system

Abstract

The system according to this embodiment aims to cultivate students' social and emotional skills and support their mental health. [Solution] The system according to the embodiment comprises an emotion recognition unit, a stress management unit, an empathy development unit, a decision support unit, and an information provision unit. The emotion recognition unit recognizes the student's emotions and supports them in expressing them appropriately. The stress management unit provides guidance on relaxation techniques and mindfulness based on the emotions recognized by the emotion recognition unit. The empathy development unit cultivates skills to understand the feelings of others and build smooth interpersonal relationships based on the techniques taught by the stress management unit. The decision support unit provides support for making ethical and responsible decisions based on the skills cultivated by the empathy development unit. The information provision unit records the student's emotional development status based on the decisions made by the decision support unit and provides this information to teachers and guardians.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

[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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the cultivation of students' social and emotional skills and the support for mental health have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to cultivate students' social and emotional skills and support mental health.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an emotion recognition unit, a stress management unit, an empathy development unit, a decision support unit, and an information provision unit. The emotion recognition unit recognizes students' emotions and supports them in expressing them appropriately. The stress management unit provides guidance on relaxation techniques and mindfulness based on the emotions recognized by the emotion recognition unit. The empathy development unit cultivates skills to understand the feelings of others and build smooth interpersonal relationships based on the techniques taught by the stress management unit. The decision support unit provides support for making ethical and responsible decisions based on the skills cultivated by the empathy development unit. The information provision unit records the emotional development status of students based on the decisions made by the decision support unit and provides this information to teachers and parents. [Effects of the Invention]

[0007] The system according to this embodiment can cultivate students' social and emotional skills and support their mental health. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is an AI agent that supports the development of students' social and emotional skills (SEL). This system aims to cultivate abilities necessary for future society, such as self-awareness, self-management, and interpersonal skills. The system also supports students' mental health, promoting healthy growth. Key features of the system include support for emotion recognition and expression, stress management, empathy and communication, decision-making support, and information provision to teachers and parents. For example, the system reads emotions from students' facial expressions and words and provides feedback on those emotions to the student. This allows students to objectively understand their own emotions and learn how to express them appropriately. Next, the system teaches students how to deep breath and meditate, helping them reduce stress. This allows students to effectively manage stress and maintain a healthy mental state. Furthermore, the system provides practice in considering things from another person's perspective through role-playing. This allows students to enhance their empathy and build good relationships. The system presents specific scenarios and asks students to consider which choice is most appropriate based on those scenarios. This allows students to develop ethical judgment and take responsible actions. Finally, the system records students' emotional progress and reports it regularly to teachers and parents. This allows teachers and parents to understand students' emotional development and provide appropriate support. This enables the system to recognize students' emotions, manage stress, cultivate empathy, support decision-making, and provide information.

[0029] The system according to this embodiment comprises an emotion recognition unit, a stress management unit, an empathy development unit, a decision support unit, and an information provision unit. The emotion recognition unit recognizes the student's emotions and supports how to express them appropriately. The emotion recognition unit, for example, reads emotions from the student's facial expressions and words and provides feedback on those emotions to the student. The emotion recognition unit can, for example, read the student's emotions using facial recognition technology. The emotion recognition unit can also, for example, read the student's emotions using voice analysis technology. The emotion recognition unit can, for example, provide verbal feedback when providing feedback on emotions to the student. The emotion recognition unit can also, for example, provide visual feedback. The stress management unit instructs relaxation techniques and mindfulness based on the emotions recognized by the emotion recognition unit. The stress management unit, for example, teaches the student how to take deep breaths and meditate. The stress management unit can, for example, instruct on breathing rhythm. The stress management unit can, for example, instruct on meditation procedures. The stress management unit can, for example, instruct on relaxation techniques. The Stress Management Department can, for example, provide advice on stress management. The Empathy Development Department cultivates skills to understand the feelings of others and build smooth interpersonal relationships based on techniques taught by the Stress Management Department. The Empathy Development Department can, for example, have participants practice thinking from another person's perspective through role-playing. The Empathy Development Department can, for example, set up roles. The Empathy Development Department can, for example, set the content of scenarios. The Empathy Development Department can, for example, conduct practice using specific scenarios. The Empathy Development Department can, for example, teach methods of giving feedback. The Decision Support Department provides support for making ethical and responsible decisions based on the skills cultivated by the Empathy Development Department. The Decision Support Department can, for example, present specific scenarios. The Decision Support Department can, for example, present scenarios that include ethical dilemmas. The Decision Support Department can, for example, present scenarios that reflect realistic situations. The Decision Support Department can, for example, present options based on ethical standards. The Decision Support Department can, for example, present options based on predicted outcomes.The Information Provision Unit records the emotional development of students based on decisions made by the Decision Support Unit and provides this information to teachers and parents. The Information Provision Unit can, for example, record the emotional progress of students. The Information Provision Unit can, for example, assess emotional stability. The Information Provision Unit can, for example, assess improvements in self-awareness. The Information Provision Unit can, for example, provide regular reports. The Information Provision Unit can, for example, provide weekly reports. The Information Provision Unit can, for example, provide monthly reports. This enables the system according to the embodiment to recognize students' emotions, manage stress, cultivate empathy, support decision-making, and provide information.

[0030] The emotion recognition unit recognizes students' emotions and supports them in expressing them appropriately. For example, it reads emotions from students' facial expressions and words and provides feedback to the students. Specifically, the emotion recognition unit uses facial recognition technology to analyze students' facial expressions and identify emotions such as joy, sadness, anger, and surprise. This involves technology that detects facial feature points and tracks changes in facial expressions in real time. It can also use voice analysis technology to analyze the tone, pitch, and rhythm of students' voices and detect changes in emotion. For example, a higher tone of voice may indicate excitement or joy, while a lower tone may indicate depression or sadness. The emotion recognition unit combines these technologies to comprehensively evaluate students' emotions and provide accurate feedback. Feedback is provided through verbal and visual feedback. Verbal feedback is used to explain the emotional state to students and help them understand how they are feeling. Visual feedback, for example, displays icons or graphs that represent the emotional state, allowing students to visually understand their emotions. This enables the emotion recognition unit to improve students' ability to recognize and express their emotions appropriately. Furthermore, the emotion recognition unit also has the function of monitoring emotional changes in real time and notifying teachers and counselors as needed. This allows for a quick response to emotional changes and supports students' mental health.

[0031] The Stress Management Department provides instruction in relaxation techniques and mindfulness based on emotions recognized by the Emotion Recognition Department. Specifically, it teaches students how to deep breath and meditate. When teaching deep breathing, students are taught how to slowly inhale and exhale to regulate their breathing rhythm. This allows students to relax and reduce stress. When teaching meditation procedures, students are taught how to close their eyes in a quiet place and focus on their breathing. Meditation has the effect of calming the mind and reducing stress. The Stress Management Department improves students' stress management abilities by having them practice these techniques. Instruction in relaxation techniques also includes muscle relaxation and imagery training. Muscle relaxation promotes overall relaxation by sequentially tensing and then releasing different parts of the body. Imagery training promotes mental and physical relaxation by visualizing pleasant scenery or relaxing situations. The Stress Management Department combines these techniques to provide students with a comprehensive stress management method. Furthermore, the Stress Management Department regularly assesses students' stress levels and provides individual advice as needed. For example, if a student is experiencing high levels of stress in a particular situation, the stress management department can provide specific advice on how to cope with that situation. This allows the stress management department to support students in effectively managing their stress and maintaining healthy mental health.

