System

Abstract

To provide a system for implementing health management based on an amount of daily exercise and diet information of users.SOLUTION: A system includes a collection unit, an analysis unit, a suggestion unit, an advice unit and a reminder unit. The collection unit collects information on an amount of daily exercise and diet of a user. The analysis unit analyzes a health condition of the user based on the information collected by the collection unit. The suggestion unit makes diet and exercise suggestions tailored to the user's health goals based on the analysis result by the analysis unit. The advice unit provides health advice for symptoms input by the user. The reminder unit sets reminders for medicine intake times and a next medical checkup date.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately manage a user's health based on the amount of daily exercise and dietary information, and there is room for improvement.

[0005] The system according to the embodiment aims to manage the user's health based on the amount of daily exercise and dietary information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, an advice unit, and a reminder unit. The collection unit collects information on the user's daily exercise amount and diet. The analysis unit analyzes the user's health condition based on the information collected by the collection unit. The suggestion unit suggests diet and exercise in accordance with the user's health goals based on the analysis results by the analysis unit. The advice unit provides health advice for symptoms input by the user. The reminder unit sets reminders for the time to take medicine and the date of the next health check. Effect of the Invention

[0007] The system according to the embodiment can manage the user's health based on the amount of daily exercise and dietary information. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system that collects information on the amount of daily exercise and diet of a user and analyzes the health condition. This system collects information on the amount of daily exercise and diet of a user, and analyzes the health condition of the user based on the collected information. Furthermore, based on the analysis result, it proposes meals and exercises that match the health goal of the user. In addition, it provides health advice for symptoms input by the user, and sets reminders for the time to take medicine and the next medical checkup date. For example, when collecting information on the amount of daily exercise and diet of a user, the information can be collected from a device such as a smart watch or a smartphone. If the user is wearing a smart watch, the information on the amount of exercise and diet can be collected from the device. Next, the health condition of the user is analyzed based on the collected information. The AI ​​analyzes the collected data and evaluates the health condition of the user. For example, if the amount of exercise is low or the diet is unbalanced, the AI ​​analyzes the health condition based on the information. Furthermore, based on the analysis result, it proposes meals and exercises that match the health goal of the user. For example, if the user wants to lose weight, the AI ​​can suggest calorie restriction and exercise. Also, if the user wants to increase muscle strength, the AI ​​can suggest high-protein meals and strength training. It also provides health advice for symptoms entered by the user. For example, if the user has a headache, the AI ​​can provide general health advice for that symptom. In addition, it can evaluate the user's stress level and provide advice on stress management. In addition, it sets reminders for the time to take medicine and the next medical checkup date. For example, the AI ​​can set reminders and notify the user so that the user does not forget to take medicine. It can also sync the next medical checkup date with a calendar app and set a reminder. In this way, the present invention is an AI assistant that provides personalized health management to the user by collecting information on the user's daily exercise and diet and analyzing the health condition. This allows the user to understand their health condition and perform appropriate health management.This enables the health management system to provide personalized health management by collecting information on the user's daily exercise and diet, analyzing their health condition, offering suggestions and advice tailored to their health goals, and setting reminders.

[0029] The health management system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, an advice unit, and a reminder unit. The collection unit collects information on the amount of exercise and meals of the user on a daily basis. The collection unit can collect information from devices such as a smart watch or a smartphone. For example, when the user wears a smart watch, the collection unit can collect information on the amount of exercise and meals from the device. In addition, when the user inputs meal information using a smartphone, the collection unit can also collect the information. Furthermore, the collection unit can collect data from an environmental sensor and analyze the health condition in consideration of the environmental conditions indicated by the collected data. The analysis unit analyzes the health condition of the user based on the information collected by the collection unit. The analysis unit, for example, analyzes the collected data using AI and evaluates the health condition of the user. For example, when the amount of exercise is low or the balance of the meal is poor, the analysis unit analyzes the health condition based on the information. The suggestion unit makes suggestions on meals and exercises that match the user's health goals based on the analysis results by the analysis unit. The suggestion unit makes suggestions that match the user's health goals using AI, for example. For example, if the user wants to lose weight, the suggestion unit can suggest calorie restriction and exercise. Also, if the user wants to increase muscle strength, the suggestion unit can suggest a high-protein diet and muscle training. The advice unit provides health advice for the symptoms input by the user. The advice unit can provide general health advice for the user's symptoms, for example, by using AI. For example, if the user feels a headache, the advice unit can provide health advice for the symptom. Furthermore, the advice unit can evaluate the user's stress level and provide advice on stress management. The reminder unit sets reminders for the time to take medicine and the date of the next medical checkup. For example, the reminder unit can set reminders using AI so that the user does not forget to take medicine, and notify the user. Also, the reminder unit can sync the date of the next medical checkup with a calendar app and set a reminder.As a result, the health management system of the embodiment can provide personalized health management by collecting information on the user's daily exercise and diet, analyzing their health condition, providing suggestions and advice tailored to their health goals, and setting reminders.

[0030] The collection unit collects information on the user's daily exercise and diet. The collection unit can collect information from devices such as a smartwatch and a smartphone. Specifically, the smartwatch collects data such as the user's heart rate, number of steps, calories burned, and exercise time in real time, and the smartphone records the diet and calorie intake entered by the user. These devices transmit data to a cloud server via Bluetooth (registered trademark) or Wi-Fi, and the data is stored in a central database. In addition, the collection unit can collect data from environmental sensors and analyze the health condition by considering the environmental conditions indicated by the collected data. For example, environmental data such as temperature, humidity, and air quality are collected, and the effects of these data on the user's exercise and diet are evaluated. The collection unit integrates these various data and provides a foundation for comprehensively understanding the user's health condition. Furthermore, the collection unit can flexibly respond to the user's lifestyle and health condition by adjusting the frequency and accuracy of data collection. For example, for users who exercise a lot, data can be collected more frequently and detailed analysis can be performed. In addition, the collection unit encrypts data and controls access to protect the user's privacy, ensuring security. This enables the collection unit to collect data efficiently and safely, improving the performance and reliability of the entire system.

[0031] The analysis unit analyzes the user's health condition based on the information collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and evaluate the user's health condition. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's exercise pattern and dietary content to detect trends and abnormalities in the health condition. For example, if the user does not exercise much or has an unbalanced diet, the health condition is analyzed based on that information. The AI ​​can evaluate changes in the user's health condition by comparing with past data and predict future risks. Furthermore, the analysis unit takes into account personal information such as the user's age, sex, weight, and height to perform a more accurate health evaluation. For example, even if the amount of exercise is the same, the impact on health differs depending on age and sex, so individual evaluation is necessary. The analysis unit provides the user with an optimal health management plan by taking these individual factors into consideration. In addition, the analysis unit can use an anomaly detection algorithm to detect patterns that are different from the norm or abnormal data and issue an early warning. For example, it can detect sudden weight gain or abnormal heart rate and alert the user. As a result, the analysis unit can not only grasp the real-time health condition, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion unit makes suggestions for meals and exercises that match the user's health goals based on the analysis results by the analysis unit. The suggestion unit makes suggestions that match the user's health goals using, for example, AI. Specifically, the AI ​​generates optimal meal plans and exercise plans based on the user's current health condition and goals. For example, if the user wants to lose weight, it can suggest calorie restriction and aerobic exercise. The AI ​​analyzes the user's meal history and suggests specific meal menus for adjusting calorie intake. In addition, if the user wants to increase muscle strength, it can suggest high-protein meals and strength training. The AI ​​analyzes the user's exercise history and provides an effective strength training plan. Furthermore, the suggestion unit can make customized suggestions according to the user's lifestyle and preferences. For example, if the user likes a specific ingredient, it suggests recipes that include that ingredient. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, it provides feedback on the results of the user's implementation of the suggested meal plan and adjusts the next suggestion based on the results. In this way, the suggestion unit can provide the user with a personalized health management plan and support the achievement of health goals.

[0033] The advice unit provides health advice for the symptoms input by the user. The advice unit can provide general health advice for the user's symptoms using, for example, AI. Specifically, when the user inputs symptoms such as headache or fatigue through the application, the AI ​​analyzes the information and provides appropriate advice. For example, in the case of a headache, the AI ​​suggests adequate hydration, taking rest, and ways to reduce stress. Furthermore, the advice unit can also evaluate the user's stress level and provide advice on stress management. The AI ​​analyzes data such as the user's heart rate and sleep patterns to evaluate the stress level. If a high stress level is detected, the AI ​​suggests stress management methods such as relaxation techniques, meditation, and moderate exercise. The advice unit can also suggest preventive measures and lifestyle improvements according to the user's health condition. For example, the AI ​​emphasizes the importance of diet and exercise to boost immunity and regular health checks. This allows the advice unit to provide specific and practical health advice to the user and support them in maintaining their health in their daily lives.

