system

Abstract

The system according to this embodiment aims to collect and analyze users' daily health data and support appropriate health management. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, an explanation unit, and an advice unit. The collection unit collects the user's daily health data. The analysis unit analyzes the data collected by the collection unit and detects changes in health status and signs of abnormalities. The proposal unit proposes an optimal check-up schedule based on the results obtained by the analysis unit. The explanation unit analyzes the results of the health check in detail based on the schedule proposed by the proposal unit and explains them to the user in an easy-to-understand manner. The advice unit provides specific and actionable health advice based on the results explained by the explanation 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to effectively collect and analyze daily health data of users and perform appropriate health management.

[0005] The system according to the embodiment aims to collect and analyze daily health data of users and support appropriate health management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, an explanation unit, and an advice unit. The data collection unit collects the user's daily health data. The analysis unit analyzes the data collected by the data collection unit and detects changes in health status and signs of abnormalities. The proposal unit proposes an optimal check-up schedule based on the results obtained by the analysis unit. The explanation unit analyzes the results of the health check in detail based on the schedule proposed by the proposal unit and explains them to the user in an easy-to-understand manner. The advice unit provides specific and actionable health advice based on the results explained by the explanation unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze users' daily health data and support appropriate health management. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 receiving 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 receiving 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 health check system according to an embodiment of the present invention is a system that continuously collects a user's daily health data and uses AI to perform advanced analysis to quickly detect changes in health status and signs of abnormalities. This health check system continuously collects a user's daily health data and uses AI to perform advanced analysis to quickly detect changes in health status and signs of abnormalities. It then proposes an optimal check-up schedule tailored to each user's lifestyle and health condition. The results of regular health checks are analyzed in detail by the AI ​​and explained to the user in an easy-to-understand manner. Furthermore, specific and actionable health advice is provided based on the results, so that the user can accurately understand their own health status and take appropriate measures. The greatest advantage of this system is that users can easily manage their health in their daily lives. Even in busy daily lives, the AI ​​constantly monitors health data and issues alerts as needed, enabling early detection and prevention of diseases and health risks. This allows users to achieve long-term health maintenance and improved quality of life. In this way, the health check system can effectively support the user's health management.

[0029] The health check system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, an explanation unit, and an advice unit. The data collection unit collects the user's daily health data. The data collection unit can collect data such as heart rate, steps, sleep patterns, and body temperature using devices such as smartwatches and fitness trackers. The data collection unit can, for example, monitor the user's heart rate in real time using a smartwatch. The data collection unit can also record the user's steps and exercise level using a fitness tracker. Furthermore, the data collection unit can analyze the user's sleep patterns using a sleep tracker. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch and detect abnormal heart rate fluctuations. A fitness tracker can record the user's steps and exercise level and understand their daily activity level. A sleep tracker can analyze the user's sleep patterns and evaluate the quality of their sleep. The analysis unit analyzes the data collected by the data collection unit and detects changes in health status or signs of abnormalities. The analysis unit can, for example, use AI to analyze collected data and detect changes in health status or signs of abnormalities. The analysis unit can, for example, use AI to detect abnormal increases in heart rate or disruptions in sleep patterns. Furthermore, the analysis unit can use AI to monitor changes in the user's health status in real time. For example, the analysis unit can use AI to detect abnormal increases in heart rate and issue an alert to the user. It can also detect disruptions in sleep patterns and suggest appropriate measures to the user. The suggestion unit proposes an optimal check-up schedule based on the results obtained by the analysis unit. For example, the suggestion unit can propose the timing of regular health check-ups considering the user's past health data and lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule. Furthermore, the suggestion unit can adjust the timing of health check-ups considering the user's lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule.The timing of health checks can also be adjusted to take into account the user's lifestyle. The explanation unit analyzes the results of the health check in detail based on the schedule proposed by the suggestion unit and explains them to the user in an easy-to-understand manner. The explanation unit can, for example, display the results of the health check in graphs and charts so that the user can understand them intuitively. The explanation unit can, for example, display the results of the health check in graphs and charts so that the user can understand them intuitively. The explanation unit can also analyze the results of the health check in detail and explain them to the user in an easy-to-understand manner. For example, the explanation unit can display the results of the health check in graphs and charts so that the user can understand them intuitively. The explanation unit can also analyze the results of the health check in detail and explain them to the user in an easy-to-understand manner. The advice unit provides specific and actionable health advice based on the results explained by the explanation unit. The advice unit can, for example, provide advice that the user can actually take action on, such as recommending dietary improvements or exercise, based on the results of the health check. The advice unit can, for example, provide advice that the user can actually take action on, such as recommending dietary improvements or exercise, based on the results of the health check. The advice unit can also provide specific and actionable health advice based on the results of the health check. For example, the advice unit can provide advice that the user can actually implement, such as dietary improvements or exercise recommendations, based on the results of the health check. This allows the health check system according to the embodiment to effectively support the user's health management. Some or all of the above-described processing in the advice unit may be performed using AI, or without AI. For example, the advice unit can provide advice using an AI model that takes the results of the health check as input and outputs specific health advice.

[0030] The data collection unit collects users' daily health data. For example, it can use devices such as smartwatches and fitness trackers to collect data such as heart rate, steps, sleep patterns, and body temperature. Specifically, smartwatches can monitor the user's heart rate in real time and detect abnormal heart rate fluctuations, allowing for continuous monitoring of the user's cardiac health. Fitness trackers record the user's steps and exercise levels, providing insight into daily activity levels and enabling detailed tracking of the user's exercise habits and activity levels. Sleep trackers analyze the user's sleep patterns and evaluate sleep quality. For instance, sleep trackers record the user's movements and heart rate fluctuations during sleep, identifying periods of deep and light sleep. This allows users to understand their sleep quality and take appropriate improvements. The data collection unit centrally manages the data collected from these devices and transmits it to a cloud server, enabling integration with other systems and departments. For example, making the collected data accessible to the analysis and proposal departments improves the overall efficiency of the system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect data and understand the user's health status in real time.