[0032] The Empathy Development Department cultivates skills to understand others' feelings and build smooth interpersonal relationships, based on techniques taught by the Stress Management Department. Specifically, it provides practice in thinking from another person's perspective through role-playing. In role-playing, students develop the ability to understand and empathize with others' viewpoints by playing different roles. For example, a scenario is set up in which one student plays the role of a friend, and another student considers how to interact with that friend. The Empathy Development Department meticulously plans the role setting and scenario content so that students can learn through experiences that closely resemble real-life situations. Practice using specific scenarios addresses realistic issues, such as bullying or conflicts with friends. This allows students to understand the emotions and perspectives of others and learn appropriate responses. The Empathy Development Department also teaches methods of feedback. After role-playing, students give each other feedback and discuss each other's actions and feelings. This allows students to understand how their actions affect others and improve their empathy skills. Furthermore, the Empathy Development Department provides opportunities for students to cooperate with others and deepen their empathy through group discussions and workshops. This allows students to practically learn skills for building smooth interpersonal relationships.

[0033] The Decision-Making Support Department provides support for making ethical and responsible decisions based on the skills cultivated by the Empathy Development Department. Specifically, it presents concrete scenarios and allows students to practice making decisions based on those scenarios. For example, it presents scenarios that include ethical dilemmas and asks students to consider how they would respond. Scenarios that reflect realistic situations address problems that students may face in daily life, such as how to help a friend in distress or how to act when witnessing bullying. The Decision-Making Support Department presents options based on ethical standards and guides students to make the best choice from among them. When presenting options, it explains the advantages and disadvantages of each option so that students can make judgments based on their own values ​​and ethical beliefs. It is also important to present options based on predicted outcomes. For example, it allows students to predict what will happen if they choose a certain option and practice making decisions based on those outcomes. This helps students understand the decision-making process and acquire the skills to act responsibly. Furthermore, the Decision-Making Support Department also provides counseling to support students with the anxiety and hesitation they feel during the decision-making process. This allows students to make decisions with confidence and increase their self-efficacy.

[0034] The Information Provision Department records and provides teachers and parents with information on students' emotional development based on decisions made by the Decision Support Department. Specifically, it records students' emotional progress and evaluates improvements in emotional stability and self-awareness. The Information Provision Department regularly monitors students' emotional changes, stress levels, and improvements in empathy skills, and records the results in detail. This allows for an objective evaluation of students' emotional development. Regular reports are made weekly and monthly, sharing students' progress with teachers and parents. The reports include specific data on students' emotional stability and improvements in self-awareness, as well as the results of stress management and empathy development initiatives. This allows teachers and parents to understand students' emotional development and provide appropriate support. Furthermore, the Information Provision Department also provides feedback on students' emotional development to the students themselves. This allows students to feel a sense of their own growth and increase their self-esteem. The Information Provision Department utilizes dedicated software and databases to streamline the data management and reporting processes. This ensures the accuracy and consistency of information and enables rapid information dissemination.

[0035] The emotion recognition unit can read emotions from students' facial expressions and words and provide feedback on those emotions to the students. For example, the emotion recognition unit can read students' emotions using facial expression recognition technology. For example, the emotion recognition unit can analyze changes in facial expressions and estimate emotions. For example, the emotion recognition unit can read students' emotions using voice analysis technology. For example, the emotion recognition unit can analyze the tone and speed of voice and estimate emotions. For example, when providing feedback on emotions to students, the emotion recognition unit can provide verbal feedback. For example, the emotion recognition unit can explain the content of emotions in words. For example, the emotion recognition unit can provide visual feedback. For example, the emotion recognition unit can display the content of emotions in graphs or charts. This allows students to objectively understand their own emotions and learn how to express them appropriately. Some or all of the above-described processes in the emotion recognition unit may be performed using, for example, generative AI, or without using generative AI. For example, the emotion recognition unit can input student facial expression data into a generating AI, which can then estimate the emotion and provide feedback on the result.

[0036] The stress management department can help students reduce stress by teaching them deep breathing and meditation techniques. For example, the stress management department can teach students how to breathe deeply. For example, the stress management department can instruct students on breathing rhythms. For example, the stress management department can teach students how to meditate. For example, the stress management department can instruct students on meditation procedures. For example, the stress management department can provide instruction on relaxation techniques. For example, the stress management department can provide advice on stress management. This allows students to effectively manage stress and maintain a healthy mental state. Some or all of the above processes in the stress management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the stress management department can input student emotional data into a generative AI, which can then suggest the most suitable relaxation technique.

[0037] The Empathy Development Department can provide students with practice in considering things from another person's perspective through role-playing. For example, the Empathy Development Department can set up the roles for role-playing. For example, the Empathy Development Department can set up the content of the scenario. For example, the Empathy Development Department can conduct practice using a specific scenario. For example, the Empathy Development Department can also provide instruction on how to give feedback. This allows students to enhance their empathy and build good interpersonal relationships. Some or all of the processes described above in the Empathy Development Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Empathy Development Department can input student emotional data into a generative AI, which can then suggest an optimal role-playing scenario.

[0038] The decision support unit can present specific scenarios and allow students to consider which choice is most appropriate based on those scenarios. For example, the decision support unit can present scenarios that include ethical dilemmas. For example, the decision support unit can present scenarios that reflect realistic situations. For example, the decision support unit can present options based on ethical standards. For example, the decision support unit can present options based on predictions of outcomes. This allows students to develop ethical judgment and take responsible actions. Some or all of the above processing in the decision support unit may be performed using, for example, generative AI, or without generative AI. For example, the decision support unit can input student emotional data into a generative AI, which can then suggest the optimal choice.

[0039] The information provision department can record students' emotional progress and report it regularly to teachers and parents. For example, the information provision department can record students' emotional progress. For example, the information provision department can assess emotional stability. For example, the information provision department can assess improvements in self-awareness. For example, the information provision department can provide regular reports. For example, the information provision department can provide weekly reports. For example, the information provision department can provide monthly reports. This allows teachers and parents to understand students' emotional growth and provide appropriate support. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision department can input student emotional data into a generative AI, which can then generate optimal report content.

[0040] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0041] The following briefly describes the processing flow for example form 1.

