system

Abstract

The system according to this embodiment aims to improve the accuracy and promote the dissemination of mental health care training. [Solution] The system according to the embodiment comprises a reception unit, a guidance unit, an analysis unit, and a feedback unit. The reception unit receives user data. The guidance unit provides training guidance based on the data received by the reception unit. The analysis unit analyzes the data in real time during the training guided by the guidance unit. The feedback unit provides feedback on the results based on the data analyzed by the analysis 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, the improvement of the accuracy and the promotion of the penetration of mental health care training have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the accuracy and promote the penetration of mental health care training.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a guidance unit, an analysis unit, and a feedback unit. The reception unit receives user data. The guidance unit provides training guidance based on the data received by the reception unit. The analysis unit analyzes the data in real time during the training guided by the guidance unit. The feedback unit provides feedback on the results based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the accuracy and promote the dissemination of mental health care training. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that solves social challenges in mental healthcare by improving the accuracy and promoting the spread of mindfulness using an AI agent. This system accepts user profiles, mental health assessment data, biofeedback data, past implementation data, latest research data, and other personal data as input. This enables personalized guidance tailored to the user's individual needs and condition. Next, the AI ​​agent acts as a high-quality virtual trainer, guiding mindfulness training. Before training, it provides a training schedule, and during training, it analyzes biodata in real time via the device and reflects this in the training menu and advice. After training, it provides feedback on the results based on the data. This mechanism can solve problems such as the shortage of specialists, the hurdles to individualized support, access limitations, and the issues of scientific evidence and effectiveness. Furthermore, because the AI ​​agent provides feedback based on the latest research results, it can enhance the effectiveness of mindfulness, which requires continuous practice. In addition, the AI ​​agent can not only support the user's mental health but also provide comprehensive health management based on biodata. As a result, users can easily receive high-quality mindfulness training from anywhere, promoting the spread of mental healthcare. This means that systems using AI agents can effectively support users' mental health care and contribute to solving social issues.

[0029] The system according to this embodiment comprises a reception unit, a guidance unit, an analysis unit, and a feedback unit. The reception unit receives user data. User data includes, but is not limited to, examples of, a profile, mental health assessment data, biofeedback data, past performance data, the latest research data, and other personal data. For example, the reception unit accepts the user's profile as input. The user's profile includes age, gender, occupation, etc. The reception unit may also accept mental health assessment data as input. Mental health assessment data includes stress levels, assessments of depressive symptoms, etc. The reception unit may also accept biofeedback data as input. Biofeedback data includes heart rate variability, skin electrical activity, etc. The reception unit may also accept past performance data as input. Past performance data includes past training history, feedback history, etc. The reception unit may also accept the latest research data as input. The latest research data includes the latest academic papers, clinical trial results, etc. The reception unit may also accept other personal data as input. Other personal data includes lifestyle data, hobby preference data, etc. The training department provides instruction based on data received by the reception department. For example, the training department can provide mindfulness training as a high-quality virtual trainer. The training department can use audio and video guides. The training department provides personalized instruction tailored to the user's individual needs and condition. For example, the training department adjusts the training menu according to the user's stress level. The training department can also suggest training menus based on the user's training history. The training department can also adjust the training menu based on user feedback. The analytics department analyzes data in real time during training conducted by the training department. For example, the analytics department analyzes biodata in real time through the device during training. The analytics department can analyze biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time.The analysis unit incorporates the data analyzed in real time into training menus and advice. For example, the analysis unit adjusts the training menu according to fluctuations in heart rate. The analysis unit can also provide advice according to fluctuations in blood pressure. The analysis unit can also adjust the training menu according to fluctuations in brain waves. The feedback unit provides feedback based on the data analyzed by the analysis unit. For example, the feedback unit provides feedback based on the data after training. The feedback unit can provide feedback on the user's training results in text or voice. The feedback unit can also provide visual feedback on the user's training results in graphs or charts. The feedback unit can also suggest the next training menu based on the user's training results. The feedback unit can also provide advice based on the user's training results. As a result, the system according to the embodiment enables personalized mindfulness training by guiding training based on the user's data and providing feedback on the results by analyzing the data in real time.

[0030] The reception desk accepts user data. This data includes, but is not limited to, profiles, mental health assessment data, biofeedback data, past training data, recent research data, and other personal data. For example, the reception desk accepts user profiles as input. User profiles include age, gender, occupation, etc. This data is important for understanding the user's basic information and helps personalize training. The reception desk may also accept mental health assessment data as input. This data includes stress levels, depressive symptom assessments, etc. This allows for understanding the user's mental health status and providing appropriate training programs. The reception desk may also accept biofeedback data as input. This data includes heart rate variability, skin electrical activity, etc. This data is important for understanding the user's physiological state in real time and is used to maximize the effectiveness of training. The reception desk may also accept past training data as input. This data includes past training history, feedback history, etc. This allows for understanding the user's past training results and challenges and reflecting them in future training sessions. The reception department can also accept the latest research data as input. This includes the latest academic papers and clinical trial results. This allows for the incorporation of the latest scientific findings into training, providing effective training. The reception department can also accept other personal data as input. This includes lifestyle data and hobby / preference data. This allows for training tailored to the individual needs and preferences of users. The reception department can centrally manage this diverse data and collaborate with other departments as needed. For example, collected data can be stored on a cloud server and made accessible to the training and analysis departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible.This allows the reception department to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The training department provides instruction based on data received by the reception department. For example, the training department can provide mindfulness training as a high-quality virtual trainer. The training department can also provide instruction using audio and video guides. Specifically, they can create a relaxing environment for the user and provide audio and video instructions on deep breathing and meditation techniques. The training department provides personalized instruction tailored to the user's individual needs and condition. For example, they can adjust the training menu according to the user's stress level. Users with high stress levels can be offered a menu that emphasizes relaxation, while users with low stress levels can be offered a menu that enhances concentration. The training department can also suggest training menus based on the user's training history. They analyze past training data to understand what kind of training the user found effective and suggest the next training menu based on that. The training department can also adjust the training menu based on user feedback. They collect feedback from users and improve the content and methods of the training. For example, if a user finds a particular training difficult, they can simplify the training or suggest an alternative method. This allows the training department to provide the optimal training for the user and maximize the effectiveness of the training. Furthermore, the training team can monitor the user's training progress in real time and adjust the training menu as needed. This allows the training team to respond flexibly to the user's condition, thereby enhancing the effectiveness of the training.

[0032] The analysis department analyzes data in real time during training sessions guided by the instruction department. For example, the analysis department analyzes biodata in real time through devices during training. Specifically, it can analyze biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time. It analyzes heart rate fluctuations to assess the user's relaxation level and stress level. It analyzes blood pressure fluctuations to assess the effectiveness of the training. It analyzes EEG fluctuations to assess the user's concentration level and relaxation level. The analysis department incorporates the data analyzed in real time into training menus and advice. For example, it adjusts the training menu according to heart rate fluctuations. If the heart rate is high, it switches to a menu that emphasizes relaxation; if the heart rate is low, it switches to a menu that enhances concentration. It can also provide advice according to blood pressure fluctuations. If blood pressure is high, it provides advice to help the user relax; if blood pressure is low, it provides advice to help the user concentrate. It can also adjust the training menu according to EEG fluctuations. If the EEG indicates a relaxed state, it provides a menu to maintain that state; if the EEG indicates a focused state, it provides a menu to maintain that state. This allows the analytics department to provide optimal training tailored to the user's condition, maximizing the effectiveness of the training. Furthermore, the analytics department can utilize historical data and statistical information to evaluate the long-term effectiveness of training. For example, it can analyze past training data to evaluate the user's training progress and effectiveness. This enables the analytics department to provide users with long-term training benefits and support their continued training.

