System

Abstract

To estimate the emotions of a student and offer the optimum education contents on the basis of the estimation results.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, a determination unit, and a correspondence unit. The acquisition unit acquires emotion estimation information for estimating the emotions of a student in an English class. The estimation unit estimates the emotions of the student on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines concrete education contents on the basis of the student emotions estimated by the estimation unit. The correspondence unit conducts the English education determined by the determination unit for the student.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately understand students' emotions and adjust educational content based on them, so there is room for improvement.

[0005] The system according to the embodiment aims to estimate the emotions of students and provide optimal educational content based on the estimated emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a corresponding unit. The acquisition unit acquires emotion estimation information for estimating emotions of students in an English classroom. The estimation unit estimates emotions of the students based on the emotion estimation information acquired by the acquisition unit. The determination unit determines specific educational content based on the emotions of the students estimated by the estimation unit. The corresponding unit implements the English education determined by the determination unit for the students. Effect of the Invention

[0007] The system according to the embodiment can estimate the emotions of students and provide optimal educational content based on the estimated emotions. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The educational robot system according to the embodiment of the present invention is a system that estimates the emotions of students in an English classroom and provides appropriate English education. The system acquires information for estimating the emotions of students, and determines and executes educational content based on the estimated emotions. For example, the educational robot system acquires information such as facial expressions, movements, voices, heart rates, blood pressure, pupil responses, brain waves, body temperature, breathing, and posted content of students. Next, the educational robot system estimates the emotions of students based on the acquired emotion estimation information. Depending on the estimated emotions, appropriate English education is determined and executed for the students. Furthermore, the educational robot system monitors the learning progress of students in real time and dynamically adjusts the learning content according to the emotions of students. In addition, the educational robot system supports communication with other students and teachers based on the emotional state of students, predicts future learning performance, and provides advice to teachers. In this way, the educational robot system can provide English education that takes into account the emotions of students.

[0029] The educational robot system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and a corresponding unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a student in an English classroom. The emotion estimation information includes, for example, the facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content of the student, but is not limited to such examples. The acquisition unit, for example, photographs the facial expression of the student in real time using a camera and acquires the facial expression data. The acquisition unit can also detect the movement of the student using a motion sensor and acquire the movement data. Furthermore, the acquisition unit can also record the voice of the student using a microphone and acquire the voice data. The estimation unit estimates the emotion of the student based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, analyzes the facial expression data of the student using a facial expression recognition algorithm and estimates the emotion. Furthermore, the estimation unit can also analyze the voice data of the student using a voice recognition algorithm and estimate the emotion. Furthermore, the estimation unit can analyze the heart rate data of the student using a heart rate analysis algorithm and estimate the emotion. The determination unit determines specific educational content based on the emotion of the student estimated by the estimation unit. For example, the determination unit determines to conduct pronunciation practice when the student is relaxed. Also, the determination unit can determine to conduct vocabulary memorization when the student is excited. Furthermore, the determination unit can determine to conduct listening practice when the student is tired. The response unit executes the English education determined by the determination unit for the student. For example, the response unit executes pronunciation practice when the student is relaxed. Also, the response unit can execute vocabulary memorization when the student is excited. Furthermore, the response unit can execute listening practice when the student is tired. In this way, the educational robot system according to the embodiment can provide appropriate English education according to the emotion of the student.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of students in an English classroom. The emotion estimation information includes, for example, facial expressions, movements, voices, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content of students, but is not limited to such examples. For example, the acquisition unit uses a camera to capture the facial expressions of students in real time and acquires facial expression data. The camera has high resolution and can capture even subtle changes in facial expressions. The acquisition unit can also detect the movements of students using a motion sensor and acquire movement data. The motion sensor detects hand movements and changes in posture with high accuracy and records them as movement data. Furthermore, the acquisition unit can also record the voice of students using a microphone and acquire voice data. The microphone has a noise canceling function and eliminates noise in the classroom to acquire clear voice data. In addition, the acquisition unit can also acquire physiological data such as the heart rate, blood pressure, body temperature, and breathing of students using a wearable device. These devices are designed in the form of a wristwatch or band so that students can wear them naturally. In addition, the acquisition unit can also use an eye tracking device to detect pupil responses and a headset to measure brain waves, allowing the acquisition unit to collect emotion estimation information from various data sources and comprehensively grasp the emotional state of students.

[0031] The estimation unit estimates the student's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses a facial expression recognition algorithm to analyze the student's facial expression data and estimate the emotion. The facial expression recognition algorithm extracts facial feature points and analyzes subtle changes in facial expressions to estimate emotions such as joy, sadness, anger, and surprise with high accuracy. The estimation unit can also analyze the student's voice data using a voice recognition algorithm to estimate the emotion. The voice recognition algorithm analyzes features such as tone, pitch, speed, and strength of the voice to detect changes in emotion. Furthermore, the estimation unit can also analyze the student's heart rate data using a heart rate analysis algorithm to estimate the emotion. The heart rate analysis algorithm analyzes the heart rate fluctuation pattern to estimate stress and relaxation states. In addition, the estimation unit can estimate a more detailed emotional state by analyzing pupil response and electroencephalogram data. The pupil response detects pupil contraction and expansion accompanying changes in emotion, and the electroencephalogram data provides clues to understanding the deeper layers of emotion by analyzing brain activity patterns. This enables the estimation unit to integrate various data sources to estimate students' emotions with high accuracy and grasp changes in emotions in real time.

[0032] The determination unit determines specific educational content based on the student's emotion estimated by the estimation unit. For example, when the student is relaxed, the determination unit determines to perform pronunciation practice. By performing pronunciation practice in a relaxed state, it is expected that pronunciation can be improved more effectively. In addition, when the student is excited, it can also determine to perform memorization of words. In an excited state, concentration increases and it is possible to memorize many words in a short time. Furthermore, when the student is tired, it can also determine to perform listening practice. Listening practice is relatively less burdensome even in a tired state and can be performed while relaxing. When determining these educational contents, the determination unit also takes into account the student's past learning history and performance data. For example, listening practice can be given priority to a student who has achieved high results in listening practice in the past. In addition, the determination unit can combine multiple emotional states to determine more complex educational content. For example, when a student is relaxed but slightly tired, a curriculum combining light pronunciation practice and listening practice can be provided. In this way, the determination unit can flexibly determine optimal educational content according to the student's emotional state and provide personalized education.

[0033] The corresponding unit executes the English education determined by the determining unit for the student. For example, when the student is relaxed, the corresponding unit executes pronunciation practice. The pronunciation practice evaluates the student's pronunciation in real time using voice recognition technology and provides feedback. In addition, when the student is excited, word memorization can be executed. The word memorization can be performed in a game format, so that the student can enjoy learning while maintaining the student's excited state. Furthermore, when the student is tired, listening practice can be executed. The listening practice can be performed while playing relaxing music or natural sounds in the background, so that the student's fatigue can be relieved and the learning effect can be improved. When executing these educational contents, the corresponding unit monitors the student's reaction in real time and adjusts the educational contents as necessary. For example, if the student begins to feel tired during the pronunciation practice, it can switch to listening practice. In addition, the corresponding unit can record the student's learning progress and use it to determine the next educational content. In this way, the corresponding unit can provide flexible education according to the student's emotional state and maximize the learning effect. Furthermore, the corresponding unit can collect the student's feedback and use it to improve the educational content. For example, if a student shows high satisfaction with a particular practice method, that method can be actively adopted, allowing the department to continue providing the best possible education.