[0042] Step 1: The emotion recognition unit recognizes students' emotions and supports them in expressing them appropriately. For example, it reads emotions from students' facial expressions and words and provides feedback to the students about those emotions. The emotion recognition unit can read students' emotions using facial recognition technology and voice analysis technology, and can provide verbal and visual feedback. Step 2: The Stress Management Department provides guidance on relaxation techniques and mindfulness based on the emotions recognized by the Emotion Recognition Department. For example, they can teach students how to take deep breaths and meditate, and guide them on breathing rhythms and meditation procedures. Step 3: The Empathy Development Department cultivates skills to understand the feelings of others and build smooth interpersonal relationships, based on techniques taught by the Stress Management Department. For example, role-playing exercises can be used to practice thinking from another person's perspective, and roles and scenarios can be set. Step 4: The Decision Support Department provides support for making ethical and responsible decisions based on the skills developed by the Empathy Development Department. For example, it can present specific scenarios, including those involving ethical dilemmas and those reflecting realistic situations. Step 5: The Information Provision Department records the emotional development of students based on decisions made by the Decision Support Department and provides this information to teachers and parents. For example, it can record students' emotional progress, assess improvements in emotional stability and self-awareness, and provide regular reports.

[0043] (Example of form 2) The system according to an embodiment of the present invention is an AI agent that supports the development of students' social and emotional skills (SEL). This system aims to cultivate abilities necessary for future society, such as self-awareness, self-management, and interpersonal skills. The system also supports students' mental health, promoting healthy growth. Key features of the system include support for emotion recognition and expression, stress management, empathy and communication, decision-making support, and information provision to teachers and parents. For example, the system reads emotions from students' facial expressions and words and provides feedback on those emotions to the student. This allows students to objectively understand their own emotions and learn how to express them appropriately. Next, the system teaches students how to deep breath and meditate, helping them reduce stress. This allows students to effectively manage stress and maintain a healthy mental state. Furthermore, the system provides practice in considering things from another person's perspective through role-playing. This allows students to enhance their empathy and build good relationships. The system presents specific scenarios and asks students to consider which choice is most appropriate based on those scenarios. This allows students to develop ethical judgment and take responsible actions. Finally, the system records students' emotional progress and reports it regularly to teachers and parents. This allows teachers and parents to understand students' emotional development and provide appropriate support. This enables the system to recognize students' emotions, manage stress, cultivate empathy, support decision-making, and provide information.

[0044] The system according to this embodiment comprises an emotion recognition unit, a stress management unit, an empathy development unit, a decision support unit, and an information provision unit. The emotion recognition unit recognizes the student's emotions and supports how to express them appropriately. The emotion recognition unit, for example, reads emotions from the student's facial expressions and words and provides feedback on those emotions to the student. The emotion recognition unit can, for example, read the student's emotions using facial recognition technology. The emotion recognition unit can also, for example, read the student's emotions using voice analysis technology. The emotion recognition unit can, for example, provide verbal feedback when providing feedback on emotions to the student. The emotion recognition unit can also, for example, provide visual feedback. The stress management unit instructs relaxation techniques and mindfulness based on the emotions recognized by the emotion recognition unit. The stress management unit, for example, teaches the student how to take deep breaths and meditate. The stress management unit can, for example, instruct on breathing rhythm. The stress management unit can, for example, instruct on meditation procedures. The stress management unit can, for example, instruct on relaxation techniques. The Stress Management Department can, for example, provide advice on stress management. The Empathy Development Department cultivates skills to understand the feelings of others and build smooth interpersonal relationships based on techniques taught by the Stress Management Department. The Empathy Development Department can, for example, have participants practice thinking from another person's perspective through role-playing. The Empathy Development Department can, for example, set up roles. The Empathy Development Department can, for example, set the content of scenarios. The Empathy Development Department can, for example, conduct practice using specific scenarios. The Empathy Development Department can, for example, teach methods of giving feedback. The Decision Support Department provides support for making ethical and responsible decisions based on the skills cultivated by the Empathy Development Department. The Decision Support Department can, for example, present specific scenarios. The Decision Support Department can, for example, present scenarios that include ethical dilemmas. The Decision Support Department can, for example, present scenarios that reflect realistic situations. The Decision Support Department can, for example, present options based on ethical standards. The Decision Support Department can, for example, present options based on predicted outcomes.The Information Provision Unit records the emotional development of students based on decisions made by the Decision Support Unit and provides this information to teachers and parents. The Information Provision Unit can, for example, record the emotional progress of students. The Information Provision Unit can, for example, assess emotional stability. The Information Provision Unit can, for example, assess improvements in self-awareness. The Information Provision Unit can, for example, provide regular reports. The Information Provision Unit can, for example, provide weekly reports. The Information Provision Unit can, for example, provide monthly reports. This enables the system according to the embodiment to recognize students' emotions, manage stress, cultivate empathy, support decision-making, and provide information.

[0045] The emotion recognition unit recognizes students' emotions and supports them in expressing them appropriately. For example, it reads emotions from students' facial expressions and words and provides feedback to the students. Specifically, the emotion recognition unit uses facial recognition technology to analyze students' facial expressions and identify emotions such as joy, sadness, anger, and surprise. This involves technology that detects facial feature points and tracks changes in facial expressions in real time. It can also use voice analysis technology to analyze the tone, pitch, and rhythm of students' voices and detect changes in emotion. For example, a higher tone of voice may indicate excitement or joy, while a lower tone may indicate depression or sadness. The emotion recognition unit combines these technologies to comprehensively evaluate students' emotions and provide accurate feedback. Feedback is provided through verbal and visual feedback. Verbal feedback is used to explain the emotional state to students and help them understand how they are feeling. Visual feedback, for example, displays icons or graphs that represent the emotional state, allowing students to visually understand their emotions. This enables the emotion recognition unit to improve students' ability to recognize and express their emotions appropriately. Furthermore, the emotion recognition unit also has the function of monitoring emotional changes in real time and notifying teachers and counselors as needed. This allows for a quick response to emotional changes and supports students' mental health.

[0046] The Stress Management Department provides instruction in relaxation techniques and mindfulness based on emotions recognized by the Emotion Recognition Department. Specifically, it teaches students how to deep breath and meditate. When teaching deep breathing, students are taught how to slowly inhale and exhale to regulate their breathing rhythm. This allows students to relax and reduce stress. When teaching meditation procedures, students are taught how to close their eyes in a quiet place and focus on their breathing. Meditation has the effect of calming the mind and reducing stress. The Stress Management Department improves students' stress management abilities by having them practice these techniques. Instruction in relaxation techniques also includes muscle relaxation and imagery training. Muscle relaxation promotes overall relaxation by sequentially tensing and then releasing different parts of the body. Imagery training promotes mental and physical relaxation by visualizing pleasant scenery or relaxing situations. The Stress Management Department combines these techniques to provide students with a comprehensive stress management method. Furthermore, the Stress Management Department regularly assesses students' stress levels and provides individual advice as needed. For example, if a student is experiencing high levels of stress in a particular situation, the stress management department can provide specific advice on how to cope with that situation. This allows the stress management department to support students in effectively managing their stress and maintaining healthy mental health.