[0033] The feedback unit provides feedback based on data analyzed by the analysis unit. For example, the feedback unit provides feedback on the results after training based on the data. Specifically, it can provide feedback on the user's training results in text or audio. It provides a detailed explanation of the user's state during training and what effects were achieved. The feedback unit can also provide visual feedback on the user's training results using graphs and charts. It can display fluctuations in heart rate, blood pressure, and brain waves in graphs, allowing the user to visually understand the effects of the training. The feedback unit can also suggest the next training menu based on the user's training results. For example, it can adjust the next training menu based on fluctuations in the user's heart rate and blood pressure. The feedback unit can also provide advice based on the user's training results. For example, it can provide advice on how to relax or how to improve concentration. In this way, the feedback unit can provide users with information to maximize the effectiveness of their training and support their continued training. Furthermore, the feedback unit can collect user feedback and use it to improve the overall system. Based on user feedback, it can improve training menus and teaching methods to provide users with more effective training. This allows the feedback unit to provide the user with optimal training and maximize the effectiveness of the training.

[0034] The feedback unit includes a health management unit that provides comprehensive health management based on biodata. The health management unit provides health management based on biodata such as heart rate, blood pressure, and electroencephalogram (EEG). The health management unit can monitor fluctuations in heart rate and issue alerts if abnormalities are detected. The health management unit can also monitor fluctuations in blood pressure and issue alerts if abnormalities are detected. The health management unit can also monitor fluctuations in electroencephalogram (EEG) and issue alerts if abnormalities are detected. The health management unit can also conduct health checkups based on biodata. The health management unit can also provide nutritional management based on biodata. The health management unit can also provide exercise guidance based on biodata. For example, the health management unit can adjust the intensity of exercise based on heart rate data. The health management unit can also provide dietary advice based on blood pressure data. The health management unit can also suggest relaxation methods based on EEG data. In this way, comprehensive health management based on biodata can provide overall support for the user's health status. Some or all of the above-described processes in the health management unit may be performed using AI, for example, or without using AI. For example, the health management department can input heart rate data into a generating AI and have the AI ​​execute health management methods.

[0035] The feedback unit includes a research results reflection unit that provides feedback based on the latest research findings. The research results reflection unit provides feedback based on, for example, the latest academic papers and clinical trial results. The research results reflection unit can collect the latest research findings and reflect them in the feedback. For example, the research results reflection unit can automatically collect the latest academic papers and reflect them in the feedback. The research results reflection unit can also automatically collect the latest clinical trial results and reflect them in the feedback. The research results reflection unit can also update the training menu based on the latest research findings. The research results reflection unit can also provide advice based on the latest research findings. For example, the research results reflection unit adjusts the training menu based on the latest academic papers. The research results reflection unit can also provide advice based on the latest clinical trial results. As a result, by providing feedback based on the latest research findings, effective training based on scientific evidence is provided. Some or all of the above processing in the research results reflection unit may be performed using, for example, AI, or not using AI. For example, the research results reflection unit can input the latest academic papers into a generating AI and have the generating AI execute the content of the feedback.

[0036] The reception desk accepts user profiles, mental health assessment data, biofeedback data, past performance data, latest research data, and other personal data as input. For example, the reception desk accepts user profiles as input. User profiles include age, gender, occupation, etc. The reception desk can also accept mental health assessment data as input. Mental health assessment data includes stress levels, depressive symptom assessments, etc. The reception desk can also accept biofeedback data as input. Biofeedback data includes heart rate variability, skin electrical activity, etc. The reception desk can also accept past performance data as input. Past performance data includes past training history, feedback history, etc. The reception desk can also accept the latest research data as input. Latest research data includes the latest academic papers, clinical trial results, etc. The reception desk can also accept other personal data as input. Other personal data includes lifestyle data, hobby preference data, etc. This allows for personalized guidance tailored to the individual needs and conditions of each user by accepting a variety of data as input. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user profile data into a generating AI and have the generating AI perform the data reception.

[0037] The training department provides mindfulness training as a high-quality virtual trainer. The training department provides instruction using, for example, audio guides or video guides. The training department provides personalized instruction tailored to the user's individual needs and condition. The training department adjusts the training menu according to, for example, the user's stress level. The training department can also suggest training menus based on the user's training history. The training department can also adjust the training menu based on user feedback. This allows for instruction by a high-quality virtual trainer, overcoming the shortage of experts and the hurdles of providing individualized support. Some or all of the above processes in the training department may be performed using, for example, AI, or not using AI. For example, the training department can input the user's training data into a generating AI and have the generating AI suggest training menus.

[0038] The analysis unit analyzes biodata in real time through the device during training and reflects this in the training menu and advice. For example, the analysis unit analyzes biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time. The analysis unit can adjust the training menu according to fluctuations in heart rate. The analysis unit can also provide advice according to fluctuations in blood pressure. The analysis unit can also adjust the training menu according to fluctuations in electroencephalogram (EEG). This allows for effective training by analyzing biodata in real time and reflecting it in the training menu and advice. 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 heart rate data into a generating AI and have the generating AI adjust the training menu.

[0039] The reception department analyzes the user's past data submission history and selects the optimal data reception method. For example, the reception department can analyze the time periods when the user previously submitted data and suggest the optimal submission time. The reception department can also analyze the devices the user has used in the past and suggest the optimal device. The reception department can also analyze the format of the data the user has submitted in the past and suggest the optimal format. In this way, by analyzing the past data submission history, the reception department can provide the user with the most suitable data reception method. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's past data submission history into a generating AI and have the generating AI select the optimal data reception method.

[0040] The reception unit filters the data upon receipt based on the user's current mental health status and areas of interest. For example, if the user's mental health status is unstable, the reception unit will only accept data that helps reduce stress. The reception unit can also accept only relevant data based on the user's areas of interest. If the user's mental health status is good, the reception unit can accept all data. This allows for the reception of more appropriate data by filtering the data based on the user's mental health status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's mental health status data into a generating AI and have the generating AI perform the data filtering.

[0041] The reception unit, upon receiving data, prioritizes the acceptance of highly relevant data, taking into account the user's geographical location. For example, if the user is in an urban area, the reception unit prioritizes the acceptance of data related to urban areas. If the user is in a suburban area, the reception unit can prioritize the acceptance of data related to suburban areas. If the user is traveling, the reception unit can also prioritize the acceptance of data related to their travel destination. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority acceptance of data.

[0042] The reception unit analyzes the user's social media activity when receiving data and accepts relevant data. For example, if the user is experiencing stress on social media, the reception unit may accept data related to stress reduction. If the user is relaxing on social media, the reception unit may accept data related to relaxation. If the user is concentrating on social media, the reception unit may also accept data related to improved concentration. In this way, relevant data can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's social media activity data into a generating AI and have the generating AI perform the acceptance of relevant data.

[0043] The training department adjusts the level of detail in training based on the user's mental health status during training sessions. For example, if the user's mental health status is unstable, the training department may provide simple training. If the user's mental health status is good, the training department may provide detailed training. If the user's mental health status is improving, the training department may also provide progressively more detailed training. By adjusting the level of detail in training according to the user's mental health status, more appropriate training is provided. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department may input the user's mental health status data into a generating AI and have the generating AI perform the adjustment of the level of detail in training.

[0044] The training unit applies different training algorithms to users based on their profiles during training. For example, the training unit may apply an appropriate training algorithm based on the user's age, gender, or occupation. By applying different training algorithms based on the user's profile, personalized training is provided. Some or all of the above processes in the training unit may be performed using AI, for example, or not. For example, the training unit may input user profile data into a generating AI and have the generating AI perform the application of the training algorithm.

