System

Abstract

An object of a system according to an embodiment is to provide an optimal health management plan based on an individual's physique, genetic information, and the like.SOLUTION: A system includes a data collection part, an analysis part, and a plan generation part. The data collection unit collects personal data of at least one of a user's physique, genetic information, lifestyle, meal content, and amount of exercise. The analysis unit analyzes the personal data collected by the data collection unit. The plan generation unit generates a health care plan optimal for the user on the basis of the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide flexible health management based on an individual's physique, genetic information, etc., and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal health management plan based on an individual's physique, genetic information, and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a plan generation unit. The data collection unit collects at least one of personal data on the user's physique, genetic information, lifestyle habits, dietary content, and amount of exercise. The analysis unit analyzes the personal data collected by the data collection unit. The plan generation unit generates an optimal health management plan for the user based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal health management plan based on an individual's physique, genetic information, and the like. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention utilizes generative AI to propose optimal health management for individuals. This system generates and provides optimal health management plans for users by collecting and analyzing personal data such as the user's physique, genetic information, lifestyle habits, dietary habits, and exercise volume. This allows the health management system to provide optimal health management plans based on the user's personal data.

[0029] A health management system according to an embodiment includes a data collection unit, an analysis unit, and a plan generation unit. The data collection unit collects at least one personal data item from a user's physique, genetic information, lifestyle habits, dietary content, and exercise amount. For example, the data collection unit collects dietary data by having the user input daily dietary information into an app. The data collection unit may use a wearable device to collect the user's exercise data. The data collection unit may use a genetic testing kit to collect the user's genetic information. The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may evaluate nutritional balance and calorie intake based on the collected dietary data. The analysis unit may evaluate exercise amount and calorie expenditure based on the collected exercise data. The analysis unit may predict the risk of certain diseases based on the collected genetic information. The plan generation unit generates an optimal health management plan for the user based on the data analyzed by the analysis unit. For example, the plan generation unit may propose an appropriate diet plan and exercise plan taking into account the user's physique and genetic information. The plan generation unit may provide a specific action plan based on the user's goals. Furthermore, the plan generation unit can modify the plan or provide additional advice depending on the user's health condition. This allows the health management system according to the embodiment to provide an optimal health management plan based on the user's personal data. For example, the user can lose weight by continuing a healthy diet or increase muscle strength by performing appropriate exercise. Furthermore, the generation AI provides continuous feedback, allowing the user to constantly monitor their health condition and make necessary adjustments.

[0030] The data collection unit can collect the user's daily dietary details and evaluate the nutritional balance or calorie intake. The data collection unit collects dietary data, for example, by having the user enter the daily dietary details into the app. For example, the app automatically analyzes the dietary details and evaluates the nutritional balance and calorie intake. The data collection unit can also collect dietary data using image analysis technology by having the user record photos of the dietary details. For example, the image analysis technology recognizes ingredients and calculates nutrients and calories. The data collection unit can also collect dietary data by having the user scan barcodes to obtain nutritional information of foods. For example, a barcode scanner reads the nutritional information of foods and compares it with a database to evaluate the nutritional balance and calorie intake. This allows the user's daily dietary details to be evaluated and their nutritional balance and calorie intake to be understood.

[0031] The plan generation unit can propose an appropriate meal plan or exercise plan taking into account the user's physique or genetic information. The plan generation unit proposes an appropriate meal plan taking into account the user's physique, for example. For example, a meal plan is provided in which the calorie intake and nutritional balance are adjusted based on the user's height, weight, and BMI. The plan generation unit can also propose a meal plan that recommends the intake of specific nutrients taking into account the user's genetic information. For example, the intake of vitamin D and omega-3 fatty acids is recommended based on the results of a genetic test. The plan generation unit can also propose an appropriate exercise plan taking into account the user's physique and genetic information. For example, an exercise plan is provided in which the type and intensity of exercise are adjusted based on the user's muscle strength and endurance. This makes it possible to provide an optimal meal plan or exercise plan based on the user's physique and genetic information.

[0032] The analysis unit can collect the user's daily weight or blood pressure and evaluate changes in their health condition. For example, the analysis unit collects the user's daily weight and evaluates changes in their health condition. For example, the analysis unit measures the user's weight every day using a scale and analyzes the data. The analysis unit can also collect the user's daily blood pressure and evaluate changes in their health condition. For example, the analysis unit measures the user's blood pressure every day using a sphygmomanometer and analyzes the data. The analysis unit can also combine the user's weight and blood pressure data to comprehensively evaluate changes in their health condition. For example, the analysis unit can analyze weight gain / loss and blood pressure fluctuations to predict health risks. This makes it possible to evaluate the user's daily weight and blood pressure and understand changes in their health condition.

