System

Abstract

An object of a system according to an embodiment is to integrally manage data of medication, meals, and exercise of a user and to individually provide appropriate guidance.SOLUTION: A system according to an embodiment includes a medication-taking management unit, a dietary guidance unit, and an exercise guidance unit. The medication management unit manages a medication schedule of the user and provides a reminder for prompting medication at an appropriate timing. The dietary guidance component analyzes the user's dietary data and suggests a nutritionally balanced dietary plan. The exercise instructor analyzes the user's exercise data and suggests an effective exercise plan.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 manage a user's medication, diet, and exercise data in an integrated manner, and provide appropriate individual guidance, so there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively manage data on medication, diet, and exercise of users and to provide appropriate individual guidance. [Means for solving the problem]

[0006] The system according to the embodiment includes a medication management unit, a dietary guidance unit, and an exercise guidance unit. The medication management unit manages the user's medication schedule and provides reminders to encourage medication at appropriate times. The dietary guidance unit analyzes the user's dietary data and proposes a nutritionally balanced meal plan. The exercise guidance unit analyzes the user's exercise data and proposes an effective exercise plan. [Effects of the Invention]

[0007] The system according to the embodiment can manage data on medication, diet, and exercise of a user in an integrated manner, and provide appropriate individual guidance. [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 healthcare management system according to an embodiment of the present invention comprehensively manages a user's medication, diet, and exercise, and uses a generative AI to provide an optimal healthcare plan, thereby enabling the healthcare management system to efficiently and effectively support the user's health management.

[0029] A healthcare management system according to an embodiment includes a medication management unit, a dietary guidance unit, and an exercise guidance unit. The medication management unit manages a user's medication schedule and provides reminders to encourage medication at appropriate times. For example, if a user needs to take medication at a specific time each day, the medication management unit sends a reminder at that time. The medication management unit also keeps a record of medication and can share the record with a doctor or pharmacist. The dietary guidance unit analyzes the user's dietary data and proposes a nutritionally balanced meal plan. For example, when a user inputs a meal record, the dietary guidance unit analyzes the data and calculates the necessary nutrient and calorie intake. The dietary guidance unit also proposes a meal plan based on the user's health condition and goals. The exercise guidance unit analyzes the user's exercise data and proposes an effective exercise plan. For example, when a user inputs an exercise record, the exercise guidance unit analyzes the data and proposes an exercise plan based on the user's physical strength and goals. The exercise guidance unit also monitors the user's exercise progress and adjusts the plan as necessary. As a result, the healthcare management system according to the embodiment can comprehensively manage the user's medication, diet, and exercise, and provide an optimal healthcare plan.

[0030] The medication management unit analyzes the user's medication history and physical condition data, evaluates the effectiveness of medication in real time, and automatically adjusts the medication schedule as needed. For example, the medication management unit uses a generation AI to analyze the user's medication history and physical condition data and evaluate the effectiveness of medication in real time. For example, it monitors fluctuations in blood pressure and blood sugar levels and automatically adjusts the medication schedule if the medication is not effective enough. The medication management unit also uses a generation AI to evaluate the effectiveness of medication based on the user's medication history and physical condition data and send an alert to the doctor as needed. For example, if a side effect is suspected, the doctor will be notified and the appropriate response will be encouraged. The medication management unit also uses a generation AI to analyze the user's medication history and physical condition data and automatically generate a report to evaluate the effectiveness of medication. For example, the report can be provided to the user as a monthly report to visualize the effectiveness of medication. This allows the effectiveness of medication to be evaluated in real time and the medication schedule to be automatically adjusted as needed.

[0031] The medication management unit can learn the user's medication patterns, identify time periods when there is a high risk of forgetting to take medication, and send reminders to urge them to take medication during those times. For example, the medication management unit uses a generation AI to learn the user's medication patterns and identify time periods when there is a high risk of forgetting to take medication. For example, based on past data, it can detect that medication is often forgotten on certain days of the week or at certain times of the day. The medication management unit can also analyze the user's medication patterns and send reminders during times when there is a high risk of forgetting to take medication. For example, it can set reminders to urge them to take medication during busy morning hours. The medication management unit can also use a generation AI to learn the user's medication patterns and not only send reminders during high-risk time periods, but also customize the content of the reminders. For example, it can send messages tailored to the user's preferences. This allows it to send reminders to urge them to take medication during times when there is a high risk of forgetting to take medication.

[0032] The medication management unit can analyze the user's lifestyle rhythm and link it with meal and exercise schedules to optimize the timing of medication. For example, the generation AI in the medication management unit analyzes the user's lifestyle rhythm and optimizes the timing of medication in conjunction with meal and exercise schedules. For example, if medication needs to be taken after a meal, a reminder is sent to coincide with the meal. The medication management unit also analyzes the user's lifestyle rhythm and optimizes the timing of medication in conjunction with exercise schedules. For example, if medication needs to be taken after exercise, a reminder is sent to coincide with the end of the exercise. The medication management unit also analyzes the user's lifestyle rhythm and optimizes the timing of medication in conjunction with meal and exercise schedules, and automatically adjusts according to changes in the lifestyle rhythm. For example, the reminder is adjusted to coincide with holiday or travel schedules. This allows the timing of medication to be optimized based on the user's lifestyle rhythm.

[0033] The medication management unit can suggest effective medication patterns by anonymizing the user's medication data and comparing it with the data of other users taking the same medication. For example, the medication management unit uses a generation AI to anonymize the user's medication data and compare it with the data of other users taking the same medication. For example, it identifies effective medication patterns and suggests them to the user. The medication management unit also anonymizes the user's medication data and compares it with the data of other users to identify common side effects and effects. For example, it suggests medication patterns with fewer side effects. The medication management unit also uses a generation AI to anonymize the user's medication data and compare it with the data of other users, and it also suggests effective medication patterns based on attributes such as region, age, and gender. For example, it provides medication patterns based on the data of users in the same age group. This makes it possible to suggest effective medication patterns.

[0034] The dietary advice unit analyzes the user's dietary history and health data, detects deficiencies or excesses of specific nutrients in real time, and can instantly adjust the meal plan. For example, the generation AI analyzes the user's dietary history and health data to detect deficiencies or excesses of specific nutrients in real time. For example, it detects deficiencies of vitamins and minerals and instantly adjusts the meal plan. Furthermore, the generation AI detects deficiencies or excesses of specific nutrients based on the user's dietary history and health data, and not only adjusts the meal plan but also suggests specific ingredients and recipes. For example, if there is a calcium deficiency, it suggests ingredients that are high in calcium. Furthermore, the generation AI analyzes the user's dietary history and health data to not only detect deficiencies or excesses of specific nutrients in real time, but also identifies the causes of the deficiency and suggests remedial measures. For example, if a specific eating habit is causing a nutrient deficiency, it provides advice to improve that habit. This allows deficiencies or excesses of specific nutrients to be detected in real time and instantly adjust the meal plan.

