system

Abstract

The system according to this embodiment aims to provide personalized health management based on the user's health status and lifestyle. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and an engagement unit. The collection unit collects the user's health status, lifestyle habits, and dietary data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The engagement unit maintains the user's motivation based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized health management based on the user's health condition and lifestyle has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized health management based on the user's health condition and lifestyle.

Means for Solving the Problems

[0007] The system according to this embodiment can provide personalized health management based on the user's health status and lifestyle. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The personalized health management agent according to an embodiment of the present invention is a system that analyzes a user's health status, lifestyle habits, and dietary data in real time and provides effective diet plans and advice for maintaining good health. This system collects the user's health status, lifestyle habits, and dietary data, and the AI ​​analyzes it to generate an optimal diet plan and advice for maintaining good health for the user. For example, the user inputs the contents of the meals they ate and records their exercise into the app. This information is collected in real time by the AI. Next, the AI ​​analyzes the collected data. Based on the user's health status, lifestyle habits, and dietary data, the AI ​​generates an optimal diet plan and advice for maintaining good health for the user. For example, it analyzes the calories and nutrients of meals the user has eaten in the past and proposes a balanced meal plan. It also provides an effective exercise plan based on the user's exercise records. Furthermore, the AI ​​is equipped with an engagement function to maintain the user's motivation. For example, it sends a message of praise when the user achieves a goal to increase the user's motivation. It also incorporates gamification elements to allow users to continue managing their health while having fun. Through this mechanism, users can find a health management method that suits them and receive help in leading a healthy life. Furthermore, personalized advice provided by AI promotes user health maintenance and contributes to reducing medical costs. For example, if a user inputs "I want to go on a diet," the AI ​​will propose an effective diet plan based on the user's current health status, lifestyle, and dietary data. Specifically, it will provide a balanced meal plan and an appropriate exercise plan to support the user in achieving their goals. In this way, the AI-powered personalized health management agent aims to transform health management into something accessible and enjoyable by monitoring the user's health status in real time and providing plans tailored to individual lifestyles. This allows the personalized health management agent to analyze the user's health status, lifestyle, and dietary data in real time and provide effective diet plans and advice for maintaining good health.

[0029] The personalized health management agent according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and an engagement unit. The data collection unit collects the user's health status, lifestyle habits, and dietary data. For example, the data collection unit collects data when the user inputs their daily activities and meals. For example, the user inputs the contents of the meals they ate and records their exercise into the app. This information is collected in real time by AI. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit generates an optimal diet plan and advice for maintaining health based on the user's health status, lifestyle habits, and dietary data. For example, it analyzes the calories and nutrients of meals the user has eaten in the past and proposes a balanced meal plan. The analysis unit can also provide an effective exercise plan based on the user's exercise records. The data provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the data provision unit provides a balanced meal menu and an appropriate exercise plan. For example, if the user inputs "I want to go on a diet," the data provision unit proposes an effective diet plan based on the user's current health status, lifestyle habits, and dietary data. The Engagement Department maintains user motivation based on the advice provided by the Service Department. For example, the Engagement Department sends praising messages when users achieve their goals. By sending praising messages when users achieve their goals, for example, it increases user motivation. The Engagement Department also incorporates gamification elements to enable users to continue managing their health while having fun. For example, the Engagement Department provides gamification elements such as a point system, badges, and rankings to enable users to continue managing their health while having fun. As a result, the personalized health management agent according to the embodiment can analyze the user's health status, lifestyle habits, and dietary data in real time and provide effective diet plans and advice for maintaining health.

[0030] The data collection unit collects user health status, lifestyle, and dietary data. Specifically, it collects data when users input their daily activities and meals. For example, users input details of meals they ate and exercise records into the app. This information is collected in real time by AI. The data collection unit automatically sends the user-input data to the cloud and stores it in a central database. Furthermore, the data collection unit can also collect data from wearable devices and smartwatches. This allows for the acquisition of detailed health data such as the user's heart rate, steps taken, and sleep patterns. For example, if a user is wearing a smartwatch, the data collection unit automatically acquires data such as heart rate, exercise level, and sleep quality from the device and updates it in real time. The data collection unit also provides a function to take and upload photos of meals to more accurately understand the user's diet. The AI ​​analyzes these photos and automatically recognizes the contents of the meals, calories, and nutrients. This eliminates the need for manual input by the user and allows for the collection of more accurate data. In addition, the data collection unit provides users with questionnaires about their lifestyle and regular health checklists to continuously monitor their health status. This allows the data collection unit to collect comprehensive data on users' health status and lifestyle habits, making it available for use by the analysis and provision units.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it generates optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. For example, it analyzes the calories and nutrients of meals the user has eaten in the past and proposes a balanced meal plan. The analysis unit can also provide effective exercise plans based on the user's exercise records. The AI ​​uses machine learning algorithms and data mining techniques to analyze this data. For example, when analyzing the user's dietary data, it calculates the calories, protein, fat, carbohydrates, and other nutrients of the meals and generates a meal plan tailored to the user's goals. Furthermore, the analysis unit analyzes the user's exercise data and proposes an optimal exercise plan based on the intensity, frequency, and type of exercise. For example, if the user is aiming to lose weight, the analysis unit can suggest high-intensity interval training (HIIT) to maximize calorie expenditure. The analysis unit also provides advice on stress management and sleep improvement based on the user's health status and lifestyle. For example, it analyzes the user's heart rate and sleep data, and if the stress level is high, it suggests relaxation techniques or meditation. This allows the analysis unit to provide personalized advice tailored to the user's health status and goals, supporting them in leading a healthy lifestyle.

[0032] The service provider offers advice based on the analysis results obtained by the analysis unit. Specifically, it provides balanced meal plans and appropriate exercise plans. For example, if a user enters "I want to lose weight," the service provider will propose an effective diet plan based on the user's current health status, lifestyle, and dietary data. The service provider provides customized meal plans and exercise plans according to the user's goals and preferences. For example, if the user is a vegetarian, the service provider will propose a balanced meal plan centered on plant-based foods. The service provider can also accommodate the user's allergies and dietary restrictions and provide an appropriate meal plan. Furthermore, the service provider monitors the user's progress and updates the advice as needed. For example, if the user reaches their target weight, the service provider will propose a new meal plan and exercise plan to maintain that weight. The service provider also provides specific recipes and exercise methods to make it easier for the user to follow the advice. For example, it provides easy-to-make recipes and cooking methods to help the user implement the suggested meal plan. Regarding exercise plans, it provides specific exercise methods and videos to support the user in exercising correctly. This allows the service provider to offer specific advice to users on how to lead a healthy lifestyle and support them in achieving their goals.

[0033] The Engagement Department maintains user motivation based on the advice provided by the Service Provider Department. Specifically, it sends praising messages when users achieve their goals. For example, sending praising messages when users achieve their goals increases user motivation. The Engagement Department also incorporates gamification elements to help users continue managing their health in an enjoyable way. For example, the Engagement Department provides gamification elements such as a point system, badges, and rankings to help users continue managing their health in an enjoyable way. Users earn points each time they achieve a specific goal, and can use those points to obtain rewards and benefits. In addition, a ranking system where users compete with each other and share their health management results increases user motivation. Furthermore, the Engagement Department collects user feedback and uses it to improve the system. For example, by having users input their opinions and impressions on the advice and gamification elements provided, the Engagement Department analyzes this information and develops more effective methods for maintaining motivation. The Engagement Department also regularly checks users' progress and provides encouraging messages and additional advice as needed. This allows the Engagement Department to provide an environment that makes it easier for users to continue managing their health and to support them in achieving their goals.

[0034] The data collection unit can collect data when users input their daily activities and dietary information. For example, the data collection unit can collect data when users input the details of meals they ate and their exercise records into the app. The data collection unit can also collect data when users input their daily activities. For example, the data collection unit can collect data when users input their step count and exercise time into the app. Furthermore, the data collection unit can also collect data when users input their dietary information. For example, the data collection unit can collect data when users input the type of meal they ate and their calorie intake into the app. In this way, the data collection unit can collect data when users input their daily activities and dietary information. Daily activities include, but are not limited to, exercise volume, step count, and activity time. Dietary information includes, but is not limited to, the type of meal, amount consumed, and timing of meals. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the data entered by the user into AI and have the AI ​​perform the data collection.