[0033] The plan generation unit can adjust the meal plan based on the user's allergy information for specific ingredients. For example, the plan generation unit adjusts the meal plan taking into account the user's allergy information for specific ingredients. For example, if the user has a nut allergy, a meal plan that does not include nuts is provided. The plan generation unit can also suggest alternative ingredients based on the user's allergy information. For example, a meal plan that uses soy milk or almond milk instead of dairy products is provided to a user who has a dairy allergy. The plan generation unit can also suggest food combinations to avoid allergic reactions based on the user's allergy information. This makes it possible to provide a safe meal plan based on the user's allergy information.

[0034] The plan generation unit can set a long-term health goal for the user and support the achievement of that goal. For example, if the user aims to lose 10 kg in one year, the plan generation unit can suggest specific steps and monitor progress. The plan generation unit can also provide support to keep the user motivated toward their health goal. For example, the plan generation unit can send regular feedback and encouraging messages. The plan generation unit can also provide plan modifications and additional advice according to the user's health goal. This makes it possible to set a long-term health goal for the user and support the achievement of that goal.

[0035] The data collection unit can predict a specific disease risk based on the user's genetic information and suggest preventive measures to address that risk. The data collection unit, for example, uses a genetic testing kit to analyze the user's genetic information and predict the specific disease risk. For example, a saliva sample is collected and genetic analysis is performed. The data collection unit can also refer to an existing genetic database to analyze the user's genetic information and predict the specific disease risk. For example, the risk can be assessed based on family history or known genetic mutations. The data collection unit can also use a machine learning algorithm to analyze the user's genetic information and predict the specific disease risk. For example, the risk can be predicted by combining genetic data and medical history data. This makes it possible to predict the disease risk based on the user's genetic information and provide appropriate preventive measures.

[0036] The data collection unit can analyze the user's lifestyle data and evaluate the user's sleep patterns or daily activity level in detail. The data collection unit, for example, uses a smartwatch to collect the user's lifestyle data. For example, it measures the sleep patterns and daily activity level and analyzes the data. The data collection unit can also use a smartphone sensor to collect the user's lifestyle data. For example, it measures the number of steps taken and the distance traveled and evaluates the daily activity level. The data collection unit can also use a dedicated app to collect the user's lifestyle data. For example, it records the contents of meals and the amount of exercise and analyzes the data. This allows the user's lifestyle data to be evaluated in detail and a precise health management plan to be provided.

[0037] The data collection unit can collect health data of the user's pet and propose a health management plan for the user and the pet. The data collection unit, for example, uses a wearable device for the pet to collect the health data of the user's pet. For example, it measures the pet's activity level and body temperature and analyzes the data. The data collection unit can also use a pet health management app to collect the health data of the user's pet. For example, it can record the pet's diet and amount of exercise and analyze the data. The data collection unit can also refer to veterinarian diagnostic data to collect the health data of the user's pet. For example, it can analyze the data based on the results of regular health checks. In this way, it is possible to collect the health data of the user's pet and propose a health management plan for the user and the pet.

[0038] The data collection unit can collect data on the user's workplace environment and provide a plan to support workplace health management. The data collection unit, for example, uses an air quality sensor in the workplace to collect the user's workplace environment data. For example, it measures temperature, humidity, and CO2 concentration and analyzes the data. The data collection unit can also use a lighting sensor in the workplace to collect the user's workplace environment data. For example, it measures illuminance and color temperature and analyzes the data. The data collection unit can also use a noise sensor in the workplace to collect the user's workplace environment data. For example, it measures noise levels and analyzes the data. In this way, it is possible to collect data on the user's workplace environment and provide a plan to support workplace health management.

[0039] The plan generation unit can propose a supplement plan that recommends the intake of specific nutrients based on the user's genetic information. The plan generation unit, for example, analyzes the user's genetic information and proposes a supplement plan that recommends the intake of specific nutrients. For example, the plan generation unit recommends the intake of vitamin D and omega-3 fatty acids. The plan generation unit can also analyze the user's genetic information and propose a supplement plan that recommends the intake of specific nutrients. For example, the plan generation unit can recommend the intake of iron and calcium. The plan generation unit can also analyze the user's genetic information and propose a supplement plan that recommends the intake of specific nutrients. For example, the plan generation unit can recommend the intake of vitamin B12 and magnesium. This makes it possible to provide a personalized supplement plan that recommends the intake of specific nutrients based on the user's genetic information.

[0040] The plan generation unit can analyze the user's exercise data and provide a plan that adjusts the timing or intensity to maximize the effect of exercise. The plan generation unit, for example, analyzes the user's exercise data and provides a plan that adjusts the timing or intensity to maximize the effect of exercise. For example, it suggests optimal exercise duration and intensity. The plan generation unit can also analyze the user's exercise data and provide a plan that adjusts the timing and intensity to maximize the effect of exercise. For example, it suggests how many times a week the user should exercise. The plan generation unit can also analyze the user's exercise data and provide a plan that adjusts the timing and intensity to maximize the effect of exercise. For example, it suggests a combination of specific exercise types. In this way, it is possible to analyze the user's exercise data and provide a plan that adjusts the timing and intensity to maximize the effect of exercise.