[0035] The dietary advice unit can learn the user's taste preferences and allergy information and suggest individually customized recipes. For example, the generation AI of the dietary advice unit learns the user's taste preferences and allergy information and suggests individually customized recipes. For example, for a user who likes spicy food, it suggests recipes that emphasize spiciness. Furthermore, the dietary advice unit uses the generation AI to suggest recipes using safe ingredients based on the user's allergy information. For example, it provides recipes that do not contain nuts to a user with a nut allergy. Furthermore, the dietary advice unit uses the generation AI to learn the user's taste preferences and allergy information and not only suggests individually customized recipes, but also suggests new recipes based on the user's eating history. For example, it suggests new recipes with variations based on recipes that were popular in the past. This makes it possible to suggest recipes that are customized based on the user's taste preferences and allergy information.

[0036] The dietary advice unit can compare the user's dietary data with that of other users and propose a meal plan based on the success stories of users with the same health goals. For example, the generation AI of the dietary advice unit compares the user's dietary data with that of other users and proposes a meal plan based on the success stories of users with the same health goals. For example, for a user aiming to lose weight, the unit references the meal plans of other users who have achieved success. The dietary advice unit also analyzes the success stories of other users with the same health goals based on the user's dietary data and proposes an effective meal plan. For example, for a user aiming to build muscle, the unit provides a high-protein meal plan of other successful users. The generation AI of the dietary advice unit not only compares the user's dietary data with that of other users and proposes a meal plan based on the success stories of users with the same health goals, but also provides detailed analysis results of the success stories. For example, the unit specifically shows the eating patterns and ingredient selection methods of successful users. This allows the unit to propose a meal plan based on the success stories of users with the same health goals.

[0037] The dietary advice unit can analyze the user's dietary data and propose meal plans that utilize seasonal and local specialties. For example, the generation AI in the dietary advice unit analyzes the user's dietary data and proposes meal plans that utilize seasonal and local specialties. For example, it provides recipes that incorporate seasonal vegetables and fruits. The dietary advice unit also proposes meal plans that utilize local specialties based on the user's dietary data. For example, it introduces traditional and regional dishes using local ingredients. The dietary advice unit also analyzes the user's dietary data using the generation AI and not only proposes meal plans that utilize seasonal and local specialties, but also provides information on the nutritional value and health benefits of local specialties. For example, it explains the nutritional value and health benefits of local specialties and suggests recipes using those ingredients. This makes it possible to propose meal plans that utilize seasonal and local specialties.

[0038] The exercise coaching unit analyzes the user's exercise history and physical condition data, evaluates the effectiveness of exercise in real time, and automatically adjusts the exercise plan as needed. For example, the exercise coaching unit uses a generation AI to analyze the user's exercise history and physical condition data and evaluate the effectiveness of exercise in real time. For example, it monitors heart rate and calorie consumption and automatically adjusts the exercise plan if the exercise effect is insufficient. The exercise coaching unit also uses a generation AI to evaluate the effectiveness of exercise based on the user's exercise history and physical condition data and send alerts to doctors and trainers as needed. For example, if excessive exercise is suspected to be risky, it notifies a specialist and encourages appropriate action. The exercise coaching unit also uses a generation AI to analyze the user's exercise history and physical condition data and automatically generate a report to evaluate the effectiveness of exercise. For example, it provides the user with a weekly report to visualize the effectiveness of exercise. This allows the effectiveness of exercise to be evaluated in real time and the exercise plan to be automatically adjusted as needed.

[0039] The exercise instructor can learn the user's exercise patterns, identify the optimal time period for maximizing the effect of exercise, and send reminders to encourage exercise at those times. In the exercise instructor, for example, the generation AI learns the user's exercise patterns and identifies the optimal time period for maximizing the effect of exercise. For example, based on past data, it detects that exercise is most effective at a specific time period. The exercise instructor also analyzes the user's exercise patterns and sends reminders at the optimal time period for maximizing the effect of exercise. For example, if exercise is most effective in the morning, it sets a reminder to encourage exercise at that time period. In addition, the generation AI learns the user's exercise patterns and not only sends reminders at the optimal time period but also customizes the content of the reminder. For example, it sends a message tailored to the user's preferences. This makes it possible to send reminders to encourage exercise at the optimal time period for maximizing the effect of exercise.

[0040] The exercise coaching unit can compare the user's exercise data with that of other users and propose an exercise plan based on the success stories of users with the same goal. For example, the generation AI in the exercise coaching unit compares the user's exercise data with that of other users and proposes an exercise plan based on the success stories of users with the same goal. For example, for a user aiming to complete a marathon, the exercise coaching unit can refer to the exercise plans of other successful users. The exercise coaching unit can also analyze the success stories of other users with the same goal based on the user's exercise data and propose an effective exercise plan. For example, for a user aiming to increase muscle strength, the exercise coaching unit can provide training plans of other successful users. The generation AI in the exercise coaching unit can compare the user's exercise data with that of other users and propose an exercise plan based on the success stories of users with the same goal, as well as provide detailed analysis results of the success stories. For example, the exercise patterns and training methods of successful users can be specifically shown. This allows the exercise coaching unit to propose an exercise plan based on the success stories of users with the same goal.

[0041] The exercise instructor can analyze the user's exercise data and propose an exercise plan according to the season and weather. For example, the generation AI in the exercise instructor analyzes the user's exercise data and proposes an exercise plan according to the season and weather. For example, it recommends indoor exercise in the summer and provides an exercise plan that incorporates cold weather protection in the winter. Furthermore, the exercise instructor not only proposes an exercise plan according to the season and weather based on the user's exercise data, but also suggests a specific exercise location and time. For example, it recommends training in an indoor gym on rainy days. Furthermore, the generation AI in the exercise instructor analyzes the user's exercise data and proposes an exercise plan according to the season and weather, but also provides points to note and advice when exercising. For example, it emphasizes the importance of hydration in the summer. This makes it possible to propose an exercise plan according to the season and weather.

[0042] The health data integration management unit integrates a user's health data and analyzes correlations to detect potential health risks early. For example, the health data integration management unit uses a generating AI to integrate a user's health data and analyze correlations to detect potential health risks early. For example, it analyzes fluctuations in blood pressure, blood sugar levels, and weight to identify the risk of diabetes and high blood pressure. The health data integration management unit also uses a generating AI to analyze correlations based on the user's health data, not only detecting potential health risks early but also suggesting specific preventive measures. For example, it suggests areas for improvement in diet and exercise. The health data integration management unit also uses a generating AI to integrate a user's health data and analyze correlations to detect potential health risks early, as well as monitoring risk fluctuations in real time. For example, it can send an immediate alert if the risk increases. This allows for early detection of potential health risks.

[0043] The health data integration management unit can analyze a user's health data and suggest preventive measures tailored to their individual health condition. For example, the generation AI in the health data integration management unit analyzes the user's health data and suggests preventive measures tailored to their individual health condition. For example, for a user at high risk of heart disease, the generation AI not only suggests preventive measures tailored to their individual health condition based on the user's health data, but also provides a specific action plan. For example, it indicates the number of times a user should exercise per week. The health data integration management unit also analyzes the user's health data and suggests preventive measures tailored to their individual health condition. It also monitors the effectiveness of the preventive measures and adjusts them as necessary. For example, it reanalyzes the health data after a preventive measure is implemented and suggests a new preventive measure if the effect is insufficient. This allows the generation AI to suggest preventive measures tailored to each individual health condition.