[0034] The determination unit determines specific English education content based on the student's emotions and attributes, and the corresponding unit can execute the specific English education content for the student based on the student's emotions and attributes. For example, the determination unit determines to perform pronunciation practice when the student is relaxed. Also, it can determine to perform word memorization when the student is excited. Furthermore, it can determine to perform listening practice when the student is tired. The corresponding unit executes the English education content determined by the determination unit for the student. For example, the corresponding unit executes pronunciation practice when the student is relaxed. Also, it can perform word memorization when the student is excited. Furthermore, it can perform listening practice when the student is tired. This makes it possible to provide individualized English education according to the student's emotions and attributes. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The determination unit determines specific dialogue content based on the student's emotion, and the corresponding unit can execute specific dialogue content based on the student's emotion. For example, the determination unit determines to execute dialogue to relax the student when the student is nervous. Also, the determination unit can determine to execute active dialogue when the student is relaxed. Furthermore, the determination unit can determine to execute dialogue to calm the student when the student is excited. The corresponding unit executes the dialogue content determined by the determination unit for the student. For example, the corresponding unit executes dialogue to relax the student when the student is nervous. Also, the corresponding unit can execute active dialogue when the student is relaxed. Furthermore, the corresponding unit can execute dialogue to calm the student when the student is excited. This makes it possible to provide more effective English education through dialogue according to the student's emotion. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The determination unit determines the dialogue content in a specific manner based on the student's emotions, and the corresponding unit can execute the dialogue content in a specific manner based on the student's emotions. For example, the determination unit determines to execute a dialogue to relax the student when the student is nervous. Also, the determination unit can determine to execute an active dialogue when the student is relaxed. Furthermore, the determination unit can determine to execute a dialogue to calm the student when the student is excited. The corresponding unit executes the dialogue content in the manner determined by the determination unit for the student. For example, the corresponding unit executes a dialogue to relax the student when the student is nervous. Also, the corresponding unit can execute an active dialogue when the student is relaxed. Furthermore, the corresponding unit can execute a dialogue to calm the student when the student is excited. This makes it possible to provide more effective English education through a dialogue in a manner according to the student's emotions. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The determination unit determines specific learning support content based on the student's emotions, and the corresponding unit can provide specific learning support content based on the student's emotions. For example, the determination unit determines to provide support for pronunciation practice when the student is relaxed. Also, the determination unit can determine to provide support for memorizing words when the student is excited. Furthermore, the corresponding unit can determine to provide support for listening practice when the student is tired. The corresponding unit provides the learning support content determined by the determination unit to the student. For example, the corresponding unit provides support for pronunciation practice when the student is relaxed. Also, the corresponding unit can provide support for memorizing words when the student is excited. Furthermore, the corresponding unit can provide support for listening practice when the student is tired. This makes it possible to provide more effective English education through learning support according to the student's emotions. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0038] The corresponding unit can monitor the learning progress of the student in real time and dynamically adjust the specific learning content based on the student's emotions. For example, the corresponding unit can monitor the student's test results and assignment submission status in real time and evaluate the learning progress. The corresponding unit can dynamically adjust the learning content based on the student's emotion data. For example, if the student is relaxed, pronunciation practice can be increased. If the student is excited, word memorization can be increased. Furthermore, if the student is tired, listening practice can be increased. This makes it possible to provide more effective learning by dynamically adjusting the learning content according to the student's learning progress and emotions. A part or all of the above-mentioned processing in the corresponding unit may be performed, for example, using AI or may be performed without using AI.

[0039] The estimation unit estimates the emotion of a student group including a plurality of students, the determination unit determines English education according to the emotion of the student group, and the corresponding unit can execute the English education determined by the determination unit for the student group. For example, the estimation unit analyzes facial expression data of the entire class and estimates the emotion of the entire class. The estimation unit can also analyze voice data of the entire class and estimate the emotion of the entire class. Furthermore, the estimation unit can analyze heart rate data of the entire class and estimate the emotion of the entire class. The determination unit determines English education according to the emotion of the student group estimated by the estimation unit. For example, the determination unit determines to conduct pronunciation practice when the entire class is relaxed. Also, it can determine to conduct word memorization when the entire class is excited. Furthermore, it can determine to conduct listening practice when the entire class is tired. The corresponding unit executes the English education determined by the determination unit for the student group. For example, the corresponding unit executes pronunciation practice when the entire class is relaxed. Also, it can execute word memorization when the entire class is excited. Furthermore, it can execute listening practice when the entire class is tired. This makes it possible to provide English education that is appropriate for the emotions of the student group. Some or all of the above-mentioned processes in the estimation unit, the determination unit, and the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The communication support unit can support communication with other students or teachers according to the emotional state of the student. For example, when the student is tense, the communication support unit supports communication to relax the student. Also, when the student is relaxed, the communication support unit can support active communication. Furthermore, when the student is excited, the communication support unit can support communication to calm the student. In this way, communication support according to the emotional state of the student can be provided. A part or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI or may be performed without using AI.

[0041] The advice unit can dynamically advise the teacher on how to teach based on the emotional state of the student. For example, if the student is tense, the advice unit advises how to teach in a way that relaxes the student. Also, if the student is relaxed, the advice unit can advise how to teach in a more proactive manner. Furthermore, if the student is excited, the advice unit can advise how to teach in a way that calms the student. This makes it possible to provide advice on how to teach based on the emotional state of the student. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The prediction unit can predict the student's future learning performance based on the transition of the student's emotional state. For example, the prediction unit analyzes the transition of the student's emotions and predicts the future learning performance. The prediction unit can also find a specific pattern from the transition of the student's emotions and predict the future learning performance. Furthermore, the prediction unit can predict the improvement of the future learning performance based on the transition of the student's emotions. This makes it possible to provide a prediction of the future learning performance based on the transition of the student's emotional state. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The identification unit can identify areas for improvement in education based on the student's emotions estimated by the estimation unit. For example, the identification unit can identify areas for improvement in education when the student is relaxed. Also, the identification unit can identify areas for improvement in education when the student is nervous. Furthermore, the identification unit can identify areas for improvement in education when the student is excited. In this way, it is possible to identify areas for improvement in education based on the student's emotions. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The acquisition unit can acquire the facial expressions of the students. For example, the acquisition unit can capture the faces of the students in real time using a camera to acquire the facial expressions. The acquisition unit can also capture an image of the moment when the student shows a specific facial expression to acquire the facial expression. Furthermore, the acquisition unit can continuously record changes in the facial expressions shown by the students during class and acquire the facial expression data. In this way, the accuracy of emotion estimation is improved by acquiring the facial expressions of the students. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The acquisition unit can acquire the student's movements. For example, the acquisition unit detects the student's movements in real time using a motion sensor and acquires the movements. The acquisition unit can also record and acquire a specific movement when the student performs the movement. Furthermore, the acquisition unit can continuously record the student's movement patterns during class and acquire the movement data. Thereby, by acquiring the student's movements, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The acquisition unit can acquire the voice of the student. For example, the acquisition unit uses a microphone to record the student's remarks in real time and acquires the voice. The acquisition unit can also capture and acquire the voice when the student makes a specific remark. Furthermore, the acquisition unit can continuously record the student's remarks during class and acquire the voice data. Thereby, by acquiring the student's voice, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The acquisition unit can acquire the heart rate of the student. For example, the acquisition unit measures the heart rate of the student in real time using a wearable device to acquire the heart rate data. The acquisition unit can also record and acquire the heart rate at the moment when the student is placed in a specific situation. Furthermore, the acquisition unit can continuously record changes in the student's heart rate during class and acquire the heart rate data. Thereby, by acquiring the student's heart rate, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The acquisition unit can acquire the blood pressure of the student. For example, the acquisition unit measures the student's blood pressure in real time using a wearable device to acquire blood pressure data. The acquisition unit can also record and acquire the blood pressure at the moment when the student is placed in a specific situation. Furthermore, the acquisition unit can continuously record changes in the student's blood pressure during class and acquire blood pressure data. Thereby, by acquiring the student's blood pressure, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The acquisition unit can acquire the pupillary response of the student. For example, the acquisition unit can observe the change in the student's pupil in real time using a camera to acquire the pupillary response. The acquisition unit can also record and acquire the pupillary response at the moment when the student is placed in a specific situation. Furthermore, the acquisition unit can continuously record the change in the student's pupillary response during class and acquire pupil data. Thereby, by acquiring the student's pupillary response, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The determination unit can determine English education according to the attributes of the student. For example, if the student is an elementary school student, the determination unit can determine that the student will memorize simple words. If the student is a junior high school student, the determination unit can also determine that the student will practice grammar. Furthermore, if the student is a high school student, the determination unit can also determine that the student will practice listening. In this way, English education according to the attributes of the student can be provided. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The determination unit can determine a dialogue according to the attributes of the student. For example, if the student is an elementary school student, the determination unit can determine to have a simple dialogue. If the student is a junior high school student, the determination unit can also determine to have a dialogue including grammar. Furthermore, if the student is a high school student, the determination unit can also determine to have a dialogue including listening. This makes it possible to provide a dialogue according to the attributes of the student. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The determination unit can determine learning support according to the attributes of the student. For example, if the student is an elementary school student, the determination unit can determine to provide support for memorizing simple words. If the student is a junior high school student, the determination unit can also determine to provide support for grammar practice. Furthermore, if the student is a high school student, the determination unit can also determine to provide support for listening practice. This makes it possible to provide learning support according to the attributes of the student. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The corresponding unit can execute English education according to the attributes of the student. For example, if the student is an elementary school student, the corresponding unit executes memorization of simple words. Also, if the student is a junior high school student, the corresponding unit can execute grammar practice. Furthermore, if the student is a high school student, the corresponding unit can execute listening practice. In this way, it is possible to provide English education according to the attributes of the student. Some or all of the above-mentioned processing in the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0054] The corresponding unit can execute a dialogue according to the attributes of the student. For example, if the student is an elementary school student, the corresponding unit executes a simple dialogue. If the student is a junior high school student, the corresponding unit can execute a dialogue including grammar. If the student is a high school student, the corresponding unit can execute a dialogue including listening. This makes it possible to provide a dialogue according to the attributes of the student. Some or all of the above-mentioned processing in the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0055] The corresponding unit can execute learning support according to the attributes of the student. For example, if the student is an elementary school student, the corresponding unit executes support for memorizing simple words. If the student is a junior high school student, the corresponding unit can also execute support for grammar practice. Furthermore, if the student is a high school student, the corresponding unit can also execute support for listening practice. This makes it possible to provide learning support according to the attributes of the student. Some or all of the above-mentioned processing in the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The communication support unit can support communication with other students according to the attributes of the student. For example, if the student is an elementary school student, the communication support unit can support simple communication. If the student is a junior high school student, the communication support unit can also support communication including grammar. Furthermore, if the student is a high school student, the communication support unit can also support communication including listening. This makes it possible to support communication with other students according to the attributes of the student. Some or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI, or may be performed without using AI.