[0045] The training department determines the priority of instruction based on the user's past training history during training sessions. For example, the training department can analyze the effectiveness of the user's past training and propose the optimal training. The training department can also analyze the frequency of the user's past training and propose the optimal training. The training department can also analyze the content of the user's past training and propose the optimal training. By prioritizing instruction based on the user's past training history, more effective training is provided. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department can input the user's past training history data into a generating AI and have the generating AI perform the determination of instruction priorities.

[0046] The training department adjusts the order of instruction based on the user's relevant health data during training sessions. For example, the training department can analyze the user's biodata and suggest an optimal training order. The training department can also analyze the user's mental health data and suggest an optimal training order. The training department can also analyze the user's feedback data and suggest an optimal training order. This allows for more effective training by adjusting the order of instruction based on the user's relevant health data. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input the user's biodata into a generating AI and have the generating AI perform the adjustment of the instruction order.

[0047] The analysis unit improves the accuracy of data analysis by considering the interrelationships of the user's biodata. For example, the analysis unit can analyze the correlation between the user's heart rate and stress level. The analysis unit can analyze the correlation between the user's sleep data and mental health status. The analysis unit can also analyze the correlation between the user's exercise data and mental health status. This improves the accuracy of the analysis by considering the interrelationships of the user's biodata. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's biodata into a generating AI and have the generating AI perform the task of improving the accuracy of the data analysis.

[0048] The analysis unit considers user attribute information when performing data analysis. For example, the analysis unit adjusts the criteria for data analysis according to the user's age. The analysis unit can also adjust the criteria for data analysis according to the user's gender. The analysis unit can also adjust the criteria for data analysis according to the user's occupation. This allows for more appropriate data analysis by considering user attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform the adjustment of the data analysis criteria.

[0049] The analysis unit considers the geographical distribution of users when performing data analysis. For example, if a user is in an urban area, the analysis unit will prioritize analyzing data related to urban areas. If a user is in a suburban area, the analysis unit can prioritize analyzing data related to suburban areas. If a user is traveling, the analysis unit can also prioritize analyzing data related to their travel destination. This allows for more appropriate data analysis by considering the geographical distribution of users. 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 geographical distribution data into a generating AI and have the generating AI perform the data analysis.

[0050] The analysis unit improves the accuracy of its analysis by referring to relevant literature for the user during data analysis. For example, the analysis unit may refer to the latest research literature on the user's mental health status. The analysis unit may also refer to the latest research literature on the user's biodata. The analysis unit may also refer to the latest research literature on the user's training data. This improves the accuracy of the analysis by referring to relevant literature for the user. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the data analysis.

[0051] The feedback unit optimizes the current feedback by referring to past feedback data when providing feedback. For example, the feedback unit analyzes the user's past feedback data to provide optimal feedback. The feedback unit can adjust the content of the feedback based on the user's past feedback data. The feedback unit can also optimize the timing of feedback by referring to the user's past feedback data. This allows the current feedback to be optimized by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback data into a generating AI and have the generating AI perform the optimization of the current feedback.

[0052] The feedback unit applies different feedback methods depending on the user's mental health state when providing feedback. For example, if the user's mental health state is unstable, the feedback unit provides relaxing feedback. If the user's mental health state is good, the feedback unit can provide detailed feedback. If the user's mental health state is improving, the feedback unit can also provide progressively more detailed feedback. This allows for more appropriate feedback to be provided by applying different feedback methods depending on the user's mental health state. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's mental health state data into a generating AI and have the generating AI execute the application of the feedback method.

[0053] The feedback unit analyzes changes in feedback based on the user's data submission timing. For example, the feedback unit provides optimal feedback based on the time of day the user submitted the data. The feedback unit can also provide optimal feedback based on the day of the week the user submitted the data. The feedback unit can also provide optimal feedback based on the season the user submitted the data. This allows for more appropriate feedback to be provided by analyzing changes in feedback based on the user's data submission timing. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user data submission timing data into a generating AI and have the generating AI perform the analysis of changes in feedback.

[0054] The feedback unit analyzes the feedback by referring to the user's relevant market data. For example, the feedback unit can refer to market data related to the user's industry to provide optimal feedback. The feedback unit can also refer to market data related to the user's occupation to provide optimal feedback. The feedback unit can also refer to market data related to the user's region to provide optimal feedback. This allows for more appropriate feedback to be provided by referring to the user's relevant market data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's relevant market data into a generating AI and have the generating AI perform the feedback analysis.

[0055] The Health Management Department analyzes the user's past health data to select the optimal health management method during health management. For example, the Health Management Department analyzes the user's past health data and provides the optimal health management method. The Health Management Department can adjust the content of health management based on the user's past health data. The Health Management Department can also optimize the timing of health management by referring to the user's past health data. In this way, by analyzing the user's past health data, it can provide the optimal health management method. Some or all of the above processes in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the user's past health data into a generating AI and have the generating AI select a health management method.

[0056] The Health Management Department selects the optimal health management method when providing health management, taking into account the user's geographical location information. For example, if the user is in an urban area, the Health Management Department can provide health management methods relevant to urban areas. If the user is in a suburban area, the Health Management Department can provide health management methods relevant to suburban areas. If the user is traveling, the Health Management Department can also provide health management methods relevant to the travel destination. In this way, the optimal health management method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the user's geographical location information into a generating AI and have the generating AI perform the selection of a health management method.

[0057] The research results reflection unit optimizes the reflection algorithm by referring to the latest research data when reflecting research results. For example, the research results reflection unit provides an optimal reflection algorithm based on the latest research data. The research results reflection unit can adjust the content of the reflection algorithm based on the latest research data. The research results reflection unit can also optimize the timing of the reflection algorithm by referring to the latest research data. As a result, the accuracy of the reflection algorithm is improved by referring to the latest research data. Some or all of the above processing in the research results reflection unit may be performed using AI, for example, or without using AI. For example, the research results reflection unit can input the latest research data into a generating AI and have the generating AI perform the optimization of the reflection algorithm.

[0058] The research results reflection unit weights the research results based on the user's data submission timing when reflecting the research results. For example, the research results reflection unit reflects the optimal research results based on the time of day the user submitted the data. The research results reflection unit can also reflect the optimal research results based on the day of the week the user submitted the data. The research results reflection unit can also reflect the optimal research results based on the season the user submitted the data. This allows for more appropriate feedback by weighting the research results based on the user's data submission timing. Some or all of the above processing in the research results reflection unit may be performed using AI, for example, or without AI. For example, the research results reflection unit can input the user's data submission timing data into a generating AI and have the generating AI perform the weighting of the research results.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The training system can analyze the user's past training data and adjust the training pace accordingly. For example, if the user previously trained quickly, the system can maintain a similar pace. If the user previously trained slowly, the system can maintain a similar pace. If the user previously interrupted training, the system can adjust the pace and resume training. This allows for more effective training by adjusting the pace based on the user's past training data.

[0061] The health management department can modify its health management approach based on the user's geographical location. For example, if the user is in an urban area, it can recommend exercise and nutrition management suitable for urban areas. If the user is in a suburban area, it can recommend exercise and nutrition management suitable for suburban areas. If the user is traveling, it can also recommend health management methods suitable for their travel destination. By changing the health management approach based on the user's geographical location, more appropriate health management can be provided.

[0062] The feedback system can analyze a user's past feedback data and adjust the content of the feedback accordingly. For example, if a user has previously preferred positive feedback, it can provide similar feedback. If a user has previously preferred detailed feedback, it can provide similar feedback. If a user has previously requested specific areas for improvement, it can provide similar feedback. By adjusting the content of feedback based on the user's past feedback data, more effective feedback can be provided.

