system

Abstract

The system according to this embodiment aims to reduce the burden on teachers and to appropriately understand the situation of each individual student. [Solution] The system according to the embodiment comprises a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors the behavior of students during class. The analysis unit analyzes the data collected by the monitoring unit. The notification unit notifies the teacher based on the results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the burden on teachers is large and it is difficult to fully grasp the situation of each student.

[0005] <8000028>The system according to the embodiment aims to reduce the burden on teachers and appropriately grasp the situation of each student.

Means for Solving the Problems

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors the situation of students during class. The analysis unit analyzes the data collected by the monitoring unit. The notification unit notifies the teacher based on the results obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment reduces the burden on teachers and allows for an appropriate understanding of each student's individual situation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is a system that reduces the burden on teachers in elementary and junior high schools in Japan and supports their interaction with students. This AI system monitors the behavior of students during class and notifies teachers if there is any abnormality. Furthermore, the AI ​​system aims to detect and prevent problems such as poor health, bullying, and truancy at an early stage through conversations with students. For example, the AI ​​system monitors the behavior of students during class. In doing so, it uses image recognition, facial expression analysis, and biosensors to analyze changes in physical condition and emotions such as tension and anxiety. For example, the AI ​​system can analyze a student's facial expression and detect signs of poor health or stress. This allows for the early detection of abnormalities in students that teachers may not easily notice. Next, the AI ​​system detects problems such as poor health, bullying, and truancy at an early stage through conversations with students. For example, the AI ​​system can ask students questions such as, "How are you feeling today?" and analyze the student's response to detect signs of poor health. Furthermore, the AI ​​system can detect signs of bullying or truancy by recording what students say and performing emotional analysis. This allows teachers to detect problems they might overlook early and take appropriate action. Furthermore, the AI ​​system explains students' situations to teachers and supports appropriate responses. For example, the AI ​​system might notify a teacher that "Student XX seems to be unwell," helping the teacher take appropriate action. It might also alert teachers that "Student XX hasn't spoken recently," enabling more personalized attention to each student. This system reduces the workload of teachers and allows for more appropriate attention to each student. For example, it is difficult for teachers to keep track of all students during class, but with the support of the AI ​​system, any changes in students can be detected early and appropriate action can be taken. In addition, by sensing problems through conversations with students, the AI ​​system can prevent problems that teachers might overlook. This prevents major problems in the educational setting, reduces the burden on teachers, and enables more appropriate attention to each student.

[0029] The AI ​​system according to this embodiment comprises a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors the behavior of students during class. The monitoring unit can, for example, analyze students' facial expressions using image recognition technology. The monitoring unit can also monitor students' physical condition using biosensors. For example, the monitoring unit can measure heart rate and skin electrical activity to detect changes in physical condition. The monitoring unit can also analyze students' emotions using facial expression analysis technology. For example, the monitoring unit can detect students' stress and anxiety based on changes in facial expressions. The analysis unit analyzes the data collected by the monitoring unit. The analysis unit can, for example, analyze the data using machine learning algorithms to detect anomalies. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit can detect outliers by comparing them with past data. The analysis unit can also analyze changes in students' emotions using emotion analysis algorithms. For example, the analysis unit can analyze the content of students' statements to detect changes in their emotions. The notification unit notifies the teacher based on the results obtained by the analysis unit. The notification unit can notify teachers, for example, using email or alerts. The notification unit can also refer to teachers' schedules and send notifications at the optimal time. For example, it can send notifications after class has ended. Furthermore, the notification unit can refer to teachers' past response history and suggest the most appropriate response. For example, it can suggest effective response methods based on past response history. This allows the AI ​​system according to the embodiment to reduce the burden on teachers and detect student abnormalities early. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that takes the results obtained by the analysis unit as input and outputs notification content.

[0030] The monitoring unit monitors students' behavior during lessons. Specifically, it uses cameras and microphones installed in the classroom to monitor students' facial expressions and what they say in real time. By using image recognition technology to analyze students' facial expressions, it can determine, for example, whether a student is smiling, concentrating, or tired. Furthermore, the monitoring unit can also monitor students' physical condition using biosensors. These biosensors, for example, are worn by students as wristband-type devices and measure heart rate and skin electrical activity. This allows for real-time detection of changes in students' physical condition, enabling immediate action if an abnormality is detected. The monitoring unit can also analyze students' emotions using facial expression analysis technology. For example, it can detect students' stress and anxiety based on changes in their facial expressions. This allows teachers to understand students' psychological states and provide appropriate support. In addition, the monitoring unit can analyze students' speech using speech recognition technology to evaluate their level of participation and understanding in the lesson. This allows teachers to gain a detailed understanding of each student's learning situation and provide individualized instruction. The monitoring unit centrally manages this data and, by coordinating with the analysis unit and notification unit as needed, can improve the overall efficiency of the system.

[0031] The analysis unit analyzes the data collected by the monitoring unit. Specifically, it can analyze the data using machine learning algorithms and detect anomalies. For example, it can analyze data such as students' heart rate and skin electrical activity to detect abnormal values ​​that exceed the normal range. It can also use data mining techniques to compare current data with past data and detect abnormal values. This allows the analysis unit to quickly grasp changes in students' physical condition and emotions and respond immediately if an anomaly occurs. Furthermore, the analysis unit can analyze changes in students' emotions using emotion analysis algorithms. For example, it can analyze the content of students' statements to detect changes in emotions. This allows teachers to understand students' psychological states in detail and provide appropriate support. Based on this data, the analysis unit can comprehensively evaluate changes in students' learning progress and physical condition and provide appropriate advice to teachers. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The notification unit notifies teachers based on the results obtained by the analysis unit. Specifically, it can notify teachers via email or alerts. For example, if an abnormality is detected in a student's health, an alert is immediately sent to the teacher's smartphone or computer. The notification unit can also refer to the teacher's schedule and send notifications at the optimal time. For example, by sending a notification after class, teachers can respond without being distracted during class. Furthermore, the notification unit can refer to the teacher's past response history and suggest the most appropriate response method. For example, it can suggest an effective response method based on past response history. This allows teachers to respond quickly and appropriately, enabling them to detect and address student abnormalities early. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that takes the results obtained by the analysis unit as input and outputs notification content. The AI ​​model can learn from past data and the teacher's response history and automatically determine the optimal notification content and timing. This allows the notification unit to reduce the burden on teachers and improve the overall efficiency of the system.

[0033] The conversation unit can collect information through conversations with students. For example, the conversation unit can ask students questions such as, "How are you feeling today?" The conversation unit can also analyze students' responses and detect signs of poor health. For example, the conversation unit can record what students say and perform sentiment analysis. This allows the conversation unit to detect early signs of poor health, bullying, or truancy in students. Some or all of the above processing in the conversation unit may be performed using AI, for example, or not using AI. For example, the conversation unit can collect information using an AI model that takes students' statements as input and outputs signs of poor health.

[0034] The recording unit can record information collected by the conversation unit. For example, the recording unit can record the content of students' statements as text data. The recording unit can also record audio data and play it back later. For example, the recording unit can save the content of students' statements as an audio file. The recording unit can also record image data. For example, the recording unit can take a picture of a student's facial expression with a camera and save it as an image file. This allows the recording unit to refer to the collected information later. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can record information using an AI model that takes the content of students' statements as input and outputs text data.

[0035] The emotion analysis unit can analyze the content recorded by the recording unit and detect changes in emotion. The emotion analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The emotion analysis unit can, for example, analyze the content of a student's statements and detect changes in emotion. It can also analyze a student's facial expressions and detect changes in emotion. For example, the emotion analysis unit can detect a student's stress or anxiety based on changes in facial expressions. It can also analyze a student's voice and detect changes in emotion. For example, the emotion analysis unit can analyze the tone and speed of the voice and detect changes in emotion. This allows the emotion analysis unit to detect problems early. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can detect changes in emotion using an AI model that takes the content of a student's statements as input and outputs changes in emotion.