[0057] The communication support unit can support communication with teachers according to the attributes of the students. For example, if the student is an elementary school student, the communication support unit can support simple communication. If the student is a junior high school student, the communication support unit can also support communication including grammar. Furthermore, if the student is a high school student, the communication support unit can also support communication including listening. This makes it possible to support communication with teachers according to the attributes of the students. Some or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The communication support unit can support group discussions according to the attributes of the students. For example, if the students are elementary school students, the communication support unit can support simple discussions. If the students are junior high school students, the communication support unit can also support discussions including grammar. If the students are high school students, the communication support unit can also support discussions including listening. This makes it possible to support group discussions according to the attributes of the students. Some or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI, or may be performed without using AI.

[0059] The advice unit can provide advice to the teacher according to the attributes of the student. The advice unit provides advice to the teacher according to the attributes of the student. For example, if the student is an elementary school student, the advice unit can advise a simple study method. If the student is a junior high school student, the advice unit can also advise a study method including grammar. Furthermore, if the student is a high school student, the advice unit can also advise a study method including listening. This makes it possible to provide advice to the teacher according to the attributes of the student. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The advice unit can provide advice on study methods according to the attributes of the student. For example, if the student is an elementary school student, the advice unit can advise on simple study methods. If the student is a junior high school student, the advice unit can also advise on study methods that include grammar. Furthermore, if the student is a high school student, the advice unit can also advise on study methods that include listening. This makes it possible to provide advice on study methods according to the attributes of the student. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The advice unit can provide advice on the learning environment according to the attributes of the student. For example, if the student is an elementary school student, the advice unit can advise a simple learning environment. If the student is a junior high school student, the advice unit can also advise a learning environment including grammar. Furthermore, if the student is a high school student, the advice unit can also advise a learning environment including listening. This makes it possible to provide advice on the learning environment according to the attributes of the student. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI.

[0062] The prediction unit can predict future learning performance based on the student's learning history. For example, the prediction unit analyzes the student's past learning history and predicts future learning performance. The prediction unit can also find a specific pattern from the student's learning history and predict future learning performance. Furthermore, the prediction unit can predict future improvement in learning performance based on the student's learning history. This makes it possible to provide a prediction of future learning performance based on the student's learning history. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0063] The prediction unit can predict future learning performance based on the attributes of the student. For example, the prediction unit can predict future learning performance when the student is an elementary school student. Also, the prediction unit can predict future learning performance when the student is a junior high school student. Furthermore, the prediction unit can predict future learning performance when the student is a high school student. This makes it possible to provide a prediction of future learning performance based on the attributes of the student. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0064] The identification unit can identify areas for improvement in education based on the student's learning history. For example, the identification unit analyzes the student's past learning history and identifies areas for improvement in education. The identification unit can also find a specific pattern from the student's learning history and identify areas for improvement in education. Furthermore, the identification unit can also identify areas for improvement in education based on the student's learning history. This makes it possible to identify areas for improvement in education based on the student's learning history. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI.

[0065] The identification unit can identify areas for improvement in education based on the attributes of the student. For example, when the student is an elementary school student, the identification unit can identify areas for improvement in education. Also, when the student is a junior high school student, the identification unit can identify areas for improvement in education. Furthermore, when the student is a high school student, the identification unit can identify areas for improvement in education. In this way, it is possible to identify areas for improvement in education based on the attributes of the student. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The educational robot system further includes a reward unit. The reward unit can provide rewards based on the student's learning results. For example, if the student completes a specific task, the reward unit can provide digital badges or points. In addition, if the student achieves high grades consecutively, the reward unit can provide special titles or benefits. Furthermore, if the student cooperates with other students to solve a task, the reward unit can provide a reward for the entire team. This can increase the student's motivation for learning.

[0068] The educational robot system further includes a reminder unit. The reminder unit can provide reminders based on the student's study schedule. For example, if the student needs to start studying at a specific time, the reminder unit can notify the student, "It's time to start studying." It can also remind the student, "Let's take a short break," when it is time to take a break. In addition, it can provide reminders such as "Let's start preparing for the test" so that the student does not forget the deadline for an important test or assignment. This allows the student to study efficiently.

[0069] The educational robot system further includes a customization unit. The customization unit can customize the learning content based on the learning style and preferences of the student. For example, if the student prefers visual learning, the customization unit can provide learning materials that make extensive use of visual aids. If the student prefers auditory learning, the customization unit can also provide audio learning materials. Furthermore, if the student prefers hands-on learning, the customization unit can also provide interactive exercises. This allows the student to progress through learning in a way that best suits him or her.

[0070] The educational robot system further includes a reflection unit. The reflection unit can provide support for students to perform self-evaluation after learning. For example, the reflection unit can prompt students to self-evaluate by presenting questions such as "What was the most difficult part of today's learning?". It can also support students to have a sense of self-improvement through questions such as "What would you like to improve in your next learning session?". It can also provide questions that encourage positive reflection such as "What was the most enjoyable part of today's learning?". This allows students to improve the quality of their learning through self-evaluation.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotions of students in an English classroom. The emotion estimation information includes the students' facial expressions, movements, voices, heart rates, blood pressure, pupil responses, brain waves, body temperature, breathing, and posted content. The acquisition unit uses a camera to capture the students' facial expressions in real time and acquires the facial expression data. It can also detect the students' movements using a motion sensor and acquire movement data. It can also record the students' voices using a microphone and acquire voice data. Step 2: The estimation unit estimates the emotion of the student based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the facial expression data of the student using a facial expression recognition algorithm to estimate the emotion. The estimation unit can also analyze the voice data of the student using a voice recognition algorithm to estimate the emotion. Furthermore, the estimation unit can analyze the heart rate data of the student using a heart rate analysis algorithm to estimate the emotion. Step 3: The determination unit determines specific educational content based on the student's emotion estimated by the estimation unit. The determination unit may determine to conduct pronunciation practice if the student is relaxed. The determination unit may also determine to conduct word memorization if the student is excited. Furthermore, the determination unit may determine to conduct listening practice if the student is tired. Step 4: The corresponding unit executes the English education determined by the determining unit for the student. The corresponding unit executes pronunciation practice when the student is relaxed. The corresponding unit can also execute word memorization when the student is excited. Furthermore, the corresponding unit can execute listening practice when the student is tired.

[0073] (Example 2) The educational robot system according to the embodiment of the present invention is a system that estimates the emotions of students in an English classroom and provides appropriate English education. The system acquires information for estimating the emotions of students, and determines and executes educational content based on the estimated emotions. For example, the educational robot system acquires information such as facial expressions, movements, voices, heart rates, blood pressure, pupil responses, brain waves, body temperature, breathing, and posted content of students. Next, the educational robot system estimates the emotions of students based on the acquired emotion estimation information. Depending on the estimated emotions, appropriate English education is determined and executed for the students. Furthermore, the educational robot system monitors the learning progress of students in real time and dynamically adjusts the learning content according to the emotions of students. In addition, the educational robot system supports communication with other students and teachers based on the emotional state of students, predicts future learning performance, and provides advice to teachers. In this way, the educational robot system can provide English education that takes into account the emotions of students.

[0074] The educational robot system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and a corresponding unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a student in an English classroom. The emotion estimation information includes, for example, the facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content of the student, but is not limited to such examples. The acquisition unit, for example, photographs the facial expression of the student in real time using a camera and acquires the facial expression data. The acquisition unit can also detect the movement of the student using a motion sensor and acquire the movement data. Furthermore, the acquisition unit can also record the voice of the student using a microphone and acquire the voice data. The estimation unit estimates the emotion of the student based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, analyzes the facial expression data of the student using a facial expression recognition algorithm and estimates the emotion. Furthermore, the estimation unit can also analyze the voice data of the student using a voice recognition algorithm and estimate the emotion. Furthermore, the estimation unit can analyze the heart rate data of the student using a heart rate analysis algorithm and estimate the emotion. The determination unit determines specific educational content based on the emotion of the student estimated by the estimation unit. For example, the determination unit determines to conduct pronunciation practice when the student is relaxed. Also, the determination unit can determine to conduct vocabulary memorization when the student is excited. Furthermore, the determination unit can determine to conduct listening practice when the student is tired. The response unit executes the English education determined by the determination unit for the student. For example, the response unit executes pronunciation practice when the student is relaxed. Also, the response unit can execute vocabulary memorization when the student is excited. Furthermore, the response unit can execute listening practice when the student is tired. In this way, the educational robot system according to the embodiment can provide appropriate English education according to the emotion of the student.