[0041] The plan generation unit can integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, the plan generation unit can integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, it can propose an exercise program for the entire family. The plan generation unit can also integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, it can propose a meal plan for the entire family. The plan generation unit can also integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, it can propose a health event for the entire family to participate in. In this way, it is possible to integrate the health data of all of the user's family members and provide a health management plan that the entire family will work on.

[0042] The plan generation unit can provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. The plan generation unit can, for example, provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. For example, it can suggest dance or yoga classes. The plan generation unit can also provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. For example, it can suggest hiking or cycling routes. The plan generation unit can also provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. For example, it can suggest the use of a sports club or fitness gym. In this way, it is possible to provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun.

[0043] The analysis unit can be used to build a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected. The analysis unit, for example, can analyze a user's health data in real time and immediately issues an alert if an abnormal value is detected. For example, it can detect abnormalities in blood pressure or heart rate. The analysis unit can also be used to build a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected. For example, it can detect abnormalities in blood sugar levels or body temperature. The analysis unit can also be used to build a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected. For example, it can detect abnormalities in oxygen saturation or respiratory rate. This makes it possible to provide a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected.

[0044] The analysis unit can track changes in the user's health condition over the long term and analyze trends to predict future health risks. The analysis unit, for example, tracks changes in the user's health condition over the long term and analyzes trends to predict future health risks. For example, it analyzes long-term fluctuations in weight and blood pressure. The analysis unit can also track changes in the user's health condition over the long term and analyzes trends to predict future health risks. For example, it analyzes long-term fluctuations in blood sugar levels and cholesterol levels. The analysis unit can also track changes in the user's health condition over the long term and analyzes trends to predict future health risks. For example, it analyzes long-term fluctuations in heart rate and oxygen saturation. This makes it possible to track changes in the user's health condition over the long term and predict future health risks.

[0045] The analysis unit can monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. The analysis unit can, for example, monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. For example, the analysis unit can analyze the pet's weight and activity level. The analysis unit can also monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. For example, the analysis unit can analyze the pet's diet and exercise level. The analysis unit can also monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. For example, the analysis unit can analyze the results of the pet's health check. This can monitor the health condition of the user's pet and provide feedback for managing the health of the pet together.

[0046] The analysis unit can monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. The analysis unit can, for example, monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. For example, it can analyze changes in air quality and lighting. The analysis unit can also monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. For example, it can analyze changes in noise levels and temperature. The analysis unit can also monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. For example, it can analyze changes in stress levels and workload. This makes it possible to monitor changes in the user's work environment and provide advice for reducing health risks in the workplace.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The health management system may further include a sleep analysis unit that collects and analyzes the user's sleep data. For example, the user wears a smartwatch while sleeping to measure and collect data on the quality and duration of sleep. The sleep analysis unit may also analyze the user's sleep patterns and provide advice to improve the quality of sleep. For example, it may suggest ways to relax before bed or an appropriate bedtime. The sleep analysis unit may also provide advice to improve daytime activity levels and concentration based on the user's sleep data. This allows for detailed analysis of the user's sleep data and provides a better health management plan.

[0049] The health management system can further include a fluid management unit that monitors the user's fluid intake. For example, the user can collect data by entering their daily fluid intake into the app. The fluid management unit can also suggest appropriate fluid intake amounts based on the user's activity level and temperature. For example, it can advise on the timing of hydration after exercise or on hot days. The fluid management unit can also predict the risk of dehydration based on the user's fluid intake and suggest preventative measures. This allows the system to manage the user's fluid intake and provide advice that is useful for maintaining health.

[0050] The health management system can also provide meal plans that take into account food allergy information based on the user's dietary data. For example, if a user registers ingredients to which they are allergic in the app, those ingredients can be excluded from the meal plan. The meal plan can also suggest substitutes for ingredients to which the user is allergic. For example, a user with a dairy allergy can be offered a meal plan that uses soy milk or almond milk. The meal plan can also suggest ingredient combinations to avoid allergic reactions. This allows the system to provide safe meal plans that take into account the user's allergy information.

[0051] The health management system can also collect data on the user's workplace environment and provide a plan to support health management at the workplace. For example, it can use air quality sensors in the workplace to measure temperature, humidity, and CO2 concentration and analyze the data. It can also suggest appropriate air conditioning settings and ventilation timing based on the workplace environment data. For example, if the CO2 concentration is high, it can issue an alert to encourage ventilation. It can also suggest adjustments to lighting and noise levels based on the workplace environment data. This allows it to provide a plan to optimize the user's workplace environment and support health management.

