System

Abstract

To provide a system according to an embodiment which generates nutrition information and exercise plans personalized on the basis of physical information of a user.SOLUTION: The system according to an embodiment includes a collection unit, a reception unit, a generation unit, and a proposition unit. The collection unit collects physical information of a user. The reception unit receives a health goal of a user. The generation unit generates nutrition information and exercise plans on the basis of the health goal received by the reception unit and the information collected by the collection unit. The proposition unit provides information generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, it is difficult to automatically generate a personalized health management plan based on a user's physical information, and there is room for improvement.

[0005] The system according to the embodiment aims to generate personalized nutritional information and exercise plans based on the user's physical information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a reception unit, a generation unit, and a provision unit. The collection unit collects physical information of a user. The reception unit receives a health goal of the user. The generation unit generates nutritional information and an exercise plan based on the information collected by the collection unit and the health goal received by the reception unit. The provision unit provides the information generated by the generation unit. Effect of the Invention

[0007] According to an embodiment, the system can generate personalized nutritional information and exercise plans based on the user's physical 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) The automatic health management platform according to the embodiment of the present invention is a system in which a user inputs personal physical information and health goals, and a generation AI generates personalized nutritional information and exercise plans. In this system, a user inputs personal physical information and health goals, and a generation AI generates nutritional information and exercise plans based on these information. The generated information is provided to the user, and the user's progress is tracked through real-time monitoring and reminder functions. In addition, consultations with experts are also possible, allowing the user to effectively manage their health while saving time and effort. In addition, monetization is achieved through partnerships and advertising revenue, and health promotion and improvement of quality of life are realized. For example, a user inputs information such as height, weight, age, sex, exercise habits, and dietary contents into an AI assistant. This information is collected by a collection unit (which may include AI processing) and accepted by a reception unit (which may include AI processing). Next, based on the information collected and accepted by the collection unit and the reception unit, a generation unit (including AI processing or generation AI processing) generates nutritional information and exercise plans. For example, a daily calorie intake and an exercise menu are suggested based on the user's target weight and exercise goals. The generated information is provided to the user by the providing unit (which may include AI processing). The user plans and executes daily meals and exercise based on the provided information. Furthermore, the monitoring unit (which may include AI processing) monitors the user's progress in real time. For example, if the user is using a smartwatch or fitness tracker, data acquired from these devices is sent to the monitoring unit, and the user's activity level and calorie consumption are tracked. Based on the monitoring results, the reminder unit (which may include AI processing) sends reminders to the user. For example, if a lack of exercise or excess calories is detected, a reminder to encourage the user to exercise or a reminder to suggest a review of the user's diet is sent. In this way, the user can effectively manage their health while saving time and effort. In addition, consultations with experts are also possible, and the user can receive advice from experts as needed. Furthermore, monetization is achieved through partnerships and advertising revenue, and health promotion and quality of life improvement are realized.This enables the automated health management platform to provide personalized nutritional information and exercise plans based on the user's physical information and health goals.

[0029] The automatic health management platform according to the embodiment includes a collection unit, a reception unit, a generation unit, and a provision unit. The collection unit collects physical information of a user. The physical information of the user includes, for example, heart rate, body temperature, blood pressure, and the like, but is not limited to such examples. The collection unit can collect such information using, for example, a smart watch or a fitness tracker. The collection unit can also collect information manually input by the user. For example, the collection unit collects information such as weight and dietary details input by the user through an application. The reception unit accepts the user's health goals. The health goals include, for example, weight loss, muscle strengthening, blood pressure management, and the like, but is not limited to such examples. The reception unit can accept, for example, the health goals input by the user through an application. The generation unit generates nutritional information and an exercise plan based on the information collected by the collection unit and the health goals accepted by the reception unit. The generation unit, for example, uses a generation AI to suggest a daily calorie intake and an exercise menu based on the user's target weight and exercise goals. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI receives a prompt such as "Calculate the daily calorie intake based on the user's target weight" and calculates the calorie intake. The provision unit provides the information generated by the generation unit to the user. The provision unit can display nutritional information and an exercise plan to the user through an application, for example. The provision unit can also notify the user of the generated information. For example, the provision unit sends a notification to the user's smartphone to inform the user of the generated information. As a result, the automatic health management platform according to the embodiment can provide personalized nutritional information and an exercise plan based on the user's physical information and health goals.

[0030] The collection unit collects the user's physical information. The user's physical information includes, but is not limited to, for example, heart rate, body temperature, and blood pressure. The collection unit can collect these pieces of information using, for example, a smart watch or a fitness tracker. Specifically, the smart watch is worn on the user's wrist, and a built-in sensor measures the heart rate, body temperature, and blood pressure in real time. These pieces of data are transmitted to a smartphone or a cloud server via Bluetooth (registered trademark) or Wi-Fi. The fitness tracker similarly monitors the user's physical activity and collects data such as the number of steps, calories burned, and sleep patterns. Furthermore, the collection unit can also collect information manually input by the user. For example, the collection unit collects information such as weight and dietary details input by the user through an application. The user can input daily dietary details, calorie intake, and weight fluctuations through the interface of the application. This allows the collection unit to collect the user's physical information from various angles and accumulate detailed health data. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis unit and generation unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The reception unit receives the health goal of the user. The health goal includes, for example, weight loss, muscle strengthening, blood pressure management, and the like, but is not limited to such examples. The reception unit can receive, for example, the health goal input by the user through the application. Specifically, the user can input his / her own health goal in detail on the setting screen of the application. For example, if the goal is weight loss, the user can input the target weight and the period to be achieved. If the goal is muscle strengthening, the user can set a specific muscle training goal and frequency. If the goal is blood pressure management, the user can input the target blood pressure value and improvements to daily lifestyle habits. This allows the reception unit to grasp the individual health goal of the user in detail and use it for subsequent processing. Furthermore, the reception unit can provide appropriate advice and reminders based on the user's health goal. For example, the reception unit periodically checks the progress of the goal set by the user and suggests reviewing or adjusting the goal as necessary. Furthermore, the reception unit can collect the user's feedback and continuously provide support for achieving the goal. This allows the reception unit to effectively support the user's health goal and maintain motivation for achieving the goal.

[0032] The generating unit generates nutritional information and an exercise plan based on the information collected by the collecting unit and the health goal accepted by the accepting unit. The generating unit, for example, uses a generating AI to suggest a daily calorie intake and an exercise menu based on the user's target weight and exercise goal. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Specifically, the generating AI receives the user's physical information and health goal as input, and generates optimal nutritional information and an exercise plan. For example, the generating AI receives a prompt "Calculate the daily calorie intake based on the user's target weight" and calculates the calorie intake. Furthermore, the generating AI considers the user's exercise history and current fitness level to suggest an appropriate exercise menu. For example, an exercise plan according to the user's needs can be generated, ranging from light exercise for beginners to high-intensity training for advanced users. In addition, the generating AI analyzes the user's diet history and nutritional balance, and suggests a meal menu containing the necessary nutrients. This allows the generating unit to provide personalized nutritional information and exercise plans according to the user's individual needs. Furthermore, the generation unit can continuously monitor the effectiveness of the generated plan and adjust the plan as necessary. For example, the generation unit can review the exercise menu and nutritional information based on the user's progress and feedback to provide a more effective plan. This allows the generation unit to strongly support the user in achieving their health goals and realize sustainable health management.

[0033] The providing unit provides the information generated by the generating unit to the user. The providing unit can display, for example, nutritional information and an exercise plan to the user through an application. Specifically, the generated nutritional information and exercise plan are visually displayed on the dashboard of the application, and the user can check the necessary information at a glance. The providing unit can also notify the user of the generated information. For example, the providing unit sends a notification to the user's smartphone to notify the user of the generated information. This allows the user to take action at the appropriate time without missing important information. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information to be provided. For example, after the user executes the provided exercise plan, the user can input the effect and impressions as feedback. Based on this feedback, the providing unit reviews and improves the plan in cooperation with the generating unit. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered using not only smartphone notifications but also email, SMS, voice calls, and the like. This allows the providing unit to quickly and reliably provide information to the user and support health management. Furthermore, the providing unit can monitor the user's progress in real time and provide reminders and advice as necessary, thereby enabling the providing unit to continuously support the user's health management and maintain motivation to achieve goals.

[0034] The generation unit includes a monitoring unit that monitors the user's progress in real time based on the generated information. The monitoring unit monitors the user's progress in real time based on the information generated by the generation unit. A specific time range of real time includes, for example, seconds, minutes, hours, etc., but is not limited to such examples. The monitoring unit, for example, analyzes data acquired from a smart watch or a fitness tracker in real time to track the user's activity level and calorie consumption. For example, the monitoring unit displays the number of steps the user has walked in a day and the calories consumed in real time. The monitoring unit can also monitor data such as the user's heart rate and body temperature in real time. For example, the monitoring unit displays an alert when the user's heart rate is abnormally high. This improves the accuracy of health management by monitoring the user's progress in real time. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from a smart watch to AI, and the AI ​​can analyze the data to monitor the user's progress.

[0035] The monitoring unit includes a reminder unit that transmits a reminder to the user based on the monitoring result. The reminder unit transmits a reminder to the user based on the monitoring result obtained by the monitoring unit. Specific contents and transmission methods of the reminder include, for example, a notification format, a transmission timing, customization of the contents, and the like, but are not limited to such examples. For example, the reminder unit transmits a reminder to encourage exercise when the user is not exercising enough. For example, the reminder unit transmits a notification to the user's smartphone saying, "You have not exercised yet today. We recommend walking for 30 minutes." The reminder unit can also transmit a reminder suggesting a review of a meal when the user is over-calorie. For example, the reminder unit transmits a notification saying, "Today's calorie intake exceeds the target. Please review your dinner menu." In this way, the reminder is transmitted based on the monitoring result to support the user's health management. A part or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input data obtained from the monitoring unit into an AI, which can then analyze the data to generate a reminder and send it to the user.

[0036] The collection unit analyzes the user's past physical information and selects an appropriate collection method. The collection unit analyzes the user's past physical information and selects the optimal collection method. A specific range of past physical information and an analysis method include, for example, data from the past week, data from the past month, etc., but are not limited to such examples. A specific type of collection method and a selection criterion include, for example, a wearable device, manual input, etc., but are not limited to such examples. The collection unit selects, for example, a collection method that can obtain the most accurate data from the user's past data. For example, the collection unit uses the wearable device when it analyzes the past data and determines that a collection method using a wearable device is the most accurate. The collection unit can also optimize the collection frequency from the user's past data. For example, when the collection unit determines that collection once a day is optimal based on the past data, it collects data once a day. Furthermore, the collection unit can also select a collection method for a specific time period from the user's past data. For example, if the collection unit determines that collection is optimal in the morning based on past data, the collection unit collects data in the morning. This allows the optimal collection method to be selected by analyzing past physical information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data into AI, which then analyzes the data and selects the optimal collection method.

[0037] The collection unit performs filtering based on the user's current health condition and lifestyle habits when collecting physical information. The collection unit performs filtering based on the user's current health condition and lifestyle habits when collecting physical information. Specific criteria and methods for filtering include, for example, the importance and reliability of data, but are not limited to such examples. For example, when the user is in good health, the collection unit collects detailed data. For example, the collection unit collects detailed data such as the user's heart rate, body temperature, and blood pressure. In addition, when the user is in poor health, the collection unit can also collect only basic data. For example, the collection unit collects basic data such as the user's weight and dietary content. Furthermore, the collection unit can select the type of data to be collected based on the user's lifestyle habits. For example, when the user has an exercise habit, the collection unit preferentially collects data related to exercise. As a result, filtering based on the current health condition and lifestyle habits improves the accuracy of the collected data. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data about the user's health condition and lifestyle habits into the AI, which can then analyze and filter the data.