[0063] The analysis unit can improve the accuracy of data analysis by considering the interrelationships of user biodata. For example, it can analyze the correlation between a user's heart rate and stress level. It can also analyze the correlation between a user's sleep data and mental health status. It can also analyze the correlation between a user's exercise data and mental health status. In this way, the accuracy of data analysis is improved by considering the interrelationships of user biodata.

[0064] The research results reflection unit can optimize the reflection algorithm by referring to the latest research data. For example, it can provide the optimal reflection algorithm based on the latest research data. It can also adjust the content of the reflection algorithm based on the latest research data. It can also optimize the timing of the reflection algorithm by referring to the latest research data. As a result, the accuracy of the reflection algorithm is improved by referring to the latest research data.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the user's data. User data includes profile, mental health assessment data, biofeedback data, past performance data, latest research data, and other personal data. For example, the user's profile includes age, gender, occupation, etc., and mental health assessment data includes stress levels and assessments of depressive symptoms. Biofeedback data includes heart rate variability and skin electrical activity, and past performance data includes past training history and feedback history. Latest research data includes the latest academic papers and clinical trial results, and other personal data includes lifestyle data and hobby / preference data. Step 2: The training department provides instruction based on the data received by the reception department. The training department can provide mindfulness training as a high-quality virtual trainer, and can use audio and video guides to guide the training. The training department provides personalized instruction tailored to the user's individual needs and condition, for example, adjusting the training menu according to the user's stress level. The training department can suggest and adjust the training menu based on the user's training history and feedback. Step 3: The analysis unit analyzes data in real time during training instructed by the training unit. The analysis unit can analyze biodata in real time through the device during training, and can analyze biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time. The analysis unit can reflect the data analyzed in real time in training menus and advice, for example, by adjusting the training menu according to heart rate fluctuations, providing advice according to blood pressure fluctuations, and adjusting the training menu according to EEG fluctuations. Step 4: The feedback unit provides feedback based on the data analyzed by the analysis unit. The feedback unit provides feedback based on the data after training and can provide feedback on the user's training results in text or audio. The feedback unit can also provide visual feedback on the user's training results using graphs and charts, and can suggest the next training menu and provide advice.

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system that solves social challenges in mental healthcare by improving the accuracy and promoting the spread of mindfulness using an AI agent. This system accepts user profiles, mental health assessment data, biofeedback data, past implementation data, latest research data, and other personal data as input. This enables personalized guidance tailored to the user's individual needs and condition. Next, the AI ​​agent acts as a high-quality virtual trainer, guiding mindfulness training. Before training, it provides a training schedule, and during training, it analyzes biodata in real time via the device and reflects this in the training menu and advice. After training, it provides feedback on the results based on the data. This mechanism can solve problems such as the shortage of specialists, the hurdles to individualized support, access limitations, and the issues of scientific evidence and effectiveness. Furthermore, because the AI ​​agent provides feedback based on the latest research results, it can enhance the effectiveness of mindfulness, which requires continuous practice. In addition, the AI ​​agent can not only support the user's mental health but also provide comprehensive health management based on biodata. As a result, users can easily receive high-quality mindfulness training from anywhere, promoting the spread of mental healthcare. This means that systems using AI agents can effectively support users' mental health care and contribute to solving social issues.

[0068] The system according to this embodiment comprises a reception unit, a guidance unit, an analysis unit, and a feedback unit. The reception unit receives user data. User data includes, but is not limited to, examples of, a profile, mental health assessment data, biofeedback data, past performance data, the latest research data, and other personal data. For example, the reception unit accepts the user's profile as input. The user's profile includes age, gender, occupation, etc. The reception unit may also accept mental health assessment data as input. Mental health assessment data includes stress levels, assessments of depressive symptoms, etc. The reception unit may also accept biofeedback data as input. Biofeedback data includes heart rate variability, skin electrical activity, etc. The reception unit may also accept past performance data as input. Past performance data includes past training history, feedback history, etc. The reception unit may also accept the latest research data as input. The latest research data includes the latest academic papers, clinical trial results, etc. The reception unit may also accept other personal data as input. Other personal data includes lifestyle data, hobby preference data, etc. The training department provides instruction based on data received by the reception department. For example, the training department can provide mindfulness training as a high-quality virtual trainer. The training department can use audio and video guides. The training department provides personalized instruction tailored to the user's individual needs and condition. For example, the training department adjusts the training menu according to the user's stress level. The training department can also suggest training menus based on the user's training history. The training department can also adjust the training menu based on user feedback. The analytics department analyzes data in real time during training conducted by the training department. For example, the analytics department analyzes biodata in real time through the device during training. The analytics department can analyze biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time.The analysis unit incorporates the data analyzed in real time into training menus and advice. For example, the analysis unit adjusts the training menu according to fluctuations in heart rate. The analysis unit can also provide advice according to fluctuations in blood pressure. The analysis unit can also adjust the training menu according to fluctuations in brain waves. The feedback unit provides feedback based on the data analyzed by the analysis unit. For example, the feedback unit provides feedback based on the data after training. The feedback unit can provide feedback on the user's training results in text or voice. The feedback unit can also provide visual feedback on the user's training results in graphs or charts. The feedback unit can also suggest the next training menu based on the user's training results. The feedback unit can also provide advice based on the user's training results. As a result, the system according to the embodiment enables personalized mindfulness training by guiding training based on the user's data and providing feedback on the results by analyzing the data in real time.

[0069] The reception desk accepts user data. This data includes, but is not limited to, profiles, mental health assessment data, biofeedback data, past training data, recent research data, and other personal data. For example, the reception desk accepts user profiles as input. User profiles include age, gender, occupation, etc. This data is important for understanding the user's basic information and helps personalize training. The reception desk may also accept mental health assessment data as input. This data includes stress levels, depressive symptom assessments, etc. This allows for understanding the user's mental health status and providing appropriate training programs. The reception desk may also accept biofeedback data as input. This data includes heart rate variability, skin electrical activity, etc. This data is important for understanding the user's physiological state in real time and is used to maximize the effectiveness of training. The reception desk may also accept past training data as input. This data includes past training history, feedback history, etc. This allows for understanding the user's past training results and challenges and reflecting them in future training sessions. The reception department can also accept the latest research data as input. This includes the latest academic papers and clinical trial results. This allows for the incorporation of the latest scientific findings into training, providing effective training. The reception department can also accept other personal data as input. This includes lifestyle data and hobby / preference data. This allows for training tailored to the individual needs and preferences of users. The reception department can centrally manage this diverse data and collaborate with other departments as needed. For example, collected data can be stored on a cloud server and made accessible to the training and analysis departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible.This allows the reception department to collect data efficiently and effectively, improving the overall performance of the system.

[0070] The training department provides instruction based on data received by the reception department. For example, the training department can provide mindfulness training as a high-quality virtual trainer. The training department can also provide instruction using audio and video guides. Specifically, they can create a relaxing environment for the user and provide audio and video instructions on deep breathing and meditation techniques. The training department provides personalized instruction tailored to the user's individual needs and condition. For example, they can adjust the training menu according to the user's stress level. Users with high stress levels can be offered a menu that emphasizes relaxation, while users with low stress levels can be offered a menu that enhances concentration. The training department can also suggest training menus based on the user's training history. They analyze past training data to understand what kind of training the user found effective and suggest the next training menu based on that. The training department can also adjust the training menu based on user feedback. They collect feedback from users and improve the content and methods of the training. For example, if a user finds a particular training difficult, they can simplify the training or suggest an alternative method. This allows the training department to provide the optimal training for the user and maximize the effectiveness of the training. Furthermore, the training team can monitor the user's training progress in real time and adjust the training menu as needed. This allows the training team to respond flexibly to the user's condition, thereby enhancing the effectiveness of the training.