[0036] The monitoring unit can monitor students' behavior using image recognition, facial expression analysis, and biosensors. For example, the monitoring unit can analyze students' facial expressions using image recognition technology. The monitoring unit can also monitor students' physical condition using biosensors. For example, the monitoring unit can measure heart rate and skin electrical activity to detect changes in physical condition. The monitoring unit can also analyze students' emotions using facial expression analysis technology. For example, the monitoring unit can detect students' stress and anxiety based on changes in their facial expressions. This allows the monitoring unit to monitor students' behavior in detail. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input student facial expression data into a generating AI and have the generating AI perform facial expression analysis.

[0037] The analysis unit can analyze data collected by the monitoring unit and detect physical ailments or emotional changes. The analysis unit can, for example, use machine learning algorithms to analyze the data and detect anomalies. The analysis unit can also analyze data using data mining techniques. For example, the analysis unit can detect outliers by comparing them with past data. The analysis unit can also analyze changes in students' emotions using emotion analysis algorithms. For example, the analysis unit can analyze the content of students' statements and detect changes in their emotions. This allows the analysis unit to detect physical ailments or emotional changes at an early stage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected by the monitoring unit into a generating AI and have the generating AI perform the detection of physical ailments or emotional changes.

[0038] The monitoring unit can refer to a student's past behavioral data, identify patterns of abnormal behavior, and enhance monitoring. For example, if a student has complained of feeling unwell in the past, the monitoring unit can enhance monitoring based on that pattern. Similarly, if a student has shown signs of bullying in the past, the monitoring unit can enhance monitoring based on that behavioral pattern. For example, if a student has shown a tendency to be absent from school in the past, the monitoring unit can enhance monitoring based on that behavioral pattern. This allows the monitoring unit to detect abnormal behavior early by enhancing monitoring based on past behavioral data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input a student's past behavioral data into a generative AI and have the generative AI identify patterns of abnormal behavior.

[0039] The monitoring unit can simultaneously collect environmental data within the classroom (temperature, humidity, noise level, etc.) during monitoring and analyze its correlation with the students' condition. For example, if the classroom temperature is high, the monitoring unit can monitor a decrease in students' concentration. Similarly, if the humidity is low, the monitoring unit can monitor signs of students' poor health. For example, if the noise level is high, the monitoring unit can monitor students' stress levels. In this way, the monitoring unit can collect environmental data within the classroom and analyze its correlation with the students' condition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the environmental data from the classroom into a generating AI and have the generating AI perform an analysis of its correlation with the students' condition.

[0040] The monitoring unit can concentrate its monitoring on specific areas by taking into account the students' seating positions during monitoring. For example, if students are sitting at the back of the classroom, the monitoring unit can focus its monitoring on that area. Similarly, if students are sitting at the front of the classroom, the monitoring unit can focus its monitoring on that area. For example, if students are sitting in the center of the classroom, the monitoring unit can focus its monitoring on that area. In this way, the monitoring unit can concentrate its monitoring on specific areas by taking into account the students' seating positions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the students' seating positions into a generating AI and have the generating AI perform monitoring of specific areas.

[0041] The monitoring unit can refer to students' home environment data during monitoring and analyze the relationship between their home environment and their behavior in the classroom. For example, if a student's home environment is unstable, the monitoring unit can focus its monitoring on their classroom behavior. Conversely, if a student's home environment is stable, the monitoring unit can prioritize monitoring other students. For example, if a student's home environment changes, the monitoring unit can reflect the impact of that change on their classroom behavior during monitoring. This allows the monitoring unit to analyze the relationship between home environment data and classroom behavior. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input students' home environment data into a generating AI and have the generating AI perform an analysis of the relationship with classroom behavior.

[0042] The analysis unit can detect anomalies by comparing current data with past data during analysis and identify the causes of those anomalies. For example, the analysis unit can detect anomalies by comparing students' physical condition data with past data. It can also detect anomalies by comparing students' behavioral data with past data. For example, the analysis unit can detect anomalies by comparing students' emotional data with past data. In this way, the analysis unit can detect anomalies and identify their causes by comparing them with past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform anomaly detection and cause identification.

[0043] The analysis unit can, during analysis, refer to the student's learning history and analyze the relationship between learning status and changes in physical condition and emotions. For example, the analysis unit can compare the student's learning history with physical condition data and analyze the relationship. The analysis unit can also compare the student's learning history with emotional data and analyze the relationship. For example, the analysis unit can compare the student's learning history with behavioral data and analyze the relationship. In this way, the analysis unit can analyze the relationship between learning status and changes in physical condition and emotions by referring to the learning history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the student's learning history into a generating AI and have the generating AI perform an analysis of the relationship with changes in physical condition and emotions.

[0044] The analysis unit can refer to students' attendance history during analysis and analyze the relationship between attendance status and changes in physical condition and emotions. For example, the analysis unit can compare students' attendance history with physical condition data and analyze the relationship. The analysis unit can also compare students' attendance history with emotional data and analyze the relationship. For example, the analysis unit can compare students' attendance history with behavioral data and analyze the relationship. In this way, the analysis unit can analyze the relationship between attendance status and changes in physical condition and emotions by referring to attendance history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input students' attendance history into a generating AI and have the generating AI perform an analysis of the relationship with changes in physical condition and emotions.

[0045] The analysis unit can, during analysis, refer to data on students' club activities and extracurricular activities and analyze the relationship between activity status and changes in physical condition and emotions. For example, the analysis unit can compare students' club activity data with physical condition data and analyze the relationship. It can also compare students' club activity data with emotional data and analyze the relationship. For example, the analysis unit can compare students' club activity data with behavioral data and analyze the relationship. In this way, the analysis unit can analyze the relationship between activity status and changes in physical condition and emotions by referring to data on club activities and extracurricular activities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input students' club activity data into a generating AI and have the generating AI perform an analysis of the relationship with changes in physical condition and emotions.

[0046] The notification unit can, at the time of notification, refer to the teacher's past response history and propose the most appropriate response method. For example, the notification unit can propose the most appropriate response method based on the teacher's past response methods. The notification unit can also propose effective response methods from the teacher's past response history. For example, the notification unit can analyze the teacher's past response history and propose the most appropriate response method. In this way, the notification unit can propose the most appropriate response method by referring to past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the teacher's past response history into a generating AI and have the generating AI execute a proposal for the most appropriate response method.

[0047] The notification unit can refer to the teacher's schedule when sending a notification and send it at the optimal time. For example, the notification unit can refer to the teacher's class schedule and send a notification after the class ends. It can also refer to the teacher's meeting schedule and send a notification after the meeting ends. For example, the notification unit can refer to the teacher's break time and send a notification during the break time. In this way, the notification unit can send a notification at the optimal time by referring to the teacher's schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the teacher's schedule data into a generating AI and have the generating AI determine the timing of the notification.

[0048] The notification unit can automatically record necessary actions in conjunction with the school's management system when a notification is sent. For example, the notification unit can automatically record the content of the notification in the school's management system. The notification unit can also automatically record the teacher's response history in the school's management system. For example, the notification unit can automatically record the student's status in the school's management system. In this way, the notification unit can automatically record necessary actions by linking with the school's management system. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the notification content into a generating AI and have the generating AI perform the recording to the school's management system.

[0049] The conversation unit can refer to a student's past statements during a conversation and ask relevant questions. For example, the conversation unit can ask relevant questions based on what the student has said in the past. It can also ask interesting questions based on the student's past statements. For example, the conversation unit can analyze a student's past statements and ask appropriate questions. In this way, the conversation unit can ask relevant questions by referring to past statements. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input a student's past statements into a generating AI and have the generating AI generate relevant questions.