[0038] When collecting physical information, the collection unit preferentially collects highly relevant information in consideration of the geographical location information of the user. When collecting physical information, the collection unit preferentially collects highly relevant information in consideration of the geographical location information of the user. Specific methods of acquiring and using geographical location information include, for example, GPS data, location information services, and the like, but are not limited to such examples. For example, when the user is at high altitude, the collection unit preferentially collects oxygen concentration and heart rate. For example, the collection unit preferentially collects data related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the collection unit can also preferentially collect air quality and noise level. For example, the collection unit preferentially collects data related to health management in urban areas based on the user's location information. Furthermore, when the user is exercising, the collection unit can also preferentially collect exercise intensity and calorie consumption. For example, the collection unit preferentially collects data related to health management during exercise based on the user's location information. In this way, highly relevant information can be preferentially collected by considering the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information to AI, which analyzes the data and preferentially collects highly relevant information.

[0039] The collection unit analyzes the user's social media activity when collecting the physical information, and collects related information. The collection unit analyzes the user's social media activity when collecting the physical information, and collects related information. Specific analysis methods and usage methods of social media activity include, for example, the content of the post, the number of likes, comments, and the like, but are not limited to such examples. For example, when the user posts about exercise on social media, the collection unit preferentially collects exercise data. For example, the collection unit analyzes the user's social media post and preferentially collects data related to exercise. In addition, when the user posts about food, the collection unit can also preferentially collect food data. For example, the collection unit analyzes the user's social media post and preferentially collects data related to food. In addition, when the user posts about stress, the collection unit can also preferentially collect stress-related data. For example, the collection unit analyzes the user's social media post and preferentially collects data related to stress. In this way, related information 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 users' social media activity into the AI, which can then analyze the data and collect relevant information.

[0040] The reception unit selects the optimal reception method by referring to the user's past goal achievement history when receiving the health goal. The reception unit selects the optimal reception method by referring to the user's past goal achievement history when receiving the health goal. The specific range and reference method of the goal achievement history include, for example, past goal achievement rate and achievement period, but are not limited to such examples. The reception unit, for example, proposes a realistic goal based on the goals the user has achieved in the past. For example, the reception unit analyzes the user's past goal achievement history and proposes a new goal based on the goals achieved in the past. The reception unit can also select the optimal goal setting method from the user's past goal achievement history. For example, the reception unit selects the most effective goal setting method based on the user's past goal achievement history. Furthermore, the reception unit can analyze the user's past goal achievement history and propose an easy-to-achieve goal. For example, the reception unit proposes an easy-to-achieve goal based on the user's past goal achievement history. As a result, the optimal reception method can be selected by referring to the past goal achievement history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input the user's past goal achievement history into the AI, which can then analyze the data and select the optimal reception method.

[0041] The reception unit customizes the goal based on the user's current health condition and lifestyle habits when receiving the health goal. The reception unit customizes the goal based on the user's current health condition and lifestyle habits when receiving the health goal. Specific methods and standards of customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the reception unit proposes a challenging goal when the user is in good health. For example, the reception unit analyzes the user's health condition and proposes a challenging goal when it is determined that the health condition is good. In addition, the reception unit can also propose a realistic goal when the user is in poor health. For example, the reception unit analyzes the user's health condition and proposes a realistic goal when it is determined that the health condition is poor. Furthermore, the reception unit can also customize an achievable goal based on the user's lifestyle. For example, the reception unit analyzes the user's lifestyle habits and proposes an achievable goal based on the lifestyle habits. As a result, an achievable goal can be set by customizing the goal based on the current health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception department can input data about the user's health condition and lifestyle habits into the AI, which can then analyze the data and customize goals.

[0042] The reception unit preferentially receives a highly relevant goal in consideration of the geographical location information of the user when receiving the health goal. The reception unit preferentially receives a highly relevant goal in consideration of the geographical location information of the user when receiving the health goal. Specific methods of acquiring and using the geographical location information include, for example, GPS data, location information services, and the like, but are not limited to such examples. For example, when the user is at high altitude, the reception unit preferentially receives a goal related to oxygen concentration and heart rate. For example, the reception unit preferentially receives a goal related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the reception unit can also preferentially receive a goal related to air quality and noise level. For example, the reception unit preferentially receives a goal related to health management in an urban area based on the user's location information. Furthermore, when the user is exercising, the reception unit can also preferentially receive a goal related to exercise intensity and calorie consumption. For example, the reception unit preferentially receives a goal related to health management during exercise based on the user's location information. In this way, by considering the geographical location information, a highly relevant goal can be set preferentially. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's location information to AI, which analyzes the data and preferentially receives highly relevant targets.

[0043] The reception unit analyzes the social media activity of the user when receiving the health goal, and receives the related goal. The reception unit analyzes the social media activity of the user when receiving the health goal, and receives the related goal. Specific analysis methods and usage methods of the social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the reception unit preferentially receives the exercise goal. For example, the reception unit analyzes the social media post of the user and preferentially receives the goal related to exercise. In addition, when the user posts about food, the reception unit can also preferentially receive the food goal. For example, the reception unit analyzes the social media post of the user and preferentially receives the goal related to food. Furthermore, when the user posts about stress, the reception unit can also preferentially receive the goal of stress reduction. For example, the reception unit analyzes the social media post of the user and preferentially receives the goal related to stress. In this way, the related goal can be set by analyzing the social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media activities into an AI, which analyzes the data and receives related goals.

[0044] The generating unit generates an optimal plan by referring to the user's past health data when generating the nutritional information and the exercise plan. The generating unit generates an optimal plan by referring to the user's past health data when generating the nutritional information and the exercise plan. The specific range and the method of referring to the past health data include, for example, data from the past year, data from the past six months, etc., but are not limited to such examples. The generating unit generates the most effective nutritional information and exercise plan from, for example, the user's past health data. For example, the generating unit analyzes the user's past data and re-proposes a plan that was effective in the past. The generating unit can also analyze the user's past health data and customize the optimal plan. For example, the generating unit generates a plan tailored to the individual health condition based on the user's past data. As a result, the optimal plan can be generated by referring to the past health data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's past health data into AI, and the AI ​​can analyze the data to generate the optimal plan.

[0045] The generating unit customizes the plan based on the user's current health condition and lifestyle habits when generating the nutrition information and the exercise plan. The generating unit customizes the plan based on the user's current health condition and lifestyle habits when generating the nutrition information and the exercise plan. Specific methods and criteria for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the generating unit generates a challenging plan when the user is in good health. For example, the generating unit analyzes the user's health condition and generates a challenging plan when it determines that the health condition is good. In addition, the generating unit can also generate a realistic plan when the user is in poor health. For example, the generating unit analyzes the user's health condition and generates a realistic plan when it determines that the health condition is poor. Furthermore, the generating unit can also customize an achievable plan based on the user's lifestyle. For example, the generating unit analyzes the user's lifestyle habits and generates an achievable plan based on the lifestyle habits. As a result, an achievable plan can be provided by customizing the plan based on the current health condition and lifestyle habits. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's health condition and lifestyle habits into AI, which can analyze the data and customize the plan.

[0046] The generating unit generates a plan with high relevance in consideration of the geographical location information of the user when generating the nutritional information and the exercise plan. The generating unit generates a plan with high relevance in consideration of the geographical location information of the user when generating the nutritional information and the exercise plan. Specific methods of acquiring and using the geographical location information include, but are not limited to, GPS data, location information services, and the like. For example, when the user is at high altitude, the generating unit generates a plan suitable for exercise at high altitude. For example, the generating unit generates a plan related to exercise at high altitude based on the user's location information. In addition, when the user is in an urban area, the generating unit can also generate a plan suitable for exercise in the urban area. For example, the generating unit generates a plan related to exercise in the urban area based on the user's location information. Furthermore, when the user is exercising, the generating unit can also generate a plan according to the exercise intensity. For example, the generating unit generates a plan related to health management during exercise based on the user's location information. As a result, by considering the geographical location information, it is possible to provide a plan with high relevance in priority. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's location information into the AI, which can then analyze the data and prioritize generating plans that are highly relevant.

[0047] The generating unit analyzes the user's social media activity when generating the nutrition information and the exercise plan, and generates a related plan. The generating unit analyzes the user's social media activity when generating the nutrition information and the exercise plan, and generates a related plan. Specific methods of analyzing and using the social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the generating unit generates an exercise plan preferentially. For example, the generating unit analyzes the user's social media post and preferentially generates a plan related to exercise. In addition, when the user posts about food, the generating unit can also generate a meal plan preferentially. For example, the generating unit analyzes the user's social media post and preferentially generates a plan related to food. Furthermore, when the user posts about stress, the generating unit can also generate a stress reduction plan preferentially. For example, the generating unit analyzes the user's social media post and preferentially generates a plan related to stress. In this way, a related plan can be provided by analyzing social media activity. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's social media activity into an AI, which analyzes the data and generates a related plan.

[0048] The providing unit selects the optimal information providing method by referring to the user's past usage history when providing information. The providing unit selects the optimal information providing method by referring to the user's past usage history when providing information. The specific range of the usage history and the reference method include, for example, past usage frequency and usage content, but are not limited to such examples. The providing unit selects the most effective information providing method, for example, from the user's past usage history. For example, the providing unit analyzes the user's past usage history and re-proposes a providing method that was effective in the past. The providing unit can also analyze the user's past usage history and provide information with high usage frequency preferentially. For example, the providing unit provides information with high usage frequency preferentially based on the user's past usage history. Furthermore, the providing unit can select a customized information providing method based on the user's past usage history. For example, the providing unit selects an information providing method tailored to individual needs based on the user's past usage history. As a result, the optimal information providing method can be selected by referring to the past usage history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input a user's past usage history into the AI, which can then analyze the data and select the optimal provision method.

[0049] The providing unit customizes the provided content based on the user's current health condition and lifestyle habits when providing information. The providing unit customizes the provided content based on the user's current health condition and lifestyle habits when providing information. Specific methods and standards for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the providing unit provides challenging information when the user is in good health. For example, the providing unit analyzes the user's health condition and provides challenging information when it is determined that the health condition is good. In addition, the providing unit can also provide realistic information when the user is in poor health. For example, the providing unit analyzes the user's health condition and provides realistic information when it is determined that the health condition is poor. Furthermore, the providing unit can also customize achievable information based on the user's lifestyle. For example, the providing unit analyzes the user's lifestyle habits and provides achievable information based on the lifestyle habits. As a result, achievable information can be provided by customizing the provided content based on the current health condition and lifestyle habits. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input data about the user's health condition and lifestyle habits into the AI, which can then analyze the data and customize the content provided.

[0050] The providing unit provides highly relevant information preferentially in consideration of the geographical location information of the user when providing information. The providing unit provides highly relevant information preferentially in consideration of the geographical location information of the user when providing information. Specific methods of acquiring and using geographical location information include, for example, GPS data, location information services, etc., but are not limited to such examples. For example, when the user is at high altitude, the providing unit preferentially provides information related to health management at high altitude. For example, the providing unit preferentially provides information related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the providing unit can also preferentially provide information related to health management in urban areas. For example, the providing unit preferentially provides information related to health management in urban areas based on the user's location information. Furthermore, when the user is exercising, the providing unit can also preferentially provide information according to the exercise intensity. For example, the providing unit preferentially provides information related to health management during exercise based on the user's location information. In this way, highly relevant information can be provided preferentially by considering the geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information to AI, which analyzes the data and provides highly relevant information preferentially.