[0035] The analysis unit can generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can generate an optimal diet plan based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can analyze the calories and nutrients of meals the user has eaten in the past and propose a balanced meal plan. The analysis unit can also provide an effective exercise plan based on the user's exercise records. For example, the analysis unit can analyze the user's exercise records and propose an appropriate exercise plan. Furthermore, the analysis unit can analyze the user's health status and generate health maintenance advice. For example, the analysis unit can analyze the user's health status and propose lifestyle improvements. This allows the system to generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. An optimal diet plan may include, but is not limited to, calorie restriction, exercise plans, and balanced meals. Health maintenance advice may include, but is not limited to, lifestyle improvements and health checklists. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health status, lifestyle, and dietary data into the AI ​​and have the AI ​​generate an optimal diet plan and advice for maintaining good health.

[0036] The service provider can provide balanced meal plans and appropriate exercise plans. For example, the service provider can provide balanced meal plans. For example, the service provider can suggest balanced meal plans based on the user's health status, lifestyle, and dietary data. The service provider can also provide appropriate exercise plans. For example, the service provider can suggest appropriate exercise plans based on the user's exercise records. In this way, by providing balanced meal plans and appropriate exercise plans, the service provider can support the user in maintaining their health. Balanced meal plans include, for example, the balance of nutrients and the selection of ingredients. Appropriate exercise plans include, for example, the type of exercise, frequency, and intensity. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health status, lifestyle, and dietary data into AI and have the AI ​​perform the task of providing balanced meal plans and appropriate exercise plans.

[0037] The engagement unit can send a message of praise to users when they achieve their goals. For example, the engagement unit can increase user motivation by sending a message of praise when a user achieves their goals. This means that sending a message of praise when a user achieves their goals can increase user motivation. The message of praise includes, but is not limited to, the wording of the message and the timing of its sending. Some or all of the above processing in the engagement unit may be performed using, for example, AI, or not using AI. For example, the engagement unit can have AI generate the message of praise to send when a user achieves their goals.

[0038] The engagement department can incorporate gamification elements to help users continue managing their health in an enjoyable way. For example, the engagement department could implement a point system, allowing users to earn points each time they manage their health. For instance, it could award points each time a user exercises or eats a balanced diet. The engagement department could also implement a badge system, allowing users to earn badges when they achieve specific goals. For example, it could award badges to users who exercise consistently for a certain period. Furthermore, the engagement department could implement a ranking system, allowing users to compete with other users. For example, it could display users' health management results in a ranking format, allowing them to compare with other users. In this way, by incorporating gamification elements, users can continue managing their health in an enjoyable way. Gamification elements include, but are not limited to, point systems, badges, and rankings. Some or all of the above processes in the engagement department may be performed using, for example, AI, or not using AI. For example, the engagement department can input user health management results into the AI, and have the AI ​​perform tasks such as awarding points and badges and generating rankings.

[0039] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose collecting data at specific time periods. The data collection unit can also collect data at specific time periods based on the user's past health data. For example, by collecting data at specific time periods based on the user's past health data, the data collection unit can collect more accurate data. Furthermore, the data collection unit can adjust the frequency of data collection based on the user's past health data. For example, by adjusting the frequency of data collection based on the user's past health data, the data collection unit can collect more detailed data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Past health data includes, but is not limited to, past weight records, blood pressure measurement results, and exercise history. The optimal data collection method includes, but is not limited to, the frequency of data collection and the devices used. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into the AI ​​and have the AI ​​select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current lifestyle and health goals during data collection. For example, the data collection unit can collect only the necessary data, taking into account the user's current lifestyle. By collecting only the necessary data, taking into account the user's current lifestyle, the data collection unit can improve data collection efficiency. The data collection unit can also prioritize the collection of relevant data based on the user's health goals. For example, by prioritizing the collection of relevant data based on the user's health goals, the data collection unit can support the user in achieving their health goals. Furthermore, the data collection unit can adjust the scope of data collection according to the user's lifestyle and health goals. For example, by adjusting the scope of data collection according to the user's lifestyle and health goals, the data collection unit can perform more effective data collection. This allows the data collection to be filtered based on the user's current lifestyle and health goals, thereby collecting only the necessary data. Current lifestyle includes, but is not limited to, work situation, home environment, and daily routines. Health goals include, but are not limited to, weight loss goals, exercise goals, and dietary improvement goals. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and health goals into the AI ​​and have the AI ​​perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region, thereby collecting region-specific health information. The data collection unit can also prioritize the collection of data related to the user's travel destination if the user is traveling. For example, if the data collection unit prioritizes the collection of data related to the user's travel destination, it can support health management at the travel destination. Furthermore, if the data collection unit is at home, it can prioritize the collection of data related to the user's home. For example, if the data collection unit is at home, it can prioritize the collection of data related to the user's home, thereby supporting health management at home. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Highly relevant data includes, but is not limited to, health information related to the user's current location and local health resources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect details of meals shared by the user on social media. For example, by collecting details of meals shared by the user on social media, the data collection unit can supplement the user's dietary data. The data collection unit can also collect exercise records shared by the user on social media. For example, by collecting exercise records shared by the user on social media, the data collection unit can supplement the user's exercise data. Furthermore, the data collection unit can also collect health-related posts shared by the user on social media. For example, by collecting health-related posts shared by the user on social media, the data collection unit can supplement the user's health data. This allows for the collection of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Relevant data includes, but is not limited to, health-related posts on social media and the user's interests. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media activity into the AI ​​and have the AI ​​collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis on important health data. For example, by performing a detailed analysis on important health data, the analysis unit can provide important information to the user. The analysis unit can also perform a simplified analysis on less important health data. For example, by performing a simplified analysis on less important health data, the analysis unit can reduce the burden on the user. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the health data. For example, by adjusting the depth of the analysis according to the importance of the health data, the analysis unit can provide more appropriate analysis results. This allows for more detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. The importance of health data includes, but is not limited to, examples such as a doctor's diagnosis or a user's self-assessment. The level of detail of the analysis includes, but is not limited to, examples such as data granularity and analysis depth. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of health data into the AI ​​and have the AI ​​adjust the level of detail in the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutrient analysis algorithm to dietary data. For example, by applying a nutrient analysis algorithm to dietary data, the analysis unit can analyze the user's diet in detail. The analysis unit can also apply an exercise effect analysis algorithm to exercise data. For example, by applying an exercise effect analysis algorithm to exercise data, the analysis unit can analyze the user's exercise effect in detail. Furthermore, the analysis unit can also apply a sleep quality analysis algorithm to sleep data. For example, by applying a sleep quality analysis algorithm to sleep data, the analysis unit can analyze the user's sleep quality in detail. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of health data. Categories of health data include, but are not limited to, dietary data, exercise data, and sleep data. Different analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input health data categories into the AI ​​and have the AI ​​apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of health data collection during analysis. For example, the analysis unit may prioritize the analysis of recently collected health data. For example, by prioritizing the analysis of recently collected health data, the analysis unit can provide the latest information. The analysis unit can also prioritize the latest data while referring to past health data. For example, by prioritizing the latest data while referring to past health data, the analysis unit can provide more accurate analysis results. Furthermore, the analysis unit can adjust the order of analysis according to the timing of health data collection. For example, by adjusting the order of analysis according to the timing of health data collection, the analysis unit can perform more effective analysis. This allows for prioritizing the analysis of the latest data by determining the priority of analysis based on the timing of health data collection. The timing of health data collection includes, but is not limited to, data timestamps and collection frequency. The priority of analysis includes, but is not limited to, data freshness and importance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the timing of health data collection into the AI ​​and have the AI ​​determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant health data. For example, by prioritizing the analysis of highly relevant health data, the analysis unit can provide more important information. The analysis unit can also adjust the order of analysis according to the relevance of health data. For example, by adjusting the order of analysis according to the relevance of health data, the analysis unit can perform more effective analysis. Furthermore, the analysis unit can postpone the analysis of less relevant health data. For example, by postponing the analysis of less relevant health data, the analysis unit can improve the efficiency of the analysis. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of health data. The relevance of health data includes, but is not limited to, data correlation and causal relationships. The order of analysis includes, but is not limited to, methods of prioritizing highly relevant data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relationships between health data into the AI ​​and have the AI ​​adjust the order of analysis.