[0047] The Empathy Development Department cultivates skills to understand others' feelings and build smooth interpersonal relationships, based on techniques taught by the Stress Management Department. Specifically, it provides practice in thinking from another person's perspective through role-playing. In role-playing, students develop the ability to understand and empathize with others' viewpoints by playing different roles. For example, a scenario is set up in which one student plays the role of a friend, and another student considers how to interact with that friend. The Empathy Development Department meticulously plans the role setting and scenario content so that students can learn through experiences that closely resemble real-life situations. Practice using specific scenarios addresses realistic issues, such as bullying or conflicts with friends. This allows students to understand the emotions and perspectives of others and learn appropriate responses. The Empathy Development Department also teaches methods of feedback. After role-playing, students give each other feedback and discuss each other's actions and feelings. This allows students to understand how their actions affect others and improve their empathy skills. Furthermore, the Empathy Development Department provides opportunities for students to cooperate with others and deepen their empathy through group discussions and workshops. This allows students to practically learn skills for building smooth interpersonal relationships.

[0048] The Decision-Making Support Department provides support for making ethical and responsible decisions based on the skills cultivated by the Empathy Development Department. Specifically, it presents concrete scenarios and allows students to practice making decisions based on those scenarios. For example, it presents scenarios that include ethical dilemmas and asks students to consider how they would respond. Scenarios that reflect realistic situations address problems that students may face in daily life, such as how to help a friend in distress or how to act when witnessing bullying. The Decision-Making Support Department presents options based on ethical standards and guides students to make the best choice from among them. When presenting options, it explains the advantages and disadvantages of each option so that students can make judgments based on their own values ​​and ethical beliefs. It is also important to present options based on predicted outcomes. For example, it allows students to predict what will happen if they choose a certain option and practice making decisions based on those outcomes. This helps students understand the decision-making process and acquire the skills to act responsibly. Furthermore, the Decision-Making Support Department also provides counseling to support students with the anxiety and hesitation they feel during the decision-making process. This allows students to make decisions with confidence and increase their self-efficacy.

[0049] The Information Provision Department records and provides teachers and parents with information on students' emotional development based on decisions made by the Decision Support Department. Specifically, it records students' emotional progress and evaluates improvements in emotional stability and self-awareness. The Information Provision Department regularly monitors students' emotional changes, stress levels, and improvements in empathy skills, and records the results in detail. This allows for an objective evaluation of students' emotional development. Regular reports are made weekly and monthly, sharing students' progress with teachers and parents. The reports include specific data on students' emotional stability and improvements in self-awareness, as well as the results of stress management and empathy development initiatives. This allows teachers and parents to understand students' emotional development and provide appropriate support. Furthermore, the Information Provision Department also provides feedback on students' emotional development to the students themselves. This allows students to feel a sense of their own growth and increase their self-esteem. The Information Provision Department utilizes dedicated software and databases to streamline the data management and reporting processes. This ensures the accuracy and consistency of information and enables rapid information dissemination.

[0050] The emotion recognition unit can read emotions from students' facial expressions and words and provide feedback on those emotions to the students. For example, the emotion recognition unit can read students' emotions using facial expression recognition technology. For example, the emotion recognition unit can analyze changes in facial expressions and estimate emotions. For example, the emotion recognition unit can read students' emotions using voice analysis technology. For example, the emotion recognition unit can analyze the tone and speed of voice and estimate emotions. For example, when providing feedback on emotions to students, the emotion recognition unit can provide verbal feedback. For example, the emotion recognition unit can explain the content of emotions in words. For example, the emotion recognition unit can provide visual feedback. For example, the emotion recognition unit can display the content of emotions in graphs or charts. This allows students to objectively understand their own emotions and learn how to express them appropriately. Some or all of the above-described processes in the emotion recognition unit may be performed using, for example, generative AI, or without using generative AI. For example, the emotion recognition unit can input student facial expression data into a generating AI, which can then estimate the emotion and provide feedback on the result.

[0051] The stress management department can help students reduce stress by teaching them deep breathing and meditation techniques. For example, the stress management department can teach students how to breathe deeply. For example, the stress management department can instruct students on breathing rhythms. For example, the stress management department can teach students how to meditate. For example, the stress management department can instruct students on meditation procedures. For example, the stress management department can provide instruction on relaxation techniques. For example, the stress management department can provide advice on stress management. This allows students to effectively manage stress and maintain a healthy mental state. Some or all of the above processes in the stress management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the stress management department can input student emotional data into a generative AI, which can then suggest the most suitable relaxation technique.

[0052] The Empathy Development Department can provide students with practice in considering things from another person's perspective through role-playing. For example, the Empathy Development Department can set up the roles for role-playing. For example, the Empathy Development Department can set up the content of the scenario. For example, the Empathy Development Department can conduct practice using a specific scenario. For example, the Empathy Development Department can also provide instruction on how to give feedback. This allows students to enhance their empathy and build good interpersonal relationships. Some or all of the processes described above in the Empathy Development Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Empathy Development Department can input student emotional data into a generative AI, which can then suggest an optimal role-playing scenario.

[0053] The decision support unit can present specific scenarios and allow students to consider which choice is most appropriate based on those scenarios. For example, the decision support unit can present scenarios that include ethical dilemmas. For example, the decision support unit can present scenarios that reflect realistic situations. For example, the decision support unit can present options based on ethical standards. For example, the decision support unit can present options based on predictions of outcomes. This allows students to develop ethical judgment and take responsible actions. Some or all of the above processing in the decision support unit may be performed using, for example, generative AI, or without generative AI. For example, the decision support unit can input student emotional data into a generative AI, which can then suggest the optimal choice.

[0054] The information provision department can record students' emotional progress and report it regularly to teachers and parents. For example, the information provision department can record students' emotional progress. For example, the information provision department can assess emotional stability. For example, the information provision department can assess improvements in self-awareness. For example, the information provision department can provide regular reports. For example, the information provision department can provide weekly reports. For example, the information provision department can provide monthly reports. This allows teachers and parents to understand students' emotional growth and provide appropriate support. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision department can input student emotional data into a generative AI, which can then generate optimal report content.

[0055] The emotion recognition unit can estimate a student's emotions and adjust the feedback method based on the estimated emotions. For example, if a student is sad, the emotion recognition unit can provide emotional feedback in a gentle tone to reassure them. If a student is angry, the emotion recognition unit can provide emotional feedback in a calm tone to calm them down. If a student is happy, the emotion recognition unit can provide emotional feedback in a positive tone to further encourage them. This improves the accuracy of emotion recognition by providing appropriate feedback according to the student's emotions. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input student emotional data into a generative AI, which can then suggest the optimal feedback method.

[0056] The emotion recognition unit can analyze a student's past emotional history and optimize an algorithm to improve the accuracy of emotion recognition. For example, the emotion recognition unit can identify specific emotional patterns based on a student's past emotional history and improve recognition accuracy. For example, the emotion recognition unit can analyze a student's emotional history, understand trends in emotional changes, and adjust the algorithm accordingly. For example, the emotion recognition unit can refer to a student's emotional history and build a feedback loop to reduce the error in emotion recognition. This allows for improved accuracy of emotion recognition based on past emotional history. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input student emotional history data into a generative AI, which can then optimize the algorithm.

[0057] The emotion recognition unit can track changes in a student's emotions in real time based on their current activities and environment. For example, if a student is concentrating during class, the emotion recognition unit can reflect that level of concentration in their emotion recognition. For example, if a student is relaxed during a break, the emotion recognition unit can reflect that level of relaxation in their emotion recognition. For example, if a student is excited during exercise, the emotion recognition unit can reflect that level of excitement in their emotion recognition. This enables real-time emotion recognition that is tailored to the student's current activities and environment. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input student activity data into a generative AI, which can then track changes in emotions in real time.