[0051] The providing unit analyzes the social media activity of the user when providing the information, and provides the related information. The providing unit analyzes the social media activity of the user when providing the information, and provides the related information. Specific methods of analyzing and using the social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the providing unit provides information about exercise preferentially. For example, the providing unit analyzes the social media post of the user and provides information related to exercise preferentially. In addition, when the user posts about food, the providing unit can also provide information about food preferentially. For example, the providing unit analyzes the social media post of the user and provides information related to food preferentially. Furthermore, when the user posts about stress, the providing unit can also provide information about stress reduction preferentially. For example, the providing unit analyzes the social media post of the user and provides information related to stress preferentially. In this way, the related information can be provided by analyzing the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input a user's social media activity into the AI, which can then analyze the data and provide relevant information.

[0052] The monitoring unit selects the optimal monitoring method by referring to the user's past health data during monitoring. The monitoring unit selects the optimal monitoring method by referring to the user's past health data during monitoring. The specific range and reference method of the past health data include, for example, data from the past year, data from the past six months, etc., but are not limited to such examples. The monitoring unit selects the most effective monitoring method, for example, from the user's past health data. For example, the monitoring unit analyzes the user's past data and re-proposes a monitoring method that was effective in the past. The monitoring unit can also analyze the user's past health data and optimize the monitoring frequency. For example, the monitoring unit determines the optimal monitoring frequency based on the user's past data. Furthermore, the monitoring unit can select a customized monitoring method based on the user's past health data. For example, the monitoring unit selects a monitoring method that meets individual needs based on the user's past data. As a result, the optimal monitoring method can be selected by referring to the past health data. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past health data into the AI, which can then analyze the data and select the optimal monitoring method.

[0053] The monitoring unit customizes the monitoring content based on the user's current health condition and lifestyle habits during monitoring. The monitoring unit customizes the monitoring content based on the user's current health condition and lifestyle habits during monitoring. Specific methods and standards for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the monitoring unit monitors detailed data when the user is in good health. For example, the monitoring unit monitors detailed data such as the user's heart rate, body temperature, and blood pressure. In addition, the monitoring unit can monitor only basic data when the user is in poor health. For example, the monitoring unit monitors basic data such as the user's weight and dietary content. Furthermore, the monitoring unit can select the type of data to be monitored based on the user's lifestyle habits. For example, when the user has an exercise habit, the monitoring unit preferentially monitors data related to exercise. As a result, by customizing the monitoring content based on the current health condition and lifestyle habits, more appropriate data can be monitored. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data about the user's health condition and lifestyle habits into the AI, which can then analyze the data and customize the monitoring content.

[0054] The monitoring unit, during monitoring, prioritizes monitoring of highly relevant data in consideration of the geographical location information of the user. The monitoring unit, during monitoring, prioritizes monitoring of highly relevant data in consideration of the geographical location information of the user. Specific methods of acquiring and using geographical location information include, for example, GPS data, location information services, and the like, but are not limited to such examples. For example, when the user is at high altitude, the monitoring unit prioritizes monitoring of data related to health management at high altitude. For example, the monitoring unit prioritizes monitoring of data related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the monitoring unit can also prioritize monitoring of data related to health management in urban areas. For example, the monitoring unit prioritizes monitoring of data related to health management in urban areas based on the user's location information. Furthermore, when the user is exercising, the monitoring unit can also prioritize monitoring of data according to the exercise intensity. For example, the monitoring unit prioritizes monitoring of data related to health management during exercise based on the user's location information. As a result, by considering the geographical location information, highly relevant data can be prioritized. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's location information to AI, which analyzes the data and monitors highly relevant data preferentially.

[0055] The monitoring unit analyzes the user's social media activity during monitoring and monitors related data. The monitoring unit analyzes the user's social media activity during monitoring and monitors related data. Specific analysis and usage methods of social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the monitoring unit preferentially monitors data related to exercise. For example, the monitoring unit analyzes the user's social media post and preferentially monitors data related to exercise. In addition, when the user posts about food, the monitoring unit can also preferentially monitor data related to food. For example, the monitoring unit analyzes the user's social media post and preferentially monitors data related to food. In addition, when the user posts about stress, the monitoring unit can also preferentially monitor data related to stress. For example, the monitoring unit analyzes the user's social media post and preferentially monitors data related to stress. In this way, the related data can be monitored by analyzing social media activity. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input a user's social media activity into the AI, which can then analyze the data and monitor related data.

[0056] The reminder unit selects an optimal reminder by referring to the user's past behavior history when sending a reminder. The reminder unit selects an optimal reminder by referring to the user's past behavior history when sending a reminder. The specific range and reference method of the behavior history include, for example, past behavior patterns and behavior frequencies, but are not limited to such examples. The reminder unit selects the most effective reminder from, for example, the user's past behavior history. For example, the reminder unit analyzes the user's past behavior history and re-suggests a reminder that was effective in the past. The reminder unit can also analyze the user's past behavior history and optimize the sending frequency of the reminder. For example, the reminder unit determines the optimal sending frequency based on the user's past behavior history. Furthermore, the reminder unit can select a customized reminder based on the user's past behavior history. For example, the reminder unit selects a reminder that meets individual needs based on the user's past behavior history. This allows the optimal reminder to be selected by referring to the past behavior history. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input the user's past behavior history into AI, which can analyze the data and select the optimal reminder.

[0057] The reminder unit customizes the reminder content based on the user's current health condition and lifestyle habits when sending a reminder. The reminder unit customizes the reminder content based on the user's current health condition and lifestyle habits when sending a reminder. Specific methods and standards for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the reminder unit transmits a challenging reminder when the user is in good health. For example, the reminder unit analyzes the user's health condition and transmits a challenging reminder when it determines that the health condition is good. In addition, the reminder unit can also transmit a realistic reminder when the user is in poor health. For example, the reminder unit analyzes the user's health condition and transmits a realistic reminder when it determines that the health condition is poor. In addition, the reminder unit can also customize an achievable reminder based on the user's lifestyle habits. For example, the reminder unit analyzes the user's lifestyle and transmits achievable reminders based on the lifestyle. This allows the reminder content to be customized based on the user's current health condition and lifestyle, thereby transmitting more appropriate reminders. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit may input data on the user's health condition and lifestyle into AI, which may analyze the data and customize the reminder content.

[0058] The reminder unit, when sending a reminder, preferentially transmits a highly relevant reminder in consideration of the geographical location information of the user. The reminder unit, when sending a reminder, preferentially transmits a highly relevant reminder in consideration of the geographical location information of the user. Specific methods of acquiring and using the geographical location information include, but are not limited to, GPS data, location information services, and the like. For example, when the user is at high altitude, the reminder unit preferentially transmits a reminder related to health management at high altitude. For example, the reminder unit preferentially transmits a reminder related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the reminder unit can also preferentially transmit a reminder related to health management in the urban area. For example, the reminder unit preferentially transmits a reminder related to health management in the urban area based on the user's location information. In addition, when the user is exercising, the reminder unit can also preferentially transmit a reminder according to the exercise intensity. For example, the reminder unit preferentially transmits reminders related to health management during exercise based on the user's location information. This allows for preferential transmission of highly relevant reminders by taking into account geographical location information. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input the user's location information into AI, which analyzes the data and preferentially transmits highly relevant reminders.

[0059] The reminder unit analyzes the user's social media activity at the time of sending a reminder, and sends a related reminder. The reminder unit analyzes the user's social media activity at the time of sending a reminder, and sends a related reminder. Specific methods of analyzing and using social media activity include, for example, the content of posts, the number of likes, comments, and the like, but are not limited to such examples. For example, when a user posts about exercise on social media, the reminder unit preferentially sends a reminder about exercise. For example, the reminder unit analyzes the user's social media posts, and preferentially sends a reminder related to exercise. In addition, when a user posts about meals, the reminder unit can also preferentially send a reminder about meals. For example, the reminder unit analyzes the user's social media posts, and preferentially sends a reminder related to meals. In addition, when a user posts about stress, the reminder unit can also preferentially send a reminder about stress reduction. For example, the reminder unit may analyze a user's social media posts and prioritize sending reminders related to stress. This allows for relevant reminders to be sent by analyzing social media activity. Some or all of the above-described processing in the reminder unit may be performed, for example, using AI or may be performed without using AI. For example, the reminder unit may input the user's social media activity into an AI, which may analyze the data and send relevant reminders.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When collecting the user's physical information, the collection unit can select the optimal collection method by referring to the user's past health data. For example, if it is determined from the past data that data collected during a specific time period is most accurate, collection is performed during that time period. If it is determined from the past data that collection using a specific device is most effective, that device is used. If it is determined from the past data that collection is optimal at a specific frequency, collection is performed at that frequency. In this way, the optimal collection method can be selected by referring to the past health data.

[0062] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, when the user is at high altitude, the oxygen concentration and heart rate are preferentially collected. Specifically, data related to health management at high altitude is collected based on the user's location information. In addition, when the user is in an urban area, air quality and noise level can also be preferentially collected. For example, data related to health management in urban areas is collected based on the user's location information. Furthermore, when the user is exercising, exercise intensity and calorie consumption can also be preferentially collected. For example, data related to health management during exercise is collected based on the user's location information. In this way, highly relevant information can be preferentially collected by considering the geographical location information.

[0063] The generating unit can generate optimal nutritional information and exercise plans by referring to the user's past health data. For example, if it is determined from the past data that a particular nutritional information or exercise plan was most effective, the generating unit re-proposes that plan. In addition, it is also possible to customize a plan tailored to a particular health condition from the past data. For example, a plan tailored to an individual health condition is generated based on the user's past data. Furthermore, it is also possible to propose a plan that will be effective in a particular period of time from the past data. For example, a plan that will be effective in a short period of time is proposed based on the past data. In this way, it is possible to generate an optimal plan by referring to the past health data.

[0064] The monitoring unit can analyze the user's social media activity and monitor related data. For example, if the user posts about exercise on social media, the data about exercise is monitored preferentially. Specifically, the user's social media posts are analyzed and data related to exercise is collected. Also, if the user posts about food, the data about food can be monitored preferentially. For example, the user's social media posts are analyzed and data related to food is collected. Furthermore, if the user posts about stress, the stress-related data can be monitored preferentially. For example, the user's social media posts are analyzed and data related to stress is collected. In this way, related data can be monitored by analyzing social media activity.

[0065] When sending a reminder, the reminder unit can select an optimal reminder by referring to the user's past behavior history. For example, if it is determined from the past behavior history that a particular reminder was most effective, the reminder unit re-suggests that reminder. Also, if it is determined from the past behavior history that it is optimal to send a reminder at a particular frequency, the reminder unit sends the reminder at that frequency. Furthermore, if it is determined from the past behavior history that it is optimal to send a reminder during a particular time period, the reminder unit sends the reminder during that time period. In this way, the optimal reminder can be selected by referring to the past behavior history.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The collection unit collects the user's physical information. The user's physical information includes heart rate, body temperature, blood pressure, etc. The collection unit can collect this information using a smart watch or fitness tracker. The collection unit also collects information manually entered by the user. For example, information such as weight and dietary details entered by the user through an application is collected. Step 2: The reception unit receives the health goal of the user. The health goal includes weight loss, muscle building, blood pressure management, etc. The reception unit can receive the health goal input by the user through the application. Step 3: The generation unit generates nutritional information and an exercise plan based on the information collected by the collection unit and the health goals accepted by the acceptance unit. The generation unit uses a generation AI to suggest a daily calorie intake and an exercise menu based on the user's target weight and exercise goals. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit can display nutritional information and an exercise plan to the user through an application. The providing unit can also notify the user of the generated information. For example, the providing unit can send a notification to the user's smartphone to notify the user of the generated information.