[0047] The service provider can adjust the level of detail in advice based on the importance of the health goal when providing advice. For example, the service provider can provide detailed advice for important health goals. For example, by providing detailed advice for important health goals, the service provider can provide important information to the user. The service provider can also provide simplified advice for less important health goals. For example, by providing simplified advice for less important health goals, the service provider can reduce the burden on the user. Furthermore, the service provider can adjust the depth of advice according to the importance of the health goal. For example, by adjusting the depth of advice according to the importance of the health goal, the service provider can provide more appropriate advice. This allows for the provision of more detailed advice for more important health goals by adjusting the level of detail in advice based on the importance of the health goal. The importance of a health goal includes, but is not limited to, a doctor's diagnosis or the user's self-assessment. The level of detail in advice includes, but is not limited to, the granularity of the information or the depth of the advice. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the importance of health goals into the AI ​​and have the AI ​​adjust the level of detail of the advice.

[0048] The service provider can apply different advice algorithms depending on the category of the health goal when providing advice. For example, for a weight loss goal, the service provider can apply a diet and exercise advice algorithm. For example, by applying a diet and exercise advice algorithm to a weight loss goal, the service provider can support the user's weight loss. The service provider can also apply a balanced lifestyle advice algorithm to a health maintenance goal. For example, by applying a balanced lifestyle advice algorithm to a health maintenance goal, the service provider can support the user's health maintenance. Furthermore, the service provider can apply a specialized advice algorithm to specific health problems. For example, by applying a specialized advice algorithm to specific health problems, the service provider can support the user in resolving their health problems. This allows for the provision of more appropriate advice by applying different advice algorithms depending on the category of the health goal. Categories of health goals include, but are not limited to, weight management, exercise habits, and dietary improvements. Different advice algorithms include, but are not limited to, rule-based, machine learning, and expert systems. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input health goal categories into the AI ​​and have the AI ​​apply different advice algorithms.

[0049] The service provider can prioritize advice based on the timeframe for achieving health goals. For example, the service provider will prioritize advice for health goals that need to be achieved urgently. For example, by prioritizing advice for health goals that need to be achieved urgently, the service provider can support the user in achieving their goals. The service provider can also provide step-by-step advice for long-term health goals. For example, by providing step-by-step advice for long-term health goals, the service provider can support the user in managing their health on an ongoing basis. Furthermore, the service provider can adjust the order of advice according to the timeframe for achieving health goals. For example, by adjusting the order of advice according to the timeframe for achieving health goals, the service provider can provide more effective advice. This means that by prioritizing advice based on the timeframe for achieving health goals, more effective advice can be provided. The timeframe for achieving health goals includes, but is not limited to, short-term goals, medium-term goals, and long-term goals. The priority of advice includes, but is not limited to, the urgency and importance of the goals. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the target date for achieving health goals into the AI ​​and have the AI ​​determine the priority of advice.

[0050] The service provider can adjust the order of advice based on the relevance of health goals when providing advice. For example, the service provider can prioritize advice for highly relevant health goals. For example, by prioritizing advice for highly relevant health goals, the service provider can support the user in achieving their goals. The service provider can also adjust the order of advice according to the relevance of health goals. For example, by adjusting the order of advice according to the relevance of health goals, the service provider can provide more effective advice. Furthermore, the service provider can postpone less relevant health goals. For example, by postponing less relevant health goals, the service provider can improve the efficiency of advice. This allows for prioritizing more relevant advice by adjusting the order of advice based on the relevance of health goals. The relevance of health goals includes, but is not limited to, correlations and causal relationships between goals. The order of advice includes, but is not limited to, methods such as prioritizing highly relevant goals. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the relevance of health goals into the AI ​​and have the AI ​​adjust the order of the advice.

[0051] The Engagement Department can analyze a user's past behavior history during engagement to select the optimal engagement method. For example, the Engagement Department can prioritize providing engagement methods that the user has preferred in the past. For example, by prioritizing engagement methods that the user has preferred in the past, the Engagement Department can increase the user's motivation. The Engagement Department can also suggest effective engagement methods based on the user's past behavior history. For example, by suggesting effective engagement methods based on the user's past behavior history, the Engagement Department can improve user engagement. Furthermore, the Engagement Department can adjust the frequency of engagement based on the user's past behavior history. For example, by adjusting the frequency of engagement based on the user's past behavior history, the Engagement Department can reduce the user's burden. This allows the optimal engagement method to be selected by analyzing the user's past behavior history. Some or all of the above processing in the Engagement Department may be performed using AI, for example, or without using AI. For example, the engagement department can input the user's past behavior history into the AI ​​and have the AI ​​select the optimal engagement method.

[0052] The engagement unit can customize the means of engagement based on the user's current life situation during engagement. For example, the engagement unit can provide appropriate means of engagement considering the user's current life situation. For example, by providing appropriate means of engagement considering the user's current life situation, the engagement unit can increase the user's motivation. The engagement unit can also provide quick and effective means of engagement when the user is busy. For example, by providing quick and effective means of engagement when the user is busy, the engagement unit can save the user's time. Furthermore, the engagement unit can provide enjoyable means of engagement when the user is relaxed. For example, by providing enjoyable means of engagement when the user is relaxed, the engagement unit can increase the user's motivation. This allows for more appropriate engagement by customizing the means of engagement based on the user's current life situation. Some or all of the above processing in the engagement unit may be performed using AI, for example, or without using AI. For example, the engagement unit can input the user's current living situation into the AI ​​and have the AI ​​customize the means of engagement.

[0053] The Engagement Unit can select the optimal engagement method by considering the user's geographical location information during engagement. For example, if the user is in a specific region, the Engagement Unit can provide engagement methods relevant to that region. For example, by providing engagement methods relevant to a specific region when the user is in a particular region, the Engagement Unit can meet the user's region-specific needs. The Engagement Unit can also provide engagement methods relevant to the user's travel destination when the user is traveling. For example, by providing engagement methods relevant to the user's travel destination when the user is traveling, the Engagement Unit can support the user's health management during their trip. Furthermore, if the Engagement Unit is at home, the Engagement Unit can provide engagement methods that can be enjoyed at home when the user is at home, for example, by providing engagement methods that can be enjoyed at home when the user is at home, the Engagement Unit can increase the user's motivation. In this way, the optimal engagement method can be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Engagement methods include, but are not limited to, local activities, health management while traveling, and engagement at home. Some or all of the above-described processes in the engagement unit may be performed using AI, for example, or not. For example, the engagement unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal engagement method.

[0054] The Engagement Department can analyze a user's social media activity during engagement and propose engagement methods. For example, the Engagement Department can propose engagement methods based on the interests and passions that the user has shared on social media. For example, by proposing engagement methods based on the interests and passions that the user has shared on social media, the Engagement Department can increase user motivation. The Engagement Department can also propose engagement methods based on the activities that the user has shared on social media. For example, by proposing engagement methods based on the activities that the user has shared on social media, the Engagement Department can improve user engagement. Furthermore, the Engagement Department can also propose engagement methods based on the friends and groups that the user has shared on social media. For example, by proposing engagement methods based on the friends and groups that the user has shared on social media, the Engagement Department can strengthen the user's social support. In this way, by analyzing a user's social media activity, the optimal engagement method can be proposed. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Engagement methods include, but are not limited to, suggestions based on interests, activities, or friends and groups. Some or all of the above-described processes in the engagement unit may be performed using AI, for example, or not using AI. For example, the engagement unit can input the user's social media activities into AI and have AI perform engagement method suggestions.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can generate an optimal diet plan based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can analyze the calories and nutrients of meals the user has eaten in the past and propose a balanced meal plan. The analysis unit can also provide an effective exercise plan based on the user's exercise records. For example, the analysis unit can analyze the user's exercise records and propose an appropriate exercise plan. Furthermore, the analysis unit can analyze the user's health status and generate health maintenance advice. For example, the analysis unit can analyze the user's health status and propose lifestyle improvements. This allows the system to generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. An optimal diet plan may include, but is not limited to, calorie restriction, exercise plans, and balanced meals. Health maintenance advice may include, but is not limited to, lifestyle improvements and health checklists. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health status, lifestyle, and dietary data into the AI ​​and have the AI ​​generate an optimal diet plan and advice for maintaining good health.

[0057] The service provider can provide balanced meal plans and appropriate exercise plans. For example, the service provider can provide balanced meal plans. For example, the service provider can suggest balanced meal plans based on the user's health status, lifestyle, and dietary data. The service provider can also provide appropriate exercise plans. For example, the service provider can suggest appropriate exercise plans based on the user's exercise records. In this way, by providing balanced meal plans and appropriate exercise plans, the service provider can support the user in maintaining their health. Balanced meal plans include, for example, the balance of nutrients and the selection of ingredients. Appropriate exercise plans include, for example, the type of exercise, frequency, and intensity. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health status, lifestyle, and dietary data into AI and have the AI ​​perform the task of providing balanced meal plans and appropriate exercise plans.

