients that help prevent physical exhaustion due to the heat in summer. For example, it suggests foods that are high in potassium and magnesium to maintain electrolyte balance. The nutrient identification unit also suggests nutrients that address seasonal health risks based on seasonal climate changes. For example, it suggests nutrients with antioxidant properties to combat hay fever in spring. In this way, seasonal health risks can be addressed by identifying necessary nutrients taking into account seasonal and climate changes.

[0108] The nutrient identification unit can analyze past dietary history and make suggestions that take into consideration long-term nutritional balance. The nutrient identification unit, for example, analyzes the user's past dietary history and identifies nutrient imbalances. For example, if vitamin D intake has been insufficient in the past, foods rich in vitamin D are suggested. The nutrient identification unit also takes long-term nutritional balance into consideration and makes suggestions to supplement the deficient nutrients based on the past dietary history. For example, if calcium intake is low, foods rich in calcium are suggested. The nutrient identification unit also suggests nutrients to support long-term health maintenance based on the user's past dietary history. For example, if iron intake has been insufficient in the past, foods rich in iron are suggested. In this way, by analyzing the user's past dietary history and making suggestions that take long-term nutritional balance into consideration, it is possible to maintain sustainable health.

[0109] The nutrient identification unit can identify nutrients related to a user's hobby by taking into account the user's hobbies and preferences. For example, if the user's hobby is sports, the nutrient identification unit will suggest nutrients that support recovery after exercise. For example, it will suggest foods that are high in protein and amino acids. Furthermore, if the user has a sweet tooth, the nutrient identification unit will suggest desserts that use healthy sweeteners. For example, it will suggest low-calorie sweets and fruits. Furthermore, if the user's hobby is gardening, the nutrient identification unit will suggest nutrients for maintaining physical strength. For example, it will suggest foods that are high in vitamins and minerals. This makes it possible to provide more personalized nutritional support by identifying nutrients by taking into account the user's hobbies and preferences.

[0110] The nutrient identification unit can identify nutrients for each occupation by taking into account the user's occupation and daily activity level. For example, if the user's occupation involves mainly desk work, the nutrient identification unit will suggest nutrients that support eye health. For example, it will suggest foods that are rich in vitamin A and lutein. Furthermore, if the user's occupation involves physical labor, the nutrient identification unit will suggest nutrients that are suitable for energy replenishment. For example, it will suggest foods that are rich in carbohydrates and protein. Furthermore, if the user's occupation involves night shifts, the nutrient identification unit will suggest nutrients for regulating the body clock. For example, it will suggest foods that are rich in melatonin. In this way, by identifying nutrients by taking into account the user's occupation and daily activity level, it is possible to provide occupation-specific health support.

[0111] The nutrient identification unit can use the emotion estimation function to make suggestions that will make the user feel positive about a specific nutrient. For example, the nutrient identification unit uses the emotion estimation function to suggest meals that will make the user feel positive about a specific nutrient. For example, when suggesting foods that are high in vitamin C, the nutrient identification unit suggests a menu that uses the user's favorite fruit. The nutrient identification unit also analyzes the user's emotional state and suggests nutrients that will elicit positive emotions. For example, the nutrient identification unit suggests foods that are high in magnesium, which is useful for reducing stress. The nutrient identification unit also uses the emotion estimation function to suggest recipes that will make the user feel positive about a specific nutrient. For example, when suggesting foods that are high in iron, the nutrient identification unit suggests recipes that use the user's favorite dishes. In this way, by making suggestions that will make the user feel positive about a specific nutrient, it is possible to promote nutrient intake.

[0112] The menu search unit can analyze the emotional state using the emotion estimation function and suggest a menu according to the emotion. For example, the menu search unit analyzes the user's emotional state and suggests a menu using ingredients that have a relaxing effect when stress is high. For example, it suggests dishes using chamomile tea or herbs. The menu search unit also uses the emotion estimation function to suggest a menu according to the user's emotional state. For example, it suggests a high-calorie menu to replenish energy when feeling low. The menu search unit also analyzes the user's emotional state and suggests a menu that will elicit positive emotions. For example, it suggests dishes using ingredients that the user likes. In this way, by analyzing the user's emotional state and suggesting menus according to the emotion, it is possible to improve user satisfaction.

[0113] The menu search unit can analyze food allergies and preferences in detail and suggest individually customized menus. For example, the menu search unit suggests menus that avoid allergies based on the user's food allergy information. For example, it suggests nut-free dishes to a user with a nut allergy. The menu search unit also analyzes the user's preferences in detail and suggests menus that suit the user's preferences. For example, it suggests spicy dishes to a user who likes spicy food. The menu search unit also integrates the user's allergy information and preferences to suggest individually customized menus. For example, it suggests dairy-free desserts to a user with a dairy allergy. In this way, by analyzing the user's food allergies and preferences in detail and suggesting individually customized menus, it is possible to improve the user's safety and satisfaction.

[0114] The menu search unit can suggest regional menus taking into account the food culture and traditions of the region. For example, the menu search unit can suggest menus using ingredients specific to the region taking into account the food culture of the user's region. For example, it can suggest dishes using local specialties. The menu search unit can also suggest menus that are familiar to the user based on traditional dishes of the region. For example, it can suggest dishes that are eaten at local festivals and events. The menu search unit can also suggest menus that suit the climate and season of the user's region. For example, it can suggest hot soups and hot pot dishes in cold regions. In this way, by suggesting menus taking into account the food culture and traditions of the user's region, it is possible to provide the user with meals that are familiar to them.