[0050] The conversation unit can provide topics to help students relax during conversations, based on their interests and concerns. For example, the conversation unit can conduct relaxing conversations based on topics that students are interested in. It can also provide relaxing topics based on students' interests. For example, the conversation unit can analyze students' interests and concerns and provide relaxing topics. This allows the conversation unit to help students relax by providing topics based on their interests and concerns. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input student interest data into a generating AI and have the generating AI generate relaxing topics.

[0051] The conversation unit can refer to the student's friendship data during a conversation and ask questions based on their relationships with friends. For example, the conversation unit can ask questions about the student's friends based on the student's friendship data. The conversation unit can also analyze the student's friendship data and ask questions based on their relationships with friends. For example, the conversation unit can refer to the student's friendship data and ask questions to deepen their relationships with friends. In this way, the conversation unit can ask questions based on their relationships with friends by referring to the friendship data. Some or all of the above processing in the conversation unit may be performed using AI, for example, or not using AI. For example, the conversation unit can input the student's friendship data into a generating AI and have the generating AI generate questions.

[0052] The conversation unit can refer to the student's learning progress during conversations and provide learning advice. For example, the conversation unit can provide appropriate advice based on the student's learning progress. Furthermore, the conversation unit can analyze the student's learning progress and suggest effective learning methods. For example, the conversation unit can refer to the student's learning progress and provide advice according to their learning progress. Thus, by referring to the learning progress, the conversation unit can provide appropriate learning advice. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input student learning progress data into a generating AI and have the generating AI generate advice.

[0053] The recording unit can refer to a student's past records during recording and highlight outliers. For example, the recording unit can compare the current student's past records and highlight outliers. The recording unit can also analyze a student's past records and highlight outliers. For example, the recording unit can refer to a student's past records and highlight outliers. In this way, the recording unit can highlight outliers by referring to past records. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input a student's past records into a generating AI and have the generating AI perform the highlighting of outliers.

[0054] The recording unit can integrate and centrally manage students' learning history and behavioral history at the time of recording. For example, the recording unit can integrate and centrally manage students' learning history and behavioral history. The recording unit can also analyze and centrally manage students' learning history and behavioral history. For example, the recording unit can refer to and centrally manage students' learning history and behavioral history. In this way, the recording unit can centrally manage learning history and behavioral history by integrating them. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI. For example, the recording unit can input students' learning history and behavioral history into a generating AI and have the generating AI perform the centralized management.

[0055] The recording unit can add feedback from students' parents to the recording process. For example, the recording unit can add feedback from students' parents to the record. The recording unit can also analyze feedback from students' parents and add it to the record. For example, the recording unit can refer to feedback from students' parents and add it to the record. This allows the recording unit to create more detailed records by adding feedback from parents. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input feedback from students' parents into a generating AI and have the generating AI add it to the record.

[0056] The recording unit can add and record data on students' club activities and extracurricular activities during the recording process. For example, the recording unit can add student club activity data to the record. It can also add student extracurricular activity data to the record. For example, the recording unit can refer to and add student club activity and extracurricular activity data to the record. This allows the recording unit to create more detailed records by adding club activity and extracurricular activity data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input student club activity and extracurricular activity data into a generating AI and have the generating AI add the data to the record.

[0057] The emotion analysis unit can identify patterns of emotional change by referring to the student's past emotional data during emotion analysis. For example, the emotion analysis unit can identify patterns of emotional change based on the student's past emotional data. The emotion analysis unit can also identify patterns of emotional change by analyzing the student's past emotional data. For example, the emotion analysis unit can identify patterns of emotional change by referring to the student's past emotional data. Thus, the emotion analysis unit can identify patterns of emotional change by referring to past emotional data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the student's past emotional data into a generating AI and have the generating AI perform the identification of patterns of emotional change.

[0058] The emotion analysis unit can analyze the relationship between changes in emotion and a student's learning status and behavioral history during emotion analysis. For example, the emotion analysis unit can compare a student's learning status with emotion data and analyze the relationship. It can also compare a student's behavioral history with emotion data and analyze the relationship. For example, the emotion analysis unit can refer to a student's learning status and behavioral history and analyze the relationship with changes in emotion. In this way, the emotion analysis unit can analyze the relationship with changes in emotion by referring to the learning status and behavioral history. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input a student's learning status and behavioral history into a generating AI and have the generating AI perform the analysis of the relationship with changes in emotion.

[0059] The emotion analysis unit can, during emotion analysis, refer to the student's home environment data and analyze the relationship between the home environment and changes in emotions. For example, the emotion analysis unit can compare the student's home environment data with emotion data and analyze the relationship. The emotion analysis unit can also analyze the student's home environment data and analyze its relationship to changes in emotions. For example, the emotion analysis unit can refer to the student's home environment data and analyze its relationship to changes in emotions. Thus, the emotion analysis unit can analyze the relationship to changes in emotions by referring to home environment data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the student's home environment data into a generating AI and have the generating AI perform the analysis of the relationship with changes in emotions.

[0060] The emotion analysis unit can, during emotion analysis, refer to the student's friendship data and analyze the relationship between friendships and changes in emotions. For example, the emotion analysis unit can compare the student's friendship data with emotion data and analyze the relationship. The emotion analysis unit can also analyze the student's friendship data and analyze its relationship with changes in emotions. For example, the emotion analysis unit can refer to the student's friendship data and analyze its relationship with changes in emotions. Thus, the emotion analysis unit can analyze the relationship with changes in emotions by referring to friendship data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the student's friendship data into a generating AI and have the generating AI perform the analysis of the relationship with changes in emotions.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The AI ​​system can also include a learning style analysis unit that analyzes students' learning styles. This unit analyzes students' learning history and behavioral data to identify the optimal learning style for each individual student. For example, if a student prefers visual learning, the unit can provide visual learning materials. Similarly, if a student prefers auditory learning, it can provide audio learning materials. Furthermore, if a student prefers practical learning, it can provide practical assignments. This allows the AI ​​system to improve learning effectiveness by providing each student with the most suitable learning style.

[0063] The AI ​​system can also be equipped with an interest analysis unit that analyzes students' interests and concerns. This unit can analyze students' statements and behavioral data to identify their interests. For example, if a student is interested in a particular subject, the unit can provide learning materials related to that subject. It can also provide information related to a particular activity if the student is interested in that activity. Furthermore, if a student is interested in a particular theme, the unit can provide assignments related to that theme. This allows the AI ​​system to customize learning content based on students' interests, thereby improving their motivation to learn.

[0064] The AI ​​system can also be equipped with a progress monitoring unit that monitors students' learning progress in real time. This unit can monitor students' learning status in real time and notify teachers of their progress. For example, it can notify teachers when a student completes an assignment. It can also notify teachers if a student falls behind in their studies. Furthermore, it can notify teachers if a student is struggling with a particular assignment. This allows the AI ​​system to grasp students' learning progress in real time and provide appropriate support.

[0065] The AI ​​system can also include an evaluation unit to assess students' learning outcomes. This unit can quantitatively evaluate students' learning performance and provide the results to teachers. For example, the evaluation unit can analyze students' test results and evaluate their grades. It can also analyze students' assignment submission status and evaluate submission rates. Furthermore, it can analyze students' learning attitudes and evaluate their motivation. This allows the AI ​​system to objectively evaluate students' learning outcomes, enabling teachers to provide appropriate feedback.

