system

Abstract

The system according to this embodiment aims to provide personalized support for mental health problems. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The provision unit provides counseling and relaxation based on the content proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized support for mental health problems is not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized support for mental health problems.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a service provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The service provision unit provides counseling and relaxation services based on the proposals made by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized support for mental health problems. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An integrated platform according to an embodiment of the present invention is a system that uses an AI agent to provide personalized support for mental health problems such as stress, depression, and anxiety. This integrated platform automatically records daily mood and activities and performs continuous status checks. Next, it provides counseling, relaxation guidance, and emergency response. This platform proposes personally optimized solutions to target groups across all generations who are facing mental challenges. For example, the integrated platform uses a 3D avatar as an AI counselor that performs natural interactions including facial expressions and gestures. Gesture recognition and generation using deep learning enables real-time responses according to the user's emotional state. For example, if the user is sad, it responds with comforting facial expressions and gentle gestures. Next, the integrated platform performs mood analysis and automatic recording using voice input and chatbots utilizing NLP (Natural Language Processing). It works in conjunction with smartphone sensors and smartwatches to automatically monitor movement patterns, exercise levels, heart rate, sleep patterns, etc., and centrally manages health status. Furthermore, the integrated platform provides personalized relaxation content that suggests meditation, breathing exercises, and music therapy based on the user's state and preferences. The platform reflects user data in real time and recommends optimal relaxation methods. It also provides real-time consultations through video calls and chats with professional counselors. Counseling records and user data are integrated to provide a consistent treatment process. Furthermore, the platform builds an online community where users facing similar problems can connect, offering educational content and online seminars on mental health. Anonymous interaction provides a safe environment for sharing experiences and supporting each other. Finally, the platform's AI detects high stress levels and provides emergency measures (e.g., emergency relaxation guides). If necessary, it automatically notifies pre-registered emergency contacts and professionals to facilitate a quick response.This platform can reduce stress and anxiety in daily life and maintain and improve users' mental health. Furthermore, it can prevent mental health problems from worsening through early intervention and preventative care. It provides easy and efficient mental healthcare, allowing users to easily manage their own mental health. The goal is to raise mental health awareness throughout society and create an easily accessible care environment. As a result, the integrated platform can comprehensively support users' mental health and provide optimal care tailored to individual needs.

[0029] The integrated platform according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. The data collection unit can collect data in cooperation with, for example, smartphone sensors or smartwatches. The data collection unit can collect heart rate data using, for example, a heart rate sensor. The data collection unit can also collect exercise data using an accelerometer. Furthermore, the data collection unit can collect movement pattern data using a GPS sensor. For example, the data collection unit can use a heart rate sensor to monitor the user's heart rate in real time and collect data. It can use an accelerometer to measure the user's exercise level and collect data. It can use a GPS sensor to track the user's movement pattern and collect data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can use, for example, machine learning algorithms to analyze the data. The analysis unit can also use, for example, statistical analysis methods to analyze the data. Furthermore, the analysis unit can also use deep learning technology to analyze the data. For example, the analysis unit can use a machine learning algorithm to analyze the collected heart rate data and determine the user's stress level. The system analyzes collected movement data using statistical analysis methods to evaluate the user's activity level. It also analyzes collected movement pattern data using deep learning techniques to identify the user's behavioral patterns. The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can suggest relaxation methods. It can also suggest counseling content. Furthermore, the proposal unit can suggest emergency response methods. For example, based on the analysis results, the proposal unit suggests meditation to the user. Based on the analysis results, it suggests breathing exercises to the user. Based on the analysis results, it suggests music therapy to the user. The service provider provides counseling and relaxation based on the suggestions made by the proposal unit. For example, the service provider can provide meditation. It can also provide breathing exercises. Furthermore, the service provider can provide music therapy. For example, the service provider provides meditation to the user based on the suggested meditation method.Based on the proposed breathing technique, the system provides the user with breathing exercises. Based on the proposed music therapy, the system provides the user with music therapy. As a result, the integrated platform according to the embodiment can consistently collect, analyze, propose, and provide data, enabling it to provide personalized support to the user.

[0030] The data collection unit collects data. For example, the data collection unit can collect data in conjunction with smartphone sensors or smartwatches. Specifically, it utilizes various sensors installed in smartphones and smartwatches to collect user biometric information and behavioral data in real time. For example, it can collect heart rate data using a heart rate sensor. Heart rate sensors are worn on the user's wrist or chest and can detect heart rate fluctuations with high accuracy. This makes it possible to monitor the user's heart rate in real time and collect data. The data collection unit can also collect exercise data using an accelerometer. The accelerometer detects the user's movements in three dimensions and accurately measures exercise such as walking, running, and climbing stairs. Furthermore, the data collection unit can collect movement pattern data using a GPS sensor. The GPS sensor acquires the user's location information with high accuracy and collects data such as movement routes, speed, and time spent at locations. For example, the data collection unit uses a heart rate sensor to monitor the user's heart rate in real time and collect data. It also uses an accelerometer to measure the user's exercise level and collect data. Using GPS sensors, the system tracks the user's movement patterns and collects data. This allows the data collection unit to comprehensively gather user biometric and behavioral data, building a comprehensive database. The collected data is sent to a cloud server, making it accessible to the analysis and proposal units. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze data using machine learning algorithms. Specifically, it integrates collected heart rate data, exercise data, and movement pattern data to analyze the user's health status and behavioral patterns in detail. For instance, it uses machine learning algorithms to analyze collected heart rate data and determine the user's stress level. By analyzing heart rate variability and patterns, it can accurately determine whether the user is experiencing stress. It also uses statistical analysis methods to analyze collected exercise data and evaluate the user's activity level. By analyzing exercise data over time, it can understand the user's exercise habits and activity patterns. Furthermore, it uses deep learning technology to analyze collected movement pattern data and identify the user's behavioral patterns. Deep learning technology can learn from large amounts of data and extract complex patterns and relationships. This allows the analysis unit to quickly and accurately analyze collected data and understand the user's health status and behavioral patterns in real time. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term health risk assessments and trend analyses. For example, based on past heart rate data, it can predict fluctuations in stress levels during specific time periods or situations and assess future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only provide real-time situational awareness but also support long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The proposal department makes suggestions based on the analysis results obtained by the analysis department. For example, the proposal department can suggest relaxation methods. Specifically, based on the analysis results, it suggests the most suitable relaxation method for the user. For example, if the user's stress level is high, the proposal department can suggest relaxation methods such as meditation, deep breathing, or yoga. The proposal department can also suggest counseling content. Based on the user's behavioral patterns and health condition, it suggests appropriate counseling content to support the user's mental health. Furthermore, the proposal department can also suggest emergency response methods. For example, if the user's heart rate suddenly increases, the proposal department can suggest resting or contacting a medical institution as an emergency response method. Based on the analysis results, the proposal department suggests meditation to the user. Based on the analysis results, it suggests breathing exercises to the user. Based on the analysis results, it suggests music therapy to the user. In this way, the proposal department can provide personalized suggestions tailored to the user's health condition and behavioral patterns, supporting the user's health management. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on feedback from users who have implemented the suggested relaxation methods, the suggestions are reviewed and more effective methods are proposed. Furthermore, the suggestion department can make suggestions tailored to the user's preferences and lifestyle. This allows the suggestion department to propose optimal health management methods to users, thereby improving their health.

[0033] The service provider will offer counseling and relaxation services based on the proposals made by the suggestion team. Specifically, they will provide users with meditation based on the suggested meditation methods. Meditation is an effective way for users to relax and restore balance to their mind and body. The service provider will provide meditation guidance in audio and video to support users in easily performing the meditation. They will also provide users with breathing exercises based on the suggested breathing techniques. Breathing exercises are an important technique for reducing stress and enhancing relaxation. The service provider will explain the breathing exercise procedure in detail and instruct users to perform it correctly. Furthermore, they will provide users with music therapy based on the suggested music therapy. Music therapy promotes relaxation of the mind and body through music and has the effect of reducing stress. The service provider will select music according to the user's preferences and situation to maximize the relaxation effect. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services offered. For example, based on feedback from users who have participated in meditation, breathing exercises, and music therapy, they will review the services offered and provide more effective methods. Furthermore, the service provider can offer customized relaxation methods tailored to the user's lifestyle and preferences. This allows the service provider to provide users with the optimal relaxation method and improve their health.