[0052] The health management system can also collect health data of the user's pet and propose a health management plan for the user and the pet. For example, a wearable device for pets can be used to measure the pet's activity level and body temperature and analyze the data. Based on the pet's health data, the system can also propose an exercise plan for the user and the pet. For example, it can suggest walk and play times. Based on the pet's health data, the system can also propose a diet plan for the pet. For example, it can suggest food amounts and ingredients based on the pet's weight and health condition. In this way, the system can collect health data of the user's pet and propose a health management plan for the user and the pet.

[0053] The health management system can also take into account the user's hobbies and interests and provide an activity plan that allows the user to manage their health while having fun. For example, if the user's hobby is dancing, dance classes and dance events can be suggested. If the user enjoys outdoor activities, hiking and cycling routes can be suggested. If the user enjoys sports, the system can suggest sports clubs and fitness gyms. In this way, the system can provide an activity plan that takes into account the user's hobbies and interests and allows the user to manage their health while having fun.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects at least one of the user's personal data, including their physique, genetic information, lifestyle habits, dietary habits, and exercise volume. For example, the data collection unit collects dietary data by having the user enter their daily dietary habits into an app, exercise data using a wearable device, and genetic information using a genetic testing kit. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, it evaluates nutritional balance and calorie intake based on the collected dietary data, evaluates exercise volume and calorie consumption based on exercise data, and predicts the risk of certain diseases based on genetic information. Step 3: The plan generation unit generates an optimal health management plan for the user based on the data analyzed by the analysis unit. For example, it considers the user's physique and genetic information to propose appropriate meal plans and exercise plans, provides a specific action plan according to the user's goals, and provides plan modifications and additional advice according to the user's health condition.

[0056] (Example 2) A health management system according to an embodiment of the present invention utilizes generative AI to propose optimal health management for individuals. This system generates and provides optimal health management plans for users by collecting and analyzing personal data such as the user's physique, genetic information, lifestyle habits, dietary habits, and exercise volume. This allows the health management system to provide optimal health management plans based on the user's personal data.

[0057] A health management system according to an embodiment includes a data collection unit, an analysis unit, and a plan generation unit. The data collection unit collects at least one personal data item from a user's physique, genetic information, lifestyle habits, dietary content, and exercise amount. For example, the data collection unit collects dietary data by having the user input daily dietary information into an app. The data collection unit may use a wearable device to collect the user's exercise data. The data collection unit may use a genetic testing kit to collect the user's genetic information. The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may evaluate nutritional balance and calorie intake based on the collected dietary data. The analysis unit may evaluate exercise amount and calorie expenditure based on the collected exercise data. The analysis unit may predict the risk of certain diseases based on the collected genetic information. The plan generation unit generates an optimal health management plan for the user based on the data analyzed by the analysis unit. For example, the plan generation unit may propose an appropriate diet plan and exercise plan taking into account the user's physique and genetic information. The plan generation unit may provide a specific action plan based on the user's goals. Furthermore, the plan generation unit can modify the plan or provide additional advice depending on the user's health condition. This allows the health management system according to the embodiment to provide an optimal health management plan based on the user's personal data. For example, the user can lose weight by continuing a healthy diet or increase muscle strength by performing appropriate exercise. Furthermore, the generation AI provides continuous feedback, allowing the user to constantly monitor their health condition and make necessary adjustments.

[0058] The data collection unit can collect the user's daily dietary details and evaluate the nutritional balance or calorie intake. The data collection unit collects dietary data, for example, by having the user enter the daily dietary details into the app. For example, the app automatically analyzes the dietary details and evaluates the nutritional balance and calorie intake. The data collection unit can also collect dietary data using image analysis technology by having the user record photos of the dietary details. For example, the image analysis technology recognizes ingredients and calculates nutrients and calories. The data collection unit can also collect dietary data by having the user scan barcodes to obtain nutritional information of foods. For example, a barcode scanner reads the nutritional information of foods and compares it with a database to evaluate the nutritional balance and calorie intake. This allows the user's daily dietary details to be evaluated and their nutritional balance and calorie intake to be understood.

[0059] The plan generation unit can propose an appropriate meal plan or exercise plan taking into account the user's physique or genetic information. The plan generation unit proposes an appropriate meal plan taking into account the user's physique, for example. For example, a meal plan is provided in which the calorie intake and nutritional balance are adjusted based on the user's height, weight, and BMI. The plan generation unit can also propose a meal plan that recommends the intake of specific nutrients taking into account the user's genetic information. For example, the intake of vitamin D and omega-3 fatty acids is recommended based on the results of a genetic test. The plan generation unit can also propose an appropriate exercise plan taking into account the user's physique and genetic information. For example, an exercise plan is provided in which the type and intensity of exercise are adjusted based on the user's muscle strength and endurance. This makes it possible to provide an optimal meal plan or exercise plan based on the user's physique and genetic information.

