system

Abstract

The system according to this embodiment aims to analyze the user's brainwaves and propose, allow the user to experience, and execute optimal actions and thought patterns. [Solution] The system according to the embodiment comprises a monitoring unit, an analysis unit, a proposal unit, a simulation unit, and a coaching unit. The monitoring unit monitors the user's brainwaves in real time. The analysis unit analyzes the data acquired by the monitoring unit. The proposal unit proposes optimal actions and thought patterns based on the analysis results obtained by the analysis unit. The simulation unit experiences the actions and thought patterns proposed by the proposal unit in a virtual environment. The coaching unit creates a concrete action plan based on the actions and thought patterns experienced by the simulation unit and tracks its progress.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, improvement of behavior and thinking patterns based on the user's brain waves has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's brain waves and propose, experience, and execute optimal behavior and thinking patterns.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an analysis unit, a proposal unit, a simulation unit, and a coaching unit. The monitoring unit monitors the user's brainwaves in real time. The analysis unit analyzes the data acquired by the monitoring unit. The proposal unit proposes optimal actions and thought patterns based on the analysis results obtained by the analysis unit. The simulation unit experiences the actions and thought patterns proposed by the proposal unit in a virtual environment. The coaching unit creates a concrete action plan based on the actions and thought patterns experienced by the simulation unit and tracks its progress. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's brainwaves and propose, allow users to experience, and execute optimal actions and thought patterns. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The behavior and thinking improvement agent system according to an embodiment of the present invention is a system that analyzes a user's thinking patterns and behaviors and provides personalized advice and training based on individual needs. This system combines neurofeedback, virtual reality (VR), and AI coaching to help users make healthy behaviors a habit and cultivate positive thinking. First, the behavior and thinking improvement agent system monitors the user's brainwaves in real time to understand their stress and concentration levels. AI analyzes this data and proposes optimal behaviors and thinking patterns. Next, the behavior and thinking improvement agent system uses virtual reality (VR) to allow the user to experience healthy behaviors in a virtual environment. Furthermore, the behavior and thinking improvement agent system creates a concrete action plan based on the user's goals and tracks progress through AI coaching. The behavior and thinking improvement agent system also provides a community function, offering a platform where users can share goals and encourage each other. For example, the behavior and thinking improvement agent system monitors the user's brainwaves in real time. For example, the behavior and thinking improvement agent system analyzes the user's brainwave data to understand their stress and concentration levels. The Behavior and Thought Improvement Agent System uses AI to analyze the user's brainwave data and propose optimal behaviors and thought patterns. For example, the Behavior and Thought Improvement Agent System uses virtual reality (VR) to allow users to experience healthy behaviors in a virtual environment. Through AI coaching, the Behavior and Thought Improvement Agent System creates concrete action plans based on the user's goals and tracks progress. The Behavior and Thought Improvement Agent System provides a community function, offering a platform where users can share goals and encourage each other. This allows the Behavior and Thought Improvement Agent System to analyze the user's thought patterns and behaviors and provide personalized advice and training based on individual needs.

[0029] The behavior and thinking improvement agent system according to the embodiment comprises a monitoring unit, an analysis unit, a proposal unit, a simulation unit, and a coaching unit. The monitoring unit monitors the user's brainwaves in real time. For example, the monitoring unit monitors the user's brainwaves in real time to understand their stress and concentration levels. The monitoring unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thinking patterns. The analysis unit analyzes the data acquired by the monitoring unit. For example, the analysis unit analyzes the user's brainwave data to understand their stress and concentration levels. The analysis unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thinking patterns. The proposal unit proposes optimal behaviors and thinking patterns based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes the user's brainwave data to understand their stress and concentration levels. The proposal unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thinking patterns. The simulation unit experiences the behaviors and thinking patterns proposed by the proposal unit in a virtual environment. The simulation unit, for example, uses virtual reality (VR) to allow the user to experience healthy behaviors in a virtual environment. The simulation unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thought patterns. The coaching unit creates a concrete action plan based on the behaviors and thought patterns experienced by the simulation unit and tracks progress. The coaching unit, for example, uses AI coaching to create a concrete action plan based on the user's goals and tracks progress. The coaching unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thought patterns. As a result, the behavior and thought improvement agent system according to this embodiment can provide personalized advice and training based on individual needs by monitoring, analyzing, proposing, simulating, and coaching the user's brainwaves in real time.

[0030] The monitoring unit monitors the user's brainwaves in real time. Specifically, it acquires brainwave data using an EEG measurement device worn by the user. This device includes sensors worn on the head and can detect even subtle fluctuations in brainwaves with high precision. The brainwave data is classified into different frequency bands such as alpha waves, beta waves, theta waves, and delta waves, and this data is collected in real time. The monitoring unit analyzes this brainwave data to understand the user's stress level and concentration state. For example, an increase in alpha waves indicates a relaxed state, and an increase in beta waves indicates a focused state. Based on this data, the user's mental state can be evaluated in real time. Furthermore, the monitoring unit uses AI to analyze the brainwave data and gain insights into the user's behavior and thought patterns. The AI ​​detects abnormal patterns by comparing them with past data and determines whether the user is experiencing stress or has decreased concentration. This allows the monitoring unit to monitor the user's mental state in real time and provide appropriate feedback as needed.

[0031] The analysis unit analyzes the data acquired by the monitoring unit. Specifically, it receives electroencephalogram (EEG) data transmitted from the monitoring unit and performs a detailed analysis using an AI algorithm. The AI ​​uses a machine learning model to identify patterns in the EEG data and evaluate the user's mental state. For example, the AI ​​analyzes the temporal fluctuations of the EEG data to identify situations in which the user is more likely to feel stressed or to have increased concentration. Furthermore, the analysis unit can integrate the user's EEG data with other physiological data (heart rate, skin electrical activity, etc.) to perform a more comprehensive analysis. This allows for a multifaceted evaluation of the user's mental state and provides more accurate insights. Based on these analysis results, the analysis unit provides data to suggest optimal behaviors and thought patterns to the user.

[0032] The suggestion department proposes optimal actions and thought patterns based on the analysis results obtained by the analysis department. Specifically, it receives data provided by the analysis department and uses AI to generate specific advice for the user. The AI ​​considers the user's past data and current mental state to provide individually customized suggestions. For example, if the user is feeling stressed, the AI ​​will suggest breathing exercises or meditation to help them relax. If their concentration is low, the AI ​​will suggest taking short breaks to improve their focus or changing the priority of specific tasks. The suggestion department notifies the user of these suggestions and encourages them to take action. Notifications are made through smartphone apps and wearable devices so that users can act immediately. Furthermore, the suggestion department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. In this way, the suggestion department can provide users with optimal actions and thought patterns and support their mental health.