[0034] The data collection unit can collect data in conjunction with smartphone sensors and smartwatches. For example, the data collection unit can collect heart rate data using a smartphone sensor. The data collection unit can also collect exercise data using a smartwatch. The data collection unit can also collect sleep pattern data using a smartphone sensor. For example, the data collection unit can use a smartphone sensor to monitor the user's heart rate in real time and collect data. It can use a smartwatch to measure the user's exercise level and collect data. It can use a smartphone sensor to track the user's sleep patterns and collect data. This allows for more detailed data collection by linking with smartphone sensors and smartwatches. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smartphone sensors and smartwatches into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze the collected data and determine the user's emotional state. For example, the analysis unit can determine the user's emotional state using facial expression analysis. The analysis unit can also determine the user's emotional state using voice analysis. The analysis unit can also determine the user's emotional state using text analysis. For example, the analysis unit can analyze the user's facial expression data using facial expression analysis and determine the emotional state. It can analyze the user's voice data using voice analysis and determine the emotional state. It can analyze the user's text data using text analysis and determine the emotional state. By determining the user's emotional state, more appropriate support can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the determination of the emotional state.

[0036] The suggestion unit can propose the optimal relaxation method based on the analysis results. For example, the suggestion unit may suggest meditation. For example, the suggestion unit may also suggest breathing exercises. For example, the suggestion unit may also suggest music therapy. For example, based on the analysis results, the suggestion unit may suggest meditation to the user. Based on the analysis results, it may suggest breathing exercises to the user. Based on the analysis results, it may suggest music therapy to the user. In this way, by suggesting the optimal relaxation method based on the analysis results, it is possible to provide support tailored to the user. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit may input the analysis results into a generative AI and have the generative AI execute the suggestion of the optimal relaxation method.

[0037] The service provider can provide meditation, breathing exercises, and music therapy based on the proposed relaxation methods. For example, the service provider can provide meditation. For example, the service provider can also provide breathing exercises. For example, the service provider can also provide music therapy. For example, the service provider can provide meditation to the user based on the proposed meditation method. For example, the service provider can provide breathing exercises to the user based on the proposed breathing method. For example, the service provider can provide music therapy to the user based on the proposed music therapy. This can improve the user's mental health by providing meditation, breathing exercises, and music therapy based on the proposed relaxation methods. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the proposed relaxation methods into a generative AI and have the generative AI perform the provision of relaxation methods.

[0038] The Emergency Response Unit can detect a user's high stress level and provide first aid. For example, the Emergency Response Unit can detect an increase in heart rate and provide first aid. The Emergency Response Unit can also detect changes in skin electrical activity and provide first aid. The Emergency Response Unit can also detect changes in breathing patterns and provide first aid. For example, the Emergency Response Unit can detect an increase in heart rate and provide relaxation techniques to the user. It can detect changes in skin electrical activity and provide emergency counseling to the user. It can detect changes in breathing patterns and provide emergency relaxation guidance to the user. This allows for the detection of a user's high stress level and the rapid provision of first aid, thereby protecting the user's mental health. Some or all of the above processes in the Emergency Response Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Emergency Response Unit can input the user's biometric data into a generative AI and have the generative AI perform the provision of first aid.

[0039] The Community Department can provide an online community where users facing similar problems can interact with each other. For example, the Community Department can provide a chat function. The Community Department can also provide a forum function. The Community Department can also provide a video call function. For example, the Community Department can provide a chat function that allows users to interact in real time. It can provide a forum function that allows users to share information. It can provide a video call function that allows users to interact face-to-face. This enables user support by providing an online community where users facing similar problems can interact. Some or all of the above processing in the Community Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Community Department can input user interaction data into a generative AI and have the generative AI suggest the optimal method of interaction.

[0040] The Ministry of Education can provide educational content and online seminars on mental health. For example, the Ministry of Education can provide video content. The Ministry of Education can also provide live seminars. The Ministry of Education can also provide text content. For example, the Ministry of Education can provide educational videos on mental health. Through live seminars, it can provide lectures by experts. Through text content, it can provide knowledge on mental health. In this way, by providing educational content and online seminars on mental health, the Ministry of Education can improve users' knowledge. Some or all of the above processing by the Ministry of Education may be performed using, for example, generative AI, or not using generative AI. For example, the Ministry of Education can input user learning data into a generative AI and have the generative AI perform the task of providing optimal educational content.

[0041] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. The data collection unit can also predict and suggest data to be collected during specific time periods based on the user's past data collection history. The data collection unit can also analyze the user's past data collection history and select the most efficient collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. It can predict and suggest data to be collected during specific time periods based on the user's past data collection history. It can analyze the user's past data collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0042] The data collection unit can filter data based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. If the user is resting, the data collection unit can also prioritize collecting data related to relaxation. If the user is working, the data collection unit can also prioritize collecting data related to work. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. If the user is resting, the data collection unit will prioritize collecting data related to relaxation. If the user is working, the data collection unit will prioritize collecting data related to work. By filtering the data based on the user's current activity status and environment, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user activity status and environment data into a generative AI and have the generative AI perform data filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, if the user is on the move, the data collection unit can also prioritize the collection of data related to movement. For example, if the user is at home, the data collection unit can also prioritize the collection of data related to home. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. If the user is on the move, the data collection unit will prioritize the collection of data related to movement. If the user is at home, the data collection unit will prioritize the collection of data related to home. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI determine the priority of the data.

[0044] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is experiencing stress on social media, the data collection unit can collect data related to stress. For example, if a user is relaxing on social media, the data collection unit can also collect data related to relaxation. For example, if a user is in a hurry on social media, the data collection unit can also collect data related to the hurried situation. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI collect the relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform a moderate analysis on data of medium importance. For example, the analysis unit can perform a detailed analysis on data of high importance. It can perform a simplified analysis on data of low importance. It can perform a moderate analysis on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply a stress analysis method to data related to stress. For example, the analysis unit can apply a relaxation analysis method to data related to relaxation. For example, the analysis unit can apply a rushed situation analysis method to data related to rushed situations. For example, the analysis unit can apply a stress analysis method to data related to stress. For example, it can apply a relaxation analysis method to data related to relaxation. For example, it can apply a rushed situation analysis method to data related to rushed situations. By applying different analysis methods depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis method.

[0047] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also perform analysis while referring to past data. The analysis unit may also focus on analyzing data from a specific period. For example, the analysis unit may prioritize the analysis of the most recent data. It may perform analysis while referring to past data. It may focus on analyzing data from a specific period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit may input the data collection period to the generative AI and have the generative AI perform the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit may also moderately analyze data of moderate relevance. For example, the analysis unit may prioritize the analysis of highly relevant data, postpone the analysis of less relevant data, and moderately analyze data of moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0049] The proposal unit can adjust the level of detail of its proposals based on the importance of the relaxation methods. For example, the proposal unit can provide detailed proposals for highly important relaxation methods. For example, it can provide simplified proposals for less important relaxation methods. For example, it can provide moderate proposals for moderately important relaxation methods. For example, the proposal unit can provide detailed proposals for highly important relaxation methods. For less important relaxation methods, it can provide simplified proposals. For moderately important relaxation methods, it can provide moderate proposals. This allows for efficient proposals by adjusting the level of detail of the proposals based on the importance of the relaxation methods. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the relaxation methods into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0050] The suggestion unit can apply different suggestion algorithms depending on the category of relaxation method when making a suggestion. For example, the suggestion unit can apply a meditation suggestion algorithm to relaxation methods related to meditation. For example, the suggestion unit can apply a breathing technique suggestion algorithm to relaxation methods related to breathing techniques. For example, the suggestion unit can apply a music therapy suggestion algorithm to relaxation methods related to music therapy. For example, the suggestion unit can apply a meditation suggestion algorithm to relaxation methods related to meditation. For relaxation methods related to breathing techniques. For relaxation methods related to music therapy. By applying different suggestion algorithms depending on the category of relaxation method, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the category of relaxation method into a generative AI and have the generative AI perform the application of the suggestion algorithm.

[0051] The proposal unit can determine the priority of proposals based on the timing of the proposed relaxation methods. For example, the proposal unit may prioritize the proposal of relaxation methods needed in the immediate future. The proposal unit may also postpone the proposal of relaxation methods needed in the future. The proposal unit may also moderately propose relaxation methods needed in the medium term. For example, the proposal unit may prioritize the proposal of relaxation methods needed in the immediate future. It may postpone the proposal of relaxation methods needed in the future. It may moderately propose relaxation methods needed in the medium term. This allows for efficient proposals by determining the priority of proposals based on the timing of the proposed relaxation methods. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit may input the timing of the proposed relaxation methods into the generative AI and have the generative AI determine the priority of the proposals.

