System

Abstract

A system according to an embodiment aims to provide personalized teaching by analyzing sensor data and feeding it back to an agent or xR.SOLUTION: A system includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects sensor data. The analysis unit analyzes the data collected by the collection unit. The feedback unit feeds back to the agent or xR based on the analysis result obtained by the analysis unit.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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately provided personalized education to accommodate the diversification of educational environments, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized education by analyzing sensor data and providing feedback to an agent or xR. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects sensor data. The analysis unit analyzes the data collected by the collection unit. The feedback unit provides feedback to an agent or xR based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized education by analyzing sensor data and providing feedback to an agent or xR. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An educational support system according to an embodiment of the present invention collects sensor data, analyzes it using a large-scale language model (LLM), and provides feedback to agents and xR (augmented reality). The educational support system provides personalized education tailored to each student's current situation and learning record. For example, the educational support system monitors students' situations in real time using various sensors installed in classrooms. These sensors include temperature, humidity, heart rate, and motion sensors. Data obtained from these sensors is collected via IoT devices. The educational support system then analyzes the collected data using an LLM. The LLM interprets the sensor data and identifies the student's current situation. For example, a high heart rate may indicate nervousness. It also refers to past learning records to evaluate the student's learning progress and level of understanding. The educational support system then feeds back the analysis results from the LLM to agents and xR. The agents provide appropriate advice and guidance to students. For example, they may suggest ways to relax to a nervous student. Furthermore, virtual classrooms and virtual teaching materials are displayed in xR, allowing students to learn in a more comprehensible manner. This allows the educational support system to provide personalized education tailored to each student. For example, more advanced learning materials can be provided to students with a high level of understanding, while basic learning materials can be provided to students with a low level of understanding. It also makes it possible for students to participate in classes from remote locations, realizing education that goes beyond the constraints of a physical classroom. This allows the educational support system to provide more effective education by respecting the strengths of each student and accommodating diverse learning styles.

[0029] The education support system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects sensor data. The sensor data includes, but is not limited to, temperature data, humidity data, heart rate data, and movement data. For example, the collection unit measures the temperature in a classroom using a temperature sensor. The collection unit can also measure the humidity in a classroom using a humidity sensor. The collection unit can also measure the heart rate of a student using a heart rate sensor. The collection unit can also detect the movement of a student using a movement sensor. For example, the temperature sensor measures temperature using a thermistor or RTD (resistance temperature detector) or other technology. The humidity sensor measures humidity using a capacitance or resistance technology. The heart rate sensor measures heart rate using a photoelectric or electrical technology. The movement sensor detects movement using an acceleration sensor or a gyro sensor or other technology. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, a data interpretation method and an algorithm used, but is not limited to, these examples. For example, the analysis unit interprets sensor data using an LLM to grasp the student's current situation. The analysis unit can also evaluate the student's learning progress and level of understanding by referring to past learning records. Furthermore, the analysis unit can estimate the student's emotions using an LLM. For example, the LLM analyzes heart rate fluctuations to determine whether the student is tense or relaxed. The feedback unit provides feedback to an agent or xR based on the analysis results obtained by the analysis unit. Feedback is provided, for example, based on a notification method to the agent or a display method in the xR, but is not limited to such examples. For example, the feedback unit provides feedback of the analysis results to the agent and appropriate advice and guidance to the student. The feedback unit can also display a virtual classroom or virtual teaching materials on the xR to allow the student to progress in learning in a way that is easier for them to understand. As a result, the education support system according to the embodiment can provide personalized education by collecting, analyzing, and providing feedback on sensor data.