[0115] The menu search unit can suggest menus for each time period, taking into account the time periods and frequency of meals. For example, the menu search unit suggests menus suitable for breakfast, lunch, and dinner, taking into account the time periods of the user's meals. For example, it suggests a high-protein menu for breakfast to replenish energy. The menu search unit also suggests appropriate snacks and light meals based on the frequency of the user's meals. For example, it suggests fruits and nuts as snacks for a user who eats three meals a day. The menu search unit also suggests menus according to the time periods of the user's meals. For example, it suggests lighter menus that are easy to digest if the user eats late at night. In this way, by suggesting menus taking into account the time periods and frequency of the user's meals, it is possible to provide more appropriate meals.

[0116] The menu search unit can take the user's meal budget into consideration and suggest a cost-effective menu. The menu search unit, for example, suggests a cost-effective menu based on the user's meal budget. For example, it suggests dishes using inexpensive, nutritious ingredients. The menu search unit also suggests a menu according to the user's budget. For example, it suggests a menu that provides a balanced meal within a limited budget. The menu search unit also takes the user's meal budget into consideration and suggests a cost-effective yet nutritious menu. For example, it suggests cost-effective dishes using seasonal ingredients. In this way, by suggesting menus taking the user's meal budget into consideration, it is possible to provide a meal with high cost performance.

[0117] The menu search unit can use the emotion estimation function to suggest a presentation method that will allow the user to enjoy their meal. For example, the menu search unit uses the emotion estimation function to suggest a food presentation method that matches the user's emotional state. For example, when the user wants to relax, it suggests a simple and beautiful presentation. The menu search unit also analyzes the user's emotional state and suggests tableware to enjoy the meal. For example, when the user wants to cheer up, it suggests brightly colored tableware. The menu search unit also uses the emotion estimation function to suggest a table setting that will allow the user to enjoy their meal. For example, it suggests a gorgeous table setting for a special occasion. In this way, meal satisfaction can be improved by suggesting a presentation method that will allow the user to enjoy their meal.

[0118] The delivery coordination unit can use the emotion estimation function to analyze the user's emotional state at the time of delivery and provide services according to the emotion. For example, the delivery coordination unit analyzes the user's emotional state at the time of delivery and attaches a message card according to the emotion. For example, if the user is feeling down, it can attach an encouraging message. The delivery coordination unit also uses the emotion estimation function to provide services according to the user's emotional state. For example, if the user is under stress, it can serve herbal tea that has a relaxing effect. The delivery coordination unit also analyzes the user's emotional state and provides a surprise according to the emotion. For example, it can serve a special dessert on a birthday. In this way, by analyzing the user's emotional state and providing services according to the emotion, it is possible to improve user satisfaction.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The health data collection unit collects the user's health data. For example, it collects data such as weight, blood pressure, heart rate, and food records. It can also collect regular health checks and medical data. For example, it collects data using a blood pressure monitor or a weight scale. Step 2: The nutrient identification unit identifies necessary nutrients based on the health data collected by the health data collection unit. For example, the generation AI analyzes the user's health data and identifies necessary nutrients such as calcium and vitamin D. It can also identify nutrients taking into account the user's allergy information. For example, it makes suggestions to avoid certain nutrients based on the allergy information. Step 3: The menu search unit searches for and suggests menus based on the nutrients identified by the nutrient identification unit. For example, the generation AI searches for menus that are high in calcium and suggests dishes such as "calcium-rich salad" or "smoothie made with calcium-fortified milk." It can also suggest menus taking into account the user's preferences and food allergies. For example, it suggests spicy dishes to a user who likes spicy food. Step 4: The delivery integration unit provides the menu suggested by the menu search unit in cooperation with a delivery service. For example, the delivery service prepares meals based on the menu suggested by the generation AI and delivers them to the user's home. This saves the user the trouble of having to prepare meals themselves.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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]

[0188] 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 health data collection unit that collects health data of a user; a nutrient identification unit that identifies necessary nutrients based on the health data; a menu search unit that searches for and suggests menus based on the nutrients; a delivery linkage unit that provides the menu in cooperation with a delivery service. A system characterized by:

2. The health data collection unit: Analyze your emotional state and assess the impact of stress and anxiety on your eating habits 2. The system of claim 1.

3. The health data collection unit: Collect lifestyle data and analyze it in combination with the health data.

2. The system of claim 1.

4. The health data collection unit: Analyze genetic information and suggest nutrients that take genetic health risks into account 2. The system of claim 1.

5. The health data collection unit: It also collects pet health data and suggests healthy eating habits for said pets.

2. The system of claim 1.

6. The health data collection unit: Collecting health data from all family members and proposing the above-mentioned menu to support the health of the entire family 2. The system of claim 1.

7. The health data collection unit: Propose ways to create an atmosphere that allows the user to enjoy their meal 2. The system of claim 1.

8. The nutrient specifying part is Analyzing emotional states and identifying the need for said nutrients according to emotions 2. The system of claim 1.

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