[0058] The emotion recognition unit can estimate a student's emotions and determine the priority of emotion recognition based on the estimated emotions. For example, if a student is showing a strong emotion, the emotion recognition unit will prioritize recognizing and responding to that emotion. For example, if a student is showing multiple emotions, the emotion recognition unit can prioritize recognizing the most important emotion. For example, if a student's emotions change rapidly, the emotion recognition unit can prioritize recognizing and responding to that change. In this way, by determining priorities according to the student's emotions, important emotions can be recognized preferentially. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input student emotion data into a generative AI, and the generative AI can determine the priority of emotions.

[0059] The emotion recognition unit can analyze changes in emotions while considering the student's geographical location information during emotion recognition. For example, if the student is at school, the emotion recognition unit can analyze changes in emotions based on the environment. For example, if the student is at home, the emotion recognition unit can analyze changes in emotions based on the environment. For example, if the student is out, the emotion recognition unit can analyze changes in emotions based on the environment. This makes it possible to analyze changes in emotions based on the student's geographical location information. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the student's geographical location data into a generative AI, and the generative AI can analyze changes in emotions.

[0060] The emotion recognition unit can analyze a student's social media activity and predict changes in emotion when recognizing an emotion. For example, the emotion recognition unit can recognize a student's emotion if they are making positive posts on social media. For example, the emotion recognition unit can recognize a student's emotion if they are making negative posts on social media. For example, the emotion recognition unit can analyze the frequency and content of a student's social media activity and predict changes in emotion. This makes it possible to predict changes in emotion based on a student's social media activity. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input a student's social media data into a generative AI, which can then predict changes in emotion.

[0061] The stress management department can estimate a student's emotions and adjust stress management methods based on those estimates. For example, if a student is feeling stressed, the stress management department may prioritize teaching relaxation techniques. If a student is relaxed, the stress management department may suggest mindfulness exercises. If a student is feeling anxious, the stress management department may teach deep breathing techniques. This allows for effective stress reduction by providing appropriate stress management methods tailored to the student's emotions. Some or all of the above processing in the stress management department may be performed using, for example, a generative AI, or without one. For example, the stress management department can input student emotional data into a generative AI, which can then suggest the optimal stress management method.

[0062] The stress management department can select the optimal relaxation technique by referring to the student's past stress history during stress management. For example, the stress management department may suggest a relaxation technique that was previously effective for the student. For example, the stress management department may analyze the student's stress history and suggest a new relaxation technique. For example, the stress management department may customize the optimal relaxation technique based on the student's stress history. This improves the effectiveness of stress management by providing the optimal relaxation technique based on past stress history. Some or all of the above processes in the stress management department may be performed using, for example, a generative AI, or without a generative AI. For example, the stress management department can input the student's stress history data into a generative AI, which can then suggest the optimal relaxation technique.

[0063] The stress management department can customize stress reduction methods based on the student's current living situation during stress management. For example, if the student is busy, the stress management department can suggest a short and effective relaxation technique. If the student has ample time, the stress management department can suggest a long meditation session. The stress management department can adjust stress reduction methods according to the student's living situation. This improves the effectiveness of stress management by providing stress reduction methods tailored to the student's living situation. Some or all of the above processing in the stress management department may be performed using, for example, a generative AI, or without a generative AI. For example, the stress management department can input student living situation data into a generative AI, which can then suggest the optimal stress reduction method.

[0064] The stress management unit can estimate a student's emotions and determine stress management priorities based on those estimated emotions. For example, if a student is experiencing high stress, the stress management unit will prioritize managing that stress. For example, if a student has multiple stressors, the stress management unit can prioritize managing the most important stressor. For example, if a student's stress levels are changing rapidly, the stress management unit can prioritize managing that change. In this way, by determining priorities according to the student's emotions, important stressors can be managed preferentially. Some or all of the above processes in the stress management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the stress management unit can input student emotion data into a generative AI, which can then determine stress management priorities.

[0065] The stress management department can provide optimal relaxation techniques during stress management, taking into account the student's geographical location. For example, if the student is at school, the stress management department can provide relaxation techniques appropriate to that environment. For example, if the student is at home, the stress management department can provide relaxation techniques appropriate to that environment. For example, if the student is out, the stress management department can provide relaxation techniques appropriate to that environment. This improves the effectiveness of stress management by providing optimal relaxation techniques based on the student's geographical location. Some or all of the above processing in the stress management department may be performed using, for example, a generative AI, or without a generative AI. For example, the stress management department can input the student's geographical location data into a generative AI, which can then propose the optimal relaxation technique.

[0066] The stress management department can analyze students' social media activity during stress management to identify the causes of stress. For example, if a student makes negative posts on social media, the stress management department can identify the cause of that stress. For example, if a student makes positive posts on social media, the stress management department can identify the cause of that stress. For example, the stress management department can analyze the frequency and content of students' social media activity to identify the causes of stress. This makes it possible to identify the causes of stress based on students' social media activity. Some or all of the above processing in the stress management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the stress management department can input students' social media data into a generative AI, and the generative AI can identify the causes of stress.

[0067] The Empathy Development Department can estimate a student's emotions and adjust the empathy development method based on the estimated emotions. For example, if a student is sad, the Empathy Development Department can prioritize teaching empathetic responses. For example, if a student is angry, the Empathy Development Department can prioritize teaching calm responses. For example, if a student is happy, the Empathy Development Department can prioritize teaching positive responses. This allows for the effective improvement of empathy by providing appropriate empathy development methods according to the student's emotions. Some or all of the above processing in the Empathy Development Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Empathy Development Department can input student emotion data into a generative AI, which can then propose the optimal empathy development method.

[0068] The Empathy Development Department can select the most suitable role-playing scenario during empathy development by referring to the student's past empathy history. For example, the Empathy Development Department may suggest a role-playing scenario that was previously effective for the student. For example, the Empathy Development Department may analyze the student's empathy history and suggest a new role-playing scenario. For example, the Empathy Development Department may customize the most suitable role-playing scenario based on the student's empathy history. This improves the effectiveness of empathy development by providing the most suitable role-playing scenario based on past empathy history. Some or all of the above processes in the Empathy Development Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Empathy Development Department can input the student's empathy history data into a generative AI, which can then suggest the most suitable role-playing scenario.

[0069] The Empathy Development Department can customize empathy training based on students' current relationships. For example, if a student is struggling with friendships, the Empathy Development Department can suggest empathy training based on those relationships. For example, if a student is struggling with family relationships, the Empathy Development Department can suggest empathy training based on those relationships. The Empathy Development Department can customize empathy training according to the student's relationships. This improves the effectiveness of empathy training by providing empathy training tailored to the student's relationships. Some or all of the above processing in the Empathy Development Department may be performed using, for example, a generative AI, or not. For example, the Empathy Development Department can input student relationship data into a generative AI, which can then suggest the most suitable empathy training.