[0052] The suggestion unit can adjust the order of suggestions based on the relevance of the relaxation methods. For example, the suggestion unit may prioritize suggesting highly relevant relaxation methods. The suggestion unit may also postpone suggesting less relevant relaxation methods. The suggestion unit may also moderately suggest relaxation methods of moderate relevance. For example, the suggestion unit may prioritize suggesting highly relevant relaxation methods. It may postpone suggesting less relevant relaxation methods. It may moderately suggest relaxation methods of moderate relevance. By adjusting the order of suggestions based on the relevance of the relaxation methods, efficient suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit may input the relevance of the relaxation methods into a generative AI and have the generative AI adjust the order of suggestions.

[0053] The service provider can select the optimal service method by referring to the user's past relaxation history when providing relaxation methods. For example, the service provider can propose the optimal service method based on the relaxation methods the user has used in the past. The service provider can also predict and propose the optimal service method for a specific time period based on the user's past relaxation history. The service provider can also analyze the user's past relaxation history and select the most effective service method. For example, the service provider can propose the optimal service method based on the relaxation methods the user has used in the past. It can predict and propose the optimal service method for a specific time period based on the user's past relaxation history. It can analyze the user's past relaxation history and select the most effective service method. This allows the service provider to select the optimal service method by referring to the user's past relaxation history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's past relaxation history into a generative AI and have the generative AI select the optimal service method.

[0054] The service provider can customize the means of providing relaxation methods based on the user's current living situation. For example, if the user is at work, the service provider can provide a short and effective relaxation method. If the user is at home, the service provider can also provide a longer relaxation method. If the user is traveling, the service provider can also provide a simple relaxation method. For example, if the user is at work, the service provider can provide a short and effective relaxation method. If the user is at home, the service provider can provide a longer relaxation method. If the user is traveling, the service provider can provide a simple relaxation method. By customizing the means of providing relaxation based on the user's current living situation, more appropriate relaxation can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input user living situation data into a generative AI and have the generative AI perform the customization of the means of providing relaxation.

[0055] The service provider can select the optimal service method when providing relaxation methods, taking into account the user's geographical location information. For example, if the user is in a specific location, the service provider can provide a relaxation method suitable for that location. For example, if the user is on the move, the service provider can also provide a relaxation method suitable for travel. For example, if the user is at home, the service provider can also provide a relaxation method suitable for home. For example, if the user is in a specific location, the service provider can provide a relaxation method suitable for that location. If the user is on the move, the service provider can provide a relaxation method suitable for travel. If the user is at home, the service provider can provide a relaxation method suitable for home. In this way, the service provider can provide the optimal relaxation method by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI select the optimal service method.

[0056] The service provider can analyze the user's social media activity and propose a means of providing relaxation methods. For example, if the user is feeling stressed on social media, the service provider can provide a relaxation method that is effective in reducing stress. For example, if the user is relaxed on social media, the service provider can also provide a relaxation method to maintain that relaxation. For example, if the user is in a hurry on social media, the service provider can also provide a relaxation method that provides quick results. For example, if the user is feeling stressed on social media, the service provider can provide a relaxation method that is effective in reducing stress. If the user is relaxed on social media, the service provider can provide a relaxation method to maintain that relaxation. If the user is in a hurry on social media, the service provider can provide a relaxation method that provides quick results. By analyzing the user's social media activity, the service provider can provide a more appropriate relaxation method. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI and have the generative AI propose a means of providing relaxation methods.

[0057] The emergency response unit can select the optimal response method by referring to the user's past stress levels during an emergency. For example, the emergency response unit proposes the optimal emergency response based on how the user responded when they experienced high stress in the past. The emergency response unit can also predict and propose the optimal emergency response method for a specific situation based on the user's past stress levels. For example, the emergency response unit can analyze the user's past stress levels and select the most effective emergency response method. For example, the emergency response unit proposes the optimal emergency response based on how the user responded when they experienced high stress in the past. It predicts and proposes the optimal emergency response method for a specific situation based on the user's past stress levels. It analyzes the user's past stress levels and selects the most effective emergency response method. This allows the optimal emergency response method to be selected by referring to the user's past stress levels. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emergency response unit can input the user's past stress levels into a generative AI and have the generative AI select the optimal response method.

[0058] The emergency response unit can select the optimal response method during an emergency, taking into account the user's geographical location information. For example, if the user is in a specific location, the emergency response unit can provide an emergency response method suitable for that location. For example, if the user is on the move, the emergency response unit can also provide an emergency response method suitable for travel. For example, if the user is at home, the emergency response unit can also provide an emergency response method suitable for home. For example, if the emergency response unit is in a specific location, it can provide an emergency response method suitable for that location. If the user is on the move, it can provide an emergency response method suitable for travel. If the user is at home, it can provide an emergency response method suitable for home. This allows the system to provide the optimal emergency response method by taking into account the user's geographical location information. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emergency response unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal response method.

[0059] The community department can select the optimal interaction method by referring to the user's past interaction history during community interactions. For example, the community department can suggest the optimal interaction method based on the interaction methods the user has used in the past. The community department can also predict and suggest the optimal interaction method for a specific time period based on the user's past interaction history. The community department can also analyze the user's past interaction history and select the most effective interaction method. For example, the community department can suggest the optimal interaction method based on the interaction methods the user has used in the past. It can predict and suggest the optimal interaction method for a specific time period based on the user's past interaction history. It can analyze the user's past interaction history and select the most effective interaction method. This allows the community department to select the optimal interaction method by referring to the user's past interaction history. Some or all of the above processing in the community department may be performed using, for example, a generative AI, or without using a generative AI. For example, the community department can input the user's past interaction history into a generative AI and have the generative AI select the optimal interaction method.

[0060] The community unit can select the optimal interaction method by considering the user's geographical location information during community interactions. For example, if the user is in a specific location, the community unit can provide an interaction method suitable for that location. For example, if the user is on the move, the community unit can also provide an interaction method suitable for travel. For example, if the user is at home, the community unit can also provide an interaction method suitable for home. For example, if the community unit is in a specific location, it can provide an interaction method suitable for that location. If the user is on the move, it can provide an interaction method suitable for travel. If the user is at home, it can provide an interaction method suitable for home. In this way, the optimal interaction method can be provided by considering the user's geographical location information. Some or all of the above processing in the community unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the community unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal interaction method.

[0061] The Ministry of Education can select the optimal delivery method for educational content by referring to the user's past learning history. For example, the Ministry of Education can propose the optimal delivery method based on the educational content the user has used in the past. The Ministry of Education can also predict and propose the optimal delivery method for a specific time period based on the user's past learning history. The Ministry of Education can also analyze the user's past learning history and select the most effective delivery method. For example, the Ministry of Education can propose the optimal delivery method based on the educational content the user has used in the past. It can predict and propose the optimal delivery method for a specific time period based on the user's past learning history. It can analyze the user's past learning history and select the most effective delivery method. In this way, the optimal delivery method can be selected by referring to the user's past learning history. Some or all of the above processes by the Ministry of Education may be performed using, for example, a generative AI, or not using a generative AI. For example, the Ministry of Education can input the user's past learning history into a generative AI and have the generative AI select the optimal delivery method.

[0062] The Ministry of Education can select the optimal delivery method for educational content by considering the user's geographical location. For example, if the user is in a specific location, the Ministry of Education can provide educational content appropriate for that location. For example, if the user is on the move, the Ministry of Education can also provide educational content appropriate for travel. For example, if the user is at home, the Ministry of Education can also provide educational content appropriate for home. For example, if the user is in a specific location, the Ministry of Education can provide educational content appropriate for that location. If the user is on the move, the Ministry of Education can provide educational content appropriate for travel. If the user is at home, the Ministry of Education can provide educational content appropriate for home. In this way, the Ministry of Education can provide the optimal educational content by considering the user's geographical location. Some or all of the above processing by the Ministry of Education may be performed using, for example, a generative AI, or without a generative AI. For example, the Ministry of Education can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The integrated platform can analyze a user's past data collection history and select the optimal collection method to support their mental health. For example, it can suggest the optimal collection method based on data the user has frequently collected in the past. It can predict and suggest data to collect at specific time periods based on the user's past data collection history. It analyzes the user's past data collection history and selects the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history.

[0065] The integrated platform can filter data collection based on the user's current activity and environment to support their mental health. For example, if the user is exercising, it prioritizes collecting exercise-related data. If the user is resting, it prioritizes collecting relaxation-related data. If the user is working, it prioritizes collecting work-related data. This allows for the collection of more relevant data by filtering it based on the user's current activity and environment.

[0066] The integrated platform can adjust the level of detail in the analysis based on the importance of the collected data, in order to support users' mental health. For example, it can perform detailed analysis on high-importance data, simplified analysis on low-importance data, and moderate analysis on medium-importance data. This allows for efficient analysis by adjusting the level of detail based on the importance of the collected data.