[0071] The analysis department analyzes data in real time during training sessions guided by the instruction department. For example, the analysis department analyzes biodata in real time through devices during training. Specifically, it can analyze biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time. It analyzes heart rate fluctuations to assess the user's relaxation level and stress level. It analyzes blood pressure fluctuations to assess the effectiveness of the training. It analyzes EEG fluctuations to assess the user's concentration level and relaxation level. The analysis department incorporates the data analyzed in real time into training menus and advice. For example, it adjusts the training menu according to heart rate fluctuations. If the heart rate is high, it switches to a menu that emphasizes relaxation; if the heart rate is low, it switches to a menu that enhances concentration. It can also provide advice according to blood pressure fluctuations. If blood pressure is high, it provides advice to help the user relax; if blood pressure is low, it provides advice to help the user concentrate. It can also adjust the training menu according to EEG fluctuations. If the EEG indicates a relaxed state, it provides a menu to maintain that state; if the EEG indicates a focused state, it provides a menu to maintain that state. This allows the analytics department to provide optimal training tailored to the user's condition, maximizing the effectiveness of the training. Furthermore, the analytics department can utilize historical data and statistical information to evaluate the long-term effectiveness of training. For example, it can analyze past training data to evaluate the user's training progress and effectiveness. This enables the analytics department to provide users with long-term training benefits and support their continued training.

[0072] The feedback unit provides feedback based on data analyzed by the analysis unit. For example, the feedback unit provides feedback on the results after training based on the data. Specifically, it can provide feedback on the user's training results in text or audio. It provides a detailed explanation of the user's state during training and what effects were achieved. The feedback unit can also provide visual feedback on the user's training results using graphs and charts. It can display fluctuations in heart rate, blood pressure, and brain waves in graphs, allowing the user to visually understand the effects of the training. The feedback unit can also suggest the next training menu based on the user's training results. For example, it can adjust the next training menu based on fluctuations in the user's heart rate and blood pressure. The feedback unit can also provide advice based on the user's training results. For example, it can provide advice on how to relax or how to improve concentration. In this way, the feedback unit can provide users with information to maximize the effectiveness of their training and support their continued training. Furthermore, the feedback unit can collect user feedback and use it to improve the overall system. Based on user feedback, it can improve training menus and teaching methods to provide users with more effective training. This allows the feedback unit to provide the user with optimal training and maximize the effectiveness of the training.

[0073] The feedback unit includes a health management unit that provides comprehensive health management based on biodata. The health management unit provides health management based on biodata such as heart rate, blood pressure, and electroencephalogram (EEG). The health management unit can monitor fluctuations in heart rate and issue alerts if abnormalities are detected. The health management unit can also monitor fluctuations in blood pressure and issue alerts if abnormalities are detected. The health management unit can also monitor fluctuations in electroencephalogram (EEG) and issue alerts if abnormalities are detected. The health management unit can also conduct health checkups based on biodata. The health management unit can also provide nutritional management based on biodata. The health management unit can also provide exercise guidance based on biodata. For example, the health management unit can adjust the intensity of exercise based on heart rate data. The health management unit can also provide dietary advice based on blood pressure data. The health management unit can also suggest relaxation methods based on EEG data. In this way, comprehensive health management based on biodata can provide overall support for the user's health status. Some or all of the above-described processes in the health management unit may be performed using AI, for example, or without using AI. For example, the health management department can input heart rate data into a generating AI and have the AI ​​execute health management methods.

[0074] The feedback unit includes a research results reflection unit that provides feedback based on the latest research findings. The research results reflection unit provides feedback based on, for example, the latest academic papers and clinical trial results. The research results reflection unit can collect the latest research findings and reflect them in the feedback. For example, the research results reflection unit can automatically collect the latest academic papers and reflect them in the feedback. The research results reflection unit can also automatically collect the latest clinical trial results and reflect them in the feedback. The research results reflection unit can also update the training menu based on the latest research findings. The research results reflection unit can also provide advice based on the latest research findings. For example, the research results reflection unit adjusts the training menu based on the latest academic papers. The research results reflection unit can also provide advice based on the latest clinical trial results. As a result, by providing feedback based on the latest research findings, effective training based on scientific evidence is provided. Some or all of the above processing in the research results reflection unit may be performed using, for example, AI, or not using AI. For example, the research results reflection unit can input the latest academic papers into a generating AI and have the generating AI execute the content of the feedback.

[0075] The reception desk accepts user profiles, mental health assessment data, biofeedback data, past performance data, latest research data, and other personal data as input. For example, the reception desk accepts user profiles as input. User profiles include age, gender, occupation, etc. The reception desk can also accept mental health assessment data as input. Mental health assessment data includes stress levels, depressive symptom assessments, etc. The reception desk can also accept biofeedback data as input. Biofeedback data includes heart rate variability, skin electrical activity, etc. The reception desk can also accept past performance data as input. Past performance data includes past training history, feedback history, etc. The reception desk can also accept the latest research data as input. Latest research data includes the latest academic papers, clinical trial results, etc. The reception desk can also accept other personal data as input. Other personal data includes lifestyle data, hobby preference data, etc. This allows for personalized guidance tailored to the individual needs and conditions of each user by accepting a variety of data as input. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user profile data into a generating AI and have the generating AI perform the data reception.

[0076] The training department provides mindfulness training as a high-quality virtual trainer. The training department provides instruction using, for example, audio guides or video guides. The training department provides personalized instruction tailored to the user's individual needs and condition. The training department adjusts the training menu according to, for example, the user's stress level. The training department can also suggest training menus based on the user's training history. The training department can also adjust the training menu based on user feedback. This allows for instruction by a high-quality virtual trainer, overcoming the shortage of experts and the hurdles of providing individualized support. Some or all of the above processes in the training department may be performed using, for example, AI, or not using AI. For example, the training department can input the user's training data into a generating AI and have the generating AI suggest training menus.

[0077] The analysis unit analyzes biodata in real time through the device during training and reflects this in the training menu and advice. For example, the analysis unit analyzes biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time. The analysis unit can adjust the training menu according to fluctuations in heart rate. The analysis unit can also provide advice according to fluctuations in blood pressure. The analysis unit can also adjust the training menu according to fluctuations in electroencephalogram (EEG). This allows for effective training by analyzing biodata in real time and reflecting it in the training menu and advice. 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 heart rate data into a generating AI and have the generating AI adjust the training menu.

[0078] The reception unit estimates the user's emotions and adjusts the timing of data reception based on the estimated emotions. For example, if the user is stressed, the reception unit will accept data during a time when the user can relax. If the user is focused, the reception unit can accept data at that time. If the user is tired, the reception unit can also accept data after they have rested. By adjusting the timing of data reception according to the user's emotions, data can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI adjust the timing of data reception.

[0079] The reception department analyzes the user's past data submission history and selects the optimal data reception method. For example, the reception department can analyze the time periods when the user previously submitted data and suggest the optimal submission time. The reception department can also analyze the devices the user has used in the past and suggest the optimal device. The reception department can also analyze the format of the data the user has submitted in the past and suggest the optimal format. In this way, by analyzing the past data submission history, the reception department can provide the user with the most suitable data reception method. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's past data submission history into a generating AI and have the generating AI select the optimal data reception method.