[0030] The collection unit can collect sensor data from temperature sensors, humidity sensors, heart rate sensors, and motion sensors. The collection unit, for example, uses a temperature sensor to measure the temperature in the classroom. Temperature sensors include thermistors, RTDs (resistance temperature detectors), thermocouples, etc. For example, thermistors have the characteristic that their resistance changes with temperature. RTDs measure temperature by utilizing the characteristic that metal resistance changes with temperature. Thermocouples measure temperature by utilizing the characteristic that a voltage is generated when a temperature difference occurs at the junction of different metals. The collection unit, for example, uses a humidity sensor to measure the humidity in the classroom. Humidity sensors include capacitive, resistive, and optical types. For example, capacitive humidity sensors have the characteristic that their capacitance changes with humidity. Resistive humidity sensors have the characteristic that their resistance changes with humidity. Optical humidity sensors measure humidity by utilizing the absorption and reflection of light. The collection unit, for example, uses a heart rate sensor to measure the student's heart rate. Heart rate sensors include photoelectric and electrical types. For example, a photoelectric heart rate sensor measures heart rate by utilizing the absorption and reflection of light. An electrical heart rate sensor measures heart rate by detecting the electrical activity of the heart. The collection unit detects student movements using, for example, a motion sensor. Motion sensors include acceleration sensors and gyro sensors. For example, an acceleration sensor has the property of measuring the acceleration of an object. A gyro sensor has the property of measuring the rotation speed of an object. This allows for the collection of a variety of sensor data to more accurately grasp the student's situation.

[0031] The analysis unit can interpret the sensor data and grasp the student's current situation. The analysis unit interprets the sensor data using, for example, the LLM. The LLM has learned large amounts of data and has advanced natural language processing capabilities. For example, the LLM can analyze heart rate fluctuations to determine whether a student is tense or relaxed. The LLM can also analyze movement data to determine whether a student is concentrating. Furthermore, the LLM can analyze temperature and humidity data to evaluate whether the classroom environment is suitable for learning. For example, the LLM can determine that a student's heart rate is high, indicating that they may be tense. The LLM can also determine from movement data that a student is less likely to be moving, indicating that they are concentrating. The LLM can also evaluate whether the classroom environment is comfortable, based on temperature and humidity data. In this way, the analysis unit can grasp the student's current situation by interpreting the sensor data.

[0032] The analysis unit can evaluate the student's learning progress and level of understanding by referring to past learning records. The analysis unit, for example, refers to past learning records. The learning records include test results, assignment submission status, study time, etc. For example, the analysis unit can evaluate the student's level of understanding by referring to test results. The analysis unit can also evaluate the student's learning progress by referring to the assignment submission status. Furthermore, the analysis unit can evaluate the student's study habits by referring to the study time. For example, the analysis unit evaluates the student's level of understanding based on the rate of correct answers on tests. The analysis unit can also evaluate the student's learning progress based on the assignment submission status. Furthermore, the analysis unit can evaluate the student's study habits based on the study time. In this way, the student's learning progress and level of understanding can be evaluated by referring to past learning records.

[0033] The feedback unit feeds back the analysis results to the agent, and is able to provide appropriate advice and guidance to the student. The feedback unit, for example, feeds back the analysis results to the agent. The agent provides appropriate advice and guidance to the student. For example, the feedback unit suggests ways to relax to a student who is feeling nervous. The feedback unit can also provide feedback praising the student's progress in learning to a student who is concentrating. The feedback unit can also provide basic guidance to a student who has a low level of understanding. For example, the feedback unit notifies the agent of the analysis results, and the agent suggests ways to relax to the student. The feedback unit notifies the agent of the analysis results, and the agent provides feedback praising the student's progress in learning. The feedback unit notifies the agent of the analysis results, and the agent provides basic guidance to the student. In this way, by feeding back the analysis results to the agent, appropriate advice and guidance can be provided to the student.

[0034] The feedback unit displays a virtual classroom or virtual teaching materials on the xR platform, allowing students to progress through their studies in a way that is easier to understand. The feedback unit, for example, displays a virtual classroom on the xR platform. The virtual classroom includes 3D models and interactive teaching materials. For example, the feedback unit provides students with a learning environment similar to that of a real classroom through the virtual classroom. The feedback unit can also display virtual teaching materials on the xR platform. The virtual teaching materials include video teaching materials and simulation teaching materials. For example, the feedback unit provides students with learning content in a visually easy-to-understand format through the virtual teaching materials. The feedback unit can also display a virtual classroom or virtual teaching materials on the xR platform, allowing students to progress through their studies interactively. For example, the feedback unit provides an environment in which students can cooperate with other students through the virtual classroom. The feedback unit can also allow students to experience actual experiments and simulations through the virtual teaching materials. As a result, displaying a virtual classroom or virtual teaching materials on the xR platform allows students to progress through their studies in a way that is easier to understand.