[0060] The analysis unit can collect the user's daily weight or blood pressure and evaluate changes in their health condition. For example, the analysis unit collects the user's daily weight and evaluates changes in their health condition. For example, the analysis unit measures the user's weight every day using a scale and analyzes the data. The analysis unit can also collect the user's daily blood pressure and evaluate changes in their health condition. For example, the analysis unit measures the user's blood pressure every day using a sphygmomanometer and analyzes the data. The analysis unit can also combine the user's weight and blood pressure data to comprehensively evaluate changes in their health condition. For example, the analysis unit can analyze weight gain / loss and blood pressure fluctuations to predict health risks. This makes it possible to evaluate the user's daily weight and blood pressure and understand changes in their health condition.

[0061] The plan generation unit can adjust the meal plan based on the user's allergy information for specific ingredients. For example, the plan generation unit adjusts the meal plan taking into account the user's allergy information for specific ingredients. For example, if the user has a nut allergy, a meal plan that does not include nuts is provided. The plan generation unit can also suggest alternative ingredients based on the user's allergy information. For example, a meal plan that uses soy milk or almond milk instead of dairy products is provided to a user who has a dairy allergy. The plan generation unit can also suggest food combinations to avoid allergic reactions based on the user's allergy information. This makes it possible to provide a safe meal plan based on the user's allergy information.

[0062] The plan generation unit can set a long-term health goal for the user and support the achievement of that goal. For example, if the user aims to lose 10 kg in one year, the plan generation unit can suggest specific steps and monitor progress. The plan generation unit can also provide support to keep the user motivated toward their health goal. For example, the plan generation unit can send regular feedback and encouraging messages. The plan generation unit can also provide plan modifications and additional advice according to the user's health goal. This makes it possible to set a long-term health goal for the user and support the achievement of that goal.

[0063] The data collection unit can estimate the user's emotional state in real time and generate a health management plan that takes into account stress levels or mood fluctuations. The data collection unit can, for example, use facial expression recognition technology to estimate the user's emotional state in real time. For example, the data collection unit can analyze the user's facial expressions through a smartphone camera to evaluate stress levels and mood fluctuations. The data collection unit can also use voice analysis technology to estimate the user's emotional state in real time. For example, the data collection unit can analyze the tone and speed of the user's voice when speaking to an app to evaluate the emotional state. The data collection unit can also use a wearable device to estimate the user's emotional state in real time. For example, the data collection unit can measure heart rate and electrodermal activity to evaluate stress levels and mood fluctuations. This allows the user's emotional state to be understood in real time and a health management plan that takes stress levels and mood fluctuations into account can be provided.

[0064] The data collection unit can predict a specific disease risk based on the user's genetic information and suggest preventive measures to address that risk. The data collection unit, for example, uses a genetic testing kit to analyze the user's genetic information and predict the specific disease risk. For example, a saliva sample is collected and genetic analysis is performed. The data collection unit can also refer to an existing genetic database to analyze the user's genetic information and predict the specific disease risk. For example, the risk can be assessed based on family history or known genetic mutations. The data collection unit can also use a machine learning algorithm to analyze the user's genetic information and predict the specific disease risk. For example, the risk can be predicted by combining genetic data and medical history data. This makes it possible to predict the disease risk based on the user's genetic information and provide appropriate preventive measures.

[0065] The data collection unit can analyze the user's lifestyle data and evaluate the user's sleep patterns or daily activity level in detail. The data collection unit, for example, uses a smartwatch to collect the user's lifestyle data. For example, it measures the sleep patterns and daily activity level and analyzes the data. The data collection unit can also use a smartphone sensor to collect the user's lifestyle data. For example, it measures the number of steps taken and the distance traveled and evaluates the daily activity level. The data collection unit can also use a dedicated app to collect the user's lifestyle data. For example, it records the contents of meals and the amount of exercise and analyzes the data. This allows the user's lifestyle data to be evaluated in detail and a precise health management plan to be provided.

[0066] The data collection unit can collect health data of the user's pet and propose a health management plan for the user and the pet. The data collection unit, for example, uses a wearable device for the pet to collect the health data of the user's pet. For example, it measures the pet's activity level and body temperature and analyzes the data. The data collection unit can also use a pet health management app to collect the health data of the user's pet. For example, it can record the pet's diet and amount of exercise and analyze the data. The data collection unit can also refer to veterinarian diagnostic data to collect the health data of the user's pet. For example, it can analyze the data based on the results of regular health checks. In this way, it is possible to collect the health data of the user's pet and propose a health management plan for the user and the pet.

[0067] The data collection unit can collect data on the user's workplace environment and provide a plan to support workplace health management. The data collection unit, for example, uses an air quality sensor in the workplace to collect the user's workplace environment data. For example, it measures temperature, humidity, and CO2 concentration and analyzes the data. The data collection unit can also use a lighting sensor in the workplace to collect the user's workplace environment data. For example, it measures illuminance and color temperature and analyzes the data. The data collection unit can also use a noise sensor in the workplace to collect the user's workplace environment data. For example, it measures noise levels and analyzes the data. In this way, it is possible to collect data on the user's workplace environment and provide a plan to support workplace health management.