[0070] The empathy development unit can estimate a student's emotions and determine the priority of empathy development based on the estimated emotions. For example, if a student is showing strong emotions, the empathy development unit will prioritize that emotion. For example, if a student is showing multiple emotions, the empathy development unit can prioritize the most important emotion. For example, if a student's emotions are changing rapidly, the empathy development unit can prioritize the change in those emotions. In this way, by determining priorities according to the student's emotions, important empathy development can be prioritized. Some or all of the above processing in the empathy development unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the empathy development unit can input student emotion data into a generative AI, and the generative AI can determine the priority of empathy development.

[0071] The Empathy Development Department can provide optimal role-playing scenarios during empathy development, taking into account the student's geographical location information. For example, if the student is at school, the Empathy Development Department can provide a role-playing scenario suitable for that environment. For example, if the student is at home, the Empathy Development Department can provide a role-playing scenario suitable for that environment. For example, if the student is out, the Empathy Development Department can provide a role-playing scenario suitable for that environment. By providing optimal role-playing scenarios based on the student's geographical location information, the effectiveness of empathy development is improved. Some or all of the above processing in the Empathy Development Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Empathy Development Department can input the student's geographical location data into a generative AI, which can then propose an optimal role-playing scenario.

[0072] The Empathy Development Department can analyze students' social media activity during empathy development and propose approaches to enhance their empathy. For example, if a student makes positive posts on social media, the Empathy Development Department can propose approaches to enhance their empathy. For example, if a student makes negative posts on social media, the Empathy Development Department can propose approaches to enhance their empathy. For example, the Empathy Development Department can analyze the frequency and content of students' social media activity and propose approaches to enhance their empathy. This makes it possible to develop approaches to enhance empathy based on students' social media activity. Some or all of the above processing in the Empathy Development Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Empathy Development Department can input students' social media data into a generative AI, and the generative AI can propose approaches to enhance empathy.

[0073] The decision support unit can estimate a student's emotions and adjust its decision support methods based on those emotions. For example, if a student is feeling anxious, the decision support unit may prioritize presenting options that minimize risk. For example, if a student is confident, the decision support unit may present challenging options. For example, if a student is undecided, the decision support unit may provide information to compare multiple options. This improves the accuracy of decision-making by providing appropriate decision support methods that are tailored to the student's emotions. Some or all of the above processing in the decision support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision support unit can input student emotion data into a generative AI, which can then propose the optimal decision support method.

[0074] The decision support unit can select the optimal scenario by referring to the student's past decision-making history when providing decision support. For example, the decision support unit can propose similar scenarios based on the student's past successful decisions. For example, the decision support unit can analyze the student's past decision-making history and propose scenarios to avoid failures. For example, the decision support unit can refer to the student's decision-making history and customize the optimal scenario. This improves the effectiveness of decision support by providing the optimal scenario based on past decision-making history. Some or all of the above processes in the decision support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision support unit can input the student's decision-making history data into a generative AI, which can then propose the optimal scenario.

[0075] The decision support unit can customize the practice exercises for developing ethical judgment skills based on the student's current situation when providing decision support. For example, if a student is facing problems at school, the decision support unit can suggest practice exercises for developing ethical judgment skills based on that situation. For example, if a student is facing problems at home, the decision support unit can suggest practice exercises for developing ethical judgment skills based on that situation. The decision support unit can customize the practice exercises for developing ethical judgment skills according to the student's current situation. This improves the effectiveness of decision support by providing practice exercises for developing ethical judgment skills that are tailored to the student's current situation. Some or all of the above processing in the decision support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision support unit can input student situation data into a generative AI, and the generative AI can suggest the most suitable practice exercises for developing ethical judgment skills.

[0076] The decision support unit can estimate a student's emotions and determine the priority of decision support based on the estimated emotions. For example, if a student is showing strong emotions, the decision support unit will prioritize that emotion. For example, if a student is showing multiple emotions, the decision support unit can prioritize the most important emotion. For example, if a student's emotions are changing rapidly, the decision support unit can prioritize that change in decision support. In this way, by determining priorities according to the student's emotions, important decision support can be prioritized. Some or all of the above processing in the decision support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision support unit can input student emotion data into a generative AI, and the generative AI can determine the priority of decision support.

[0077] The decision support unit can provide the optimal scenario when providing decision support, taking into account the student's geographical location information. For example, if the student is at school, the decision support unit can provide a scenario appropriate to that environment. For example, if the student is at home, the decision support unit can provide a scenario appropriate to that environment. For example, if the student is out, the decision support unit can provide a scenario appropriate to that environment. This improves the effectiveness of decision support by providing the optimal scenario based on the student's geographical location information. Some or all of the above processing in the decision support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision support unit can input the student's geographical location data into a generative AI, which can then propose the optimal scenario.

[0078] The decision support unit can analyze students' social media activity and predict the impact of decisions when providing decision support. For example, the decision support unit can predict the impact of a decision if a student makes positive posts on social media. For example, the decision support unit can predict the impact of a decision if a student makes negative posts on social media. For example, the decision support unit can analyze the frequency and content of students' social media activity and predict the impact of decisions. This makes it possible to predict the impact of decisions based on students' social media activity. Some or all of the above processing in the decision support unit may be performed using, for example, generative AI, or without generative AI. For example, the decision support unit can input students' social media data into generative AI, and the generative AI can predict the impact of decisions.

[0079] The information provision unit can estimate students' emotions and adjust the method of information provision based on the estimated emotions. For example, if a student is feeling stressed, the information provision unit can provide concise and easy-to-understand information. For example, if a student is relaxed, the information provision unit can provide detailed information. For example, if a student is feeling anxious, the information provision unit can provide reassuring information. This improves the effectiveness of information provision by providing appropriate information methods according to students' emotions. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input student emotion data into a generative AI, which can then propose the optimal method of information provision.

[0080] The information provision department can select the optimal reporting method by referring to the student's past emotional development history when providing information. For example, the information provision department can select the optimal reporting method based on the student's past development history. For example, the information provision department can analyze the student's development history and customize the report content. For example, the information provision department can refer to the student's development history and propose the optimal reporting method. This improves the effectiveness of information provision by providing the optimal reporting method based on the student's past emotional development history. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provision department can input the student's development history data into a generative AI, and the generative AI can propose the optimal reporting method.

[0081] The information provision department can customize the content of reports to teachers and parents based on the student's current situation when providing information. For example, if a student is facing problems at school, the information provision department can provide a report based on that situation. For example, if a student is facing problems at home, the information provision department can provide a report based on that situation. The information provision department can customize the content of reports according to the student's current situation. This improves the effectiveness of information provision by providing reports that are tailored to the student's current situation. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision department can input student situation data into a generative AI, and the generative AI can suggest the most appropriate report content.

[0082] The information provision unit can estimate a student's emotions and determine the priority of information provision based on the estimated emotions. For example, if a student is showing strong emotions, the information provision unit will prioritize providing information on those emotions. For example, if a student is showing multiple emotions, the information provision unit can prioritize providing information on the most important emotion. For example, if a student's emotions are changing rapidly, the information provision unit can prioritize providing information on that change. In this way, by determining priorities according to the student's emotions, important information can be prioritized. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input student emotion data into a generative AI, and the generative AI can determine the priority of information provision.