[0068] (Example 2) The automatic health management platform according to the embodiment of the present invention is a system in which a user inputs personal physical information and health goals, and a generation AI generates personalized nutritional information and exercise plans. In this system, a user inputs personal physical information and health goals, and a generation AI generates nutritional information and exercise plans based on these information. The generated information is provided to the user, and the user's progress is tracked through real-time monitoring and reminder functions. In addition, consultations with experts are also possible, allowing the user to effectively manage their health while saving time and effort. In addition, monetization is achieved through partnerships and advertising revenue, and health promotion and improvement of quality of life are realized. For example, a user inputs information such as height, weight, age, sex, exercise habits, and dietary contents into an AI assistant. This information is collected by a collection unit (which may include AI processing) and accepted by a reception unit (which may include AI processing). Next, based on the information collected and accepted by the collection unit and the reception unit, a generation unit (including AI processing or generation AI processing) generates nutritional information and exercise plans. For example, a daily calorie intake and an exercise menu are suggested based on the user's target weight and exercise goals. The generated information is provided to the user by the providing unit (which may include AI processing). The user plans and executes daily meals and exercise based on the provided information. Furthermore, the monitoring unit (which may include AI processing) monitors the user's progress in real time. For example, if the user is using a smartwatch or fitness tracker, data acquired from these devices is sent to the monitoring unit, and the user's activity level and calorie consumption are tracked. Based on the monitoring results, the reminder unit (which may include AI processing) sends reminders to the user. For example, if a lack of exercise or excess calories is detected, a reminder to encourage the user to exercise or a reminder to suggest a review of the user's diet is sent. In this way, the user can effectively manage their health while saving time and effort. In addition, consultations with experts are also possible, and the user can receive advice from experts as needed. Furthermore, monetization is achieved through partnerships and advertising revenue, and health promotion and quality of life improvement are realized.This enables the automated health management platform to provide personalized nutritional information and exercise plans based on the user's physical information and health goals.

[0069] The automatic health management platform according to the embodiment includes a collection unit, a reception unit, a generation unit, and a provision unit. The collection unit collects physical information of a user. The physical information of the user includes, for example, heart rate, body temperature, blood pressure, and the like, but is not limited to such examples. The collection unit can collect such information using, for example, a smart watch or a fitness tracker. The collection unit can also collect information manually input by the user. For example, the collection unit collects information such as weight and dietary details input by the user through an application. The reception unit accepts the user's health goals. The health goals include, for example, weight loss, muscle strengthening, blood pressure management, and the like, but is not limited to such examples. The reception unit can accept, for example, the health goals input by the user through an application. The generation unit generates nutritional information and an exercise plan based on the information collected by the collection unit and the health goals accepted by the reception unit. The generation unit, for example, uses a generation AI to suggest a daily calorie intake and an exercise menu based on the user's target weight and exercise goals. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI receives a prompt such as "Calculate the daily calorie intake based on the user's target weight" and calculates the calorie intake. The provision unit provides the information generated by the generation unit to the user. The provision unit can display nutritional information and an exercise plan to the user through an application, for example. The provision unit can also notify the user of the generated information. For example, the provision unit sends a notification to the user's smartphone to inform the user of the generated information. As a result, the automatic health management platform according to the embodiment can provide personalized nutritional information and an exercise plan based on the user's physical information and health goals.

[0070] The collection unit collects the user's physical information. The user's physical information includes, but is not limited to, for example, heart rate, body temperature, and blood pressure. The collection unit can collect these pieces of information using, for example, a smart watch or a fitness tracker. Specifically, the smart watch is worn on the user's wrist, and a built-in sensor measures the heart rate, body temperature, and blood pressure in real time. These pieces of data are transmitted to a smartphone or a cloud server via Bluetooth or Wi-Fi. The fitness tracker similarly monitors the user's physical activity and collects data such as the number of steps, calories burned, and sleep patterns. In addition, the collection unit can also collect information manually entered by the user. For example, the collection unit collects information such as weight and dietary content entered by the user through an application. The user can input daily dietary content, calorie intake, and weight fluctuations through the interface of the application. This allows the collection unit to collect the user's physical information from various angles and accumulate detailed health data. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the analysis unit and generation unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0071] The reception unit receives the health goal of the user. The health goal includes, for example, weight loss, muscle strengthening, blood pressure management, and the like, but is not limited to such examples. The reception unit can receive, for example, the health goal input by the user through the application. Specifically, the user can input his / her own health goal in detail on the setting screen of the application. For example, if the goal is weight loss, the user can input the target weight and the period to be achieved. If the goal is muscle strengthening, the user can set a specific muscle training goal and frequency. If the goal is blood pressure management, the user can input the target blood pressure value and improvements to daily lifestyle habits. This allows the reception unit to grasp the individual health goal of the user in detail and use it for subsequent processing. Furthermore, the reception unit can provide appropriate advice and reminders based on the user's health goal. For example, the reception unit periodically checks the progress of the goal set by the user and suggests reviewing or adjusting the goal as necessary. Furthermore, the reception unit can collect the user's feedback and continuously provide support for achieving the goal. This allows the reception unit to effectively support the user's health goal and maintain motivation for achieving the goal.

[0072] The generating unit generates nutritional information and an exercise plan based on the information collected by the collecting unit and the health goal accepted by the accepting unit. The generating unit, for example, uses a generating AI to suggest a daily calorie intake and an exercise menu based on the user's target weight and exercise goal. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Specifically, the generating AI receives the user's physical information and health goal as input, and generates optimal nutritional information and an exercise plan. For example, the generating AI receives a prompt "Calculate the daily calorie intake based on the user's target weight" and calculates the calorie intake. Furthermore, the generating AI considers the user's exercise history and current fitness level to suggest an appropriate exercise menu. For example, an exercise plan according to the user's needs can be generated, ranging from light exercise for beginners to high-intensity training for advanced users. In addition, the generating AI analyzes the user's diet history and nutritional balance, and suggests a meal menu containing the necessary nutrients. This allows the generating unit to provide personalized nutritional information and exercise plans according to the user's individual needs. Furthermore, the generation unit can continuously monitor the effectiveness of the generated plan and adjust the plan as necessary. For example, the generation unit can review the exercise menu and nutritional information based on the user's progress and feedback to provide a more effective plan. This allows the generation unit to strongly support the user in achieving their health goals and realize sustainable health management.

[0073] The providing unit provides the information generated by the generating unit to the user. The providing unit can display, for example, nutritional information and an exercise plan to the user through an application. Specifically, the generated nutritional information and exercise plan are visually displayed on the dashboard of the application, and the user can check the necessary information at a glance. The providing unit can also notify the user of the generated information. For example, the providing unit sends a notification to the user's smartphone to notify the user of the generated information. This allows the user to take action at the appropriate time without missing important information. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information to be provided. For example, after the user executes the provided exercise plan, the user can input the effect and impressions as feedback. Based on this feedback, the providing unit reviews and improves the plan in cooperation with the generating unit. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered using not only smartphone notifications but also email, SMS, voice calls, and the like. This allows the providing unit to quickly and reliably provide information to the user and support health management. Furthermore, the providing unit can monitor the user's progress in real time and provide reminders and advice as necessary, thereby enabling the providing unit to continuously support the user's health management and maintain motivation to achieve goals.

[0074] The generation unit includes a monitoring unit that monitors the user's progress in real time based on the generated information. The monitoring unit monitors the user's progress in real time based on the information generated by the generation unit. A specific time range of real time includes, for example, seconds, minutes, hours, etc., but is not limited to such examples. The monitoring unit, for example, analyzes data acquired from a smart watch or a fitness tracker in real time to track the user's activity level and calorie consumption. For example, the monitoring unit displays the number of steps the user has walked in a day and the calories consumed in real time. The monitoring unit can also monitor data such as the user's heart rate and body temperature in real time. For example, the monitoring unit displays an alert when the user's heart rate is abnormally high. This improves the accuracy of health management by monitoring the user's progress in real time. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from a smart watch to AI, and the AI ​​can analyze the data to monitor the user's progress.

[0075] The monitoring unit includes a reminder unit that transmits a reminder to the user based on the monitoring result. The reminder unit transmits a reminder to the user based on the monitoring result obtained by the monitoring unit. Specific contents and transmission methods of the reminder include, for example, a notification format, a transmission timing, customization of the contents, and the like, but are not limited to such examples. For example, the reminder unit transmits a reminder to encourage exercise when the user is not exercising enough. For example, the reminder unit transmits a notification to the user's smartphone saying, "You have not exercised yet today. We recommend walking for 30 minutes." The reminder unit can also transmit a reminder suggesting a review of a meal when the user is over-calorie. For example, the reminder unit transmits a notification saying, "Today's calorie intake exceeds the target. Please review your dinner menu." In this way, the reminder is transmitted based on the monitoring result to support the user's health management. A part or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input data obtained from the monitoring unit into an AI, which can then analyze the data to generate a reminder and send it to the user.

[0076] The collection unit estimates the user's emotion and adjusts the collection timing of the physical information using a specific algorithm based on the estimated user's emotion. The collection unit estimates the user's emotion and adjusts the collection timing of the physical information based on the estimated user's emotion. Specific estimation methods and standards for emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. Specific adjustment methods and standards for the collection timing include, but are not limited to, time zones and the user's activity status. For example, when the user is feeling stressed, the collection unit delays the collection timing to collect data in a relaxed state. For example, the collection unit captures the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and delays the collection timing when the stress is high. In addition, the collection unit can also collect physical information immediately when the user is relaxed. For example, the collection unit analyzes the user's voice and collects data immediately when it is determined that the user is relaxed. Furthermore, the collection unit can also shorten the collection timing to collect information quickly when the user is in a hurry. For example, when it is determined that the user is in a hurry based on the user's self-reporting, the collection unit shortens the collection timing. This allows physical information to be collected at a more appropriate timing by adjusting the collection timing according to the user's emotions. The emotion estimation is realized by 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. Some 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 image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The collection unit analyzes the user's past physical information and selects an appropriate collection method. The collection unit analyzes the user's past physical information and selects the optimal collection method. A specific range of past physical information and an analysis method include, for example, data from the past week, data from the past month, etc., but are not limited to such examples. A specific type of collection method and a selection criterion include, for example, a wearable device, manual input, etc., but are not limited to such examples. The collection unit selects, for example, a collection method that can obtain the most accurate data from the user's past data. For example, the collection unit uses the wearable device when it analyzes the past data and determines that a collection method using a wearable device is the most accurate. The collection unit can also optimize the collection frequency from the user's past data. For example, when the collection unit determines that collection once a day is optimal based on the past data, it collects data once a day. Furthermore, the collection unit can also select a collection method for a specific time period from the user's past data. For example, if the collection unit determines that collection is optimal in the morning based on past data, the collection unit collects data in the morning. This allows the optimal collection method to be selected by analyzing past physical information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data into AI, which then analyzes the data and selects the optimal collection method.

[0078] The collection unit performs filtering based on the user's current health condition and lifestyle habits when collecting physical information. The collection unit performs filtering based on the user's current health condition and lifestyle habits when collecting physical information. Specific criteria and methods for filtering include, for example, the importance and reliability of data, but are not limited to such examples. For example, when the user is in good health, the collection unit collects detailed data. For example, the collection unit collects detailed data such as the user's heart rate, body temperature, and blood pressure. In addition, when the user is in poor health, the collection unit can also collect only basic data. For example, the collection unit collects basic data such as the user's weight and dietary content. Furthermore, the collection unit can select the type of data to be collected based on the user's lifestyle habits. For example, when the user has an exercise habit, the collection unit preferentially collects data related to exercise. As a result, filtering based on the current health condition and lifestyle habits improves the accuracy of the collected data. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data about the user's health condition and lifestyle habits into the AI, which can then analyze and filter the data.