[0068] The data collection unit can use the emotion estimation function to analyze the emotional state of the user when eating or exercising, and provide advice to elicit positive emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotional state of the user when eating. For example, it analyzes facial expressions and voice while eating, and provides advice to elicit positive emotions. The data collection unit can also use the emotion estimation function to analyze the emotional state of the user when exercising. For example, it analyzes facial expressions and voice while exercising, and provides advice to elicit positive emotions. The data collection unit can also use the emotion estimation function to analyze the emotional state of the user when relaxing. For example, it analyzes facial expressions and voice while relaxing, and provides advice to elicit positive emotions. In this way, the user's emotional state can be analyzed, and advice to elicit positive emotions can be provided.

[0069] The plan generation unit can generate a mental support plan for maintaining motivation by taking into account the user's emotional state. The plan generation unit can, for example, generate a mental support plan for maintaining motivation by taking into account the user's emotional state. For example, it can provide positive messages or words of encouragement. The plan generation unit can also suggest relaxation exercises or meditation by taking into account the user's emotional state. For example, it can suggest relaxation exercises or meditation. The plan generation unit can also suggest keeping an emotional diary by taking into account the user's emotional state and recording emotional fluctuations. In this way, it is possible to provide a mental support plan for maintaining motivation by taking into account the user's emotional state.

[0070] The plan generation unit can propose a supplement plan that recommends the intake of specific nutrients based on the user's genetic information. The plan generation unit, for example, analyzes the user's genetic information and proposes a supplement plan that recommends the intake of specific nutrients. For example, the plan generation unit recommends the intake of vitamin D and omega-3 fatty acids. The plan generation unit can also analyze the user's genetic information and propose a supplement plan that recommends the intake of specific nutrients. For example, the plan generation unit can recommend the intake of iron and calcium. The plan generation unit can also analyze the user's genetic information and propose a supplement plan that recommends the intake of specific nutrients. For example, the plan generation unit can recommend the intake of vitamin B12 and magnesium. This makes it possible to provide a personalized supplement plan that recommends the intake of specific nutrients based on the user's genetic information.

[0071] The plan generation unit can analyze the user's exercise data and provide a plan that adjusts the timing or intensity to maximize the effect of exercise. The plan generation unit, for example, analyzes the user's exercise data and provides a plan that adjusts the timing or intensity to maximize the effect of exercise. For example, it suggests optimal exercise duration and intensity. The plan generation unit can also analyze the user's exercise data and provide a plan that adjusts the timing and intensity to maximize the effect of exercise. For example, it suggests how many times a week the user should exercise. The plan generation unit can also analyze the user's exercise data and provide a plan that adjusts the timing and intensity to maximize the effect of exercise. For example, it suggests a combination of specific exercise types. In this way, it is possible to analyze the user's exercise data and provide a plan that adjusts the timing and intensity to maximize the effect of exercise.

[0072] The plan generation unit can integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, the plan generation unit can integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, it can propose an exercise program for the entire family. The plan generation unit can also integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, it can propose a meal plan for the entire family. The plan generation unit can also integrate the health data of all of the user's family members and propose a health management plan that the entire family will work on. For example, it can propose a health event for the entire family to participate in. In this way, it is possible to integrate the health data of all of the user's family members and provide a health management plan that the entire family will work on.

[0073] The plan generation unit can provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. The plan generation unit can, for example, provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. For example, it can suggest dance or yoga classes. The plan generation unit can also provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. For example, it can suggest hiking or cycling routes. The plan generation unit can also provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun. For example, it can suggest the use of a sports club or fitness gym. In this way, it is possible to provide an activity plan that takes into account the user's hobbies and interests, allowing the user to manage their health while having fun.

[0074] The plan generation unit can use the emotion estimation function to generate a health management plan tailored to the time period when the user is most relaxed. The plan generation unit, for example, uses the emotion estimation function to generate a health management plan tailored to the time period when the user is most relaxed. For example, meditation or stretching can be suggested to suit the time period when the user is most relaxed. The plan generation unit can also use the emotion estimation function to generate a health management plan tailored to the time period when the user is most relaxed. For example, aromatherapy can be suggested to suit the time period when the user is most relaxed. The plan generation unit can also use the emotion estimation function to generate a health management plan tailored to the time period when the user is most relaxed. For example, music therapy can be suggested to suit the time period when the user is most relaxed. This makes it possible to provide a health management plan tailored to the time period when the user is most relaxed.