[0031] The analysis unit analyzes data collected by the data collection unit to detect changes in health status and signs of abnormalities. For example, the analysis unit can use AI to analyze the collected data and detect changes in health status and signs of abnormalities. Specifically, AI can detect abnormal increases in heart rate or disruptions in sleep patterns. For instance, AI can analyze a user's heart rate data and detect abnormal fluctuations that exceed the normal range. This allows users to detect heart problems early and take appropriate measures. AI can also analyze a user's sleep patterns and detect decreased sleep quality or irregular sleep patterns. This allows users to recognize their sleep problems and take steps to improve them. Furthermore, the analysis unit can use AI to monitor changes in the user's health status in real time. For example, AI can continuously analyze a user's heart rate data and issue alerts if abnormal fluctuations are detected. AI can also continuously analyze a user's sleep patterns and suggest appropriate measures if a decrease in sleep quality is detected. This allows the analysis unit to quickly and accurately analyze collected data and understand the user's health status in real time. Furthermore, the analytics department can utilize historical data and statistical information to analyze long-term changes and trends in health status. For example, based on past heart rate data, it can predict changes in a user's cardiac health and assess future risks. This allows the analytics department to not only monitor the situation in real time but also to provide long-term health management, comprehensively supporting the user's health.

[0032] The proposal department proposes an optimal check-up schedule based on the results obtained by the analysis department. For example, the proposal department can suggest the timing of regular health checks by considering the user's past health data and lifestyle. Specifically, the proposal department can analyze the user's past health data and propose an optimal check-up schedule. For example, based on the user's heart rate data and sleep patterns, it can suggest regular cardiac examinations and sleep evaluations. The proposal department can also adjust the timing of health checks by considering the user's lifestyle. For example, if the user has just started exercising, it can suggest regular fitness checks to evaluate the effects of the exercise. Furthermore, the proposal department can flexibly adjust the check-up schedule in response to changes in the user's health condition. For example, if an abnormality is detected in the user's heart rate data, it can promptly suggest a cardiac examination. This allows the proposal department to continuously monitor the user's health condition and propose health checks at the optimal time. In addition, the proposal department can continuously improve the accuracy and effectiveness of its suggestions based on user feedback. For example, by providing feedback on the results of the suggested check-ups, the proposal department can adjust the next suggestion more appropriately. This allows the proposal department to effectively support users' health management and contribute to maintaining and improving their health status.

[0033] The explanation unit analyzes the results of the health check in detail based on the schedule proposed by the proposal unit and explains them to the user in an easy-to-understand manner. For example, the explanation unit can display the health check results in graphs and charts so that the user can understand them intuitively. Specifically, the explanation unit can visually display heart rate variability and changes in sleep patterns so that the user can grasp their health status at a glance. The explanation unit can also analyze the health check results in detail and explain them to the user in an easy-to-understand manner. For example, if abnormal fluctuations in heart rate are detected, it can explain the cause and impact in detail and provide information so that the user can take appropriate measures. Furthermore, the explanation unit can continuously monitor changes in the user's health status and provide regular reports. For example, it can create monthly and annual reports so that the user can understand changes in their health status over the long term. In this way, the explanation unit can support the user in accurately understanding their health status and taking appropriate measures. In addition, the explanation unit can continuously improve the accuracy and effectiveness of the explanation content based on user feedback. For example, it can evaluate whether the user understood the explanation content and revise the explanation method as needed. This allows the explanatory section to provide users with clear and effective explanations, supporting their health management.

[0034] The advice unit provides specific and actionable health advice based on the results explained by the explanation unit. For example, based on the results of a health check, the advice unit can provide advice that users can actually implement, such as recommendations for dietary improvements or exercise. Specifically, the advice unit can recommend dietary improvements and exercise based on the user's heart rate data and sleep patterns. For example, if abnormal fluctuations in heart rate are detected, it can suggest ways of eating and exercising that do not strain the heart. Also, if the quality of sleep is poor, it can suggest ways to improve the sleep environment or relaxation methods. Furthermore, the advice unit can provide individually customized advice according to the user's lifestyle and health condition. For example, if the user has a specific health goal, it can propose a specific action plan to achieve that goal. In this way, the advice unit can provide specific advice that users can actually implement and support the improvement of their health condition. In addition, the advice unit can continuously improve the accuracy and effectiveness of its advice based on user feedback. For example, by providing feedback on the results of the user implementing the suggested advice, the advice unit can adjust the content of the next advice more appropriately. In this way, the advice unit can effectively support the user's health management and contribute to maintaining and improving their health condition. Some or all of the processing described above in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice using an AI model that takes the results of a health check as input and outputs specific health advice.

[0035] The data collection unit can collect data such as heart rate, steps, sleep patterns, and body temperature using devices such as smartwatches and fitness trackers. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch. The data collection unit can also record the user's steps and exercise level using a fitness tracker. The data collection unit can also analyze the user's sleep patterns using a sleep tracker. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch and detect abnormal heart rate fluctuations. Fitness trackers can record the user's steps and exercise level, allowing for an understanding of daily activity levels. Sleep trackers can analyze the user's sleep patterns and evaluate sleep quality. This allows for the efficient collection of the user's daily health data using devices such as smartwatches and fitness trackers. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smartwatches and fitness trackers into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can analyze the collected data and detect changes in health status or signs of abnormalities. For example, the analysis unit can use AI to analyze the collected data and detect changes in health status or signs of abnormalities. For example, the analysis unit can use AI to detect abnormal increases in heart rate or disruptions in sleep patterns. The analysis unit can also use AI to monitor changes in the user's health status in real time. For example, the analysis unit can use AI to detect abnormal increases in heart rate and issue an alert to the user. The analysis unit can also detect disruptions in sleep patterns and suggest appropriate measures to the user. This allows for early detection of changes in health status or signs of abnormalities by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0037] The suggestion unit can propose the timing of regular health checks, taking into account the user's past health data and lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule. The suggestion unit can also adjust the timing of health checks, taking into account the user's lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule. The suggestion unit can also adjust the timing of health checks, taking into account the user's lifestyle. This allows the suggestion unit to propose the optimal timing for health checks by taking into account the user's past health data and lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past health data into a generating AI and have the generating AI propose an optimal check-up schedule.