[0079] The collection unit estimates the user's emotion and determines the priority of the physical information to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of the physical information to be collected based on the estimated user's emotion. Specific methods and criteria for determining the priority include, for example, the importance and urgency of data, but are not limited to such examples. For example, when the user is feeling stressed, the collection unit preferentially collects stress-related data. For example, the collection unit preferentially collects stress-related data such as the user's heart rate and electrodermal activity. In addition, the collection unit can also collect overall health data when the user is relaxed. For example, the collection unit collects overall health data such as the user's weight, dietary content, and amount of exercise. Furthermore, the collection unit can also preferentially collect only basic data when the user is in a hurry. For example, the collection unit preferentially collects basic data such as the user's weight and dietary content. In this way, important data can be preferentially collected by determining the priority of the physical information to be collected based on 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some 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 may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0080] When collecting physical information, the collection unit preferentially collects highly relevant information in consideration of the geographical location information of the user. When collecting physical information, the collection unit preferentially collects highly relevant information in consideration of the geographical location information of the user. Specific methods of acquiring and using geographical location information include, for example, GPS data, location information services, and the like, but are not limited to such examples. For example, when the user is at high altitude, the collection unit preferentially collects oxygen concentration and heart rate. For example, the collection unit preferentially collects data related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the collection unit can also preferentially collect air quality and noise level. For example, the collection unit preferentially collects data related to health management in urban areas based on the user's location information. Furthermore, when the user is exercising, the collection unit can also preferentially collect exercise intensity and calorie consumption. For example, the collection unit preferentially collects data related to health management during exercise based on the user's location information. In this way, highly relevant information can be preferentially collected by considering the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information to AI, which analyzes the data and preferentially collects highly relevant information.

[0081] The collection unit analyzes the user's social media activity when collecting the physical information, and collects related information. The collection unit analyzes the user's social media activity when collecting the physical information, and collects related information. Specific analysis methods and usage methods of social media activity include, for example, the content of the post, the number of likes, comments, and the like, but are not limited to such examples. For example, when the user posts about exercise on social media, the collection unit preferentially collects exercise data. For example, the collection unit analyzes the user's social media post and preferentially collects data related to exercise. In addition, when the user posts about food, the collection unit can also preferentially collect food data. For example, the collection unit analyzes the user's social media post and preferentially collects data related to food. In addition, when the user posts about stress, the collection unit can also preferentially collect stress-related data. For example, the collection unit analyzes the user's social media post and preferentially collects data related to stress. In this way, related information 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 users' social media activity into the AI, which can then analyze the data and collect relevant information.

[0082] The reception unit estimates the user's emotions and adjusts the reception method of the health goal based on the estimated user's emotions. The reception unit estimates the user's emotions and adjusts the reception method of the health goal based on the estimated user's emotions. Specific types of reception methods and adjustment methods include, for example, online forms, interactive interfaces, and the like, but are not limited to such examples. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input procedures. For example, the reception unit captures the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and provides a simple interface when the stress is high. In addition, the reception unit can provide detailed input options and suggest a customizable input method when the user is relaxed. For example, the reception unit analyzes the user's voice and provides detailed input options when it is determined that the user is relaxed. Furthermore, the reception unit can prioritize voice input when the user is in a hurry, allowing the user to quickly input health goals. For example, the reception unit prioritizes voice input when it is determined that the user is in a hurry based on the user's self-report. This allows a more appropriate health goal to be set by adjusting the reception method based on the user's emotions. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI or may be performed without using an AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0083] The reception unit selects the optimal reception method by referring to the user's past goal achievement history when receiving the health goal. The reception unit selects the optimal reception method by referring to the user's past goal achievement history when receiving the health goal. The specific range and reference method of the goal achievement history include, for example, past goal achievement rate and achievement period, but are not limited to such examples. The reception unit, for example, proposes a realistic goal based on the goals the user has achieved in the past. For example, the reception unit analyzes the user's past goal achievement history and proposes a new goal based on the goals achieved in the past. The reception unit can also select the optimal goal setting method from the user's past goal achievement history. For example, the reception unit selects the most effective goal setting method based on the user's past goal achievement history. Furthermore, the reception unit can analyze the user's past goal achievement history and propose an easy-to-achieve goal. For example, the reception unit proposes an easy-to-achieve goal based on the user's past goal achievement history. As a result, the optimal reception method can be selected by referring to the past goal achievement history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input the user's past goal achievement history into the AI, which can then analyze the data and select the optimal reception method.

[0084] The reception unit customizes the goal based on the user's current health condition and lifestyle habits when receiving the health goal. The reception unit customizes the goal based on the user's current health condition and lifestyle habits when receiving the health goal. Specific methods and standards of customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the reception unit proposes a challenging goal when the user is in good health. For example, the reception unit analyzes the user's health condition and proposes a challenging goal when it is determined that the health condition is good. In addition, the reception unit can also propose a realistic goal when the user is in poor health. For example, the reception unit analyzes the user's health condition and proposes a realistic goal when it is determined that the health condition is poor. Furthermore, the reception unit can also customize an achievable goal based on the user's lifestyle. For example, the reception unit analyzes the user's lifestyle habits and proposes an achievable goal based on the lifestyle habits. As a result, an achievable goal can be set by customizing the goal based on the current health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception department can input data about the user's health condition and lifestyle habits into the AI, which can then analyze the data and customize goals.

[0085] The reception unit estimates the user's emotion and determines the priority of the health goals to be received based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of the health goals to be received based on the estimated user's emotion. Specific methods and criteria for determining the priority include, for example, the importance and urgency of the goal, but are not limited to such examples. For example, when the user is feeling stressed, the reception unit preferentially receives the goal of stress reduction. For example, the reception unit captures the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and preferentially receives the goal of stress reduction when the stress is high. In addition, the reception unit can also receive the overall health goal when the user is relaxed. For example, the reception unit analyzes the user's voice and receives the overall health goal when it is determined that the user is relaxed. Furthermore, the reception unit can also preferentially receive the short-term goal when the user is in a hurry. For example, the reception unit preferentially receives the short-term goal when it is determined that the user is in a hurry based on the user's self-report. This allows important goals to be set preferentially by determining the priority of health goals based on the user's emotions. The emotion estimation is realized by 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. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0086] The reception unit preferentially receives a highly relevant goal in consideration of the geographical location information of the user when receiving the health goal. The reception unit preferentially receives a highly relevant goal in consideration of the geographical location information of the user when receiving the health goal. Specific methods of acquiring and using the geographical location information include, for example, GPS data, location information services, and the like, but are not limited to such examples. For example, when the user is at high altitude, the reception unit preferentially receives a goal related to oxygen concentration and heart rate. For example, the reception unit preferentially receives a goal related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the reception unit can also preferentially receive a goal related to air quality and noise level. For example, the reception unit preferentially receives a goal related to health management in an urban area based on the user's location information. Furthermore, when the user is exercising, the reception unit can also preferentially receive a goal related to exercise intensity and calorie consumption. For example, the reception unit preferentially receives a goal related to health management during exercise based on the user's location information. In this way, by considering the geographical location information, a highly relevant goal can be set preferentially. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's location information to AI, which analyzes the data and preferentially receives highly relevant targets.

[0087] The reception unit analyzes the social media activity of the user when receiving the health goal, and receives the related goal. The reception unit analyzes the social media activity of the user when receiving the health goal, and receives the related goal. Specific analysis methods and usage methods of the social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the reception unit preferentially receives the exercise goal. For example, the reception unit analyzes the social media post of the user and preferentially receives the goal related to exercise. In addition, when the user posts about food, the reception unit can also preferentially receive the food goal. For example, the reception unit analyzes the social media post of the user and preferentially receives the goal related to food. Furthermore, when the user posts about stress, the reception unit can also preferentially receive the goal of stress reduction. For example, the reception unit analyzes the social media post of the user and preferentially receives the goal related to stress. In this way, the related goal can be set by analyzing the social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media activities into an AI, which analyzes the data and receives related goals.

[0088] The generation unit estimates the user's emotion and adjusts the generation method of the nutrition information and the exercise plan based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the generation method of the nutrition information and the exercise plan based on the estimated user's emotion. Specific adjustment methods and standards of the generation method include, for example, the selection of an algorithm, the use of data, and the like, but are not limited to such examples. For example, when the user is feeling stressed, the generation unit generates nutrition information and an exercise plan that are effective for stress reduction. For example, the generation unit takes a picture of the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and generates a plan that is effective for stress reduction when the stress is high. In addition, the generation unit can also generate nutrition information and an exercise plan that promotes overall health when the user is relaxed. For example, the generation unit analyzes the user's voice, and generates a plan that promotes overall health when it is determined that the user is relaxed. Furthermore, the generation unit can also generate nutrition information and an exercise plan that are effective in a short period of time when the user is in a hurry. For example, when it is determined that the user is in a hurry based on the user's self-report, the generation unit generates a plan that is effective in a short period of time. This allows the generation method to be adjusted based on the user's emotions, thereby providing more appropriate nutritional information and exercise plans. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0089] The generating unit generates an optimal plan by referring to the user's past health data when generating the nutritional information and the exercise plan. The generating unit generates an optimal plan by referring to the user's past health data when generating the nutritional information and the exercise plan. The specific range and the method of referring to the past health data include, for example, data from the past year, data from the past six months, etc., but are not limited to such examples. The generating unit generates the most effective nutritional information and exercise plan from, for example, the user's past health data. For example, the generating unit analyzes the user's past data and re-proposes a plan that was effective in the past. The generating unit can also analyze the user's past health data and customize the optimal plan. For example, the generating unit generates a plan tailored to the individual health condition based on the user's past data. As a result, the optimal plan can be generated by referring to the past health data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's past health data into AI, and the AI ​​can analyze the data to generate the optimal plan.

[0090] The generating unit customizes the plan based on the user's current health condition and lifestyle habits when generating the nutrition information and the exercise plan. The generating unit customizes the plan based on the user's current health condition and lifestyle habits when generating the nutrition information and the exercise plan. Specific methods and criteria for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the generating unit generates a challenging plan when the user is in good health. For example, the generating unit analyzes the user's health condition and generates a challenging plan when it determines that the health condition is good. In addition, the generating unit can also generate a realistic plan when the user is in poor health. For example, the generating unit analyzes the user's health condition and generates a realistic plan when it determines that the health condition is poor. Furthermore, the generating unit can also customize an achievable plan based on the user's lifestyle. For example, the generating unit analyzes the user's lifestyle habits and generates an achievable plan based on the lifestyle habits. As a result, an achievable plan can be provided by customizing the plan based on the current health condition and lifestyle habits. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's health condition and lifestyle habits into AI, which can analyze the data and customize the plan.

[0091] The generation unit estimates the user's emotion and determines the priority of the nutrition information and exercise plan to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion and determines the priority of the nutrition information and exercise plan to be generated based on the estimated user's emotion. Specific methods and criteria for determining the priority include, for example, the importance and urgency of the plan, but are not limited to such examples. For example, when the user is feeling stressed, the generation unit preferentially generates a plan that is effective for stress reduction. For example, the generation unit takes a picture of the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and preferentially generates a plan that is effective for stress reduction when the stress is high. In addition, the generation unit can also preferentially generate a plan that promotes overall health when the user is relaxed. For example, the generation unit analyzes the user's voice and preferentially generates a plan that promotes overall health when it is determined that the user is relaxed. Furthermore, the generation unit can also preferentially generate a plan that will be effective in a short period of time when the user is in a hurry. For example, when the generation unit determines that the user is in a hurry based on the user's self-report, it preferentially generates a plan that will be effective in a short period of time. This allows important plans to be provided preferentially by determining the priority order of nutritional information and exercise plans based on the user's emotions. The emotion estimation is realized by 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. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0092] The generating unit generates a plan with high relevance in consideration of the geographical location information of the user when generating the nutritional information and the exercise plan. The generating unit generates a plan with high relevance in consideration of the geographical location information of the user when generating the nutritional information and the exercise plan. Specific methods of acquiring and using the geographical location information include, but are not limited to, GPS data, location information services, and the like. For example, when the user is at high altitude, the generating unit generates a plan suitable for exercise at high altitude. For example, the generating unit generates a plan related to exercise at high altitude based on the user's location information. In addition, when the user is in an urban area, the generating unit can also generate a plan suitable for exercise in the urban area. For example, the generating unit generates a plan related to exercise in the urban area based on the user's location information. Furthermore, when the user is exercising, the generating unit can also generate a plan according to the exercise intensity. For example, the generating unit generates a plan related to health management during exercise based on the user's location information. As a result, by considering the geographical location information, it is possible to provide a plan with high relevance in priority. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's location information into the AI, which can then analyze the data and prioritize generating plans that are highly relevant.