[0058] The engagement unit can send a message of praise to users when they achieve their goals. For example, the engagement unit can increase user motivation by sending a message of praise when a user achieves their goals. This means that sending a message of praise when a user achieves their goals can increase user motivation. The message of praise includes, but is not limited to, the wording of the message and the timing of its sending. Some or all of the above processing in the engagement unit may be performed using, for example, AI, or not using AI. For example, the engagement unit can have AI generate the message of praise to send when a user achieves their goals.

[0059] The engagement department can incorporate gamification elements to help users continue managing their health in an enjoyable way. For example, the engagement department could implement a point system, allowing users to earn points each time they manage their health. For instance, it could award points each time a user exercises or eats a balanced diet. The engagement department could also implement a badge system, allowing users to earn badges when they achieve specific goals. For example, it could award badges to users who exercise consistently for a certain period. Furthermore, the engagement department could implement a ranking system, allowing users to compete with other users. For example, it could display users' health management results in a ranking format, allowing them to compare with other users. In this way, by incorporating gamification elements, users can continue managing their health in an enjoyable way. Gamification elements include, but are not limited to, point systems, badges, and rankings. Some or all of the above processes in the engagement department may be performed using, for example, AI, or not using AI. For example, the engagement department can input user health management results into the AI, and have the AI ​​perform tasks such as awarding points and badges and generating rankings.

[0060] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose collecting data at specific time periods. The data collection unit can also collect data at specific time periods based on the user's past health data. For example, by collecting data at specific time periods based on the user's past health data, the data collection unit can collect more accurate data. Furthermore, the data collection unit can adjust the frequency of data collection based on the user's past health data. For example, by adjusting the frequency of data collection based on the user's past health data, the data collection unit can collect more detailed data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Past health data includes, but is not limited to, past weight records, blood pressure measurement results, and exercise history. The optimal data collection method includes, but is not limited to, the frequency of data collection and the devices used. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into the AI ​​and have the AI ​​select the optimal data collection method.

[0061] The data collection unit can filter data based on the user's current lifestyle and health goals during data collection. For example, the data collection unit can collect only the necessary data, taking into account the user's current lifestyle. By collecting only the necessary data, taking into account the user's current lifestyle, the data collection unit can improve data collection efficiency. The data collection unit can also prioritize the collection of relevant data based on the user's health goals. For example, by prioritizing the collection of relevant data based on the user's health goals, the data collection unit can support the user in achieving their health goals. Furthermore, the data collection unit can adjust the scope of data collection according to the user's lifestyle and health goals. For example, by adjusting the scope of data collection according to the user's lifestyle and health goals, the data collection unit can perform more effective data collection. This allows the data collection to be filtered based on the user's current lifestyle and health goals, thereby collecting only the necessary data. Current lifestyle includes, but is not limited to, work situation, home environment, and daily routines. Health goals include, but are not limited to, weight loss goals, exercise goals, and dietary improvement goals. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and health goals into the AI ​​and have the AI ​​perform data filtering.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user health status, lifestyle, and dietary data. For example, data is collected when users input their daily activities and meals into the app. This information is collected in real time by AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, based on the user's health status, lifestyle, and dietary data, it generates an optimal diet plan and advice for maintaining good health for the user. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. For example, it provides a balanced meal plan or an appropriate exercise plan. Step 4: The Engagement team maintains user motivation based on the advice provided by the Service Provider team. For example, they send praising messages when users achieve their goals. They also incorporate gamification elements to help users continue managing their health while having fun.

[0064] (Example of form 2) The personalized health management agent according to an embodiment of the present invention is a system that analyzes a user's health status, lifestyle habits, and dietary data in real time and provides effective diet plans and advice for maintaining good health. This system collects the user's health status, lifestyle habits, and dietary data, and the AI ​​analyzes it to generate an optimal diet plan and advice for maintaining good health for the user. For example, the user inputs the contents of the meals they ate and records their exercise into the app. This information is collected in real time by the AI. Next, the AI ​​analyzes the collected data. Based on the user's health status, lifestyle habits, and dietary data, the AI ​​generates an optimal diet plan and advice for maintaining good health for the user. For example, it analyzes the calories and nutrients of meals the user has eaten in the past and proposes a balanced meal plan. It also provides an effective exercise plan based on the user's exercise records. Furthermore, the AI ​​is equipped with an engagement function to maintain the user's motivation. For example, it sends a message of praise when the user achieves a goal to increase the user's motivation. It also incorporates gamification elements to allow users to continue managing their health while having fun. Through this mechanism, users can find a health management method that suits them and receive help in leading a healthy life. Furthermore, personalized advice provided by AI promotes user health maintenance and contributes to reducing medical costs. For example, if a user inputs "I want to go on a diet," the AI ​​will propose an effective diet plan based on the user's current health status, lifestyle, and dietary data. Specifically, it will provide a balanced meal plan and an appropriate exercise plan to support the user in achieving their goals. In this way, the AI-powered personalized health management agent aims to transform health management into something accessible and enjoyable by monitoring the user's health status in real time and providing plans tailored to individual lifestyles. This allows the personalized health management agent to analyze the user's health status, lifestyle, and dietary data in real time and provide effective diet plans and advice for maintaining good health.

[0065] The personalized health management agent according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and an engagement unit. The data collection unit collects the user's health status, lifestyle habits, and dietary data. For example, the data collection unit collects data when the user inputs their daily activities and meals. For example, the user inputs the contents of the meals they ate and records their exercise into the app. This information is collected in real time by AI. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit generates an optimal diet plan and advice for maintaining health based on the user's health status, lifestyle habits, and dietary data. For example, it analyzes the calories and nutrients of meals the user has eaten in the past and proposes a balanced meal plan. The analysis unit can also provide an effective exercise plan based on the user's exercise records. The data provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the data provision unit provides a balanced meal menu and an appropriate exercise plan. For example, if the user inputs "I want to go on a diet," the data provision unit proposes an effective diet plan based on the user's current health status, lifestyle habits, and dietary data. The Engagement Department maintains user motivation based on the advice provided by the Service Department. For example, the Engagement Department sends praising messages when users achieve their goals. By sending praising messages when users achieve their goals, for example, it increases user motivation. The Engagement Department also incorporates gamification elements to enable users to continue managing their health while having fun. For example, the Engagement Department provides gamification elements such as a point system, badges, and rankings to enable users to continue managing their health while having fun. As a result, the personalized health management agent according to the embodiment can analyze the user's health status, lifestyle habits, and dietary data in real time and provide effective diet plans and advice for maintaining health.

[0066] The data collection unit collects user health status, lifestyle, and dietary data. Specifically, it collects data when users input their daily activities and meals. For example, users input details of meals they ate and exercise records into the app. This information is collected in real time by AI. The data collection unit automatically sends the user-input data to the cloud and stores it in a central database. Furthermore, the data collection unit can also collect data from wearable devices and smartwatches. This allows for the acquisition of detailed health data such as the user's heart rate, steps taken, and sleep patterns. For example, if a user is wearing a smartwatch, the data collection unit automatically acquires data such as heart rate, exercise level, and sleep quality from the device and updates it in real time. The data collection unit also provides a function to take and upload photos of meals to more accurately understand the user's diet. The AI ​​analyzes these photos and automatically recognizes the contents of the meals, calories, and nutrients. This eliminates the need for manual input by the user and allows for the collection of more accurate data. In addition, the data collection unit provides users with questionnaires about their lifestyle and regular health checklists to continuously monitor their health status. This allows the data collection unit to collect comprehensive data on users' health status and lifestyle habits, making it available for use by the analysis and provision units.