[0066] The AI ​​system can also be equipped with an environment optimization unit to further optimize the students' learning environment. This unit can monitor the students' learning environment and provide an optimal environment. For example, it can monitor the classroom temperature and humidity to provide a comfortable environment. It can also adjust the classroom lighting to provide appropriate brightness. Furthermore, it can monitor the classroom noise level to provide a quiet environment. In this way, the AI ​​system can optimize the students' learning environment and improve learning effectiveness.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The monitoring unit monitors the students' behavior during class. For example, it uses image recognition technology to analyze students' facial expressions and biosensors to measure heart rate and skin electrical activity to detect changes in their physical condition. It can also use facial expression analysis technology to analyze students' emotions and detect stress and anxiety. Step 2: The analysis unit analyzes the data collected by the monitoring unit. For example, it analyzes the data using machine learning algorithms and data mining techniques to detect anomalies. It can also analyze changes in students' emotions using sentiment analysis algorithms and detect changes in emotions by analyzing the content of students' statements. Step 3: The notification unit notifies the teacher based on the results obtained by the analysis unit. For example, it can notify the teacher using email or alerts, and it can also refer to the teacher's schedule to send notifications at the optimal time. It can also refer to the teacher's past response history and suggest the best course of action. Processing in the notification unit may also be done using AI, and notifications can be sent using an AI model that takes the results obtained by the analysis unit as input and outputs the notification content.

[0069] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system that reduces the burden on teachers in elementary and junior high schools in Japan and supports their interaction with students. This AI system monitors the behavior of students during class and notifies teachers if there is any abnormality. Furthermore, the AI ​​system aims to detect and prevent problems such as poor health, bullying, and truancy at an early stage through conversations with students. For example, the AI ​​system monitors the behavior of students during class. In doing so, it uses image recognition, facial expression analysis, and biosensors to analyze changes in physical condition and emotions such as tension and anxiety. For example, the AI ​​system can analyze a student's facial expression and detect signs of poor health or stress. This allows for the early detection of abnormalities in students that teachers may not easily notice. Next, the AI ​​system detects problems such as poor health, bullying, and truancy at an early stage through conversations with students. For example, the AI ​​system can ask students questions such as, "How are you feeling today?" and analyze the student's response to detect signs of poor health. Furthermore, the AI ​​system can detect signs of bullying or truancy by recording what students say and performing emotional analysis. This allows teachers to detect problems they might overlook early and take appropriate action. Furthermore, the AI ​​system explains students' situations to teachers and supports appropriate responses. For example, the AI ​​system might notify a teacher that "Student XX seems to be unwell," helping the teacher take appropriate action. It might also alert teachers that "Student XX hasn't spoken recently," enabling more personalized attention to each student. This system reduces the workload of teachers and allows for more appropriate attention to each student. For example, it is difficult for teachers to keep track of all students during class, but with the support of the AI ​​system, any changes in students can be detected early and appropriate action can be taken. In addition, by sensing problems through conversations with students, the AI ​​system can prevent problems that teachers might overlook. This prevents major problems in the educational setting, reduces the burden on teachers, and enables more appropriate attention to each student.

[0070] The AI ​​system according to this embodiment comprises a monitoring unit, an analysis unit, and a notification unit. The monitoring unit monitors the behavior of students during class. The monitoring unit can, for example, analyze students' facial expressions using image recognition technology. The monitoring unit can also monitor students' physical condition using biosensors. For example, the monitoring unit can measure heart rate and skin electrical activity to detect changes in physical condition. The monitoring unit can also analyze students' emotions using facial expression analysis technology. For example, the monitoring unit can detect students' stress and anxiety based on changes in facial expressions. The analysis unit analyzes the data collected by the monitoring unit. The analysis unit can, for example, analyze the data using machine learning algorithms to detect anomalies. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit can detect outliers by comparing them with past data. The analysis unit can also analyze changes in students' emotions using emotion analysis algorithms. For example, the analysis unit can analyze the content of students' statements to detect changes in their emotions. The notification unit notifies the teacher based on the results obtained by the analysis unit. The notification unit can notify teachers, for example, using email or alerts. The notification unit can also refer to teachers' schedules and send notifications at the optimal time. For example, it can send notifications after class has ended. Furthermore, the notification unit can refer to teachers' past response history and suggest the most appropriate response. For example, it can suggest effective response methods based on past response history. This allows the AI ​​system according to the embodiment to reduce the burden on teachers and detect student abnormalities early. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that takes the results obtained by the analysis unit as input and outputs notification content.

[0071] The monitoring unit monitors students' behavior during lessons. Specifically, it uses cameras and microphones installed in the classroom to monitor students' facial expressions and what they say in real time. By using image recognition technology to analyze students' facial expressions, it can determine, for example, whether a student is smiling, concentrating, or tired. Furthermore, the monitoring unit can also monitor students' physical condition using biosensors. These biosensors, for example, are worn by students as wristband-type devices and measure heart rate and skin electrical activity. This allows for real-time detection of changes in students' physical condition, enabling immediate action if an abnormality is detected. The monitoring unit can also analyze students' emotions using facial expression analysis technology. For example, it can detect students' stress and anxiety based on changes in their facial expressions. This allows teachers to understand students' psychological states and provide appropriate support. In addition, the monitoring unit can analyze students' speech using speech recognition technology to evaluate their level of participation and understanding in the lesson. This allows teachers to gain a detailed understanding of each student's learning situation and provide individualized instruction. The monitoring unit centrally manages this data and, by coordinating with the analysis unit and notification unit as needed, can improve the overall efficiency of the system.

[0072] The analysis unit analyzes the data collected by the monitoring unit. Specifically, it can analyze the data using machine learning algorithms and detect anomalies. For example, it can analyze data such as students' heart rate and skin electrical activity to detect abnormal values ​​that exceed the normal range. It can also use data mining techniques to compare current data with past data and detect abnormal values. This allows the analysis unit to quickly grasp changes in students' physical condition and emotions and respond immediately if an anomaly occurs. Furthermore, the analysis unit can analyze changes in students' emotions using emotion analysis algorithms. For example, it can analyze the content of students' statements to detect changes in emotions. This allows teachers to understand students' psychological states in detail and provide appropriate support. Based on this data, the analysis unit can comprehensively evaluate changes in students' learning progress and physical condition and provide appropriate advice to teachers. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0073] The notification unit notifies teachers based on the results obtained by the analysis unit. Specifically, it can notify teachers via email or alerts. For example, if an abnormality is detected in a student's health, an alert is immediately sent to the teacher's smartphone or computer. The notification unit can also refer to the teacher's schedule and send notifications at the optimal time. For example, by sending a notification after class, teachers can respond without being distracted during class. Furthermore, the notification unit can refer to the teacher's past response history and suggest the most appropriate response method. For example, it can suggest an effective response method based on past response history. This allows teachers to respond quickly and appropriately, enabling them to detect and address student abnormalities early. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that takes the results obtained by the analysis unit as input and outputs notification content. The AI ​​model can learn from past data and the teacher's response history and automatically determine the optimal notification content and timing. This allows the notification unit to reduce the burden on teachers and improve the overall efficiency of the system.

[0074] The conversation unit can collect information through conversations with students. For example, the conversation unit can ask students questions such as, "How are you feeling today?" The conversation unit can also analyze students' responses and detect signs of poor health. For example, the conversation unit can record what students say and perform sentiment analysis. This allows the conversation unit to detect early signs of poor health, bullying, or truancy in students. Some or all of the above processing in the conversation unit may be performed using AI, for example, or not using AI. For example, the conversation unit can collect information using an AI model that takes students' statements as input and outputs signs of poor health.

[0075] The recording unit can record information collected by the conversation unit. For example, the recording unit can record the content of students' statements as text data. The recording unit can also record audio data and play it back later. For example, the recording unit can save the content of students' statements as an audio file. The recording unit can also record image data. For example, the recording unit can take a picture of a student's facial expression with a camera and save it as an image file. This allows the recording unit to refer to the collected information later. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can record information using an AI model that takes the content of students' statements as input and outputs text data.

[0076] The emotion analysis unit can analyze the content recorded by the recording unit and detect changes in emotion. The emotion analysis unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The emotion analysis unit can, for example, analyze the content of a student's statements and detect changes in emotion. It can also analyze a student's facial expressions and detect changes in emotion. For example, the emotion analysis unit can detect a student's stress or anxiety based on changes in facial expressions. It can also analyze a student's voice and detect changes in emotion. For example, the emotion analysis unit can analyze the tone and speed of the voice and detect changes in emotion. This allows the emotion analysis unit to detect problems early. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can detect changes in emotion using an AI model that takes the content of a student's statements as input and outputs changes in emotion.