[0033] The Simulation Department allows users to experience the behaviors and thought patterns proposed by the Proposal Department in a virtual environment. Specifically, it uses virtual reality (VR) technology to provide users with the opportunity to practice the proposed behaviors in a virtual environment. For example, if a user is suggested to meditate to reduce stress, they can practice meditation in a VR environment with guidance. The VR environment provides a space where users can concentrate without interference from the real world, allowing them to effectively experience the proposed behaviors. The Simulation Department uses AI to analyze the user's brainwave data in real time and evaluate the user's reactions in the virtual environment. This allows the department to understand how effectively the user is practicing the proposed behaviors and provide feedback as needed. Furthermore, the Simulation Department supports users in practicing the behaviors they experienced in the virtual environment in the real world. In this way, the Simulation Department helps users effectively learn the proposed behaviors and incorporate them into their daily lives.

[0034] The coaching department creates concrete action plans based on the behavioral and thought patterns experienced by the simulation department and tracks progress. Specifically, it creates concrete action plans based on the user's goals through AI coaching. The AI ​​considers the user's past data and current state to set realistic and achievable goals. For example, if the user's goal is to reduce stress, the AI ​​will suggest relaxation methods and stress management techniques that can be practiced daily. The coaching department provides these action plans to the user and supports their implementation. Furthermore, the coaching department tracks the user's progress and provides regular feedback. It evaluates whether the user is on track towards their goals or facing difficulties and adjusts the action plan as needed. In this way, the coaching department provides concrete support for the user to achieve their goals and promotes continuous improvement. In addition, the coaching department collects user feedback and continuously improves the accuracy and effectiveness of the AI ​​algorithm. This allows the coaching department to provide optimal support to the user and effectively assist in improving their behavior and thinking.

[0035] The monitoring unit can detect anomalies by referring to the user's past brainwave data during monitoring. For example, the monitoring unit can compare past brainwave data with current data to detect abnormal patterns. The monitoring unit can also compare past stress levels with current stress levels to detect abnormal fluctuations. The monitoring unit can also compare past concentration data with current data to detect abnormal declines. This allows for early detection of anomalies by referring to past brainwave data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input past brainwave data into a generating AI and have the generating AI perform anomaly detection.

[0036] The monitoring unit can analyze brainwave fluctuations by combining the user's lifestyle data during monitoring. For example, the monitoring unit can combine the user's sleep data with brainwave data to analyze brainwave fluctuations caused by sleep deprivation. The monitoring unit can also combine the user's diet data with brainwave data to analyze the effects of diet. The monitoring unit can also combine the user's exercise data with brainwave data to analyze the effects of exercise. By combining lifestyle data, brainwave fluctuations can be analyzed more accurately. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input lifestyle data into a generating AI and have the generating AI perform the analysis of brainwave fluctuations.

[0037] The monitoring unit can analyze electroencephalogram (EEG) fluctuations in conjunction with the user's physical activity data during monitoring. For example, the monitoring unit can combine the user's exercise data and EEG data to analyze the effects of exercise. The monitoring unit can also combine the user's heart rate data and EEG data to analyze heart rate fluctuations. The monitoring unit can also combine the user's respiratory data and EEG data to analyze the effects of respiration. This allows for a more accurate analysis of EEG fluctuations by using physical activity data in conjunction with EEG data. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input physical activity data into a generating AI and have the generating AI perform the analysis of EEG fluctuations.

[0038] The monitoring unit can analyze EEG fluctuations by combining the user's sleep data during monitoring. For example, the monitoring unit can combine the user's sleep data and EEG data to analyze EEG fluctuations caused by sleep deprivation. The monitoring unit can also combine the user's sleep quality and EEG data to analyze the effects of sleep. The monitoring unit can also combine the user's sleep pattern and EEG data to analyze the effects of sleep. By combining sleep data, EEG fluctuations can be analyzed more accurately. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input sleep data into a generating AI and have the generating AI perform the analysis of EEG fluctuations.

[0039] The analysis unit can correct the analysis results by referring to the user's past stress levels during the analysis. For example, the analysis unit can compare past stress levels with the current stress level and correct the analysis results. The analysis unit can also correct the current analysis results by considering fluctuations in past stress levels. The analysis unit can also correct the current analysis results based on trends in past stress levels. This allows for more accurate correction of the analysis results by referring to past stress levels. 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 past stress level data into a generating AI and have the generating AI perform the correction of the analysis results.

[0040] The analysis unit can analyze stress levels and concentration levels by combining the user's dietary data during analysis. For example, the analysis unit can combine the user's dietary data with stress levels to analyze the effects of diet. The analysis unit can also combine the user's dietary data with fluctuations in concentration to analyze the effects of diet. The analysis unit can also combine the user's dietary data with electroencephalogram (EEG) data to analyze the effects of diet. This allows for a more accurate analysis of stress levels and concentration levels by combining dietary data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input dietary data into a generating AI and have the generating AI perform the analysis of stress levels and concentration levels.

[0041] The analysis unit can analyze stress levels and concentration levels by combining the user's exercise data with other data during the analysis process. For example, the analysis unit can combine the user's exercise data with stress levels to analyze the effects of exercise. The analysis unit can also combine the user's exercise data with fluctuations in concentration levels to analyze the effects of exercise. The analysis unit can also combine the user's exercise data with electroencephalogram (EEG) data to analyze the effects of exercise. This allows for a more accurate analysis of stress levels and concentration levels by combining exercise data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input exercise data into a generating AI and have the generating AI perform the analysis of stress levels and concentration levels.

[0042] The analysis unit can analyze stress levels and concentration states by combining the user's social activity data during analysis. For example, the analysis unit can combine the user's social activity data with stress levels to analyze the impact of social activities. The analysis unit can also combine the user's social activity data with fluctuations in concentration to analyze the impact of social activities. The analysis unit can also combine the user's social activity data with electroencephalogram (EEG) data to analyze the impact of social activities. This allows for a more accurate analysis of stress levels and concentration states by combining social activity data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input social activity data into a generating AI and have the generating AI perform the analysis of stress levels and concentration states.

[0043] The suggestion unit can propose the optimal behavioral pattern by referring to the user's past behavioral history when making a suggestion. For example, the suggestion unit can propose the optimal behavioral pattern based on the user's past behavioral history. The suggestion unit can also propose an effective behavioral pattern from the user's past behavioral history. The suggestion unit can also analyze the user's past behavioral history and propose the most effective behavioral pattern. In this way, the optimal behavioral pattern can be proposed by referring to past behavioral history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input past behavioral history data into a generating AI and have the generating AI execute the proposal of the optimal behavioral pattern.

