system

Abstract

The system according to this embodiment aims to analyze the user's biometric information and provide specific suggestions and support for optimizing their health status. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a visualization unit, an improvement proposal unit, and a linkage unit. The collection unit acquires the user's biometric information. The analysis unit analyzes the biometric information acquired by the collection unit. The proposal unit proposes music tailored to the user's condition based on the information analyzed by the analysis unit. The visualization unit visualizes health indicators based on the music proposed by the proposal unit. The improvement proposal unit proposes improvements to lifestyle habits based on the health indicators visualized by the visualization unit. The linkage unit supports consultation with medical professionals based on the improvements proposed by the improvement proposal 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 conventional technology, specific proposals and supports for optimizing the health state based on the user's biological information have not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's biological information and provide specific proposals and supports for optimizing the health state.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a visualization unit, an improvement suggestion unit, and a collaboration unit. The data collection unit acquires the user's biometric information. The analysis unit analyzes the biometric information acquired by the data collection unit. The suggestion unit suggests music tailored to the user's condition based on the information analyzed by the analysis unit. The visualization unit visualizes health indicators based on the music suggested by the suggestion unit. The improvement suggestion unit suggests improvements to lifestyle habits based on the health indicators visualized by the visualization unit. The collaboration unit supports consultation with medical professionals based on the improvements suggested by the improvement suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's biometric information and provide specific suggestions and support for optimizing their health status. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) A health monitoring system according to an embodiment of the present invention is a system that optimizes a user's health status using music. This system acquires the user's biometric information from a wearable device and analyzes it with AI to understand the user's health status. Furthermore, it suggests relaxation music or active music tailored to the user's state, aiming to reduce stress levels and improve sleep quality. For example, the health monitoring system acquires the user's biometric information from a wearable device. For example, it collects data such as heart rate and sleep patterns. Next, the health monitoring system uses AI to analyze the acquired biometric information. The AI ​​analyzes the collected data to understand the user's health status. For example, it detects fluctuations in heart rate and abnormalities in sleep patterns to evaluate the user's stress level and sleep quality. Furthermore, the health monitoring system suggests relaxation music or active music tailored to the user's state. Based on the analysis results, the AI ​​selects the optimal music for the user. For example, it suggests relaxation music for users with high stress levels and active music for users in an active state. The health monitoring system also visualizes health indicators in graphs and reports and provides them to the user. Based on the analysis results, the AI ​​visually displays the user's health status. For example, the system creates and provides users with graphs of heart rate variability and sleep patterns. Furthermore, the health monitoring system supports lifestyle improvements by suggesting exercise and meditation synchronized with music. The AI ​​suggests appropriate exercise and meditation based on the user's condition. For example, it suggests meditation while listening to relaxation music or exercise while listening to active music. If necessary, the health monitoring system supports consultation with medical professionals. The AI ​​suggests consultation with medical professionals according to the user's health condition. For example, it recommends consultation with a professional for users with high stress levels. This allows users to easily improve their health and enhance their quality of life. For example, a reduction in stress levels and an improvement in sleep quality can be expected. In addition, the visualization of health indicators makes it easier for users to understand their own health status. Furthermore, the suggestion of exercise and meditation synchronized with music promotes improvements in lifestyle.By supporting consultations with medical professionals as needed, users' health management becomes more comprehensive. This allows the health monitoring system to optimize the user's health status and improve their quality of life.

[0029] The health monitoring system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a visualization unit, an improvement suggestion unit, and a linkage unit. The data collection unit acquires the user's biometric information. The data collection unit acquires, for example, heart rate and sleep patterns from a wearable device. The data collection unit uses, for example, a wearable device worn by the user to measure heart rate. The data collection unit uses, for example, a wearable device worn by the user during sleep to measure sleep patterns. The data collection unit acquires, for example, heart rate and sleep pattern data in real time. The analysis unit analyzes the biometric information acquired by the data collection unit. The analysis unit analyzes, for example, heart rate fluctuations and evaluates the user's stress level. The analysis unit analyzes, for example, abnormalities in sleep patterns and evaluates the quality of the user's sleep. The analysis unit uses, for example, AI to analyze heart rate and sleep pattern data. The suggestion unit suggests music tailored to the user's condition based on the information analyzed by the analysis unit. The suggestion unit suggests relaxation music to users with high stress levels. The suggestion unit, for example, suggests active music to users who are in an active state. The suggestion unit, for example, selects the most suitable music according to the user's state. The visualization unit visualizes health indicators based on the music suggested by the suggestion unit. The visualization unit, for example, displays heart rate fluctuations in a graph. The visualization unit, for example, creates a graph of sleep patterns. The visualization unit, for example, displays health indicators in a report. The improvement suggestion unit suggests lifestyle improvements based on the health indicators visualized by the visualization unit. The improvement suggestion unit, for example, suggests meditation while listening to relaxation music. The improvement suggestion unit, for example, suggests exercise while listening to active music. The improvement suggestion unit, for example, suggests appropriate exercise and meditation according to the user's state. The collaboration unit supports consultation with medical professionals based on the improvements suggested by the improvement suggestion unit. The collaboration unit, for example, recommends consultation with a professional to users with high stress levels. The collaboration unit, for example, suggests consultation with a medical professional according to the user's health condition. The collaboration unit, for example, supports users in consulting with medical professionals as needed.As a result, the health monitoring system according to this embodiment can optimize the user's health status and improve their quality of life.

[0030] The data collection unit acquires the user's biometric information. For example, it acquires heart rate and sleep patterns from wearable devices. Specifically, it uses wearable devices worn by the user to measure heart rate. These devices are worn on the wrist or chest and measure heart rate in real time using optical or electrical heart rate sensors. Furthermore, the data collection unit measures sleep patterns using wearable devices worn by the user during sleep. These devices use accelerometers and gyroscopes to detect the user's movements and posture and evaluate the depth and quality of sleep. The data collection unit acquires this data in real time and transmits it to a central database. This allows the data collection unit to continuously monitor the user's biometric information and quickly detect changes in their health status. In addition, with the user's consent, the data collection unit can also collect additional biometric information. For example, dedicated sensors can be used to collect data such as blood pressure, body temperature, and blood oxygen saturation. This allows the data collection unit to acquire more biometric information and comprehensively evaluate the user's health status. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes biometric information acquired by the data collection unit. Specifically, it analyzes heart rate variability to assess the user's stress level. The analysis unit uses AI to analyze heart rate data in real time, detecting heart rate variability patterns and anomalies. For example, if the heart rate rises sharply, it is determined that the user is likely experiencing stress. It also analyzes abnormal sleep patterns to assess the user's sleep quality. The AI ​​analyzes sleep data to evaluate the proportion of deep sleep and light sleep, the number of awakenings during sleep, etc. This allows for a quantitative assessment of the user's sleep quality and a determination of the need for improvement. Furthermore, the analysis unit can also analyze long-term health trends by utilizing past data and statistical information. For example, based on heart rate data from the past few months, it can analyze fluctuations in the user's stress level to understand increases and decreases in stress during specific periods or situations. It can also analyze sleep data over a long period to evaluate changes in the user's sleep patterns and the effects of improvements. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term health management and trend analysis, enabling a comprehensive assessment of the user's health status.

[0032] The suggestion department proposes music tailored to the user's state based on information analyzed by the analysis department. Specifically, it suggests relaxation music to users with high stress levels. Relaxation music is music with a slow tempo and incorporates natural sounds to promote relaxation. It also suggests active music to users who are active. Active music is music with a fast tempo and energetic rhythm to support the user's activity. The suggestion department uses AI to select the optimal music for the user's state. The AI ​​analyzes data such as the user's heart rate and sleep patterns, and proposes the optimal music based on the user's current state and preferences. For example, if a user is feeling stressed, the AI ​​suggests relaxation music to help the user relax. If a user is exercising, the AI ​​suggests active music to support the user's exercise. In this way, the suggestion department can provide music tailored to the user's state and optimize the user's health. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by providing feedback on the suggested music, the AI ​​can learn the user's preferences and reactions and reflect them in future suggestions. This allows the proposal department to provide optimal music tailored to the user's needs and improve the user's health.