[0075] The acquisition unit acquires emotion estimation information for estimating the emotion of students in an English classroom. The emotion estimation information includes, for example, facial expressions, movements, voices, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content of students, but is not limited to such examples. For example, the acquisition unit uses a camera to capture the facial expressions of students in real time and acquires facial expression data. The camera has high resolution and can capture even subtle changes in facial expressions. The acquisition unit can also detect the movements of students using a motion sensor and acquire movement data. The motion sensor detects hand movements and changes in posture with high accuracy and records them as movement data. Furthermore, the acquisition unit can also record the voice of students using a microphone and acquire voice data. The microphone has a noise canceling function and eliminates noise in the classroom to acquire clear voice data. In addition, the acquisition unit can also acquire physiological data such as the heart rate, blood pressure, body temperature, and breathing of students using a wearable device. These devices are designed in the form of a wristwatch or band so that students can wear them naturally. In addition, the acquisition unit can also use an eye tracking device to detect pupil responses and a headset to measure brain waves, allowing the acquisition unit to collect emotion estimation information from various data sources and comprehensively grasp the emotional state of students.

[0076] The estimation unit estimates the student's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses a facial expression recognition algorithm to analyze the student's facial expression data and estimate the emotion. The facial expression recognition algorithm extracts facial feature points and analyzes subtle changes in facial expressions to estimate emotions such as joy, sadness, anger, and surprise with high accuracy. The estimation unit can also analyze the student's voice data using a voice recognition algorithm to estimate the emotion. The voice recognition algorithm analyzes features such as tone, pitch, speed, and strength of the voice to detect changes in emotion. Furthermore, the estimation unit can also analyze the student's heart rate data using a heart rate analysis algorithm to estimate the emotion. The heart rate analysis algorithm analyzes the heart rate fluctuation pattern to estimate stress and relaxation states. In addition, the estimation unit can estimate a more detailed emotional state by analyzing pupil response and electroencephalogram data. The pupil response detects pupil contraction and expansion accompanying changes in emotion, and the electroencephalogram data provides clues to understanding the deeper layers of emotion by analyzing brain activity patterns. This enables the estimation unit to integrate various data sources to estimate students' emotions with high accuracy and grasp changes in emotions in real time.

[0077] The determination unit determines specific educational content based on the student's emotion estimated by the estimation unit. For example, when the student is relaxed, the determination unit determines to perform pronunciation practice. By performing pronunciation practice in a relaxed state, it is expected that pronunciation can be improved more effectively. In addition, when the student is excited, it can also determine to perform memorization of words. In an excited state, concentration increases and it is possible to memorize many words in a short time. Furthermore, when the student is tired, it can also determine to perform listening practice. Listening practice is relatively less burdensome even in a tired state and can be performed while relaxing. When determining these educational contents, the determination unit also takes into account the student's past learning history and performance data. For example, listening practice can be given priority to a student who has achieved high results in listening practice in the past. In addition, the determination unit can combine multiple emotional states to determine more complex educational content. For example, when a student is relaxed but slightly tired, a curriculum combining light pronunciation practice and listening practice can be provided. In this way, the determination unit can flexibly determine optimal educational content according to the student's emotional state and provide personalized education.

[0078] The corresponding unit executes the English education determined by the determining unit for the student. For example, when the student is relaxed, the corresponding unit executes pronunciation practice. The pronunciation practice evaluates the student's pronunciation in real time using voice recognition technology and provides feedback. In addition, when the student is excited, word memorization can be executed. The word memorization can be performed in a game format, so that the student can enjoy learning while maintaining the student's excited state. Furthermore, when the student is tired, listening practice can be executed. The listening practice can be performed while playing relaxing music or natural sounds in the background, so that the student's fatigue can be relieved and the learning effect can be improved. When executing these educational contents, the corresponding unit monitors the student's reaction in real time and adjusts the educational contents as necessary. For example, if the student begins to feel tired during the pronunciation practice, it can switch to listening practice. In addition, the corresponding unit can record the student's learning progress and use it to determine the next educational content. In this way, the corresponding unit can provide flexible education according to the student's emotional state and maximize the learning effect. Furthermore, the corresponding unit can collect the student's feedback and use it to improve the educational content. For example, if a student shows high satisfaction with a particular practice method, that method can be actively adopted, allowing the department to continue providing the best possible education.

[0079] The determination unit determines specific English education content based on the student's emotions and attributes, and the corresponding unit can execute the specific English education content for the student based on the student's emotions and attributes. For example, the determination unit determines to perform pronunciation practice when the student is relaxed. Also, it can determine to perform word memorization when the student is excited. Furthermore, it can determine to perform listening practice when the student is tired. The corresponding unit executes the English education content determined by the determination unit for the student. For example, the corresponding unit executes pronunciation practice when the student is relaxed. Also, it can perform word memorization when the student is excited. Furthermore, it can perform listening practice when the student is tired. This makes it possible to provide individualized English education according to the student's emotions and attributes. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The determination unit determines specific dialogue content based on the student's emotion, and the corresponding unit can execute specific dialogue content based on the student's emotion. For example, the determination unit determines to execute dialogue to relax the student when the student is nervous. Also, the determination unit can determine to execute active dialogue when the student is relaxed. Furthermore, the determination unit can determine to execute dialogue to calm the student when the student is excited. The corresponding unit executes the dialogue content determined by the determination unit for the student. For example, the corresponding unit executes dialogue to relax the student when the student is nervous. Also, the corresponding unit can execute active dialogue when the student is relaxed. Furthermore, the corresponding unit can execute dialogue to calm the student when the student is excited. This makes it possible to provide more effective English education through dialogue according to the student's emotion. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0081] The determination unit determines the dialogue content in a specific manner based on the student's emotions, and the corresponding unit can execute the dialogue content in a specific manner based on the student's emotions. For example, the determination unit determines to execute a dialogue to relax the student when the student is nervous. Also, the determination unit can determine to execute an active dialogue when the student is relaxed. Furthermore, the determination unit can determine to execute a dialogue to calm the student when the student is excited. The corresponding unit executes the dialogue content in the manner determined by the determination unit for the student. For example, the corresponding unit executes a dialogue to relax the student when the student is nervous. Also, the corresponding unit can execute an active dialogue when the student is relaxed. Furthermore, the corresponding unit can execute a dialogue to calm the student when the student is excited. This makes it possible to provide more effective English education through a dialogue in a manner according to the student's emotions. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The determination unit determines specific learning support content based on the student's emotions, and the corresponding unit can provide specific learning support content based on the student's emotions. For example, the determination unit determines to provide support for pronunciation practice when the student is relaxed. Also, the determination unit can determine to provide support for memorizing words when the student is excited. Furthermore, the corresponding unit can determine to provide support for listening practice when the student is tired. The corresponding unit provides the learning support content determined by the determination unit to the student. For example, the corresponding unit provides support for pronunciation practice when the student is relaxed. Also, the corresponding unit can provide support for memorizing words when the student is excited. Furthermore, the corresponding unit can provide support for listening practice when the student is tired. This makes it possible to provide more effective English education through learning support according to the student's emotions. A part or all of the above-mentioned processing in the determination unit and the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The corresponding unit can monitor the learning progress of the student in real time and dynamically adjust the specific learning content based on the student's emotions. For example, the corresponding unit can monitor the student's test results and assignment submission status in real time and evaluate the learning progress. The corresponding unit can dynamically adjust the learning content based on the student's emotion data. For example, if the student is relaxed, pronunciation practice can be increased. If the student is excited, word memorization can be increased. Furthermore, if the student is tired, listening practice can be increased. This makes it possible to provide more effective learning by dynamically adjusting the learning content according to the student's learning progress and emotions. A part or all of the above-mentioned processing in the corresponding unit may be performed, for example, using AI or may be performed without using AI.

[0084] The estimation unit estimates the emotion of a student group including a plurality of students, the determination unit determines English education according to the emotion of the student group, and the corresponding unit can execute the English education determined by the determination unit for the student group. For example, the estimation unit analyzes facial expression data of the entire class and estimates the emotion of the entire class. The estimation unit can also analyze voice data of the entire class and estimate the emotion of the entire class. Furthermore, the estimation unit can analyze heart rate data of the entire class and estimate the emotion of the entire class. The determination unit determines English education according to the emotion of the student group estimated by the estimation unit. For example, the determination unit determines to conduct pronunciation practice when the entire class is relaxed. Also, it can determine to conduct word memorization when the entire class is excited. Furthermore, it can determine to conduct listening practice when the entire class is tired. The corresponding unit executes the English education determined by the determination unit for the student group. For example, the corresponding unit executes pronunciation practice when the entire class is relaxed. Also, it can execute word memorization when the entire class is excited. Furthermore, it can execute listening practice when the entire class is tired. This makes it possible to provide English education that is appropriate for the emotions of the student group. Some or all of the above-mentioned processes in the estimation unit, the determination unit, and the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The communication support unit can support communication with other students or teachers according to the emotional state of the student. For example, when the student is tense, the communication support unit supports communication to relax the student. Also, when the student is relaxed, the communication support unit can support active communication. Furthermore, when the student is excited, the communication support unit can support communication to calm the student. In this way, communication support according to the emotional state of the student can be provided. A part or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI or may be performed without using AI.