[0038] The explanation unit can display the results of the health check in graphs and charts, making them intuitively understandable to the user. For example, the explanation unit can display the results of the health check in graphs and charts, making them intuitively understandable to the user. The explanation unit can also, for example, analyze the results of the health check in detail and explain them clearly to the user. For example, the explanation unit can display the results of the health check in graphs and charts, making them intuitively understandable to the user. The explanation unit can also, for example, analyze the results of the health check in detail and explain them clearly to the user. This allows the user to intuitively understand the results of the health check by displaying them in graphs and charts. Some or all of the above-described processes in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the results of the health check into a generating AI and have the generating AI suggest a method for displaying the results.

[0039] The advice unit can provide specific and actionable health advice based on the results. For example, based on the results of a health check, the advice unit can provide advice that the user can actually take action on, such as recommendations for dietary improvements or exercise. By providing specific and actionable health advice based on the results, the advice unit can enable the user to take appropriate measures. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the results of a health check into a generating AI and have the generating AI propose specific health advice.

[0040] 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 the user's past heart rate data and collect data during periods of high heart rate variability. For example, the data collection unit can analyze the user's past sleep patterns and optimize data collection during sleep. For example, the data collection unit can analyze the user's past exercise data and prioritize data collection after exercise. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing 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 a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect data related to relaxation. For example, if the user is working, the data collection unit can collect data related to stress levels. This allows for the collection of highly relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity and environmental data into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and heart rate data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. For example, if the user is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing 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 a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts on social media indicating they are stressed, the data collection unit can collect data related to their stress level. For example, if the user posts about exercise, the data collection unit can prioritize collecting exercise data. For example, if the user posts about food, the data collection unit can collect data related to food. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0044] 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 can perform a detailed analysis and create a detailed report for important data. For example, the analysis unit can perform a simplified analysis and create a concise report for less important data. For example, the analysis unit can perform an analysis with a moderate level of detail for data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] 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 heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise volume analysis algorithm to step count data. By applying different analysis algorithms depending on the category of health data, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and apply an appropriate analysis algorithm to the generating AI.

[0046] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may analyze current data while referring to past data. For example, the analysis unit may focus on analyzing data collected during a specific period. This allows for efficient analysis by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the health data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of analysis.

[0048] The proposal unit can adjust the level of detail in its proposals based on the importance of the health checks. For example, the proposal unit can provide detailed proposals for important health checks, simple proposals for less important health checks, and proposals with a moderate level of detail for health checks of moderate importance. This allows for efficient proposals by adjusting the level of detail based on the importance of the health checks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health checks into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0049] The suggestion unit can apply different suggestion algorithms depending on the user's lifestyle when making suggestions. For example, if the user is a night owl, the suggestion unit can make suggestions suitable for the night. For example, if the user is an early riser, the suggestion unit can make suggestions suitable for the morning. For example, the suggestion unit can adjust the timing of suggestions according to the user's lifestyle. This allows for suggestions tailored to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and apply the suggestion algorithm to the generating AI.

[0050] The proposal unit can determine the priority of proposals based on the timing of health data collection when making a proposal. For example, the proposal unit can make proposals based on recently collected data. For example, the proposal unit can make proposals based on current data while referring to past data. For example, the proposal unit can focus on reflecting data collected during a specific period in its proposals. This allows for efficient proposals by determining the priority of proposals based on the timing of health data collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of health data during the proposal process. For example, the proposal unit can make proposals based on highly relevant data. For example, the proposal unit can postpone proposals based on less relevant data. For example, the proposal unit can dynamically adjust the order of proposals according to the relevance of the data. This allows for efficient proposals by adjusting the order of proposals based on the relevance of health data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of proposals.

[0052] The explanation unit can adjust the level of detail in its explanation based on the importance of the health check results. For example, it can provide a detailed explanation for important results, a simplified explanation for less important results, and a moderate level of detail for results of moderate importance. This allows for efficient explanations by adjusting the level of detail based on the importance of the health check results. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the importance of the health check results into a generating AI and have the generating AI adjust the level of detail in the explanation.

[0053] The explanation unit can apply different explanation algorithms depending on the category of health data during explanation. For example, the explanation unit can apply a heart rate variability analysis algorithm to heart rate data and explain the results. For example, the explanation unit can apply a sleep stage analysis algorithm to sleep data and explain the results. For example, the explanation unit can apply an exercise volume analysis algorithm to step count data and explain the results. This allows explanations to be provided according to the category of health data. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the category of health data into a generating AI and have the generating AI apply an appropriate explanation algorithm.

[0054] The explanation unit can determine the priority of explanations based on the timing of health data collection during the explanation process. For example, the explanation unit can provide explanations based on recently collected data. For example, the explanation unit can provide explanations based on current data while referring to past data. For example, the explanation unit can focus on incorporating data collected during a specific period into the explanation. This allows for efficient explanations by determining the priority of explanations based on the timing of health data collection. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of explanations.

[0055] The explanation unit can adjust the order of explanations based on the relevance of health data during the explanation process. For example, the explanation unit can explain based on highly relevant data. For example, the explanation unit can explain less relevant data later. For example, the explanation unit can dynamically adjust the order of explanations according to the relevance of the data. This allows for efficient explanations by adjusting the order of explanations based on the relevance of health data. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of explanations.

[0056] The advice unit can adjust the level of detail of its advice based on the importance of the health check results. For example, the advice unit can provide detailed advice for important results, simple advice for less important results, and advice with a moderate level of detail for results of moderate importance. This allows for efficient advice by adjusting the level of detail based on the importance of the health check results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of the health check results into a generating AI and have the generating AI adjust the level of detail of the advice.

[0057] The advice unit can apply different advice algorithms depending on the category of health data when providing advice. For example, the advice unit can apply a heart rate variability analysis algorithm to heart rate data and provide advice based on the results. For example, the advice unit can apply a sleep stage analysis algorithm to sleep data and provide advice based on the results. For example, the advice unit can apply an exercise volume analysis algorithm to step count data and provide advice based on the results. This allows for advice tailored to the category of health data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the category of health data into a generating AI and apply an appropriate advice algorithm to the generating AI.