[0044] The health data integration management unit can compare the user's health data with that of other users and propose a health management plan based on success stories of users with the same health condition. For example, the generation AI in the health data integration management unit compares the user's health data with that of other users and proposes a health management plan based on success stories of users with the same health condition. For example, it uses plans from other users who have successfully managed diabetes as a reference. The health data integration management unit can also analyze success stories of other users with the same health condition based on the user's health data and propose an effective health management plan. For example, it can provide diet and exercise plans from other users who have successfully managed high blood pressure. The generation AI in the health data integration management unit can compare the user's health data with that of other users and propose a health management plan based on success stories of users with the same health condition, as well as provide detailed analysis results of the success stories. For example, it can show the lifestyle habits and specific actions of successful users. This allows it to propose a health management plan based on success stories of users with the same health condition.

[0045] The health data integration management unit can analyze the user's health data and propose a health management plan tailored to the characteristics of the season and region. For example, the generation AI in the health data integration management unit analyzes the user's health data and proposes a health management plan tailored to the characteristics of the season and region. For example, it recommends taking vitamin D in winter and suggests heatstroke prevention measures in summer. The health data integration management unit not only proposes a health management plan tailored to the characteristics of the season and region based on the user's health data, but also provides specific action plans. For example, it shows exercise plans and meal plans for each season. The health data integration management unit also analyzes the user's health data and proposes a health management plan tailored to the characteristics of the season and region, but also provides information on local medical institutions and health facilities. For example, it introduces information on local health events and checkups. This makes it possible to propose a health management plan tailored to the characteristics of the season and region.

[0046] The personalized healthcare plan provider analyzes the user's health data and goals and continuously updates the optimal healthcare plan in real time. For example, the AI ​​generation analyzes the user's health data and goals and continuously updates the optimal healthcare plan in real time. For example, it adjusts diet and exercise plans based on fluctuations in weight and blood pressure. The personalized healthcare plan provider not only continuously updates the optimal healthcare plan in real time based on the user's health data and goals, but also provides specific action plans. For example, it presents weekly exercise menus and meal plans. The personalized healthcare plan provider not only analyzes the user's health data and goals and continuously updates the optimal healthcare plan in real time, but also monitors the effectiveness of the plan and adjusts it as necessary. For example, it reanalyzes the health data after the plan is implemented and suggests a new plan if the effectiveness is insufficient. This allows the optimal healthcare plan to be continuously updated in real time.

[0047] The personalized healthcare plan providing unit can learn the user's lifestyle and environmental data and propose an individually customized healthcare plan. For example, the generation AI of the personalized healthcare plan providing unit learns the user's lifestyle and environmental data and proposes an individually customized healthcare plan. For example, a plan incorporating relaxation techniques is provided for a user who is highly stressed at work. The personalized healthcare plan providing unit not only proposes an individually customized healthcare plan based on the user's lifestyle and environmental data, but also provides a specific action plan. For example, it shows an exercise plan that utilizes commuting time. The personalized healthcare plan providing unit not only proposes an individually customized healthcare plan based on the user's lifestyle and environmental data, but also monitors the effectiveness of the plan and adjusts it as needed. For example, the plan is automatically updated according to changes in lifestyle. This makes it possible to propose an individually customized healthcare plan.

[0048] The personalized healthcare plan providing unit can compare the user's healthcare plan with other users and propose a plan based on success stories of users with the same goal. For example, the generation AI in the personalized healthcare plan providing unit compares the user's healthcare plan with other users and proposes a plan based on success stories of users with the same goal. For example, for a user aiming to lose weight, the unit references plans of other users who have been successful. The personalized healthcare plan providing unit also analyzes success stories of other users with the same goal based on the user's healthcare plan and proposes an effective plan. For example, for a user aiming to increase muscle strength, the unit provides training plans of other users who have been successful. The personalized healthcare plan providing unit also compares the user's healthcare plan with other users and proposes a plan based on success stories of users with the same goal, and provides detailed analysis results of the success stories. For example, it shows the lifestyle habits and specific actions of successful users. This makes it possible to propose a plan based on success stories of users with the same goal.

[0049] The personalized healthcare plan providing unit can analyze the user's healthcare plan and propose a plan that suits the characteristics of the season and region. For example, the generation AI in the personalized healthcare plan providing unit analyzes the user's healthcare plan and proposes a plan that suits the characteristics of the season and region. For example, it recommends taking vitamin D in winter and suggests measures to prevent heatstroke in summer. The personalized healthcare plan providing unit not only proposes a plan that suits the characteristics of the season and region based on the user's healthcare plan, but also provides a specific action plan. For example, it shows exercise plans and meal plans for each season. The generation AI in the personalized healthcare plan providing unit analyzes the user's healthcare plan and proposes a plan that suits the characteristics of the season and region, but also provides information on local medical institutions and health facilities. For example, it introduces information on local health events and checkups. This makes it possible to propose a plan that suits the characteristics of the season and region.

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

[0051] The healthcare management system can further analyze the user's sleep data and provide advice to improve the quality of sleep. For example, based on the sleep data, it can suggest appropriate bedtimes and wake-up times for the user. It can also analyze the sleep data and provide a specific action plan to improve the quality of sleep. For example, it can suggest improving the bedroom environment or a relaxation routine. It can also analyze the user's sleep patterns based on the sleep data and provide customized advice to improve the quality of sleep. For example, it can suggest using specific music or a meditation app. In this way, it can provide advice to improve the quality of the user's sleep.

[0052] The healthcare management system can further monitor the user's stress level and provide advice for stress management. For example, based on the stress level, it can suggest relaxation methods or activities for stress relief. It can also analyze the stress level and provide a specific action plan for stress management. For example, it can suggest yoga or deep breathing exercises. It can also analyze the user's stress pattern based on the stress level and provide customized advice for stress management. For example, it can set up a routine for relaxation at specific times of the day. In this way, it can monitor the user's stress level and provide advice for stress management.

[0053] The healthcare management system can further monitor the user's water intake and provide reminders to encourage proper hydration. For example, based on the amount of water intake, it can suggest appropriate times for the user to hydrate. It can also analyze the amount of water intake and provide a specific action plan for proper hydration. For example, it can suggest drinking water at specific times during the day. It can also analyze the user's hydration patterns based on the amount of water intake and provide customized advice for proper hydration. For example, it can suggest drinking water after exercise or before meals. In this way, it is possible to monitor the user's water intake and provide reminders to encourage proper hydration.

[0054] The healthcare management system can further analyze the user's exercise data and provide advice to maximize the effects of exercise. For example, based on the exercise data, it can suggest the type and intensity of exercise that is suitable for the user. It can also analyze the exercise data and provide a specific action plan to maximize the effects of exercise. For example, it can indicate how many times a week the user should exercise. It can also analyze the user's exercise patterns based on the exercise data and provide customized advice to maximize the effects of exercise. For example, it can suggest exercising at a specific time of day. In this way, it is possible to analyze the user's exercise data and provide advice to maximize the effects of exercise.