[0086] The advice unit can dynamically advise the teacher on how to teach based on the emotional state of the student. For example, if the student is tense, the advice unit advises how to teach in a way that relaxes the student. Also, if the student is relaxed, the advice unit can advise how to teach in a more proactive manner. Furthermore, if the student is excited, the advice unit can advise how to teach in a way that calms the student. This makes it possible to provide advice on how to teach based on the emotional state of the student. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0087] The prediction unit can predict the student's future learning performance based on the transition of the student's emotional state. For example, the prediction unit analyzes the transition of the student's emotions and predicts the future learning performance. The prediction unit can also find a specific pattern from the transition of the student's emotions and predict the future learning performance. Furthermore, the prediction unit can predict the improvement of the future learning performance based on the transition of the student's emotions. This makes it possible to provide a prediction of the future learning performance based on the transition of the student's emotional state. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0088] The identification unit can identify areas for improvement in education based on the student's emotions estimated by the estimation unit. For example, the identification unit can identify areas for improvement in education when the student is relaxed. Also, the identification unit can identify areas for improvement in education when the student is nervous. Furthermore, the identification unit can identify areas for improvement in education when the student is excited. In this way, it is possible to identify areas for improvement in education based on the student's emotions. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The acquisition unit can acquire the facial expressions of the students. For example, the acquisition unit can capture the faces of the students in real time using a camera to acquire the facial expressions. The acquisition unit can also capture an image of the moment when the student shows a specific facial expression to acquire the facial expression. Furthermore, the acquisition unit can continuously record changes in the facial expressions shown by the students during class and acquire the facial expression data. In this way, the accuracy of emotion estimation is improved by acquiring the facial expressions of the students. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The acquisition unit can acquire the student's movements. For example, the acquisition unit detects the student's movements in real time using a motion sensor and acquires the movements. The acquisition unit can also record and acquire a specific movement when the student performs the movement. Furthermore, the acquisition unit can continuously record the student's movement patterns during class and acquire the movement data. Thereby, by acquiring the student's movements, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The acquisition unit can acquire the voice of the student. For example, the acquisition unit uses a microphone to record the student's remarks in real time and acquires the voice. The acquisition unit can also capture and acquire the voice when the student makes a specific remark. Furthermore, the acquisition unit can continuously record the student's remarks during class and acquire the voice data. Thereby, by acquiring the student's voice, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0092] The acquisition unit can acquire the heart rate of the student. For example, the acquisition unit measures the heart rate of the student in real time using a wearable device to acquire the heart rate data. The acquisition unit can also record and acquire the heart rate at the moment when the student is placed in a specific situation. Furthermore, the acquisition unit can continuously record changes in the student's heart rate during class and acquire the heart rate data. Thereby, by acquiring the student's heart rate, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The acquisition unit can acquire the blood pressure of the student. For example, the acquisition unit measures the student's blood pressure in real time using a wearable device to acquire blood pressure data. The acquisition unit can also record and acquire the blood pressure at the moment when the student is placed in a specific situation. Furthermore, the acquisition unit can continuously record changes in the student's blood pressure during class and acquire blood pressure data. Thereby, by acquiring the student's blood pressure, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0094] The acquisition unit can acquire the pupillary response of the student. For example, the acquisition unit can observe the change in the student's pupil in real time using a camera to acquire the pupillary response. The acquisition unit can also record and acquire the pupillary response at the moment when the student is placed in a specific situation. Furthermore, the acquisition unit can continuously record the change in the student's pupillary response during class and acquire pupil data. Thereby, by acquiring the student's pupillary response, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0095] The estimation unit can estimate emotions based on the student's facial expression. For example, the estimation unit detects the student's smile and estimates an emotion of joy. The estimation unit can also detect the student's frown and estimate an emotion of stress or anxiety. Furthermore, the estimation unit can detect the sparkle in the student's eyes and estimate an emotion of interest or concern. As a result, the accuracy of emotion estimation is improved by estimating emotions based on the student's facial expression. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0096] The estimation unit can estimate the emotion based on the student's behavior. For example, the estimation unit detects the student's behavior of raising his / her hand and estimates a positive emotion. The estimation unit can also detect the student's behavior of looking down and estimate the emotion of depression or fatigue. Furthermore, the estimation unit can detect the student's behavior of crossing his / her legs and estimate the emotion of relaxation. As a result, the emotion estimation accuracy is improved by estimating the emotion based on the student's behavior. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0097] The estimation unit can estimate emotions based on the student's voice. For example, the estimation unit analyzes the tone of the student's voice and estimates emotions such as excitement and joy. The estimation unit can also analyze the speed of the student's voice and estimate emotions such as tension and impatience. Furthermore, the estimation unit can analyze the volume of the student's voice and estimate emotions such as confidence and anxiety. As a result, the accuracy of emotion estimation is improved by estimating emotions based on the student's voice. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0098] The estimation unit can estimate the emotion based on the heart rate of the student. For example, when the heart rate of the student increases, the estimation unit estimates the emotion of excitement or tension. In addition, when the heart rate of the student is stable, the estimation unit can also estimate the emotion of relaxation. Furthermore, when the heart rate of the student decreases, the estimation unit can also estimate the emotion of fatigue or depression. As a result, the emotion estimation based on the heart rate of the student improves the accuracy of the emotion estimation. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0099] The estimation unit can estimate the emotion based on the student's blood pressure. For example, when the student's blood pressure rises, the estimation unit estimates the emotion of tension or stress. In addition, when the student's blood pressure is stable, the estimation unit can also estimate the emotion of relaxation. Furthermore, when the student's blood pressure drops, the estimation unit can also estimate the emotion of fatigue or depression. As a result, the emotion estimation based on the student's blood pressure improves the accuracy of the emotion estimation. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0100] The estimation unit can estimate the emotion based on the pupil response of the student. For example, when the pupil of the student expands, the estimation unit estimates the emotion of excitement or surprise. Also, when the pupil of the student contracts, the estimation unit can estimate the emotion of concentration or tension. Furthermore, the estimation unit can continuously observe the change of the student's pupil and estimate the transition of the emotion. As a result, the emotion is estimated based on the pupil response of the student, and the accuracy of the emotion estimation is improved. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0101] The determination unit can determine English education according to the student's emotions. For example, the determination unit determines to practice pronunciation when the student is relaxed. Also, the determination unit can determine to memorize words when the student is excited. Furthermore, the determination unit can determine to practice listening when the student is tired. In this way, English education according to the student's emotions can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI.

[0102] The determination unit can determine a dialogue according to the student's emotions. For example, if the student is nervous, the determination unit can determine to have a dialogue to relax the student. Also, if the student is relaxed, the determination unit can determine to have an active dialogue. Furthermore, if the student is excited, the determination unit can determine to have a dialogue to calm the student. This makes it possible to provide a dialogue according to the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI.

[0103] The determination unit can determine learning support according to the student's emotions. For example, when the student is relaxed, the determination unit determines to provide support for pronunciation practice. Also, when the student is excited, the determination unit can determine to provide support for memorizing words. Furthermore, when the student is tired, the determination unit can determine to provide support for listening practice. This makes it possible to provide learning support according to the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI.

[0104] The determination unit can determine English education according to the attributes of the student. For example, if the student is an elementary school student, the determination unit can determine that the student will memorize simple words. If the student is a junior high school student, the determination unit can also determine that the student will practice grammar. Furthermore, if the student is a high school student, the determination unit can also determine that the student will practice listening. In this way, English education according to the attributes of the student can be provided. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0105] The determination unit can determine a dialogue according to the attributes of the student. For example, if the student is an elementary school student, the determination unit can determine to have a simple dialogue. If the student is a junior high school student, the determination unit can also determine to have a dialogue including grammar. Furthermore, if the student is a high school student, the determination unit can also determine to have a dialogue including listening. This makes it possible to provide a dialogue according to the attributes of the student. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI.

[0106] The determination unit can determine learning support according to the attributes of the student. For example, if the student is an elementary school student, the determination unit can determine to provide support for memorizing simple words. If the student is a junior high school student, the determination unit can also determine to provide support for grammar practice. Furthermore, if the student is a high school student, the determination unit can also determine to provide support for listening practice. This makes it possible to provide learning support according to the attributes of the student. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI.

[0107] The corresponding unit can execute English education according to the student's emotions. For example, when the student is relaxed, the corresponding unit executes pronunciation practice. When the student is excited, the corresponding unit can also execute word memorization. Furthermore, when the student is tired, the corresponding unit can execute listening practice. This makes it possible to provide English education according to the student's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the corresponding unit may be performed, for example, using an AI, or may be performed without using an AI.

[0108] The corresponding unit can execute a dialogue according to the student's emotions. For example, if the student is nervous, the corresponding unit executes a dialogue to relax the student. Also, if the student is relaxed, the corresponding unit can execute a proactive dialogue. Furthermore, if the student is excited, the corresponding unit can execute a dialogue to calm the student. This makes it possible to provide a dialogue according to the student's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the corresponding unit may be performed, for example, using an AI, or may be performed without using an AI.

[0109] The corresponding unit can execute learning support according to the student's emotions. For example, when the student is relaxed, the corresponding unit executes support for pronunciation practice. Also, when the student is excited, the corresponding unit can execute support for memorizing words. Furthermore, when the student is tired, the corresponding unit can execute support for listening practice. This makes it possible to provide learning support according to the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the corresponding unit may be performed using, for example, an AI, or may be performed without using an AI.