[0034] The reminder section sets reminders for the time to take medicine and the date of the next medical checkup. For example, the reminder section can use AI to set reminders and notify the user so that the user does not forget the time to take medicine. Specifically, when the user inputs the medicine intake schedule into the application, the AI ​​sets a reminder based on that information and sends a notification at the specified time. The notification is performed by a method such as a push notification on the smartphone, a voice alert, or vibration. The reminder section can also sync the next medical checkup date with a calendar app and set a reminder. When the user inputs the medical checkup date into the application, the AI ​​syncs that information with the calendar app and sends a reminder the day before the medical checkup date. Furthermore, the reminder section can also set reminders for regular exercise and meals. For example, if the user sets a certain time every day to exercise, the AI ​​will send a reminder at that time to encourage the user to exercise. In this way, the reminder section provides support for the user to continuously manage their health and ensure that they do not forget important tasks. In addition, the reminder unit can adjust the contents and timing of the reminder based on the user's feedback to provide more effective notifications. This allows the reminder unit to provide personalized reminders to the user and support the user in continuing health management.

[0035] The collection unit can collect information on the amount of exercise and food of the user from at least one device of the user's smartwatch. The collection unit, for example, uses a smartwatch to collect information on the amount of exercise and food of the user. For example, when the user wears a smartwatch, the collection unit can collect information on the amount of exercise and food from the device. The collection unit can also collect information on the food of the user using a smartphone. For example, when the user inputs food information using a smartphone, the collection unit can collect the information. Thereby, by collecting information from the smartwatch or the smartphone, the user's daily amount of exercise and food information can be accurately obtained. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartwatch to AI and have the AI ​​perform analysis of the data.

[0036] The advice unit can evaluate the user's stress level and provide appropriate advice on stress management. The advice unit can, for example, use AI to evaluate the user's stress level and provide advice on stress management. For example, the advice unit can analyze the user's heart rate variability and evaluate the stress level. The advice unit can also evaluate the stress level based on the user's questionnaire results. Furthermore, the advice unit can provide appropriate stress management advice based on the user's stress level. For example, the advice unit can suggest a relaxation method or a stress relief method when the user shows a high stress level. This makes it possible to support the user's mental health by evaluating the user's stress level and providing appropriate stress management advice. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's heart rate variability data into AI and cause AI to execute stress level evaluation.

[0037] The reminder unit can synchronize with the user's calendar app and set a reminder appropriately in the calendar app. The reminder unit can, for example, synchronize with the user's calendar app and set a reminder. For example, the reminder unit can set the next medical checkup date in the user's calendar app and notify the user of the reminder. The reminder unit can also set the time to take medicine in the user's calendar app and notify the user of the reminder. In this way, by synchronizing with the calendar app, the user can receive the notification at an appropriate time without missing the reminder. A part or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input data from the calendar app into AI and have the AI ​​execute the setting of the reminder.

[0038] The collection unit collects data from the environmental sensor, and the analysis unit can analyze the health condition based on the environmental conditions indicated by the data collected by the collection unit. The collection unit collects data using, for example, an environmental sensor. For example, the collection unit can collect data from environmental sensors such as a temperature sensor, a humidity sensor, and an air quality sensor. The analysis unit analyzes the health condition based on the environmental conditions indicated by the data collected by the collection unit. For example, the analysis unit can evaluate the user's health condition taking into account environmental conditions such as temperature, humidity, and air quality. This makes it possible to more accurately analyze the user's health condition by taking into account the data from the environmental sensor. Some or all of the above-mentioned processing in the collection unit and the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the environmental sensor to AI and have the AI ​​perform analysis of the data.

[0039] The advice unit can provide the user with appropriate personalized health advice based on the user's genetic information. The advice unit can, for example, use AI to analyze the user's genetic information and provide personalized health advice. For example, the advice unit can evaluate the risk of a specific disease based on the user's DNA analysis results or genetic test results and provide appropriate health advice. The advice unit can also suggest appropriate exercise plans and meal plans based on the user's genetic information. This makes it possible to support optimal health management for the user by providing advice based on the genetic information. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or without using AI. For example, the advice unit can input the user's genetic information into AI and cause the AI ​​to generate health advice.

[0040] The suggestion unit can suggest the intensity and type of exercise based on the user's exercise habits. The suggestion unit, for example, uses AI to analyze the user's exercise habits and suggest the intensity and type of exercise. For example, the suggestion unit can suggest an appropriate exercise plan based on how many times a week the user exercises and what kind of exercise the user performs. The suggestion unit can also adjust the intensity of exercise based on the user's exercise habits. For example, the suggestion unit can suggest light exercise if the user is a beginner, and suggest higher intensity exercise if the user is an experienced user. This makes it possible to provide an effective exercise plan by making suggestions based on the user's exercise habits. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's exercise habit data into AI and cause AI to generate an exercise plan.

[0041] The suggestion unit can suggest suitable nearby exercise facilities and health food stores based on the user's geographical location information. The suggestion unit, for example, uses AI to analyze the user's geographical location information and suggests suitable nearby exercise facilities and health food stores. For example, when the user is looking for exercise facilities around his / her home, the suggestion unit can suggest nearby gyms and parks. In addition, when the user is looking for a health food store at a travel destination, the suggestion unit can also suggest nearby health food stores. This makes it possible to support convenient health management by making suggestions based on the user's location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's location information data into AI and have the AI ​​execute the suggestion of appropriate facilities and stores.

[0042] The reminder unit can cooperate with the user's smart home device and notify the user of the reminder by voice, smell, or brightness. The reminder unit cooperates with the user's smart home device using, for example, AI to notify the user of the reminder. For example, the reminder unit can notify the user of the reminder by voice through the user's smart speaker. The reminder unit can also notify the user of the reminder by brightness using the user's smart light. Furthermore, the reminder unit can also notify the user of the reminder by smell using the user's smart diffuser. This allows the user to be notified of the reminder in various ways by cooperating with the smart home device. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input data of the smart home device into AI and cause the AI ​​to execute a reminder notification method.

[0043] The suggestion unit can give a reward to the user when the user achieves a specific health goal. The suggestion unit, for example, uses AI to evaluate the achievement of the user's health goal and gives the reward. For example, the suggestion unit can provide a coupon for health food as a reward when the user achieves a weight loss goal. In addition, the suggestion unit can provide a free gym voucher as a reward when the user achieves an exercise goal. Furthermore, the suggestion unit can give bonus points as a reward when the user achieves a health goal. This makes it possible to increase the motivation of the user by giving a reward when the health goal is achieved. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the achievement data of the user's health goal into AI and cause AI to execute the granting of the reward.

[0044] The collection unit can analyze the user's past exercise and meal history and select an appropriate collection method. The collection unit can, for example, use AI to analyze the user's past exercise and meal history and select the optimal collection method. For example, the collection unit can select the optimal collection method based on the user's frequent exercise and meal patterns in the past. The collection unit can also select a method for collecting data for a specific time period from the user's past exercise history. Furthermore, the collection unit can analyze the user's meal history and collect information in accordance with the timing of meals. This allows information to be collected efficiently by selecting the optimal collection method based on the past history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past exercise and meal history data into AI and cause AI to select the optimal collection method.

[0045] The collection unit can perform filtering based on the user's current health condition and lifestyle habits at the time of collection. The collection unit performs filtering based on the user's current health condition and lifestyle habits at the time of collection, for example, using AI. For example, the collection unit can collect only necessary information taking into account the user's current health condition. In addition, the collection unit can also preferentially collect highly relevant information based on the user's lifestyle habits. Furthermore, when the user's health condition is deteriorating, the collection unit can limit the information to be collected to reduce the burden. As a result, by performing filtering based on the current health condition and lifestyle habits, only necessary information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and lifestyle habit data to AI and have the AI ​​perform filtering.

[0046] The collection unit can preferentially collect highly relevant information based on the geographical location information of the user at the time of collection. The collection unit preferentially collects highly relevant information by taking into account the geographical location information of the user at the time of collection, for example, using AI. For example, when the user is in a specific area, the collection unit can preferentially collect information related to the area. In addition, when the user is traveling, the collection unit can also preferentially collect information about the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect information around the home. In this way, by taking into account the geographical location information, highly relevant information can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to AI and cause AI to execute collection of highly relevant information.

[0047] The collection unit can analyze the user's social media activity at the time of collection and collect related information. The collection unit can, for example, use AI to analyze the user's social media activity at the time of collection and collect related information. For example, the collection unit can collect exercise and diet information shared by the user on social media. The collection unit can also collect health information of interest from the user's social media activity. Furthermore, the collection unit can also collect information from health-related accounts followed by the user. In this way, information based on the user's interests can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media data into AI and cause AI to collect related information.

[0048] The analysis unit can predict the current health state based on the past health data during the analysis. For example, the analysis unit can predict the current athletic ability based on the user's past exercise data. The analysis unit can also predict the current nutritional state based on the user's past dietary data. Furthermore, the analysis unit can predict the current health risk based on the user's past health checkup data. This makes it possible to more accurately predict the current health state by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past health data into AI and cause the AI ​​to predict the current health state.

[0049] The analysis unit can evaluate the health condition based on the genetic information of the user during the analysis. The analysis unit, for example, uses AI to analyze the genetic information of the user and evaluate the health condition. For example, the analysis unit can evaluate the risk of a specific disease based on the DNA analysis result or the genetic test result of the user. The analysis unit can also propose an appropriate exercise plan or meal plan based on the genetic information of the user. This makes it possible to evaluate the optimal health condition for the user by taking the genetic information into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the genetic information of the user to AI and have the AI ​​execute the evaluation of the health condition.