[0055] The healthcare management system can further analyze the user's dietary data and provide advice to improve diet quality. For example, based on the dietary data, it can suggest ingredients and recipes that are suitable for the user. It can also analyze the dietary data and provide a specific action plan to improve diet quality. For example, it can present a nutritionally balanced diet plan. It can also analyze the user's dietary patterns based on the dietary data and provide customized advice to improve diet quality. For example, it can suggest incorporating specific ingredients. In this way, it is possible to analyze the user's dietary data and provide advice to improve diet quality.

[0056] The healthcare management system can further analyze the user's health data and provide advice for early detection of health risks. For example, based on the health data, it can suggest preventive measures and health management methods suitable for the user. It can also analyze the health data and provide a specific action plan for early detection of health risks. For example, it can recommend regular health checks or specific health habits. It can also analyze the user's health patterns based on the health data and provide customized advice for early detection of health risks. For example, it can suggest consulting a doctor if certain symptoms appear. In this way, it is possible to analyze the user's health data and provide advice for early detection of health risks.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The medication management unit manages the user's medication schedule and provides reminders to take medication at the appropriate time. For example, if a user needs to take their medication at a specific time every day, a reminder will be sent at that time. The medication management unit also keeps a record of medication use and can share this information with doctors and pharmacists. Step 2: The dietary advice unit analyzes the user's dietary data and proposes a nutritionally balanced meal plan. For example, when the user enters a meal record, the data is analyzed and the necessary nutrients and calorie intake are calculated. The dietary advice unit also proposes a meal plan based on the user's health condition and goals. Step 3: The exercise coaching unit analyzes the user's exercise data and proposes an effective exercise plan. For example, when the user inputs their exercise record, the data is analyzed and an exercise plan is proposed based on the user's physical strength and goals. The exercise coaching unit also monitors the user's exercise progress and adjusts the plan as necessary.

[0059] (Example 2) A healthcare management system according to an embodiment of the present invention comprehensively manages a user's medication, diet, and exercise, and uses a generative AI to provide an optimal healthcare plan, thereby enabling the healthcare management system to efficiently and effectively support the user's health management.

[0060] A healthcare management system according to an embodiment includes a medication management unit, a dietary guidance unit, and an exercise guidance unit. The medication management unit manages a user's medication schedule and provides reminders to encourage medication at appropriate times. For example, if a user needs to take medication at a specific time each day, the medication management unit sends a reminder at that time. The medication management unit also keeps a record of medication and can share the record with a doctor or pharmacist. The dietary guidance unit analyzes the user's dietary data and proposes a nutritionally balanced meal plan. For example, when a user inputs a meal record, the dietary guidance unit analyzes the data and calculates the necessary nutrient and calorie intake. The dietary guidance unit also proposes a meal plan based on the user's health condition and goals. The exercise guidance unit analyzes the user's exercise data and proposes an effective exercise plan. For example, when a user inputs an exercise record, the exercise guidance unit analyzes the data and proposes an exercise plan based on the user's physical strength and goals. The exercise guidance unit also monitors the user's exercise progress and adjusts the plan as necessary. As a result, the healthcare management system according to the embodiment can comprehensively manage the user's medication, diet, and exercise, and provide an optimal healthcare plan.

[0061] The medication management unit analyzes the user's medication history and physical condition data, evaluates the effectiveness of medication in real time, and automatically adjusts the medication schedule as needed. For example, the medication management unit uses a generation AI to analyze the user's medication history and physical condition data and evaluate the effectiveness of medication in real time. For example, it monitors fluctuations in blood pressure and blood sugar levels and automatically adjusts the medication schedule if the medication is not effective enough. The medication management unit also uses a generation AI to evaluate the effectiveness of medication based on the user's medication history and physical condition data and send an alert to the doctor as needed. For example, if a side effect is suspected, the doctor will be notified and the appropriate response will be encouraged. The medication management unit also uses a generation AI to analyze the user's medication history and physical condition data and automatically generate a report to evaluate the effectiveness of medication. For example, the report can be provided to the user as a monthly report to visualize the effectiveness of medication. This allows the effectiveness of medication to be evaluated in real time and the medication schedule to be automatically adjusted as needed.

[0062] The medication management unit can learn the user's medication patterns, identify time periods when there is a high risk of forgetting to take medication, and send reminders to urge them to take medication during those times. For example, the medication management unit uses a generation AI to learn the user's medication patterns and identify time periods when there is a high risk of forgetting to take medication. For example, based on past data, it can detect that medication is often forgotten on certain days of the week or at certain times of the day. The medication management unit can also analyze the user's medication patterns and send reminders during times when there is a high risk of forgetting to take medication. For example, it can set reminders to urge them to take medication during busy morning hours. The medication management unit can also use a generation AI to learn the user's medication patterns and not only send reminders during high-risk time periods, but also customize the content of the reminders. For example, it can send messages tailored to the user's preferences. This allows it to send reminders to urge them to take medication during times when there is a high risk of forgetting to take medication.

[0063] The medication management unit can use the emotion estimation function to analyze the user's emotional state and provide advice on how to relax when stress or anxiety is high, thereby supporting continued medication intake. The medication management unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide advice on how to relax when stress or anxiety is high. For example, it can suggest deep breathing or meditation. The medication management unit can also analyze the user's emotional state and not only provide advice on how to relax when stress or anxiety is high, but also send messages to reaffirm the importance of taking medication. For example, it can explain the impact of continuing to take medication on health. The medication management unit can also use the emotion estimation function to analyze the user's emotional state and not only provide advice on how to relax when stress or anxiety is high, but also suggest music or videos with a relaxing effect. For example, it can play relaxing music. This can provide advice on how to relax when stress or anxiety is high, thereby supporting continued medication intake.

[0064] The medication management unit can analyze the user's lifestyle rhythm and link it with meal and exercise schedules to optimize the timing of medication. For example, the generation AI in the medication management unit analyzes the user's lifestyle rhythm and optimizes the timing of medication in conjunction with meal and exercise schedules. For example, if medication needs to be taken after a meal, a reminder is sent to coincide with the meal. The medication management unit also analyzes the user's lifestyle rhythm and optimizes the timing of medication in conjunction with exercise schedules. For example, if medication needs to be taken after exercise, a reminder is sent to coincide with the end of the exercise. The medication management unit also analyzes the user's lifestyle rhythm and optimizes the timing of medication in conjunction with meal and exercise schedules, and automatically adjusts according to changes in the lifestyle rhythm. For example, the reminder is adjusted to coincide with holiday or travel schedules. This allows the timing of medication to be optimized based on the user's lifestyle rhythm.

[0065] The medication management unit can suggest effective medication patterns by anonymizing the user's medication data and comparing it with the data of other users taking the same medication. For example, the medication management unit uses a generation AI to anonymize the user's medication data and compare it with the data of other users taking the same medication. For example, it identifies effective medication patterns and suggests them to the user. The medication management unit also anonymizes the user's medication data and compares it with the data of other users to identify common side effects and effects. For example, it suggests medication patterns with fewer side effects. The medication management unit also uses a generation AI to anonymize the user's medication data and compare it with the data of other users, and it also suggests effective medication patterns based on attributes such as region, age, and gender. For example, it provides medication patterns based on the data of users in the same age group. This makes it possible to suggest effective medication patterns.