[0035] The collection unit can analyze the student's past sensor data and determine appropriate sensor placement. For example, the collection unit can analyze the student's past heart rate data and determine the most effective position for placing the heart rate sensor. The collection unit can also place the movement sensor in the optimal position in the classroom based on the student's past movement data. The collection unit can also analyze the student's past temperature and humidity data and place the temperature sensor and humidity sensor in the optimal position. For example, the collection unit analyzes the heart rate data and places the heart rate sensor on the student's wrist or chest. The collection unit also places the movement sensor on the ceiling or wall of the classroom based on the movement data. The collection unit also analyzes the temperature and humidity data and places the temperature sensor and humidity sensor in the center or corner of the classroom. In this way, the optimal sensor placement can be determined by analyzing the past sensor data.

[0036] When collecting sensor data, the collection unit can filter the data based on the student's current activity. For example, the collection unit prioritizes collecting data from the heart rate sensor and the motion sensor during class, and prioritizes collecting data from the temperature sensor and the humidity sensor during breaks. The collection unit can also filter the motion sensor data to measure the student's concentration level when the student is studying independently. The collection unit can also filter the heart rate sensor data to measure the student's stress level when the student is participating in a group activity. For example, the collection unit prioritizes collecting data from the heart rate sensor and the motion sensor during class to understand the student's concentration level and tension. The collection unit prioritizes collecting data from the temperature sensor and the humidity sensor during breaks to optimize the classroom environment. The collection unit can measure the student's concentration level by filtering the motion sensor data during self-study. This allows for more relevant data to be collected by filtering sensor data based on the student's current activity.

[0037] When collecting sensor data, the collection unit can select an appropriate collection means depending on the student's input method. For example, if the student uses voice input, the collection unit prioritizes collecting voice sensor data. Furthermore, if the student uses text input, the collection unit can also collect keyboard keystroke data. Furthermore, if the student uses motion input, the collection unit can also prioritize collecting motion sensor data. For example, the collection unit prioritizes collecting voice sensor data from students using voice input to understand the student's spoken content and emotions. Furthermore, the collection unit prioritizes collecting keyboard keystroke data from students using text input to evaluate the student's input speed and accuracy. Furthermore, the collection unit prioritizes collecting motion sensor data from students using motion input to understand the student's movement patterns and concentration levels. This allows for more effective data collection by selecting the optimal collection means depending on the student's input method.

[0038] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the student. For example, when a student is in a classroom, the collection unit prioritizes collecting data from the temperature sensor and humidity sensor in the classroom. Furthermore, when a student is in a schoolyard, the collection unit can also prioritize collecting data from the motion sensor. Furthermore, when a student is in a library, the collection unit can also prioritize collecting data from the heart rate sensor. For example, the collection unit optimizes the classroom environment by prioritizedly collecting data from the temperature sensor and humidity sensor of students in the classroom. Furthermore, the collection unit prioritizes collecting data from the motion sensor of students in the schoolyard to understand the student's exercise volume and activity level. Furthermore, the collection unit prioritizes collecting data from the heart rate sensor of students in the library to understand the student's concentration level and stress level. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the student.

[0039] The collection unit can analyze the student's social media activities and collect related data when collecting sensor data. For example, if a student feels stressed because of social media, the collection unit can prioritize collecting heart rate sensor data. Furthermore, if a student is relaxing because of social media, the collection unit can also prioritize collecting motion sensor data. Furthermore, if a student is concentrating on social media, the collection unit can prioritize collecting temperature and humidity sensor data. For example, the collection unit prioritizes collecting heart rate sensor data from a student who feels stressed because of social media to understand the student's stress level. Furthermore, the collection unit prioritizes collecting motion sensor data from a student who is relaxing because of social media to understand the student's state of relaxation. Furthermore, the collection unit prioritizes collecting temperature and humidity sensor data from a student who is concentrating on social media to understand the student's level of concentration. In this way, related data can be collected by analyzing the student's social media activities.