[0067] The integrated platform can apply different analytical methods depending on the data category during analysis to support users' mental health. For example, it can apply a stress analysis method to stress-related data, a relaxation analysis method to relaxation-related data, and a rushed situation analysis method to rushed situation-related data. This allows for more appropriate analysis by applying different analytical methods depending on the data category.

[0068] The integrated platform can adjust the level of detail in relaxation suggestions based on the importance of the suggested relaxation method, in order to support the user's mental health. For example, it can provide detailed suggestions for highly important relaxation methods, simplified suggestions for less important methods, and appropriate suggestions for moderately important methods. This allows for more efficient suggestions by adjusting the level of detail based on the importance of the relaxation method.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects data. The data collection unit can collect data in conjunction with, for example, smartphone sensors or smartwatches. The data collection unit collects heart rate data using a heart rate sensor, activity data using an accelerometer, and movement pattern data using a GPS sensor. For example, the data collection unit monitors the user's heart rate in real time using a heart rate sensor and collects data. It measures the user's activity level using an accelerometer and collects data. It tracks the user's movement pattern using a GPS sensor and collects data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms, statistical analysis methods, and deep learning techniques. For example, the analysis unit uses machine learning algorithms to analyze collected heart rate data and determine the user's stress level. It uses statistical analysis methods to analyze collected exercise data and evaluate the user's activity level. It uses deep learning techniques to analyze collected movement pattern data and identify the user's behavioral patterns. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department proposes relaxation methods, counseling content, and emergency response methods. For example, based on the analysis results, the proposal department proposes meditation, breathing exercises, and music therapy to the user. Step 4: The service provider provides counseling and relaxation based on the proposals made by the suggestion provider. The service provider offers meditation, breathing exercises, and music therapy. For example, the service provider provides meditation to the user based on the suggested meditation method, breathing exercises to the user based on the suggested breathing method, and music therapy to the user based on the suggested music therapy.

[0071] (Example of form 2) An integrated platform according to an embodiment of the present invention is a system that uses an AI agent to provide personalized support for mental health problems such as stress, depression, and anxiety. This integrated platform automatically records daily mood and activities and performs continuous status checks. Next, it provides counseling, relaxation guidance, and emergency response. This platform proposes personally optimized solutions to target groups across all generations who are facing mental challenges. For example, the integrated platform uses a 3D avatar as an AI counselor that performs natural interactions including facial expressions and gestures. Gesture recognition and generation using deep learning enables real-time responses according to the user's emotional state. For example, if the user is sad, it responds with comforting facial expressions and gentle gestures. Next, the integrated platform performs mood analysis and automatic recording using voice input and chatbots utilizing NLP (Natural Language Processing). It works in conjunction with smartphone sensors and smartwatches to automatically monitor movement patterns, exercise levels, heart rate, sleep patterns, etc., and centrally manages health status. Furthermore, the integrated platform provides personalized relaxation content that suggests meditation, breathing exercises, and music therapy based on the user's state and preferences. The platform reflects user data in real time and recommends optimal relaxation methods. It also provides real-time consultations through video calls and chats with professional counselors. Counseling records and user data are integrated to provide a consistent treatment process. Furthermore, the platform builds an online community where users facing similar problems can connect, offering educational content and online seminars on mental health. Anonymous interaction provides a safe environment for sharing experiences and supporting each other. Finally, the platform's AI detects high stress levels and provides emergency measures (e.g., emergency relaxation guides). If necessary, it automatically notifies pre-registered emergency contacts and professionals to facilitate a quick response.This platform can reduce stress and anxiety in daily life and maintain and improve users' mental health. Furthermore, it can prevent mental health problems from worsening through early intervention and preventative care. It provides easy and efficient mental healthcare, allowing users to easily manage their own mental health. The goal is to raise mental health awareness throughout society and create an easily accessible care environment. As a result, the integrated platform can comprehensively support users' mental health and provide optimal care tailored to individual needs.

[0072] The integrated platform according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. The data collection unit can collect data in cooperation with, for example, smartphone sensors or smartwatches. The data collection unit can collect heart rate data using, for example, a heart rate sensor. The data collection unit can also collect exercise data using an accelerometer. Furthermore, the data collection unit can collect movement pattern data using a GPS sensor. For example, the data collection unit can use a heart rate sensor to monitor the user's heart rate in real time and collect data. It can use an accelerometer to measure the user's exercise level and collect data. It can use a GPS sensor to track the user's movement pattern and collect data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can use, for example, machine learning algorithms to analyze the data. The analysis unit can also use, for example, statistical analysis methods to analyze the data. Furthermore, the analysis unit can also use deep learning technology to analyze the data. For example, the analysis unit can use a machine learning algorithm to analyze the collected heart rate data and determine the user's stress level. The system analyzes collected movement data using statistical analysis methods to evaluate the user's activity level. It also analyzes collected movement pattern data using deep learning techniques to identify the user's behavioral patterns. The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can suggest relaxation methods. It can also suggest counseling content. Furthermore, the proposal unit can suggest emergency response methods. For example, based on the analysis results, the proposal unit suggests meditation to the user. Based on the analysis results, it suggests breathing exercises to the user. Based on the analysis results, it suggests music therapy to the user. The service provider provides counseling and relaxation based on the suggestions made by the proposal unit. For example, the service provider can provide meditation. It can also provide breathing exercises. Furthermore, the service provider can provide music therapy. For example, the service provider provides meditation to the user based on the suggested meditation method.Based on the proposed breathing technique, the system provides the user with breathing exercises. Based on the proposed music therapy, the system provides the user with music therapy. As a result, the integrated platform according to the embodiment can consistently collect, analyze, propose, and provide data, enabling it to provide personalized support to the user.

[0073] The data collection unit collects data. For example, the data collection unit can collect data in conjunction with smartphone sensors or smartwatches. Specifically, it utilizes various sensors installed in smartphones and smartwatches to collect user biometric information and behavioral data in real time. For example, it can collect heart rate data using a heart rate sensor. Heart rate sensors are worn on the user's wrist or chest and can detect heart rate fluctuations with high accuracy. This makes it possible to monitor the user's heart rate in real time and collect data. The data collection unit can also collect exercise data using an accelerometer. The accelerometer detects the user's movements in three dimensions and accurately measures exercise such as walking, running, and climbing stairs. Furthermore, the data collection unit can collect movement pattern data using a GPS sensor. The GPS sensor acquires the user's location information with high accuracy and collects data such as movement routes, speed, and time spent at locations. For example, the data collection unit uses a heart rate sensor to monitor the user's heart rate in real time and collect data. It also uses an accelerometer to measure the user's exercise level and collect data. Using GPS sensors, the system tracks the user's movement patterns and collects data. This allows the data collection unit to comprehensively gather user biometric and behavioral data, building a comprehensive database. The collected data is sent to a cloud server, making it accessible to the analysis and proposal units. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze data using machine learning algorithms. Specifically, it integrates collected heart rate data, exercise data, and movement pattern data to analyze the user's health status and behavioral patterns in detail. For instance, it uses machine learning algorithms to analyze collected heart rate data and determine the user's stress level. By analyzing heart rate variability and patterns, it can accurately determine whether the user is experiencing stress. It also uses statistical analysis methods to analyze collected exercise data and evaluate the user's activity level. By analyzing exercise data over time, it can understand the user's exercise habits and activity patterns. Furthermore, it uses deep learning technology to analyze collected movement pattern data and identify the user's behavioral patterns. Deep learning technology can learn from large amounts of data and extract complex patterns and relationships. This allows the analysis unit to quickly and accurately analyze collected data and understand the user's health status and behavioral patterns in real time. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term health risk assessments and trend analyses. For example, based on past heart rate data, it can predict fluctuations in stress levels during specific time periods or situations and assess future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only provide real-time situational awareness but also support long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0075] The proposal department makes suggestions based on the analysis results obtained by the analysis department. For example, the proposal department can suggest relaxation methods. Specifically, based on the analysis results, it suggests the most suitable relaxation method for the user. For example, if the user's stress level is high, the proposal department can suggest relaxation methods such as meditation, deep breathing, or yoga. The proposal department can also suggest counseling content. Based on the user's behavioral patterns and health condition, it suggests appropriate counseling content to support the user's mental health. Furthermore, the proposal department can also suggest emergency response methods. For example, if the user's heart rate suddenly increases, the proposal department can suggest resting or contacting a medical institution as an emergency response method. Based on the analysis results, the proposal department suggests meditation to the user. Based on the analysis results, it suggests breathing exercises to the user. Based on the analysis results, it suggests music therapy to the user. In this way, the proposal department can provide personalized suggestions tailored to the user's health condition and behavioral patterns, supporting the user's health management. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on feedback from users who have implemented the suggested relaxation methods, the suggestions are reviewed and more effective methods are proposed. Furthermore, the suggestion department can make suggestions tailored to the user's preferences and lifestyle. This allows the suggestion department to propose optimal health management methods to users, thereby improving their health.