[0083] The information provision department can provide the most suitable reporting method when providing information, taking into account the student's geographical location. For example, if the student is at school, the information provision department can provide a reporting method appropriate to that environment. For example, if the student is at home, the information provision department can provide a reporting method appropriate to that environment. For example, if the student is out, the information provision department can provide a reporting method appropriate to that environment. This improves the effectiveness of information provision by providing the most suitable reporting method based on the student's geographical location. Some or all of the above processing in the information provision department may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provision department can input the student's geographical location data into a generating AI, which can then propose the most suitable reporting method.

[0084] The information provision department can analyze students' social media activity and predict their emotional development when providing information. For example, the information provision department can predict a student's development if they make positive posts on social media. For example, the information provision department can predict a student's development if they make negative posts on social media. For example, the information provision department can analyze the frequency and content of a student's social media activity and predict their emotional development. This makes it possible to predict emotional development based on a student's social media activity. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision department can input a student's social media data into a generative AI, which can then predict their emotional development.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] In addition to recognizing students' emotions, the system can also track changes in emotions in real time. For example, the emotion recognition unit can reflect a student's level of concentration during class in its emotional recognition. Similarly, it can reflect a student's level of relaxation during breaks. Furthermore, it can reflect a student's level of excitement during exercise in its emotional recognition. This enables real-time emotional recognition tailored to a student's current activity and environment.

[0087] The system can have the ability to predict changes in emotions based on students' emotional recognition. For example, the emotion recognition unit can analyze a student's past emotional history and identify specific emotional patterns. It can also understand trends in emotional change based on the student's emotional history and adjust the algorithm accordingly. Furthermore, it can refer to the student's emotional history to build a feedback loop to reduce errors in emotion recognition. This allows for improved accuracy of emotion recognition based on past emotional history.

[0088] The system can have a function to determine the priority of emotions based on the student's emotional recognition. For example, if a student is exhibiting a strong emotion, the emotion recognition unit can prioritize recognizing and responding to that emotion. Also, if a student is exhibiting multiple emotions, it can prioritize recognizing the most important emotion. Furthermore, if a student's emotional change is rapid, it can prioritize recognizing and responding to that change. In this way, by determining priorities according to the student's emotions, important emotions can be recognized preferentially.

[0089] The system can be equipped with the ability to track changes in students' emotions in real time, based on their emotional recognition. For example, the emotional recognition unit can reflect a student's level of concentration in their emotional recognition when they are focused during class. It can also reflect a student's level of relaxation during breaks. Furthermore, it can reflect a student's level of excitement during exercise. This enables real-time emotional recognition tailored to a student's current activity and environment.

[0090] The system can have the ability to predict changes in emotions based on students' emotional recognition. For example, the emotion recognition unit can analyze a student's past emotional history and identify specific emotional patterns. It can also understand trends in emotional change based on the student's emotional history and adjust the algorithm accordingly. Furthermore, it can refer to the student's emotional history to build a feedback loop to reduce errors in emotion recognition. This allows for improved accuracy of emotion recognition based on past emotional history.

[0091] The system can have a function to determine the priority of emotions based on the student's emotional recognition. For example, if a student is exhibiting a strong emotion, the emotion recognition unit can prioritize recognizing and responding to that emotion. Also, if a student is exhibiting multiple emotions, it can prioritize recognizing the most important emotion. Furthermore, if a student's emotional change is rapid, it can prioritize recognizing and responding to that change. In this way, by determining priorities according to the student's emotions, important emotions can be recognized preferentially.

[0092] The system can be equipped with the ability to track changes in students' emotions in real time, based on their emotional recognition. For example, the emotional recognition unit can reflect a student's level of concentration in their emotional recognition when they are focused during class. It can also reflect a student's level of relaxation during breaks. Furthermore, it can reflect a student's level of excitement during exercise. This enables real-time emotional recognition tailored to a student's current activity and environment.

[0093] The system can have the ability to predict changes in emotions based on students' emotional recognition. For example, the emotion recognition unit can analyze a student's past emotional history and identify specific emotional patterns. It can also understand trends in emotional change based on the student's emotional history and adjust the algorithm accordingly. Furthermore, it can refer to the student's emotional history to build a feedback loop to reduce errors in emotion recognition. This allows for improved accuracy of emotion recognition based on past emotional history.

[0094] The system can have a function to determine the priority of emotions based on the student's emotional recognition. For example, if a student is exhibiting a strong emotion, the emotion recognition unit can prioritize recognizing and responding to that emotion. Also, if a student is exhibiting multiple emotions, it can prioritize recognizing the most important emotion. Furthermore, if a student's emotional change is rapid, it can prioritize recognizing and responding to that change. In this way, by determining priorities according to the student's emotions, important emotions can be recognized preferentially.

[0095] The system can be equipped with the ability to track changes in students' emotions in real time, based on their emotional recognition. For example, the emotional recognition unit can reflect a student's level of concentration in their emotional recognition when they are focused during class. It can also reflect a student's level of relaxation during breaks. Furthermore, it can reflect a student's level of excitement during exercise. This enables real-time emotional recognition tailored to a student's current activity and environment.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The emotion recognition unit recognizes students' emotions and supports them in expressing them appropriately. For example, it reads emotions from students' facial expressions and words and provides feedback to the students about those emotions. The emotion recognition unit can read students' emotions using facial recognition technology and voice analysis technology, and can provide verbal and visual feedback. Step 2: The Stress Management Department provides guidance on relaxation techniques and mindfulness based on the emotions recognized by the Emotion Recognition Department. For example, they can teach students how to take deep breaths and meditate, and guide them on breathing rhythms and meditation procedures. Step 3: The Empathy Development Department cultivates skills to understand the feelings of others and build smooth interpersonal relationships, based on techniques taught by the Stress Management Department. For example, role-playing exercises can be used to practice thinking from another person's perspective, and roles and scenarios can be set. Step 4: The Decision Support Department provides support for making ethical and responsible decisions based on the skills developed by the Empathy Development Department. For example, it can present specific scenarios, including those involving ethical dilemmas and those reflecting realistic situations. Step 5: The Information Provision Department records the emotional development of students based on decisions made by the Decision Support Department and provides this information to teachers and parents. For example, it can record students' emotional progress, assess improvements in emotional stability and self-awareness, and provide regular reports.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the emotion recognition unit, stress management unit, empathy development unit, decision support unit, and information provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 38B of the smart device 14 to detect the student's facial expressions and words, and the control unit 46A recognizes the emotion. The stress management unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides instruction in relaxation techniques and mindfulness. The empathy development unit is implemented in the specific processing unit 46A of the smart device 14 and provides practice in thinking from another person's perspective through role-playing. The decision support unit is implemented in the specific processing unit 290 of the data processing unit 12 and cultivates ethical judgment by presenting concrete scenarios. The information provision unit is implemented in the specific processing unit 46A of the smart device 14 and records the student's emotional progress and reports it to teachers and parents periodically. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the emotion recognition unit, stress management unit, empathy development unit, decision support unit, and information provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect the student's facial expressions and words, and the control unit 46A recognizes the emotion. The stress management unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides instruction on relaxation techniques and mindfulness. The empathy development unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides practice in thinking from another person's perspective through role-playing. The decision support unit is implemented in the specific processing unit 290 of the data processing unit 12 and cultivates ethical judgment by presenting concrete scenarios. The information provision unit is implemented in the specific processing unit 46A of the smart glasses 214 and records the student's emotional progress and reports it to teachers and parents periodically. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the emotion recognition unit, stress management unit, empathy development unit, decision support unit, and information provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect the student's facial expressions and words, and the control unit 46A recognizes the emotion. The stress management unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides instruction in relaxation techniques and mindfulness. The empathy development unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides practice in thinking from another person's perspective through role-playing. The decision support unit is implemented in the specific processing unit 290 of the data processing unit 12 and cultivates ethical judgment by presenting concrete scenarios. The information provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and records the student's emotional progress and reports it to teachers and parents periodically. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In 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.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 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.