[0058] The advice unit can determine the priority of advice based on the timing of health data collection. For example, the advice unit can provide advice based on recently collected data. For example, the advice unit can provide advice based on current data while referring to past data. For example, the advice unit can focus on incorporating data collected during a specific period into its advice. This allows for efficient advice by determining the priority of advice based on the timing of health data collection. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

[0059] The advice unit can adjust the order of advice based on the relevance of health data when providing advice. For example, the advice unit can provide advice based on highly relevant data. For example, the advice unit can postpone providing advice on less relevant data. For example, the advice unit can dynamically adjust the order of advice according to the relevance of the data. This allows for efficient advice by adjusting the order of advice based on the relevance of health data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of advice.

[0060] The advice unit can apply different advice algorithms depending on the user's lifestyle when providing advice. For example, if the user is a night owl, the advice unit can provide advice suitable for the night. For example, if the user is an early riser, the advice unit can provide advice suitable for the morning. For example, the advice unit can adjust the timing of advice according to the user's lifestyle. This allows for advice tailored to the user's lifestyle. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's lifestyle data into a generating AI and apply an advice algorithm to the generating AI.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] 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 the user's past heart rate data and collect data during periods of high heart rate variability. For example, the data collection unit can analyze the user's past sleep patterns and optimize data collection during sleep. For example, the data collection unit can analyze the user's past exercise data and prioritize data collection after exercise. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing 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 a generating AI and have the generating AI select the optimal data collection method.

[0063] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect data related to relaxation. For example, if the user is working, the data collection unit can collect data related to stress levels. This allows for the collection of highly relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity and environmental data into a generating AI and have the generating AI perform data filtering.

[0064] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and heart rate data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. For example, if the user is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing 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 a generating AI and have the generating AI perform the collection of highly relevant data.

[0065] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts on social media indicating they are stressed, the data collection unit can collect data related to their stress level. For example, if the user posts about exercise, the data collection unit can prioritize collecting exercise data. For example, if the user posts about food, the data collection unit can collect data related to food. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0066] 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 can perform a detailed analysis and create a detailed report for important data. For example, the analysis unit can perform a simplified analysis and create a concise report for less important data. For example, the analysis unit can perform an analysis with a moderate level of detail for data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0067] 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 heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise volume analysis algorithm to step count data. By applying different analysis algorithms depending on the category of health data, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and apply an appropriate analysis algorithm to the generating AI.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects the user's daily health data. The data collection unit uses devices such as smartwatches and fitness trackers to collect data such as heart rate, steps, sleep patterns, and body temperature. For example, a smartwatch can be used to monitor the user's heart rate in real time, a fitness tracker can be used to record steps and exercise levels, and a sleep tracker can be used to analyze sleep patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit to detect changes in health status or signs of abnormalities. The analysis unit can use AI to detect abnormal increases in heart rate or disruptions in sleep patterns and issue alerts to the user. Step 3: The proposal department proposes an optimal check-up schedule based on the results obtained by the analysis department. The proposal department can suggest the timing of regular health check-ups, taking into account the user's past health data and lifestyle. Step 4: The explanation unit analyzes the health check results in detail based on the schedule proposed by the proposal unit and explains them clearly to the user. The explanation unit displays the health check results in graphs and charts to allow the user to understand them intuitively. Step 5: The advice section provides specific, actionable health advice based on the results explained by the explanation section. Based on the health check results, the advice section provides advice that the user can actually implement, such as recommendations for dietary improvements or exercise.

[0070] (Example of form 2) The health check system according to an embodiment of the present invention is a system that continuously collects a user's daily health data and uses AI to perform advanced analysis to quickly detect changes in health status and signs of abnormalities. This health check system continuously collects a user's daily health data and uses AI to perform advanced analysis to quickly detect changes in health status and signs of abnormalities. It then proposes an optimal check-up schedule tailored to each user's lifestyle and health condition. The results of regular health checks are analyzed in detail by the AI ​​and explained to the user in an easy-to-understand manner. Furthermore, specific and actionable health advice is provided based on the results, so that the user can accurately understand their own health status and take appropriate measures. The greatest advantage of this system is that users can easily manage their health in their daily lives. Even in busy daily lives, the AI ​​constantly monitors health data and issues alerts as needed, enabling early detection and prevention of diseases and health risks. This allows users to achieve long-term health maintenance and improved quality of life. In this way, the health check system can effectively support the user's health management.

[0071] The health check system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, an explanation unit, and an advice unit. The data collection unit collects the user's daily health data. The data collection unit can collect data such as heart rate, steps, sleep patterns, and body temperature using devices such as smartwatches and fitness trackers. The data collection unit can, for example, monitor the user's heart rate in real time using a smartwatch. The data collection unit can also record the user's steps and exercise level using a fitness tracker. Furthermore, the data collection unit can analyze the user's sleep patterns using a sleep tracker. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch and detect abnormal heart rate fluctuations. A fitness tracker can record the user's steps and exercise level and understand their daily activity level. A sleep tracker can analyze the user's sleep patterns and evaluate the quality of their sleep. The analysis unit analyzes the data collected by the data collection unit and detects changes in health status or signs of abnormalities. The analysis unit can, for example, use AI to analyze collected data and detect changes in health status or signs of abnormalities. The analysis unit can, for example, use AI to detect abnormal increases in heart rate or disruptions in sleep patterns. Furthermore, the analysis unit can use AI to monitor changes in the user's health status in real time. For example, the analysis unit can use AI to detect abnormal increases in heart rate and issue an alert to the user. It can also detect disruptions in sleep patterns and suggest appropriate measures to the user. The suggestion unit proposes an optimal check-up schedule based on the results obtained by the analysis unit. For example, the suggestion unit can propose the timing of regular health check-ups considering the user's past health data and lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule. Furthermore, the suggestion unit can adjust the timing of health check-ups considering the user's lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule.The timing of health checks can also be adjusted to take into account the user's lifestyle. The explanation unit analyzes the results of the health check in detail based on the schedule proposed by the suggestion unit and explains them to the user in an easy-to-understand manner. The explanation unit can, for example, display the results of the health check in graphs and charts so that the user can understand them intuitively. The explanation unit can, for example, display the results of the health check in graphs and charts so that the user can understand them intuitively. The explanation unit can also analyze the results of the health check in detail and explain them to the user in an easy-to-understand manner. For example, the explanation unit can display the results of the health check in graphs and charts so that the user can understand them intuitively. The explanation unit can also analyze the results of the health check in detail and explain them to the user in an easy-to-understand manner. The advice unit provides specific and actionable health advice based on the results explained by the explanation unit. The advice unit can, for example, provide advice that the user can actually take action on, such as recommending dietary improvements or exercise, based on the results of the health check. The advice unit can, for example, provide advice that the user can actually take action on, such as recommending dietary improvements or exercise, based on the results of the health check. The advice unit can also provide specific and actionable health advice based on the results of the health check. For example, the advice unit can provide advice that the user can actually implement, such as dietary improvements or exercise recommendations, based on the results of the health check. This allows the health check system according to the embodiment to effectively support the user's health management. Some or all of the above-described processing in the advice unit may be performed using AI, or without AI. For example, the advice unit can provide advice using an AI model that takes the results of the health check as input and outputs specific health advice.