[0033] The visualization unit visualizes health indicators based on the music proposed by the proposal unit. Specifically, it displays heart rate variability in a graph. The heart rate variability graph visually shows the change in heart rate after the user listens to relaxation music, allowing them to confirm the effect of the music. It also creates a sleep pattern graph. The sleep pattern graph visually shows the depth and quality of the user's sleep, helping the user understand how they can improve their sleep. Furthermore, it displays health indicators in a report. The report summarizes the user's heart rate and sleep pattern data and provides a comprehensive evaluation of the user's health status. This allows the visualization unit to provide information that helps the user understand their health status and take concrete actions to improve it. In addition, the visualization unit can link the user's health data with other systems and devices. For example, the user's health data can be displayed in a smartphone app or web portal, allowing the user to check their health status at any time. The visualization unit can also customize how the data is displayed, changing the format of graphs and reports according to the user's preferences and needs. This allows the visualization unit to provide health data in a user-friendly and easy-to-understand format, supporting users in managing their health.

[0034] The Improvement Suggestion Department proposes lifestyle improvements based on health indicators visualized by the Visualization Department. Specifically, it suggests meditation while listening to relaxation music. Meditation has the effect of reducing user stress and promoting mental and physical relaxation. It also suggests exercise while listening to active music. Exercise has the effect of improving the user's physical fitness and overall health. The Improvement Suggestion Department makes appropriate exercise and meditation suggestions according to the user's condition. For example, based on the user's heart rate data, if the stress level is high, it suggests meditation while listening to relaxation music, and if the user is active, it suggests exercise while listening to active music. In this way, the Improvement Suggestion Department can optimize the user's health and improve their quality of life. Furthermore, the Improvement Suggestion Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by providing feedback on the suggested exercise and meditation, the AI ​​can learn from the user's reactions and reflect them in future suggestions. In this way, the Improvement Suggestion Department can provide optimal improvement suggestions that meet the user's needs and improve the user's health.

[0035] The Collaboration Department supports consultations with medical professionals based on improvements proposed by the Improvement Proposal Department. Specifically, it recommends consultations with professionals to users with high stress levels. The Collaboration Department suggests consultations with medical professionals according to the user's health condition. For example, based on the user's heart rate data and sleep pattern data, it recommends consultations with professionals if the user has a high stress level or poor sleep quality. The Collaboration Department supports users in consulting with medical professionals as needed. For example, the Collaboration Department provides a reservation system for users to consult with professionals, making it easy for users to make reservations. The Collaboration Department also shares the user's health data with professionals, making it easier for professionals to understand the user's health condition. This allows the Collaboration Department to help users receive appropriate medical support and optimize the user's health condition. Furthermore, the Collaboration Department can collect user feedback and continuously improve the accuracy and effectiveness of the collaboration. For example, based on the feedback provided by users after consulting with professionals, the Collaboration Department can review and improve the collaboration. This allows the Collaboration Department to provide optimal medical support for users and improve their health condition.

[0036] The data collection unit can acquire heart rate and sleep patterns from wearable devices. For example, the data collection unit uses a wearable device worn by the user to measure heart rate. For example, the data collection unit uses a wearable device worn by the user while sleeping to measure sleep patterns. For example, the data collection unit acquires heart rate and sleep pattern data in real time. This allows for accurate collection of the user's biometric information by acquiring heart rate and sleep patterns from wearable devices. 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 heart rate and sleep pattern data acquired from wearable devices into a generating AI, which can analyze the data to understand the user's health status.

[0037] The analysis unit can analyze acquired biometric information to understand the user's health status. For example, the analysis unit can analyze heart rate variability to evaluate the user's stress level. For example, the analysis unit can analyze abnormal sleep patterns to evaluate the user's sleep quality. For example, the analysis unit can use AI to analyze heart rate and sleep pattern data. This allows for an accurate understanding of the user's health status by analyzing the acquired biometric information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input acquired biometric information into a generating AI, which can then analyze the data to understand the user's health status.

[0038] The suggestion unit can suggest relaxation music or active music tailored to the user's state. For example, the suggestion unit can suggest relaxation music to users with high stress levels. For example, the suggestion unit can suggest active music to users who are in an active state. For example, the suggestion unit can select the optimal music according to the user's state. By suggesting music tailored to the user's state, it is possible to reduce stress levels and improve sleep quality. 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 data about the user's state into a generating AI, which can analyze the data and suggest the optimal music.

[0039] The visualization unit can display health indicators in graphs and reports. For example, the visualization unit can display heart rate fluctuations in a graph. For example, the visualization unit can create a graph of sleep patterns. For example, the visualization unit can display health indicators in a report. This makes it easier for users to understand their own health status by displaying health indicators in graphs and reports. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input health indicator data into a generating AI, and the generating AI can analyze the data to create graphs and reports.

[0040] The improvement suggestion unit can suggest exercise and meditation synchronized with music. For example, the improvement suggestion unit may suggest meditation while listening to relaxation music. For example, the improvement suggestion unit may suggest exercise while listening to active music. For example, the improvement suggestion unit may suggest appropriate exercise and meditation according to the user's condition. In this way, by suggesting exercise and meditation synchronized with music, it is possible to support the improvement of lifestyle habits. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data about the user's condition into a generating AI, and the generating AI can analyze the data to suggest the optimal exercise and meditation.

[0041] The collaboration unit can support consultations with medical professionals as needed. For example, the collaboration unit may recommend consultations with professionals to users with high stress levels. For example, the collaboration unit may suggest consultations with medical professionals according to the user's health condition. For example, the collaboration unit may support users in consulting with medical professionals as needed. This enhances user health management by supporting consultations with medical professionals as needed. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit may input data on the user's health condition into a generating AI, which can then analyze the data and suggest consultations with medical professionals.

[0042] The data collection unit can analyze the user's past biometric information history and select the optimal acquisition method. For example, the data collection unit can analyze the user's past heart rate data to determine the optimal acquisition timing. For example, the data collection unit can analyze the user's past sleep patterns to select the optimal acquisition method. For example, the data collection unit can analyze the user's past exercise data to select the optimal acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past biometric information history. 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 biometric information history into a generating AI, which can then analyze the data and select the optimal acquisition method.

[0043] The data collection unit can filter biometric information based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit will acquire biometric information suitable for exercise. For example, if the user is relaxed, the data collection unit will acquire biometric information suitable for relaxation. For example, if the user is working, the data collection unit will acquire biometric information suitable for work. By filtering based on the user's current activity status and environment, appropriate biometric information can be obtained. 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 data about the user's current activity status and environment into a generating AI, which can then analyze the data to obtain appropriate biometric information.

[0044] The data collection unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring biometric information. For example, if the user is at home, the data collection unit will prioritize the acquisition of biometric information related to relaxation. If the user is at work, the data collection unit will prioritize the acquisition of biometric information related to stress. If the user is exercising, the data collection unit will prioritize the acquisition of biometric information related to exercise. This allows for the priority acquisition of highly relevant biometric information 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, which can then analyze the data and prioritize the acquisition of highly relevant biometric information.

[0045] The data collection unit can analyze the user's social media activity when acquiring biometric information and obtain relevant information. For example, if the user is experiencing stress on social media, the data collection unit prioritizes acquiring heart rate. For example, if the user is relaxing on social media, the data collection unit prioritizes acquiring sleep patterns. For example, if the user posts information about exercise on social media, the data collection unit prioritizes acquiring exercise-related biometric information. In this way, relevant biometric information can be obtained 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 data about the user's social media activity into a generating AI, which can then analyze the data and obtain relevant biometric information.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during the analysis. For example, the analysis unit performs a detailed analysis if there is a large variation in heart rate. For example, the analysis unit performs a detailed analysis if there is an abnormality in the sleep pattern. For example, the analysis unit performs a detailed analysis if there is an abnormality in the exercise data. In this way, by adjusting the level of detail of the analysis based on the importance of the biological information, important information can be analyzed in detail. 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 data on the importance of biological information into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of biometric information during analysis. For example, the analysis unit applies a heart rate analysis algorithm to heart rate data. For example, the analysis unit applies a sleep analysis algorithm to sleep pattern data. For example, the analysis unit applies an exercise analysis algorithm to exercise data. By applying different analysis algorithms depending on the category of biometric information, appropriate analysis can be performed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the category of biometric information into a generating AI, and the generating AI can analyze the data and apply different analysis algorithms.