[0044] The suggestion unit can propose behavioral patterns by combining the user's health data during the proposal process. For example, the suggestion unit can propose the optimal behavioral pattern by combining the user's health data and behavioral history. The suggestion unit can also propose healthy behavioral patterns based on the user's health data. The suggestion unit can also propose the optimal behavioral pattern by combining the user's health data and electroencephalogram (EEG) data. This allows for the proposal of more appropriate behavioral patterns by combining health data. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input health data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0045] The suggestion unit can propose behavioral patterns while considering the user's hobbies and interests. For example, the suggestion unit can propose the optimal behavioral pattern based on the user's hobbies and interests. The suggestion unit can also propose effective behavioral patterns while considering the user's hobbies and interests. The suggestion unit can analyze the user's hobbies and interests and propose the most effective behavioral pattern. This allows for the proposal of more effective behavioral patterns by considering hobbies and interests. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input hobby and interest data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0046] The suggestion unit can propose behavioral patterns while considering the user's hobbies and interests. For example, the suggestion unit can propose the optimal behavioral pattern based on the user's hobbies and interests. The suggestion unit can also propose effective behavioral patterns while considering the user's hobbies and interests. The suggestion unit can analyze the user's hobbies and interests and propose the most effective behavioral pattern. This allows for the proposal of more effective behavioral patterns by considering hobbies and interests. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input hobby and interest data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0047] The suggestion unit can propose behavioral patterns by combining the user's occupational data during the proposal process. For example, the suggestion unit can propose the optimal behavioral pattern by combining the user's occupational data and behavioral history. The suggestion unit can also propose effective behavioral patterns based on the user's occupational data. The suggestion unit can also propose the optimal behavioral pattern by combining the user's occupational data and electroencephalogram (EEG) data. This allows for the proposal of more appropriate behavioral patterns by combining occupational data. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input occupational data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0048] The simulation unit can customize the virtual environment during simulation by referring to the user's past experience data. For example, the simulation unit can customize the virtual environment based on environments in which the user was able to relax in the past. The simulation unit can also customize the virtual environment based on environments in which the user was able to concentrate in the past. The simulation unit can also customize the virtual environment to avoid environments in which the user was stressed in the past. In this way, a more effective virtual environment can be provided by referring to past experience data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input past experience data into a generating AI and have the generating AI perform the customization of the virtual environment.

[0049] The simulation unit can set up a virtual environment by combining the user's health data during the simulation. For example, the simulation unit can set up a relaxing virtual environment based on the user's health data. The simulation unit can also set up a virtual environment that enhances concentration based on the user's health data. The simulation unit can also set up a virtual environment that reduces stress based on the user's health data. In this way, a more effective virtual environment can be provided by combining health data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input health data into a generating AI and have the generating AI perform the virtual environment setting.

[0050] The simulation unit can set up a virtual environment during simulation, taking into account the user's hobbies and interests. For example, the simulation unit can set up a relaxing virtual environment based on the user's hobbies and interests. The simulation unit can also set up a virtual environment that enhances concentration based on the user's hobbies and interests. The simulation unit can also set up a virtual environment that reduces stress based on the user's hobbies and interests. In this way, a more effective virtual environment can be provided by taking hobbies and interests into consideration. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input hobby and interest data into a generating AI and have the generating AI perform the virtual environment setting.

[0051] The simulation unit can set up a virtual environment by combining the user's occupational data during simulation. For example, the simulation unit can set up a relaxing virtual environment based on the user's occupational data. The simulation unit can also set up a virtual environment that enhances concentration based on the user's occupational data. The simulation unit can also set up a virtual environment that reduces stress based on the user's occupational data. In this way, a more effective virtual environment can be provided by combining occupational data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input occupational data into a generating AI and have the generating AI perform the setting of the virtual environment.

[0052] The coaching department can create a specific action plan during coaching sessions by referring to the user's past behavioral history. For example, the coaching department can create a specific action plan based on the user's past behavioral history. The coaching department can also create an effective action plan from the user's past behavioral history. The coaching department can also analyze the user's past behavioral history and create the most effective action plan. This allows for the creation of a more effective action plan by referring to past behavioral history. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input past behavioral history data into a generating AI and have the generating AI create an action plan.

[0053] The coaching department can create action plans by combining the user's health data during coaching sessions. For example, the coaching department can create specific action plans based on the user's health data. The coaching department can also create healthy action plans based on the user's health data. The coaching department can also create optimal action plans by combining the user's health data and behavioral history. This allows for the creation of more effective action plans by combining health data. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input health data into a generating AI and have the generating AI create an action plan.

[0054] The coaching department can create action plans during coaching sessions, taking into account the user's hobbies and interests. For example, the coaching department can create specific action plans based on the user's hobbies and interests. The coaching department can also create effective action plans by taking into account the user's hobbies and interests. The coaching department can also analyze the user's hobbies and interests and create the most effective action plan. This allows for the creation of more effective action plans by considering hobbies and interests. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input hobby and interest data into a generating AI and have the generating AI create the action plan.

[0055] The coaching department can create action plans by combining the user's occupational data during coaching sessions. For example, the coaching department can create specific action plans based on the user's occupational data. The coaching department can also create effective action plans based on the user's occupational data. The coaching department can also create optimal action plans by combining the user's occupational data and behavioral history. This allows for the creation of more effective action plans by combining occupational data. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input occupational data into a generating AI and have the generating AI create action plans.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The monitoring unit not only monitors the user's brainwave data in real time, but can also simultaneously acquire biosignals such as the user's heart rate and skin electrical activity. This allows for a more accurate understanding of the user's stress level and concentration state. For example, if the monitoring unit detects a sudden increase in the user's heart rate, it can determine that stress levels are high and suggest appropriate relaxation methods. Furthermore, by detecting changes in skin electrical activity, it can grasp changes in the user's emotions in real time and provide advice as needed. In addition, by combining and analyzing these biosignals, it is possible to comprehensively evaluate the user's health condition and provide a more effective training plan.

[0058] The monitoring unit can detect anomalies by referring to the user's past brainwave data during monitoring. For example, it can compare past brainwave data with current data to detect abnormal patterns. It can also compare past stress levels with current stress levels to detect abnormal fluctuations. Furthermore, it can compare past concentration data with current data to detect abnormal declines. This allows for early detection of anomalies by referring to past brainwave data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input past brainwave data into a generating AI and have the generating AI perform anomaly detection.

[0059] The monitoring unit can analyze brainwave fluctuations by combining the user's lifestyle data during monitoring. For example, it can combine the user's sleep data with brainwave data to analyze brainwave fluctuations caused by sleep deprivation. It can also combine the user's diet data with brainwave data to analyze the effects of diet. Furthermore, it can combine the user's exercise data with brainwave data to analyze the effects of exercise. By combining lifestyle data in this way, brainwave fluctuations can be analyzed more accurately. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input lifestyle data into a generating AI and have the generating AI perform the analysis of brainwave fluctuations.