[0050] The analysis unit can evaluate the health state based on the user's living environment data during the analysis. The analysis unit, for example, uses AI to refer to the user's living environment data and evaluate the health state. For example, the analysis unit can evaluate a health risk based on the user's living environment data. The analysis unit can also evaluate a stress level based on the user's work environment data. Furthermore, the analysis unit can provide appropriate health advice based on the user's living environment data. This makes it possible to more accurately evaluate the user's health state by referring to the living environment data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's living environment data into AI and cause the AI ​​to perform an evaluation of the health state.

[0051] The analysis unit can predict the health state based on the user's diet and exercise history during the analysis. The analysis unit can predict the health state by referring to the user's diet and exercise history using, for example, AI. For example, the analysis unit can evaluate the nutritional balance based on the user's diet history. The analysis unit can also evaluate the exercise ability based on the user's exercise history. Furthermore, the analysis unit can predict health risks based on the user's diet and exercise history. This makes it possible to provide the user with appropriate health management by predicting the health state based on the diet and exercise history. Part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's diet and exercise history data into AI and cause the AI ​​to execute a health state prediction.

[0052] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health goal at the time of suggestion. The suggestion unit, for example, uses AI to evaluate the importance of the health goal at the time of suggestion and adjust the level of detail of the proposal. For example, the suggestion unit can make a detailed proposal when the health goal is high. In addition, the suggestion unit can make a moderately detailed proposal when the health goal is medium. Furthermore, the suggestion unit can make a concise proposal when the health goal is low. As a result, the level of detail of the proposal can be adjusted according to the importance of the health goal, thereby providing an optimal proposal to the user. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's health goal data into AI and cause AI to adjust the level of detail of the proposal.

[0053] The suggestion unit can apply different suggestion algorithms according to the user's exercise habits and food preferences at the time of suggestion. The suggestion unit, for example, uses AI to analyze the user's exercise habits and food preferences at the time of suggestion and applies different suggestion algorithms. For example, if the user likes exercise, the suggestion unit can make exercise-centered suggestions. Also, if the user places importance on food, the suggestion unit can make food-centered suggestions. Furthermore, the suggestion unit can make balanced suggestions based on the user's exercise habits and food preferences. This makes it possible to make more personalized suggestions by making suggestions according to the user's exercise habits and food preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise habits and food preference data into AI and cause AI to apply the suggestion algorithm.

[0054] The suggestion unit can suggest suitable nearby exercise facilities and health food stores based on the user's geographical location information at the time of suggestion. The suggestion unit, for example, uses AI to analyze the user's geographical location information at the time of suggestion, and suggests suitable nearby exercise facilities and health food stores. For example, when the user is looking for exercise facilities around his / her home, the suggestion unit can suggest nearby gyms and parks. In addition, when the user is looking for a health food store at a travel destination, the suggestion unit can also suggest nearby health food stores. This makes it possible to make suggestions based on geographical location information, making it possible to make suggestions that are highly convenient for the user. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information data into AI and have the AI ​​execute suggestions for appropriate facilities and stores.

[0055] The suggestion unit can provide a reward when the user achieves a specific health goal at the time of suggestion. The suggestion unit, for example, uses AI to evaluate the achievement of the user's health goal at the time of suggestion and provides a reward. For example, the suggestion unit can provide a coupon for health food as a reward when the user achieves a weight loss goal. In addition, the suggestion unit can provide a free gym voucher as a reward when the user achieves an exercise goal. Furthermore, the suggestion unit can provide bonus points as a reward when the user achieves a health goal. This makes it possible to increase the motivation of the user by providing a reward when the health goal is achieved. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the achievement data of the user's health goal into AI and cause AI to execute the granting of the reward.

[0056] The advice unit can provide appropriate advice by referring to the user's past health data when providing advice. The advice unit can provide optimal advice by referring to the user's past health data when providing advice, for example, using AI. For example, the advice unit can provide appropriate exercise advice based on the user's past exercise data. The advice unit can also provide balanced diet advice based on the user's past diet data. Furthermore, the advice unit can provide advice on health risks based on the user's past health checkup data. This makes it possible to provide optimal advice to the user by referring to the past health data. Part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input the user's past health data into AI and cause the AI ​​to generate advice.

[0057] The advice unit can provide personalized advice based on the genetic information of the user at the time of giving advice. The advice unit can, for example, use AI to analyze the genetic information of the user at the time of giving advice and provide personalized advice. For example, the advice unit can provide advice on the risk of a specific disease based on the user's DNA analysis results or genetic test results. The advice unit can also suggest an appropriate exercise plan or meal plan based on the user's genetic information. This makes it possible to provide optimal advice to the user by taking the genetic information into consideration. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input the user's genetic information into AI and cause the AI ​​to generate advice.

[0058] The advice unit can provide appropriate advice by referring to the user's living environment data when giving advice. The advice unit can provide optimal advice by referring to the user's living environment data when giving advice, for example, using AI. For example, the advice unit can provide advice on health risks based on the user's living environment data. The advice unit can also provide advice on stress management based on the user's work environment data. Furthermore, the advice unit can provide appropriate health advice based on the user's living environment data. Thereby, optimal advice can be provided to the user by referring to the living environment data. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input the user's living environment data into AI and cause the AI ​​to generate advice.

[0059] The advice unit can provide relevant advice by analyzing the user's social media activity at the time of advice. The advice unit can provide relevant advice by, for example, using AI to analyze the user's social media activity at the time of advice. For example, the advice unit can provide relevant advice based on health information shared by the user on social media. The advice unit can also provide advice based on health information of interest from the user's social media activity. Furthermore, the advice unit can also provide advice based on information obtained from health-related accounts followed by the user. In this way, by analyzing social media activity, relevant advice can be provided to the user. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's social media data into AI and cause AI to execute generation of relevant advice.

[0060] The reminder unit can provide an appropriate reminder by referring to the user's past behavior history when setting a reminder. The reminder unit can provide an optimal reminder by referring to the user's past behavior history when setting a reminder, for example, using AI. For example, the reminder unit can set an optimal reminder based on the user's past drug intake history. The reminder unit can also set a reminder for the next medical checkup date based on the user's past medical checkup history. Furthermore, the reminder unit can analyze the user's past behavior patterns and provide an optimal reminder. As a result, the optimal reminder can be provided to the user by referring to the past behavior history. A part or all of the above-mentioned processing in the reminder unit can be performed, for example, using AI, or can be performed without using AI. For example, the reminder unit can input the user's past behavior history data into AI and cause AI to generate a reminder.

[0061] The reminder unit can synchronize with the user's calendar app when setting a reminder and customize the reminder. The reminder unit can synchronize with the user's calendar app when setting a reminder, for example, using AI, and customize the reminder. For example, the reminder unit can set a reminder by referring to a schedule registered in the user's calendar. The reminder unit can also set a reminder related to a specific event from the user's calendar information. Furthermore, the reminder unit can provide an optimal reminder that matches the schedule based on the user's calendar information. As a result, by synchronizing with the calendar app, it is possible to provide the user with an optimal reminder. A part or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input the user's calendar information into AI and cause AI to customize the reminder.

[0062] The reminder unit can cooperate with the user's smart home device when setting a reminder and notify the user by voice, smell, or brightness. The reminder unit cooperates with the user's smart home device when setting a reminder, for example, using AI, and notifies the user of the reminder. For example, the reminder unit can notify the user of the reminder by voice through the user's smart speaker. The reminder unit can also notify the user of the reminder by brightness using the user's smart light. Furthermore, the reminder unit can also notify the user of the reminder by smell using the user's smart diffuser. This allows the user to be notified of the reminder in various ways by cooperating with the smart home device. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input data of the smart home device into AI and cause the AI ​​to execute a reminder notification method.

[0063] The reminder unit can provide an appropriate reminder based on the geographical location information of the user when setting a reminder. The reminder unit, for example, uses AI to consider the geographical location information of the user when setting a reminder and provides an optimal reminder. For example, when the user is in a specific location, the reminder unit can provide a reminder related to that location. In addition, when the user is moving, the reminder unit can also provide a reminder related to the destination. Furthermore, the reminder unit can also provide an optimal reminder based on the geographical location information of the user. In this way, by considering the geographical location information, an optimal reminder can be provided to the user. A part or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input the geographical location information data of the user to AI and cause the AI ​​to generate a reminder.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The health management system further monitors the user's sleep pattern, and the analysis unit can evaluate the user's health condition based on the collected sleep data. For example, the collection unit can collect sleep data from a smart watch or a smart bed, and the analysis unit can evaluate the quality and duration of sleep. The suggestion unit can provide advice for improving the quality of sleep. For example, the suggestion unit can suggest an appropriate bedtime and relaxation method to the user. This enables health management that takes into account the user's sleep pattern.

[0066] The health management system further monitors the user's water intake, and the analysis unit can evaluate the user's health condition based on the collected water intake data. For example, the collection unit can collect water intake data from a smart bottle or a smart watch, and the analysis unit can evaluate the user's water intake. The suggestion unit can suggest an appropriate water intake. For example, the suggestion unit can suggest to the user that the user set a daily water intake goal and take in water at an appropriate time. This enables health management that takes into account the user's water intake.

[0067] The health management system further monitors the user's heart rate, and the analysis unit can evaluate the user's health condition based on the collected heart rate data. For example, the collection unit can collect heart rate data from a smart watch or a heart rate sensor, and the analysis unit can evaluate the fluctuation of the user's heart rate. The suggestion unit can provide advice for stabilizing the heart rate. For example, the suggestion unit can suggest relaxation methods and appropriate exercises to the user. This enables health management that takes the user's heart rate into consideration.