[0093] The generating unit analyzes the user's social media activity when generating the nutrition information and the exercise plan, and generates a related plan. The generating unit analyzes the user's social media activity when generating the nutrition information and the exercise plan, and generates a related plan. Specific methods of analyzing and using the social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the generating unit generates an exercise plan preferentially. For example, the generating unit analyzes the user's social media post and preferentially generates a plan related to exercise. In addition, when the user posts about food, the generating unit can also generate a meal plan preferentially. For example, the generating unit analyzes the user's social media post and preferentially generates a plan related to food. Furthermore, when the user posts about stress, the generating unit can also generate a stress reduction plan preferentially. For example, the generating unit analyzes the user's social media post and preferentially generates a plan related to stress. In this way, a related plan can be provided by analyzing social media activity. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's social media activity into an AI, which analyzes the data and generates a related plan.

[0094] The providing unit estimates the user's emotion and adjusts the information providing method based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the information providing method based on the estimated user's emotion. Specific adjustment methods and standards for the providing method include, for example, notification format, transmission timing, and content customization, but are not limited to such examples. For example, when the user is stressed, the providing unit selects a simple and highly visible information providing method. For example, the providing unit takes a picture of the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and selects a simple and highly visible information providing method when the stress is high. In addition, the providing unit can also select a providing method including detailed information when the user is relaxed. For example, the providing unit analyzes the user's voice and selects a providing method including detailed information when it is determined that the user is relaxed. Furthermore, the providing unit can also select an information providing method that covers the main points when the user is in a hurry. For example, when the providing unit determines that the user is in a hurry based on the user's self-report, it selects an information providing method that covers the main points. This allows more appropriate information to be provided by adjusting the method of providing information based on the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0095] The providing unit selects the optimal information providing method by referring to the user's past usage history when providing information. The providing unit selects the optimal information providing method by referring to the user's past usage history when providing information. The specific range of the usage history and the reference method include, for example, past usage frequency and usage content, but are not limited to such examples. The providing unit selects the most effective information providing method, for example, from the user's past usage history. For example, the providing unit analyzes the user's past usage history and re-proposes a providing method that was effective in the past. The providing unit can also analyze the user's past usage history and provide information with high usage frequency preferentially. For example, the providing unit provides information with high usage frequency preferentially based on the user's past usage history. Furthermore, the providing unit can select a customized information providing method based on the user's past usage history. For example, the providing unit selects an information providing method tailored to individual needs based on the user's past usage history. As a result, the optimal information providing method can be selected by referring to the past usage history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input a user's past usage history into the AI, which can then analyze the data and select the optimal provision method.

[0096] The providing unit customizes the provided content based on the user's current health condition and lifestyle habits when providing information. The providing unit customizes the provided content based on the user's current health condition and lifestyle habits when providing information. Specific methods and standards for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the providing unit provides challenging information when the user is in good health. For example, the providing unit analyzes the user's health condition and provides challenging information when it is determined that the health condition is good. In addition, the providing unit can also provide realistic information when the user is in poor health. For example, the providing unit analyzes the user's health condition and provides realistic information when it is determined that the health condition is poor. Furthermore, the providing unit can also customize achievable information based on the user's lifestyle. For example, the providing unit analyzes the user's lifestyle habits and provides achievable information based on the lifestyle habits. As a result, achievable information can be provided by customizing the provided content based on the current health condition and lifestyle habits. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input data about the user's health condition and lifestyle habits into the AI, which can then analyze the data and customize the content provided.

[0097] The providing unit estimates the user's emotion and determines the priority of information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority of information to be provided based on the estimated user's emotion. Specific methods and criteria for determining the priority include, for example, the importance and urgency of information, but are not limited to such examples. For example, when the user is feeling stressed, the providing unit preferentially provides information that is effective for reducing stress. For example, the providing unit captures the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and preferentially provides information that is effective for reducing stress when the stress is high. In addition, the providing unit can also preferentially provide information that promotes overall health when the user is relaxed. For example, the providing unit analyzes the user's voice and preferentially provides information that promotes overall health when it is determined that the user is relaxed. Furthermore, the providing unit can also preferentially provide information that is effective in a short period of time when the user is in a hurry. For example, when the providing unit determines that the user is in a hurry based on the user's self-report, it preferentially provides information that is effective in a short period of time. In this way, important information can be preferentially provided by determining the priority of information based on the user's emotion. The estimation of emotions 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0098] The providing unit provides highly relevant information preferentially in consideration of the geographical location information of the user when providing information. The providing unit provides highly relevant information preferentially in consideration of the geographical location information of the user when providing information. Specific methods of acquiring and using geographical location information include, for example, GPS data, location information services, etc., but are not limited to such examples. For example, when the user is at high altitude, the providing unit preferentially provides information related to health management at high altitude. For example, the providing unit preferentially provides information related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the providing unit can also preferentially provide information related to health management in urban areas. For example, the providing unit preferentially provides information related to health management in urban areas based on the user's location information. Furthermore, when the user is exercising, the providing unit can also preferentially provide information according to the exercise intensity. For example, the providing unit preferentially provides information related to health management during exercise based on the user's location information. In this way, highly relevant information can be provided preferentially by considering the geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information to AI, which analyzes the data and provides highly relevant information preferentially.

[0099] The providing unit analyzes the social media activity of the user when providing the information, and provides the related information. The providing unit analyzes the social media activity of the user when providing the information, and provides the related information. Specific methods of analyzing and using the social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the providing unit provides information about exercise preferentially. For example, the providing unit analyzes the social media post of the user and provides information related to exercise preferentially. In addition, when the user posts about food, the providing unit can also provide information about food preferentially. For example, the providing unit analyzes the social media post of the user and provides information related to food preferentially. Furthermore, when the user posts about stress, the providing unit can also provide information about stress reduction preferentially. For example, the providing unit analyzes the social media post of the user and provides information related to stress preferentially. In this way, the related information can be provided by analyzing the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input a user's social media activity into the AI, which can then analyze the data and provide relevant information.

[0100] The monitoring unit estimates the user's emotions and adjusts the monitoring method based on the estimated user's emotions. The monitoring unit estimates the user's emotions and adjusts the monitoring method based on the estimated user's emotions. Specific adjustment methods and standards for the monitoring method include, for example, the frequency of data acquisition, the sensor used, and the like, but are not limited to such examples. For example, when the user is feeling stressed, the monitoring unit preferentially monitors stress-related data. For example, the monitoring unit captures the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and preferentially monitors stress-related data when the stress is high. In addition, the monitoring unit can also monitor overall health data when the user is relaxed. For example, the monitoring unit analyzes the user's voice and monitors overall health data when it is determined that the user is relaxed. Furthermore, the monitoring unit can also preferentially monitor only basic data when the user is in a hurry. For example, when the monitoring unit determines that the user is in a hurry based on the user's self-report, it preferentially monitors only basic data. This allows more appropriate data to be monitored by adjusting the monitoring method based on the user's emotions. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using an AI or may be performed without using an AI. For example, the monitoring unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0101] The monitoring unit selects the optimal monitoring method by referring to the user's past health data during monitoring. The monitoring unit selects the optimal monitoring method by referring to the user's past health data during monitoring. The specific range and reference method of the past health data include, for example, data from the past year, data from the past six months, etc., but are not limited to such examples. The monitoring unit selects the most effective monitoring method, for example, from the user's past health data. For example, the monitoring unit analyzes the user's past data and re-proposes a monitoring method that was effective in the past. The monitoring unit can also analyze the user's past health data and optimize the monitoring frequency. For example, the monitoring unit determines the optimal monitoring frequency based on the user's past data. Furthermore, the monitoring unit can select a customized monitoring method based on the user's past health data. For example, the monitoring unit selects a monitoring method that meets individual needs based on the user's past data. As a result, the optimal monitoring method can be selected by referring to the past health data. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past health data into the AI, which can then analyze the data and select the optimal monitoring method.

[0102] The monitoring unit customizes the monitoring content based on the user's current health condition and lifestyle habits during monitoring. The monitoring unit customizes the monitoring content based on the user's current health condition and lifestyle habits during monitoring. Specific methods and standards for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the monitoring unit monitors detailed data when the user is in good health. For example, the monitoring unit monitors detailed data such as the user's heart rate, body temperature, and blood pressure. In addition, the monitoring unit can monitor only basic data when the user is in poor health. For example, the monitoring unit monitors basic data such as the user's weight and dietary content. Furthermore, the monitoring unit can select the type of data to be monitored based on the user's lifestyle habits. For example, when the user has an exercise habit, the monitoring unit preferentially monitors data related to exercise. As a result, by customizing the monitoring content based on the current health condition and lifestyle habits, more appropriate data can be monitored. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data about the user's health condition and lifestyle habits into the AI, which can then analyze the data and customize the monitoring content.

[0103] The monitoring unit estimates the user's emotions and determines the monitoring priority based on the estimated user's emotions. The monitoring unit estimates the user's emotions and determines the monitoring priority based on the estimated user's emotions. Specific methods and criteria for determining the priority include, for example, the importance and urgency of data, but are not limited to such examples. For example, when the user is feeling stressed, the monitoring unit preferentially monitors stress-related data. For example, the monitoring unit captures the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and preferentially monitors stress-related data when the stress is high. In addition, the monitoring unit can also monitor overall health data when the user is relaxed. For example, the monitoring unit analyzes the user's voice and monitors overall health data when it is determined that the user is relaxed. Furthermore, the monitoring unit can also preferentially monitor only basic data when the user is in a hurry. For example, when the monitoring unit determines that the user is in a hurry based on the user's self-report, it preferentially monitors only basic data. In this way, important data can be preferentially monitored by determining the monitoring priority based on the user's emotions. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using an AI or may be performed without using an AI. For example, the monitoring unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0104] The monitoring unit, during monitoring, prioritizes monitoring of highly relevant data in consideration of the geographical location information of the user. The monitoring unit, during monitoring, prioritizes monitoring of highly relevant data in consideration of the geographical location information of the user. Specific methods of acquiring and using geographical location information include, for example, GPS data, location information services, and the like, but are not limited to such examples. For example, when the user is at high altitude, the monitoring unit prioritizes monitoring of data related to health management at high altitude. For example, the monitoring unit prioritizes monitoring of data related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the monitoring unit can also prioritize monitoring of data related to health management in urban areas. For example, the monitoring unit prioritizes monitoring of data related to health management in urban areas based on the user's location information. Furthermore, when the user is exercising, the monitoring unit can also prioritize monitoring of data according to the exercise intensity. For example, the monitoring unit prioritizes monitoring of data related to health management during exercise based on the user's location information. As a result, by considering the geographical location information, highly relevant data can be prioritized. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's location information to AI, which analyzes the data and monitors highly relevant data preferentially.