[0048] The analysis unit can weight the analysis based on the timing of acquisition of biological information. For example, the analysis unit may weight recently acquired biological information for analysis. For example, the analysis unit may weight past biological information for analysis. For example, the analysis unit may weight biological information acquired during a specific period for analysis. By weighting the analysis based on the timing of acquisition of biological information, appropriate 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 data regarding the timing of acquisition of biological information into a generating AI, and the generating AI can analyze the data and perform weighting.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the biological information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant biological information. For example, the analysis unit may postpone the analysis of less relevant biological information. For example, the analysis unit may focus on analyzing highly relevant biological information. By adjusting the order of analysis based on the relevance of the biological information, efficient 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 data on the relevance of biological information into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0050] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the music. For example, the suggestion unit provides detailed suggestions for important music, and concise suggestions for unimportant music. The suggestion unit adjusts the level of detail of its suggestions according to importance. This allows for detailed suggestions for important music by adjusting the level of detail based on the importance of the music. 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 data on the importance of the music into a generating AI, which can then analyze the data and adjust the level of detail of the suggestions.

[0051] The suggestion unit can apply different suggestion algorithms depending on the music category when making suggestions. For example, the suggestion unit applies a relaxation suggestion algorithm to relaxation music. For example, the suggestion unit applies an active suggestion algorithm to active music. The suggestion unit applies different suggestion algorithms depending on the category. This allows for appropriate suggestions to be made by applying different suggestion algorithms depending on the music category. 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 data about the music category into a generating AI, which can then analyze the data and apply different suggestion algorithms.

[0052] The proposal unit can determine the priority of proposals based on the release dates of the music. For example, the proposal unit may prioritize proposing music with upcoming release dates. For example, the proposal unit may postpone proposing music with later release dates. For example, the proposal unit may adjust the priority of proposals according to the release dates. This allows proposals to be made at the appropriate time by determining the priority of proposals based on the release dates of the music. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input data on the release dates of the music into a generating AI, which can then analyze the data to determine the priority of proposals.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the music during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant music. For example, the suggestion unit may postpone suggesting less relevant music. For example, the suggestion unit may adjust the order of suggestions according to their relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the music. 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 data on the relevance of the music into a generating AI, which can then analyze the data and adjust the order of suggestions.

[0054] The visualization unit can adjust the level of detail of the visualization based on the importance of the health indicators during visualization. For example, the visualization unit provides detailed graphs for important health indicators. For example, the visualization unit provides simplified graphs for unimportant health indicators. The visualization unit adjusts the level of detail of the visualization according to importance. This allows important health indicators to be visualized in detail by adjusting the level of detail of the visualization based on the importance of the health indicators. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on the importance of health indicators into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the visualization.

[0055] The visualization unit can apply different visualization methods depending on the category of health indicators during visualization. For example, the visualization unit applies a heart rate graph to heart rate data. For example, the visualization unit applies a sleep graph to sleep pattern data. For example, the visualization unit applies an exercise graph to exercise data. By applying different visualization methods depending on the category of health indicators, appropriate visualization can be achieved. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data related to the category of health indicators into a generating AI, which can then analyze the data and apply different visualization methods.

[0056] The visualization unit can weight the visualization based on when the health indicators were acquired. For example, the visualization unit may weight recently acquired health indicators for visualization. For example, the visualization unit may weight past health indicators for visualization. For example, the visualization unit may weight health indicators acquired during a specific period for visualization. This allows for appropriate visualization by weighting the visualization based on when the health indicators were acquired. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on when the health indicators were acquired into a generating AI, which can then analyze the data and perform weighting.

[0057] The visualization unit can adjust the order of visualization based on the relevance of health indicators during visualization. For example, the visualization unit may prioritize the visualization of highly relevant health indicators. For example, the visualization unit may delay the visualization of less relevant health indicators. For example, the visualization unit may adjust the order of visualization according to relevance. This allows for efficient visualization by adjusting the order of visualization based on the relevance of health indicators. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit may input data on the relevance of health indicators into a generating AI, and the generating AI may analyze the data and adjust the order of visualization.

[0058] The improvement suggestion unit can analyze the user's past lifestyle habits and select the most suitable improvement suggestion when making suggestions. For example, the improvement suggestion unit can analyze the user's past exercise habits and make the most suitable exercise suggestion. For example, the improvement suggestion unit can analyze the user's past eating habits and make the most suitable diet suggestion. For example, the improvement suggestion unit can analyze the user's past sleep habits and make the most suitable sleep suggestion. In this way, the optimal improvement suggestion can be made by analyzing the user's past lifestyle habits. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data on the user's past lifestyle habits into a generating AI, which can then analyze the data and select the most suitable improvement suggestion.

[0059] The improvement suggestion unit can customize the means of improvement suggestions based on the user's current living situation. For example, if the user is busy, the improvement suggestion unit will provide quick suggestions. If the user is relaxed, the improvement suggestion unit will provide detailed suggestions. If the user is in a hurry, the improvement suggestion unit will provide concise suggestions. By customizing the means of improvement suggestions based on the user's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data about the user's current living situation into a generating AI, which can then analyze the data and customize the means of improvement suggestions.

[0060] The improvement suggestion unit can select the most suitable improvement suggestion by considering the user's geographical location information when making suggestions. For example, if the user is at home, the improvement suggestion unit will suggest improvements that can be done at home. For example, if the user is at work, the improvement suggestion unit will suggest improvements that can be done at work. For example, if the user is out, the improvement suggestion unit will suggest improvements that can be done while out. In this way, the optimal improvement suggestion can be made by considering the user's geographical location information. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data and select the most suitable improvement suggestion.

[0061] The improvement suggestion unit can analyze the user's social media activity and propose means of improvement when making improvement suggestions. For example, if the user is experiencing stress on social media, the improvement suggestion unit will make suggestions for stress reduction. For example, if the user is relaxing on social media, the improvement suggestion unit will make suggestions for relaxation. For example, if the user is posting information about exercise on social media, the improvement suggestion unit will make suggestions for exercise. In this way, appropriate improvement suggestions can be made by analyzing the user's social media activity. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data on the user's social media activity into a generating AI, which can then analyze the data and propose means of improvement.

[0062] The collaboration unit can select the most suitable medical professional based on the user's health condition during the collaboration process. For example, the collaboration unit can select a stress specialist based on the user's stress level. For example, the collaboration unit can select a sleep specialist based on the user's sleep patterns. For example, the collaboration unit can select an exercise specialist based on the user's exercise levels. This allows for appropriate consultation by selecting the most suitable medical professional based on the user's health condition. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's health condition into a generating AI, which can then analyze the data and select the most suitable medical professional.

[0063] The collaboration unit can optimize the consultation content by referring to the user's past medical history during the collaboration process. For example, the collaboration unit can refer to the user's past stress history and optimize the consultation content related to stress. For example, the collaboration unit can refer to the user's past sleep history and optimize the consultation content related to sleep. For example, the collaboration unit can refer to the user's past exercise history and optimize the consultation content related to exercise. This allows for appropriate consultation content to be provided by referring to the user's past medical history. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's past medical history into a generating AI, which can then analyze the data and optimize the consultation content.

[0064] The collaboration unit can select the most suitable medical professional by considering the user's geographical location information during the collaboration process. For example, if the user is at home, the collaboration unit will select a medical professional near their home. For example, if the user is at work, the collaboration unit will select a medical professional near their workplace. For example, if the user is out, the collaboration unit will select a medical professional near their current location. In this way, the most suitable medical professional can be selected by considering the user's geographical location information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location information into a generating AI, which can then analyze the data and select the most suitable medical professional.

[0065] The collaboration unit can analyze the user's social media activity during collaboration to optimize the consultation content. For example, if the user is experiencing stress on social media, the collaboration unit will optimize the consultation content related to stress. For example, if the user is relaxing on social media, the collaboration unit will optimize the consultation content related to relaxation. For example, if the user is posting information about exercise on social media, the collaboration unit will optimize the consultation content related to exercise. In this way, appropriate consultation content can be provided by analyzing the user's social media activity. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's social media activity into a generating AI, which can then analyze the data to optimize the consultation content.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] A health monitoring system can acquire a user's dietary information and analyze their nutritional balance. For example, the data collection unit acquires dietary information from an application where the user records their meals. The analysis unit analyzes the acquired dietary information and evaluates the user's nutritional balance. The suggestion unit proposes an appropriate meal plan to the user based on the analysis results. This allows the user to understand their own diet and maintain a healthy eating lifestyle.