[0076] The service provider will offer counseling and relaxation services based on the proposals made by the suggestion team. Specifically, they will provide users with meditation based on the suggested meditation methods. Meditation is an effective way for users to relax and restore balance to their mind and body. The service provider will provide meditation guidance in audio and video to support users in easily performing the meditation. They will also provide users with breathing exercises based on the suggested breathing techniques. Breathing exercises are an important technique for reducing stress and enhancing relaxation. The service provider will explain the breathing exercise procedure in detail and instruct users to perform it correctly. Furthermore, they will provide users with music therapy based on the suggested music therapy. Music therapy promotes relaxation of the mind and body through music and has the effect of reducing stress. The service provider will select music according to the user's preferences and situation to maximize the relaxation effect. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services offered. For example, based on feedback from users who have participated in meditation, breathing exercises, and music therapy, they will review the services offered and provide more effective methods. Furthermore, the service provider can offer customized relaxation methods tailored to the user's lifestyle and preferences. This allows the service provider to provide users with the optimal relaxation method and improve their health.

[0077] The data collection unit can collect data in conjunction with smartphone sensors and smartwatches. For example, the data collection unit can collect heart rate data using a smartphone sensor. The data collection unit can also collect exercise data using a smartwatch. The data collection unit can also collect sleep pattern data using a smartphone sensor. For example, the data collection unit can use a smartphone sensor to monitor the user's heart rate in real time and collect data. It can use a smartwatch to measure the user's exercise level and collect data. It can use a smartphone sensor to track the user's sleep patterns and collect data. This allows for more detailed data collection by linking with smartphone sensors and smartwatches. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smartphone sensors and smartwatches into a generating AI and have the generating AI perform data analysis.

[0078] The analysis unit can analyze the collected data and determine the user's emotional state. For example, the analysis unit can determine the user's emotional state using facial expression analysis. The analysis unit can also determine the user's emotional state using voice analysis. The analysis unit can also determine the user's emotional state using text analysis. For example, the analysis unit can analyze the user's facial expression data using facial expression analysis and determine the emotional state. It can analyze the user's voice data using voice analysis and determine the emotional state. It can analyze the user's text data using text analysis and determine the emotional state. By determining the user's emotional state, more appropriate support can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the determination of the emotional state.

[0079] The suggestion unit can propose the optimal relaxation method based on the analysis results. For example, the suggestion unit may suggest meditation. For example, the suggestion unit may also suggest breathing exercises. For example, the suggestion unit may also suggest music therapy. For example, based on the analysis results, the suggestion unit may suggest meditation to the user. Based on the analysis results, it may suggest breathing exercises to the user. Based on the analysis results, it may suggest music therapy to the user. In this way, by suggesting the optimal relaxation method based on the analysis results, it is possible to provide support tailored to the user. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit may input the analysis results into a generative AI and have the generative AI execute the suggestion of the optimal relaxation method.

[0080] The service provider can provide meditation, breathing exercises, and music therapy based on the proposed relaxation methods. For example, the service provider can provide meditation. For example, the service provider can also provide breathing exercises. For example, the service provider can also provide music therapy. For example, the service provider can provide meditation to the user based on the proposed meditation method. For example, the service provider can provide breathing exercises to the user based on the proposed breathing method. For example, the service provider can provide music therapy to the user based on the proposed music therapy. This can improve the user's mental health by providing meditation, breathing exercises, and music therapy based on the proposed relaxation methods. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the proposed relaxation methods into a generative AI and have the generative AI perform the provision of relaxation methods.

[0081] The Emergency Response Unit can detect a user's high stress level and provide first aid. For example, the Emergency Response Unit can detect an increase in heart rate and provide first aid. The Emergency Response Unit can also detect changes in skin electrical activity and provide first aid. The Emergency Response Unit can also detect changes in breathing patterns and provide first aid. For example, the Emergency Response Unit can detect an increase in heart rate and provide relaxation techniques to the user. It can detect changes in skin electrical activity and provide emergency counseling to the user. It can detect changes in breathing patterns and provide emergency relaxation guidance to the user. This allows for the detection of a user's high stress level and the rapid provision of first aid, thereby protecting the user's mental health. Some or all of the above processes in the Emergency Response Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Emergency Response Unit can input the user's biometric data into a generative AI and have the generative AI perform the provision of first aid.

[0082] The Community Department can provide an online community where users facing similar problems can interact with each other. For example, the Community Department can provide a chat function. The Community Department can also provide a forum function. The Community Department can also provide a video call function. For example, the Community Department can provide a chat function that allows users to interact in real time. It can provide a forum function that allows users to share information. It can provide a video call function that allows users to interact face-to-face. This enables user support by providing an online community where users facing similar problems can interact. Some or all of the above processing in the Community Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Community Department can input user interaction data into a generative AI and have the generative AI suggest the optimal method of interaction.

[0083] The Ministry of Education can provide educational content and online seminars on mental health. For example, the Ministry of Education can provide video content. The Ministry of Education can also provide live seminars. The Ministry of Education can also provide text content. For example, the Ministry of Education can provide educational videos on mental health. Through live seminars, it can provide lectures by experts. Through text content, it can provide knowledge on mental health. In this way, by providing educational content and online seminars on mental health, the Ministry of Education can improve users' knowledge. Some or all of the above processing by the Ministry of Education may be performed using, for example, generative AI, or not using generative AI. For example, the Ministry of Education can input user learning data into a generative AI and have the generative AI perform the task of providing optimal educational content.

[0084] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the frequency of data collection to collect more detailed information. If the user is relaxed, the data collection unit can also decrease the frequency of data collection to reduce the user's burden. If the user is in a hurry, the data collection unit can also adjust the frequency of data collection to quickly collect necessary information. For example, if the user is stressed, the data collection unit can increase the frequency of data collection to collect more detailed information. If the user is relaxed, the data collection unit can decrease the frequency of data collection to reduce the user's burden. If the user is in a hurry, the data collection unit can adjust the frequency of data collection to quickly collect necessary information. This allows for more appropriate data collection by adjusting the frequency of data collection based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of data collection.

[0085] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. The data collection unit can also predict and suggest data to be collected during specific time periods based on the user's past data collection history. The data collection unit can also analyze the user's past data collection history and select the most efficient collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. It can predict and suggest data to be collected during specific time periods based on the user's past data collection history. It can analyze the user's past data collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0086] The data collection unit can filter data based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. If the user is resting, the data collection unit can also prioritize collecting data related to relaxation. If the user is working, the data collection unit can also prioritize collecting data related to work. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. If the user is resting, the data collection unit will prioritize collecting data related to relaxation. If the user is working, the data collection unit will prioritize collecting data related to work. By filtering the data based on the user's current activity status and environment, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user activity status and environment data into a generative AI and have the generative AI perform data filtering.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress. For example, if the user is relaxed, the data collection unit may also prioritize collecting data related to relaxation. For example, if the user is in a hurry, the data collection unit may also prioritize collecting data related to the hurried situation. In this way, by determining the priority of data to collect based on the user's emotions, more important data can be collected preferentially. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data prioritization.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, if the user is on the move, the data collection unit can also prioritize the collection of data related to movement. For example, if the user is at home, the data collection unit can also prioritize the collection of data related to home. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. If the user is on the move, the data collection unit will prioritize the collection of data related to movement. If the user is at home, the data collection unit will prioritize the collection of data related to home. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI determine the priority of the data.

[0089] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is experiencing stress on social media, the data collection unit can collect data related to stress. For example, if a user is relaxing on social media, the data collection unit can also collect data related to relaxation. For example, if a user is in a hurry on social media, the data collection unit can also collect data related to the hurried situation. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI collect the relevant data.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize data related to stress in its analysis. For example, if the user is relaxed, the analysis unit can also prioritize data related to relaxation in its analysis. For example, if the user is in a hurry, the analysis unit can also prioritize data related to the hurried situation in its analysis. For example, if the user is stressed, the analysis unit will prioritize data related to stress in its analysis. If the user is relaxed, the analysis unit will prioritize data related to relaxation in its analysis. If the user is in a hurry, the analysis unit will prioritize data related to the hurried situation in its analysis. By adjusting the analysis algorithm based on the user's emotions, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform a moderate analysis on data of medium importance. For example, the analysis unit can perform a detailed analysis on data of high importance. It can perform a simplified analysis on data of low importance. It can perform a moderate analysis on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0092] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply a stress analysis method to data related to stress. For example, the analysis unit can apply a relaxation analysis method to data related to relaxation. For example, the analysis unit can apply a rushed situation analysis method to data related to rushed situations. For example, the analysis unit can apply a stress analysis method to data related to stress. For example, it can apply a relaxation analysis method to data related to relaxation. For example, it can apply a rushed situation analysis method to data related to rushed situations. By applying different analysis methods depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis method.