[0077] The monitoring unit can monitor students' behavior using image recognition, facial expression analysis, and biosensors. For example, the monitoring unit can analyze students' facial expressions using image recognition technology. The monitoring unit can also monitor students' physical condition using biosensors. For example, the monitoring unit can measure heart rate and skin electrical activity to detect changes in physical condition. The monitoring unit can also analyze students' emotions using facial expression analysis technology. For example, the monitoring unit can detect students' stress and anxiety based on changes in their facial expressions. This allows the monitoring unit to monitor students' behavior in detail. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input student facial expression data into a generating AI and have the generating AI perform facial expression analysis.

[0078] The analysis unit can analyze data collected by the monitoring unit and detect physical ailments or emotional changes. The analysis unit can, for example, use machine learning algorithms to analyze the data and detect anomalies. The analysis unit can also analyze data using data mining techniques. For example, the analysis unit can detect outliers by comparing them with past data. The analysis unit can also analyze changes in students' emotions using emotion analysis algorithms. For example, the analysis unit can analyze the content of students' statements and detect changes in their emotions. This allows the analysis unit to detect physical ailments or emotional changes at an early stage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data collected by the monitoring unit into a generating AI and have the generating AI perform the detection of physical ailments or emotional changes.

[0079] The monitoring unit can estimate a student's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if a student is stressed, the monitoring unit can increase the monitoring frequency to detect abnormalities early. Conversely, if a student is relaxed, the monitoring unit can lower the monitoring frequency to respect their privacy. For example, if a student is tense, the monitoring unit can appropriately adjust the monitoring frequency to avoid excessive surveillance. This allows the monitoring unit to perform more appropriate monitoring by adjusting the monitoring frequency according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input student emotion data into the generative AI and have the generative AI adjust the monitoring frequency.

[0080] The monitoring unit can refer to a student's past behavioral data, identify patterns of abnormal behavior, and enhance monitoring. For example, if a student has complained of feeling unwell in the past, the monitoring unit can enhance monitoring based on that pattern. Similarly, if a student has shown signs of bullying in the past, the monitoring unit can enhance monitoring based on that behavioral pattern. For example, if a student has shown a tendency to be absent from school in the past, the monitoring unit can enhance monitoring based on that behavioral pattern. This allows the monitoring unit to detect abnormal behavior early by enhancing monitoring based on past behavioral data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input a student's past behavioral data into a generative AI and have the generative AI identify patterns of abnormal behavior.

[0081] The monitoring unit can simultaneously collect environmental data within the classroom (temperature, humidity, noise level, etc.) during monitoring and analyze its correlation with the students' condition. For example, if the classroom temperature is high, the monitoring unit can monitor a decrease in students' concentration. Similarly, if the humidity is low, the monitoring unit can monitor signs of students' poor health. For example, if the noise level is high, the monitoring unit can monitor students' stress levels. In this way, the monitoring unit can collect environmental data within the classroom and analyze its correlation with the students' condition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the environmental data from the classroom into a generating AI and have the generating AI perform an analysis of its correlation with the students' condition.

[0082] The monitoring unit can estimate students' emotions and determine monitoring priorities based on the estimated emotions. For example, if a student is feeling stressed, the monitoring unit can prioritize monitoring that student. Conversely, if a student is relaxed, the monitoring unit can prioritize monitoring other students. For example, if a student is tense, the monitoring unit can monitor that student appropriately. This allows the monitoring unit to perform more appropriate monitoring by determining monitoring priorities according to students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input student emotion data into a generative AI and have the generative AI determine monitoring priorities.

[0083] The monitoring unit can concentrate its monitoring on specific areas by taking into account the students' seating positions during monitoring. For example, if students are sitting at the back of the classroom, the monitoring unit can focus its monitoring on that area. Similarly, if students are sitting at the front of the classroom, the monitoring unit can focus its monitoring on that area. For example, if students are sitting in the center of the classroom, the monitoring unit can focus its monitoring on that area. In this way, the monitoring unit can concentrate its monitoring on specific areas by taking into account the students' seating positions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the students' seating positions into a generating AI and have the generating AI perform monitoring of specific areas.

[0084] The monitoring unit can refer to students' home environment data during monitoring and analyze the relationship between their home environment and their behavior in the classroom. For example, if a student's home environment is unstable, the monitoring unit can focus its monitoring on their classroom behavior. Conversely, if a student's home environment is stable, the monitoring unit can prioritize monitoring other students. For example, if a student's home environment changes, the monitoring unit can reflect the impact of that change on their classroom behavior during monitoring. This allows the monitoring unit to analyze the relationship between home environment data and classroom behavior. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input students' home environment data into a generating AI and have the generating AI perform an analysis of the relationship with classroom behavior.

[0085] The analysis unit can estimate a student's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if a student is stressed, the analysis unit can prioritize stress-related data in its analysis. Similarly, if a student is relaxed, the analysis unit can prioritize relaxation-related data in its analysis. For example, if a student is tense, the analysis unit can prioritize tension-related data in its analysis. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis algorithm according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0086] The analysis unit can detect anomalies by comparing current data with past data during analysis and identify the causes of those anomalies. For example, the analysis unit can detect anomalies by comparing students' physical condition data with past data. It can also detect anomalies by comparing students' behavioral data with past data. For example, the analysis unit can detect anomalies by comparing students' emotional data with past data. In this way, the analysis unit can detect anomalies and identify their causes by comparing them with past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform anomaly detection and cause identification.

[0087] The analysis unit can, during analysis, refer to the student's learning history and analyze the relationship between learning status and changes in physical condition and emotions. For example, the analysis unit can compare the student's learning history with physical condition data and analyze the relationship. The analysis unit can also compare the student's learning history with emotional data and analyze the relationship. For example, the analysis unit can compare the student's learning history with behavioral data and analyze the relationship. In this way, the analysis unit can analyze the relationship between learning status and changes in physical condition and emotions by referring to the learning history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the student's learning history into a generating AI and have the generating AI perform an analysis of the relationship with changes in physical condition and emotions.

[0088] The analysis unit can estimate a student's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if a student is stressed, the analysis unit can provide a simple and highly visible display method. If a student is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if a student is tense, the analysis unit can provide a concise display method. This allows the analysis unit to provide a more appropriate display by adjusting the display method of the analysis results according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0089] The analysis unit can refer to students' attendance history during analysis and analyze the relationship between attendance status and changes in physical condition and emotions. For example, the analysis unit can compare students' attendance history with physical condition data and analyze the relationship. The analysis unit can also compare students' attendance history with emotional data and analyze the relationship. For example, the analysis unit can compare students' attendance history with behavioral data and analyze the relationship. In this way, the analysis unit can analyze the relationship between attendance status and changes in physical condition and emotions by referring to attendance history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input students' attendance history into a generating AI and have the generating AI perform an analysis of the relationship with changes in physical condition and emotions.

[0090] The analysis unit can, during analysis, refer to data on students' club activities and extracurricular activities and analyze the relationship between activity status and changes in physical condition and emotions. For example, the analysis unit can compare students' club activity data with physical condition data and analyze the relationship. It can also compare students' club activity data with emotional data and analyze the relationship. For example, the analysis unit can compare students' club activity data with behavioral data and analyze the relationship. In this way, the analysis unit can analyze the relationship between activity status and changes in physical condition and emotions by referring to data on club activities and extracurricular activities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input students' club activity data into a generating AI and have the generating AI perform an analysis of the relationship with changes in physical condition and emotions.