[0040] When collecting sensor data, the collection unit can customize the collection method by reflecting the student's past feedback. For example, if a student has requested collection of heart rate sensor data in the past, the collection unit prioritizes collection of heart rate sensor data. Furthermore, if a student has requested collection of motion sensor data in the past, the collection unit can also prioritize collection of motion sensor data. Furthermore, if a student has requested collection of temperature and humidity sensor data in the past, the collection unit can also prioritize collection of temperature and humidity sensor data. For example, the collection unit prioritizes collection of heart rate sensor data from a student who requested collection of heart rate sensor data to understand the student's heart rate. Furthermore, the collection unit prioritizes collection of motion sensor data from a student who requested collection of motion sensor data to understand the student's motion patterns. Furthermore, the collection unit prioritizes collection of temperature and humidity sensor data from a student who requested collection of temperature and humidity sensor data to optimize the student's learning environment. This allows the collection method to be customized by reflecting the student's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the sensor data. For example, if data from a heart rate sensor is important, the analysis unit performs a detailed analysis. Also, if data from a motion sensor is important, the analysis unit can also perform a detailed analysis. Furthermore, if data from a temperature sensor and a humidity sensor is important, the analysis unit can also perform a detailed analysis. For example, if data from a heart rate sensor is important, the analysis unit performs a detailed analysis using a heart rate variability analysis algorithm. Also, if data from a motion sensor is important, the analysis unit performs a detailed analysis using a motion analysis algorithm. Furthermore, if data from a temperature sensor and a humidity sensor is important, the analysis unit performs a detailed analysis using an environmental analysis algorithm. In this way, by adjusting the level of detail of the analysis based on the importance of the sensor data, more detailed analysis can be performed on more important data.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of sensor data. For example, the analysis unit applies a heart rate variability analysis algorithm to data from a heart rate sensor. The analysis unit can also apply a motion analysis algorithm to data from a motion sensor. The analysis unit can also apply an environment analysis algorithm to data from a temperature sensor and a humidity sensor. For example, the analysis unit applies a heart rate variability analysis algorithm to data from a heart rate sensor to analyze heart rate fluctuations in detail. The analysis unit applies a motion analysis algorithm to data from a motion sensor to analyze students' motion patterns in detail. The analysis unit also applies an environment analysis algorithm to data from a temperature sensor and a humidity sensor to analyze the environment in the classroom in detail. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of sensor data.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. For example, the analysis unit can improve the accuracy of the current heart rate data analysis by referring to the student's past heart rate data analysis results. The analysis unit can also improve the accuracy of the current movement data analysis by referring to the student's past movement data analysis results. Furthermore, the analysis unit can improve the accuracy of the current environmental data analysis by referring to the student's past temperature and humidity data analysis results. For example, the analysis unit adjusts the algorithm for analyzing the current heart rate data based on the past heart rate data analysis results. The analysis unit can also adjust the algorithm for analyzing the current movement data based on the past movement data analysis results. Furthermore, the analysis unit adjusts the algorithm for analyzing the current environmental data based on the past temperature and humidity data analysis results. In this way, the accuracy of the current analysis can be improved by referring to the student's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the sensor data was collected. For example, the analysis unit prioritizes analysis of the most recent heart rate data. The analysis unit can also prioritize analysis of the most recent movement data. Furthermore, the analysis unit can also prioritize analysis of the most recent temperature and humidity data. For example, the analysis unit prioritizes analysis of the most recent heart rate data to understand the current heart rate state of the student. The analysis unit prioritizes analysis of the most recent movement data to understand the current movement pattern of the student. Furthermore, the analysis unit prioritizes analysis of the most recent temperature and humidity data to understand the current environment in the classroom. In this way, by determining the priority of analysis based on the time when the sensor data was collected, the most recent data can be analyzed preferentially.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sensor data. For example, if there is a high relevance between heart rate data and movement data, the analysis unit prioritizes analyzing these data. Furthermore, if there is a high relevance between temperature data and humidity data, the analysis unit can also prioritize analyzing these data. For example, if there is a high relevance between heart rate data and movement data, the analysis unit prioritizes analyzing these data to comprehensively grasp the heart rate status and movement patterns of students. Furthermore, if there is a high relevance between temperature data and humidity data, the analysis unit prioritizes analyzing these data to comprehensively grasp the environment in the classroom. Furthermore, if there is a high relevance between heart rate data and temperature data, the analysis unit prioritizes analyzing these data to comprehensively grasp the heart rate status of students and the environment in the classroom. Thus, by adjusting the order of analysis based on the relevance of the sensor data, it is possible to prioritize analyzing more relevant data.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. For example, if the student is a beginner, the analysis unit can provide the analysis results while avoiding technical terminology. Also, if the student is an intermediate learner, the analysis unit can provide the analysis results using appropriate technical terminology. Furthermore, if the student is an advanced learner, the analysis unit can provide the analysis results using detailed technical terminology. For example, the analysis unit can provide the analysis results to beginner students while avoiding technical terminology, making the analysis results easier for the student to understand. Also, the analysis unit can provide the analysis results to intermediate students using appropriate technical terminology, encouraging the student to deeply understand the analysis results. Furthermore, the analysis unit can provide the analysis results to advanced students using detailed technical terminology, helping the student deepen their technical knowledge. In this way, by adjusting the use of technical terminology in the analysis according to the student's level of expertise, it is possible to provide analysis results that are easier to understand.