[0072] The data collection unit collects users' daily health data. For example, it can use devices such as smartwatches and fitness trackers to collect data such as heart rate, steps, sleep patterns, and body temperature. Specifically, smartwatches can monitor the user's heart rate in real time and detect abnormal heart rate fluctuations, allowing for continuous monitoring of the user's cardiac health. Fitness trackers record the user's steps and exercise levels, providing insight into daily activity levels and enabling detailed tracking of the user's exercise habits and activity levels. Sleep trackers analyze the user's sleep patterns and evaluate sleep quality. For instance, sleep trackers record the user's movements and heart rate fluctuations during sleep, identifying periods of deep and light sleep. This allows users to understand their sleep quality and take appropriate improvements. The data collection unit centrally manages the data collected from these devices and transmits it to a cloud server, enabling integration with other systems and departments. For example, making the collected data accessible to the analysis and proposal departments improves the overall efficiency of the system. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect data and understand the user's health status in real time.

[0073] The analysis unit analyzes data collected by the data collection unit to detect changes in health status and signs of abnormalities. For example, the analysis unit can use AI to analyze the collected data and detect changes in health status and signs of abnormalities. Specifically, AI can detect abnormal increases in heart rate or disruptions in sleep patterns. For instance, AI can analyze a user's heart rate data and detect abnormal fluctuations that exceed the normal range. This allows users to detect heart problems early and take appropriate measures. AI can also analyze a user's sleep patterns and detect decreased sleep quality or irregular sleep patterns. This allows users to recognize their sleep problems and take steps to improve them. Furthermore, the analysis unit can use AI to monitor changes in the user's health status in real time. For example, AI can continuously analyze a user's heart rate data and issue alerts if abnormal fluctuations are detected. AI can also continuously analyze a user's sleep patterns and suggest appropriate measures if a decrease in sleep quality is detected. This allows the analysis unit to quickly and accurately analyze collected data and understand the user's health status in real time. Furthermore, the analytics department can utilize historical data and statistical information to analyze long-term changes and trends in health status. For example, based on past heart rate data, it can predict changes in a user's cardiac health and assess future risks. This allows the analytics department to not only monitor the situation in real time but also to provide long-term health management, comprehensively supporting the user's health.

[0074] The proposal department proposes an optimal check-up schedule based on the results obtained by the analysis department. For example, the proposal department can suggest the timing of regular health checks by considering the user's past health data and lifestyle. Specifically, the proposal department can analyze the user's past health data and propose an optimal check-up schedule. For example, based on the user's heart rate data and sleep patterns, it can suggest regular cardiac examinations and sleep evaluations. The proposal department can also adjust the timing of health checks by considering the user's lifestyle. For example, if the user has just started exercising, it can suggest regular fitness checks to evaluate the effects of the exercise. Furthermore, the proposal department can flexibly adjust the check-up schedule in response to changes in the user's health condition. For example, if an abnormality is detected in the user's heart rate data, it can promptly suggest a cardiac examination. This allows the proposal department to continuously monitor the user's health condition and propose health checks at the optimal time. In addition, the proposal department can continuously improve the accuracy and effectiveness of its suggestions based on user feedback. For example, by providing feedback on the results of the suggested check-ups, the proposal department can adjust the next suggestion more appropriately. This allows the proposal department to effectively support users' health management and contribute to maintaining and improving their health status.

[0075] The explanation unit analyzes the results of the health check in detail based on the schedule proposed by the proposal unit and explains them to the user in an easy-to-understand manner. For example, the explanation unit can display the health check results in graphs and charts so that the user can understand them intuitively. Specifically, the explanation unit can visually display heart rate variability and changes in sleep patterns so that the user can grasp their health status at a glance. The explanation unit can also analyze the health check results in detail and explain them to the user in an easy-to-understand manner. For example, if abnormal fluctuations in heart rate are detected, it can explain the cause and impact in detail and provide information so that the user can take appropriate measures. Furthermore, the explanation unit can continuously monitor changes in the user's health status and provide regular reports. For example, it can create monthly and annual reports so that the user can understand changes in their health status over the long term. In this way, the explanation unit can support the user in accurately understanding their health status and taking appropriate measures. In addition, the explanation unit can continuously improve the accuracy and effectiveness of the explanation content based on user feedback. For example, it can evaluate whether the user understood the explanation content and revise the explanation method as needed. This allows the explanatory section to provide users with clear and effective explanations, supporting their health management.

[0076] The advice unit provides specific and actionable health advice based on the results explained by the explanation unit. For example, based on the results of a health check, the advice unit can provide advice that users can actually implement, such as recommendations for dietary improvements or exercise. Specifically, the advice unit can recommend dietary improvements and exercise based on the user's heart rate data and sleep patterns. For example, if abnormal fluctuations in heart rate are detected, it can suggest ways of eating and exercising that do not strain the heart. Also, if the quality of sleep is poor, it can suggest ways to improve the sleep environment or relaxation methods. Furthermore, the advice unit can provide individually customized advice according to the user's lifestyle and health condition. For example, if the user has a specific health goal, it can propose a specific action plan to achieve that goal. In this way, the advice unit can provide specific advice that users can actually implement and support the improvement of their health condition. In addition, the advice unit can continuously improve the accuracy and effectiveness of its advice based on user feedback. For example, by providing feedback on the results of the user implementing the suggested advice, the advice unit can adjust the content of the next advice more appropriately. In this way, the advice unit can effectively support the user's health management and contribute to maintaining and improving their health condition. Some or all of the processing described above in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice using an AI model that takes the results of a health check as input and outputs specific health advice.