[0091] The notification unit can estimate a student's emotions and adjust the urgency of the notification based on the estimated emotions. For example, if a student is feeling stressed, the notification unit can send a high-urgency notification to the teacher. Conversely, if a student is relaxed, the notification unit can send a low-urgency notification to the teacher. For example, if a student is feeling anxious, the notification unit can send a notification of moderate urgency to the teacher. This allows the notification unit to provide more appropriate notifications by adjusting the urgency of the notification according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input student emotion data into a generative AI and have the generative AI adjust the urgency of the notification.

[0092] The notification unit can, at the time of notification, refer to the teacher's past response history and propose the most appropriate response method. For example, the notification unit can propose the most appropriate response method based on the teacher's past response methods. The notification unit can also propose effective response methods from the teacher's past response history. For example, the notification unit can analyze the teacher's past response history and propose the most appropriate response method. In this way, the notification unit can propose the most appropriate response method by referring to past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the teacher's past response history into a generating AI and have the generating AI execute a proposal for the most appropriate response method.

[0093] The notification unit can estimate a student's emotions and customize the content of the notification based on the estimated emotions. For example, if a student is feeling stressed, the notification unit can notify them of ways to reduce stress. It can also notify them of ways to maintain relaxation if they are relaxed. Similarly, if a student is feeling anxious, the notification unit can notify them of ways to alleviate anxiety. This allows the notification unit to provide more appropriate notifications by customizing the content according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input student emotion data into a generative AI and have the generative AI customize the notification content.

[0094] The notification unit can refer to the teacher's schedule when sending a notification and send it at the optimal time. For example, the notification unit can refer to the teacher's class schedule and send a notification after the class ends. It can also refer to the teacher's meeting schedule and send a notification after the meeting ends. For example, the notification unit can refer to the teacher's break time and send a notification during the break time. In this way, the notification unit can send a notification at the optimal time by referring to the teacher's schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the teacher's schedule data into a generating AI and have the generating AI determine the timing of the notification.

[0095] The notification unit can automatically record necessary actions in conjunction with the school's management system when a notification is sent. For example, the notification unit can automatically record the content of the notification in the school's management system. The notification unit can also automatically record the teacher's response history in the school's management system. For example, the notification unit can automatically record the student's status in the school's management system. In this way, the notification unit can automatically record necessary actions by linking with the school's management system. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the notification content into a generating AI and have the generating AI perform the recording to the school's management system.

[0096] The conversation unit can estimate a student's emotions and adjust the tone and content of the conversation based on the estimated emotions. For example, if a student is stressed, the conversation unit can speak in a gentle tone. Conversely, if a student is relaxed, the conversation unit can speak in a cheerful tone. For example, if a student is nervous, the conversation unit can speak in a calm tone. In this way, the conversation unit can have a more appropriate conversation by adjusting the tone and content of the conversation according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using AI, for example, or not using AI. For example, the conversation unit can input student emotion data into a generative AI and have the generative AI adjust the tone and content of the conversation.

[0097] The conversation unit can refer to a student's past statements during a conversation and ask relevant questions. For example, the conversation unit can ask relevant questions based on what the student has said in the past. It can also ask interesting questions based on the student's past statements. For example, the conversation unit can analyze a student's past statements and ask appropriate questions. In this way, the conversation unit can ask relevant questions by referring to past statements. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input a student's past statements into a generating AI and have the generating AI generate relevant questions.

[0098] The conversation unit can provide topics to help students relax during conversations, based on their interests and concerns. For example, the conversation unit can conduct relaxing conversations based on topics that students are interested in. It can also provide relaxing topics based on students' interests. For example, the conversation unit can analyze students' interests and concerns and provide relaxing topics. This allows the conversation unit to help students relax by providing topics based on their interests and concerns. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input student interest data into a generating AI and have the generating AI generate relaxing topics.

[0099] The conversation unit can estimate a student's emotions and adjust the frequency of conversation based on the estimated emotions. For example, if a student is feeling stressed, the conversation unit can increase the frequency of conversation to provide support. Conversely, if a student is relaxed, the conversation unit can decrease the frequency of conversation to respect their privacy. For example, if a student is nervous, the conversation unit can appropriately adjust the frequency of conversation to avoid excessive interference. This allows the conversation unit to have more appropriate conversations by adjusting the frequency of conversation according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using AI, for example, or not using AI. For example, the conversation unit can input student emotion data into a generative AI and have the generative AI adjust the frequency of conversation.

[0100] The conversation unit can refer to the student's friendship data during a conversation and ask questions based on their relationships with friends. For example, the conversation unit can ask questions about the student's friends based on the student's friendship data. The conversation unit can also analyze the student's friendship data and ask questions based on their relationships with friends. For example, the conversation unit can refer to the student's friendship data and ask questions to deepen their relationships with friends. In this way, the conversation unit can ask questions based on their relationships with friends by referring to the friendship data. Some or all of the above processing in the conversation unit may be performed using AI, for example, or not using AI. For example, the conversation unit can input the student's friendship data into a generating AI and have the generating AI generate questions.

[0101] The conversation unit can refer to the student's learning progress during conversations and provide learning advice. For example, the conversation unit can provide appropriate advice based on the student's learning progress. Furthermore, the conversation unit can analyze the student's learning progress and suggest effective learning methods. For example, the conversation unit can refer to the student's learning progress and provide advice according to their learning progress. Thus, by referring to the learning progress, the conversation unit can provide appropriate learning advice. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input student learning progress data into a generating AI and have the generating AI generate advice.

[0102] The recording unit can estimate a student's emotions and adjust the level of detail in the recording based on the estimated emotions. For example, if a student is stressed, the recording unit can make a detailed record to detect abnormalities early. Conversely, if a student is relaxed, the recording unit can make a simplified record to respect privacy. For example, if a student is tense, the recording unit can make a record with an appropriate level of detail to avoid excessive monitoring. This allows the recording unit to make more appropriate records by adjusting the level of detail in the recording according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input student emotion data into a generative AI and have the generative AI adjust the level of detail in the recording.

[0103] The recording unit can refer to a student's past records during recording and highlight outliers. For example, the recording unit can compare the current student's past records and highlight outliers. The recording unit can also analyze a student's past records and highlight outliers. For example, the recording unit can refer to a student's past records and highlight outliers. In this way, the recording unit can highlight outliers by referring to past records. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input a student's past records into a generating AI and have the generating AI perform the highlighting of outliers.

[0104] The recording unit can integrate and centrally manage students' learning history and behavioral history at the time of recording. For example, the recording unit can integrate and centrally manage students' learning history and behavioral history. The recording unit can also analyze and centrally manage students' learning history and behavioral history. For example, the recording unit can refer to and centrally manage students' learning history and behavioral history. In this way, the recording unit can centrally manage learning history and behavioral history by integrating them. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI. For example, the recording unit can input students' learning history and behavioral history into a generating AI and have the generating AI perform the centralized management.

[0105] The recording unit can estimate students' emotions and determine recording priorities based on the estimated emotions. For example, if a student is feeling stressed, the recording unit can prioritize recording that student. Conversely, if a student is relaxed, the recording unit can prioritize recording other students. For example, if a student is tense, the recording unit can record that student appropriately. This allows the recording unit to prioritize recordings according to students' emotions, enabling more appropriate recordings. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, or not. For example, the recording unit can input student emotion data into a generative AI and have the generative AI determine recording priorities.

[0106] The recording unit can add feedback from students' parents to the recording process. For example, the recording unit can add feedback from students' parents to the record. The recording unit can also analyze feedback from students' parents and add it to the record. For example, the recording unit can refer to feedback from students' parents and add it to the record. This allows the recording unit to create more detailed records by adding feedback from parents. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input feedback from students' parents into a generating AI and have the generating AI add it to the record.

[0107] The recording unit can add and record data on students' club activities and extracurricular activities during the recording process. For example, the recording unit can add student club activity data to the record. It can also add student extracurricular activity data to the record. For example, the recording unit can refer to and add student club activity and extracurricular activity data to the record. This allows the recording unit to create more detailed records by adding club activity and extracurricular activity data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input student club activity and extracurricular activity data into a generating AI and have the generating AI add the data to the record.