[0067] The analysis unit analyzes the data collected by the data collection unit. Specifically, it generates optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. For example, it analyzes the calories and nutrients of meals the user has eaten in the past and proposes a balanced meal plan. The analysis unit can also provide effective exercise plans based on the user's exercise records. The AI ​​uses machine learning algorithms and data mining techniques to analyze this data. For example, when analyzing the user's dietary data, it calculates the calories, protein, fat, carbohydrates, and other nutrients of the meals and generates a meal plan tailored to the user's goals. Furthermore, the analysis unit analyzes the user's exercise data and proposes an optimal exercise plan based on the intensity, frequency, and type of exercise. For example, if the user is aiming to lose weight, the analysis unit can suggest high-intensity interval training (HIIT) to maximize calorie expenditure. The analysis unit also provides advice on stress management and sleep improvement based on the user's health status and lifestyle. For example, it analyzes the user's heart rate and sleep data, and if the stress level is high, it suggests relaxation techniques or meditation. This allows the analysis unit to provide personalized advice tailored to the user's health status and goals, supporting them in leading a healthy lifestyle.

[0068] The service provider offers advice based on the analysis results obtained by the analysis unit. Specifically, it provides balanced meal plans and appropriate exercise plans. For example, if a user enters "I want to lose weight," the service provider will propose an effective diet plan based on the user's current health status, lifestyle, and dietary data. The service provider provides customized meal plans and exercise plans according to the user's goals and preferences. For example, if the user is a vegetarian, the service provider will propose a balanced meal plan centered on plant-based foods. The service provider can also accommodate the user's allergies and dietary restrictions and provide an appropriate meal plan. Furthermore, the service provider monitors the user's progress and updates the advice as needed. For example, if the user reaches their target weight, the service provider will propose a new meal plan and exercise plan to maintain that weight. The service provider also provides specific recipes and exercise methods to make it easier for the user to follow the advice. For example, it provides easy-to-make recipes and cooking methods to help the user implement the suggested meal plan. Regarding exercise plans, it provides specific exercise methods and videos to support the user in exercising correctly. This allows the service provider to offer specific advice to users on how to lead a healthy lifestyle and support them in achieving their goals.

[0069] The Engagement Department maintains user motivation based on the advice provided by the Service Provider Department. Specifically, it sends praising messages when users achieve their goals. For example, sending praising messages when users achieve their goals increases user motivation. The Engagement Department also incorporates gamification elements to help users continue managing their health in an enjoyable way. For example, the Engagement Department provides gamification elements such as a point system, badges, and rankings to help users continue managing their health in an enjoyable way. Users earn points each time they achieve a specific goal, and can use those points to obtain rewards and benefits. In addition, a ranking system where users compete with each other and share their health management results increases user motivation. Furthermore, the Engagement Department collects user feedback and uses it to improve the system. For example, by having users input their opinions and impressions on the advice and gamification elements provided, the Engagement Department analyzes this information and develops more effective methods for maintaining motivation. The Engagement Department also regularly checks users' progress and provides encouraging messages and additional advice as needed. This allows the Engagement Department to provide an environment that makes it easier for users to continue managing their health and to support them in achieving their goals.

[0070] The data collection unit can collect data when users input their daily activities and dietary information. For example, the data collection unit can collect data when users input the details of meals they ate and their exercise records into the app. The data collection unit can also collect data when users input their daily activities. For example, the data collection unit can collect data when users input their step count and exercise time into the app. Furthermore, the data collection unit can also collect data when users input their dietary information. For example, the data collection unit can collect data when users input the type of meal they ate and their calorie intake into the app. In this way, the data collection unit can collect data when users input their daily activities and dietary information. Daily activities include, but are not limited to, exercise volume, step count, and activity time. Dietary information includes, but is not limited to, the type of meal, amount consumed, and timing of meals. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the data entered by the user into AI and have the AI ​​perform the data collection.

[0071] The analysis unit can generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can generate an optimal diet plan based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can analyze the calories and nutrients of meals the user has eaten in the past and propose a balanced meal plan. The analysis unit can also provide an effective exercise plan based on the user's exercise records. For example, the analysis unit can analyze the user's exercise records and propose an appropriate exercise plan. Furthermore, the analysis unit can analyze the user's health status and generate health maintenance advice. For example, the analysis unit can analyze the user's health status and propose lifestyle improvements. This allows the system to generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. An optimal diet plan may include, but is not limited to, calorie restriction, exercise plans, and balanced meals. Health maintenance advice may include, but is not limited to, lifestyle improvements and health checklists. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health status, lifestyle, and dietary data into the AI ​​and have the AI ​​generate an optimal diet plan and advice for maintaining good health.

[0072] The service provider can provide balanced meal plans and appropriate exercise plans. For example, the service provider can provide balanced meal plans. For example, the service provider can suggest balanced meal plans based on the user's health status, lifestyle, and dietary data. The service provider can also provide appropriate exercise plans. For example, the service provider can suggest appropriate exercise plans based on the user's exercise records. In this way, by providing balanced meal plans and appropriate exercise plans, the service provider can support the user in maintaining their health. Balanced meal plans include, for example, the balance of nutrients and the selection of ingredients. Appropriate exercise plans include, for example, the type of exercise, frequency, and intensity. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health status, lifestyle, and dietary data into AI and have the AI ​​perform the task of providing balanced meal plans and appropriate exercise plans.

[0073] The engagement unit can send a message of praise to users when they achieve their goals. For example, the engagement unit can increase user motivation by sending a message of praise when a user achieves their goals. This means that sending a message of praise when a user achieves their goals can increase user motivation. The message of praise includes, but is not limited to, the wording of the message and the timing of its sending. Some or all of the above processing in the engagement unit may be performed using, for example, AI, or not using AI. For example, the engagement unit can have AI generate the message of praise to send when a user achieves their goals.

[0074] The engagement department can incorporate gamification elements to help users continue managing their health in an enjoyable way. For example, the engagement department could implement a point system, allowing users to earn points each time they manage their health. For instance, it could award points each time a user exercises or eats a balanced diet. The engagement department could also implement a badge system, allowing users to earn badges when they achieve specific goals. For example, it could award badges to users who exercise consistently for a certain period. Furthermore, the engagement department could implement a ranking system, allowing users to compete with other users. For example, it could display users' health management results in a ranking format, allowing them to compare with other users. In this way, by incorporating gamification elements, users can continue managing their health in an enjoyable way. Gamification elements include, but are not limited to, point systems, badges, and rankings. Some or all of the above processes in the engagement department may be performed using, for example, AI, or not using AI. For example, the engagement department can input user health management results into the AI, and have the AI ​​perform tasks such as awarding points and badges and generating rankings.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, by collecting data during times when the user is relaxed, the data collection unit can collect more accurate data. Also, if the user is busy, the data collection unit can collect data during times when the user is free. For example, by collecting data during times when the user is free, the data collection unit can reduce the burden on the user. Furthermore, if the user is relaxed, the data collection unit can increase the frequency of data collection. For example, by increasing the frequency of data collection when the user is relaxed, the data collection unit can collect more detailed data. In this way, adjusting the timing of data collection based on the user's emotions enables more effective data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of data collection.

[0076] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose collecting data at specific time periods. The data collection unit can also collect data at specific time periods based on the user's past health data. For example, by collecting data at specific time periods based on the user's past health data, the data collection unit can collect more accurate data. Furthermore, the data collection unit can adjust the frequency of data collection based on the user's past health data. For example, by adjusting the frequency of data collection based on the user's past health data, the data collection unit can collect more detailed data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Past health data includes, but is not limited to, past weight records, blood pressure measurement results, and exercise history. The optimal data collection method includes, but is not limited to, the frequency of data collection and the devices used. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into the AI ​​and have the AI ​​select the optimal data collection method.