[0047] When providing feedback, the feedback unit can select an appropriate feedback method by analyzing the student's past feedback history. For example, the feedback unit prioritizes the use of feedback methods that the student has previously preferred. The feedback unit can also select feedback methods that have been effective for the student in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided. For example, the feedback unit makes it easier for the student to accept feedback by preferentially using feedback methods that the student has previously preferred. Furthermore, the feedback unit enhances the student's learning effectiveness by selecting feedback methods that have been effective in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided, allowing the student to continue learning without feeling stressed. In this way, the optimal feedback method can be selected by analyzing the student's past feedback history.

[0048] The feedback unit can customize the content of the feedback based on the student's current learning situation when providing feedback. For example, if the student has a high level of understanding, the feedback unit can provide feedback with more advanced content. Also, if the student has a low level of understanding, the feedback unit can provide feedback with basic content. Furthermore, if the student's progress is lagging, the feedback unit can provide feedback to encourage progress. For example, the feedback unit can increase the student's motivation to learn by providing feedback with more advanced content to a student with a high level of understanding. Also, the feedback unit can deepen the student's understanding by providing feedback with basic content to a student with a low level of understanding. Furthermore, the feedback unit can improve the student's learning pace by providing feedback to encourage progress to a student who is lagging behind. In this way, more effective feedback can be provided by customizing the content of the feedback based on the student's current learning situation.

[0049] The feedback unit can improve the feedback method by reflecting the student's feedback when providing feedback. For example, if the student has a positive reaction to the feedback, the feedback unit continues that method. Also, if the student has a negative reaction to the feedback, the feedback unit can improve that method. Furthermore, if the student does not react to the feedback, the feedback unit can try a different method. For example, the feedback unit can maintain the student's motivation to learn by continuing the feedback method that has shown a positive reaction. Also, the feedback unit can reduce the student's stress by improving the feedback method that has shown a negative reaction. Furthermore, the feedback unit can find a feedback method that is suitable for the student by changing the feedback method that has shown no reaction. In this way, the feedback method can be improved by reflecting the student's feedback.

[0050] The feedback unit can select an appropriate feedback method by taking into account the geographical location information of the student when providing feedback. For example, the feedback unit provides direct feedback when the student is in a classroom. The feedback unit can also provide online feedback when the student is at home. Furthermore, the feedback unit can provide feedback via a mobile device when the student is on the move. For example, the feedback unit can provide direct feedback to a student in a classroom, allowing the student to respond immediately. The feedback unit can also provide online feedback to a student at home, helping the student to continue learning at home. Furthermore, the feedback unit can provide feedback via a mobile device to a student on the move, allowing the student to continue learning anywhere. In this way, the optimal feedback method can be selected by taking into account the geographical location information of the student.

[0051] When providing feedback, the feedback unit can analyze the student's social media activity and suggest a means of providing feedback. For example, if the student is active on social media, the feedback unit can provide feedback through social media. Also, if the student is feeling stressed because of social media, the feedback unit can provide feedback on ways to relax. Furthermore, if the student is concentrating on social media, the feedback unit can provide feedback praising the student's progress in learning. For example, the feedback unit makes the student more receptive to feedback by providing feedback through social media to a student who is active on social media. Also, the feedback unit reduces the student's stress by providing feedback on ways to relax to a student who is feeling stressed because of social media. Furthermore, the feedback unit increases the student's motivation by providing feedback praising the student's progress in learning to a student who is concentrating on social media. In this way, by analyzing the student's social media activity, more appropriate means of feedback can be suggested.