[0066] The medication management unit can use the emotion estimation function to introduce messages and a reward system that motivate the user so that they have positive feelings about taking their medication. For example, the medication management unit can use the emotion estimation function to send messages that motivate the user so that they have positive feelings about taking their medication. For example, the medication management unit can introduce words of encouragement or success stories. The medication management unit can also analyze the user's emotional state and introduce a reward system that motivates the user so that they have positive feelings about taking their medication. For example, a system can be provided in which points can be earned by continuing to take medication and exchanged for rewards. The medication management unit can also use the emotion estimation function to introduce messages and a reward system that motivate the user so that they have positive feelings about taking their medication, as well as send customized messages tailored to the user's preferences. For example, the medication management unit can send messages using the user's favorite character. This makes it possible to introduce messages and a reward system that motivate the user so that they have positive feelings about taking their medication.

[0067] The dietary advice unit analyzes the user's dietary history and health data, detects deficiencies or excesses of specific nutrients in real time, and can instantly adjust the meal plan. For example, the generation AI analyzes the user's dietary history and health data to detect deficiencies or excesses of specific nutrients in real time. For example, it detects deficiencies of vitamins and minerals and instantly adjusts the meal plan. Furthermore, the generation AI detects deficiencies or excesses of specific nutrients based on the user's dietary history and health data, and not only adjusts the meal plan but also suggests specific ingredients and recipes. For example, if there is a calcium deficiency, it suggests ingredients that are high in calcium. Furthermore, the generation AI analyzes the user's dietary history and health data to not only detect deficiencies or excesses of specific nutrients in real time, but also identifies the causes of the deficiency and suggests remedial measures. For example, if a specific eating habit is causing a nutrient deficiency, it provides advice to improve that habit. This allows deficiencies or excesses of specific nutrients to be detected in real time and instantly adjust the meal plan.

[0068] The dietary advice unit can learn the user's taste preferences and allergy information and suggest individually customized recipes. For example, the generation AI of the dietary advice unit learns the user's taste preferences and allergy information and suggests individually customized recipes. For example, for a user who likes spicy food, it suggests recipes that emphasize spiciness. Furthermore, the dietary advice unit uses the generation AI to suggest recipes using safe ingredients based on the user's allergy information. For example, it provides recipes that do not contain nuts to a user with a nut allergy. Furthermore, the dietary advice unit uses the generation AI to learn the user's taste preferences and allergy information and not only suggests individually customized recipes, but also suggests new recipes based on the user's eating history. For example, it suggests new recipes with variations based on recipes that were popular in the past. This makes it possible to suggest recipes that are customized based on the user's taste preferences and allergy information.

[0069] The dietary advice unit can use the emotion estimation function to analyze the user's emotions toward a meal and provide advice to increase meal satisfaction. The dietary advice unit, for example, uses the emotion estimation function to analyze the user's emotions toward a meal in real time and provide advice to increase satisfaction. For example, it makes suggestions to create a relaxing environment while eating. The dietary advice unit also analyzes the user's emotions toward a meal and provides specific advice to increase satisfaction. For example, it makes suggestions to improve the presentation and plating of the meal. The dietary advice unit also uses the emotion estimation function to analyze the user's emotions toward a meal and not only provides advice to increase satisfaction, but also suggests entertainment elements to elicit positive emotions while eating. For example, it provides music and videos that can be enjoyed while eating. In this way, the user's emotions toward a meal can be analyzed and advice to increase meal satisfaction can be provided.

[0070] The dietary advice unit can compare the user's dietary data with that of other users and propose a meal plan based on the success stories of users with the same health goals. For example, the generation AI of the dietary advice unit compares the user's dietary data with that of other users and proposes a meal plan based on the success stories of users with the same health goals. For example, for a user aiming to lose weight, the unit references the meal plans of other users who have achieved success. The dietary advice unit also analyzes the success stories of other users with the same health goals based on the user's dietary data and proposes an effective meal plan. For example, for a user aiming to build muscle, the unit provides a high-protein meal plan of other successful users. The generation AI of the dietary advice unit not only compares the user's dietary data with that of other users and proposes a meal plan based on the success stories of users with the same health goals, but also provides detailed analysis results of the success stories. For example, the unit specifically shows the eating patterns and ingredient selection methods of successful users. This allows the unit to propose a meal plan based on the success stories of users with the same health goals.

[0071] The dietary advice unit can analyze the user's dietary data and propose meal plans that utilize seasonal and local specialties. For example, the generation AI in the dietary advice unit analyzes the user's dietary data and proposes meal plans that utilize seasonal and local specialties. For example, it provides recipes that incorporate seasonal vegetables and fruits. The dietary advice unit also proposes meal plans that utilize local specialties based on the user's dietary data. For example, it introduces traditional and regional dishes using local ingredients. The dietary advice unit also analyzes the user's dietary data using the generation AI and not only proposes meal plans that utilize seasonal and local specialties, but also provides information on the nutritional value and health benefits of local specialties. For example, it explains the nutritional value and health benefits of local specialties and suggests recipes using those ingredients. This makes it possible to propose meal plans that utilize seasonal and local specialties.

[0072] The dietary advice unit can use the emotion estimation function to introduce entertainment elements to enhance the enjoyment of meals so that the user will have positive feelings about meals. The dietary advice unit, for example, uses the emotion estimation function to introduce entertainment elements to enhance the enjoyment of meals so that the user will have positive feelings about meals. For example, it provides quizzes and games that can be enjoyed while eating. The dietary advice unit also analyzes the user's emotional state and suggests entertainment elements to enhance the enjoyment of meals so that the user will have positive feelings about meals. For example, it provides enjoyable videos and music that can be viewed while eating. The dietary advice unit not only uses the emotion estimation function to introduce entertainment elements to enhance the enjoyment of meals so that the user will have positive feelings about meals, but also provides customized entertainment tailored to the user's preferences. For example, it provides content that incorporates the user's favorite themes and characters. This makes it possible to introduce entertainment elements to enhance the enjoyment of meals so that the user will have positive feelings about meals.

[0073] The exercise coaching unit analyzes the user's exercise history and physical condition data, evaluates the effectiveness of exercise in real time, and automatically adjusts the exercise plan as needed. For example, the exercise coaching unit uses a generation AI to analyze the user's exercise history and physical condition data and evaluate the effectiveness of exercise in real time. For example, it monitors heart rate and calorie consumption and automatically adjusts the exercise plan if the exercise effect is insufficient. The exercise coaching unit also uses a generation AI to evaluate the effectiveness of exercise based on the user's exercise history and physical condition data and send alerts to doctors and trainers as needed. For example, if excessive exercise is suspected to be risky, it notifies a specialist and encourages appropriate action. The exercise coaching unit also uses a generation AI to analyze the user's exercise history and physical condition data and automatically generate a report to evaluate the effectiveness of exercise. For example, it provides the user with a weekly report to visualize the effectiveness of exercise. This allows the effectiveness of exercise to be evaluated in real time and the exercise plan to be automatically adjusted as needed.