[0068] A health monitoring system can acquire a user's exercise history and analyze their exercise patterns. For example, the data collection unit acquires exercise history from the user's fitness tracker. The analysis unit analyzes the acquired exercise history and evaluates the user's exercise patterns. The suggestion unit proposes an appropriate exercise plan to the user based on the analysis results. This allows the user to understand their own exercise habits and maintain healthy exercise routines.

[0069] A health monitoring system can monitor a user's fluid intake and suggest appropriate hydration. For example, the data collection unit acquires data from an application that records the amount of water the user drinks. The analysis unit analyzes the acquired data and evaluates the user's fluid intake. The suggestion unit suggests appropriate hydration to the user based on the analysis results. This allows the user to maintain appropriate hydration and stay healthy.

[0070] A health monitoring system can monitor a user's sleep environment and suggest improvements. For example, the data collection unit acquires the temperature and humidity of the user's bedroom from sensors. The analysis unit analyzes the acquired data and evaluates the user's sleep environment. Based on the analysis results, the suggestion unit makes appropriate suggestions for improving the user's sleep environment. This allows the user to maintain a comfortable sleep environment and improve the quality of their sleep.

[0071] A health monitoring system can monitor a user's stress level in real time and offer suggestions for stress reduction. For example, the data collection unit acquires the user's heart rate and skin electrical activity from sensors. The analysis unit analyzes the acquired data and evaluates the user's stress level. Based on the analysis results, the suggestion unit proposes appropriate stress reduction methods to the user. This allows the user to manage stress and maintain their health.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit acquires the user's biometric information. For example, the data collection unit acquires heart rate and sleep patterns from wearable devices. The data collection unit uses a wearable device worn by the user to measure heart rate and a wearable device worn by the user while sleeping to measure sleep patterns. The data collection unit acquires heart rate and sleep pattern data in real time. Step 2: The analysis unit analyzes the biometric information acquired by the data collection unit. For example, the analysis unit analyzes heart rate variability to assess the user's stress level. The analysis unit analyzes abnormalities in sleep patterns to assess the user's sleep quality. The analysis unit uses AI to analyze heart rate and sleep pattern data. Step 3: The suggestion unit proposes music tailored to the user's state based on the information analyzed by the analysis unit. For example, the suggestion unit will suggest relaxation music to users with high stress levels and active music to users who are in an active state. The suggestion unit selects the optimal music according to the user's state. Step 4: The visualization unit visualizes health indicators based on the music proposed by the proposal unit. For example, the visualization unit displays heart rate variability in a graph and creates a sleep pattern graph. The visualization unit displays the health indicators in a report. Step 5: The Improvement Suggestion Department proposes lifestyle improvements based on the health indicators visualized by the Visualization Department. For example, the Improvement Suggestion Department might suggest meditation while listening to relaxation music or exercise while listening to active music. The Improvement Suggestion Department will suggest appropriate exercise and meditation based on the user's condition. Step 6: The Liaison Department supports consultations with medical professionals based on the improvements proposed by the Improvement Proposal Department. For example, the Liaison Department recommends consultations with professionals for users with high stress levels and suggests consultations with medical professionals according to the user's health condition. The Liaison Department supports users in consulting with medical professionals as needed.

[0074] (Example of form 2) A health monitoring system according to an embodiment of the present invention is a system that optimizes a user's health status using music. This system acquires the user's biometric information from a wearable device and analyzes it with AI to understand the user's health status. Furthermore, it suggests relaxation music or active music tailored to the user's state, aiming to reduce stress levels and improve sleep quality. For example, the health monitoring system acquires the user's biometric information from a wearable device. For example, it collects data such as heart rate and sleep patterns. Next, the health monitoring system uses AI to analyze the acquired biometric information. The AI ​​analyzes the collected data to understand the user's health status. For example, it detects fluctuations in heart rate and abnormalities in sleep patterns to evaluate the user's stress level and sleep quality. Furthermore, the health monitoring system suggests relaxation music or active music tailored to the user's state. Based on the analysis results, the AI ​​selects the optimal music for the user. For example, it suggests relaxation music for users with high stress levels and active music for users in an active state. The health monitoring system also visualizes health indicators in graphs and reports and provides them to the user. Based on the analysis results, the AI ​​visually displays the user's health status. For example, the system creates and provides users with graphs of heart rate variability and sleep patterns. Furthermore, the health monitoring system supports lifestyle improvements by suggesting exercise and meditation synchronized with music. The AI ​​suggests appropriate exercise and meditation based on the user's condition. For example, it suggests meditation while listening to relaxation music or exercise while listening to active music. If necessary, the health monitoring system supports consultation with medical professionals. The AI ​​suggests consultation with medical professionals according to the user's health condition. For example, it recommends consultation with a professional for users with high stress levels. This allows users to easily improve their health and enhance their quality of life. For example, a reduction in stress levels and an improvement in sleep quality can be expected. In addition, the visualization of health indicators makes it easier for users to understand their own health status. Furthermore, the suggestion of exercise and meditation synchronized with music promotes improvements in lifestyle.By supporting consultations with medical professionals as needed, users' health management becomes more comprehensive. This allows the health monitoring system to optimize the user's health status and improve their quality of life.

[0075] The health monitoring system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a visualization unit, an improvement suggestion unit, and a linkage unit. The data collection unit acquires the user's biometric information. The data collection unit acquires, for example, heart rate and sleep patterns from a wearable device. The data collection unit uses, for example, a wearable device worn by the user to measure heart rate. The data collection unit uses, for example, a wearable device worn by the user during sleep to measure sleep patterns. The data collection unit acquires, for example, heart rate and sleep pattern data in real time. The analysis unit analyzes the biometric information acquired by the data collection unit. The analysis unit analyzes, for example, heart rate fluctuations and evaluates the user's stress level. The analysis unit analyzes, for example, abnormalities in sleep patterns and evaluates the quality of the user's sleep. The analysis unit uses, for example, AI to analyze heart rate and sleep pattern data. The suggestion unit suggests music tailored to the user's condition based on the information analyzed by the analysis unit. The suggestion unit suggests relaxation music to users with high stress levels. The suggestion unit, for example, suggests active music to users who are in an active state. The suggestion unit, for example, selects the most suitable music according to the user's state. The visualization unit visualizes health indicators based on the music suggested by the suggestion unit. The visualization unit, for example, displays heart rate fluctuations in a graph. The visualization unit, for example, creates a graph of sleep patterns. The visualization unit, for example, displays health indicators in a report. The improvement suggestion unit suggests lifestyle improvements based on the health indicators visualized by the visualization unit. The improvement suggestion unit, for example, suggests meditation while listening to relaxation music. The improvement suggestion unit, for example, suggests exercise while listening to active music. The improvement suggestion unit, for example, suggests appropriate exercise and meditation according to the user's state. The collaboration unit supports consultation with medical professionals based on the improvements suggested by the improvement suggestion unit. The collaboration unit, for example, recommends consultation with a professional to users with high stress levels. The collaboration unit, for example, suggests consultation with a medical professional according to the user's health condition. The collaboration unit, for example, supports users in consulting with medical professionals as needed.As a result, the health monitoring system according to this embodiment can optimize the user's health status and improve their quality of life.

[0076] The data collection unit acquires the user's biometric information. For example, it acquires heart rate and sleep patterns from wearable devices. Specifically, it uses wearable devices worn by the user to measure heart rate. These devices are worn on the wrist or chest and measure heart rate in real time using optical or electrical heart rate sensors. Furthermore, the data collection unit measures sleep patterns using wearable devices worn by the user during sleep. These devices use accelerometers and gyroscopes to detect the user's movements and posture and evaluate the depth and quality of sleep. The data collection unit acquires this data in real time and transmits it to a central database. This allows the data collection unit to continuously monitor the user's biometric information and quickly detect changes in their health status. In addition, with the user's consent, the data collection unit can also collect additional biometric information. For example, dedicated sensors can be used to collect data such as blood pressure, body temperature, and blood oxygen saturation. This allows the data collection unit to acquire more biometric information and comprehensively evaluate the user's health status. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0077] The analysis unit analyzes biometric information acquired by the data collection unit. Specifically, it analyzes heart rate variability to assess the user's stress level. The analysis unit uses AI to analyze heart rate data in real time, detecting heart rate variability patterns and anomalies. For example, if the heart rate rises sharply, it is determined that the user is likely experiencing stress. It also analyzes abnormal sleep patterns to assess the user's sleep quality. The AI ​​analyzes sleep data to evaluate the proportion of deep sleep and light sleep, the number of awakenings during sleep, etc. This allows for a quantitative assessment of the user's sleep quality and a determination of the need for improvement. Furthermore, the analysis unit can also analyze long-term health trends by utilizing past data and statistical information. For example, based on heart rate data from the past few months, it can analyze fluctuations in the user's stress level to understand increases and decreases in stress during specific periods or situations. It can also analyze sleep data over a long period to evaluate changes in the user's sleep patterns and the effects of improvements. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term health management and trend analysis, enabling a comprehensive assessment of the user's health status.