[0080] The reception unit filters the data upon receipt based on the user's current mental health status and areas of interest. For example, if the user's mental health status is unstable, the reception unit will only accept data that helps reduce stress. The reception unit can also accept only relevant data based on the user's areas of interest. If the user's mental health status is good, the reception unit can accept all data. This allows for the reception of more appropriate data by filtering the data based on the user's mental health status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's mental health status data into a generating AI and have the generating AI perform the data filtering.

[0081] The reception unit estimates the user's emotions and determines the priority of data to accept based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize accepting data related to stress reduction. If the user is relaxed, the reception unit can accept all data equally. If the user is focused, the reception unit may also prioritize accepting data related to improving concentration. In this way, by prioritizing data according to the user's emotions, more important data 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 reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0082] The reception unit, upon receiving data, prioritizes the acceptance of highly relevant data, taking into account the user's geographical location. For example, if the user is in an urban area, the reception unit prioritizes the acceptance of data related to urban areas. If the user is in a suburban area, the reception unit can prioritize the acceptance of data related to suburban areas. If the user is traveling, the reception unit can also prioritize the acceptance of data related to their travel destination. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority acceptance of data.

[0083] The reception unit analyzes the user's social media activity when receiving data and accepts relevant data. For example, if the user is experiencing stress on social media, the reception unit may accept data related to stress reduction. If the user is relaxing on social media, the reception unit may accept data related to relaxation. If the user is concentrating on social media, the reception unit may also accept data related to improved concentration. In this way, relevant data can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's social media activity data into a generating AI and have the generating AI perform the acceptance of relevant data.

[0084] The training unit estimates the user's emotions and adjusts the training presentation based on the estimated emotions. For example, if the user is stressed, the training unit uses a relaxing presentation. If the user is relaxed, the training unit can use a more detailed presentation. If the user is focused, the training unit can also use a presentation that enhances concentration. By adjusting the training presentation according to the user's emotions, more effective training is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI or not. For example, the training unit can input user emotion data into a generative AI and have the generative AI adjust the training presentation.

[0085] The training department adjusts the level of detail in training based on the user's mental health status during training sessions. For example, if the user's mental health status is unstable, the training department may provide simple training. If the user's mental health status is good, the training department may provide detailed training. If the user's mental health status is improving, the training department may also provide progressively more detailed training. By adjusting the level of detail in training according to the user's mental health status, more appropriate training is provided. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department may input the user's mental health status data into a generating AI and have the generating AI perform the adjustment of the level of detail in training.

[0086] The training unit applies different training algorithms to users based on their profiles during training. For example, the training unit may apply an appropriate training algorithm based on the user's age, gender, or occupation. By applying different training algorithms based on the user's profile, personalized training is provided. Some or all of the above processes in the training unit may be performed using AI, for example, or not. For example, the training unit may input user profile data into a generating AI and have the generating AI perform the application of the training algorithm.

[0087] The instruction unit estimates the user's emotions and adjusts the training length based on the estimated emotions. For example, if the user is stressed, the instruction unit may provide a shorter training session. If the user is relaxed, the instruction unit may provide a longer training session. If the user is focused, the instruction unit may also provide training to improve their concentration. By adjusting the training length according to the user's emotions, more appropriate training is provided. 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 instruction unit may be performed using AI or not using AI. For example, the instruction unit may input user emotion data into a generative AI and have the generative AI adjust the training length.

[0088] The training department determines the priority of instruction based on the user's past training history during training sessions. For example, the training department can analyze the effectiveness of the user's past training and propose the optimal training. The training department can also analyze the frequency of the user's past training and propose the optimal training. The training department can also analyze the content of the user's past training and propose the optimal training. By prioritizing instruction based on the user's past training history, more effective training is provided. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department can input the user's past training history data into a generating AI and have the generating AI perform the determination of instruction priorities.

[0089] The training department adjusts the order of instruction based on the user's relevant health data during training sessions. For example, the training department can analyze the user's biodata and suggest an optimal training order. The training department can also analyze the user's mental health data and suggest an optimal training order. The training department can also analyze the user's feedback data and suggest an optimal training order. This allows for more effective training by adjusting the order of instruction based on the user's relevant health data. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input the user's biodata into a generating AI and have the generating AI perform the adjustment of the instruction order.

[0090] The analysis unit estimates the user's emotions and adjusts the data analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit prioritizes analyzing data related to stress reduction. If the user is relaxed, the analysis unit can analyze all data equally. If the user is focused, the analysis unit can also prioritize analyzing data related to improved concentration. This allows for more appropriate data analysis by adjusting the data analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis criteria.

[0091] The analysis unit improves the accuracy of data analysis by considering the interrelationships of the user's biodata. For example, the analysis unit can analyze the correlation between the user's heart rate and stress level. The analysis unit can analyze the correlation between the user's sleep data and mental health status. The analysis unit can also analyze the correlation between the user's exercise data and mental health status. This improves the accuracy of the analysis by considering the interrelationships of the user's biodata. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's biodata into a generating AI and have the generating AI perform the task of improving the accuracy of the data analysis.

[0092] The analysis unit considers user attribute information when performing data analysis. For example, the analysis unit adjusts the criteria for data analysis according to the user's age. The analysis unit can also adjust the criteria for data analysis according to the user's gender. The analysis unit can also adjust the criteria for data analysis according to the user's occupation. This allows for more appropriate data analysis by considering user attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform the adjustment of the data analysis criteria.

[0093] The analysis unit estimates the user's emotions and adjusts the display order of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit prioritizes displaying analysis results related to stress reduction. If the user is relaxed, the analysis unit can display all analysis results equally. If the user is focused, the analysis unit can also prioritize displaying analysis results related to improving concentration. This provides more appropriate feedback by adjusting the display order of analysis results according to 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 not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the analysis results.

[0094] The analysis unit considers the geographical distribution of users when performing data analysis. For example, if a user is in an urban area, the analysis unit will prioritize analyzing data related to urban areas. If a user is in a suburban area, the analysis unit can prioritize analyzing data related to suburban areas. If a user is traveling, the analysis unit can also prioritize analyzing data related to their travel destination. This allows for more appropriate data analysis by considering the geographical distribution of users. 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 geographical distribution data into a generating AI and have the generating AI perform the data analysis.

[0095] The analysis unit improves the accuracy of its analysis by referring to relevant literature for the user during data analysis. For example, the analysis unit may refer to the latest research literature on the user's mental health status. The analysis unit may also refer to the latest research literature on the user's biodata. The analysis unit may also refer to the latest research literature on the user's training data. This improves the accuracy of the analysis by referring to relevant literature for the user. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the data analysis.

[0096] The feedback unit estimates the user's emotions and adjusts how the feedback is displayed based on the estimated emotions. For example, if the user is stressed, the feedback unit provides relaxing feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is focused, the feedback unit can also provide feedback to enhance their concentration. By adjusting how the feedback is displayed according to the user's emotions, more appropriate feedback is provided. 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 feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust how the feedback is displayed.

[0097] The feedback unit optimizes the current feedback by referring to past feedback data when providing feedback. For example, the feedback unit analyzes the user's past feedback data to provide optimal feedback. The feedback unit can adjust the content of the feedback based on the user's past feedback data. The feedback unit can also optimize the timing of feedback by referring to the user's past feedback data. This allows the current feedback to be optimized by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback data into a generating AI and have the generating AI perform the optimization of the current feedback.

[0098] The feedback unit applies different feedback methods depending on the user's mental health state when providing feedback. For example, if the user's mental health state is unstable, the feedback unit provides relaxing feedback. If the user's mental health state is good, the feedback unit can provide detailed feedback. If the user's mental health state is improving, the feedback unit can also provide progressively more detailed feedback. This allows for more appropriate feedback to be provided by applying different feedback methods depending on the user's mental health state. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's mental health state data into a generating AI and have the generating AI execute the application of the feedback method.