[0074] The exercise instructor can learn the user's exercise patterns, identify the optimal time period for maximizing the effect of exercise, and send reminders to encourage exercise at those times. In the exercise instructor, for example, the generation AI learns the user's exercise patterns and identifies the optimal time period for maximizing the effect of exercise. For example, based on past data, it detects that exercise is most effective at a specific time period. The exercise instructor also analyzes the user's exercise patterns and sends reminders at the optimal time period for maximizing the effect of exercise. For example, if exercise is most effective in the morning, it sets a reminder to encourage exercise at that time period. In addition, the generation AI learns the user's exercise patterns and not only sends reminders at the optimal time period but also customizes the content of the reminder. For example, it sends a message tailored to the user's preferences. This makes it possible to send reminders to encourage exercise at the optimal time period for maximizing the effect of exercise.

[0075] The exercise instructor can use the emotion estimation function to analyze the user's emotional state and send encouraging messages when motivation is low, thereby supporting the user's continued exercise. The exercise instructor, for example, can use the emotion estimation function to analyze the user's emotional state in real time and send encouraging messages when motivation is low. For example, the exercise instructor can introduce positive words or success stories. The exercise instructor can also analyze the user's emotional state and not only send encouraging messages when motivation is low, but also suggest specific action plans. For example, the exercise instructor can provide effective exercise menus in a short amount of time. The exercise instructor can also analyze the user's emotional state using the emotion estimation function and not only send encouraging messages when motivation is low, but also send customized messages tailored to the user's preferences. For example, the exercise instructor can quote famous quotes from the user's favorite athletes. This allows the exercise instructor to send encouraging messages when motivation is low, thereby supporting the user's continued exercise.

[0076] The exercise coaching unit can compare the user's exercise data with that of other users and propose an exercise plan based on the success stories of users with the same goal. For example, the generation AI in the exercise coaching unit compares the user's exercise data with that of other users and proposes an exercise plan based on the success stories of users with the same goal. For example, for a user aiming to complete a marathon, the exercise coaching unit can refer to the exercise plans of other successful users. The exercise coaching unit can also analyze the success stories of other users with the same goal based on the user's exercise data and propose an effective exercise plan. For example, for a user aiming to increase muscle strength, the exercise coaching unit can provide training plans of other successful users. The generation AI in the exercise coaching unit can compare the user's exercise data with that of other users and propose an exercise plan based on the success stories of users with the same goal, as well as provide detailed analysis results of the success stories. For example, the exercise patterns and training methods of successful users can be specifically shown. This allows the exercise coaching unit to propose an exercise plan based on the success stories of users with the same goal.

[0077] The exercise instructor can analyze the user's exercise data and propose an exercise plan according to the season and weather. For example, the generation AI in the exercise instructor analyzes the user's exercise data and proposes an exercise plan according to the season and weather. For example, it recommends indoor exercise in the summer and provides an exercise plan that incorporates cold weather protection in the winter. Furthermore, the exercise instructor not only proposes an exercise plan according to the season and weather based on the user's exercise data, but also suggests a specific exercise location and time. For example, it recommends training in an indoor gym on rainy days. Furthermore, the generation AI in the exercise instructor analyzes the user's exercise data and proposes an exercise plan according to the season and weather, but also provides points to note and advice when exercising. For example, it emphasizes the importance of hydration in the summer. This makes it possible to propose an exercise plan according to the season and weather.

[0078] The exercise instructor can use the emotion estimation function to introduce game elements to enhance the enjoyment of exercise so that the user will have positive feelings toward exercise. The exercise instructor, for example, uses the emotion estimation function to introduce game elements to enhance the enjoyment of exercise so that the user will have positive feelings toward exercise. For example, a system is provided in which points are earned according to the degree of exercise achievement and rankings are competed for. The exercise instructor also analyzes the user's emotional state and suggests game elements to enhance the enjoyment of exercise so that the user will have positive feelings toward exercise. For example, mini-games and challenges that can be enjoyed while exercising are provided. The exercise instructor not only uses the emotion estimation function to introduce game elements to enhance the enjoyment of exercise so that the user will have positive feelings toward exercise, but also provides customized games tailored to the user's preferences. For example, a game incorporating the user's favorite character or theme is provided. This makes it possible to introduce game elements to enhance the enjoyment of exercise so that the user will have positive feelings toward exercise.

[0079] The health data integration management unit integrates a user's health data and analyzes correlations to detect potential health risks early. For example, the health data integration management unit uses a generating AI to integrate a user's health data and analyze correlations to detect potential health risks early. For example, it analyzes fluctuations in blood pressure, blood sugar levels, and weight to identify the risk of diabetes and high blood pressure. The health data integration management unit also uses a generating AI to analyze correlations based on the user's health data, not only detecting potential health risks early but also suggesting specific preventive measures. For example, it suggests areas for improvement in diet and exercise. The health data integration management unit also uses a generating AI to integrate a user's health data and analyze correlations to detect potential health risks early, as well as monitoring risk fluctuations in real time. For example, it can send an immediate alert if the risk increases. This allows for early detection of potential health risks.

[0080] The health data integration management unit can analyze a user's health data and suggest preventive measures tailored to their individual health condition. For example, the generation AI in the health data integration management unit analyzes the user's health data and suggests preventive measures tailored to their individual health condition. For example, for a user at high risk of heart disease, the generation AI not only suggests preventive measures tailored to their individual health condition based on the user's health data, but also provides a specific action plan. For example, it indicates the number of times a user should exercise per week. The health data integration management unit also analyzes the user's health data and suggests preventive measures tailored to their individual health condition. It also monitors the effectiveness of the preventive measures and adjusts them as necessary. For example, it reanalyzes the health data after a preventive measure is implemented and suggests a new preventive measure if the effect is insufficient. This allows the generation AI to suggest preventive measures tailored to each individual health condition.

[0081] The health data integration management unit can use the emotion estimation function to integrate the user's emotional state with health data and evaluate the impact of emotional fluctuations on health. For example, the health data integration management unit can use the emotion estimation function to integrate the user's emotional state with health data and evaluate the impact of emotional fluctuations on health. For example, it can analyze the impact of stress on blood pressure and heart rate. The health data integration management unit can also analyze the user's emotional state and integrate it with health data to not only evaluate the impact of emotional fluctuations on health but also suggest specific countermeasures. For example, it can provide relaxation methods for stress management. The health data integration management unit can also use the emotion estimation function to integrate the user's emotional state with health data and not only evaluate the impact of emotional fluctuations on health but also visualize the impact of emotional fluctuations on health. For example, it can display a graph showing the correlation between emotional fluctuations and health data. This allows the impact of emotional fluctuations on health to be evaluated.

[0082] The health data integration management unit can compare the user's health data with that of other users and propose a health management plan based on success stories of users with the same health condition. For example, the generation AI in the health data integration management unit compares the user's health data with that of other users and proposes a health management plan based on success stories of users with the same health condition. For example, it uses plans from other users who have successfully managed diabetes as a reference. The health data integration management unit can also analyze success stories of other users with the same health condition based on the user's health data and propose an effective health management plan. For example, it can provide diet and exercise plans from other users who have successfully managed high blood pressure. The generation AI in the health data integration management unit can compare the user's health data with that of other users and propose a health management plan based on success stories of users with the same health condition, as well as provide detailed analysis results of the success stories. For example, it can show the lifestyle habits and specific actions of successful users. This allows it to propose a health management plan based on success stories of users with the same health condition.