[0077] The data collection unit can filter data based on the user's current lifestyle and health goals during data collection. For example, the data collection unit can collect only the necessary data, taking into account the user's current lifestyle. By collecting only the necessary data, taking into account the user's current lifestyle, the data collection unit can improve data collection efficiency. The data collection unit can also prioritize the collection of relevant data based on the user's health goals. For example, by prioritizing the collection of relevant data based on the user's health goals, the data collection unit can support the user in achieving their health goals. Furthermore, the data collection unit can adjust the scope of data collection according to the user's lifestyle and health goals. For example, by adjusting the scope of data collection according to the user's lifestyle and health goals, the data collection unit can perform more effective data collection. This allows the data collection to be filtered based on the user's current lifestyle and health goals, thereby collecting only the necessary data. Current lifestyle includes, but is not limited to, work situation, home environment, and daily routines. Health goals include, but are not limited to, weight loss goals, exercise goals, and dietary improvement goals. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and health goals into the AI ​​and have the AI ​​perform data filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, by prioritizing the collection of stress-related data when the user is stressed, the data collection unit can support the user's stress management. The data collection unit can also prioritize collecting relaxation-related data when the user is relaxed. For example, by prioritizing the collection of relaxation-related data when the user is relaxed, the data collection unit can help maintain the user's relaxed state. Furthermore, if the user is tired, the data collection unit can also prioritize collecting fatigue-related data. For example, by prioritizing the collection of fatigue-related data when the user is tired, the data collection unit can support the user's fatigue management. In this way, by determining the priority of data to collect based on the user's emotions, more important data can be collected preferentially. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and have AI determine the priority of the data to be collected.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region, thereby collecting region-specific health information. The data collection unit can also prioritize the collection of data related to the user's travel destination if the user is traveling. For example, if the data collection unit prioritizes the collection of data related to the user's travel destination, it can support health management at the travel destination. Furthermore, if the data collection unit is at home, it can prioritize the collection of data related to the user's home. For example, if the data collection unit is at home, it can prioritize the collection of data related to the user's home, thereby supporting health management at home. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Highly relevant data includes, but is not limited to, health information related to the user's current location and local health resources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect details of meals shared by the user on social media. For example, by collecting details of meals shared by the user on social media, the data collection unit can supplement the user's dietary data. The data collection unit can also collect exercise records shared by the user on social media. For example, by collecting exercise records shared by the user on social media, the data collection unit can supplement the user's exercise data. Furthermore, the data collection unit can also collect health-related posts shared by the user on social media. For example, by collecting health-related posts shared by the user on social media, the data collection unit can supplement the user's health data. This allows for the collection of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Relevant data includes, but is not limited to, health-related posts on social media and the user's interests. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media activity into the AI ​​and have the AI ​​collect the relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis result. For example, by providing a simple analysis result when the user is stressed, the analysis unit can reduce the user's burden. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, by providing a detailed analysis result when the user is relaxed, the analysis unit can deepen the user's understanding. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, by providing a concise analysis result when the user is in a hurry, the analysis unit can save the user's time. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the way the analysis is presented.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis on important health data. For example, by performing a detailed analysis on important health data, the analysis unit can provide important information to the user. The analysis unit can also perform a simplified analysis on less important health data. For example, by performing a simplified analysis on less important health data, the analysis unit can reduce the burden on the user. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the health data. For example, by adjusting the depth of the analysis according to the importance of the health data, the analysis unit can provide more appropriate analysis results. This allows for more detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. The importance of health data includes, but is not limited to, examples such as a doctor's diagnosis or a user's self-assessment. The level of detail of the analysis includes, but is not limited to, examples such as data granularity and analysis depth. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of health data into the AI ​​and have the AI ​​adjust the level of detail in the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutrient analysis algorithm to dietary data. For example, by applying a nutrient analysis algorithm to dietary data, the analysis unit can analyze the user's diet in detail. The analysis unit can also apply an exercise effect analysis algorithm to exercise data. For example, by applying an exercise effect analysis algorithm to exercise data, the analysis unit can analyze the user's exercise effect in detail. Furthermore, the analysis unit can also apply a sleep quality analysis algorithm to sleep data. For example, by applying a sleep quality analysis algorithm to sleep data, the analysis unit can analyze the user's sleep quality in detail. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of health data. Categories of health data include, but are not limited to, dietary data, exercise data, and sleep data. Different analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input health data categories into the AI ​​and have the AI ​​apply different analysis algorithms.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, by providing a short, concise analysis result when the user is in a hurry, the analysis unit can save the user time. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, by providing a detailed analysis result when the user is relaxed, the analysis unit can deepen the user's understanding. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, by providing a visually stimulating analysis result when the user is excited, the analysis unit can attract the user's interest. This allows for the provision of more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the timing of health data collection during analysis. For example, the analysis unit may prioritize the analysis of recently collected health data. For example, by prioritizing the analysis of recently collected health data, the analysis unit can provide the latest information. The analysis unit can also prioritize the latest data while referring to past health data. For example, by prioritizing the latest data while referring to past health data, the analysis unit can provide more accurate analysis results. Furthermore, the analysis unit can adjust the order of analysis according to the timing of health data collection. For example, by adjusting the order of analysis according to the timing of health data collection, the analysis unit can perform more effective analysis. This allows for prioritizing the analysis of the latest data by determining the priority of analysis based on the timing of health data collection. The timing of health data collection includes, but is not limited to, data timestamps and collection frequency. The priority of analysis includes, but is not limited to, data freshness and importance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the timing of health data collection into the AI ​​and have the AI ​​determine the priority of the analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant health data. For example, by prioritizing the analysis of highly relevant health data, the analysis unit can provide more important information. The analysis unit can also adjust the order of analysis according to the relevance of health data. For example, by adjusting the order of analysis according to the relevance of health data, the analysis unit can perform more effective analysis. Furthermore, the analysis unit can postpone the analysis of less relevant health data. For example, by postponing the analysis of less relevant health data, the analysis unit can improve the efficiency of the analysis. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of health data. The relevance of health data includes, but is not limited to, data correlation and causal relationships. The order of analysis includes, but is not limited to, methods of prioritizing highly relevant data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relationships between health data into the AI ​​and have the AI ​​adjust the order of analysis.

[0087] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand advice. For example, if the user is stressed, the service provider can reduce the user's burden by providing simple and easy-to-understand advice. The service provider can also provide detailed advice if the user is relaxed. For example, if the user is relaxed, the service provider can deepen the user's understanding by providing detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise advice. For example, if the service provider is in a hurry, the service provider can save the user's time by providing concise advice. This allows for the provision of more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​adjust how advice is expressed.

[0088] The service provider can adjust the level of detail in advice based on the importance of the health goal when providing advice. For example, the service provider can provide detailed advice for important health goals. For example, by providing detailed advice for important health goals, the service provider can provide important information to the user. The service provider can also provide simplified advice for less important health goals. For example, by providing simplified advice for less important health goals, the service provider can reduce the burden on the user. Furthermore, the service provider can adjust the depth of advice according to the importance of the health goal. For example, by adjusting the depth of advice according to the importance of the health goal, the service provider can provide more appropriate advice. This allows for the provision of more detailed advice for more important health goals by adjusting the level of detail in advice based on the importance of the health goal. The importance of a health goal includes, but is not limited to, a doctor's diagnosis or the user's self-assessment. The level of detail in advice includes, but is not limited to, the granularity of the information or the depth of the advice. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the importance of health goals into the AI ​​and have the AI ​​adjust the level of detail of the advice.

[0089] The service provider can apply different advice algorithms depending on the category of the health goal when providing advice. For example, for a weight loss goal, the service provider can apply a diet and exercise advice algorithm. For example, by applying a diet and exercise advice algorithm to a weight loss goal, the service provider can support the user's weight loss. The service provider can also apply a balanced lifestyle advice algorithm to a health maintenance goal. For example, by applying a balanced lifestyle advice algorithm to a health maintenance goal, the service provider can support the user's health maintenance. Furthermore, the service provider can apply a specialized advice algorithm to specific health problems. For example, by applying a specialized advice algorithm to specific health problems, the service provider can support the user in resolving their health problems. This allows for the provision of more appropriate advice by applying different advice algorithms depending on the category of the health goal. Categories of health goals include, but are not limited to, weight management, exercise habits, and dietary improvements. Different advice algorithms include, but are not limited to, rule-based, machine learning, and expert systems. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input health goal categories into the AI ​​and have the AI ​​apply different advice algorithms.

[0090] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. For example, by providing short, concise advice when the user is in a hurry, the service provider can save the user's time. The service provider can also provide detailed advice when the user is relaxed. For example, by providing detailed advice when the user is relaxed, the service provider can deepen the user's understanding. Furthermore, if the user is excited, the service provider can provide visually stimulating advice. For example, by providing visually stimulating advice when the user is excited, the service provider can capture the user's interest. This allows for more appropriate advice to be provided by adjusting the length of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​adjust the length of the advice.

[0091] The service provider can prioritize advice based on the timeframe for achieving health goals. For example, the service provider will prioritize advice for health goals that need to be achieved urgently. For example, by prioritizing advice for health goals that need to be achieved urgently, the service provider can support the user in achieving their goals. The service provider can also provide step-by-step advice for long-term health goals. For example, by providing step-by-step advice for long-term health goals, the service provider can support the user in managing their health on an ongoing basis. Furthermore, the service provider can adjust the order of advice according to the timeframe for achieving health goals. For example, by adjusting the order of advice according to the timeframe for achieving health goals, the service provider can provide more effective advice. This means that by prioritizing advice based on the timeframe for achieving health goals, more effective advice can be provided. The timeframe for achieving health goals includes, but is not limited to, short-term goals, medium-term goals, and long-term goals. The priority of advice includes, but is not limited to, the urgency and importance of the goals. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the target date for achieving health goals into the AI ​​and have the AI ​​determine the priority of advice.