[0105] The monitoring unit analyzes the user's social media activity during monitoring and monitors related data. The monitoring unit analyzes the user's social media activity during monitoring and monitors related data. Specific analysis and usage methods of social media activity include, for example, the content of the post, the number of likes, and comments, but are not limited to such examples. For example, when the user posts about exercise on social media, the monitoring unit preferentially monitors data related to exercise. For example, the monitoring unit analyzes the user's social media post and preferentially monitors data related to exercise. In addition, when the user posts about food, the monitoring unit can also preferentially monitor data related to food. For example, the monitoring unit analyzes the user's social media post and preferentially monitors data related to food. In addition, when the user posts about stress, the monitoring unit can also preferentially monitor data related to stress. For example, the monitoring unit analyzes the user's social media post and preferentially monitors data related to stress. In this way, the related data can be monitored by analyzing social media activity. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input a user's social media activity into the AI, which can then analyze the data and monitor related data.

[0106] The reminder unit estimates the user's emotion and adjusts the reminder sending method based on the estimated user's emotion. The reminder unit estimates the user's emotion and adjusts the reminder sending method based on the estimated user's emotion. Specific adjustment methods and standards for the sending method include, for example, notification format, sending timing, content customization, etc., but are not limited to such examples. For example, if the user is feeling stressed, the reminder unit sends a reminder in gentle words. For example, the reminder unit takes a picture of the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and sends a reminder in gentle words if the stress is high. The reminder unit can also send a detailed reminder if the user is relaxed. For example, the reminder unit analyzes the user's voice and sends a detailed reminder if it is determined that the user is relaxed. Furthermore, the reminder unit can also send a concise reminder if the user is in a hurry. For example, the reminder unit sends a brief reminder if it is determined that the user is in a hurry based on the user's self-report. This allows a more appropriate reminder to be sent by adjusting the reminder sending method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., 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, for example, using an AI, or may be performed without using an AI. For example, the reminder unit can input image data of the user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0107] The reminder unit selects an optimal reminder by referring to the user's past behavior history when sending a reminder. The reminder unit selects an optimal reminder by referring to the user's past behavior history when sending a reminder. The specific range and reference method of the behavior history include, for example, past behavior patterns and behavior frequencies, but are not limited to such examples. The reminder unit selects the most effective reminder from, for example, the user's past behavior history. For example, the reminder unit analyzes the user's past behavior history and re-suggests a reminder that was effective in the past. The reminder unit can also analyze the user's past behavior history and optimize the sending frequency of the reminder. For example, the reminder unit determines the optimal sending frequency based on the user's past behavior history. Furthermore, the reminder unit can select a customized reminder based on the user's past behavior history. For example, the reminder unit selects a reminder that meets individual needs based on the user's past behavior history. This allows the optimal reminder to be selected by referring to the past behavior history. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input the user's past behavior history into AI, which can analyze the data and select the optimal reminder.

[0108] The reminder unit customizes the reminder content based on the user's current health condition and lifestyle habits when sending a reminder. The reminder unit customizes the reminder content based on the user's current health condition and lifestyle habits when sending a reminder. Specific methods and standards for customization include, for example, individual health conditions, adjustments based on lifestyle habits, and the like, but are not limited to such examples. For example, the reminder unit transmits a challenging reminder when the user is in good health. For example, the reminder unit analyzes the user's health condition and transmits a challenging reminder when it determines that the health condition is good. In addition, the reminder unit can also transmit a realistic reminder when the user is in poor health. For example, the reminder unit analyzes the user's health condition and transmits a realistic reminder when it determines that the health condition is poor. In addition, the reminder unit can also customize an achievable reminder based on the user's lifestyle habits. For example, the reminder unit analyzes the user's lifestyle and transmits achievable reminders based on the lifestyle. This allows the reminder content to be customized based on the user's current health condition and lifestyle, thereby transmitting more appropriate reminders. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit may input data on the user's health condition and lifestyle into AI, which may analyze the data and customize the reminder content.

[0109] The reminder unit estimates the user's emotion and determines the priority of the reminder based on the estimated user's emotion. The reminder unit estimates the user's emotion and determines the priority of the reminder based on the estimated user's emotion. Specific methods and criteria for determining the priority include, for example, the importance and urgency of the reminder, but are not limited to such examples. For example, when the user is feeling stressed, the reminder unit preferentially transmits a reminder that is effective in reducing stress. For example, the reminder unit takes a picture of the user's facial expression with a camera, evaluates the stress level using an emotion estimation algorithm, and preferentially transmits a reminder that is effective in reducing stress when the stress is high. In addition, the reminder unit can also preferentially transmit a reminder that promotes overall health when the user is relaxed. For example, the reminder unit analyzes the user's voice and, when it is determined that the user is relaxed, preferentially transmits a reminder that promotes overall health. Furthermore, when the user is in a hurry, the reminder unit can also preferentially transmit a reminder that is effective in a short period of time. For example, when the reminder unit determines that the user is in a hurry based on the user's self-report, the reminder unit preferentially sends a reminder that will be effective in a short time. This allows important reminders to be preferentially sent by determining the priority of reminders based on the user's emotions. 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 image data of the user taken by a camera to the generation AI and cause the generation AI to execute an estimation of the user's emotions.

[0110] The reminder unit, when sending a reminder, preferentially transmits a highly relevant reminder in consideration of the geographical location information of the user. The reminder unit, when sending a reminder, preferentially transmits a highly relevant reminder in consideration of the geographical location information of the user. Specific methods of acquiring and using the geographical location information include, but are not limited to, GPS data, location information services, and the like. For example, when the user is at high altitude, the reminder unit preferentially transmits a reminder related to health management at high altitude. For example, the reminder unit preferentially transmits a reminder related to health management at high altitude based on the user's location information. In addition, when the user is in an urban area, the reminder unit can also preferentially transmit a reminder related to health management in the urban area. For example, the reminder unit preferentially transmits a reminder related to health management in the urban area based on the user's location information. In addition, when the user is exercising, the reminder unit can also preferentially transmit a reminder according to the exercise intensity. For example, the reminder unit preferentially transmits reminders related to health management during exercise based on the user's location information. This allows for preferential transmission of highly relevant reminders by taking into account geographical location information. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input the user's location information into AI, which analyzes the data and preferentially transmits highly relevant reminders.

[0111] The reminder unit analyzes the user's social media activity at the time of sending a reminder, and sends a related reminder. The reminder unit analyzes the user's social media activity at the time of sending a reminder, and sends a related reminder. Specific methods of analyzing and using social media activity include, for example, the content of posts, the number of likes, comments, and the like, but are not limited to such examples. For example, when a user posts about exercise on social media, the reminder unit preferentially sends a reminder about exercise. For example, the reminder unit analyzes the user's social media posts, and preferentially sends a reminder related to exercise. In addition, when a user posts about meals, the reminder unit can also preferentially send a reminder about meals. For example, the reminder unit analyzes the user's social media posts, and preferentially sends a reminder related to meals. In addition, when a user posts about stress, the reminder unit can also preferentially send a reminder about stress reduction. For example, the reminder unit may analyze a user's social media posts and prioritize sending reminders related to stress. This allows for relevant reminders to be sent by analyzing social media activity. Some or all of the above-described processing in the reminder unit may be performed, for example, using AI or may be performed without using AI. For example, the reminder unit may input the user's social media activity into an AI, which may analyze the data and send relevant reminders.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] When collecting the user's physical information, the collection unit can select the optimal collection method by referring to the user's past health data. For example, if it is determined from the past data that data collected during a specific time period is most accurate, collection is performed during that time period. If it is determined from the past data that collection using a specific device is most effective, that device is used. If it is determined from the past data that collection is optimal at a specific frequency, collection is performed at that frequency. In this way, the optimal collection method can be selected by referring to the past health data.

[0114] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. For example, when the user is feeling stressed, stress-related data is monitored with priority. Specifically, data such as the user's heart rate and electrodermal activity is collected with priority. In addition, when the user is relaxed, overall health data can be monitored. For example, data such as the user's weight, diet, and amount of exercise is collected. Furthermore, when the user is in a hurry, only basic data can be monitored with priority. For example, basic data such as the user's weight and diet is collected. In this way, more appropriate data can be monitored by adjusting the monitoring method based on the user's emotions.

[0115] The reminder unit can estimate the user's emotions and adjust the reminder sending method based on the estimated user's emotions. For example, if the user is feeling stressed, a reminder is sent in kind words. Specifically, a message such as "Why don't you try to relax a little today?" is sent. Also, if the user is relaxed, a detailed reminder can be sent. For example, a message such as "We recommend that you walk for 30 minutes today" is sent. Furthermore, if the user is in a hurry, a concise reminder can be sent. For example, a message such as "Don't forget to exercise!" is sent. This allows more appropriate reminders to be sent by adjusting the reminder sending method based on the user's emotions.

[0116] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, when the user is at high altitude, the oxygen concentration and heart rate are preferentially collected. Specifically, data related to health management at high altitude is collected based on the user's location information. In addition, when the user is in an urban area, air quality and noise level can also be preferentially collected. For example, data related to health management in urban areas is collected based on the user's location information. Furthermore, when the user is exercising, exercise intensity and calorie consumption can also be preferentially collected. For example, data related to health management during exercise is collected based on the user's location information. In this way, highly relevant information can be preferentially collected by considering the geographical location information.

[0117] The reception unit can estimate the user's emotions and adjust the reception method of the health goal based on the estimated user's emotions. For example, when the user is feeling stressed, a simple interface is provided and the input procedure is minimized. Specifically, the user's facial expression is captured by a camera, and the stress level is evaluated using an emotion estimation algorithm, and when the stress is high, a simple interface is provided. In addition, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. For example, when the user's voice is analyzed and it is determined that the user is relaxed, detailed input options are provided. Furthermore, when the user is in a hurry, voice input can be prioritized so that the health goal can be input quickly. For example, when it is determined that the user is in a hurry based on the user's self-report, voice input is prioritized. In this way, by adjusting the reception method based on the user's emotions, more appropriate health goals can be set.

[0118] The generating unit can generate optimal nutritional information and exercise plans by referring to the user's past health data. For example, if it is determined from the past data that a particular nutritional information or exercise plan was most effective, the generating unit re-proposes that plan. In addition, it is also possible to customize a plan tailored to a particular health condition from the past data. For example, a plan tailored to an individual health condition is generated based on the user's past data. Furthermore, it is also possible to propose a plan that will be effective in a particular period of time from the past data. For example, a plan that will be effective in a short period of time is proposed based on the past data. In this way, it is possible to generate an optimal plan by referring to the past health data.

[0119] The providing unit can estimate the user's emotions and adjust the information providing method based on the estimated user's emotions. For example, when the user is feeling stressed, a simple and highly visible information providing method is selected. Specifically, the user's facial expression is photographed by a camera, and the stress level is evaluated using an emotion estimation algorithm, and when the stress level is high, a simple and highly visible information providing method is selected. In addition, when the user is relaxed, a providing method including detailed information can be selected. For example, when the user's voice is analyzed and it is determined that the user is relaxed, a providing method including detailed information is selected. Furthermore, when the user is in a hurry, an information providing method that focuses on the main points can be selected. For example, when it is determined that the user is in a hurry based on the user's self-report, an information providing method that focuses on the main points is selected. In this way, by adjusting the information providing method based on the user's emotions, more appropriate information can be provided.

[0120] The monitoring unit can analyze the user's social media activity and monitor related data. For example, if the user posts about exercise on social media, the data about exercise is monitored preferentially. Specifically, the user's social media posts are analyzed and data related to exercise is collected. Also, if the user posts about food, the data about food can be monitored preferentially. For example, the user's social media posts are analyzed and data related to food is collected. Furthermore, if the user posts about stress, the stress-related data can be monitored preferentially. For example, the user's social media posts are analyzed and data related to stress is collected. In this way, related data can be monitored by analyzing social media activity.