[0068] The health management system further monitors the user's body temperature, and the analysis unit can evaluate the user's health condition based on the collected body temperature data. For example, the collection unit can collect body temperature data from a smart watch or a thermometer, and the analysis unit can evaluate fluctuations in the user's body temperature. The suggestion unit can provide advice for stabilizing the body temperature. For example, the suggestion unit can suggest appropriate clothing and environmental adjustments to the user. This enables health management that takes the user's body temperature into consideration.

[0069] The health management system further monitors the user's blood pressure, and the analysis unit can evaluate the user's health condition based on the collected blood pressure data. For example, the collection unit can collect blood pressure data from a smart watch or a blood pressure monitor, and the analysis unit can evaluate fluctuations in the user's blood pressure. The suggestion unit can provide advice for stabilizing blood pressure. For example, the suggestion unit can suggest appropriate meals and exercise to the user. This enables health management that takes the user's blood pressure into consideration.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The collection unit collects information on the user's daily exercise and diet. The collection unit can collect information from devices such as a smartwatch or a smartphone. If the user wears a smartwatch, the collection unit collects information on the exercise and diet from the device. If the user inputs diet information using a smartphone, the collection unit can also collect the information. In addition, the collection unit can collect data from environmental sensors and analyze the health condition taking into account the environmental conditions indicated by the collected data. Step 2: The analysis unit analyzes the user's health condition based on the information collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and evaluates the user's health condition. If the user does not exercise much or has an unbalanced diet, the health condition is analyzed based on that information. Step 3: The suggestion unit makes suggestions for diet and exercise that match the user's health goals based on the analysis results by the analysis unit. The suggestion unit makes suggestions that match the user's health goals, for example, using AI. If the user wants to lose weight, it can suggest calorie restriction and exercise. Also, if the user wants to increase muscle strength, it can suggest a high-protein diet and strength training. Step 4: The advice unit provides health advice for the symptoms input by the user. The advice unit can, for example, use AI to provide general health advice for the user's symptoms. If the user experiences a headache, health advice can be provided for that symptom. In addition, the advice unit can also assess the user's stress level and provide advice on stress management. Step 5: The reminder section sets reminders for the time to take medicine and the date of the next medical checkup. For example, the reminder section can use AI to set reminders and notify the user so that the user does not forget to take medicine. The reminder section can also sync the next medical checkup date with a calendar app and set a reminder.

[0072] (Example 2) A health management system according to an embodiment of the present invention is a system that collects information on the amount of daily exercise and diet of a user and analyzes the health condition. This system collects information on the amount of daily exercise and diet of a user, and analyzes the health condition of the user based on the collected information. Furthermore, based on the analysis result, it proposes meals and exercises that match the health goal of the user. In addition, it provides health advice for symptoms input by the user, and sets reminders for the time to take medicine and the next medical checkup date. For example, when collecting information on the amount of daily exercise and diet of a user, the information can be collected from a device such as a smart watch or a smartphone. If the user is wearing a smart watch, the information on the amount of exercise and diet can be collected from the device. Next, the health condition of the user is analyzed based on the collected information. The AI ​​analyzes the collected data and evaluates the health condition of the user. For example, if the amount of exercise is low or the diet is unbalanced, the AI ​​analyzes the health condition based on the information. Furthermore, based on the analysis result, it proposes meals and exercises that match the health goal of the user. For example, if the user wants to lose weight, the AI ​​can suggest calorie restriction and exercise. Also, if the user wants to increase muscle strength, the AI ​​can suggest high-protein meals and strength training. It also provides health advice for symptoms entered by the user. For example, if the user has a headache, the AI ​​can provide general health advice for that symptom. In addition, it can evaluate the user's stress level and provide advice on stress management. In addition, it sets reminders for the time to take medicine and the next medical checkup date. For example, the AI ​​can set reminders and notify the user so that the user does not forget to take medicine. It can also sync the next medical checkup date with a calendar app and set a reminder. In this way, the present invention is an AI assistant that provides personalized health management to the user by collecting information on the user's daily exercise and diet and analyzing the health condition. This allows the user to understand their health condition and perform appropriate health management.This enables the health management system to provide personalized health management by collecting information on the user's daily exercise and diet, analyzing their health condition, offering suggestions and advice tailored to their health goals, and setting reminders.

[0073] The health management system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, an advice unit, and a reminder unit. The collection unit collects information on the amount of exercise and meals of the user on a daily basis. The collection unit can collect information from devices such as a smart watch or a smartphone. For example, when the user wears a smart watch, the collection unit can collect information on the amount of exercise and meals from the device. In addition, when the user inputs meal information using a smartphone, the collection unit can also collect the information. Furthermore, the collection unit can collect data from an environmental sensor and analyze the health condition in consideration of the environmental conditions indicated by the collected data. The analysis unit analyzes the health condition of the user based on the information collected by the collection unit. The analysis unit, for example, analyzes the collected data using AI and evaluates the health condition of the user. For example, when the amount of exercise is low or the balance of the meal is poor, the analysis unit analyzes the health condition based on the information. The suggestion unit makes suggestions on meals and exercises that match the user's health goals based on the analysis results by the analysis unit. The suggestion unit makes suggestions that match the user's health goals using AI, for example. For example, if the user wants to lose weight, the suggestion unit can suggest calorie restriction and exercise. Also, if the user wants to increase muscle strength, the suggestion unit can suggest a high-protein diet and muscle training. The advice unit provides health advice for the symptoms input by the user. The advice unit can provide general health advice for the user's symptoms, for example, by using AI. For example, if the user feels a headache, the advice unit can provide health advice for the symptom. Furthermore, the advice unit can evaluate the user's stress level and provide advice on stress management. The reminder unit sets reminders for the time to take medicine and the date of the next medical checkup. For example, the reminder unit can set reminders using AI so that the user does not forget to take medicine, and notify the user. Also, the reminder unit can sync the date of the next medical checkup with a calendar app and set a reminder.As a result, the health management system of the embodiment can provide personalized health management by collecting information on the user's daily exercise and diet, analyzing their health condition, providing suggestions and advice tailored to their health goals, and setting reminders.

[0074] The collection unit collects information on the user's daily exercise and diet. The collection unit can collect information from devices such as a smartwatch and a smartphone. Specifically, the smartwatch collects data such as the user's heart rate, number of steps, calories burned, and exercise time in real time, and the smartphone records the diet and calorie intake entered by the user. These devices transmit data to a cloud server via Bluetooth or Wi-Fi, and the data is stored in a central database. In addition, the collection unit can collect data from environmental sensors and analyze the health condition by considering the environmental conditions indicated by the collected data. For example, environmental data such as temperature, humidity, and air quality are collected, and the impact of these data on the user's exercise and diet is evaluated. The collection unit integrates these diverse data and provides a foundation for comprehensively understanding the user's health condition. Furthermore, the collection unit can flexibly respond to the user's lifestyle and health condition by adjusting the frequency and accuracy of data collection. For example, for users who exercise a lot, data can be collected more frequently and detailed analysis can be performed. In addition, the collection unit encrypts data and controls access to protect the user's privacy, ensuring security. This enables the collection unit to collect data efficiently and safely, improving the performance and reliability of the entire system.

[0075] The analysis unit analyzes the user's health condition based on the information collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and evaluate the user's health condition. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's exercise pattern and dietary content to detect trends and abnormalities in the health condition. For example, if the user does not exercise much or has an unbalanced diet, the health condition is analyzed based on that information. The AI ​​can evaluate changes in the user's health condition by comparing with past data and predict future risks. Furthermore, the analysis unit takes into account personal information such as the user's age, sex, weight, and height to perform a more accurate health evaluation. For example, even if the amount of exercise is the same, the impact on health differs depending on age and sex, so individual evaluation is necessary. The analysis unit provides the user with an optimal health management plan by taking these individual factors into consideration. In addition, the analysis unit can use an anomaly detection algorithm to detect patterns that are different from the norm or abnormal data and issue an early warning. For example, it can detect sudden weight gain or abnormal heart rate and alert the user. As a result, the analysis unit can not only grasp the real-time health condition, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0076] The suggestion unit makes suggestions for meals and exercises that match the user's health goals based on the analysis results by the analysis unit. The suggestion unit makes suggestions that match the user's health goals using, for example, AI. Specifically, the AI ​​generates optimal meal plans and exercise plans based on the user's current health condition and goals. For example, if the user wants to lose weight, it can suggest calorie restriction and aerobic exercise. The AI ​​analyzes the user's meal history and suggests specific meal menus for adjusting calorie intake. In addition, if the user wants to increase muscle strength, it can suggest high-protein meals and strength training. The AI ​​analyzes the user's exercise history and provides an effective strength training plan. Furthermore, the suggestion unit can make customized suggestions according to the user's lifestyle and preferences. For example, if the user likes a specific ingredient, it suggests recipes that include that ingredient. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, it provides feedback on the results of the user's implementation of the suggested meal plan and adjusts the next suggestion based on the results. In this way, the suggestion unit can provide the user with a personalized health management plan and support the achievement of health goals.