[0078] The suggestion department proposes music tailored to the user's state based on information analyzed by the analysis department. Specifically, it suggests relaxation music to users with high stress levels. Relaxation music is music with a slow tempo and incorporates natural sounds to promote relaxation. It also suggests active music to users who are active. Active music is music with a fast tempo and energetic rhythm to support the user's activity. The suggestion department uses AI to select the optimal music for the user's state. The AI ​​analyzes data such as the user's heart rate and sleep patterns, and proposes the optimal music based on the user's current state and preferences. For example, if a user is feeling stressed, the AI ​​suggests relaxation music to help the user relax. If a user is exercising, the AI ​​suggests active music to support the user's exercise. In this way, the suggestion department can provide music tailored to the user's state and optimize the user's health. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by providing feedback on the suggested music, the AI ​​can learn the user's preferences and reactions and reflect them in future suggestions. This allows the proposal department to provide optimal music tailored to the user's needs and improve the user's health.

[0079] The visualization unit visualizes health indicators based on the music proposed by the proposal unit. Specifically, it displays heart rate variability in a graph. The heart rate variability graph visually shows the change in heart rate after the user listens to relaxation music, allowing them to confirm the effect of the music. It also creates a sleep pattern graph. The sleep pattern graph visually shows the depth and quality of the user's sleep, helping the user understand how they can improve their sleep. Furthermore, it displays health indicators in a report. The report summarizes the user's heart rate and sleep pattern data and provides a comprehensive evaluation of the user's health status. This allows the visualization unit to provide information that helps the user understand their health status and take concrete actions to improve it. In addition, the visualization unit can link the user's health data with other systems and devices. For example, the user's health data can be displayed in a smartphone app or web portal, allowing the user to check their health status at any time. The visualization unit can also customize how the data is displayed, changing the format of graphs and reports according to the user's preferences and needs. This allows the visualization unit to provide health data in a user-friendly and easy-to-understand format, supporting users in managing their health.

[0080] The Improvement Suggestion Department proposes lifestyle improvements based on health indicators visualized by the Visualization Department. Specifically, it suggests meditation while listening to relaxation music. Meditation has the effect of reducing user stress and promoting mental and physical relaxation. It also suggests exercise while listening to active music. Exercise has the effect of improving the user's physical fitness and overall health. The Improvement Suggestion Department makes appropriate exercise and meditation suggestions according to the user's condition. For example, based on the user's heart rate data, if the stress level is high, it suggests meditation while listening to relaxation music, and if the user is active, it suggests exercise while listening to active music. In this way, the Improvement Suggestion Department can optimize the user's health and improve their quality of life. Furthermore, the Improvement Suggestion Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by providing feedback on the suggested exercise and meditation, the AI ​​can learn from the user's reactions and reflect them in future suggestions. In this way, the Improvement Suggestion Department can provide optimal improvement suggestions that meet the user's needs and improve the user's health.

[0081] The Collaboration Department supports consultations with medical professionals based on improvements proposed by the Improvement Proposal Department. Specifically, it recommends consultations with professionals to users with high stress levels. The Collaboration Department suggests consultations with medical professionals according to the user's health condition. For example, based on the user's heart rate data and sleep pattern data, it recommends consultations with professionals if the user has a high stress level or poor sleep quality. The Collaboration Department supports users in consulting with medical professionals as needed. For example, the Collaboration Department provides a reservation system for users to consult with professionals, making it easy for users to make reservations. The Collaboration Department also shares the user's health data with professionals, making it easier for professionals to understand the user's health condition. This allows the Collaboration Department to help users receive appropriate medical support and optimize the user's health condition. Furthermore, the Collaboration Department can collect user feedback and continuously improve the accuracy and effectiveness of the collaboration. For example, based on the feedback provided by users after consulting with professionals, the Collaboration Department can review and improve the collaboration. This allows the Collaboration Department to provide optimal medical support for users and improve their health condition.

[0082] The data collection unit can acquire heart rate and sleep patterns from wearable devices. For example, the data collection unit uses a wearable device worn by the user to measure heart rate. For example, the data collection unit uses a wearable device worn by the user while sleeping to measure sleep patterns. For example, the data collection unit acquires heart rate and sleep pattern data in real time. This allows for accurate collection of the user's biometric information by acquiring heart rate and sleep patterns from wearable devices. 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 heart rate and sleep pattern data acquired from wearable devices into a generating AI, which can analyze the data to understand the user's health status.

[0083] The analysis unit can analyze acquired biometric information to understand the user's health status. For example, the analysis unit can analyze heart rate variability to evaluate the user's stress level. For example, the analysis unit can analyze abnormal sleep patterns to evaluate the user's sleep quality. For example, the analysis unit can use AI to analyze heart rate and sleep pattern data. This allows for an accurate understanding of the user's health status by analyzing the acquired biometric information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input acquired biometric information into a generating AI, which can then analyze the data to understand the user's health status.

[0084] The suggestion unit can suggest relaxation music or active music tailored to the user's state. For example, the suggestion unit can suggest relaxation music to users with high stress levels. For example, the suggestion unit can suggest active music to users who are in an active state. For example, the suggestion unit can select the optimal music according to the user's state. By suggesting music tailored to the user's state, it is possible to reduce stress levels and improve sleep quality. 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 data about the user's state into a generating AI, which can analyze the data and suggest the optimal music.

[0085] The visualization unit can display health indicators in graphs and reports. For example, the visualization unit can display heart rate fluctuations in a graph. For example, the visualization unit can create a graph of sleep patterns. For example, the visualization unit can display health indicators in a report. This makes it easier for users to understand their own health status by displaying health indicators in graphs and reports. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input health indicator data into a generating AI, and the generating AI can analyze the data to create graphs and reports.

[0086] The improvement suggestion unit can suggest exercise and meditation synchronized with music. For example, the improvement suggestion unit may suggest meditation while listening to relaxation music. For example, the improvement suggestion unit may suggest exercise while listening to active music. For example, the improvement suggestion unit may suggest appropriate exercise and meditation according to the user's condition. In this way, by suggesting exercise and meditation synchronized with music, it is possible to support the improvement of lifestyle habits. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data about the user's condition into a generating AI, and the generating AI can analyze the data to suggest the optimal exercise and meditation.

[0087] The collaboration unit can support consultations with medical professionals as needed. For example, the collaboration unit may recommend consultations with professionals to users with high stress levels. For example, the collaboration unit may suggest consultations with medical professionals according to the user's health condition. For example, the collaboration unit may support users in consulting with medical professionals as needed. This enhances user health management by supporting consultations with medical professionals as needed. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit may input data on the user's health condition into a generating AI, which can then analyze the data and suggest consultations with medical professionals.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of biometric data acquisition based on the estimated emotions. For example, if the user is stressed, the data collection unit will acquire biometric data when the user is relaxed. For example, if the user is relaxed, the data collection unit will acquire biometric data periodically. For example, if the user is in a hurry, the data collection unit will acquire biometric data in a short amount of time. By adjusting the timing of biometric data acquisition based on the user's emotions, biometric data can be acquired at a more appropriate time. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotion and adjust the timing of biometric data acquisition.

[0089] The data collection unit can analyze the user's past biometric information history and select the optimal acquisition method. For example, the data collection unit can analyze the user's past heart rate data to determine the optimal acquisition timing. For example, the data collection unit can analyze the user's past sleep patterns to select the optimal acquisition method. For example, the data collection unit can analyze the user's past exercise data to select the optimal acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past biometric information history. 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 biometric information history into a generating AI, which can then analyze the data and select the optimal acquisition method.

[0090] The data collection unit can filter biometric information based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit will acquire biometric information suitable for exercise. For example, if the user is relaxed, the data collection unit will acquire biometric information suitable for relaxation. For example, if the user is working, the data collection unit will acquire biometric information suitable for work. By filtering based on the user's current activity status and environment, appropriate biometric information can be obtained. 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 data about the user's current activity status and environment into a generating AI, which can then analyze the data to obtain appropriate biometric information.