[0099] The feedback unit estimates the user's emotions and adjusts the importance of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit prioritizes providing stress-reducing feedback. If the user is relaxed, the feedback unit can provide all feedback equally. If the user is focused, the feedback unit can also prioritize providing feedback on improving concentration. This allows for more appropriate feedback to be provided by adjusting the importance of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the importance of feedback.

[0100] The feedback unit analyzes changes in feedback based on the user's data submission timing. For example, the feedback unit provides optimal feedback based on the time of day the user submitted the data. The feedback unit can also provide optimal feedback based on the day of the week the user submitted the data. The feedback unit can also provide optimal feedback based on the season the user submitted the data. This allows for more appropriate feedback to be provided by analyzing changes in feedback based on the user's data submission timing. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user data submission timing data into a generating AI and have the generating AI perform the analysis of changes in feedback.

[0101] The feedback unit analyzes the feedback by referring to the user's relevant market data. For example, the feedback unit can refer to market data related to the user's industry to provide optimal feedback. The feedback unit can also refer to market data related to the user's occupation to provide optimal feedback. The feedback unit can also refer to market data related to the user's region to provide optimal feedback. This allows for more appropriate feedback to be provided by referring to the user's relevant market data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's relevant market data into a generating AI and have the generating AI perform the feedback analysis.

[0102] The health management department estimates the user's emotions and adjusts health management methods based on the estimated emotions. For example, if the user is stressed, the health management department provides health management methods related to stress reduction. If the user is relaxed, the health management department can provide all health management methods equally. If the user is focused, the health management department can also provide health management methods related to improving concentration. In this way, more appropriate health management is provided by adjusting health management methods according to 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 health management department may be performed using AI, for example, or not using AI. For example, the health management department can input user emotion data into a generative AI and have the generative AI perform the adjustment of health management methods.

[0103] The Health Management Department analyzes the user's past health data to select the optimal health management method during health management. For example, the Health Management Department analyzes the user's past health data and provides the optimal health management method. The Health Management Department can adjust the content of health management based on the user's past health data. The Health Management Department can also optimize the timing of health management by referring to the user's past health data. In this way, by analyzing the user's past health data, it can provide the optimal health management method. Some or all of the above processes in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the user's past health data into a generating AI and have the generating AI select a health management method.

[0104] The health management department estimates the user's emotions and determines the priority of health management based on the estimated emotions. For example, if the user is stressed, the health management department will prioritize providing health management related to stress reduction. If the user is relaxed, the health management department can provide all health management equally. If the user is focused, the health management department can also prioritize providing health management related to improving concentration. In this way, more appropriate health management is provided by determining the priority of health management according to 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 health management department may be performed using AI, for example, or not using AI. For example, the health management department can input user emotion data into a generative AI and have the generative AI perform the determination of health management priorities.

[0105] The Health Management Department selects the optimal health management method when providing health management, taking into account the user's geographical location information. For example, if the user is in an urban area, the Health Management Department can provide health management methods relevant to urban areas. If the user is in a suburban area, the Health Management Department can provide health management methods relevant to suburban areas. If the user is traveling, the Health Management Department can also provide health management methods relevant to the travel destination. In this way, the optimal health management method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the user's geographical location information into a generating AI and have the generating AI perform the selection of a health management method.

[0106] The research results reflection unit estimates the user's emotions and adjusts how research results are reflected based on the estimated user emotions. For example, if the user is stressed, the research results reflection unit will prioritize reflecting research results related to stress reduction. If the user is relaxed, the research results reflection unit can reflect all research results equally. If the user is focused, the research results reflection unit can also prioritize reflecting research results related to improving concentration. This allows for more appropriate feedback by adjusting how research results are reflected according to the user's emotions. 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 research results reflection unit may be performed using AI, for example, or without AI. For example, the research results reflection unit can input user emotion data into a generative AI and have the generative AI adjust how research results are reflected.

[0107] The research results reflection unit optimizes the reflection algorithm by referring to the latest research data when reflecting research results. For example, the research results reflection unit provides an optimal reflection algorithm based on the latest research data. The research results reflection unit can adjust the content of the reflection algorithm based on the latest research data. The research results reflection unit can also optimize the timing of the reflection algorithm by referring to the latest research data. As a result, the accuracy of the reflection algorithm is improved by referring to the latest research data. Some or all of the above processing in the research results reflection unit may be performed using AI, for example, or without using AI. For example, the research results reflection unit can input the latest research data into a generating AI and have the generating AI perform the optimization of the reflection algorithm.

[0108] The research results reflection unit estimates the user's emotions and adjusts the frequency of reflecting research results based on the estimated user emotions. For example, if the user is stressed, the research results reflection unit will frequently reflect research results related to stress reduction. If the user is relaxed, the research results reflection unit can reflect all research results equally. If the user is focused, the research results reflection unit can also frequently reflect research results related to improving concentration. This allows for more appropriate feedback by adjusting the frequency of reflecting research results according to the user's emotions. 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 research results reflection unit may be performed using AI, for example, or without AI. For example, the research results reflection unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of reflecting research results.

[0109] The research results reflection unit weights the research results based on the user's data submission timing when reflecting the research results. For example, the research results reflection unit reflects the optimal research results based on the time of day the user submitted the data. The research results reflection unit can also reflect the optimal research results based on the day of the week the user submitted the data. The research results reflection unit can also reflect the optimal research results based on the season the user submitted the data. This allows for more appropriate feedback by weighting the research results based on the user's data submission timing. Some or all of the above processing in the research results reflection unit may be performed using AI, for example, or without AI. For example, the research results reflection unit can input the user's data submission timing data into a generating AI and have the generating AI perform the weighting of the research results.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The training system can estimate the user's emotions and adjust the training pace based on those estimates. For example, if the user is stressed, the training pace can be slowed to promote relaxation. If the user is relaxed, the training can proceed at the normal pace. If the user is focused, the training pace can be increased to maintain their concentration. By adjusting the training pace according to the user's emotions, more effective training can be provided.

[0112] The health management department can estimate the user's emotions and change its health management approach based on those estimates. For example, if the user is stressed, it can recommend relaxation techniques to reduce stress. If the user is relaxed, it can recommend exercise and nutritional management to maintain their health. If the user is focused, it can recommend mental training to improve their concentration. By changing the health management approach according to the user's emotions, more appropriate health management can be provided.

[0113] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, it can emphasize and provide positive feedback. If the user is relaxed, it can provide detailed feedback. If the user is focused, it can provide feedback that includes specific areas for improvement. In this way, by adjusting the content of the feedback according to the user's emotions, more effective feedback is provided.

[0114] The analytics department can estimate the user's emotions and prioritize data analysis based on those estimates. For example, if the user is stressed, data related to stress reduction will be prioritized. If the user is relaxed, all data can be analyzed equally. If the user is focused, data related to improving concentration can also be prioritized. This allows for more appropriate data analysis by prioritizing data analysis according to the user's emotions.

[0115] The research results reflection unit can estimate the user's emotions and adjust how research results are reflected based on those estimated emotions. For example, if the user is stressed, research results related to stress reduction will be prioritized. If the user is relaxed, all research results can be reflected equally. If the user is focused, research results related to improving concentration can also be prioritized. This allows for more appropriate feedback to be provided by adjusting how research results are reflected according to the user's emotions.

[0116] The training system can analyze the user's past training data and adjust the training pace accordingly. For example, if the user previously trained quickly, the system can maintain a similar pace. If the user previously trained slowly, the system can maintain a similar pace. If the user previously interrupted training, the system can adjust the pace and resume training. This allows for more effective training by adjusting the pace based on the user's past training data.