[0077] The advice unit provides health advice for the symptoms input by the user. The advice unit can provide general health advice for the user's symptoms using, for example, AI. Specifically, when the user inputs symptoms such as headache or fatigue through the application, the AI ​​analyzes the information and provides appropriate advice. For example, in the case of a headache, the AI ​​suggests adequate hydration, taking rest, and ways to reduce stress. Furthermore, the advice unit can also evaluate the user's stress level and provide advice on stress management. The AI ​​analyzes data such as the user's heart rate and sleep patterns to evaluate the stress level. If a high stress level is detected, the AI ​​suggests stress management methods such as relaxation techniques, meditation, and moderate exercise. The advice unit can also suggest preventive measures and lifestyle improvements according to the user's health condition. For example, the AI ​​emphasizes the importance of diet and exercise to boost immunity and regular health checks. This allows the advice unit to provide specific and practical health advice to the user and support them in maintaining their health in their daily lives.

[0078] The reminder section sets reminders for the time to take medicine and the date of the next medical checkup. For example, the reminder section can use AI to set reminders and notify the user so that the user does not forget the time to take medicine. Specifically, when the user inputs the medicine intake schedule into the application, the AI ​​sets a reminder based on that information and sends a notification at the specified time. The notification is performed by a method such as a push notification on the smartphone, a voice alert, or vibration. The reminder section can also sync the next medical checkup date with a calendar app and set a reminder. When the user inputs the medical checkup date into the application, the AI ​​syncs that information with the calendar app and sends a reminder the day before the medical checkup date. Furthermore, the reminder section can also set reminders for regular exercise and meals. For example, if the user sets a certain time every day to exercise, the AI ​​will send a reminder at that time to encourage the user to exercise. In this way, the reminder section provides support for the user to continuously manage their health and ensure that they do not forget important tasks. In addition, the reminder unit can adjust the contents and timing of the reminder based on the user's feedback to provide more effective notifications. This allows the reminder unit to provide personalized reminders to the user and support the user in continuing health management.

[0079] The collection unit can collect information on the amount of exercise and food of the user from at least one device of the user's smartwatch. The collection unit, for example, uses a smartwatch to collect information on the amount of exercise and food of the user. For example, when the user wears a smartwatch, the collection unit can collect information on the amount of exercise and food from the device. The collection unit can also collect information on the food of the user using a smartphone. For example, when the user inputs food information using a smartphone, the collection unit can collect the information. Thereby, by collecting information from the smartwatch or the smartphone, the user's daily amount of exercise and food information can be accurately obtained. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartwatch to AI and have the AI ​​perform analysis of the data.

[0080] The advice unit can evaluate the user's stress level and provide appropriate advice on stress management. The advice unit can, for example, use AI to evaluate the user's stress level and provide advice on stress management. For example, the advice unit can analyze the user's heart rate variability and evaluate the stress level. The advice unit can also evaluate the stress level based on the user's questionnaire results. Furthermore, the advice unit can provide appropriate stress management advice based on the user's stress level. For example, the advice unit can suggest a relaxation method or a stress relief method when the user shows a high stress level. This makes it possible to support the user's mental health by evaluating the user's stress level and providing appropriate stress management advice. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's heart rate variability data into AI and cause AI to execute stress level evaluation.

[0081] The reminder unit can synchronize with the user's calendar app and set a reminder appropriately in the calendar app. The reminder unit can, for example, synchronize with the user's calendar app and set a reminder. For example, the reminder unit can set the next medical checkup date in the user's calendar app and notify the user of the reminder. The reminder unit can also set the time to take medicine in the user's calendar app and notify the user of the reminder. In this way, by synchronizing with the calendar app, the user can receive the notification at an appropriate time without missing the reminder. A part or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input data from the calendar app into AI and have the AI ​​execute the setting of the reminder.

[0082] The collection unit collects data from the environmental sensor, and the analysis unit can analyze the health condition based on the environmental conditions indicated by the data collected by the collection unit. The collection unit collects data using, for example, an environmental sensor. For example, the collection unit can collect data from environmental sensors such as a temperature sensor, a humidity sensor, and an air quality sensor. The analysis unit analyzes the health condition based on the environmental conditions indicated by the data collected by the collection unit. For example, the analysis unit can evaluate the user's health condition taking into account environmental conditions such as temperature, humidity, and air quality. This makes it possible to more accurately analyze the user's health condition by taking into account the data from the environmental sensor. Some or all of the above-mentioned processing in the collection unit and the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the environmental sensor to AI and have the AI ​​perform analysis of the data.

[0083] The advice unit can provide the user with appropriate personalized health advice based on the user's genetic information. The advice unit can, for example, use AI to analyze the user's genetic information and provide personalized health advice. For example, the advice unit can evaluate the risk of a specific disease based on the user's DNA analysis results or genetic test results and provide appropriate health advice. The advice unit can also suggest appropriate exercise plans and meal plans based on the user's genetic information. This makes it possible to support optimal health management for the user by providing advice based on the genetic information. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or without using AI. For example, the advice unit can input the user's genetic information into AI and cause the AI ​​to generate health advice.

[0084] The suggestion unit can suggest the intensity and type of exercise based on the user's exercise habits. The suggestion unit, for example, uses AI to analyze the user's exercise habits and suggest the intensity and type of exercise. For example, the suggestion unit can suggest an appropriate exercise plan based on how many times a week the user exercises and what kind of exercise the user performs. The suggestion unit can also adjust the intensity of exercise based on the user's exercise habits. For example, the suggestion unit can suggest light exercise if the user is a beginner, and suggest higher intensity exercise if the user is an experienced user. This makes it possible to provide an effective exercise plan by making suggestions based on the user's exercise habits. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's exercise habit data into AI and cause AI to generate an exercise plan.

[0085] The suggestion unit can suggest suitable nearby exercise facilities and health food stores based on the user's geographical location information. The suggestion unit, for example, uses AI to analyze the user's geographical location information and suggests suitable nearby exercise facilities and health food stores. For example, when the user is looking for exercise facilities around his / her home, the suggestion unit can suggest nearby gyms and parks. In addition, when the user is looking for a health food store at a travel destination, the suggestion unit can also suggest nearby health food stores. This makes it possible to support convenient health management by making suggestions based on the user's location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's location information data into AI and have the AI ​​execute the suggestion of appropriate facilities and stores.

[0086] The reminder unit can cooperate with the user's smart home device and notify the user of the reminder by voice, smell, or brightness. The reminder unit cooperates with the user's smart home device using, for example, AI to notify the user of the reminder. For example, the reminder unit can notify the user of the reminder by voice through the user's smart speaker. The reminder unit can also notify the user of the reminder by brightness using the user's smart light. Furthermore, the reminder unit can also notify the user of the reminder by smell using the user's smart diffuser. This allows the user to be notified of the reminder in various ways by cooperating with the smart home device. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input data of the smart home device into AI and cause the AI ​​to execute a reminder notification method.

[0087] The suggestion unit can give a reward to the user when the user achieves a specific health goal. The suggestion unit, for example, uses AI to evaluate the achievement of the user's health goal and gives the reward. For example, the suggestion unit can provide a coupon for health food as a reward when the user achieves a weight loss goal. In addition, the suggestion unit can provide a free gym voucher as a reward when the user achieves an exercise goal. Furthermore, the suggestion unit can give bonus points as a reward when the user achieves a health goal. This makes it possible to increase the motivation of the user by giving a reward when the health goal is achieved. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the achievement data of the user's health goal into AI and cause AI to execute the granting of the reward.

[0088] The collection unit can estimate the user's emotion and adjust the collection timing of the amount of exercise and meal information based on the estimated emotion of the user. The collection unit estimates the user's emotion using, for example, an emotion engine or a generation AI, and adjusts the collection timing. For example, when the user is stressed, the collection unit can delay the collection timing to collect information in a relaxed state. Also, when the user is relaxed, the collection unit can collect information at a normal timing. Furthermore, when the user is in a hurry, the collection unit can also advance the collection timing to collect information quickly. This allows information to be collected at a more appropriate timing by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing.

[0089] The collection unit can analyze the user's past exercise and meal history and select an appropriate collection method. The collection unit can, for example, use AI to analyze the user's past exercise and meal history and select the optimal collection method. For example, the collection unit can select the optimal collection method based on the user's frequent exercise and meal patterns in the past. The collection unit can also select a method for collecting data for a specific time period from the user's past exercise history. Furthermore, the collection unit can analyze the user's meal history and collect information in accordance with the timing of meals. This allows information to be collected efficiently by selecting the optimal collection method based on the past history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past exercise and meal history data into AI and cause AI to select the optimal collection method.

[0090] The collection unit can perform filtering based on the user's current health condition and lifestyle habits at the time of collection. The collection unit performs filtering based on the user's current health condition and lifestyle habits at the time of collection, for example, using AI. For example, the collection unit can collect only necessary information taking into account the user's current health condition. In addition, the collection unit can also preferentially collect highly relevant information based on the user's lifestyle habits. Furthermore, when the user's health condition is deteriorating, the collection unit can limit the information to be collected to reduce the burden. As a result, by performing filtering based on the current health condition and lifestyle habits, only necessary information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and lifestyle habit data to AI and have the AI ​​perform filtering.