[0083] The health data integration management unit can analyze the user's health data and propose a health management plan tailored to the characteristics of the season and region. For example, the generation AI in the health data integration management unit analyzes the user's health data and proposes a health management plan tailored to the characteristics of the season and region. For example, it recommends taking vitamin D in winter and suggests heatstroke prevention measures in summer. The health data integration management unit not only proposes a health management plan tailored to the characteristics of the season and region based on the user's health data, but also provides specific action plans. For example, it shows exercise plans and meal plans for each season. The health data integration management unit also analyzes the user's health data and proposes a health management plan tailored to the characteristics of the season and region, but also provides information on local medical institutions and health facilities. For example, it introduces information on local health events and checkups. This makes it possible to propose a health management plan tailored to the characteristics of the season and region.

[0084] The health data integrated management unit can use the emotion estimation function to introduce entertainment elements to enhance the enjoyment of health management so that the user will have positive feelings toward health management. For example, the health data integrated management unit uses the emotion estimation function to introduce entertainment elements to enhance the enjoyment of health management so that the user will have positive feelings toward health management. For example, a system is provided in which points are earned according to the degree of health management achievement and rankings are competed for. The health data integrated management unit also analyzes the user's emotional state and suggests entertainment elements to enhance the enjoyment of health management so that the user will have positive feelings toward health management. For example, mini-games and challenges that can be enjoyed during health management are provided. The health data integrated management unit not only uses the emotion estimation function to introduce entertainment elements to enhance the enjoyment of health management so that the user will have positive feelings toward health management, but also provides customized entertainment tailored to the user's preferences. For example, content incorporating the user's favorite themes and characters is provided. This makes it possible to introduce entertainment elements to enhance the enjoyment of health management so that the user will have positive feelings toward health management.

[0085] The personalized healthcare plan provider analyzes the user's health data and goals and continuously updates the optimal healthcare plan in real time. For example, the AI ​​generation analyzes the user's health data and goals and continuously updates the optimal healthcare plan in real time. For example, it adjusts diet and exercise plans based on fluctuations in weight and blood pressure. The personalized healthcare plan provider not only continuously updates the optimal healthcare plan in real time based on the user's health data and goals, but also provides specific action plans. For example, it presents weekly exercise menus and meal plans. The personalized healthcare plan provider not only analyzes the user's health data and goals and continuously updates the optimal healthcare plan in real time, but also monitors the effectiveness of the plan and adjusts it as necessary. For example, it reanalyzes the health data after the plan is implemented and suggests a new plan if the effectiveness is insufficient. This allows the optimal healthcare plan to be continuously updated in real time.

[0086] The personalized healthcare plan providing unit can learn the user's lifestyle and environmental data and propose an individually customized healthcare plan. For example, the generation AI of the personalized healthcare plan providing unit learns the user's lifestyle and environmental data and proposes an individually customized healthcare plan. For example, a plan incorporating relaxation techniques is provided for a user who is highly stressed at work. The personalized healthcare plan providing unit not only proposes an individually customized healthcare plan based on the user's lifestyle and environmental data, but also provides a specific action plan. For example, it shows an exercise plan that utilizes commuting time. The personalized healthcare plan providing unit not only proposes an individually customized healthcare plan based on the user's lifestyle and environmental data, but also monitors the effectiveness of the plan and adjusts it as needed. For example, the plan is automatically updated according to changes in lifestyle. This makes it possible to propose an individually customized healthcare plan.

[0087] The personalized healthcare plan providing unit can use the emotion estimation function to analyze the user's emotional state and provide an action plan to increase motivation. For example, the personalized healthcare plan providing unit can use the emotion estimation function to analyze the user's emotional state in real time and provide an action plan to increase motivation. For example, it can introduce positive words and success stories. The personalized healthcare plan providing unit can also analyze the user's emotional state and provide a specific action plan to increase motivation. For example, it can set goals that can be achieved in a short period of time, allowing the user to feel a sense of accomplishment. The personalized healthcare plan providing unit can also use the emotion estimation function to analyze the user's emotional state and provide an action plan to increase motivation, as well as provide a customized plan tailored to the user's preferences. For example, it can provide a plan that incorporates the user's favorite activities and hobbies. This makes it possible to provide an action plan to increase motivation.

[0088] The personalized healthcare plan providing unit can compare the user's healthcare plan with other users and propose a plan based on success stories of users with the same goal. For example, the generation AI in the personalized healthcare plan providing unit compares the user's healthcare plan with other users and proposes a plan based on success stories of users with the same goal. For example, for a user aiming to lose weight, the unit references plans of other users who have been successful. The personalized healthcare plan providing unit also analyzes success stories of other users with the same goal based on the user's healthcare plan and proposes an effective plan. For example, for a user aiming to increase muscle strength, the unit provides training plans of other users who have been successful. The personalized healthcare plan providing unit also compares the user's healthcare plan with other users and proposes a plan based on success stories of users with the same goal, and provides detailed analysis results of the success stories. For example, it shows the lifestyle habits and specific actions of successful users. This makes it possible to propose a plan based on success stories of users with the same goal.

[0089] The personalized healthcare plan providing unit can analyze the user's healthcare plan and propose a plan that suits the characteristics of the season and region. For example, the generation AI in the personalized healthcare plan providing unit analyzes the user's healthcare plan and proposes a plan that suits the characteristics of the season and region. For example, it recommends taking vitamin D in winter and suggests measures to prevent heatstroke in summer. The personalized healthcare plan providing unit not only proposes a plan that suits the characteristics of the season and region based on the user's healthcare plan, but also provides a specific action plan. For example, it shows exercise plans and meal plans for each season. The generation AI in the personalized healthcare plan providing unit analyzes the user's healthcare plan and proposes a plan that suits the characteristics of the season and region, but also provides information on local medical institutions and health facilities. For example, it introduces information on local health events and checkups. This makes it possible to propose a plan that suits the characteristics of the season and region.

[0090] The personalized healthcare plan providing unit can use the emotion estimation function to introduce entertainment elements to enhance the enjoyment of the plan so that the user will have positive feelings toward the healthcare plan. The personalized healthcare plan providing unit, for example, uses the emotion estimation function to introduce entertainment elements to enhance the enjoyment of the plan so that the user will have positive feelings toward the healthcare plan. For example, a system is provided in which points are earned according to the degree of plan completion and rankings are competed for. The personalized healthcare plan providing unit also analyzes the user's emotional state and suggests entertainment elements to enhance the enjoyment of the plan so that the user will have positive feelings toward the healthcare plan. For example, mini-games and challenges that can be enjoyed while planning are provided. The personalized healthcare plan providing unit not only uses the emotion estimation function to introduce entertainment elements to enhance the enjoyment of the plan so that the user will have positive feelings toward the healthcare plan, but also provides customized entertainment tailored to the user's preferences. For example, it provides content incorporating the user's favorite themes and characters. This makes it possible to introduce entertainment elements to enhance the enjoyment of the plan so that the user will have positive feelings toward the healthcare plan.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The healthcare management system can further analyze the user's sleep data and provide advice to improve the quality of sleep. For example, based on the sleep data, it can suggest appropriate bedtimes and wake-up times for the user. It can also analyze the sleep data and provide a specific action plan to improve the quality of sleep. For example, it can suggest improving the bedroom environment or a relaxation routine. It can also analyze the user's sleep patterns based on the sleep data and provide customized advice to improve the quality of sleep. For example, it can suggest using specific music or a meditation app. In this way, it can provide advice to improve the quality of the user's sleep.