[0110] The corresponding unit can execute English education according to the attributes of the student. For example, if the student is an elementary school student, the corresponding unit executes memorization of simple words. Also, if the student is a junior high school student, the corresponding unit can execute grammar practice. Furthermore, if the student is a high school student, the corresponding unit can execute listening practice. In this way, it is possible to provide English education according to the attributes of the student. Some or all of the above-mentioned processing in the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0111] The corresponding unit can execute a dialogue according to the attributes of the student. For example, if the student is an elementary school student, the corresponding unit executes a simple dialogue. If the student is a junior high school student, the corresponding unit can execute a dialogue including grammar. If the student is a high school student, the corresponding unit can execute a dialogue including listening. This makes it possible to provide a dialogue according to the attributes of the student. Some or all of the above-mentioned processing in the corresponding unit may be performed using, for example, AI, or may be performed without using AI.

[0112] The corresponding unit can execute learning support according to the attributes of the student. For example, if the student is an elementary school student, the corresponding unit executes support for memorizing simple words. If the student is a junior high school student, the corresponding unit can also execute support for grammar practice. Furthermore, if the student is a high school student, the corresponding unit can also execute support for listening practice. This makes it possible to provide learning support according to the attributes of the student. Some or all of the above-mentioned processing in the corresponding unit may be performed, for example, using AI, or may be performed without using AI.

[0113] The communication support unit can support communication with other students according to the student's emotions. For example, when a student is nervous, the communication support unit supports communication to relax the student. Also, when a student is relaxed, the communication support unit can support active communication. Furthermore, when a student is excited, the communication support unit can support communication to calm the student. This makes it possible to support communication with other students according to the student's emotions. Estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI, or may be performed without using AI.

[0114] The communication support unit can support communication with the teacher according to the student's emotions. For example, when the student is nervous, the communication support unit supports communication to relax the student. Also, when the student is relaxed, the communication support unit can support active communication. Furthermore, when the student is excited, the communication support unit can support communication to calm the student. This makes it possible to support communication with the teacher according to the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the communication support unit may be performed, for example, using an AI, or may be performed without using an AI.

[0115] The communication support unit can support group discussions according to the emotions of students. For example, when a student is nervous, the communication support unit supports a discussion to relax the student. Also, when a student is relaxed, the communication support unit can support an active discussion. Furthermore, when a student is excited, the communication support unit can support a discussion to calm the student. This makes it possible to support group discussions according to the emotions of students. Estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the communication support unit may be performed, for example, using an AI, or may be performed without using an AI.

[0116] The communication support unit can support communication with other students according to the attributes of the student. For example, if the student is an elementary school student, the communication support unit can support simple communication. If the student is a junior high school student, the communication support unit can also support communication including grammar. Furthermore, if the student is a high school student, the communication support unit can also support communication including listening. This makes it possible to support communication with other students according to the attributes of the student. Some or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI, or may be performed without using AI.

[0117] The communication support unit can support communication with teachers according to the attributes of the students. For example, if the student is an elementary school student, the communication support unit can support simple communication. If the student is a junior high school student, the communication support unit can also support communication including grammar. Furthermore, if the student is a high school student, the communication support unit can also support communication including listening. This makes it possible to support communication with teachers according to the attributes of the students. Some or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI, or may be performed without using AI.

[0118] The communication support unit can support group discussions according to the attributes of the students. For example, if the students are elementary school students, the communication support unit can support simple discussions. If the students are junior high school students, the communication support unit can also support discussions including grammar. If the students are high school students, the communication support unit can also support discussions including listening. This makes it possible to support group discussions according to the attributes of the students. Some or all of the above-mentioned processing in the communication support unit may be performed, for example, using AI, or may be performed without using AI.

[0119] The advice unit can provide advice to the teacher according to the student's emotions. For example, if the student is nervous, the advice unit provides advice to relax the student. Also, if the student is relaxed, the advice unit can provide proactive advice. Furthermore, if the student is excited, the advice unit can provide advice to calm the student. This makes it possible to provide advice to the teacher according to the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI.

[0120] The advice unit can provide advice on the study method according to the student's emotions. For example, if the student is nervous, the advice unit advises a study method to relax the student. Also, if the student is relaxed, the advice unit can advise an active study method. Furthermore, if the student is excited, the advice unit can advise a study method to calm the student. This makes it possible to provide advice on the study method according to the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI.

[0121] The advice unit can provide advice on the learning environment according to the student's emotions. For example, if the student is nervous, the advice unit advises a learning environment for relaxing the student. Also, if the student is relaxed, the advice unit can advise a positive learning environment. Furthermore, if the student is excited, the advice unit can advise a learning environment for calming the student. This makes it possible to provide advice on the learning environment according to the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI.

[0122] The advice unit can provide advice to the teacher according to the attributes of the student. For example, if the student is an elementary school student, the advice unit can advise a simple study method. If the student is a junior high school student, the advice unit can also advise a study method that includes grammar. Furthermore, if the student is a high school student, the advice unit can also advise a study method that includes listening. This makes it possible to provide advice to the teacher according to the attributes of the student. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0123] The advice unit can provide advice on study methods according to the attributes of the student. For example, if the student is an elementary school student, the advice unit can advise on simple study methods. If the student is a junior high school student, the advice unit can also advise on study methods that include grammar. Furthermore, if the student is a high school student, the advice unit can also advise on study methods that include listening. This makes it possible to provide advice on study methods according to the attributes of the student. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0124] The advice unit can provide advice on the learning environment according to the attributes of the student. For example, if the student is an elementary school student, the advice unit can advise a simple learning environment. If the student is a junior high school student, the advice unit can also advise a learning environment including grammar. Furthermore, if the student is a high school student, the advice unit can also advise a learning environment including listening. This makes it possible to provide advice on the learning environment according to the attributes of the student. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0125] The prediction unit can predict future learning performance based on the student's emotions. For example, the prediction unit can predict that the future learning performance will improve if the student is relaxed. Also, the prediction unit can predict that the future learning performance will decrease if the student is nervous. Furthermore, the prediction unit can predict that the future learning performance will fluctuate if the student is excited. This can provide a prediction of the future learning performance based on the student's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using an AI, or may be performed without using an AI.

[0126] The prediction unit can predict future learning performance based on the student's learning history. For example, the prediction unit analyzes the student's past learning history and predicts future learning performance. The prediction unit can also find a specific pattern from the student's learning history and predict future learning performance. Furthermore, the prediction unit can predict future improvement in learning performance based on the student's learning history. This makes it possible to provide a prediction of future learning performance based on the student's learning history. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0127] The prediction unit can predict future learning performance based on the attributes of the student. For example, the prediction unit can predict future learning performance when the student is an elementary school student. Also, the prediction unit can predict future learning performance when the student is a junior high school student. Furthermore, the prediction unit can predict future learning performance when the student is a high school student. This makes it possible to provide a prediction of future learning performance based on the attributes of the student. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0128] The prediction unit can predict future learning performance based on the change in the student's emotions. For example, the prediction unit analyzes the change in the student's emotions and predicts the future learning performance. The prediction unit can also find a specific pattern from the change in the student's emotions and predict the future learning performance. Furthermore, the prediction unit can predict the improvement of future learning performance based on the change in the student's emotions. This makes it possible to provide a prediction of the future learning performance based on the change in the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI.

[0129] The prediction unit can predict the future learning performance based on the student's learning history and emotional transition. For example, the prediction unit analyzes the student's learning history and emotional transition to predict the future learning performance. The prediction unit can also find a specific pattern from the student's learning history and emotional transition to predict the future learning performance. Furthermore, the prediction unit can predict the improvement of future learning performance based on the student's learning history and emotional transition. This makes it possible to provide a prediction of the future learning performance based on the student's learning history and emotional transition. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI.

[0130] The prediction unit can predict future learning performance based on the student's attributes and emotional transitions. For example, the prediction unit analyzes the student's attributes and emotional transitions to predict future learning performance. The prediction unit can also find a specific pattern from the student's attributes and emotional transitions to predict future learning performance. Furthermore, the prediction unit can predict future learning performance improvement based on the student's attributes and emotional transitions. This makes it possible to provide a prediction of future learning performance based on the student's attributes and emotional transitions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI.

[0131] The identification unit can identify areas for improvement in education based on the student's emotions. For example, the identification unit can identify areas for improvement in education when the student is relaxed. Also, the identification unit can identify areas for improvement in education when the student is nervous. Furthermore, the identification unit can identify areas for improvement in education when the student is excited. This makes it possible to identify areas for improvement in education based on the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI.

[0132] The identification unit can identify areas for improvement in education based on the student's learning history. For example, the identification unit analyzes the student's past learning history and identifies areas for improvement in education. The identification unit can also find a specific pattern from the student's learning history and identify areas for improvement in education. Furthermore, the identification unit can also identify areas for improvement in education based on the student's learning history. This makes it possible to identify areas for improvement in education based on the student's learning history. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI.

[0133] The identification unit can identify areas for improvement in education based on the attributes of the student. For example, when the student is an elementary school student, the identification unit can identify areas for improvement in education. Also, when the student is a junior high school student, the identification unit can identify areas for improvement in education. Furthermore, when the student is a high school student, the identification unit can identify areas for improvement in education. In this way, it is possible to identify areas for improvement in education based on the attributes of the student. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0134] The identification unit can identify areas for improvement in education based on the transition of the student's emotions. For example, the identification unit analyzes the transition of the student's emotions and identifies areas for improvement in education. The identification unit can also find a specific pattern from the transition of the student's emotions and identify areas for improvement in education. Furthermore, the identification unit can also identify areas for improvement in education based on the transition of the student's emotions. This makes it possible to identify areas for improvement in education based on the transition of the student's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI.