[0092] The service provider can adjust the order of advice based on the relevance of health goals when providing advice. For example, the service provider can prioritize advice for highly relevant health goals. For example, by prioritizing advice for highly relevant health goals, the service provider can support the user in achieving their goals. The service provider can also adjust the order of advice according to the relevance of health goals. For example, by adjusting the order of advice according to the relevance of health goals, the service provider can provide more effective advice. Furthermore, the service provider can postpone less relevant health goals. For example, by postponing less relevant health goals, the service provider can improve the efficiency of advice. This allows for prioritizing more relevant advice by adjusting the order of advice based on the relevance of health goals. The relevance of health goals includes, but is not limited to, correlations and causal relationships between goals. The order of advice includes, but is not limited to, methods such as prioritizing highly relevant goals. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the relevance of health goals into the AI ​​and have the AI ​​adjust the order of the advice.

[0093] The engagement unit can estimate the user's emotions and adjust the engagement method based on the estimated emotions. For example, if the user is feeling stressed, the engagement unit can provide a relaxing engagement method. For example, by providing a relaxing engagement method when the user is feeling stressed, the engagement unit can reduce the user's stress. Also, if the user is relaxed, the engagement unit can provide an enjoyable engagement method. For example, by providing an enjoyable engagement method when the user is relaxed, the engagement unit can increase the user's motivation. Furthermore, if the user is in a hurry, the engagement unit can provide a quick and effective engagement method. For example, by providing a quick and effective engagement method when the user is in a hurry, the engagement unit can save the user's time. In this way, by adjusting the engagement method based on the user's emotions, more effective engagement can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal-generating AI. Some or all of the above-described processes in the engagement unit may be performed using AI, or not using AI. For example, the engagement unit may input user sentiment data into the AI ​​and have the AI ​​adjust the method of engagement.

[0094] The Engagement Department can analyze a user's past behavior history during engagement to select the optimal engagement method. For example, the Engagement Department can prioritize providing engagement methods that the user has preferred in the past. For example, by prioritizing engagement methods that the user has preferred in the past, the Engagement Department can increase the user's motivation. The Engagement Department can also suggest effective engagement methods based on the user's past behavior history. For example, by suggesting effective engagement methods based on the user's past behavior history, the Engagement Department can improve user engagement. Furthermore, the Engagement Department can adjust the frequency of engagement based on the user's past behavior history. For example, by adjusting the frequency of engagement based on the user's past behavior history, the Engagement Department can reduce the user's burden. This allows the optimal engagement method to be selected by analyzing the user's past behavior history. Some or all of the above processing in the Engagement Department may be performed using AI, for example, or without using AI. For example, the engagement department can input the user's past behavior history into the AI ​​and have the AI ​​select the optimal engagement method.

[0095] The engagement unit can customize the means of engagement based on the user's current life situation during engagement. For example, the engagement unit can provide appropriate means of engagement considering the user's current life situation. For example, by providing appropriate means of engagement considering the user's current life situation, the engagement unit can increase the user's motivation. The engagement unit can also provide quick and effective means of engagement when the user is busy. For example, by providing quick and effective means of engagement when the user is busy, the engagement unit can save the user's time. Furthermore, the engagement unit can provide enjoyable means of engagement when the user is relaxed. For example, by providing enjoyable means of engagement when the user is relaxed, the engagement unit can increase the user's motivation. This allows for more appropriate engagement by customizing the means of engagement based on the user's current life situation. Some or all of the above processing in the engagement unit may be performed using AI, for example, or without using AI. For example, the engagement unit can input the user's current living situation into the AI ​​and have the AI ​​customize the means of engagement.

[0096] The engagement unit can estimate the user's emotions and prioritize engagements based on those emotions. For example, if the user is stressed, the engagement unit will prioritize engagements related to stress reduction. By prioritizing stress-reducing engagements when the user is stressed, the engagement unit can reduce the user's stress. The engagement unit can also prioritize enjoyable engagements when the user is relaxed. By prioritizing enjoyable engagements when the user is relaxed, the engagement unit can increase the user's motivation. Furthermore, if the user is in a hurry, the engagement unit can prioritize quick and effective engagements. By prioritizing quick and effective engagements when the user is in a hurry, the engagement unit can save the user's time. In this way, more effective engagements can be provided by determining the priority of engagements based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal-generating AI. Some or all of the above-described processes in the engagement unit may be performed using AI, or not using AI. For example, the engagement unit may input user sentiment data into the AI ​​and have the AI ​​determine the priority of engagements.

[0097] The Engagement Unit can select the optimal engagement method by considering the user's geographical location information during engagement. For example, if the user is in a specific region, the Engagement Unit can provide engagement methods relevant to that region. For example, by providing engagement methods relevant to a specific region when the user is in a particular region, the Engagement Unit can meet the user's region-specific needs. The Engagement Unit can also provide engagement methods relevant to the user's travel destination when the user is traveling. For example, by providing engagement methods relevant to the user's travel destination when the user is traveling, the Engagement Unit can support the user's health management during their trip. Furthermore, if the Engagement Unit is at home, the Engagement Unit can provide engagement methods that can be enjoyed at home when the user is at home, for example, by providing engagement methods that can be enjoyed at home when the user is at home, the Engagement Unit can increase the user's motivation. In this way, the optimal engagement method can be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Engagement methods include, but are not limited to, local activities, health management while traveling, and engagement at home. Some or all of the above-described processes in the engagement unit may be performed using AI, for example, or not. For example, the engagement unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal engagement method.

[0098] The Engagement Department can analyze a user's social media activity during engagement and propose engagement methods. For example, the Engagement Department can propose engagement methods based on the interests and passions that the user has shared on social media. For example, by proposing engagement methods based on the interests and passions that the user has shared on social media, the Engagement Department can increase user motivation. The Engagement Department can also propose engagement methods based on the activities that the user has shared on social media. For example, by proposing engagement methods based on the activities that the user has shared on social media, the Engagement Department can improve user engagement. Furthermore, the Engagement Department can also propose engagement methods based on the friends and groups that the user has shared on social media. For example, by proposing engagement methods based on the friends and groups that the user has shared on social media, the Engagement Department can strengthen the user's social support. In this way, by analyzing a user's social media activity, the optimal engagement method can be proposed. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Engagement methods include, but are not limited to, suggestions based on interests, activities, or friends and groups. Some or all of the above-described processes in the engagement unit may be performed using AI, for example, or not using AI. For example, the engagement unit can input the user's social media activities into AI and have AI perform engagement method suggestions.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analysis unit can generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can generate an optimal diet plan based on the user's health status, lifestyle, and dietary data. For example, the analysis unit can analyze the calories and nutrients of meals the user has eaten in the past and propose a balanced meal plan. The analysis unit can also provide an effective exercise plan based on the user's exercise records. For example, the analysis unit can analyze the user's exercise records and propose an appropriate exercise plan. Furthermore, the analysis unit can analyze the user's health status and generate health maintenance advice. For example, the analysis unit can analyze the user's health status and propose lifestyle improvements. This allows the system to generate optimal diet plans and health maintenance advice based on the user's health status, lifestyle, and dietary data. An optimal diet plan may include, but is not limited to, calorie restriction, exercise plans, and balanced meals. Health maintenance advice may include, but is not limited to, lifestyle improvements and health checklists. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health status, lifestyle, and dietary data into the AI ​​and have the AI ​​generate an optimal diet plan and advice for maintaining good health.

[0101] The service provider can provide balanced meal plans and appropriate exercise plans. For example, the service provider can provide balanced meal plans. For example, the service provider can suggest balanced meal plans based on the user's health status, lifestyle, and dietary data. The service provider can also provide appropriate exercise plans. For example, the service provider can suggest appropriate exercise plans based on the user's exercise records. In this way, by providing balanced meal plans and appropriate exercise plans, the service provider can support the user in maintaining their health. Balanced meal plans include, for example, the balance of nutrients and the selection of ingredients. Appropriate exercise plans include, for example, the type of exercise, frequency, and intensity. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health status, lifestyle, and dietary data into AI and have the AI ​​perform the task of providing balanced meal plans and appropriate exercise plans.