[0077] The data collection unit can collect data such as heart rate, steps, sleep patterns, and body temperature using devices such as smartwatches and fitness trackers. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch. The data collection unit can also record the user's steps and exercise level using a fitness tracker. The data collection unit can also analyze the user's sleep patterns using a sleep tracker. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch and detect abnormal heart rate fluctuations. Fitness trackers can record the user's steps and exercise level, allowing for an understanding of daily activity levels. Sleep trackers can analyze the user's sleep patterns and evaluate sleep quality. This allows for the efficient collection of the user's daily health data using devices such as smartwatches and fitness trackers. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smartwatches and fitness trackers into a generating AI and have the generating AI perform data analysis.

[0078] The analysis unit can analyze the collected data and detect changes in health status or signs of abnormalities. For example, the analysis unit can use AI to analyze the collected data and detect changes in health status or signs of abnormalities. For example, the analysis unit can use AI to detect abnormal increases in heart rate or disruptions in sleep patterns. The analysis unit can also use AI to monitor changes in the user's health status in real time. For example, the analysis unit can use AI to detect abnormal increases in heart rate and issue an alert to the user. The analysis unit can also detect disruptions in sleep patterns and suggest appropriate measures to the user. This allows for early detection of changes in health status or signs of abnormalities by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0079] The suggestion unit can propose the timing of regular health checks, taking into account the user's past health data and lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule. The suggestion unit can also adjust the timing of health checks, taking into account the user's lifestyle. For example, the suggestion unit can analyze the user's past health data and propose an optimal check-up schedule. The suggestion unit can also adjust the timing of health checks, taking into account the user's lifestyle. This allows the suggestion unit to propose the optimal timing for health checks by taking into account the user's past health data and lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past health data into a generating AI and have the generating AI propose an optimal check-up schedule.

[0080] The explanation unit can display the results of the health check in graphs and charts, making them intuitively understandable to the user. For example, the explanation unit can display the results of the health check in graphs and charts, making them intuitively understandable to the user. The explanation unit can also, for example, analyze the results of the health check in detail and explain them clearly to the user. For example, the explanation unit can display the results of the health check in graphs and charts, making them intuitively understandable to the user. The explanation unit can also, for example, analyze the results of the health check in detail and explain them clearly to the user. This allows the user to intuitively understand the results of the health check by displaying them in graphs and charts. Some or all of the above-described processes in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the results of the health check into a generating AI and have the generating AI suggest a method for displaying the results.

[0081] The advice unit can provide specific and actionable health advice based on the results. For example, based on the results of a health check, the advice unit can provide advice that the user can actually take action on, such as recommendations for dietary improvements or exercise. By providing specific and actionable health advice based on the results, the advice unit can enable the user to take appropriate measures. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the results of a health check into a generating AI and have the generating AI propose specific health advice.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect health data during times when the user is relaxed. If the user is relaxed, the data collection unit can collect health data at the normal collection timing. If the user is in a hurry, the data collection unit can prioritize collecting only data that can be collected in a short amount of time. By adjusting the timing of health data collection based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0083] 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 the user's past heart rate data and collect data during periods of high heart rate variability. For example, the data collection unit can analyze the user's past sleep patterns and optimize data collection during sleep. For example, the data collection unit can analyze the user's past exercise data and prioritize data collection after exercise. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing 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 a generating AI and have the generating AI select the optimal data collection method.

[0084] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect data related to relaxation. For example, if the user is working, the data collection unit can collect data related to stress levels. This allows for the collection of highly relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity and environmental data into a generating AI and have the generating AI perform data filtering.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data related to stress levels. For example, if the user is relaxed, the data collection unit can collect overall health data in a balanced manner. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This ensures that important data is collected preferentially by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and heart rate data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. For example, if the user is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing 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 a generating AI and have the generating AI perform the collection of highly relevant data.

[0087] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts on social media indicating they are stressed, the data collection unit can collect data related to their stress level. For example, if the user posts about exercise, the data collection unit can prioritize collecting exercise data. For example, if the user posts about food, the data collection unit can collect data related to food. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0088] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit will focus on analyzing data related to stress levels. If the user is relaxed, the analysis unit can analyze overall health data in a balanced way. If the user is in a hurry, the analysis unit can quickly analyze only the important data. This allows for more appropriate analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0089] 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 can perform a detailed analysis and create a detailed report for important data. For example, the analysis unit can perform a simplified analysis and create a concise report for less important data. For example, the analysis unit can perform an analysis with a moderate level of detail for data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0090] 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 heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise volume analysis algorithm to step count data. By applying different analysis algorithms depending on the category of health data, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and apply an appropriate analysis algorithm to the generating AI.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above 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 a generative AI and have the generative AI adjust the display method of the analysis results.

[0092] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may analyze current data while referring to past data. For example, the analysis unit may focus on analyzing data collected during a specific period. This allows for efficient analysis by determining the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the health data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of analysis.

[0094] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0095] The proposal unit can adjust the level of detail in its proposals based on the importance of the health checks. For example, the proposal unit can provide detailed proposals for important health checks, simple proposals for less important health checks, and proposals with a moderate level of detail for health checks of moderate importance. This allows for efficient proposals by adjusting the level of detail based on the importance of the health checks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health checks into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0096] The suggestion unit can apply different suggestion algorithms depending on the user's lifestyle when making suggestions. For example, if the user is a night owl, the suggestion unit can make suggestions suitable for the night. For example, if the user is an early riser, the suggestion unit can make suggestions suitable for the morning. For example, the suggestion unit can adjust the timing of suggestions according to the user's lifestyle. This allows for suggestions tailored to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and apply the suggestion algorithm to the generating AI.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. If the user is in a hurry, for example, the suggestion unit can provide suggestions that can be quickly understood. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0098] The proposal unit can determine the priority of proposals based on the timing of health data collection when making a proposal. For example, the proposal unit can make proposals based on recently collected data. For example, the proposal unit can make proposals based on current data while referring to past data. For example, the proposal unit can focus on reflecting data collected during a specific period in its proposals. This allows for efficient proposals by determining the priority of proposals based on the timing of health data collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of proposals.