[0093] 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 provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0094] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also perform analysis while referring to past data. The analysis unit may also focus on analyzing data from a specific period. For example, the analysis unit may prioritize the analysis of the most recent data. It may perform analysis while referring to past data. It may focus on analyzing data from a specific period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit may input the data collection period to the generative AI and have the generative AI perform the determination of the analysis priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit may also moderately analyze data of moderate relevance. For example, the analysis unit may prioritize the analysis of highly relevant data, postpone the analysis of less relevant data, and moderately analyze data of moderate relevance. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0096] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make suggestions using a gentle expression. If the user is relaxed, the suggestion unit may also make suggestions using a detailed expression. If the user is in a hurry, the suggestion unit may also make suggestions using a concise expression. For example, if the user is stressed, the suggestion unit will make suggestions using a gentle expression. If the user is relaxed, the suggestion unit will make suggestions using a detailed expression. If the user is in a hurry, the suggestion unit will make suggestions using a concise expression. By adjusting the way the suggestion is expressed based on the user's emotions, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way the suggestion is expressed.

[0097] The proposal unit can adjust the level of detail of its proposals based on the importance of the relaxation methods. For example, the proposal unit can provide detailed proposals for highly important relaxation methods. For example, it can provide simplified proposals for less important relaxation methods. For example, it can provide moderate proposals for moderately important relaxation methods. For example, the proposal unit can provide detailed proposals for highly important relaxation methods. For less important relaxation methods, it can provide simplified proposals. For moderately important relaxation methods, it can provide moderate proposals. This allows for efficient proposals by adjusting the level of detail of the proposals based on the importance of the relaxation methods. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the relaxation methods into the generative AI and have the generative AI adjust the level of detail of the proposals.

[0098] The suggestion unit can apply different suggestion algorithms depending on the category of relaxation method when making a suggestion. For example, the suggestion unit can apply a meditation suggestion algorithm to relaxation methods related to meditation. For example, the suggestion unit can apply a breathing technique suggestion algorithm to relaxation methods related to breathing techniques. For example, the suggestion unit can apply a music therapy suggestion algorithm to relaxation methods related to music therapy. For example, the suggestion unit can apply a meditation suggestion algorithm to relaxation methods related to meditation. For relaxation methods related to breathing techniques. For relaxation methods related to music therapy. By applying different suggestion algorithms depending on the category of relaxation method, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the category of relaxation method into a generative AI and have the generative AI perform the application of the suggestion algorithm.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, the suggestion unit may make longer suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit may make quick and concise suggestions. For example, if the user is stressed, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, it will make longer suggestions that include detailed explanations. If the user is in a hurry, it will make quick and concise suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0100] The proposal unit can determine the priority of proposals based on the timing of the proposed relaxation methods. For example, the proposal unit may prioritize the proposal of relaxation methods needed in the immediate future. The proposal unit may also postpone the proposal of relaxation methods needed in the future. The proposal unit may also moderately propose relaxation methods needed in the medium term. For example, the proposal unit may prioritize the proposal of relaxation methods needed in the immediate future. It may postpone the proposal of relaxation methods needed in the future. It may moderately propose relaxation methods needed in the medium term. This allows for efficient proposals by determining the priority of proposals based on the timing of the proposed relaxation methods. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit may input the timing of the proposed relaxation methods into the generative AI and have the generative AI determine the priority of the proposals.

[0101] The suggestion unit can adjust the order of suggestions based on the relevance of the relaxation methods. For example, the suggestion unit may prioritize suggesting highly relevant relaxation methods. The suggestion unit may also postpone suggesting less relevant relaxation methods. The suggestion unit may also moderately suggest relaxation methods of moderate relevance. For example, the suggestion unit may prioritize suggesting highly relevant relaxation methods. It may postpone suggesting less relevant relaxation methods. It may moderately suggest relaxation methods of moderate relevance. By adjusting the order of suggestions based on the relevance of the relaxation methods, efficient suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit may input the relevance of the relaxation methods into a generative AI and have the generative AI adjust the order of suggestions.

[0102] The service provider can estimate the user's emotions and adjust the method of providing relaxation based on the estimated emotions. For example, if the user is stressed, the service provider can provide relaxation using a gentle method. If the user is relaxed, the service provider can also provide relaxation using a detailed method. If the user is in a hurry, the service provider can also provide relaxation using a concise method. For example, if the user is stressed, the service provider can provide relaxation using a gentle method. If the user is relaxed, the service provider can provide relaxation using a detailed method. If the user is in a hurry, the service provider can provide relaxation using a concise method. By adjusting the method of providing relaxation based on the user's emotions, more appropriate relaxation can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the method of provision.

[0103] The service provider can select the optimal service method by referring to the user's past relaxation history when providing relaxation methods. For example, the service provider can propose the optimal service method based on the relaxation methods the user has used in the past. The service provider can also predict and propose the optimal service method for a specific time period based on the user's past relaxation history. The service provider can also analyze the user's past relaxation history and select the most effective service method. For example, the service provider can propose the optimal service method based on the relaxation methods the user has used in the past. It can predict and propose the optimal service method for a specific time period based on the user's past relaxation history. It can analyze the user's past relaxation history and select the most effective service method. This allows the service provider to select the optimal service method by referring to the user's past relaxation history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's past relaxation history into a generative AI and have the generative AI select the optimal service method.

[0104] The service provider can customize the means of providing relaxation methods based on the user's current living situation. For example, if the user is at work, the service provider can provide a short and effective relaxation method. If the user is at home, the service provider can also provide a longer relaxation method. If the user is traveling, the service provider can also provide a simple relaxation method. For example, if the user is at work, the service provider can provide a short and effective relaxation method. If the user is at home, the service provider can provide a longer relaxation method. If the user is traveling, the service provider can provide a simple relaxation method. By customizing the means of providing relaxation based on the user's current living situation, more appropriate relaxation can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input user living situation data into a generative AI and have the generative AI perform the customization of the means of providing relaxation.

[0105] The service provider can estimate the user's emotions and determine the priority of relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the service provider will prioritize providing relaxation methods that are effective in reducing stress. For example, if the user is relaxed, the service provider may also prioritize providing relaxation methods that help maintain that relaxation. For example, if the user is in a hurry, the service provider may also prioritize providing relaxation methods that provide quick results. For example, if the user is feeling stressed, the service provider will prioritize providing relaxation methods that are effective in reducing stress. If the user is relaxed, the service provider will prioritize providing relaxation methods that help maintain that relaxation. If the user is in a hurry, the service provider will prioritize providing relaxation methods that provide quick results. By determining the priority of relaxation methods based on the user's emotions, more appropriate relaxation can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of relaxation methods.

[0106] The service provider can select the optimal service method when providing relaxation methods, taking into account the user's geographical location information. For example, if the user is in a specific location, the service provider can provide a relaxation method suitable for that location. For example, if the user is on the move, the service provider can also provide a relaxation method suitable for travel. For example, if the user is at home, the service provider can also provide a relaxation method suitable for home. For example, if the user is in a specific location, the service provider can provide a relaxation method suitable for that location. If the user is on the move, the service provider can provide a relaxation method suitable for travel. If the user is at home, the service provider can provide a relaxation method suitable for home. In this way, the service provider can provide the optimal relaxation method by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI select the optimal service method.

[0107] The service provider can analyze the user's social media activity and propose a means of providing relaxation methods. For example, if the user is feeling stressed on social media, the service provider can provide a relaxation method that is effective in reducing stress. For example, if the user is relaxed on social media, the service provider can also provide a relaxation method to maintain that relaxation. For example, if the user is in a hurry on social media, the service provider can also provide a relaxation method that provides quick results. For example, if the user is feeling stressed on social media, the service provider can provide a relaxation method that is effective in reducing stress. If the user is relaxed on social media, the service provider can provide a relaxation method to maintain that relaxation. If the user is in a hurry on social media, the service provider can provide a relaxation method that provides quick results. By analyzing the user's social media activity, the service provider can provide a more appropriate relaxation method. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI and have the generative AI propose a means of providing relaxation methods.

[0108] The emergency response unit can estimate the user's emotions and adjust the emergency response method based on the estimated user emotions. For example, if the user is experiencing high stress, the emergency response unit can quickly take emergency action to reduce stress. For example, if the user is in a panic state, the emergency response unit can also take emergency action to calm the user. For example, if the user is depressed, the emergency response unit can also take emergency action to comfort the user. For example, if the user is experiencing high stress, the emergency response unit can quickly take emergency action to reduce stress. If the user is in a panic state, it can take emergency action to calm the user. If the user is depressed, it can take emergency action to comfort the user. By adjusting the emergency response method based on the user's emotions, a more appropriate emergency response becomes possible. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the emergency response unit can input user emotion data into a generative AI and have the generative AI adjust the emergency response method.