[0121] When sending a reminder, the reminder unit can select an optimal reminder by referring to the user's past behavior history. For example, if it is determined from the past behavior history that a particular reminder was most effective, the reminder unit re-suggests that reminder. Also, if it is determined from the past behavior history that it is optimal to send a reminder at a particular frequency, the reminder unit sends the reminder at that frequency. Furthermore, if it is determined from the past behavior history that it is optimal to send a reminder during a particular time period, the reminder unit sends the reminder during that time period. In this way, the optimal reminder can be selected by referring to the past behavior history.

[0122] The generating unit can estimate the user's emotions and adjust the generation method of the nutritional information and the exercise plan based on the estimated user's emotions. For example, when the user is feeling stressed, the generating unit generates nutritional information and an exercise plan that are effective for stress reduction. Specifically, the user's facial expression is photographed by a camera, and the stress level is evaluated using an emotion estimation algorithm, and when the stress level is high, a plan that is effective for stress reduction is generated. In addition, when the user is relaxed, nutritional information and an exercise plan that promotes overall health can be generated. For example, when the user's voice is analyzed and it is determined that the user is relaxed, a plan that promotes overall health is generated. Furthermore, when the user is in a hurry, nutritional information and an exercise plan that are effective in a short period of time can be generated. For example, when it is determined that the user is in a hurry based on the user's self-report, a plan that is effective in a short period of time is generated. In this way, by adjusting the generation method based on the user's emotions, more appropriate nutritional information and exercise plans can be provided.

[0123] The process flow of the second embodiment will be briefly described below.

[0124] Step 1: The collection unit collects the user's physical information. The user's physical information includes heart rate, body temperature, blood pressure, etc. The collection unit can collect this information using a smart watch or fitness tracker. The collection unit also collects information manually entered by the user. For example, information such as weight and dietary details entered by the user through an application is collected. Step 2: The reception unit receives the health goal of the user. The health goal includes weight loss, muscle building, blood pressure management, etc. The reception unit can receive the health goal input by the user through the application. Step 3: The generation unit generates nutritional information and an exercise plan based on the information collected by the collection unit and the health goals accepted by the acceptance unit. The generation unit uses a generation AI to suggest a daily calorie intake and an exercise menu based on the user's target weight and exercise goals. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit can display nutritional information and an exercise plan to the user through an application. The providing unit can also notify the user of the generated information. For example, the providing unit can send a notification to the user's smartphone to notify the user of the generated information.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, each of the multiple elements including the collection unit, the reception unit, the generation unit, the provision unit, the monitoring unit, and the reminder unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's physical information using the camera 42 and the microphone 38B of the smart device 14, and transmits the data collected by the control unit 46A to the data processing device 12. The reception unit receives the user's health goal using the touch panel 38A and the microphone 38B of the smart device 14, and transmits the data received by the control unit 46A to the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates nutritional information and an exercise plan based on the information obtained from the collection unit and the reception unit. The provision unit provides the user with the generated information using the display 40A and the speaker 40B of the smart device 14. The monitoring unit monitors the user's progress in real time using the sensor of the smart device 14 and the specific processing unit 290 of the data processing device 12. The reminder unit generates a reminder by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and transmits the reminder to 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.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, each of the multiple elements including the collection unit, the reception unit, the generation unit, the provision unit, the monitoring unit, and the reminder unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's physical information using the camera 42 and the microphone 238 of the smart glasses 214, and transmits the data collected by the control unit 46A to the data processing device 12. The reception unit receives the user's health goal using the microphone 238 of the smart glasses 214, and transmits the data received by the control unit 46A to the data processing device 12. The generation unit is realized by a specific processing unit 290 of the data processing device 12, and generates nutritional information and an exercise plan based on the information obtained from the collection unit and the reception unit. The provision unit provides the user with the information generated using the display and the speaker 240 of the smart glasses 214. The monitoring unit monitors the user's progress in real time using the sensor of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The reminder unit generates a reminder by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and transmits the reminder to 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.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] For example, each of the multiple elements including the collection unit, the reception unit, the generation unit, the provision unit, the monitoring unit, and the reminder unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's physical information using the camera 42 and the microphone 238 of the headset type terminal 314, and transmits the data collected by the control unit 46A to the data processing device 12. The reception unit receives the user's health goal using the microphone 238 of the headset type terminal 314, and transmits the data received by the control unit 46A to the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates nutritional information and an exercise plan based on the information obtained from the collection unit and the reception unit. The provision unit provides the user with the generated information using the display 343 and the speaker 240 of the headset type terminal 314. The monitoring unit monitors the user's progress in real time using the sensor of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The reminder unit generates a reminder by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and transmits it to 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.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] For example, each of the multiple elements including the collection unit, the reception unit, the generation unit, the provision unit, the monitoring unit, and the reminder unit is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's physical information using the camera 42 and the microphone 238 of the robot 414, and transmits the data collected by the control unit 46A to the data processing device 12. The reception unit receives the user's health goal using the microphone 238 of the robot 414, and transmits the data received by the control unit 46A to the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates nutritional information and an exercise plan based on the information obtained from the collection unit and the reception unit. The provision unit provides the user with the information generated using the display and the speaker 240 of the robot 414. The monitoring unit monitors the user's progress in real time using the sensor of the robot 414 and the specific processing unit 290 of the data processing device 12. The reminder unit generates a reminder by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and transmits it to 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Appendix 1) A collection unit that collects physical information of a user; A reception unit that receives a health goal of the user; A generating unit that generates nutritional information and an exercise plan based on the information collected by the collecting unit and the health goal accepted by the accepting unit; A providing unit that provides the information generated by the generating unit. A system characterized by: (Appendix 2) The generation unit is A monitoring unit that monitors the progress of the user in real time based on the generated information. 2. The system of claim 1, (Appendix 3) The monitoring unit is A reminder unit that sends a reminder to the user based on the monitoring result. 3. The system of claim 2, (Appendix 4) The collecting unit includes: The user's emotions are estimated, and the timing of collecting body information is adjusted using a specific algorithm based on the estimated user's emotions. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze the user's past physical information and select the appropriate collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting physical information, it is filtered based on the user's current health condition and lifestyle habits. 2. The system of claim 1, (Appendix 7) The collecting unit includes: The user's emotions are estimated, and the priority of the collected body information is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting body information, the system prioritizes collection of information that is more relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting physical information, we analyze the user's social media activity to collect related information. 2. The system of claim 1, (Appendix 10) The reception unit is To estimate a user's emotion and adjust a method of accepting a health goal based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting a health goal, the optimal acceptance method is selected by referring to the user's past goal achievement history. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting health goals, customize goals based on the user's current health and lifestyle habits 2. The system of claim 1, (Appendix 13) The reception unit is Estimate the user's emotions and prioritize the health goals to be accepted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 14) The reception unit is When accepting health goals, the system takes into account the user's geographic location information to prioritize goals that are more relevant to them. 2. The system of claim 1, (Appendix 15) The reception unit is When accepting health goals, analyze the user's social media activity and accept related goals. 2. The system of claim 1, (Appendix 16) The generation unit is To estimate a user's emotion and adjust a method for generating nutritional information and an exercise plan based on the estimated user's emotion 2. The system of claim 1, (Appendix 17) The generation unit is When generating nutritional information and exercise plans, the system references the user's past health data to generate optimal plans. 2. The system of claim 1, (Appendix 18) The generation unit is When generating nutrition information and exercise plans, customize the plan based on the user's current health and lifestyle habits 2. The system of claim 1, (Appendix 19) The generation unit is To estimate a user's emotion and determine a priority of nutritional information and an exercise plan to be generated based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The generation unit is When generating nutrition information and exercise plans, the plan is generated with priority given to the user's geographic location information. 2. The system of claim 1, (Appendix 21) The generation unit is When generating nutritional information and exercise plans, analyze the user's social media activity and generate relevant plans 2. The system of claim 1, (Appendix 22) The providing unit is To estimate a user's emotion and adjust a method of providing information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The providing unit is When providing information, the optimal method of providing the information is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 24) The providing unit is When providing information, customize the content based on the user's current health and lifestyle habits 2. The system of claim 1, (Appendix 25) The providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, the system takes into account the user's geographic location information and prioritizes providing information that is highly relevant 2. The system of claim 1, (Appendix 27) The providing unit is When you provide information, analyze your social media activity and provide you with relevant information 2. The system of claim 1, (Appendix 28) The monitoring unit is Estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. 3. The system of claim 2, (Appendix 29) The monitoring unit is When monitoring, the optimal monitoring method is selected by referring to the user's past health data. 3. The system of claim 2, (Appendix 30) The monitoring unit is During monitoring, customize the monitoring content based on the user's current health condition and lifestyle habits 3. The system of claim 2, (Appendix 31) The monitoring unit is Estimate user emotions and determine monitoring priorities based on the estimated user emotions 3. The system of claim 2, (Appendix 32) The monitoring unit is During monitoring, the geographic location of the user is taken into consideration to prioritize monitoring of the most relevant data. 3. The system of claim 2, (Appendix 33) The monitoring unit is During monitoring, analyze the user's social media activities and monitor related data. 3. The system of claim 2, (Appendix 34) The reminder unit is Infer user emotions and adjust how reminders are sent based on the inferred user emotions 4. The system of claim 3. (Appendix 35) The reminder unit includes: When sending a reminder, the system selects the most appropriate reminder by referring to the user's past behavior history. 4. The system of claim 3. (Appendix 36) The reminder unit includes: When sending reminders, customize the content of the reminder based on the user's current health status and lifestyle habits 4. The system of claim 3. (Appendix 37) The reminder unit is Infer user emotions and prioritize reminders based on the inferred user emotions 4. The system of claim 3. (Appendix 38) The reminder unit is When sending reminders, the geographic location of the user is taken into consideration to prioritize sending more relevant reminders. 4. The system of claim 3. (Appendix 39) The reminder unit is When sending reminders, analyze the user's social media activity to send relevant reminders 4. The system of claim 3. [Explanation of symbols]

[0197] 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 physical information of a user; A reception unit that receives a health goal of the user; A generating unit that generates nutritional information and an exercise plan based on the information collected by the collecting unit and the health goal accepted by the accepting unit; A providing unit that provides the information generated by the generating unit. A system characterized by:

2. The generation unit is A monitoring unit that monitors the progress of the user in real time based on the information generated by the generating unit.

2. The system of claim 1 .

3. The monitoring unit is A reminder unit that sends a reminder to the user based on the monitoring result.

3. The system of claim 2.

4. The collecting unit includes: The emotion of the user is estimated, and a timing of collecting physical information is adjusted using a specific algorithm based on the estimated emotion of the user.

2. The system of claim 1 .

5. The collecting unit includes: Analyzing the user's past physical information and selecting an appropriate collection method 2. The system of claim 1 .

6. The collecting unit includes: When collecting physical information, filtering is performed based on the user's current health condition and lifestyle habits.

2. The system of claim 1 .

7. The collecting unit includes: An emotion of the user is estimated, and a priority order of physical information to be collected is determined based on the estimated emotion of the user.

2. The system of claim 1 .

8. The collecting unit includes: When collecting physical information, information highly relevant to the user is preferentially collected based on the user's geographic location information.

2. The system of claim 1 .

9. The collecting unit includes: When collecting physical information, the user's social media activity is analyzed to collect related information.

2. The system of claim 1 .

10. The reception unit is Estimating the user's emotion and adjusting a method of accepting the health goal based on the estimated emotion of the user.

2. The system of claim 1 .

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