[0102] The engagement unit can send a message of praise to users when they achieve their goals. For example, the engagement unit can increase user motivation by sending a message of praise when a user achieves their goals. This means that sending a message of praise when a user achieves their goals can increase user motivation. The message of praise includes, but is not limited to, the wording of the message and the timing of its sending. Some or all of the above processing in the engagement unit may be performed using, for example, AI, or not using AI. For example, the engagement unit can have AI generate the message of praise to send when a user achieves their goals.

[0103] The engagement department can incorporate gamification elements to help users continue managing their health in an enjoyable way. For example, the engagement department could implement a point system, allowing users to earn points each time they manage their health. For instance, it could award points each time a user exercises or eats a balanced diet. The engagement department could also implement a badge system, allowing users to earn badges when they achieve specific goals. For example, it could award badges to users who exercise consistently for a certain period. Furthermore, the engagement department could implement a ranking system, allowing users to compete with other users. For example, it could display users' health management results in a ranking format, allowing them to compare with other users. In this way, by incorporating gamification elements, users can continue managing their health in an enjoyable way. Gamification elements include, but are not limited to, point systems, badges, and rankings. Some or all of the above processes in the engagement department may be performed using, for example, AI, or not using AI. For example, the engagement department can input user health management results into the AI, and have the AI ​​perform tasks such as awarding points and badges and generating rankings.

[0104] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, by collecting data during times when the user is relaxed, the data collection unit can collect more accurate data. Also, if the user is busy, the data collection unit can collect data during times when the user is free. For example, by collecting data during times when the user is free, the data collection unit can reduce the burden on the user. Furthermore, if the user is relaxed, the data collection unit can increase the frequency of data collection. For example, by increasing the frequency of data collection when the user is relaxed, the data collection unit can collect more detailed data. In this way, adjusting the timing of data collection based on the user's emotions enables more effective data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of data collection.

[0105] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose the optimal data collection method. For example, the data collection unit can analyze trends in data previously entered by the user and propose collecting data at specific time periods. The data collection unit can also collect data at specific time periods based on the user's past health data. For example, by collecting data at specific time periods based on the user's past health data, the data collection unit can collect more accurate data. Furthermore, the data collection unit can adjust the frequency of data collection based on the user's past health data. For example, by adjusting the frequency of data collection based on the user's past health data, the data collection unit can collect more detailed data. This allows the optimal data collection method to be selected by analyzing the user's past health data. Past health data includes, but is not limited to, past weight records, blood pressure measurement results, and exercise history. The optimal data collection method includes, but is not limited to, the frequency of data collection and the devices used. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into the AI ​​and have the AI ​​select the optimal data collection method.

[0106] The data collection unit can filter data based on the user's current lifestyle and health goals during data collection. For example, the data collection unit can collect only the necessary data, taking into account the user's current lifestyle. By collecting only the necessary data, taking into account the user's current lifestyle, the data collection unit can improve data collection efficiency. The data collection unit can also prioritize the collection of relevant data based on the user's health goals. For example, by prioritizing the collection of relevant data based on the user's health goals, the data collection unit can support the user in achieving their health goals. Furthermore, the data collection unit can adjust the scope of data collection according to the user's lifestyle and health goals. For example, by adjusting the scope of data collection according to the user's lifestyle and health goals, the data collection unit can perform more effective data collection. This allows the data collection to be filtered based on the user's current lifestyle and health goals, thereby collecting only the necessary data. Current lifestyle includes, but is not limited to, work situation, home environment, and daily routines. Health goals include, but are not limited to, weight loss goals, exercise goals, and dietary improvement goals. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and health goals into the AI ​​and have the AI ​​perform data filtering.

[0107] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, by prioritizing the collection of stress-related data when the user is stressed, the data collection unit can support the user's stress management. The data collection unit can also prioritize collecting relaxation-related data when the user is relaxed. For example, by prioritizing the collection of relaxation-related data when the user is relaxed, the data collection unit can help maintain the user's relaxed state. Furthermore, if the user is tired, the data collection unit can also prioritize collecting fatigue-related data. For example, by prioritizing the collection of fatigue-related data when the user is tired, the data collection unit can support the user's fatigue management. In this way, by determining the priority of data to collect based on the user's emotions, more important data can be collected preferentially. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and have AI determine the priority of the data to be collected.

[0108] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis result. For example, by providing a simple analysis result when the user is stressed, the analysis unit can reduce the user's burden. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, by providing a detailed analysis result when the user is relaxed, the analysis unit can deepen the user's understanding. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, by providing a concise analysis result when the user is in a hurry, the analysis unit can save the user's time. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the way the analysis is presented.

[0109] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand advice. For example, if the user is stressed, the service provider can reduce the user's burden by providing simple and easy-to-understand advice. The service provider can also provide detailed advice if the user is relaxed. For example, if the user is relaxed, the service provider can deepen the user's understanding by providing detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise advice. For example, if the service provider is in a hurry, the service provider can save the user's time by providing concise advice. This allows for the provision of more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​adjust how advice is expressed.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects user health status, lifestyle, and dietary data. For example, data is collected when users input their daily activities and meals into the app. This information is collected in real time by AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, based on the user's health status, lifestyle, and dietary data, it generates an optimal diet plan and advice for maintaining good health for the user. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. For example, it provides a balanced meal plan or an appropriate exercise plan. Step 4: The Engagement team maintains user motivation based on the advice provided by the Service Provider team. For example, they send praising messages when users achieve their goals. They also incorporate gamification elements to help users continue managing their health while having fun.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and engagement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects data when the user inputs their daily activities and diet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to generate an optimal diet plan and health maintenance advice for the user. The provision unit is implemented by the control unit 46A of the smart device 14 and provides advice based on the analysis results. The engagement unit is implemented by the control unit 46A of the smart device 14 and provides messages and gamification elements to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and engagement unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects data when the user inputs their daily activities and diet. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to generate an optimal diet plan and health maintenance advice for the user. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides advice based on the analysis results. The engagement unit is implemented by the control unit 46A of the smart glasses 214 and provides messages and gamification elements to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and engagement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects data when the user inputs their daily activities and diet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to generate an optimal diet plan and health maintenance advice for the user. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides advice based on the analysis results. The engagement unit is implemented by the control unit 46A of the headset terminal 314 and provides messages and gamification elements to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and engagement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects data when the user inputs their daily activities and diet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to generate an optimal diet plan and health maintenance advice for the user. The provision unit is implemented by the control unit 46A of the robot 414 and provides advice based on the analysis results. The engagement unit is implemented by the control unit 46A of the robot 414 and provides messages and gamification elements to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A data collection unit that collects user health status, lifestyle habits, and dietary data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The system includes an engagement unit that maintains user motivation based on the advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is collected when users input their daily activities and dietary information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the user's health status, lifestyle, and dietary data, the system generates an optimal diet plan and health maintenance advice for the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide balanced meal plans and appropriate exercise plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned engagement unit is, Send a message of praise when the user achieves their goal. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned engagement unit is, By incorporating gamification elements, users can continue managing their health while having fun. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and health goals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health goal. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of the health goal. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, prioritize the advice based on the timeframe for achieving health goals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, adjust the order of advice based on its relevance to your health goals. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned engagement unit is, It estimates user emotions and adjusts engagement methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned engagement unit is, During engagement, the system analyzes the user's past behavior history to select the most suitable engagement method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned engagement unit is, During engagement, customize the means of engagement based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned engagement unit is, It estimates user emotions and determines engagement priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned engagement unit is, During engagement, the optimal engagement method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned engagement unit is, During engagement, we analyze users' social media activity and suggest ways to engage them. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user health status, lifestyle habits, and dietary data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The system includes an engagement unit that maintains user motivation based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is Data is collected when users input their daily activities and dietary information. The system according to feature 1.

3. The aforementioned analysis unit, Based on the user's health status, lifestyle, and dietary data, the system generates an optimal diet plan and health maintenance advice for the user. The system according to feature 1.

4. The aforementioned supply unit is, We provide balanced meal plans and appropriate exercise plans. The system according to feature 1.

5. The aforementioned engagement unit is, Send a message of praise when the user achieves their goal. The system according to feature 1.

6. The aforementioned engagement unit is, By incorporating gamification elements, users can continue managing their health while having fun. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and health goals. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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