[0108] The emotion analysis unit can estimate a student's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if a student is stressed, the emotion analysis unit can prioritize stress-related data in its analysis. Similarly, if a student is relaxed, the emotion analysis unit can prioritize relaxation-related data in its analysis. For example, if a student is tense, the emotion analysis unit can prioritize tension-related data in its analysis. This allows the emotion analysis unit to perform a more appropriate analysis by adjusting the analysis algorithm according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, or not using AI. For example, the emotion analysis unit can input student emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0109] The emotion analysis unit can identify patterns of emotional change by referring to the student's past emotional data during emotion analysis. For example, the emotion analysis unit can identify patterns of emotional change based on the student's past emotional data. The emotion analysis unit can also identify patterns of emotional change by analyzing the student's past emotional data. For example, the emotion analysis unit can identify patterns of emotional change by referring to the student's past emotional data. Thus, the emotion analysis unit can identify patterns of emotional change by referring to past emotional data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the student's past emotional data into a generating AI and have the generating AI perform the identification of patterns of emotional change.

[0110] The emotion analysis unit can analyze the relationship between changes in emotion and a student's learning status and behavioral history during emotion analysis. For example, the emotion analysis unit can compare a student's learning status with emotion data and analyze the relationship. It can also compare a student's behavioral history with emotion data and analyze the relationship. For example, the emotion analysis unit can refer to a student's learning status and behavioral history and analyze the relationship with changes in emotion. In this way, the emotion analysis unit can analyze the relationship with changes in emotion by referring to the learning status and behavioral history. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input a student's learning status and behavioral history into a generating AI and have the generating AI perform the analysis of the relationship with changes in emotion.

[0111] The emotion analysis unit can estimate a student's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if a student is stressed, the emotion analysis unit can provide a simple and highly visible display method. If a student is relaxed, the emotion analysis unit can also provide a display method that includes detailed information. For example, if a student is tense, the emotion analysis unit can provide a concise display method. In this way, the emotion analysis unit can provide a more appropriate display by adjusting the display method of the analysis results according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input student emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0112] The emotion analysis unit can, during emotion analysis, refer to the student's home environment data and analyze the relationship between the home environment and changes in emotions. For example, the emotion analysis unit can compare the student's home environment data with emotion data and analyze the relationship. The emotion analysis unit can also analyze the student's home environment data and analyze its relationship to changes in emotions. For example, the emotion analysis unit can refer to the student's home environment data and analyze its relationship to changes in emotions. Thus, the emotion analysis unit can analyze the relationship to changes in emotions by referring to home environment data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the student's home environment data into a generating AI and have the generating AI perform the analysis of the relationship with changes in emotions.

[0113] The emotion analysis unit can, during emotion analysis, refer to the student's friendship data and analyze the relationship between friendships and changes in emotions. For example, the emotion analysis unit can compare the student's friendship data with emotion data and analyze the relationship. The emotion analysis unit can also analyze the student's friendship data and analyze its relationship with changes in emotions. For example, the emotion analysis unit can refer to the student's friendship data and analyze its relationship with changes in emotions. Thus, the emotion analysis unit can analyze the relationship with changes in emotions by referring to friendship data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the student's friendship data into a generating AI and have the generating AI perform the analysis of the relationship with changes in emotions.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The AI ​​system can also include a learning style analysis unit that analyzes students' learning styles. This unit analyzes students' learning history and behavioral data to identify the optimal learning style for each individual student. For example, if a student prefers visual learning, the unit can provide visual learning materials. Similarly, if a student prefers auditory learning, it can provide audio learning materials. Furthermore, if a student prefers practical learning, it can provide practical assignments. This allows the AI ​​system to improve learning effectiveness by providing each student with the most suitable learning style.

[0116] The AI ​​system can also be equipped with an interest analysis unit that analyzes students' interests and concerns. This unit can analyze students' statements and behavioral data to identify their interests. For example, if a student is interested in a particular subject, the unit can provide learning materials related to that subject. It can also provide information related to a particular activity if the student is interested in that activity. Furthermore, if a student is interested in a particular theme, the unit can provide assignments related to that theme. This allows the AI ​​system to customize learning content based on students' interests, thereby improving their motivation to learn.

[0117] The AI ​​system can also be equipped with a progress monitoring unit that monitors students' learning progress in real time. This unit can monitor students' learning status in real time and notify teachers of their progress. For example, it can notify teachers when a student completes an assignment. It can also notify teachers if a student falls behind in their studies. Furthermore, it can notify teachers if a student is struggling with a particular assignment. This allows the AI ​​system to grasp students' learning progress in real time and provide appropriate support.

[0118] The AI ​​system can also include an evaluation unit to assess students' learning outcomes. This unit can quantitatively evaluate students' learning performance and provide the results to teachers. For example, the evaluation unit can analyze students' test results and evaluate their grades. It can also analyze students' assignment submission status and evaluate submission rates. Furthermore, it can analyze students' learning attitudes and evaluate their motivation. This allows the AI ​​system to objectively evaluate students' learning outcomes, enabling teachers to provide appropriate feedback.

[0119] The AI ​​system can also be equipped with an environment optimization unit to further optimize the students' learning environment. This unit can monitor the students' learning environment and provide an optimal environment. For example, it can monitor the classroom temperature and humidity to provide a comfortable environment. It can also adjust the classroom lighting to provide appropriate brightness. Furthermore, it can monitor the classroom noise level to provide a quiet environment. In this way, the AI ​​system can optimize the students' learning environment and improve learning effectiveness.

[0120] The AI ​​system may also include an emotion adaptation unit that estimates students' emotions and adjusts learning content based on those emotions. The emotion adaptation unit can analyze students' emotions and provide learning content appropriate to those emotions. For example, if a student is feeling stressed, the emotion adaptation unit can provide relaxing learning content. Conversely, if a student is relaxed, it can provide challenging learning content. Furthermore, if a student is excited, it can provide learning content that enhances concentration. This allows the AI ​​system to adjust learning content according to students' emotions, thereby improving learning effectiveness.

[0121] The AI ​​system may also include an emotion progress adjustment unit that estimates the student's emotions and adjusts the learning progress based on those emotions. The emotion progress adjustment unit can analyze the student's emotions and provide learning progress that corresponds to those emotions. For example, if the student is feeling stressed, the emotion progress adjustment unit can slow down the learning progress. Conversely, if the student is relaxed, the emotion progress adjustment unit can accelerate the learning progress. Furthermore, if the student is excited, the emotion progress adjustment unit can appropriately adjust the learning progress. In this way, the AI ​​system can adjust the learning progress according to the student's emotions and improve learning effectiveness.

[0122] The AI ​​system may also include an emotional feedback unit that estimates the student's emotions and provides learning feedback based on those emotions. The emotional feedback unit can analyze the student's emotions and provide feedback appropriate to those emotions. For example, if the student is feeling stressed, the emotional feedback unit can provide encouraging feedback. If the student is relaxed, the emotional feedback unit can also provide specific areas for improvement. Furthermore, if the student is excited, the emotional feedback unit can provide instructions for the next step. This allows the AI ​​system to provide feedback according to the student's emotions, thereby improving learning effectiveness.

[0123] The AI ​​system can also be equipped with an emotion motivation unit that estimates students' emotions and improves their learning motivation based on those estimated emotions. This emotion motivation unit can analyze students' emotions and provide motivation-enhancing measures tailored to those emotions. For example, if a student is feeling stressed, it can provide a relaxing environment. If a student is relaxed, it can also provide challenging tasks. Furthermore, if a student is excited, it can provide activities to improve their concentration. This allows the AI ​​system to improve motivation and learning effectiveness based on students' emotions.