[0091] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, an emotion engine or a generation AI, and determines the priority of information to be collected. For example, when the user is feeling stressed, the collection unit can preferentially collect information related to stress. Also, when the user is relaxed, the collection unit can collect information with normal priority. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect important information. As a result, important information can be preferentially collected by determining the priority of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0092] The collection unit can preferentially collect highly relevant information based on the geographical location information of the user at the time of collection. The collection unit preferentially collects highly relevant information by taking into account the geographical location information of the user at the time of collection, for example, using AI. For example, when the user is in a specific area, the collection unit can preferentially collect information related to the area. In addition, when the user is traveling, the collection unit can also preferentially collect information about the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect information around the home. In this way, by taking into account the geographical location information, highly relevant information can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to AI and cause AI to execute collection of highly relevant information.

[0093] The collection unit can analyze the user's social media activity at the time of collection and collect related information. The collection unit can, for example, use AI to analyze the user's social media activity at the time of collection and collect related information. For example, the collection unit can collect exercise and diet information shared by the user on social media. The collection unit can also collect health information of interest from the user's social media activity. Furthermore, the collection unit can also collect information from health-related accounts followed by the user. In this way, information based on the user's interests can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media data into AI and cause AI to collect related information.

[0094] The analysis unit can estimate the user's emotion and adjust the analysis method of the health condition based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, an emotion engine or a generation AI, and adjust the analysis method of the health condition. For example, when the user is feeling stressed, the analysis unit can prioritize the analysis of the health condition related to stress. In addition, when the user is relaxed, the analysis unit can apply a normal analysis method. Furthermore, when the user is in a hurry, the analysis unit can also apply a method for performing analysis quickly. This allows for a more appropriate analysis of the health condition by adjusting the analysis method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis method.

[0095] The analysis unit can predict the current health state based on the past health data during the analysis. For example, the analysis unit can predict the current athletic ability based on the user's past exercise data. The analysis unit can also predict the current nutritional state based on the user's past dietary data. Furthermore, the analysis unit can predict the current health risk based on the user's past health checkup data. This makes it possible to more accurately predict the current health state by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past health data into AI and cause the AI ​​to predict the current health state.

[0096] The analysis unit can evaluate the health condition based on the genetic information of the user during the analysis. The analysis unit, for example, uses AI to analyze the genetic information of the user and evaluate the health condition. For example, the analysis unit can evaluate the risk of a specific disease based on the DNA analysis result or the genetic test result of the user. The analysis unit can also propose an appropriate exercise plan or meal plan based on the genetic information of the user. This makes it possible to evaluate the optimal health condition for the user by taking the genetic information into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the genetic information of the user to AI and have the AI ​​execute the evaluation of the health condition.

[0097] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, an emotion engine or a generation AI, and adjusts the display method of the analysis result. For example, the analysis unit can provide a simple and highly visible display method when the user is nervous. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide the analysis result in a more appropriate form by adjusting the display method according to the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0098] The analysis unit can evaluate the health state based on the user's living environment data during the analysis. The analysis unit, for example, uses AI to refer to the user's living environment data and evaluate the health state. For example, the analysis unit can evaluate a health risk based on the user's living environment data. The analysis unit can also evaluate a stress level based on the user's work environment data. Furthermore, the analysis unit can provide appropriate health advice based on the user's living environment data. This makes it possible to more accurately evaluate the user's health state by referring to the living environment data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's living environment data into AI and cause the AI ​​to perform an evaluation of the health state.

[0099] The analysis unit can predict the health state based on the user's diet and exercise history during the analysis. The analysis unit can predict the health state by referring to the user's diet and exercise history using, for example, AI. For example, the analysis unit can evaluate the nutritional balance based on the user's diet history. The analysis unit can also evaluate the exercise ability based on the user's exercise history. Furthermore, the analysis unit can predict health risks based on the user's diet and exercise history. This makes it possible to provide the user with appropriate health management by predicting the health state based on the diet and exercise history. Part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's diet and exercise history data into AI and cause the AI ​​to execute a health state prediction.

[0100] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, an emotion engine or a generation AI, and adjusts the expression method of the proposal. For example, the suggestion unit can make a simple and easy-to-understand suggestion when the user is stressed. In addition, the suggestion unit can make a detailed suggestion when the user is relaxed. Furthermore, the suggestion unit can make a suggestion that can be quickly implemented when the user is in a hurry. This enables more effective suggestions by adjusting the expression method of the proposal according to the user's emotion. The estimation of the emotion is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the proposal.

[0101] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health goal at the time of suggestion. The suggestion unit, for example, uses AI to evaluate the importance of the health goal at the time of suggestion and adjust the level of detail of the proposal. For example, the suggestion unit can make a detailed proposal when the health goal is high. In addition, the suggestion unit can make a moderately detailed proposal when the health goal is medium. Furthermore, the suggestion unit can make a concise proposal when the health goal is low. As a result, the level of detail of the proposal can be adjusted according to the importance of the health goal, thereby providing an optimal proposal to the user. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's health goal data into AI and cause AI to adjust the level of detail of the proposal.

[0102] The suggestion unit can apply different suggestion algorithms according to the user's exercise habits and food preferences at the time of suggestion. The suggestion unit, for example, uses AI to analyze the user's exercise habits and food preferences at the time of suggestion and applies different suggestion algorithms. For example, if the user likes exercise, the suggestion unit can make exercise-centered suggestions. Also, if the user places importance on food, the suggestion unit can make food-centered suggestions. Furthermore, the suggestion unit can make balanced suggestions based on the user's exercise habits and food preferences. This makes it possible to make more personalized suggestions by making suggestions according to the user's exercise habits and food preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise habits and food preference data into AI and cause AI to apply the suggestion algorithm.

[0103] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, an emotion engine or a generation AI, and adjusts the length of the proposal. For example, the suggestion unit can make a short and to the point proposal when the user is stressed. The suggestion unit can also make a detailed proposal when the user is relaxed. Furthermore, the suggestion unit can also make a short proposal that can be quickly implemented when the user is in a hurry. This allows for more effective proposals by adjusting the length of the proposal according to the user's emotion. The estimation of the emotion is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the proposal.

[0104] The suggestion unit can suggest suitable nearby exercise facilities and health food stores based on the user's geographical location information at the time of suggestion. The suggestion unit, for example, uses AI to analyze the user's geographical location information at the time of suggestion, and suggests suitable nearby exercise facilities and health food stores. For example, when the user is looking for exercise facilities around his / her home, the suggestion unit can suggest nearby gyms and parks. In addition, when the user is looking for a health food store at a travel destination, the suggestion unit can also suggest nearby health food stores. This makes it possible to make suggestions based on geographical location information, making it possible to make suggestions that are highly convenient for the user. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information data into AI and have the AI ​​execute suggestions for appropriate facilities and stores.

[0105] The suggestion unit can provide a reward when the user achieves a specific health goal at the time of suggestion. The suggestion unit, for example, uses AI to evaluate the achievement of the user's health goal at the time of suggestion and provides a reward. For example, the suggestion unit can provide a coupon for health food as a reward when the user achieves a weight loss goal. In addition, the suggestion unit can provide a free gym voucher as a reward when the user achieves an exercise goal. Furthermore, the suggestion unit can provide bonus points as a reward when the user achieves a health goal. This makes it possible to increase the motivation of the user by providing a reward when the health goal is achieved. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the achievement data of the user's health goal into AI and cause AI to execute the granting of the reward.

[0106] The advice unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. The advice unit can estimate the user's emotion using, for example, an emotion engine or a generation AI, and adjust the way of expressing advice. For example, the advice unit can provide simple and easy-to-understand advice when the user is stressed. In addition, the advice unit can provide detailed advice when the user is relaxed. Furthermore, the advice unit can provide advice that can be quickly implemented when the user is in a hurry. This enables more effective advice by adjusting the way of expressing advice according to the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing advice.

[0107] The advice unit can provide appropriate advice by referring to the user's past health data when providing advice. The advice unit can provide optimal advice by referring to the user's past health data when providing advice, for example, using AI. For example, the advice unit can provide appropriate exercise advice based on the user's past exercise data. The advice unit can also provide balanced diet advice based on the user's past diet data. Furthermore, the advice unit can provide advice on health risks based on the user's past health checkup data. This makes it possible to provide optimal advice to the user by referring to the past health data. Part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input the user's past health data into AI and cause the AI ​​to generate advice.

[0108] The advice unit can provide personalized advice based on the genetic information of the user at the time of giving advice. The advice unit can, for example, use AI to analyze the genetic information of the user at the time of giving advice and provide personalized advice. For example, the advice unit can provide advice on the risk of a specific disease based on the user's DNA analysis results or genetic test results. The advice unit can also suggest an appropriate exercise plan or meal plan based on the user's genetic information. This makes it possible to provide optimal advice to the user by taking the genetic information into consideration. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input the user's genetic information into AI and cause the AI ​​to generate advice.

[0109] The advice unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. The advice unit can estimate the user's emotion using, for example, an emotion engine or a generation AI, and determine the priority of advice. For example, when the user is feeling stressed, the advice unit can prioritize advice related to stress management. Also, when the user is relaxed, the advice unit can provide advice with normal priority. Furthermore, when the user is in a hurry, the advice unit can also provide important advice with priority. Thereby, by determining the priority of advice according to the user's emotion, important advice can be provided with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0110] The advice unit can provide appropriate advice by referring to the user's living environment data when giving advice. The advice unit can provide optimal advice by referring to the user's living environment data when giving advice, for example, using AI. For example, the advice unit can provide advice on health risks based on the user's living environment data. The advice unit can also provide advice on stress management based on the user's work environment data. Furthermore, the advice unit can provide appropriate health advice based on the user's living environment data. Thereby, optimal advice can be provided to the user by referring to the living environment data. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input the user's living environment data into AI and cause the AI ​​to generate advice.