[0060] The monitoring unit can analyze electroencephalogram (EEG) fluctuations in conjunction with the user's physical activity data during monitoring. For example, it can combine the user's exercise data and EEG data to analyze the effects of exercise. It can also combine the user's heart rate data and EEG data to analyze heart rate fluctuations. Furthermore, it can combine the user's respiratory data and EEG data to analyze the effects of respiration. This allows for a more accurate analysis of EEG fluctuations by using physical activity data in conjunction with EEG data. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input physical activity data into a generating AI and have the generating AI perform the analysis of EEG fluctuations.

[0061] The analysis unit can correct the analysis results by referring to the user's past stress levels during the analysis. For example, it can compare past stress levels with current stress levels and correct the analysis results. It can also correct the current analysis results by considering fluctuations in past stress levels. Furthermore, it can correct the current analysis results based on trends in past stress levels. This allows for more accurate correction of the analysis results by referring to past stress levels. 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 past stress level data into a generating AI and have the generating AI perform the correction of the analysis results.

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

[0063] Step 1: The monitoring unit monitors the user's brainwaves in real time. For example, it monitors the user's brainwaves in real time to understand their stress levels and concentration levels. Step 2: The analysis unit analyzes the data acquired by the monitoring unit. For example, it analyzes the user's electroencephalogram (EEG) data to understand their stress levels and concentration levels. Step 3: The proposal unit proposes optimal behaviors and thought patterns based on the analysis results obtained by the analysis unit. For example, it analyzes the user's brainwave data to understand their stress and concentration levels and proposes optimal behaviors and thought patterns. Step 4: The simulation team experiences the behaviors and thought patterns proposed by the proposal team in a virtual environment. For example, virtual reality (VR) is used to allow users to experience healthy behaviors in a virtual environment. Step 5: The coaching department creates a concrete action plan based on the behaviors and thought patterns experienced by the simulation department and tracks its progress. For example, through AI coaching, they create a concrete action plan based on the user's goals and track its progress.

[0064] (Example of form 2) The behavior and thinking improvement agent system according to an embodiment of the present invention is a system that analyzes a user's thinking patterns and behaviors and provides personalized advice and training based on individual needs. This system combines neurofeedback, virtual reality (VR), and AI coaching to help users make healthy behaviors a habit and cultivate positive thinking. First, the behavior and thinking improvement agent system monitors the user's brainwaves in real time to understand their stress and concentration levels. AI analyzes this data and proposes optimal behaviors and thinking patterns. Next, the behavior and thinking improvement agent system uses virtual reality (VR) to allow the user to experience healthy behaviors in a virtual environment. Furthermore, the behavior and thinking improvement agent system creates a concrete action plan based on the user's goals and tracks progress through AI coaching. The behavior and thinking improvement agent system also provides a community function, offering a platform where users can share goals and encourage each other. For example, the behavior and thinking improvement agent system monitors the user's brainwaves in real time. For example, the behavior and thinking improvement agent system analyzes the user's brainwave data to understand their stress and concentration levels. The Behavior and Thought Improvement Agent System uses AI to analyze the user's brainwave data and propose optimal behaviors and thought patterns. For example, the Behavior and Thought Improvement Agent System uses virtual reality (VR) to allow users to experience healthy behaviors in a virtual environment. Through AI coaching, the Behavior and Thought Improvement Agent System creates concrete action plans based on the user's goals and tracks progress. The Behavior and Thought Improvement Agent System provides a community function, offering a platform where users can share goals and encourage each other. This allows the Behavior and Thought Improvement Agent System to analyze the user's thought patterns and behaviors and provide personalized advice and training based on individual needs.

[0065] The behavior and thinking improvement agent system according to the embodiment comprises a monitoring unit, an analysis unit, a proposal unit, a simulation unit, and a coaching unit. The monitoring unit monitors the user's brainwaves in real time. For example, the monitoring unit monitors the user's brainwaves in real time to understand their stress and concentration levels. The monitoring unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thinking patterns. The analysis unit analyzes the data acquired by the monitoring unit. For example, the analysis unit analyzes the user's brainwave data to understand their stress and concentration levels. The analysis unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thinking patterns. The proposal unit proposes optimal behaviors and thinking patterns based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes the user's brainwave data to understand their stress and concentration levels. The proposal unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thinking patterns. The simulation unit experiences the behaviors and thinking patterns proposed by the proposal unit in a virtual environment. The simulation unit, for example, uses virtual reality (VR) to allow the user to experience healthy behaviors in a virtual environment. The simulation unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thought patterns. The coaching unit creates a concrete action plan based on the behaviors and thought patterns experienced by the simulation unit and tracks progress. The coaching unit, for example, uses AI coaching to create a concrete action plan based on the user's goals and tracks progress. The coaching unit uses AI to analyze the user's brainwave data and proposes optimal behaviors and thought patterns. As a result, the behavior and thought improvement agent system according to this embodiment can provide personalized advice and training based on individual needs by monitoring, analyzing, proposing, simulating, and coaching the user's brainwaves in real time.

[0066] The monitoring unit monitors the user's brainwaves in real time. Specifically, it acquires brainwave data using an EEG measurement device worn by the user. This device includes sensors worn on the head and can detect even subtle fluctuations in brainwaves with high precision. The brainwave data is classified into different frequency bands such as alpha waves, beta waves, theta waves, and delta waves, and this data is collected in real time. The monitoring unit analyzes this brainwave data to understand the user's stress level and concentration state. For example, an increase in alpha waves indicates a relaxed state, and an increase in beta waves indicates a focused state. Based on this data, the user's mental state can be evaluated in real time. Furthermore, the monitoring unit uses AI to analyze the brainwave data and gain insights into the user's behavior and thought patterns. The AI ​​detects abnormal patterns by comparing them with past data and determines whether the user is experiencing stress or has decreased concentration. This allows the monitoring unit to monitor the user's mental state in real time and provide appropriate feedback as needed.

[0067] The analysis unit analyzes the data acquired by the monitoring unit. Specifically, it receives electroencephalogram (EEG) data transmitted from the monitoring unit and performs a detailed analysis using an AI algorithm. The AI ​​uses a machine learning model to identify patterns in the EEG data and evaluate the user's mental state. For example, the AI ​​analyzes the temporal fluctuations of the EEG data to identify situations in which the user is more likely to feel stressed or to have increased concentration. Furthermore, the analysis unit can integrate the user's EEG data with other physiological data (heart rate, skin electrical activity, etc.) to perform a more comprehensive analysis. This allows for a multifaceted evaluation of the user's mental state and provides more accurate insights. Based on these analysis results, the analysis unit provides data to suggest optimal behaviors and thought patterns to the user.