[0093] The healthcare management system can further monitor the user's stress level and provide advice for stress management. For example, based on the stress level, it can suggest relaxation methods or activities for stress relief. It can also analyze the stress level and provide a specific action plan for stress management. For example, it can suggest yoga or deep breathing exercises. It can also analyze the user's stress pattern based on the stress level and provide customized advice for stress management. For example, it can set up a routine for relaxation at specific times of the day. In this way, it can monitor the user's stress level and provide advice for stress management.

[0094] The healthcare management system can further monitor the user's water intake and provide reminders to encourage proper hydration. For example, based on the amount of water intake, it can suggest appropriate times for the user to hydrate. It can also analyze the amount of water intake and provide a specific action plan for proper hydration. For example, it can suggest drinking water at specific times during the day. It can also analyze the user's hydration patterns based on the amount of water intake and provide customized advice for proper hydration. For example, it can suggest drinking water after exercise or before meals. In this way, it is possible to monitor the user's water intake and provide reminders to encourage proper hydration.

[0095] The healthcare management system can also analyze the user's emotional state and provide a healthcare plan according to emotional fluctuations. For example, based on the emotional state, it can suggest relaxation activities or methods for relieving stress. It can also analyze the emotional state and provide a specific action plan according to emotional fluctuations. For example, it can suggest relaxation methods when the user is emotionally unstable. It can also analyze the user's emotional patterns based on the emotional state and provide customized advice according to emotional fluctuations. For example, it can set up a routine for relaxing at a specific time of day. In this way, it can analyze the user's emotional state and provide a healthcare plan according to emotional fluctuations.

[0096] The healthcare management system can further analyze the user's exercise data and provide advice to maximize the effects of exercise. For example, based on the exercise data, it can suggest the type and intensity of exercise that is suitable for the user. It can also analyze the exercise data and provide a specific action plan to maximize the effects of exercise. For example, it can indicate how many times a week the user should exercise. It can also analyze the user's exercise patterns based on the exercise data and provide customized advice to maximize the effects of exercise. For example, it can suggest exercising at a specific time of day. In this way, it is possible to analyze the user's exercise data and provide advice to maximize the effects of exercise.

[0097] The healthcare management system can further analyze the user's dietary data and provide advice to improve diet quality. For example, based on the dietary data, it can suggest ingredients and recipes that are suitable for the user. It can also analyze the dietary data and provide a specific action plan to improve diet quality. For example, it can present a nutritionally balanced diet plan. It can also analyze the user's dietary patterns based on the dietary data and provide customized advice to improve diet quality. For example, it can suggest incorporating specific ingredients. In this way, it is possible to analyze the user's dietary data and provide advice to improve diet quality.

[0098] The healthcare management system can also analyze the user's emotional state and provide an exercise plan according to emotional fluctuations. For example, based on the emotional state, it can suggest relaxation exercises or stress relief activities. It can also analyze the emotional state and provide a specific exercise plan according to emotional fluctuations. For example, it can suggest yoga or meditation when the user is emotionally unstable. It can also analyze the user's emotional patterns based on the emotional state and provide a customized exercise plan according to emotional fluctuations. For example, it can suggest doing relaxation exercises at specific times of the day. In this way, it is possible to analyze the user's emotional state and provide an exercise plan according to emotional fluctuations.

[0099] The healthcare management system can further analyze the user's health data and provide advice for early detection of health risks. For example, based on the health data, it can suggest preventive measures and health management methods suitable for the user. It can also analyze the health data and provide a specific action plan for early detection of health risks. For example, it can recommend regular health checks or specific health habits. It can also analyze the user's health patterns based on the health data and provide customized advice for early detection of health risks. For example, it can suggest consulting a doctor if certain symptoms appear. In this way, it is possible to analyze the user's health data and provide advice for early detection of health risks.

[0100] The healthcare management system can further analyze the user's emotional state and provide a meal plan according to the emotional fluctuations. For example, based on the emotional state, it can suggest ingredients for relaxation or recipes for stress relief. It can also analyze the emotional state and provide a specific meal plan according to the emotional fluctuations. For example, it can suggest incorporating ingredients that have a relaxing effect when you are emotionally unstable. It can also analyze the user's emotional patterns based on the emotional state and provide a customized meal plan according to the emotional fluctuations. For example, it can suggest meals that are relaxing at specific times of the day. In this way, it is possible to analyze the user's emotional state and provide a meal plan according to the emotional fluctuations.

[0101] The healthcare management system can further analyze the user's emotional state and provide a healthcare management plan according to the emotional fluctuations. For example, based on the emotional state, it can suggest relaxation activities or methods for relieving stress. It can also analyze the emotional state and provide a specific healthcare management plan according to the emotional fluctuations. For example, it can suggest relaxation methods when the user is emotionally unstable. It can also analyze the user's emotional patterns based on the emotional state and provide a customized healthcare management plan according to the emotional fluctuations. For example, it can set a routine for relaxing at a specific time of day. In this way, it can analyze the user's emotional state and provide a healthcare management plan according to the emotional fluctuations.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The medication management unit manages the user's medication schedule and provides reminders to take medication at the appropriate time. For example, if a user needs to take their medication at a specific time every day, a reminder will be sent at that time. The medication management unit also keeps a record of medication use and can share this information with doctors and pharmacists. Step 2: The dietary advice unit analyzes the user's dietary data and proposes a nutritionally balanced meal plan. For example, when the user enters a meal record, the data is analyzed and the necessary nutrients and calorie intake are calculated. The dietary advice unit also proposes a meal plan based on the user's health condition and goals. Step 3: The exercise coaching unit analyzes the user's exercise data and proposes an effective exercise plan. For example, when the user inputs their exercise record, the data is analyzed and an exercise plan is proposed based on the user's physical strength and goals. The exercise coaching unit also monitors the user's exercise progress and adjusts the plan as necessary.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 medication management unit that manages the user's medication schedule and provides reminders to encourage medication at appropriate times; A dietary advice department analyzes the user's dietary data and proposes nutritionally balanced meal plans; and an exercise instruction unit that analyzes the user's exercise data and proposes an effective exercise plan. A system characterized by:

2. The medication management unit Analyze the user's medication history and physical condition data, evaluate the effectiveness of medication in real time, and automatically adjust the medication schedule as needed.

2. The system of claim 1.

3. The medication management unit The system learns the user's medication patterns, identifies time periods when there is a high risk of forgetting to take medication, and sends the reminder during those time periods to urge the user to pay particular attention.

2. The system of claim 1.

4. The medication management unit Analyzing the user's emotional state and providing advice on how to relax when stress or anxiety is high, thereby supporting continued medication use 2. The system of claim 1.

5. The medication management unit Analyze the user's daily rhythm and link it with meal and exercise schedules to optimize the timing of medication.

2. The system of claim 1.

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