[0109] The emergency response unit can select the optimal response method by referring to the user's past stress levels during an emergency. For example, the emergency response unit proposes the optimal emergency response based on how the user responded when they experienced high stress in the past. The emergency response unit can also predict and propose the optimal emergency response method for a specific situation based on the user's past stress levels. For example, the emergency response unit can analyze the user's past stress levels and select the most effective emergency response method. For example, the emergency response unit proposes the optimal emergency response based on how the user responded when they experienced high stress in the past. It predicts and proposes the optimal emergency response method for a specific situation based on the user's past stress levels. It analyzes the user's past stress levels and selects the most effective emergency response method. This allows the optimal emergency response method to be selected by referring to the user's past stress levels. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emergency response unit can input the user's past stress levels into a generative AI and have the generative AI select the optimal response method.

[0110] The emergency response unit can estimate the user's emotions and determine the priority of emergency responses based on the estimated emotions. For example, if the user is experiencing high stress, the emergency response unit will provide a rapid emergency response. For example, if the user is in a state of panic, the emergency response unit may also prioritize emergency responses. For example, if the user is depressed, the emergency response unit may also provide emergency responses at an appropriate time. For example, if the user is experiencing high stress, the emergency response unit will provide a rapid emergency response. If the user is in a state of panic, it will provide emergency responses at an appropriate time. This allows for more appropriate emergency responses by determining the priority of emergency responses based on the user's emotions. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the emergency response unit can input user emotion data into a generative AI and have the generative AI determine the priority of emergency responses.

[0111] The emergency response unit can select the optimal response method during an emergency, taking into account the user's geographical location information. For example, if the user is in a specific location, the emergency response unit can provide an emergency response method suitable for that location. For example, if the user is on the move, the emergency response unit can also provide an emergency response method suitable for travel. For example, if the user is at home, the emergency response unit can also provide an emergency response method suitable for home. For example, if the emergency response unit is in a specific location, it can provide an emergency response method suitable for that location. If the user is on the move, it can provide an emergency response method suitable for travel. If the user is at home, it can provide an emergency response method suitable for home. This allows the system to provide the optimal emergency response method by taking into account the user's geographical location information. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emergency response unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal response method.

[0112] The community unit can estimate the user's emotions and adjust the community's interaction methods based on those emotions. For example, if the user is stressed, the community unit can provide a calm interaction method. If the user is relaxed, the community unit can also provide a lively interaction method. If the user is in a hurry, the community unit can also provide a concise interaction method. For example, if the user is stressed, the community unit can provide a calm interaction method. If the user is relaxed, the community unit can provide a lively interaction method. If the user is in a hurry, the community unit can provide a concise interaction method. By adjusting the community's interaction methods based on the user's emotions, more appropriate interactions become possible. Some or all of the above processing in the community unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the community unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the interaction method.

[0113] The community department can select the optimal interaction method by referring to the user's past interaction history during community interactions. For example, the community department can suggest the optimal interaction method based on the interaction methods the user has used in the past. The community department can also predict and suggest the optimal interaction method for a specific time period based on the user's past interaction history. The community department can also analyze the user's past interaction history and select the most effective interaction method. For example, the community department can suggest the optimal interaction method based on the interaction methods the user has used in the past. It can predict and suggest the optimal interaction method for a specific time period based on the user's past interaction history. It can analyze the user's past interaction history and select the most effective interaction method. This allows the community department to select the optimal interaction method by referring to the user's past interaction history. Some or all of the above processing in the community department may be performed using, for example, a generative AI, or without using a generative AI. For example, the community department can input the user's past interaction history into a generative AI and have the generative AI select the optimal interaction method.

[0114] The community unit can estimate the user's emotions and prioritize communities based on those emotions. For example, if a user is stressed, the community unit will prioritize providing communities that are effective in reducing stress. If a user is relaxed, the community unit can also prioritize providing communities that help maintain that relaxation. If a user is in a hurry, the community unit can also prioritize providing communities that provide quick results. For example, if a user is stressed, the community unit will prioritize providing communities that are effective in reducing stress. If a user is relaxed, the community unit will prioritize providing communities that help maintain that relaxation. If a user is in a hurry, the community unit will prioritize providing communities that provide quick results. By prioritizing communities based on the user's emotions, more appropriate communities can be provided. Some or all of the above processing in the community unit may be performed using, for example, generative AI, or not using generative AI. For example, the community unit can input user emotion data into generative AI and have the generative AI determine the priority of communities.

[0115] The community unit can select the optimal interaction method by considering the user's geographical location information during community interactions. For example, if the user is in a specific location, the community unit can provide an interaction method suitable for that location. For example, if the user is on the move, the community unit can also provide an interaction method suitable for travel. For example, if the user is at home, the community unit can also provide an interaction method suitable for home. For example, if the community unit is in a specific location, it can provide an interaction method suitable for that location. If the user is on the move, it can provide an interaction method suitable for travel. If the user is at home, it can provide an interaction method suitable for home. In this way, the optimal interaction method can be provided by considering the user's geographical location information. Some or all of the above processing in the community unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the community unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal interaction method.

[0116] The Ministry of Education can estimate the user's emotions and adjust the way educational content is delivered based on those emotions. For example, if the user is stressed, the Ministry of Education can deliver the educational content in a calm manner. If the user is relaxed, the Ministry of Education can also deliver the educational content in a detailed manner. If the user is in a hurry, the Ministry of Education can also deliver the educational content in a concise manner. For example, if the user is stressed, the Ministry of Education can deliver the educational content in a calm manner. If the user is relaxed, the Ministry of Education can deliver the educational content in a detailed manner. If the user is in a hurry, the Ministry of Education can deliver the educational content in a concise manner. By adjusting the way educational content is delivered based on the user's emotions, more appropriate education can be provided. Some or all of the above processing by the Ministry of Education may be performed using, for example, generative AI, or not using generative AI. For example, the Ministry of Education can input user emotion data into generative AI and have the generative AI adjust the way educational content is delivered.

[0117] The Ministry of Education can select the optimal delivery method for educational content by referring to the user's past learning history. For example, the Ministry of Education can propose the optimal delivery method based on the educational content the user has used in the past. The Ministry of Education can also predict and propose the optimal delivery method for a specific time period based on the user's past learning history. The Ministry of Education can also analyze the user's past learning history and select the most effective delivery method. For example, the Ministry of Education can propose the optimal delivery method based on the educational content the user has used in the past. It can predict and propose the optimal delivery method for a specific time period based on the user's past learning history. It can analyze the user's past learning history and select the most effective delivery method. In this way, the optimal delivery method can be selected by referring to the user's past learning history. Some or all of the above processes by the Ministry of Education may be performed using, for example, a generative AI, or not using a generative AI. For example, the Ministry of Education can input the user's past learning history into a generative AI and have the generative AI select the optimal delivery method.

[0118] The Ministry of Education can estimate a user's emotions and prioritize educational content based on those emotions. For example, if a user is stressed, the Ministry of Education can prioritize providing educational content that is effective in reducing stress. If a user is relaxed, the Ministry of Education can also prioritize providing educational content that helps maintain that relaxation. If a user is in a hurry, the Ministry of Education can also prioritize providing educational content that provides quick results. For example, if a user is stressed, the Ministry of Education can prioritize providing educational content that is effective in reducing stress. If a user is relaxed, the Ministry of Education can prioritize providing educational content that helps maintain that relaxation. If a user is in a hurry, the Ministry of Education can prioritize providing educational content that provides quick results. By prioritizing educational content based on user emotions, more appropriate education can be provided. Some or all of the above processing in the Ministry of Education may be performed using, for example, generative AI, or not using generative AI. For example, the Ministry of Education can input user emotion data into generative AI and have the generative AI determine the priority of educational content.

[0119] The Ministry of Education can select the optimal delivery method for educational content by considering the user's geographical location. For example, if the user is in a specific location, the Ministry of Education can provide educational content appropriate for that location. For example, if the user is on the move, the Ministry of Education can also provide educational content appropriate for travel. For example, if the user is at home, the Ministry of Education can also provide educational content appropriate for home. For example, if the user is in a specific location, the Ministry of Education can provide educational content appropriate for that location. If the user is on the move, the Ministry of Education can provide educational content appropriate for travel. If the user is at home, the Ministry of Education can provide educational content appropriate for home. In this way, the Ministry of Education can provide the optimal educational content by considering the user's geographical location. Some or all of the above processing by the Ministry of Education may be performed using, for example, a generative AI, or without a generative AI. For example, the Ministry of Education can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The integrated platform can estimate a user's emotions and adjust counseling content based on those emotions to support their mental health. For example, if a user is stressed, the counseling content can be adjusted to be calm and reassuring. If a user is relaxed, the counseling content can be adjusted to encourage deeper self-exploration. If a user is in a hurry, the counseling content can be adjusted to be short and effective. In this way, more effective support can be provided by adjusting counseling content based on the user's emotions.