[0068] The suggestion department proposes optimal actions and thought patterns based on the analysis results obtained by the analysis department. Specifically, it receives data provided by the analysis department and uses AI to generate specific advice for the user. The AI ​​considers the user's past data and current mental state to provide individually customized suggestions. For example, if the user is feeling stressed, the AI ​​will suggest breathing exercises or meditation to help them relax. If their concentration is low, the AI ​​will suggest taking short breaks to improve their focus or changing the priority of specific tasks. The suggestion department notifies the user of these suggestions and encourages them to take action. Notifications are made through smartphone apps and wearable devices so that users can act immediately. Furthermore, the suggestion department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. In this way, the suggestion department can provide users with optimal actions and thought patterns and support their mental health.

[0069] The Simulation Department allows users to experience the behaviors and thought patterns proposed by the Proposal Department in a virtual environment. Specifically, it uses virtual reality (VR) technology to provide users with the opportunity to practice the proposed behaviors in a virtual environment. For example, if a user is suggested to meditate to reduce stress, they can practice meditation in a VR environment with guidance. The VR environment provides a space where users can concentrate without interference from the real world, allowing them to effectively experience the proposed behaviors. The Simulation Department uses AI to analyze the user's brainwave data in real time and evaluate the user's reactions in the virtual environment. This allows the department to understand how effectively the user is practicing the proposed behaviors and provide feedback as needed. Furthermore, the Simulation Department supports users in practicing the behaviors they experienced in the virtual environment in the real world. In this way, the Simulation Department helps users effectively learn the proposed behaviors and incorporate them into their daily lives.

[0070] The coaching department creates concrete action plans based on the behavioral and thought patterns experienced by the simulation department and tracks progress. Specifically, it creates concrete action plans based on the user's goals through AI coaching. The AI ​​considers the user's past data and current state to set realistic and achievable goals. For example, if the user's goal is to reduce stress, the AI ​​will suggest relaxation methods and stress management techniques that can be practiced daily. The coaching department provides these action plans to the user and supports their implementation. Furthermore, the coaching department tracks the user's progress and provides regular feedback. It evaluates whether the user is on track towards their goals or facing difficulties and adjusts the action plan as needed. In this way, the coaching department provides concrete support for the user to achieve their goals and promotes continuous improvement. In addition, the coaching department collects user feedback and continuously improves the accuracy and effectiveness of the AI ​​algorithm. This allows the coaching department to provide optimal support to the user and effectively assist in improving their behavior and thinking.

[0071] The monitoring unit can estimate the user's emotions and adjust the frequency of EEG monitoring based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to grasp the stress level in real time. If the user is relaxed, the monitoring unit can also lower the monitoring frequency to acquire only the necessary data. If the user is focused, the monitoring unit can also adjust the frequency to monitor fluctuations in concentration in detail. This allows for the acquisition of more appropriate data by adjusting the monitoring frequency according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the monitoring frequency based on emotions.

[0072] The monitoring unit can detect anomalies by referring to the user's past brainwave data during monitoring. For example, the monitoring unit can compare past brainwave data with current data to detect abnormal patterns. The monitoring unit can also compare past stress levels with current stress levels to detect abnormal fluctuations. The monitoring unit can also compare past concentration data with current data to detect abnormal declines. This allows for early detection of anomalies by referring to past brainwave data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input past brainwave data into a generating AI and have the generating AI perform anomaly detection.

[0073] The monitoring unit can analyze brainwave fluctuations by combining the user's lifestyle data during monitoring. For example, the monitoring unit can combine the user's sleep data with brainwave data to analyze brainwave fluctuations caused by sleep deprivation. The monitoring unit can also combine the user's diet data with brainwave data to analyze the effects of diet. The monitoring unit can also combine the user's exercise data with brainwave data to analyze the effects of exercise. By combining lifestyle data, brainwave fluctuations can be analyzed more accurately. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input lifestyle data into a generating AI and have the generating AI perform the analysis of brainwave fluctuations.

[0074] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. If the user is focused, the monitoring unit can also visually display fluctuations in concentration levels in an easy-to-understand manner. This makes it possible to provide highly visible information by adjusting the display method according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the display method.

[0075] The monitoring unit can analyze electroencephalogram (EEG) fluctuations in conjunction with the user's physical activity data during monitoring. For example, the monitoring unit can combine the user's exercise data and EEG data to analyze the effects of exercise. The monitoring unit can also combine the user's heart rate data and EEG data to analyze heart rate fluctuations. The monitoring unit can also combine the user's respiratory data and EEG data to analyze the effects of respiration. This allows for a more accurate analysis of EEG fluctuations by using physical activity data in conjunction with EEG data. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input physical activity data into a generating AI and have the generating AI perform the analysis of EEG fluctuations.

[0076] The monitoring unit can analyze EEG fluctuations by combining the user's sleep data during monitoring. For example, the monitoring unit can combine the user's sleep data and EEG data to analyze EEG fluctuations caused by sleep deprivation. The monitoring unit can also combine the user's sleep quality and EEG data to analyze the effects of sleep. The monitoring unit can also combine the user's sleep pattern and EEG data to analyze the effects of sleep. By combining sleep data, EEG fluctuations can be analyzed more accurately. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input sleep data into a generating AI and have the generating AI perform the analysis of EEG fluctuations.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use an algorithm that analyzes the stress level in detail. If the user is relaxed, the analysis unit can also use an algorithm that analyzes the state of relaxation. If the user is focused, the analysis unit can also use an algorithm that analyzes fluctuations in concentration. By adjusting the analysis algorithm according to the user's emotions, more accurate analysis results can be obtained. 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 generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0078] The analysis unit can correct the analysis results by referring to the user's past stress levels during the analysis. For example, the analysis unit can compare past stress levels with the current stress level and correct the analysis results. The analysis unit can also correct the current analysis results by considering fluctuations in past stress levels. The analysis unit can also correct the current analysis results based on trends in past stress levels. This allows for more accurate correction of the analysis results by referring to past stress levels. 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 past stress level data into a generating AI and have the generating AI perform the correction of the analysis results.

[0079] The analysis unit can analyze stress levels and concentration levels by combining the user's dietary data during analysis. For example, the analysis unit can combine the user's dietary data with stress levels to analyze the effects of diet. The analysis unit can also combine the user's dietary data with fluctuations in concentration to analyze the effects of diet. The analysis unit can also combine the user's dietary data with electroencephalogram (EEG) data to analyze the effects of diet. This allows for a more accurate analysis of stress levels and concentration levels by combining dietary data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input dietary data into a generating AI and have the generating AI perform the analysis of stress levels and concentration levels.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is focused, the analysis unit can also visually display fluctuations in concentration levels in an easy-to-understand manner. This makes it possible to provide highly visible information by adjusting the display method according to the user's emotions. 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 generating AI and have the generating AI perform the adjustment of the display method.