[0150] Each of the multiple elements described above, including the emotion recognition unit, stress management unit, empathy development unit, decision support unit, and information provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the emotion recognition unit uses the camera 42 and microphone 238 of the robot 414 to detect the student's facial expressions and words, and the control unit 46A recognizes the emotion. The stress management unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides instruction in relaxation techniques and mindfulness. The empathy development unit is implemented in the specific processing unit 46A of the robot 414 and provides practice in thinking from another person's perspective through role-playing. The decision support unit is implemented in the specific processing unit 290 of the data processing unit 12 and cultivates ethical judgment by presenting concrete scenarios. The information provision unit is implemented in the specific processing unit 46A of the robot 414 and records the student's emotional progress and reports it to teachers and parents periodically. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) An emotion recognition unit that recognizes students' emotions and supports them in expressing them appropriately, A stress management unit provides guidance on relaxation techniques and mindfulness based on the emotions recognized by the aforementioned emotion recognition unit, Based on the techniques taught by the aforementioned Stress Management Department, the Empathy Development Department cultivates skills to understand the feelings of others and build smooth interpersonal relationships. The Decision Support Department provides support for making ethical and responsible decisions based on the skills cultivated by the aforementioned Empathy Development Department, The system includes an information provision unit that records the emotional development status of students based on decisions made by the aforementioned decision support unit and provides this information to teachers and guardians. A system characterized by the following features. (Note 2) The emotion recognition unit, Read students' emotions from their facial expressions and words, and provide feedback to the students based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned stress management unit, We teach students deep breathing and meditation techniques to help them reduce stress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Empathy Development Department Through role-playing, students can practice thinking from another person's perspective. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned decision support unit, Present specific scenarios and ask the reader to consider which choice is most appropriate based on those scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned information provision unit, Record the emotional progress of students and report it regularly to teachers and parents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The emotion recognition unit, The system estimates students' emotions and adjusts the feedback method for emotion recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The emotion recognition unit, Analyze students' past emotional history and optimize algorithms to improve the accuracy of emotion recognition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The emotion recognition unit, During emotion recognition, emotional changes are tracked in real time based on the student's current activities and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The emotion recognition unit, The system estimates students' emotions and determines the priority of emotion recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The emotion recognition unit, When recognizing emotions, the system analyzes changes in emotions while considering the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The emotion recognition unit, During emotion recognition, we analyze students' social media activity and predict changes in their emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned stress management unit, The system estimates students' emotions and adjusts stress management methods based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned stress management unit, During stress management, the optimal relaxation technique is selected by referring to the student's past stress history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned stress management unit, When managing stress, customize stress reduction methods based on the student's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned stress management unit, The system estimates students' emotions and determines stress management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned stress management unit, When managing stress, we provide optimal relaxation techniques while considering the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned stress management unit, During stress management, analyze students' social media activity to identify the causes of stress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Empathy Development Department We estimate students' emotions and adjust empathy development methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Empathy Development Department When fostering empathy, the most suitable role-playing scenario is selected by referring to the student's past empathy history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Empathy Development Department When fostering empathy, customize exercises to enhance empathy based on students' current relationships. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Empathy Development Department The system estimates students' emotions and determines priorities for empathy development based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Empathy Development Department When fostering empathy, provide optimal role-playing scenarios that take into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Empathy Development Department In fostering empathy, we analyze students' social media activity and propose approaches to enhance their empathy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned decision support unit, We estimate students' emotions and adjust decision-making support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned decision support unit, When providing decision support, the system selects the optimal scenario by referring to the student's past decision-making history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned decision support unit, When providing decision-making support, customize exercises to develop students' ethical judgment based on their current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned decision support unit, The system estimates students' emotions and prioritizes decision-making support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned decision support unit, When providing decision support, we consider students' geographical location information to provide the optimal scenario. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned decision support unit, When providing decision support, we analyze students' social media activity and predict the impact on decisions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned information provision unit, We estimate students' emotions and adjust the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned information provision unit, When providing information, the most appropriate reporting method will be selected by referring to the student's past emotional development history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned information provision unit, When providing information, the content of reports to teachers and parents will be customized based on the student's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned information provision unit, The system estimates students' emotions and determines the priority of information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned information provision unit, When providing information, we will consider the student's geographical location and provide the most appropriate reporting method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned information provision unit, When providing information, we analyze students' social media activity and predict their emotional development. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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. An emotion recognition unit that recognizes students' emotions and supports them in expressing them appropriately, A stress management unit provides guidance on relaxation techniques and mindfulness based on the emotions recognized by the aforementioned emotion recognition unit, Based on the techniques taught by the aforementioned Stress Management Department, the Empathy Development Department cultivates skills to understand the feelings of others and build smooth interpersonal relationships. The Decision Support Department provides support for making ethical and responsible decisions based on the skills cultivated by the aforementioned Empathy Development Department, The system includes an information provision unit that records the emotional development status of students based on decisions made by the aforementioned decision support unit and provides this information to teachers and guardians. A system characterized by the following features.

2. The emotion recognition unit, Read students' emotions from their facial expressions and words, and provide feedback to the students based on those emotions. The system according to feature 1.

3. The aforementioned stress management unit, We teach students deep breathing and meditation techniques to help them reduce stress. The system according to feature 1.

4. The aforementioned Empathy Development Department Through role-playing, students can practice thinking from another person's perspective. The system according to feature 1.

5. The aforementioned decision support unit, Present specific scenarios and ask the reader to consider which choice is most appropriate based on those scenarios. The system according to feature 1.

6. The aforementioned information provision unit, Record the emotional progress of students and report it regularly to teachers and parents. The system according to feature 1.

7. The emotion recognition unit, The system estimates students' emotions and adjusts the feedback method for emotion recognition based on the estimated emotions. The system according to feature 1.

8. The emotion recognition unit, Analyze students' past emotional history and optimize algorithms to improve the accuracy of emotion recognition. The system according to feature 1.

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