[0117] The health management department can modify its health management approach based on the user's geographical location. For example, if the user is in an urban area, it can recommend exercise and nutrition management suitable for urban areas. If the user is in a suburban area, it can recommend exercise and nutrition management suitable for suburban areas. If the user is traveling, it can also recommend health management methods suitable for their travel destination. By changing the health management approach based on the user's geographical location, more appropriate health management can be provided.

[0118] The feedback system can analyze a user's past feedback data and adjust the content of the feedback accordingly. For example, if a user has previously preferred positive feedback, it can provide similar feedback. If a user has previously preferred detailed feedback, it can provide similar feedback. If a user has previously requested specific areas for improvement, it can provide similar feedback. By adjusting the content of feedback based on the user's past feedback data, more effective feedback can be provided.

[0119] The analysis unit can improve the accuracy of data analysis by considering the interrelationships of user biodata. For example, it can analyze the correlation between a user's heart rate and stress level. It can also analyze the correlation between a user's sleep data and mental health status. It can also analyze the correlation between a user's exercise data and mental health status. In this way, the accuracy of data analysis is improved by considering the interrelationships of user biodata.

[0120] The research results reflection unit can optimize the reflection algorithm by referring to the latest research data. For example, it can provide the optimal reflection algorithm based on the latest research data. It can also adjust the content of the reflection algorithm based on the latest research data. It can also optimize the timing of the reflection algorithm by referring to the latest research data. As a result, the accuracy of the reflection algorithm is improved by referring to the latest research data.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives the user's data. User data includes profile, mental health assessment data, biofeedback data, past performance data, latest research data, and other personal data. For example, the user's profile includes age, gender, occupation, etc., and mental health assessment data includes stress levels and assessments of depressive symptoms. Biofeedback data includes heart rate variability and skin electrical activity, and past performance data includes past training history and feedback history. Latest research data includes the latest academic papers and clinical trial results, and other personal data includes lifestyle data and hobby / preference data. Step 2: The training department provides instruction based on the data received by the reception department. The training department can provide mindfulness training as a high-quality virtual trainer, and can use audio and video guides to guide the training. The training department provides personalized instruction tailored to the user's individual needs and condition, for example, adjusting the training menu according to the user's stress level. The training department can suggest and adjust the training menu based on the user's training history and feedback. Step 3: The analysis unit analyzes data in real time during training instructed by the training unit. The analysis unit can analyze biodata in real time through the device during training, and can analyze biodata such as heart rate, blood pressure, and electroencephalogram (EEG) in real time. The analysis unit can reflect the data analyzed in real time in training menus and advice, for example, by adjusting the training menu according to heart rate fluctuations, providing advice according to blood pressure fluctuations, and adjusting the training menu according to EEG fluctuations. Step 4: The feedback unit provides feedback based on the data analyzed by the analysis unit. The feedback unit provides feedback based on the data after training and can provide feedback on the user's training results in text or audio. The feedback unit can also provide visual feedback on the user's training results using graphs and charts, and can suggest the next training menu and provide advice.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, instruction unit, analysis unit, feedback unit, and health management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's profile and mental health assessment data. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides personalized training guidance to the user. The analysis unit is implemented by, for example, the control unit 46A of the smart device 14 and analyzes biodata in real time during training. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides feedback on the training results. The health management unit is implemented by, for example, the control unit 46A of the smart device 14 and performs comprehensive health management based on biodata. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, instruction unit, analysis unit, feedback unit, and health management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's profile and mental health assessment data. The instruction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides personalized training guidance to the user. The analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214 and analyzes biodata in real time during training. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides feedback on the training results. The health management unit is implemented, for example, by the control unit 46A of the smart glasses 214 and performs comprehensive health management based on biodata. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, instruction unit, analysis unit, feedback unit, and health management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's profile and mental health assessment data. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides personalized training guidance to the user. The analysis unit is implemented by, for example, the control unit 46A of the headset terminal 314 and analyzes biodata in real time during training. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides feedback on the training results. The health management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs comprehensive health management based on biodata. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, instruction unit, analysis unit, feedback unit, and health management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's profile and mental health assessment data. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides personalized training guidance to the user. The analysis unit is implemented by, for example, the control unit 46A of the robot 414 and analyzes biodata in real time during training. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides feedback on the training results. The health management unit is implemented by, for example, the control unit 46A of the robot 414 and performs comprehensive health management based on biodata. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception desk that accepts user data, Based on the data received by the reception department, the training department provides instruction, The analysis unit analyzes data in real time during training conducted by the aforementioned leadership unit, The system includes a feedback unit that provides feedback based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned feedback unit is The company has a health management department that provides comprehensive health management based on biodata. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is It includes a research results reflection unit that provides feedback based on the latest research findings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system accepts user profiles, mental health assessment data, biofeedback data, past implementation data, latest research data, and other personal data as input. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned leadership, I provide mindfulness training as a high-quality virtual trainer. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Biodata is analyzed in real time through the device during training, and this is reflected in training menus and advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of data reception based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past data submission history to select the optimal data acceptance method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving data, filtering is performed based on the user's current mental health status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the data to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving data, the system prioritizes accepting data that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving data, the system analyzes the user's social media activity and accepts relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned leadership, It estimates the user's emotions and adjusts the training's representation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned leadership, During training sessions, adjust the level of detail based on the user's mental health status. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned leadership, During training sessions, different instruction algorithms are applied depending on the user's profile. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned leadership, It estimates the user's emotions and adjusts the training length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned leadership, During training sessions, the system prioritizes instruction based on the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned leadership, During training sessions, the order of instruction is adjusted based on the user's relevant health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate user sentiment and adjust the criteria for data analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When analyzing data, consider the interrelationships between users' biodata to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When performing data analysis, consider user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When performing data analysis, consider the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When analyzing data, referencing relevant user literature improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, we optimize the current feedback by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, different feedback methods are applied depending on the user's mental health status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts the importance of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we analyze how the feedback changes based on when the user submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we analyze the feedback by referring to relevant market data from the user. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned health management department, It estimates the user's emotions and adjusts health management methods based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned health management department, During health management, the system analyzes the user's past health data to select the optimal health management method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned health management department, It estimates the user's emotions and determines health management priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned health management department, When managing health, the system selects the optimal health management method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned research results reflection unit is, We estimate user emotions and adjust how research results are reflected based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned research results reflection unit is, When reflecting research results, the reflection algorithm is optimized by referring to the latest research data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned research results reflection unit is, We estimate the user's emotions and adjust the frequency of reflecting research findings based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned research results reflection unit is, When reflecting research results, the results will be weighted based on when users submitted their data. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0195] 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 reception desk that accepts user data, Based on the data received by the reception department, the training department provides instruction, The analysis unit analyzes data in real time during training conducted by the aforementioned leadership unit, The system includes a feedback unit that provides feedback based on the data analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned feedback unit is The company has a health management department that provides comprehensive health management based on biodata. The system according to feature 1.

3. The aforementioned feedback unit is It includes a research results reflection unit that provides feedback based on the latest research findings. The system according to feature 1.

4. The aforementioned reception unit is The system accepts user profiles, mental health assessment data, biofeedback data, past implementation data, latest research data, and other personal data as input. The system according to feature 1.

5. The aforementioned leadership, I provide mindfulness training as a high-quality virtual trainer. The system according to feature 1.

6. The aforementioned analysis unit is Biodata is analyzed in real time through the device during training, and this is reflected in training menus and advice. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past data submission history to select the optimal data acceptance method. The system according to feature 1.

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