[0081] The analysis unit can analyze stress levels and concentration levels by combining the user's exercise data with other data during the analysis process. For example, the analysis unit can combine the user's exercise data with stress levels to analyze the effects of exercise. The analysis unit can also combine the user's exercise data with fluctuations in concentration levels to analyze the effects of exercise. The analysis unit can also combine the user's exercise data with electroencephalogram (EEG) data to analyze the effects of exercise. This allows for a more accurate analysis of stress levels and concentration levels by combining exercise data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input exercise data into a generating AI and have the generating AI perform the analysis of stress levels and concentration levels.

[0082] The analysis unit can analyze stress levels and concentration states by combining the user's social activity data during analysis. For example, the analysis unit can combine the user's social activity data with stress levels to analyze the impact of social activities. The analysis unit can also combine the user's social activity data with fluctuations in concentration to analyze the impact of social activities. The analysis unit can also combine the user's social activity data with electroencephalogram (EEG) data to analyze the impact of social activities. This allows for a more accurate analysis of stress levels and concentration states by combining social activity data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input social activity data into a generating AI and have the generating AI perform the analysis of stress levels and concentration states.

[0083] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit can suggest actions to help them relax. If the user is relaxed, the suggestion unit can also suggest actions to improve their concentration. If the user is focused, the suggestion unit can also suggest actions to maintain their concentration. By adjusting the suggestions according to the user's emotions, the suggestion unit can propose more appropriate actions and thought patterns. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generating AI and have the generating AI adjust the suggestions.

[0084] The suggestion unit can propose the optimal behavioral pattern by referring to the user's past behavioral history when making a suggestion. For example, the suggestion unit can propose the optimal behavioral pattern based on the user's past behavioral history. The suggestion unit can also propose an effective behavioral pattern from the user's past behavioral history. The suggestion unit can also analyze the user's past behavioral history and propose the most effective behavioral pattern. In this way, the optimal behavioral pattern can be proposed by referring to past behavioral history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input past behavioral history data into a generating AI and have the generating AI execute the proposal of the optimal behavioral pattern.

[0085] The suggestion unit can propose behavioral patterns by combining the user's health data during the proposal process. For example, the suggestion unit can propose the optimal behavioral pattern by combining the user's health data and behavioral history. The suggestion unit can also propose healthy behavioral patterns based on the user's health data. The suggestion unit can also propose the optimal behavioral pattern by combining the user's health data and electroencephalogram (EEG) data. This allows for the proposal of more appropriate behavioral patterns by combining health data. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input health data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0086] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit will prioritize suggestions for stress reduction. If the user is relaxed, the suggestion unit may also prioritize suggestions for improving concentration. If the user is focused, the suggestion unit may also prioritize suggestions for maintaining concentration. By determining the priority of suggestions according to the user's emotions, more effective suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generating AI and have the generating AI determine the priority of suggestions.

[0087] The suggestion unit can propose behavioral patterns while considering the user's hobbies and interests. For example, the suggestion unit can propose the optimal behavioral pattern based on the user's hobbies and interests. The suggestion unit can also propose effective behavioral patterns while considering the user's hobbies and interests. The suggestion unit can analyze the user's hobbies and interests and propose the most effective behavioral pattern. This allows for the proposal of more effective behavioral patterns by considering hobbies and interests. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input hobby and interest data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0088] The suggestion unit can propose behavioral patterns while considering the user's hobbies and interests. For example, the suggestion unit can propose the optimal behavioral pattern based on the user's hobbies and interests. The suggestion unit can also propose effective behavioral patterns while considering the user's hobbies and interests. The suggestion unit can analyze the user's hobbies and interests and propose the most effective behavioral pattern. This allows for the proposal of more effective behavioral patterns by considering hobbies and interests. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input hobby and interest data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0089] The suggestion unit can propose behavioral patterns by combining the user's occupational data during the proposal process. For example, the suggestion unit can propose the optimal behavioral pattern by combining the user's occupational data and behavioral history. The suggestion unit can also propose effective behavioral patterns based on the user's occupational data. The suggestion unit can also propose the optimal behavioral pattern by combining the user's occupational data and electroencephalogram (EEG) data. This allows for the proposal of more appropriate behavioral patterns by combining occupational data. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input occupational data into a generating AI and have the generating AI execute the behavioral pattern proposal.

[0090] The simulation unit can estimate the user's emotions and adjust the virtual environment settings based on those emotions. For example, if the user is feeling stressed, the simulation unit can set up a relaxing virtual environment. If the user is relaxed, the simulation unit can also set up a virtual environment that enhances concentration. If the user is focused, the simulation unit can also set up a virtual environment that maintains concentration. By adjusting the virtual environment settings according to the user's emotions, a more effective simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI perform the virtual environment settings.

[0091] The simulation unit can customize the virtual environment during simulation by referring to the user's past experience data. For example, the simulation unit can customize the virtual environment based on environments in which the user was able to relax in the past. The simulation unit can also customize the virtual environment based on environments in which the user was able to concentrate in the past. The simulation unit can also customize the virtual environment to avoid environments in which the user was stressed in the past. In this way, a more effective virtual environment can be provided by referring to past experience data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input past experience data into a generating AI and have the generating AI perform the customization of the virtual environment.

[0092] The simulation unit can set up a virtual environment by combining the user's health data during the simulation. For example, the simulation unit can set up a relaxing virtual environment based on the user's health data. The simulation unit can also set up a virtual environment that enhances concentration based on the user's health data. The simulation unit can also set up a virtual environment that reduces stress based on the user's health data. In this way, a more effective virtual environment can be provided by combining health data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input health data into a generating AI and have the generating AI perform the virtual environment setting.

[0093] The simulation unit can estimate the user's emotions and adjust the order of simulations based on the estimated emotions. For example, if the user is stressed, the simulation unit will prioritize simulations that promote relaxation. If the user is relaxed, the simulation unit may also prioritize simulations that enhance concentration. If the user is focused, the simulation unit may also prioritize simulations that maintain concentration. By adjusting the order of simulations according to the user's emotions, more effective simulations become possible. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI adjust the order of simulations.

[0094] The simulation unit can set up a virtual environment during simulation, taking into account the user's hobbies and interests. For example, the simulation unit can set up a relaxing virtual environment based on the user's hobbies and interests. The simulation unit can also set up a virtual environment that enhances concentration based on the user's hobbies and interests. The simulation unit can also set up a virtual environment that reduces stress based on the user's hobbies and interests. In this way, a more effective virtual environment can be provided by taking hobbies and interests into consideration. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input hobby and interest data into a generating AI and have the generating AI perform the virtual environment setting.