[0052] When providing feedback, the feedback unit can customize the feedback method by reflecting the student's past feedback. For example, the feedback unit prioritizes the use of feedback methods that the student has previously preferred. The feedback unit can also select feedback methods that have been effective for the student in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided. For example, the feedback unit makes it easier for the student to accept feedback by preferentially using feedback methods that the student has previously preferred. Furthermore, the feedback unit enhances the student's learning effectiveness by selecting feedback methods that have been effective in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided, allowing the student to continue learning without feeling stressed. In this way, the feedback method can be customized by reflecting the student's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can also refer to weather data when interpreting sensor data. For example, the analysis unit can collect external weather data and predict fluctuations in temperature and humidity in the classroom. The analysis unit can also identify factors that affect students' physical condition and concentration based on the weather data. Furthermore, the analysis unit can use the weather data to make suggestions for optimizing the classroom environment. This allows for more accurate analysis by referring to weather data.

[0055] When collecting sensor data, the collection unit can adjust the placement of sensors based on the individual learning style of each student. For example, sensors that collect visual information can be placed more prominently for students with a visual learning style. Audio sensors can also be placed more prominently for students with an auditory learning style. Furthermore, movement sensors can be placed more prominently for students with a learning style that involves movement. This allows for sensor placement according to each student's learning style, resulting in more effective data collection.

[0056] The analysis unit can use AI pattern recognition technology when interpreting sensor data. For example, the analysis unit can use AI to learn students' behavioral patterns and detect abnormal behavior. The analysis unit can also use AI to analyze students' learning patterns and suggest effective learning methods. Furthermore, the analysis unit can use AI to analyze students' emotional patterns and predict emotional fluctuations. This makes it possible to perform more advanced analysis using AI pattern recognition technology.

[0057] The analysis unit can analyze learning trends when referring to past learning records. For example, the analysis unit can graph the student's learning progress based on past learning records and visually display the trends. The analysis unit can also identify areas in the student's learning that need strengthening based on the learning trends. Furthermore, the analysis unit can propose a learning plan for the student based on the learning trends. In this way, analyzing learning trends can more effectively support student learning.

[0058] The feedback unit can customize the character settings of the agent when feeding back the analysis results to the agent. For example, the feedback unit can change the agent's appearance or voice according to the student's preferences. The feedback unit can also adjust the agent's teaching method according to the student's learning style. Furthermore, the feedback unit can change the agent's facial expression or tone according to the student's emotions. In this way, by customizing the agent's character settings, it is possible to provide more friendly feedback to students.

[0059] The feedback unit can add interactive elements when displaying virtual classrooms or virtual teaching materials on xR. For example, the feedback unit can provide a function for students to collaborate with other students in the virtual classroom to solve assignments. The feedback unit can also incorporate quizzes and mini-games into the virtual teaching materials. Furthermore, the feedback unit can provide a function for students to communicate with teachers in real time in the virtual classroom. This can increase students' motivation to learn by adding interactive elements.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects sensor data. The sensor data includes temperature data, humidity data, heart rate data, and movement data. For example, the collection unit measures the temperature in the classroom using a temperature sensor and the humidity in the classroom using a humidity sensor. It also measures the students' heart rates using a heart rate sensor and detects the students' movements using a movement sensor. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is based on the data interpretation method and algorithm used. For example, the analysis unit uses LLM to interpret sensor data and understand the student's current situation. It can also refer to past learning records to evaluate the student's learning progress and level of understanding, and even estimate the student's emotions. Step 3: The feedback unit provides feedback to the agent and xR based on the analysis results obtained by the analysis unit. For example, the feedback unit may provide the agent with feedback on the analysis results and provide appropriate advice and guidance to the student. It may also be possible to display virtual classrooms and virtual teaching materials on the xR screen, allowing students to progress through their studies in a way that is easier for them to understand.

[0062] (Example 2) An educational support system according to an embodiment of the present invention collects sensor data, analyzes it using a large-scale language model (LLM), and provides feedback to agents and xR (augmented reality). The educational support system provides personalized education tailored to each student's current situation and learning record. For example, the educational support system monitors students' situations in real time using various sensors installed in classrooms. These sensors include temperature, humidity, heart rate, and motion sensors. Data obtained from these sensors is collected via IoT devices. The educational support system then analyzes the collected data using an LLM. The LLM interprets the sensor data and identifies the student's current situation. For example, a high heart rate may indicate nervousness. It also refers to past learning records to evaluate the student's learning progress and level of understanding. The educational support system then feeds back the analysis results from the LLM to agents and xR. The agents provide appropriate advice and guidance to students. For example, they may suggest ways to relax to a nervous student. Furthermore, virtual classrooms and virtual teaching materials are displayed in xR, allowing students to learn in a more comprehensible manner. This allows the educational support system to provide personalized education tailored to each student. For example, more advanced learning materials can be provided to students with a high level of understanding, while basic learning materials can be provided to students with a low level of understanding. It also makes it possible for students to participate in classes from remote locations, realizing education that goes beyond the constraints of a physical classroom. This allows the educational support system to provide more effective education by respecting the strengths of each student and accommodating diverse learning styles.