[0099] The proposal unit can adjust the order of proposals based on the relevance of health data during the proposal process. For example, the proposal unit can make proposals based on highly relevant data. For example, the proposal unit can postpone proposals based on less relevant data. For example, the proposal unit can dynamically adjust the order of proposals according to the relevance of the data. This allows for efficient proposals by adjusting the order of proposals based on the relevance of health data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of proposals.

[0100] The explanation unit can estimate the user's emotions and adjust the way the explanation is presented based on the estimated emotions. For example, if the user is stressed, the explanation unit can provide a simple and easy-to-understand explanation. If the user is relaxed, the explanation unit can provide a detailed explanation. If the user is in a hurry, the explanation unit can provide a concise explanation. By adjusting the way the explanation is presented based on the user's emotions, a more appropriate explanation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with 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 explanation unit may be performed using AI or not. For example, the explanation unit can input user emotion data into the generative AI and have the generative AI adjust the way the explanation is presented.

[0101] The explanation unit can adjust the level of detail in its explanation based on the importance of the health check results. For example, it can provide a detailed explanation for important results, a simplified explanation for less important results, and a moderate level of detail for results of moderate importance. This allows for efficient explanations by adjusting the level of detail based on the importance of the health check results. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the importance of the health check results into a generating AI and have the generating AI adjust the level of detail in the explanation.

[0102] The explanation unit can apply different explanation algorithms depending on the category of health data during explanation. For example, the explanation unit can apply a heart rate variability analysis algorithm to heart rate data and explain the results. For example, the explanation unit can apply a sleep stage analysis algorithm to sleep data and explain the results. For example, the explanation unit can apply an exercise volume analysis algorithm to step count data and explain the results. This allows explanations to be provided according to the category of health data. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the category of health data into a generating AI and have the generating AI apply an appropriate explanation algorithm.

[0103] The explanation unit can estimate the user's emotions and adjust the length of the explanation based on the estimated emotions. For example, if the user is stressed, the explanation unit can provide a short, concise explanation. For example, if the user is relaxed, the explanation unit can provide a detailed explanation. For example, if the user is in a hurry, the explanation unit can provide a quickly understandable explanation. This allows for more appropriate explanations by adjusting the length of the explanation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 explanation unit may be performed using AI or not. For example, the explanation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the explanation.

[0104] The explanation unit can determine the priority of explanations based on the timing of health data collection during the explanation process. For example, the explanation unit can provide explanations based on recently collected data. For example, the explanation unit can provide explanations based on current data while referring to past data. For example, the explanation unit can focus on incorporating data collected during a specific period into the explanation. This allows for efficient explanations by determining the priority of explanations based on the timing of health data collection. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of explanations.

[0105] The explanation unit can adjust the order of explanations based on the relevance of health data during the explanation process. For example, the explanation unit can explain based on highly relevant data. For example, the explanation unit can explain less relevant data later. For example, the explanation unit can dynamically adjust the order of explanations according to the relevance of the data. This allows for efficient explanations by adjusting the order of explanations based on the relevance of health data. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of explanations.

[0106] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide simple and easy-to-understand advice. If the user is relaxed, the advice unit can provide advice that includes detailed information. If the user is in a hurry, the advice unit can provide concise advice. By adjusting the way it expresses advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses advice.

[0107] The advice unit can adjust the level of detail of its advice based on the importance of the health check results. For example, the advice unit can provide detailed advice for important results, simple advice for less important results, and advice with a moderate level of detail for results of moderate importance. This allows for efficient advice by adjusting the level of detail based on the importance of the health check results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of the health check results into a generating AI and have the generating AI adjust the level of detail of the advice.

[0108] The advice unit can apply different advice algorithms depending on the category of health data when providing advice. For example, the advice unit can apply a heart rate variability analysis algorithm to heart rate data and provide advice based on the results. For example, the advice unit can apply a sleep stage analysis algorithm to sleep data and provide advice based on the results. For example, the advice unit can apply an exercise volume analysis algorithm to step count data and provide advice based on the results. This allows for advice tailored to the category of health data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the category of health data into a generating AI and apply an appropriate advice algorithm to the generating AI.

[0109] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide short, concise advice. If the user is relaxed, for example, the advice unit can provide detailed advice. If the user is in a hurry, for example, the advice unit can provide advice that can be quickly understood. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0110] The advice unit can determine the priority of advice based on the timing of health data collection. For example, the advice unit can provide advice based on recently collected data. For example, the advice unit can provide advice based on current data while referring to past data. For example, the advice unit can focus on incorporating data collected during a specific period into its advice. This allows for efficient advice by determining the priority of advice based on the timing of health data collection. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of advice.

[0111] The advice unit can adjust the order of advice based on the relevance of health data when providing advice. For example, the advice unit can provide advice based on highly relevant data. For example, the advice unit can postpone providing advice on less relevant data. For example, the advice unit can dynamically adjust the order of advice according to the relevance of the data. This allows for efficient advice by adjusting the order of advice based on the relevance of health data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the relevance of health data into a generating AI and have the generating AI adjust the order of advice.

[0112] The advice unit can apply different advice algorithms depending on the user's lifestyle when providing advice. For example, if the user is a night owl, the advice unit can provide advice suitable for the night. For example, if the user is an early riser, the advice unit can provide advice suitable for the morning. For example, the advice unit can adjust the timing of advice according to the user's lifestyle. This allows for advice tailored to the user's lifestyle. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's lifestyle data into a generating AI and apply an advice algorithm to the generating AI.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect health data during times when the user is relaxed. If the user is relaxed, the data collection unit can collect health data at the normal collection timing. If the user is in a hurry, the data collection unit can prioritize collecting only data that can be collected in a short amount of time. By adjusting the timing of health data collection based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0115] 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 the user's past heart rate data and collect data during periods of high heart rate variability. For example, the data collection unit can analyze the user's past sleep patterns and optimize data collection during sleep. For example, the data collection unit can analyze the user's past exercise data and prioritize data collection after exercise. This allows the optimal data collection method to be selected by analyzing the user's past health data. Some or all of the above processing 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 a generating AI and have the generating AI select the optimal data collection method.

[0116] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect data related to relaxation. For example, if the user is working, the data collection unit can collect data related to stress levels. This allows for the collection of highly relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity and environmental data into a generating AI and have the generating AI perform data filtering.