[0135] The identification unit can identify areas for improvement in education based on the student's learning history and emotional transitions. For example, the identification unit analyzes the student's learning history and emotional transitions to identify areas for improvement in education. The identification unit can also find a specific pattern from the student's learning history and emotional transitions to identify areas for improvement in education. The identification unit can also identify areas for improvement in education based on the student's learning history and emotional transitions. This makes it possible to identify areas for improvement in education based on the student's learning history and emotional transitions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI.

[0136] The identification unit can identify areas for improvement in education based on the transition of the student's attributes and emotions. For example, the identification unit analyzes the transition of the student's attributes and emotions to identify areas for improvement in education. The identification unit can also find a specific pattern from the transition of the student's attributes and emotions to identify areas for improvement in education. Furthermore, the identification unit can also identify areas for improvement in education based on the transition of the student's attributes and emotions. This makes it possible to identify areas for improvement in education based on the transition of the student's attributes and emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The educational robot system further includes a feedback unit. The feedback unit can provide feedback to the student based on the emotion estimation result and the learning progress of the student. For example, if the student is relaxed, the feedback unit can provide positive feedback such as "You are learning at a good pace." If the student is nervous, the feedback unit can also provide advice such as "Take a short break and relax." If the student is excited, the feedback unit can also suggest specific actions such as "Take a deep breath to stay focused." This allows the student to receive appropriate feedback according to his or her emotional state, improving the learning effect.

[0139] The educational robot system further includes a reward unit. The reward unit can provide rewards based on the student's learning results. For example, if the student completes a specific task, the reward unit can provide digital badges or points. In addition, if the student achieves high grades consecutively, the reward unit can provide special titles or benefits. Furthermore, if the student cooperates with other students to solve a task, the reward unit can provide a reward for the entire team. This can increase the student's motivation for learning.

[0140] The educational robot system further includes a reminder unit. The reminder unit can provide reminders based on the student's study schedule. For example, if the student needs to start studying at a specific time, the reminder unit can notify the student, "It's time to start studying." It can also remind the student, "Let's take a short break," when it is time to take a break. In addition, it can provide reminders such as "Let's start preparing for the test" so that the student does not forget the deadline for an important test or assignment. This allows the student to study efficiently.

[0141] The educational robot system further includes a customization unit. The customization unit can customize the learning content based on the student's learning style and preferences. For example, if the student prefers visual learning, the customization unit can provide learning materials that make extensive use of visual aids. If the student prefers auditory learning, the customization unit can also provide audio learning materials. Furthermore, if the student prefers hands-on learning, the customization unit can also provide interactive exercises. This allows the student to progress through learning in a way that best suits him or her.

[0142] The educational robot system further includes a reflection unit. The reflection unit can provide support for students to perform self-evaluation after learning. For example, the reflection unit can prompt students to self-evaluate by presenting questions such as "What was the most difficult part of today's learning?". It can also support students to have a sense of self-improvement through questions such as "What would you like to improve in your next learning session?". It can also provide questions that encourage positive reflection such as "What was the most enjoyable part of today's learning?". This allows students to improve the quality of their learning through self-evaluation.

[0143] The educational robot system further includes an emotion sharing unit. The emotion sharing unit can support students to share their emotions with other students and teachers. For example, if a student is relaxed, the emotion sharing unit can share a message such as "I was able to study in a relaxed manner today." If a student is nervous, the emotion sharing unit can also share an emotion such as "I'm a little nervous." If a student is excited, the emotion sharing unit can also share an emotion such as "I'm very excited to learn something new." This allows students to improve their learning environment by sharing their emotions with others.

[0144] The educational robot system further includes an emotion history unit. The emotion history unit records the transition of the student's emotions and can refer to the past emotional states. For example, the emotion history unit records whether the student was relaxed in a particular lesson. It can also record whether the student was nervous before a test. It can also record whether the student was excited when learning a new subject. This allows the student to look back on the transition of his / her emotions and understand the learning pattern.

[0145] The educational robot system further includes an emotion prediction unit. The emotion prediction unit can predict the student's future emotion state based on the student's current emotion state and past emotion history. For example, the emotion prediction unit can predict that the student is likely to be nervous when working on a specific task. It can also predict that the student is likely to be excited when learning a new subject. It can also predict that the student is likely to be tired after a long period of study. This allows the educational robot system to grasp the student's emotion state in advance and provide appropriate support.

[0146] The educational robot system further includes an emotion adjustment unit. The emotion adjustment unit can provide support for adjusting the student's emotion state. For example, if the student is tense, the emotion adjustment unit can suggest breathing techniques to relax the student. If the student is excited, the emotion adjustment unit can also suggest meditation to improve concentration. If the student is tired, the emotion adjustment unit can also suggest stretching to refresh the student. This allows the student to appropriately adjust his / her emotion state and concentrate on learning.

[0147] The educational robot system further includes an emotion feedback unit. The emotion feedback unit can provide feedback based on the student's emotion state. For example, if the student is relaxed, the emotion feedback unit can provide positive feedback such as "You are learning at a good pace." If the student is tense, the emotion feedback unit can provide advice such as "Take a short break and relax." If the student is excited, the emotion feedback unit can suggest specific actions such as "Take a deep breath to stay focused." This allows the student to receive appropriate feedback according to his or her emotion state, improving the learning effect.

[0148] The process flow of the second embodiment will be briefly described below.

[0149] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotions of students in an English classroom. The emotion estimation information includes the students' facial expressions, movements, voices, heart rates, blood pressure, pupil responses, brain waves, body temperature, breathing, and posted content. The acquisition unit uses a camera to capture the students' facial expressions in real time and acquires the facial expression data. It can also detect the students' movements using a motion sensor and acquire movement data. It can also record the students' voices using a microphone and acquire voice data. Step 2: The estimation unit estimates the emotion of the student based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the facial expression data of the student using a facial expression recognition algorithm to estimate the emotion. The estimation unit can also analyze the voice data of the student using a voice recognition algorithm to estimate the emotion. Furthermore, the estimation unit can analyze the heart rate data of the student using a heart rate analysis algorithm to estimate the emotion. Step 3: The determination unit determines specific educational content based on the student's emotion estimated by the estimation unit. The determination unit may determine to conduct pronunciation practice if the student is relaxed. The determination unit may also determine to conduct word memorization if the student is excited. Furthermore, the determination unit may determine to conduct listening practice if the student is tired. Step 4: The corresponding unit executes the English education determined by the determining unit for the student. The corresponding unit executes pronunciation practice when the student is relaxed. The corresponding unit can also execute word memorization when the student is excited. Furthermore, the corresponding unit can execute listening practice when the student is tired.

[0150] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0152] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] For example, the acquisition unit of the educational robot system is realized by either the data processing device 12 or the smart device 14. For example, the camera 42 is used to capture the facial expression of the student in real time, and facial expression data is acquired. For example, a motion sensor is used to detect the student's movement, and movement data is acquired. For example, the microphone 38B is used to record the student's voice, and voice data is acquired. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, a facial expression recognition algorithm is used to analyze the student's facial expression data, and emotions are estimated. For example, a voice recognition algorithm is used to analyze the student's voice data, and emotions are estimated. The determination unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, when the student is relaxed, it is determined to perform pronunciation practice. The correspondence unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, when the student is relaxed, pronunciation practice is performed. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0156] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0161] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0169] For example, the acquisition unit of the educational robot system is realized by either the data processing device 12 or the smart glasses 214. For example, the camera 42 is used to capture the student's facial expression in real time to acquire facial expression data. For example, a motion sensor is used to detect the student's movement to acquire movement data. For example, the microphone 238 is used to record the student's voice to acquire voice data. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, a facial expression recognition algorithm is used to analyze the student's facial expression data to estimate emotions. For example, a voice recognition algorithm is used to analyze the student's voice data to estimate emotions. The decision unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, if the student is relaxed, it is determined to perform pronunciation practice. The response unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, if the student is relaxed, pronunciation practice is performed. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0172] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0177] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0180] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0184] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0185] For example, the acquisition unit of the educational robot system is realized by either the data processing device 12 or the headset type terminal 314. For example, the camera 42 is used to capture the facial expression of the student in real time, and facial expression data is acquired. For example, a motion sensor is used to detect the student's movement, and movement data is acquired. For example, the microphone 238 is used to record the student's voice, and voice data is acquired. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. For example, a facial expression recognition algorithm is used to analyze the student's facial expression data, and emotions are estimated. For example, a voice recognition algorithm is used to analyze the student's voice data, and emotions are estimated. The decision unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. For example, when the student is relaxed, it is determined to perform pronunciation practice. The response unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. For example, when the student is relaxed, pronunciation practice is performed. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 7, a 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.