[0091] The data collection unit can estimate the user's emotions and determine the priority of biometric information to acquire based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes acquiring heart rate. If the user is relaxed, the data collection unit prioritizes acquiring sleep patterns. If the user is in a hurry, the data collection unit prioritizes biometric information that can be acquired quickly. This allows for the priority acquisition of important biometric information by determining the priority of biometric information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of biometric information to acquire.

[0092] The data collection unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring biometric information. For example, if the user is at home, the data collection unit will prioritize the acquisition of biometric information related to relaxation. If the user is at work, the data collection unit will prioritize the acquisition of biometric information related to stress. If the user is exercising, the data collection unit will prioritize the acquisition of biometric information related to exercise. This allows for the priority acquisition of highly relevant biometric information 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, which can then analyze the data and prioritize the acquisition of highly relevant biometric information.

[0093] The data collection unit can analyze the user's social media activity when acquiring biometric information and obtain relevant information. For example, if the user is experiencing stress on social media, the data collection unit prioritizes acquiring heart rate. For example, if the user is relaxing on social media, the data collection unit prioritizes acquiring sleep patterns. For example, if the user posts information about exercise on social media, the data collection unit prioritizes acquiring exercise-related biometric information. In this way, relevant biometric information can be obtained 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 data about the user's social media activity into a generating AI, which can then analyze the data and obtain relevant biometric information.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit will focus on analyzing stress-related data. For example, if the user is relaxed, the analysis unit will focus on analyzing relaxation-related data. For example, if the user is in a hurry, the analysis unit will prioritize analyzing data that can be analyzed quickly. By adjusting the analysis method based on the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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, which can estimate emotions and adjust the analysis method.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during the analysis. For example, the analysis unit performs a detailed analysis if there is a large variation in heart rate. For example, the analysis unit performs a detailed analysis if there is an abnormality in the sleep pattern. For example, the analysis unit performs a detailed analysis if there is an abnormality in the exercise data. In this way, by adjusting the level of detail of the analysis based on the importance of the biological information, important information can be analyzed in detail. 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 data on the importance of biological information into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the category of biometric information during analysis. For example, the analysis unit applies a heart rate analysis algorithm to heart rate data. For example, the analysis unit applies a sleep analysis algorithm to sleep pattern data. For example, the analysis unit applies an exercise analysis algorithm to exercise data. By applying different analysis algorithms depending on the category of biometric information, appropriate analysis can be performed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the category of biometric information into a generating AI, and the generating AI can analyze the data and apply different analysis algorithms.

[0097] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of stress-related data. For example, if the user is relaxed, the analysis unit will prioritize the analysis of relaxation-related data. For example, if the user is in a hurry, the analysis unit will prioritize the analysis of data that can be analyzed quickly. This allows for the priority of analysis of important data by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above 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, which can estimate emotions and determine the priority of analysis.

[0098] The analysis unit can weight the analysis based on the timing of acquisition of biological information. For example, the analysis unit may weight recently acquired biological information for analysis. For example, the analysis unit may weight past biological information for analysis. For example, the analysis unit may weight biological information acquired during a specific period for analysis. By weighting the analysis based on the timing of acquisition of biological information, appropriate 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 data regarding the timing of acquisition of biological information into a generating AI, and the generating AI can analyze the data and perform weighting.

[0099] The analysis unit can adjust the order of analysis based on the relevance of the biological information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant biological information. For example, the analysis unit may postpone the analysis of less relevant biological information. For example, the analysis unit may focus on analyzing highly relevant biological information. By adjusting the order of analysis based on the relevance of the biological information, efficient 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 data on the relevance of biological information into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0100] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present suggestions in a relaxing way. If the user is relaxed, the suggestion unit will present suggestions in a detailed way. If the user is in a hurry, the suggestion unit will present suggestions in a concise way. By adjusting the way it presents suggestions 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, which can estimate the emotion and adjust the way it presents suggestions.

[0101] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the music. For example, the suggestion unit provides detailed suggestions for important music, and concise suggestions for unimportant music. The suggestion unit adjusts the level of detail of its suggestions according to importance. This allows for detailed suggestions for important music by adjusting the level of detail based on the importance of the music. 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 data on the importance of the music into a generating AI, which can then analyze the data and adjust the level of detail of the suggestions.

[0102] The suggestion unit can apply different suggestion algorithms depending on the music category when making suggestions. For example, the suggestion unit applies a relaxation suggestion algorithm to relaxation music. For example, the suggestion unit applies an active suggestion algorithm to active music. The suggestion unit applies different suggestion algorithms depending on the category. This allows for appropriate suggestions to be made by applying different suggestion algorithms depending on the music category. 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 data about the music category into a generating AI, which can then analyze the data and apply different suggestion algorithms.

[0103] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make a short suggestion. For example, if the user is relaxed, the suggestion unit will make a long suggestion. For example, if the user is in a hurry, the suggestion unit will make a concise suggestion. By adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the length of the suggestion.

[0104] The proposal unit can determine the priority of proposals based on the release dates of the music. For example, the proposal unit may prioritize proposing music with upcoming release dates. For example, the proposal unit may postpone proposing music with later release dates. For example, the proposal unit may adjust the priority of proposals according to the release dates. This allows proposals to be made at the appropriate time by determining the priority of proposals based on the release dates of the music. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input data on the release dates of the music into a generating AI, which can then analyze the data to determine the priority of proposals.

[0105] The suggestion unit can adjust the order of suggestions based on the relevance of the music during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant music. For example, the suggestion unit may postpone suggesting less relevant music. For example, the suggestion unit may adjust the order of suggestions according to their relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the music. 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 data on the relevance of the music into a generating AI, which can then analyze the data and adjust the order of suggestions.

[0106] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit will visualize with a simple graph. If the user is relaxed, the visualization unit will visualize with a detailed graph. If the user is in a hurry, the visualization unit will visualize with a concise graph. This allows for more appropriate visualization by adjusting the visualization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the visualization method.

[0107] The visualization unit can adjust the level of detail of the visualization based on the importance of the health indicators during visualization. For example, the visualization unit provides detailed graphs for important health indicators. For example, the visualization unit provides simplified graphs for unimportant health indicators. The visualization unit adjusts the level of detail of the visualization according to importance. This allows important health indicators to be visualized in detail by adjusting the level of detail of the visualization based on the importance of the health indicators. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on the importance of health indicators into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the visualization.

[0108] The visualization unit can apply different visualization methods depending on the category of health indicators during visualization. For example, the visualization unit applies a heart rate graph to heart rate data. For example, the visualization unit applies a sleep graph to sleep pattern data. For example, the visualization unit applies an exercise graph to exercise data. By applying different visualization methods depending on the category of health indicators, appropriate visualization can be achieved. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data related to the category of health indicators into a generating AI, which can then analyze the data and apply different visualization methods.

[0109] The visualization unit can estimate the user's emotions and determine visualization priorities based on the estimated emotions. For example, if the user is stressed, the visualization unit will prioritize the visualization of stress-related health indicators. For example, if the user is relaxed, the visualization unit will prioritize the visualization of relaxation-related health indicators. For example, if the user is in a hurry, the visualization unit will prioritize the visualization of health indicators that can be visualized concisely. In this way, by determining visualization priorities based on the user's emotions, important health indicators can be visualized preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into a generative AI, which can estimate emotions and determine visualization priorities.

[0110] The visualization unit can weight the visualization based on when the health indicators were acquired. For example, the visualization unit may weight recently acquired health indicators for visualization. For example, the visualization unit may weight past health indicators for visualization. For example, the visualization unit may weight health indicators acquired during a specific period for visualization. This allows for appropriate visualization by weighting the visualization based on when the health indicators were acquired. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on when the health indicators were acquired into a generating AI, which can then analyze the data and perform weighting.

[0111] The visualization unit can adjust the order of visualization based on the relevance of health indicators during visualization. For example, the visualization unit may prioritize the visualization of highly relevant health indicators. For example, the visualization unit may delay the visualization of less relevant health indicators. For example, the visualization unit may adjust the order of visualization according to relevance. This allows for efficient visualization by adjusting the order of visualization based on the relevance of health indicators. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit may input data on the relevance of health indicators into a generating AI, and the generating AI may analyze the data and adjust the order of visualization.