[0111] The advice unit can provide relevant advice by analyzing the user's social media activity at the time of advice. The advice unit can provide relevant advice by, for example, using AI to analyze the user's social media activity at the time of advice. For example, the advice unit can provide relevant advice based on health information shared by the user on social media. The advice unit can also provide advice based on health information of interest from the user's social media activity. Furthermore, the advice unit can also provide advice based on information obtained from health-related accounts followed by the user. In this way, by analyzing social media activity, relevant advice can be provided to the user. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's social media data into AI and cause AI to execute generation of relevant advice.

[0112] The reminder unit can estimate the user's emotion and adjust the reminder notification method based on the estimated user's emotion. The reminder unit estimates the user's emotion using, for example, an emotion engine or a generation AI, and adjusts the reminder notification method. For example, the reminder unit can notify the user of a reminder with a gentle sound when the user is stressed. The reminder unit can also notify the user of a reminder with a normal sound when the user is relaxed. Furthermore, the reminder unit can also apply a method of prompt notification when the user is in a hurry. This enables more effective reminder notification by adjusting the notification method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the user's emotional data into the generation AI and have the generation AI adjust the notification method.

[0113] The reminder unit can provide an appropriate reminder by referring to the user's past behavior history when setting a reminder. The reminder unit can provide an optimal reminder by referring to the user's past behavior history when setting a reminder, for example, using AI. For example, the reminder unit can set an optimal reminder based on the user's past drug intake history. The reminder unit can also set a reminder for the next medical checkup date based on the user's past medical checkup history. Furthermore, the reminder unit can analyze the user's past behavior patterns and provide an optimal reminder. As a result, the optimal reminder can be provided to the user by referring to the past behavior history. A part or all of the above-mentioned processing in the reminder unit can be performed, for example, using AI, or can be performed without using AI. For example, the reminder unit can input the user's past behavior history data into AI and cause AI to generate a reminder.

[0114] The reminder unit can synchronize with the user's calendar app when setting a reminder and customize the reminder. The reminder unit can synchronize with the user's calendar app when setting a reminder, for example, using AI, and customize the reminder. For example, the reminder unit can set a reminder by referring to a schedule registered in the user's calendar. The reminder unit can also set a reminder related to a specific event from the user's calendar information. Furthermore, the reminder unit can provide an optimal reminder that matches the schedule based on the user's calendar information. As a result, by synchronizing with the calendar app, it is possible to provide the user with an optimal reminder. A part or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input the user's calendar information into AI and cause AI to customize the reminder.

[0115] The reminder unit can estimate the user's emotion and determine the priority of the reminder based on the estimated user's emotion. The reminder unit can estimate the user's emotion using, for example, an emotion engine or a generation AI, and determine the priority of the reminder. For example, when the user is stressed, the reminder unit can give priority to important reminders. Also, when the user is relaxed, the reminder unit can give priority to reminders to be notified quickly. In addition, when the user is in a hurry, the reminder unit can also give priority to reminders to be notified quickly. In this way, by determining the priority of reminders according to the user's emotion, important reminders can be given priority. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of reminders.

[0116] The reminder unit can cooperate with the user's smart home device when setting a reminder and notify the user by voice, smell, or brightness. The reminder unit cooperates with the user's smart home device when setting a reminder, for example, using AI, and notifies the user of the reminder. For example, the reminder unit can notify the user of the reminder by voice through the user's smart speaker. The reminder unit can also notify the user of the reminder by brightness using the user's smart light. Furthermore, the reminder unit can also notify the user of the reminder by smell using the user's smart diffuser. This allows the user to be notified of the reminder in various ways by cooperating with the smart home device. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input data of the smart home device into AI and cause the AI ​​to execute a reminder notification method.

[0117] The reminder unit can provide an appropriate reminder based on the geographical location information of the user when setting a reminder. The reminder unit, for example, uses AI to consider the geographical location information of the user when setting a reminder and provides an optimal reminder. For example, when the user is in a specific location, the reminder unit can provide a reminder related to that location. In addition, when the user is moving, the reminder unit can also provide a reminder related to the destination. Furthermore, the reminder unit can also provide an optimal reminder based on the geographical location information of the user. In this way, by considering the geographical location information, an optimal reminder can be provided to the user. A part or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminder unit can input the geographical location information data of the user to AI and cause the AI ​​to generate a reminder.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The health management system further monitors the user's sleep pattern, and the analysis unit can evaluate the user's health condition based on the collected sleep data. For example, the collection unit can collect sleep data from a smart watch or a smart bed, and the analysis unit can evaluate the quality and duration of sleep. The suggestion unit can provide advice for improving the quality of sleep. For example, the suggestion unit can suggest an appropriate bedtime and relaxation method to the user. This enables health management that takes into account the user's sleep pattern.

[0120] The health management system further monitors the user's water intake, and the analysis unit can evaluate the user's health condition based on the collected water intake data. For example, the collection unit can collect water intake data from a smart bottle or a smart watch, and the analysis unit can evaluate the user's water intake. The suggestion unit can suggest an appropriate water intake. For example, the suggestion unit can suggest to the user that the user set a daily water intake goal and take in water at an appropriate time. This enables health management that takes into account the user's water intake.

[0121] The health management system further monitors the user's heart rate, and the analysis unit can evaluate the user's health condition based on the collected heart rate data. For example, the collection unit can collect heart rate data from a smart watch or a heart rate sensor, and the analysis unit can evaluate the fluctuation of the user's heart rate. The suggestion unit can provide advice for stabilizing the heart rate. For example, the suggestion unit can suggest relaxation methods and appropriate exercises to the user. This enables health management that takes the user's heart rate into consideration.

[0122] The health management system further monitors the user's body temperature, and the analysis unit can evaluate the user's health condition based on the collected body temperature data. For example, the collection unit can collect body temperature data from a smart watch or a thermometer, and the analysis unit can evaluate fluctuations in the user's body temperature. The suggestion unit can provide advice for stabilizing the body temperature. For example, the suggestion unit can suggest appropriate clothing and environmental adjustments to the user. This enables health management that takes the user's body temperature into consideration.

[0123] The health management system further monitors the user's blood pressure, and the analysis unit can evaluate the user's health condition based on the collected blood pressure data. For example, the collection unit can collect blood pressure data from a smart watch or a blood pressure monitor, and the analysis unit can evaluate fluctuations in the user's blood pressure. The suggestion unit can provide advice for stabilizing blood pressure. For example, the suggestion unit can suggest appropriate meals and exercise to the user. This enables health management that takes the user's blood pressure into consideration.

[0124] The health management system can further estimate the user's emotions and provide stress management advice based on the estimated user's emotions. For example, the analysis unit can evaluate the stress level based on the user's emotion data, and the suggestion unit can provide advice for reducing stress. For example, the suggestion unit can suggest relaxation methods and stress relief methods to the user. This enables stress management that takes the user's emotions into consideration.

[0125] The health management system can further estimate the user's emotions and adjust the suggested exercises based on the estimated user's emotions. For example, the suggestion unit can suggest relaxing exercises when the user is stressed, or suggest higher intensity exercises when the user is relaxed. This makes it possible to suggest exercises according to the user's emotions.

[0126] The health management system can further estimate the user's emotions and adjust the meal suggestions based on the estimated user's emotions. For example, the suggestion unit can suggest a meal that will relax the user when the user is stressed. Also, the suggestion unit can suggest a nutritionally balanced meal when the user is relaxed. This makes it possible to suggest meals according to the user's emotions.

[0127] The health management system can further estimate the user's emotions and adjust the way health advice is presented based on the estimated user's emotions. For example, the advice unit can provide simple and easy-to-understand advice when the user is feeling stressed, and can provide detailed advice when the user is relaxed. This makes it possible to provide advice according to the user's emotions.

[0128] The health management system can further estimate the user's emotions and adjust the reminder notification method based on the estimated user's emotions. For example, the reminder unit can notify the user of a reminder with a gentle sound when the user is stressed, and can notify the user of a reminder with a normal sound when the user is relaxed. This makes it possible to notify the user of a reminder according to the user's emotions.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The collection unit collects information on the user's daily exercise and diet. The collection unit can collect information from devices such as a smartwatch or a smartphone. If the user wears a smartwatch, the collection unit collects information on the exercise and diet from the device. If the user inputs diet information using a smartphone, the collection unit can also collect the information. In addition, the collection unit can collect data from environmental sensors and analyze the health condition taking into account the environmental conditions indicated by the collected data. Step 2: The analysis unit analyzes the user's health condition based on the information collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected data and evaluates the user's health condition. If the user does not exercise much or has an unbalanced diet, the health condition is analyzed based on that information. Step 3: The suggestion unit makes suggestions for diet and exercise that match the user's health goals based on the analysis results by the analysis unit. The suggestion unit makes suggestions that match the user's health goals, for example, using AI. If the user wants to lose weight, it can suggest calorie restriction and exercise. Also, if the user wants to increase muscle strength, it can suggest a high-protein diet and strength training. Step 4: The advice unit provides health advice for the symptoms input by the user. The advice unit can, for example, use AI to provide general health advice for the user's symptoms. If the user experiences a headache, health advice can be provided for that symptom. In addition, the advice unit can also assess the user's stress level and provide advice on stress management. Step 5: The reminder section sets reminders for the time to take medicine and the date of the next medical checkup. For example, the reminder section can use AI to set reminders and notify the user so that the user does not forget to take medicine. The reminder section can also sync the next medical checkup date with a calendar app and set a reminder.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0133] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the camera 42 and the microphone 38B of the smart device 14 can be used to collect information on the user's daily exercise amount and diet. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the user's health condition. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and makes suggestions on diet and exercise according to the user's health goals. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides health advice for symptoms input by the user. The reminder unit is realized by, for example, the control unit 46A of the smart device 14, and sets reminders for the time to take medicine and the date of the next health check, and notifies the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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, the RAM 30, and the 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] 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 performed in a secure state.