[0122] The integrated platform can support users' mental health by analyzing their past emotional data and suggesting optimal relaxation methods. For example, it can suggest relaxation methods that were effective when the user was stressed in the past, methods that the user used when they were relaxed in the past, and methods that were effective when the user was rushed in the past. In this way, by analyzing the user's past emotional data, it can suggest the most suitable relaxation methods.

[0123] The integrated platform can estimate a user's emotions and adjust the delivery of relaxation content based on those emotions to support their mental health. For example, if a user is stressed, calming music or meditation can be provided. If a user is relaxed, content that promotes deep relaxation can be provided. If a user is in a hurry, short, effective relaxation content can be provided. This allows for more effective support by tailoring the delivery of relaxation content based on the user's emotions.

[0124] The integrated platform can support users' mental health by estimating their emotions and adjusting emergency response methods based on those estimates. For example, if a user is experiencing high stress, it can quickly implement stress-reducing emergency responses. If a user is panicking, it can implement calming emergency responses. If a user is depressed, it can implement comforting emergency responses. This allows for more appropriate emergency responses by adjusting them based on the user's emotions.

[0125] The integrated platform can support users' mental health by estimating their emotions and adjusting community interactions based on those emotions. For example, if a user is stressed, it can provide a calm interaction style; if a user is relaxed, it can provide an active interaction style; and if a user is in a hurry, it can provide a concise interaction style. This allows for more appropriate interactions by adjusting community interactions based on users' emotions.

[0126] The integrated platform can analyze a user's past data collection history and select the optimal collection method to support their mental health. For example, it can suggest the optimal collection method based on data the user has frequently collected in the past. It can predict and suggest data to collect at specific time periods based on the user's past data collection history. It analyzes the user's past data collection history and selects the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history.

[0127] The integrated platform can filter data collection based on the user's current activity and environment to support their mental health. For example, if the user is exercising, it prioritizes collecting exercise-related data. If the user is resting, it prioritizes collecting relaxation-related data. If the user is working, it prioritizes collecting work-related data. This allows for the collection of more relevant data by filtering it based on the user's current activity and environment.

[0128] The integrated platform can adjust the level of detail in the analysis based on the importance of the collected data, in order to support users' mental health. For example, it can perform detailed analysis on high-importance data, simplified analysis on low-importance data, and moderate analysis on medium-importance data. This allows for efficient analysis by adjusting the level of detail based on the importance of the collected data.

[0129] The integrated platform can apply different analytical methods depending on the data category during analysis to support users' mental health. For example, it can apply a stress analysis method to stress-related data, a relaxation analysis method to relaxation-related data, and a rushed situation analysis method to rushed situation-related data. This allows for more appropriate analysis by applying different analytical methods depending on the data category.

[0130] The integrated platform can adjust the level of detail in relaxation suggestions based on the importance of the suggested relaxation method, in order to support the user's mental health. For example, it can provide detailed suggestions for highly important relaxation methods, simplified suggestions for less important methods, and appropriate suggestions for moderately important methods. This allows for more efficient suggestions by adjusting the level of detail based on the importance of the relaxation method.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects data. The data collection unit can collect data in conjunction with, for example, smartphone sensors or smartwatches. The data collection unit collects heart rate data using a heart rate sensor, activity data using an accelerometer, and movement pattern data using a GPS sensor. For example, the data collection unit monitors the user's heart rate in real time using a heart rate sensor and collects data. It measures the user's activity level using an accelerometer and collects data. It tracks the user's movement pattern using a GPS sensor and collects data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms, statistical analysis methods, and deep learning techniques. For example, the analysis unit uses machine learning algorithms to analyze collected heart rate data and determine the user's stress level. It uses statistical analysis methods to analyze collected exercise data and evaluate the user's activity level. It uses deep learning techniques to analyze collected movement pattern data and identify the user's behavioral patterns. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department proposes relaxation methods, counseling content, and emergency response methods. For example, based on the analysis results, the proposal department proposes meditation, breathing exercises, and music therapy to the user. Step 4: The service provider provides counseling and relaxation based on the proposals made by the suggestion provider. The service provider offers meditation, breathing exercises, and music therapy. For example, the service provider provides meditation to the user based on the suggested meditation method, breathing exercises to the user based on the suggested breathing method, and music therapy to the user based on the suggested music therapy.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, emergency response unit, community unit, and education unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the proposal unit and provision unit are implemented by the specific processing unit 290 of the data processing unit 12. The emergency response unit monitors the user's biometric data using the sensors of the smart device 14 and provides first aid using the specific processing unit 290 of the data processing unit 12. The community unit supports interaction between users using the communication functions of the smart device 14, and the education unit provides educational content 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.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, emergency response unit, community unit, and education unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the sensors of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the proposal unit and provision unit are implemented by the specific processing unit 290 of the data processing unit 12. The emergency response unit monitors the user's biometric data using the sensors of the smart glasses 214 and provides first aid using the specific processing unit 290 of the data processing unit 12. The community unit supports interaction between users using the communication function of the smart glasses 214, and the education unit provides educational content 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.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, emergency response unit, community unit, and education unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the proposal unit and provision unit are implemented by the specific processing unit 290 of the data processing unit 12. The emergency response unit monitors the user's biometric data using the sensors of the headset terminal 314 and provides first aid using the specific processing unit 290 of the data processing unit 12. The community unit supports interaction between users using the communication functions of the headset terminal 314, and the education unit provides educational content using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, emergency response unit, community unit, and education unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the sensors of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the proposal unit and provision unit are implemented by the specific processing unit 290 of the data processing unit 12. The emergency response unit monitors the user's biometric data using the sensors of the robot 414 and provides first aid using the specific processing unit 290 of the data processing unit 12. The community unit supports interaction between users using the communication functions of the robot 414, and the education unit provides educational content using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, The system comprises a provision unit that provides counseling and relaxation based on the content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data in conjunction with smartphone sensors and smartwatches. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine the user's emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We will propose the optimal relaxation method based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Meditation, breathing exercises, and music therapy are provided based on the proposed relaxation methods. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features an emergency response unit that detects high stress levels in users and provides first aid. The system described in Appendix 1, characterized by the features described herein. (Note 7) It features a community section that provides an online community where users facing similar problems can interact with each other. The system described in Appendix 1, characterized by the features described herein. (Note 8) The department has an education division that provides educational content and online seminars on mental health. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the relaxation method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of relaxation method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on the timing of the proposed relaxation methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the relaxation methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the method of providing relaxation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing relaxation methods, the system selects the most suitable method by referring to the user's past relaxation history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing relaxation methods, customize the methods of provision based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and prioritizes relaxation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing relaxation methods, the optimal method of delivery is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing relaxation methods, we analyze users' social media activity and propose delivery methods accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned emergency response unit, It estimates the user's emotions and adjusts emergency response methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned emergency response unit, During emergency situations, the system selects the optimal response method by referring to the user's past stress levels. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned emergency response unit, It estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned emergency response unit, During emergency response, the system selects the optimal response method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned community department, It estimates user emotions and adjusts community interaction methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned community department, When users interact within the community, the system selects the most suitable method of interaction by referring to their past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned community department, It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned community department, When users interact within the community, the system selects the most appropriate method of interaction by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned Ministry of Education, We estimate user emotions and adjust the delivery method of educational content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned Ministry of Education, When providing educational content, the system selects the optimal delivery method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned Ministry of Education, It estimates user sentiment and prioritizes educational content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned Ministry of Education, When providing educational content, the optimal delivery method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, The system comprises a provision unit that provides counseling and relaxation based on the content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data in conjunction with smartphone sensors and smartwatches. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to determine the user's emotional state. The system according to feature 1.

4. The aforementioned proposal section is, We will propose the optimal relaxation method based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Meditation, breathing exercises, and music therapy are provided based on the proposed relaxation methods. The system according to feature 1.

6. It features an emergency response unit that detects high stress levels in users and provides first aid. The system according to feature 1.

7. It features a community section that provides an online community where users facing similar problems can interact with each other. The system according to feature 1.

8. The department has an education division that provides educational content and online seminars on mental health. The system according to feature 1.

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