[0095] The simulation unit can set up a virtual environment by combining the user's occupational data during simulation. For example, the simulation unit can set up a relaxing virtual environment based on the user's occupational data. The simulation unit can also set up a virtual environment that enhances concentration based on the user's occupational data. The simulation unit can also set up a virtual environment that reduces stress based on the user's occupational data. In this way, a more effective virtual environment can be provided by combining occupational data. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input occupational data into a generating AI and have the generating AI perform the setting of the virtual environment.

[0096] The coaching unit can estimate the user's emotions and adjust the coaching content based on those emotions. For example, if the user is feeling stressed, the coaching unit can provide coaching content to help them relax. If the user is relaxed, the coaching unit can also provide coaching content to improve their concentration. If the user is focused, the coaching unit can also provide coaching content to help them maintain their concentration. By adjusting the coaching content according to the user's emotions, more effective coaching becomes possible. Some or all of the above-described processes in the coaching unit may be performed using AI or not. For example, the coaching unit can input user emotion data into a generating AI and have the generating AI adjust the coaching content.

[0097] The coaching department can create a specific action plan during coaching sessions by referring to the user's past behavioral history. For example, the coaching department can create a specific action plan based on the user's past behavioral history. The coaching department can also create an effective action plan from the user's past behavioral history. The coaching department can also analyze the user's past behavioral history and create the most effective action plan. This allows for the creation of a more effective action plan by referring to past behavioral history. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input past behavioral history data into a generating AI and have the generating AI create an action plan.

[0098] The coaching department can create action plans by combining the user's health data during coaching sessions. For example, the coaching department can create specific action plans based on the user's health data. The coaching department can also create healthy action plans based on the user's health data. The coaching department can also create optimal action plans by combining the user's health data and behavioral history. This allows for the creation of more effective action plans by combining health data. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input health data into a generating AI and have the generating AI create an action plan.

[0099] The coaching unit can estimate the user's emotions and adjust the frequency of coaching based on those emotions. For example, if the user is stressed, the coaching unit can provide frequent coaching to help reduce stress. If the user is relaxed, the coaching unit can reduce the frequency of coaching and provide it only when necessary. If the user is focused, the coaching unit can provide coaching at an appropriate frequency to maintain their focus. By adjusting the frequency of coaching according to the user's emotions, more effective coaching becomes possible. Some or all of the above processes in the coaching unit may be performed using AI or not. For example, the coaching unit can input user emotion data into a generating AI and have the generating AI adjust the frequency of coaching.

[0100] The coaching department can create action plans during coaching sessions, taking into account the user's hobbies and interests. For example, the coaching department can create specific action plans based on the user's hobbies and interests. The coaching department can also create effective action plans by taking into account the user's hobbies and interests. The coaching department can also analyze the user's hobbies and interests and create the most effective action plan. This allows for the creation of more effective action plans by considering hobbies and interests. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input hobby and interest data into a generating AI and have the generating AI create the action plan.

[0101] The coaching department can create action plans by combining the user's occupational data during coaching sessions. For example, the coaching department can create specific action plans based on the user's occupational data. The coaching department can also create effective action plans based on the user's occupational data. The coaching department can also create optimal action plans by combining the user's occupational data and behavioral history. This allows for the creation of more effective action plans by combining occupational data. Some or all of the above processes in the coaching department may be performed using AI or not. For example, the coaching department can input occupational data into a generating AI and have the generating AI create action plans.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The monitoring unit not only monitors the user's brainwave data in real time, but can also simultaneously acquire biosignals such as the user's heart rate and skin electrical activity. This allows for a more accurate understanding of the user's stress level and concentration state. For example, if the monitoring unit detects a sudden increase in the user's heart rate, it can determine that stress levels are high and suggest appropriate relaxation methods. Furthermore, by detecting changes in skin electrical activity, it can grasp changes in the user's emotions in real time and provide advice as needed. In addition, by combining and analyzing these biosignals, it is possible to comprehensively evaluate the user's health condition and provide a more effective training plan.

[0104] The monitoring unit can estimate the user's emotions and adjust the frequency of EEG monitoring based on the estimated emotions. For example, if the user is stressed, the monitoring frequency can be increased to grasp the stress level in real time. If the user is relaxed, the monitoring frequency can be lowered to acquire only the necessary data. Furthermore, if the user is focused, the frequency can be adjusted to monitor fluctuations in concentration in detail. This allows for the acquisition of more appropriate data by adjusting the monitoring frequency according to the user's emotions. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the monitoring frequency based on emotions.

[0105] The monitoring unit can detect anomalies by referring to the user's past brainwave data during monitoring. For example, it can compare past brainwave data with current data to detect abnormal patterns. It can also compare past stress levels with current stress levels to detect abnormal fluctuations. Furthermore, it can compare past concentration data with current data to detect abnormal declines. This allows for early detection of anomalies by referring to past brainwave data. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input past brainwave data into a generating AI and have the generating AI perform anomaly detection.

[0106] The monitoring unit can analyze brainwave fluctuations by combining the user's lifestyle data during monitoring. For example, it can combine the user's sleep data with brainwave data to analyze brainwave fluctuations caused by sleep deprivation. It can also combine the user's diet data with brainwave data to analyze the effects of diet. Furthermore, it can combine the user's exercise data with brainwave data to analyze the effects of exercise. By combining lifestyle data in this way, brainwave fluctuations can be analyzed more accurately. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input lifestyle data into a generating AI and have the generating AI perform the analysis of brainwave fluctuations.

[0107] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is focused, it can visually display fluctuations in their concentration level in an easy-to-understand manner. In this way, by adjusting the display method according to the user's emotions, it becomes possible to provide highly visible information. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the display method.

[0108] The monitoring unit can analyze electroencephalogram (EEG) fluctuations in conjunction with the user's physical activity data during monitoring. For example, it can combine the user's exercise data and EEG data to analyze the effects of exercise. It can also combine the user's heart rate data and EEG data to analyze heart rate fluctuations. Furthermore, it can combine the user's respiratory data and EEG data to analyze the effects of respiration. This allows for a more accurate analysis of EEG fluctuations by using physical activity data in conjunction with EEG data. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input physical activity data into a generating AI and have the generating AI perform the analysis of EEG fluctuations.

[0109] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, it can use an algorithm that analyzes the stress level in detail. If the user is relaxed, it can also use an algorithm that analyzes the relaxed state. Furthermore, if the user is focused, it can also use an algorithm that analyzes fluctuations in concentration. By adjusting the analysis algorithm according to the user's emotions, more accurate analysis results can be obtained. 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 generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0110] The analysis unit can correct the analysis results by referring to the user's past stress levels during the analysis. For example, it can compare past stress levels with current stress levels and correct the analysis results. It can also correct the current analysis results by considering fluctuations in past stress levels. Furthermore, it can correct the current analysis results based on trends in past stress levels. This allows for more accurate correction of the analysis results by referring to past stress levels. 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 past stress level data into a generating AI and have the generating AI perform the correction of the analysis results.