[0124] The AI ​​system may also include an emotional support unit that estimates students' emotions and provides learning support based on those emotions. This emotional support unit can analyze students' emotions and provide support tailored to those emotions. For example, if a student is feeling stressed, it can offer relaxing activities. If a student is relaxed, it can also support their learning progress. Furthermore, if a student is agitated, it can provide support to improve their concentration. This allows the AI ​​system to provide learning support according to students' emotions, thereby improving learning effectiveness.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The monitoring unit monitors the students' behavior during class. For example, it uses image recognition technology to analyze students' facial expressions and biosensors to measure heart rate and skin electrical activity to detect changes in their physical condition. It can also use facial expression analysis technology to analyze students' emotions and detect stress and anxiety. Step 2: The analysis unit analyzes the data collected by the monitoring unit. For example, it analyzes the data using machine learning algorithms and data mining techniques to detect anomalies. It can also analyze changes in students' emotions using sentiment analysis algorithms and detect changes in emotions by analyzing the content of students' statements. Step 3: The notification unit notifies the teacher based on the results obtained by the analysis unit. For example, it can notify the teacher using email or alerts, and it can also refer to the teacher's schedule to send notifications at the optimal time. It can also refer to the teacher's past response history and suggest the best course of action. Processing in the notification unit may also be done using AI, and notifications can be sent using an AI model that takes the results obtained by the analysis unit as input and outputs the notification content.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, conversation unit, recording unit, and emotion analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the student's behavior using the camera 42 and biosensors of the smart device 14 and collects data using the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to detect anomalies. The notification unit is implemented, for example, by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12, and notifies the teacher at an appropriate time. The conversation unit conducts conversations with students using the microphone 38B of the smart device 14 and collects information using the control unit 46A. The recording unit records information in, for example, the storage 50 of the smart device 14 or the database 24 of the data processing unit 12. The emotion analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the content of the student's statements and facial expressions to detect changes in emotion. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, conversation unit, recording unit, and emotion analysis unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the student's behavior using the camera 42 and biosensors of the smart glasses 214 and collects data using the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to detect anomalies. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12, and notifies the teacher at an appropriate time. The conversation unit engages in conversation with the student using the microphone 238 of the smart glasses 214 and collects information using the control unit 46A. The recording unit records the information in, for example, the storage 50 of the smart glasses 214 or the database 24 of the data processing unit 12. The emotion analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the student's statements and facial expressions to detect changes in emotion. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, conversation unit, recording unit, and emotion analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the student's condition using the camera 42 and biosensors of the headset terminal 314 and collects data using the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to detect anomalies. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12, and notifies the teacher at an appropriate time. The conversation unit conducts conversations with students using the microphone 238 of the headset terminal 314 and collects information using the control unit 46A. The recording unit records information in, for example, the storage 50 of the headset terminal 314 or the database 24 of the data processing unit 12. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and detects changes in emotion by analyzing the content of students' statements and facial expressions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the monitoring unit, analysis unit, notification unit, conversation unit, recording unit, and emotion analysis unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the students' behavior using the camera 42 and biosensors of the robot 414 and collects data using the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to detect anomalies. The notification unit is implemented, for example, by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing unit 12, and notifies the teacher at an appropriate time. The conversation unit engages in conversation with students using the microphone 238 of the robot 414 and collects information using the control unit 46A. The recording unit records information in, for example, the storage 50 of the robot 414 or the database 24 of the data processing unit 12. The emotion analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the content of students' statements and facial expressions to detect changes in their emotions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) The monitoring department monitors the students' behavior during class, An analysis unit analyzes the data collected by the monitoring unit, The system includes a notification unit that notifies the teacher based on the results obtained by the analysis unit. A system characterized by the following features. (Note 2) It includes a conversation department that collects information through conversations with students. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a recording unit that records information collected by the aforementioned conversation unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) The system includes an emotion analysis unit that analyzes the content recorded by the recording unit and detects changes in emotion. The system described in Appendix 3, characterized by the features described herein. (Note 5) The monitoring unit, We monitor students' behavior using image recognition, facial expression analysis, and biosensors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The monitoring unit analyzes the data collected to detect physical ailments and emotional changes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, The system estimates the students' emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, Referencing students' past behavioral data will help identify patterns of abnormal behavior and strengthen monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, During monitoring, environmental data within the classroom is collected simultaneously and analyzed for its correlation with the students' condition. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, The system estimates students' emotions and prioritizes monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, During monitoring, the monitoring is concentrated on specific areas, taking into account the students' seating locations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The monitoring unit, During monitoring, data on students' home environments will be referenced to analyze the relationship between their home situation and their behavior in the classroom. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the students' emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, anomalies are detected by comparing them with past data, and the cause of the anomalies is identified. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the student's learning history is referenced to analyze the relationship between their learning status and changes in their physical condition and emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates students' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the student's attendance history is referenced to analyze the relationship between attendance status and changes in physical and emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, data on students' club activities and extracurricular activities will be referenced to analyze the relationship between their activity levels and changes in their physical condition and emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The system estimates students' emotions and adjusts the urgency of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When notifying, the system will refer to the teacher's past response history and suggest the most appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates students' emotions and customizes the content of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When sending notifications, the teacher's schedule is referenced to ensure the notification is sent at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, Upon notification, the system will integrate with the school's management system and automatically record the necessary actions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned conversation section is, The system estimates the students' emotions and adjusts the tone and content of the conversation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned conversation section is, During conversations, refer to the student's past statements and ask related questions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned conversation section is, During conversations, provide topics that help students relax, based on their interests and concerns. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned conversation section is, The system estimates the students' emotions and adjusts the frequency of conversations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned conversation section is, During conversations, refer to the student's friendship data and ask questions based on their relationships with friends. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned conversation section is, During conversations, refer to the student's learning progress and provide advice regarding their studies. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned recording unit is The system estimates the students' emotions and adjusts the level of detail in the recording based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned recording unit is During recording, the system references the student's past records and highlights any outliers. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned recording unit is During recording, student learning history and behavioral history are integrated and centrally managed. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned recording unit is The system estimates students' emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned recording unit is When recording, add and record feedback from the student's parents. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned recording unit is When recording, add and record data on students' club activities and extracurricular activities. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned emotion analysis unit, The system estimates the students' emotions and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned emotion analysis unit, During emotion analysis, we refer to the student's past emotional data to identify patterns in emotional change. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned emotion analysis unit, During emotion analysis, we refer to students' learning progress and behavioral history and analyze their relationship to changes in their emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned emotion analysis unit, The system estimates students' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned emotion analysis unit, During emotion analysis, we refer to data on students' home environments and analyze the relationship between their home situation and changes in their emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned emotion analysis unit, During emotion analysis, we refer to students' friendship data and analyze the relationship between friendships and changes in emotions. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0199] 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. The monitoring department monitors the students' behavior during class, An analysis unit analyzes the data collected by the monitoring unit, The system includes a notification unit that notifies the teacher based on the results obtained by the analysis unit. A system characterized by the following features.

2. It includes a conversation department that collects information through conversations with students. The system according to feature 1.

3. It includes a recording unit that records information collected by the aforementioned conversation unit. The system according to feature 2.

4. The system includes an emotion analysis unit that analyzes the content recorded by the recording unit and detects changes in emotion. The system according to claim 3.

5. The monitoring unit, We monitor students' behavior using image recognition, facial expression analysis, and biosensors. The system according to feature 1.

6. The aforementioned analysis unit, The monitoring unit analyzes the data collected to detect physical ailments and emotional changes. The system according to feature 1.

7. The monitoring unit, The system estimates the students' emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

8. The monitoring unit, Referencing students' past behavioral data will help identify patterns of abnormal behavior and strengthen monitoring. The system according to feature 1.

9. The monitoring unit, During monitoring, environmental data within the classroom is collected simultaneously and analyzed for its correlation with the students' condition. The system according to feature 1.

10. The monitoring unit, The system estimates students' emotions and prioritizes monitoring based on those estimated emotions. The system according to feature 1.

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