[0112] The improvement suggestion unit can estimate the user's emotions and adjust the method of improvement suggestions based on the estimated user emotions. For example, if the user is stressed, the improvement suggestion unit will provide relaxing suggestions. For example, if the user is relaxed, the improvement suggestion unit will provide detailed suggestions. For example, if the user is in a hurry, the improvement suggestion unit will provide concise suggestions. In this way, by adjusting the method of improvement suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement suggestion unit may be performed using AI or not using AI. For example, the improvement suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the method of improvement suggestions.

[0113] The improvement suggestion unit can analyze the user's past lifestyle habits and select the most suitable improvement suggestion when making suggestions. For example, the improvement suggestion unit can analyze the user's past exercise habits and make the most suitable exercise suggestion. For example, the improvement suggestion unit can analyze the user's past eating habits and make the most suitable diet suggestion. For example, the improvement suggestion unit can analyze the user's past sleep habits and make the most suitable sleep suggestion. In this way, the optimal improvement suggestion can be made by analyzing the user's past lifestyle habits. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data on the user's past lifestyle habits into a generating AI, which can then analyze the data and select the most suitable improvement suggestion.

[0114] The improvement suggestion unit can customize the means of improvement suggestions based on the user's current living situation. For example, if the user is busy, the improvement suggestion unit will provide quick suggestions. If the user is relaxed, the improvement suggestion unit will provide detailed suggestions. If the user is in a hurry, the improvement suggestion unit will provide concise suggestions. By customizing the means of improvement suggestions based on the user's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data about the user's current living situation into a generating AI, which can then analyze the data and customize the means of improvement suggestions.

[0115] The improvement suggestion unit can estimate the user's emotions and determine the priority of improvement suggestions based on the estimated emotions. For example, if the user is feeling stressed, the improvement suggestion unit will prioritize stress reduction suggestions. For example, if the user is relaxed, the improvement suggestion unit will prioritize relaxation suggestions. For example, if the user is in a hurry, the improvement suggestion unit will prioritize suggestions that can be implemented quickly. In this way, by determining the priority of improvement suggestions based on the user's emotions, important improvement suggestions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement suggestion unit may be performed using AI, or not using AI. For example, the improvement suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of improvement suggestions.

[0116] The improvement suggestion unit can select the most suitable improvement suggestion by considering the user's geographical location information when making suggestions. For example, if the user is at home, the improvement suggestion unit will suggest improvements that can be done at home. For example, if the user is at work, the improvement suggestion unit will suggest improvements that can be done at work. For example, if the user is out, the improvement suggestion unit will suggest improvements that can be done while out. In this way, the optimal improvement suggestion can be made by considering the user's geographical location information. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data and select the most suitable improvement suggestion.

[0117] The improvement suggestion unit can analyze the user's social media activity and propose means of improvement when making improvement suggestions. For example, if the user is experiencing stress on social media, the improvement suggestion unit will make suggestions for stress reduction. For example, if the user is relaxing on social media, the improvement suggestion unit will make suggestions for relaxation. For example, if the user is posting information about exercise on social media, the improvement suggestion unit will make suggestions for exercise. In this way, appropriate improvement suggestions can be made by analyzing the user's social media activity. Some or all of the above processing in the improvement suggestion unit may be performed using AI, for example, or without AI. For example, the improvement suggestion unit can input data on the user's social media activity into a generating AI, which can then analyze the data and propose means of improvement.

[0118] The collaboration unit can estimate the user's emotions and adjust the timing of consultations with medical professionals based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit will promptly suggest consultations with medical professionals. For example, if the user is relaxed, the collaboration unit will suggest consultations with medical professionals regularly. For example, if the user is in a hurry, the collaboration unit will suggest consultations with medical professionals in a short amount of time. This allows for consultations to take place at the appropriate time by adjusting the timing of consultations with medical professionals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of consultations with medical professionals.

[0119] The collaboration unit can select the most suitable medical professional based on the user's health condition during the collaboration process. For example, the collaboration unit can select a stress specialist based on the user's stress level. For example, the collaboration unit can select a sleep specialist based on the user's sleep patterns. For example, the collaboration unit can select an exercise specialist based on the user's exercise levels. This allows for appropriate consultation by selecting the most suitable medical professional based on the user's health condition. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's health condition into a generating AI, which can then analyze the data and select the most suitable medical professional.

[0120] The collaboration unit can optimize the consultation content by referring to the user's past medical history during the collaboration process. For example, the collaboration unit can refer to the user's past stress history and optimize the consultation content related to stress. For example, the collaboration unit can refer to the user's past sleep history and optimize the consultation content related to sleep. For example, the collaboration unit can refer to the user's past exercise history and optimize the consultation content related to exercise. This allows for appropriate consultation content to be provided by referring to the user's past medical history. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's past medical history into a generating AI, which can then analyze the data and optimize the consultation content.

[0121] The collaboration unit can estimate the user's emotions and determine the priority of consultations with medical professionals based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit will prioritize consultations related to stress. For example, if the user is relaxed, the collaboration unit will prioritize consultations related to relaxation. For example, if the user is in a hurry, the collaboration unit will prioritize consultations that can be completed in a short time. In this way, important consultations can be prioritized by determining the priority of consultations with medical professionals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of consultations with medical professionals.

[0122] The collaboration unit can select the most suitable medical professional by considering the user's geographical location information during the collaboration process. For example, if the user is at home, the collaboration unit will select a medical professional near their home. For example, if the user is at work, the collaboration unit will select a medical professional near their workplace. For example, if the user is out, the collaboration unit will select a medical professional near their current location. In this way, the most suitable medical professional can be selected by considering the user's geographical location information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location information into a generating AI, which can then analyze the data and select the most suitable medical professional.

[0123] The collaboration unit can analyze the user's social media activity during collaboration to optimize the consultation content. For example, if the user is experiencing stress on social media, the collaboration unit will optimize the consultation content related to stress. For example, if the user is relaxing on social media, the collaboration unit will optimize the consultation content related to relaxation. For example, if the user is posting information about exercise on social media, the collaboration unit will optimize the consultation content related to exercise. In this way, appropriate consultation content can be provided by analyzing the user's social media activity. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's social media activity into a generating AI, which can then analyze the data to optimize the consultation content.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] A health monitoring system can acquire a user's dietary information and analyze their nutritional balance. For example, the data collection unit acquires dietary information from an application where the user records their meals. The analysis unit analyzes the acquired dietary information and evaluates the user's nutritional balance. The suggestion unit proposes an appropriate meal plan to the user based on the analysis results. This allows the user to understand their own diet and maintain a healthy eating lifestyle.

[0126] A health monitoring system can acquire a user's exercise history and analyze their exercise patterns. For example, the data collection unit acquires exercise history from the user's fitness tracker. The analysis unit analyzes the acquired exercise history and evaluates the user's exercise patterns. The suggestion unit proposes an appropriate exercise plan to the user based on the analysis results. This allows the user to understand their own exercise habits and maintain healthy exercise routines.

[0127] A health monitoring system can monitor a user's fluid intake and suggest appropriate hydration. For example, the data collection unit acquires data from an application that records the amount of water the user drinks. The analysis unit analyzes the acquired data and evaluates the user's fluid intake. The suggestion unit suggests appropriate hydration to the user based on the analysis results. This allows the user to maintain appropriate hydration and stay healthy.

[0128] A health monitoring system can monitor a user's sleep environment and suggest improvements. For example, the data collection unit acquires the temperature and humidity of the user's bedroom from sensors. The analysis unit analyzes the acquired data and evaluates the user's sleep environment. Based on the analysis results, the suggestion unit makes appropriate suggestions for improving the user's sleep environment. This allows the user to maintain a comfortable sleep environment and improve the quality of their sleep.

[0129] A health monitoring system can monitor a user's stress level in real time and offer suggestions for stress reduction. For example, the data collection unit acquires the user's heart rate and skin electrical activity from sensors. The analysis unit analyzes the acquired data and evaluates the user's stress level. Based on the analysis results, the suggestion unit proposes appropriate stress reduction methods to the user. This allows the user to manage stress and maintain their health.

[0130] The health monitoring system can estimate the user's emotions and select music based on those emotions. For example, if the user is feeling sad, the system will suggest uplifting music. If the user is relaxed, the system will suggest music to maintain that relaxation. If the user is excited, the system will suggest calming music. By providing music tailored to the user's emotions, the system can help stabilize their feelings.