[0063] The education support system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects sensor data. The sensor data includes, but is not limited to, temperature data, humidity data, heart rate data, and movement data. For example, the collection unit measures the temperature in a classroom using a temperature sensor. The collection unit can also measure the humidity in a classroom using a humidity sensor. The collection unit can also measure the heart rate of a student using a heart rate sensor. The collection unit can also detect the movement of a student using a movement sensor. For example, the temperature sensor measures temperature using a thermistor or RTD (resistance temperature detector) or other technology. The humidity sensor measures humidity using a capacitance or resistance technology. The heart rate sensor measures heart rate using a photoelectric or electrical technology. The movement sensor detects movement using an acceleration sensor or a gyro sensor or other technology. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, a data interpretation method and an algorithm used, but is not limited to, these examples. For example, the analysis unit interprets sensor data using an LLM to grasp the student's current situation. The analysis unit can also evaluate the student's learning progress and level of understanding by referring to past learning records. Furthermore, the analysis unit can estimate the student's emotions using an LLM. For example, the LLM analyzes heart rate fluctuations to determine whether the student is tense or relaxed. The feedback unit provides feedback to an agent or xR based on the analysis results obtained by the analysis unit. Feedback is provided, for example, based on a notification method to the agent or a display method in the xR, but is not limited to such examples. For example, the feedback unit provides feedback of the analysis results to the agent and appropriate advice and guidance to the student. The feedback unit can also display a virtual classroom or virtual teaching materials on the xR to allow the student to progress in learning in a way that is easier for them to understand. As a result, the education support system according to the embodiment can provide personalized education by collecting, analyzing, and providing feedback on sensor data.

[0064] The collection unit can collect sensor data from temperature sensors, humidity sensors, heart rate sensors, and motion sensors. The collection unit, for example, uses a temperature sensor to measure the temperature in the classroom. Temperature sensors include thermistors, RTDs (resistance temperature detectors), thermocouples, etc. For example, thermistors have the characteristic that their resistance changes with temperature. RTDs measure temperature by utilizing the characteristic that metal resistance changes with temperature. Thermocouples measure temperature by utilizing the characteristic that a voltage is generated when a temperature difference occurs at the junction of different metals. The collection unit, for example, uses a humidity sensor to measure the humidity in the classroom. Humidity sensors include capacitive, resistive, and optical types. For example, capacitive humidity sensors have the characteristic that their capacitance changes with humidity. Resistive humidity sensors have the characteristic that their resistance changes with humidity. Optical humidity sensors measure humidity by utilizing the absorption and reflection of light. The collection unit, for example, uses a heart rate sensor to measure the student's heart rate. Heart rate sensors include photoelectric and electrical types. For example, a photoelectric heart rate sensor measures heart rate by utilizing the absorption and reflection of light. An electrical heart rate sensor measures heart rate by detecting the electrical activity of the heart. The collection unit detects student movements using, for example, a motion sensor. Motion sensors include acceleration sensors and gyro sensors. For example, an acceleration sensor has the property of measuring the acceleration of an object. A gyro sensor has the property of measuring the rotation speed of an object. This allows for the collection of a variety of sensor data to more accurately grasp the student's situation.

[0065] The analysis unit can interpret the sensor data and grasp the student's current situation. The analysis unit interprets the sensor data using, for example, the LLM. The LLM has learned large amounts of data and has advanced natural language processing capabilities. For example, the LLM can analyze heart rate fluctuations to determine whether a student is tense or relaxed. The LLM can also analyze movement data to determine whether a student is concentrating. Furthermore, the LLM can analyze temperature and humidity data to evaluate whether the classroom environment is suitable for learning. For example, the LLM can determine that a student's heart rate is high, indicating that they may be tense. The LLM can also determine from movement data that a student is less likely to be moving, indicating that they are concentrating. The LLM can also evaluate whether the classroom environment is comfortable, based on temperature and humidity data. In this way, the analysis unit can grasp the student's current situation by interpreting the sensor data.