[0188] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0193] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0194] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0195] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0196] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0197] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0198] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0199] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0200] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0201] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0202] For example, the acquisition unit of the educational robot system is realized by either the data processing device 12 or the robot 414. For example, the camera 42 is used to capture the facial expression of the student in real time, and facial expression data is acquired. For example, a motion sensor is used to detect the student's movement, and movement data is acquired. For example, the microphone 238 is used to record the student's voice, and voice data is acquired. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, a facial expression recognition algorithm is used to analyze the student's facial expression data, and emotions are estimated. For example, a voice recognition algorithm is used to analyze the student's voice data, and emotions are estimated. The decision unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, when the student is relaxed, it is determined to perform pronunciation practice. The correspondence unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, when the student is relaxed, pronunciation practice is performed. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0203] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0204] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0206] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0208] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0210] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0211] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0212] In addition, 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 upon request from the data processing device 12.

[0213] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0214] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0215] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0216] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0217] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0219] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0221] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating emotions of students in an English classroom; an estimation unit that estimates an emotion of the student based on the emotion estimation information acquired by the acquisition unit; A determination unit that determines specific educational content based on the emotion of the student estimated by the estimation unit; a response unit that implements the English education determined by the determination unit for the student; A system characterized by: (Appendix 2) The determination unit is Determine specific English education content based on the student's feelings and the student's attributes; The corresponding portion is Implementing specific English education content for the student based on the student's emotions and attributes 2. The system of claim 1, (Appendix 3) The determination unit is determining specific dialogue content based on the student's feelings; The corresponding portion is Implement specific dialogue content based on the student's emotions 2. The system of claim 1, (Appendix 4) The determination unit is determining a specific dialogue content based on the student's feelings; The corresponding portion is Execute dialogue content in a specific manner based on the student's emotions 2. The system of claim 1, (Appendix 5) The determination unit is Determine specific learning support content based on the student's feelings; The corresponding portion is Provide specific learning support based on the students' feelings 2. The system of claim 1, (Appendix 6) The corresponding portion is monitoring the student's learning progress in real time; Dynamically adjust specific learning content based on the student's emotions. 2. The system of claim 1, (Appendix 7) The estimation unit is Estimating emotions of a group of students including a plurality of said students; The determination unit is Decide on English education according to the feelings of the group of students; The corresponding portion is The English education determined by the determination unit is carried out for the group of students. 2. The system of claim 1, (Appendix 8) Depending on the emotional state of the student, Equipping students with a communication support department to help them communicate with other students or teachers 2. The system of claim 1, (Appendix 9) Based on the emotional state of the student, Equipped with an advice module that dynamically advises teachers on teaching methods 2. The system of claim 1, (Appendix 10) Based on the change in the student's emotional state, A prediction unit for predicting the future learning performance of the student. 2. The system of claim 1, (Appendix 11) Based on the emotion of the student estimated by the estimation unit, Establish a department to identify areas for improvement in education 2. The system of claim 1, (Appendix 12) The acquisition unit is Capture students' facial expressions 2. The system of claim 1, (Appendix 13) The acquisition unit is Capture student actions 2. The system of claim 1, (Appendix 14) The acquisition unit is Capture student audio 2. The system of claim 1, (Appendix 15) The acquisition unit is Get the student's heart rate 2. The system of claim 1, (Appendix 16) The acquisition unit is Get the student's blood pressure 2. The system of claim 1, (Appendix 17) The acquisition unit is Obtaining pupillary responses 2. The system of claim 1, (Appendix 18) The estimation unit is Inferring emotions based on students' facial expressions 2. The system of claim 1, (Appendix 19) The estimation unit is Inferring emotions based on student behavior 2. The system of claim 1, (Appendix 20) The estimation unit is Estimate emotions based on student voice 2. The system of claim 1, (Appendix 21) The estimation unit is Inferring a student's emotions based on their heart rate 2. The system of claim 1, (Appendix 22) The estimation unit is Inferring students' emotions based on their blood pressure 2. The system of claim 1, (Appendix 23) The estimation unit is Estimating emotions based on pupil responses 2. The system of claim 1, (Appendix 24) The determination unit is Deciding English education based on students' emotions 2. The system of claim 1, (Appendix 25) The determination unit is Decide on dialogue based on students' emotions 2. The system of claim 1, (Appendix 26) The determination unit is Deciding on learning support based on students' emotions 2. The system of claim 1, (Appendix 27) The determination unit is Decide on English education according to students' attributes 2. The system of claim 1, (Appendix 28) The determination unit is Determine dialogue based on student attributes 2. The system of claim 1, (Appendix 29) The determination unit is Determine learning support according to student attributes 2. The system of claim 1, (Appendix 30) The corresponding portion is Implementing English education that responds to students' emotions 2. The system of claim 1, (Appendix 31) The corresponding portion is Engage in dialogue that responds to students' emotions 2. The system of claim 1, (Appendix 32) The corresponding portion is Implementing learning support based on students' emotions 2. The system of claim 1, (Appendix 33) The corresponding portion is Implementing English education according to students' attributes 2. The system of claim 1, (Appendix 34) The corresponding portion is Execute dialogue according to student attributes 2. The system of claim 1, (Appendix 35) The corresponding portion is Provide learning support according to student attributes 2. The system of claim 1, (Appendix 36) The communication support unit includes: Help students communicate with others according to their emotions 3. The system of claim 2, (Appendix 37) The communication support unit includes: Support students to communicate with teachers according to their emotions 3. The system of claim 2, (Appendix 38) The communication support unit includes: Support group discussions according to students' emotions 3. The system of claim 2, (Appendix 39) The communication support unit includes: Support communication with other students according to their attributes 3. The system of claim 2, (Appendix 40) The communication support unit includes: Support communication with teachers according to students' attributes 3. The system of claim 2, (Appendix 41) The communication support unit includes: Support group discussions according to student attributes 3. The system of claim 2, (Appendix 42) The advice unit is Providing advice to teachers based on student emotions 4. The system of claim 3. (Appendix 43) The advice unit is Offer students advice on how to study based on their emotions 4. The system of claim 3. (Appendix 44) The advice unit is Offer advice on the learning environment based on students' emotions 4. The system of claim 3. (Appendix 45) The advice unit is Providing advice to teachers based on student attributes 4. The system of claim 3. (Appendix 46) The advice unit is Providing advice on study methods based on student attributes 4. The system of claim 3. (Appendix 47) The advice unit is Providing advice on learning environments based on student attributes 4. The system of claim 3. (Appendix 48) The prediction unit is Predicting future learning performance based on students' emotions 2. The system of claim 1, (Appendix 49) The prediction unit is Predicting future learning performance based on students' learning history 2. The system of claim 1, (Appendix 50) The prediction unit is Predict future learning performance based on student attributes 2. The system of claim 1, (Appendix 51) The prediction unit is Predicting future learning performance based on student emotion transitions 2. The system of claim 1, (Appendix 52) The prediction unit is Predicting future learning performance based on students' learning history and emotional transitions 2. The system of claim 1, (Appendix 53) The prediction unit is Predicting future learning performance based on student attributes and emotional transitions 2. The system of claim 1, (Appendix 54) The identification unit is Identify areas for improvement in education based on student sentiment 2. The system of claim 1, (Appendix 55) The identification unit is Identify areas for improvement in education based on students' learning histories 2. The system of claim 1, (Appendix 56) The identification unit is Identify areas for improvement in education based on student attributes 2. The system of claim 1, (Appendix 57) The identification unit is Identifying areas for improvement in education based on student emotional transitions 2. The system of claim 1, (Appendix 58) The identification unit is Identify areas for improvement in education based on students' learning histories and emotional transitions 2. The system of claim 1, (Appendix 59) The identification unit is Identify areas for improvement in education based on student demographics and emotional transitions 2. The system of claim 1, [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires emotion estimation information for estimating emotions of students in an English classroom; an estimation unit that estimates an emotion of the student based on the emotion estimation information acquired by the acquisition unit; A determination unit that determines specific educational content based on the emotion of the student estimated by the estimation unit; a response unit that implements the English education determined by the determination unit for the student; A system characterized by:

2. The determination unit is Determine specific English education content based on the student's feelings and the student's attributes; The corresponding portion is Implementing specific English education content for the student based on the student's emotions and attributes 2. The system of claim 1 .

3. The determination unit is determining specific dialogue content based on the student's feelings; The corresponding portion is Implement specific dialogue content based on the student's emotions 2. The system of claim 1 .

4. The determination unit is determining a specific dialogue content based on the student's feelings; The corresponding portion is Execute dialogue content in a specific manner based on the student's emotions 2. The system of claim 1 .

5. The determination unit is Determine specific learning support content based on the student's feelings; The corresponding portion is Provide specific learning support based on the students' feelings 2. The system of claim 1 .

6. The corresponding portion is monitoring the student's learning progress in real time; Dynamically adjust specific learning content based on the student's emotions.

2. The system of claim 1 .

7. The estimation unit is Estimating emotions of a group of students including a plurality of said students; The determination unit is Decide on English education according to the feelings of the group of students; The corresponding portion is The English education determined by the determination unit is carried out for the group of students.

2. The system of claim 1 .

8. Depending on the emotional state of the student, Equipping students with a communication support department to help them communicate with other students or teachers 2. The system of claim 1 .

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