[0075] The analysis unit can monitor the user's emotional state in real time and provide feedback to reduce stress or anxiety. For example, the analysis unit can monitor the user's emotional state in real time and provide feedback to reduce stress or anxiety. For example, the analysis unit can suggest relaxation exercises. The analysis unit can also monitor the user's emotional state in real time and provide feedback to reduce stress or anxiety. For example, the analysis unit can provide guidance on deep breathing or meditation. The analysis unit can also monitor the user's emotional state in real time and provide feedback to reduce stress or anxiety. For example, the analysis unit can send positive messages. This makes it possible to monitor the user's emotional state in real time and provide feedback to reduce stress or anxiety.

[0076] The analysis unit can be used to build a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected. The analysis unit, for example, can analyze a user's health data in real time and immediately issues an alert if an abnormal value is detected. For example, it can detect abnormalities in blood pressure or heart rate. The analysis unit can also be used to build a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected. For example, it can detect abnormalities in blood sugar levels or body temperature. The analysis unit can also be used to build a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected. For example, it can detect abnormalities in oxygen saturation or respiratory rate. This makes it possible to provide a system that analyzes a user's health data and immediately issues an alert if an abnormal value is detected.

[0077] The analysis unit can track changes in the user's health condition over the long term and analyze trends to predict future health risks. The analysis unit, for example, tracks changes in the user's health condition over the long term and analyzes trends to predict future health risks. For example, it analyzes long-term fluctuations in weight and blood pressure. The analysis unit can also track changes in the user's health condition over the long term and analyzes trends to predict future health risks. For example, it analyzes long-term fluctuations in blood sugar levels and cholesterol levels. The analysis unit can also track changes in the user's health condition over the long term and analyzes trends to predict future health risks. For example, it analyzes long-term fluctuations in heart rate and oxygen saturation. This makes it possible to track changes in the user's health condition over the long term and predict future health risks.

[0078] The analysis unit can monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. The analysis unit can, for example, monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. For example, the analysis unit can analyze the pet's weight and activity level. The analysis unit can also monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. For example, the analysis unit can analyze the pet's diet and exercise level. The analysis unit can also monitor the health condition of the user's pet and provide feedback for managing the health of the pet together. For example, the analysis unit can analyze the results of the pet's health check. This can monitor the health condition of the user's pet and provide feedback for managing the health of the pet together.

[0079] The analysis unit can monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. The analysis unit can, for example, monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. For example, it can analyze changes in air quality and lighting. The analysis unit can also monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. For example, it can analyze changes in noise levels and temperature. The analysis unit can also monitor changes in the user's work environment and provide advice for reducing health risks in the workplace. For example, it can analyze changes in stress levels and workload. This makes it possible to monitor changes in the user's work environment and provide advice for reducing health risks in the workplace.

[0080] The analysis unit can use the emotion estimation function to provide feedback according to the user's emotional state and support the user in maintaining positive emotions. The analysis unit can, for example, use the emotion estimation function to provide feedback according to the user's emotional state and support the user in maintaining positive emotions. For example, the analysis unit can send a positive message. The analysis unit can also use the emotion estimation function to provide feedback according to the user's emotional state and support the user in maintaining positive emotions. For example, the analysis unit can suggest relaxation exercises. The analysis unit can also use the emotion estimation function to provide feedback according to the user's emotional state and support the user in maintaining positive emotions. For example, the analysis unit can suggest keeping an emotion diary. This can provide feedback according to the user's emotional state and support the user in maintaining positive emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The health management system may further include a sleep analysis unit that collects and analyzes the user's sleep data. For example, the user wears a smartwatch while sleeping to measure and collect data on the quality and duration of sleep. The sleep analysis unit may also analyze the user's sleep patterns and provide advice to improve the quality of sleep. For example, it may suggest ways to relax before bed or an appropriate bedtime. The sleep analysis unit may also provide advice to improve daytime activity levels and concentration based on the user's sleep data. This allows for detailed analysis of the user's sleep data and provides a better health management plan.

[0083] The health management system can further include a fluid management unit that monitors the user's fluid intake. For example, the user can collect data by entering their daily fluid intake into the app. The fluid management unit can also suggest appropriate fluid intake amounts based on the user's activity level and temperature. For example, it can advise on the timing of hydration after exercise or on hot days. The fluid management unit can also predict the risk of dehydration based on the user's fluid intake and suggest preventative measures. This allows the system to manage the user's fluid intake and provide advice that is useful for maintaining health.

[0084] The health management system can further estimate the user's emotional state and provide a stress management plan based on the estimated emotion. For example, the system can analyze the user's facial expressions and voice to evaluate the user's stress level. The stress management plan can also suggest relaxation exercises or meditations depending on the user's emotional state. For example, it can suggest deep breathing or yoga sessions. The stress management plan can also provide positive messages or words of encouragement depending on the user's emotional state. This allows the system to provide a stress management plan that takes the user's emotional state into consideration.