[0131] A health monitoring system can estimate a user's emotions and suggest exercise based on those emotions. For example, if the user is feeling stressed, the system might suggest relaxing yoga. If the user is relaxed, it might suggest light jogging. If the user is excited, it might suggest exercise to release energy. In this way, by suggesting exercises tailored to the user's emotions, the system can support a healthy lifestyle.

[0132] The health monitoring system can estimate the user's emotions and suggest meals based on those emotions. For example, if the user is feeling sad, the suggestion unit will suggest a meal to lift their mood. If the user is relaxed, the suggestion unit will suggest a meal to maintain that relaxation. If the user is excited, the suggestion unit will suggest a meal to calm them down. In this way, by providing meals tailored to the user's emotions, it is possible to promote emotional stability.

[0133] The health monitoring system can estimate the user's emotions and adjust the sleep environment based on those emotions. For example, if the user is feeling stressed, the improvement suggestion unit will suggest a relaxing environment. If the user is relaxed, the improvement suggestion unit will suggest an environment that maintains that relaxation. If the user is agitated, the improvement suggestion unit will suggest a calming environment. In this way, by providing a sleep environment tailored to the user's emotions, the quality of sleep can be improved.

[0134] The health monitoring system can estimate the user's emotions and adjust the visualization method of health indicators based on those emotions. For example, if the user is stressed, the visualization unit will display a simple graph. If the user is relaxed, the visualization unit will display a detailed graph. If the user is in a hurry, the visualization unit will display a concise graph. This allows for more appropriate visualization by adjusting the visualization method based on the user's emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The data collection unit acquires the user's biometric information. For example, the data collection unit acquires heart rate and sleep patterns from wearable devices. The data collection unit uses a wearable device worn by the user to measure heart rate and a wearable device worn by the user while sleeping to measure sleep patterns. The data collection unit acquires heart rate and sleep pattern data in real time. Step 2: The analysis unit analyzes the biometric information acquired by the data collection unit. For example, the analysis unit analyzes heart rate variability to assess the user's stress level. The analysis unit analyzes abnormalities in sleep patterns to assess the user's sleep quality. The analysis unit uses AI to analyze heart rate and sleep pattern data. Step 3: The suggestion unit proposes music tailored to the user's state based on the information analyzed by the analysis unit. For example, the suggestion unit will suggest relaxation music to users with high stress levels and active music to users who are in an active state. The suggestion unit selects the optimal music according to the user's state. Step 4: The visualization unit visualizes health indicators based on the music proposed by the proposal unit. For example, the visualization unit displays heart rate variability in a graph and creates a sleep pattern graph. The visualization unit displays the health indicators in a report. Step 5: The Improvement Suggestion Department proposes lifestyle improvements based on the health indicators visualized by the Visualization Department. For example, the Improvement Suggestion Department might suggest meditation while listening to relaxation music or exercise while listening to active music. The Improvement Suggestion Department will suggest appropriate exercise and meditation based on the user's condition. Step 6: The Liaison Department supports consultations with medical professionals based on the improvements proposed by the Improvement Proposal Department. For example, the Liaison Department recommends consultations with professionals for users with high stress levels and suggests consultations with medical professionals according to the user's health condition. The Liaison Department supports users in consulting with medical professionals as needed.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, improvement proposal unit, and collaboration 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 acquires heart rate and sleep patterns from the wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired biometric information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests music tailored to the user's condition. The visualization unit is implemented by the control unit 46A of the smart device 14 and displays health indicators in graphs and reports. The improvement proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests improvements to lifestyle habits. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports consultation with medical professionals. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, improvement proposal unit, and collaboration 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 acquires heart rate and sleep patterns from the wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired biometric information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests music tailored to the user's condition. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and displays health indicators in graphs and reports. The improvement proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests improvements to lifestyle habits. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports consultation with medical professionals. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, improvement proposal unit, and collaboration 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 acquires heart rate and sleep patterns from the wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired biometric information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests music tailored to the user's condition. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and displays health indicators in graphs and reports. The improvement proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests improvements to lifestyle habits. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports consultation with medical professionals. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, improvement proposal unit, and collaboration unit, is implemented in at least one of the following: 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 acquires heart rate and sleep patterns from a wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired biometric information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests music tailored to the user's condition. The visualization unit is implemented by the control unit 46A of the robot 414 and displays health indicators in graphs and reports. The improvement proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests improvements to lifestyle habits. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports consultation with medical professionals. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A collection unit that acquires the user's biometric information, An analysis unit analyzes the biological information acquired by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, a suggestion unit proposes music tailored to the user's condition, Based on the music proposed by the aforementioned proposal unit, a visualization unit visualizes health indicators, Based on the health indicators visualized by the aforementioned visualization unit, an improvement suggestion unit proposes improvements to lifestyle habits, The system includes a liaison department that supports consultations with medical professionals based on the improvements proposed by the aforementioned improvement proposal department. A system characterized by the following features. (Note 2) The aforementioned collection unit is Obtaining heart rate and sleep patterns from wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The acquired biometric information is analyzed to understand the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest relaxation music and active music tailored to the user's state. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned visualization unit, Display health indicators in graphs and reports. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned improvement proposal department, We propose exercise and meditation linked to music. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned linkage unit is, We provide support for consultations with medical professionals as needed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of biometric data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past biometric data history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When acquiring biometric information, 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 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of biometric information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When acquiring biometric information, the system prioritizes the acquisition of highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When acquiring biometric information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the biological information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of biological information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis is weighted based on when the biological information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the biological information. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the music. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the music category. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on the timing of music delivery. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on their musical relevance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, When visualizing, adjust the level of detail based on the importance of the health indicators. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, When visualizing data, different visualization methods are applied depending on the category of health indicator. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visualization unit, When visualizing the data, weighting is applied based on when the health indicators were acquired. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned visualization unit, When visualizing, adjust the order of visualizations based on the relevance of health indicators. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned improvement proposal department, It estimates the user's emotions and adjusts the method of suggesting improvements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned improvement proposal department, When proposing improvements, the system analyzes the user's past lifestyle habits to select the most suitable improvement suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned improvement proposal department, When suggesting improvements, customize the method of suggestion based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned improvement proposal department, It estimates user emotions and prioritizes improvement suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned improvement proposal department, When submitting improvement suggestions, the system selects the most suitable suggestions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned improvement proposal department, When proposing improvements, we analyze users' social media activity and suggest methods for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the timing of consultations with medical professionals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned linkage unit is, During integration, the system selects the most suitable medical professional based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned linkage unit is, During integration, the system optimizes the consultation content by referencing the user's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned linkage unit is, The system estimates the user's emotions and prioritizes consultations with medical professionals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned linkage unit is, During integration, the system selects the most suitable medical professional by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned linkage unit is, During integration, the system analyzes the user's social media activity to optimize the consultation content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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 collection unit that acquires the user's biometric information, An analysis unit analyzes the biological information acquired by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, a suggestion unit proposes music tailored to the user's condition, Based on the music proposed by the aforementioned proposal unit, a visualization unit visualizes health indicators, Based on the health indicators visualized by the aforementioned visualization unit, an improvement suggestion unit proposes improvements to lifestyle habits, The system includes a liaison department that supports consultations with medical professionals based on the improvements proposed by the aforementioned improvement proposal department. A system characterized by the following features.

2. The aforementioned collection unit is Obtaining heart rate and sleep patterns from wearable devices. The system according to feature 1.

3. The aforementioned analysis unit, The acquired biometric information is analyzed to understand the user's health status. The system according to feature 1.

4. The aforementioned proposal section is, We suggest relaxation music and active music tailored to the user's state. The system according to feature 1.

5. The aforementioned visualization unit, Display health indicators in graphs and reports. The system according to feature 1.

6. The aforementioned improvement proposal department, We propose exercise and meditation linked to music. The system according to feature 1.

7. The aforementioned linkage unit is, We provide support for consultations with medical professionals as needed. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of biometric data acquisition based on the estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past biometric data history and select the optimal acquisition method. The system according to feature 1.

10. The aforementioned collection unit is When acquiring biometric information, filtering is performed based on the user's current activity status and environment. The system according to feature 1.

Images