[0142] Fig. 4 shows an example of 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0148] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] For example, the collection unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the camera 42 and the microphone 238 of the smart glasses 214 can be used to collect information on the user's daily exercise amount and diet. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the user's health condition. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and makes suggestions on diet and exercise according to the user's health goals. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides health advice for symptoms input by the user. The reminder unit is realized by, for example, the control unit 46A of the smart glasses 214, and sets reminders for the time to take medicine and the date of the next health check, and notifies the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] 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, the RAM 30, and the 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.

[0154] 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.

[0155] 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 the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0157] 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 performed in a secure state.

[0158] 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.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 identification processing unit 290 using these models.

[0162] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] 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 voice indicating a user input for 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.

[0164] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0166] For example, the collection unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the camera 42 and the microphone 238 of the headset type terminal 314 can be used to collect information on the user's daily exercise amount and diet. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the user's health condition. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and makes suggestions on diet and exercise according to the user's health goals. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides health advice for symptoms input by the user. The reminder unit is realized by, for example, the control unit 46A of the headset type terminal 314, and sets reminders for the time to take medicine and the date of the next health check, and notifies the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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, the RAM 30, and the 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.

[0170] 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.

[0171] 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 the voice according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0173] 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 performed in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0175] 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.

[0176] 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.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0178] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0179] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0180] 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 a voice indicating a user input for 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.

[0181] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0182] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0183] For example, the collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the camera 42 and the microphone 238 of the robot 414 can be used to collect information on the user's daily exercise amount and diet. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the user's health condition. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and makes suggestions on diet and exercise according to the user's health goals. The advice unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides health advice for symptoms input by the user. The reminder unit is realized by, for example, the control unit 46A of the robot 414, and sets reminders for the time to take medicine and the date of the next health check, and notifies the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0185] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0187] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0188] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0189] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0191] 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.

[0192] 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 Universal Serial Bus (USB) 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.

[0193] In addition, 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 upon request from the data processing device 12.

[0194] 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.

[0195] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0196] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0197] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0198] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0200] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0202] (Appendix 1) A collection unit that collects information on the amount of daily exercise and meals of a user; an analysis unit that analyzes a health condition of the user based on the information collected by the collection unit; A suggestion unit that suggests meals and exercises in accordance with the health goals of the user based on the analysis results by the analysis unit; an advice unit that provides health advice for the symptoms input by the user; A reminder unit for setting a reminder for the time to take medicine and the date of the next medical checkup. A system characterized by: (Appendix 2) The collecting unit includes: Collecting information on the user's activity and diet from at least one device of the user's smartwatch 2. The system of claim 1, (Appendix 3) The advice unit, Evaluating the user's stress level and providing appropriate advice regarding stress management 2. The system of claim 1, (Appendix 4) The reminder unit includes: Sync with the user's calendar app and set the reminder appropriately in the calendar app. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collecting data from environmental sensors; The analysis unit includes: Analyzing health conditions based on environmental conditions indicated by the data collected by the collecting unit 2. The system of claim 1, (Appendix 6) The advice unit, Providing personalized and appropriate health advice to the user based on the user's genetic information. 2. The system of claim 1, (Appendix 7) The suggestion unit, Suggesting the intensity and type of exercise based on the user's exercise habits 2. The system of claim 1, (Appendix 8) The suggestion unit, Suggesting suitable nearby exercise facilities and health food stores based on the user's geographic location information 2. The system of claim 1, (Appendix 9) The reminder unit includes: The system cooperates with the user's smart home devices to notify the user of the reminder by sound, smell, or brightness. 2. The system of claim 1, (Appendix 10) The suggestion unit, Rewarding the user when the user achieves certain health goals 2. The system of claim 1, (Appendix 11) The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of exercise amount and meal information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Analyze the user's past exercise and diet history and select the appropriate collection method 2. The system of claim 1, (Appendix 13) The collecting unit includes: At the time of collection, filtering is performed based on the user's current health status and lifestyle habits. 2. The system of claim 1, (Appendix 14) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The collecting unit includes: At the time of collection, the collection of information that is most relevant is prioritized based on the user's geographic location. 2. The system of claim 1, (Appendix 16) The collecting unit includes: At the time of collection, we analyze your social media activity and collect related information. 2. The system of claim 1, (Appendix 17) The analysis unit includes: To estimate a user's emotion and adjust a health condition analysis method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 18) The analysis unit includes: During analysis, predict your current health status based on your past health data 2. The system of claim 1, (Appendix 19) The analysis unit includes: During analysis, the health status of the user is evaluated based on the user's genetic information. 2. The system of claim 1, (Appendix 20) The analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 21) The analysis unit includes: During analysis, the health status of the user is evaluated based on the user's living environment data. 2. The system of claim 1, (Appendix 22) The analysis unit includes: During analysis, predict health status based on the user's diet and exercise history 2. The system of claim 1, (Appendix 23) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of your health goal 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the user's exercise habits and dietary preferences. 2. The system of claim 1, (Appendix 26) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 27) The suggestion unit, At the time of suggestion, the system will suggest suitable nearby exercise facilities and health food stores based on the user's geographic location. 2. The system of claim 1, (Appendix 28) The suggestion unit, When suggested, reward users when they achieve certain health goals 2. The system of claim 1, (Appendix 29) The advice unit, Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The advice unit, When providing advice, the system refers to the user's past health data to provide appropriate advice. 2. The system of claim 1, (Appendix 31) The advice unit, When providing advice, the system provides personalized advice based on the user's genetic information. 2. The system of claim 1, (Appendix 32) The advice unit, Estimate the user's emotions and prioritize advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 33) The advice unit, When providing advice, the system refers to the user's living environment data and provides appropriate advice. 2. The system of claim 1, (Appendix 34) The advice unit, When providing advice, the social media activity of the user is analyzed to provide relevant advice. 2. The system of claim 1, (Appendix 35) The reminder unit includes: Infer user emotions and adjust reminder notification methods based on the estimated user emotions 2. The system of claim 1, (Appendix 36) The reminder unit includes: When setting a reminder, provide appropriate reminders by referring to the user's past actions. 2. The system of claim 1, (Appendix 37) The reminder unit includes: When setting a reminder, sync with the user's calendar app and customize the reminder 2. The system of claim 1, (Appendix 38) The reminder unit includes: Infer user emotions and prioritize reminders based on the inferred user emotions 2. The system of claim 1, (Appendix 39) The reminder unit includes: When a reminder is set, it will link with the user's smart home devices and notify them with sound, smell, or light. 2. The system of claim 1, (Appendix 40) The reminder unit includes: When setting a reminder, provide appropriate reminders based on the user's geographic location 2. The system of claim 1, [Explanation of symbols]

[0203] 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 collection unit that collects information on the amount of daily exercise and meals of a user; an analysis unit that analyzes a health condition of the user based on the information collected by the collection unit; A suggestion unit that suggests meals and exercises in accordance with the health goals of the user based on the analysis results by the analysis unit; an advice unit that provides health advice for the symptoms input by the user; A reminder unit for setting a reminder for the time to take medicine and the date of the next medical checkup. A system characterized by:

2. The collecting unit includes: Collecting information on the user's activity and diet from at least one device of the user's smartwatch 2. The system of claim 1 .

3. The advice unit, Evaluating the user's stress level and providing appropriate advice regarding stress management 2. The system of claim 1 .

4. The reminder unit includes: Sync with the user's calendar app and set the reminder appropriately in the calendar app.

2. The system of claim 1 .

5. The collecting unit includes: Collecting data from environmental sensors; The analysis unit includes: Analyzing health conditions based on environmental conditions indicated by the data collected by the collecting unit 2. The system of claim 1 .

6. The advice unit, Providing personalized and appropriate health advice to the user based on the user's genetic information.

2. The system of claim 1 .

7. The suggestion unit, Suggesting the intensity and type of exercise based on the user's exercise habits 2. The system of claim 1 .

8. The suggestion unit, Suggesting suitable nearby exercise facilities and health food stores based on the user's geographic location information 2. The system of claim 1 .

9. The reminder unit includes: The system cooperates with the user's smart home devices to notify the user of the reminder by sound, smell, or brightness.

2. The system of claim 1 .

10. The suggestion unit, Rewarding the user when the user achieves certain health goals 2. The system of claim 1 .

Images