[0085] The health management system can also provide meal plans that take into account food allergy information based on the user's dietary data. For example, if a user registers ingredients to which they are allergic in the app, those ingredients can be excluded from the meal plan. The meal plan can also suggest substitutes for ingredients to which the user is allergic. For example, a user with a dairy allergy can be offered a meal plan that uses soy milk or almond milk. The meal plan can also suggest ingredient combinations to avoid allergic reactions. This allows the system to provide safe meal plans that take into account the user's allergy information.

[0086] The health management system can also estimate the user's emotional state and adjust the exercise plan based on the estimated emotion. For example, it can analyze the user's facial expressions and voice to evaluate their motivation for exercise. The exercise plan can also adjust the type and intensity of exercise according to the user's emotional state. For example, if stress is high, it can suggest yoga, which has a relaxing effect, and if positive emotions are strong, it can suggest high-intensity training. The exercise plan can also adjust the timing of exercise according to the user's emotional state. This makes it possible to provide an exercise plan that takes the user's emotional state into account.

[0087] The health management system can also collect data on the user's workplace environment and provide a plan to support health management at the workplace. For example, it can use air quality sensors in the workplace to measure temperature, humidity, and CO2 concentration and analyze the data. It can also suggest appropriate air conditioning settings and ventilation timing based on the workplace environment data. For example, if the CO2 concentration is high, it can issue an alert to encourage ventilation. It can also suggest adjustments to lighting and noise levels based on the workplace environment data. This allows it to provide a plan to optimize the user's workplace environment and support health management.

[0088] The health management system can further estimate the user's emotional state and adjust the meal plan based on the estimated emotion. For example, the system can analyze the user's facial expressions and voice to evaluate their emotions regarding meals. The meal plan can also adjust ingredients and menus according to the user's emotional state. For example, if the user is highly stressed, the system can suggest a menu using ingredients with a relaxing effect, and if the user is feeling very positive, the system can suggest a menu suitable for replenishing energy. The meal plan can also adjust the timing of meals according to the user's emotional state. This allows the system to provide a meal plan that takes the user's emotional state into consideration.

[0089] The health management system can also collect health data of the user's pet and propose a health management plan for the user and the pet. For example, a wearable device for pets can be used to measure the pet's activity level and body temperature and analyze the data. Based on the pet's health data, the system can also propose an exercise plan for the user and the pet. For example, it can suggest walk and play times. Based on the pet's health data, the system can also propose a diet plan for the pet. For example, it can suggest food amounts and ingredients based on the pet's weight and health condition. In this way, the system can collect health data of the user's pet and propose a health management plan for the user and the pet.

[0090] The health management system can further estimate the user's emotional state and provide a relaxation plan based on the estimated emotion. For example, the system can analyze the user's facial expressions and voice to evaluate the user's level of relaxation. The relaxation plan can also suggest relaxation methods according to the user's emotional state. For example, it can suggest meditation or deep breathing if stress is high, and light stretching if positive emotions are strong. The relaxation plan can also adjust the timing of relaxation according to the user's emotional state. This makes it possible to provide a relaxation plan that takes the user's emotional state into consideration.

[0091] The health management system can also take into account the user's hobbies and interests and provide an activity plan that allows the user to manage their health while having fun. For example, if the user's hobby is dancing, dance classes and dance events can be suggested. If the user enjoys outdoor activities, hiking and cycling routes can be suggested. If the user enjoys sports, the system can suggest sports clubs and fitness gyms. In this way, the system can provide an activity plan that takes into account the user's hobbies and interests and allows the user to manage their health while having fun.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The data collection unit collects at least one of the user's personal data, including their physique, genetic information, lifestyle habits, dietary habits, and exercise volume. For example, the data collection unit collects dietary data by having the user enter their daily dietary habits into an app, exercise data using a wearable device, and genetic information using a genetic testing kit. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, it evaluates nutritional balance and calorie intake based on the collected dietary data, evaluates exercise volume and calorie consumption based on exercise data, and predicts the risk of certain diseases based on genetic information. Step 3: The plan generation unit generates an optimal health management plan for the user based on the data analyzed by the analysis unit. For example, it considers the user's physique and genetic information to propose appropriate meal plans and exercise plans, provides a specific action plan according to the user's goals, and provides plan modifications and additional advice according to the user's health condition.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 system equipped with a generative AI, a data collection unit that collects at least one of personal data of the user including physique, genetic information, lifestyle habits, dietary content, and amount of exercise; an analysis unit that analyzes the personal data collected by the data collection unit; a plan generation unit that generates an optimal health management plan for the user based on the data analyzed by the analysis unit. A system characterized by:

2. The data collection unit Collecting the user's daily dietary information and evaluating nutritional balance or calorie intake 2. The system of claim 1.

3. The plan generation unit Proposing an appropriate diet plan or exercise plan taking into account the user's physique or genetic information 2. The system of claim 1.

4. The analysis unit Collecting the user's daily weight or blood pressure to assess changes in health status 2. The system of claim 1.

5. The plan generation unit Tailoring meal plans based on the user's allergy information to specific ingredients 2. The system of claim 1.

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