[0111] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest actions to help them relax. If the user is relaxed, it can also suggest actions to improve their concentration. Furthermore, if the user is focused, it can suggest actions to maintain that concentration. By adjusting the suggestions according to the user's emotions, it can suggest more appropriate actions and thought patterns. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generating AI and have the generating AI adjust the suggestions.

[0112] The simulation unit can estimate the user's emotions and adjust the virtual environment settings based on those emotions. For example, if the user is stressed, it can set up a relaxing virtual environment. If the user is relaxed, it can also set up a virtual environment that enhances concentration. Furthermore, if the user is focused, it can set up a virtual environment that maintains concentration. By adjusting the virtual environment settings according to the user's emotions, a more effective simulation becomes possible. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI execute the virtual environment settings.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The monitoring unit monitors the user's brainwaves in real time. For example, it monitors the user's brainwaves in real time to understand their stress levels and concentration levels. Step 2: The analysis unit analyzes the data acquired by the monitoring unit. For example, it analyzes the user's electroencephalogram (EEG) data to understand their stress levels and concentration levels. Step 3: The proposal unit proposes optimal behaviors and thought patterns based on the analysis results obtained by the analysis unit. For example, it analyzes the user's brainwave data to understand their stress and concentration levels and proposes optimal behaviors and thought patterns. Step 4: The simulation team experiences the behaviors and thought patterns proposed by the proposal team in a virtual environment. For example, virtual reality (VR) is used to allow users to experience healthy behaviors in a virtual environment. Step 5: The coaching department creates a concrete action plan based on the behaviors and thought patterns experienced by the simulation department and tracks its progress. For example, through AI coaching, they create a concrete action plan based on the user's goals and track its progress.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the monitoring unit, analysis unit, proposal unit, simulation unit, and coaching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the user's brainwaves in real time using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the user's brainwave data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes optimal actions and thought patterns using the specific processing unit 290 of the data processing unit 12. The simulation unit provides an experience in a virtual environment using virtual reality (VR) via the control unit 46A of the smart device 14. The coaching unit creates a concrete action plan and tracks progress using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the monitoring unit, analysis unit, proposal unit, simulation unit, and coaching unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the user's brainwaves in real time using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the user's brainwave data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes optimal actions and thought patterns using the specific processing unit 290 of the data processing unit 12. The simulation unit provides an experience in a virtual environment using virtual reality (VR) using the control unit 46A of the smart glasses 214. The coaching unit creates a concrete action plan and tracks progress using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the monitoring unit, analysis unit, proposal unit, simulation unit, and coaching unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the user's brainwaves in real time using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the user's brainwave data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes optimal actions and thought patterns using the specific processing unit 290 of the data processing unit 12. The simulation unit provides an experience in a virtual environment using virtual reality (VR) via the control unit 46A of the headset terminal 314. The coaching unit creates a concrete action plan and tracks its progress using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the monitoring unit, analysis unit, proposal unit, simulation unit, and coaching unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the user's brainwaves in real time using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the user's brainwave data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes optimal actions and thought patterns using the specific processing unit 290 of the data processing unit 12. The simulation unit provides an experience in a virtual environment using virtual reality (VR) via the control unit 46A of the robot 414. The coaching unit creates a concrete action plan and tracks progress using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A monitoring unit that monitors the user's brainwaves in real time, An analysis unit analyzes the data acquired by the monitoring unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal actions and thought patterns. A simulation unit that allows users to experience the behavioral and thought patterns proposed by the aforementioned proposal unit in a virtual environment, The system includes a coaching unit that creates a concrete action plan based on the behaviors and thought patterns experienced by the simulation unit and tracks its progress. A system characterized by the following features. (Note 2) The monitoring unit, The system estimates the user's emotions and adjusts the frequency of EEG monitoring based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, During monitoring, the system detects anomalies by referring to the user's past EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, During monitoring, the system analyzes changes in brainwaves by combining the user's lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, During monitoring, the system analyzes EEG fluctuations in conjunction with the user's physical activity data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, During monitoring, the system analyzes brainwave fluctuations by combining the user's sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the analysis results are corrected by referring to the user's past stress levels. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, the user's dietary data is combined to analyze their stress levels and concentration levels. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the user's exercise data is used in conjunction with other data to analyze their stress levels and concentration levels. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the system combines the user's social activity data to analyze their stress levels and concentration levels. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, we refer to the user's past behavioral history to propose the optimal behavioral pattern. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we combine the user's health data to suggest behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, we propose behavioral patterns that take into account the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, we propose behavioral patterns that take into account the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we combine the user's occupational data to suggest behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, It estimates the user's emotions and adjusts the virtual environment settings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, During simulation, the virtual environment is customized by referencing the user's past experience data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During simulation, the virtual environment is configured by combining the user's health data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During simulation, the virtual environment is configured taking into account the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During simulation, the virtual environment is configured by combining the user's occupational data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned coaching department, The system estimates the user's emotions and adjusts the coaching content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned coaching department, During coaching sessions, we create specific action plans by referring to the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned coaching department, During coaching sessions, we combine the user's health data to create an action plan. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned coaching department, It estimates the user's emotions and adjusts the frequency of coaching based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned coaching department, During coaching sessions, we create action plans that take into account the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned coaching department, During coaching sessions, we create action plans by combining the user's occupational data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 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 monitoring unit that monitors the user's brainwaves in real time, An analysis unit analyzes the data acquired by the monitoring unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal actions and thought patterns. A simulation unit that allows users to experience the behavioral and thought patterns proposed by the aforementioned proposal unit in a virtual environment, The system includes a coaching unit that creates a concrete action plan based on the behaviors and thought patterns experienced by the simulation unit and tracks its progress. A system characterized by the following features.

2. The monitoring unit, The system estimates the user's emotions and adjusts the frequency of EEG monitoring based on the estimated emotions. The system according to feature 1.

3. The monitoring unit, During monitoring, the system detects anomalies by referring to the user's past EEG data. The system according to feature 1.

4. The monitoring unit, During monitoring, the system analyzes changes in brainwaves by combining the user's lifestyle data. The system according to feature 1.

5. The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system according to feature 1.

6. The monitoring unit, During monitoring, the system analyzes EEG fluctuations in conjunction with the user's physical activity data. The system according to feature 1.

7. The monitoring unit, During monitoring, the system analyzes brainwave fluctuations by combining the user's sleep data. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

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