[0117] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data related to stress levels. For example, if the user is relaxed, the data collection unit can collect overall health data in a balanced manner. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This ensures that important data is collected preferentially by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0118] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and heart rate data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. For example, if the user is indoors, the data collection unit can prioritize the collection of data related to the indoor environment. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing 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 a generating AI and have the generating AI perform the collection of highly relevant data.

[0119] The data collection unit can analyze the user's social media activity and collect relevant data when collecting health data. For example, if the user posts on social media indicating they are stressed, the data collection unit can collect data related to their stress level. For example, if the user posts about exercise, the data collection unit can prioritize collecting exercise data. For example, if the user posts about food, the data collection unit can collect data related to food. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0120] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit will focus on analyzing data related to stress levels. If the user is relaxed, the analysis unit can analyze overall health data in a balanced way. If the user is in a hurry, the analysis unit can quickly analyze only the important data. This allows for more appropriate analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0121] 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 can perform a detailed analysis and create a detailed report for important data. For example, the analysis unit can perform a simplified analysis and create a concise report for less important data. For example, the analysis unit can perform an analysis with a moderate level of detail for data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0122] 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 heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise volume analysis algorithm to step count data. By applying different analysis algorithms depending on the category of health data, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and apply an appropriate analysis algorithm to the generating AI.

[0123] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above 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 a generative AI and have the generative AI adjust the display method of the analysis results.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The data collection unit collects the user's daily health data. The data collection unit uses devices such as smartwatches and fitness trackers to collect data such as heart rate, steps, sleep patterns, and body temperature. For example, a smartwatch can be used to monitor the user's heart rate in real time, a fitness tracker can be used to record steps and exercise levels, and a sleep tracker can be used to analyze sleep patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit to detect changes in health status or signs of abnormalities. The analysis unit can use AI to detect abnormal increases in heart rate or disruptions in sleep patterns and issue alerts to the user. Step 3: The proposal department proposes an optimal check-up schedule based on the results obtained by the analysis department. The proposal department can suggest the timing of regular health check-ups, taking into account the user's past health data and lifestyle. Step 4: The explanation unit analyzes the health check results in detail based on the schedule proposed by the proposal unit and explains them clearly to the user. The explanation unit displays the health check results in graphs and charts to allow the user to understand them intuitively. Step 5: The advice section provides specific, actionable health advice based on the results explained by the explanation section. Based on the health check results, the advice section provides advice that the user can actually implement, such as recommendations for dietary improvements or exercise.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, explanation unit, and advice 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 the user's health data using a smartwatch or fitness tracker. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal check-up schedule. The explanation unit is implemented by the control unit 46A of the smart device 14 and explains the results of the health check to the user in an easy-to-understand manner. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific health advice. 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.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, explanation unit, and advice 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 the user's health data using a smartwatch or fitness tracker. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal check-up schedule. The explanation unit is implemented by the control unit 46A of the smart glasses 214 and explains the results of the health check to the user in an easy-to-understand manner. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific health advice. 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.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, explanation unit, and advice 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 the user's health data using a smartwatch or fitness tracker. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal check-up schedule. The explanation unit is implemented by the control unit 46A of the headset terminal 314 and explains the results of the health check to the user in an easy-to-understand manner. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific health advice. 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.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, explanation unit, and advice unit, is implemented by, for example, 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 the user's health data using a smartwatch or fitness tracker. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal check-up schedule. The explanation unit is implemented by, for example, the control unit 46A of the robot 414 and explains the results of the health check to the user in an easy-to-understand manner. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides specific health advice. 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A data collection unit that collects users' daily health data, An analysis unit analyzes the data collected by the aforementioned collection unit and detects changes in health status or signs of abnormalities, Based on the results obtained by the analysis unit, a proposal unit proposes an optimal check-up schedule. Based on the schedule proposed by the aforementioned proposal unit, the explanation unit analyzes the results of the health check in detail and explains them to the user in an easy-to-understand manner. The system includes an advice unit that provides specific and actionable health advice based on the results explained by the explanatory unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Use devices such as smartwatches and fitness trackers to collect data such as heart rate, steps, sleep patterns, and body temperature. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to detect changes in health status and signs of abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest the timing of regular health checks, taking into account the user's past health data and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 5) The above explanatory section is, The results of health checks are displayed in graphs and charts to allow users to understand them intuitively. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, We provide specific, actionable health advice based on the results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health 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 When collecting health data, filtering is performed based on the user's current activity status and environment. 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 health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail 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 is During analysis, different analytical 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 is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis 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 is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the health check. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the health data will be collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The above explanatory section is, It estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The above explanatory section is, During the explanation, adjust the level of detail based on the importance of the health check results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The above explanatory section is, During explanation, different explanation algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The above explanatory section is, It estimates the user's emotions and adjusts the length of the explanation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The above explanatory section is, During the explanation, prioritize the explanation based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The above explanatory section is, During the explanation, adjust the order of explanations based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, 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 32) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the health check results. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advice section, When providing advice, we prioritize the advice based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned advice section, When providing advice, we adjust the order of advice based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 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 users' daily health data, An analysis unit analyzes the data collected by the aforementioned collection unit and detects changes in health status or signs of abnormalities. Based on the results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal check-up schedule. Based on the schedule proposed by the aforementioned proposal unit, the explanation unit analyzes the results of the health check in detail and explains them to the user in an easy-to-understand manner. The system includes an advice unit that provides specific and actionable health advice based on the results explained by the explanatory unit. A system characterized by the following features.

2. The aforementioned collection unit is Use devices such as smartwatches and fitness trackers to collect data such as heart rate, steps, sleep patterns, and body temperature. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to detect changes in health status and signs of abnormalities. The system according to feature 1.

4. The aforementioned proposal section is, We suggest the timing of regular health checks, taking into account the user's past health data and lifestyle. The system according to feature 1.

5. The above explanatory section is, The results of health checks are displayed in graphs and charts to allow users to understand them intuitively. The system according to feature 1.

6. The aforementioned advice section, We provide specific, actionable health advice based on the results. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health 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 When collecting health data, filtering is performed based on the user's current activity level and environment. 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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