[0066] The analysis unit can evaluate the student's learning progress and level of understanding by referring to past learning records. The analysis unit, for example, refers to past learning records. The learning records include test results, assignment submission status, study time, etc. For example, the analysis unit can evaluate the student's level of understanding by referring to test results. The analysis unit can also evaluate the student's learning progress by referring to the assignment submission status. Furthermore, the analysis unit can evaluate the student's study habits by referring to the study time. For example, the analysis unit evaluates the student's level of understanding based on the rate of correct answers on tests. The analysis unit can also evaluate the student's learning progress based on the assignment submission status. Furthermore, the analysis unit can evaluate the student's study habits based on the study time. In this way, the student's learning progress and level of understanding can be evaluated by referring to past learning records.

[0067] The feedback unit feeds back the analysis results to the agent, and is able to provide appropriate advice and guidance to the student. The feedback unit, for example, feeds back the analysis results to the agent. The agent provides appropriate advice and guidance to the student. For example, the feedback unit suggests ways to relax to a student who is feeling nervous. The feedback unit can also provide feedback praising the student's progress in learning to a student who is concentrating. The feedback unit can also provide basic guidance to a student who has a low level of understanding. For example, the feedback unit notifies the agent of the analysis results, and the agent suggests ways to relax to the student. The feedback unit notifies the agent of the analysis results, and the agent provides feedback praising the student's progress in learning. The feedback unit notifies the agent of the analysis results, and the agent provides basic guidance to the student. In this way, by feeding back the analysis results to the agent, appropriate advice and guidance can be provided to the student.

[0068] The feedback unit displays a virtual classroom or virtual teaching materials on the xR platform, allowing students to progress through their studies in a way that is easier to understand. The feedback unit, for example, displays a virtual classroom on the xR platform. The virtual classroom includes 3D models and interactive teaching materials. For example, the feedback unit provides students with a learning environment similar to that of a real classroom through the virtual classroom. The feedback unit can also display virtual teaching materials on the xR platform. The virtual teaching materials include video teaching materials and simulation teaching materials. For example, the feedback unit provides students with learning content in a visually easy-to-understand format through the virtual teaching materials. The feedback unit can also display a virtual classroom or virtual teaching materials on the xR platform, allowing students to progress through their studies interactively. For example, the feedback unit provides an environment in which students can cooperate with other students through the virtual classroom. The feedback unit can also allow students to experience actual experiments and simulations through the virtual teaching materials. As a result, displaying a virtual classroom or virtual teaching materials on the xR platform allows students to progress through their studies in a way that is easier to understand.

[0069] The collection unit can estimate a student's emotions and adjust the frequency of sensor data collection based on the estimated student's emotions. For example, if a student is nervous, the collection unit can increase the frequency of data collection from the heart rate sensor and monitor heart rate fluctuations in real time. Furthermore, if a student is relaxed, the collection unit can decrease the frequency of data collection from the motion sensor and collect only the minimum amount of data necessary. Furthermore, if a student is concentrating, the collection unit can adjust the frequency of data collection from the temperature sensor and humidity sensor to maintain a comfortable learning environment. For example, the collection unit can increase the frequency of data collection from the heart rate sensor to grasp a student's state of tension in real time and take appropriate action. Furthermore, the collection unit can decrease the frequency of data collection from the motion sensor to efficiently collect data from relaxed students. Furthermore, the collection unit can optimize the learning environment for concentrating students by adjusting the frequency of data collection from the temperature sensor and humidity sensor. This allows for more appropriate data collection by adjusting the frequency of sensor data collection based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0070] The collection unit can analyze the student's past sensor data and determine appropriate sensor placement. For example, the collection unit can analyze the student's past heart rate data and determine the most effective position for placing the heart rate sensor. The collection unit can also place the movement sensor in the optimal position in the classroom based on the student's past movement data. The collection unit can also analyze the student's past temperature and humidity data and place the temperature sensor and humidity sensor in the optimal position. For example, the collection unit analyzes the heart rate data and places the heart rate sensor on the student's wrist or chest. The collection unit also places the movement sensor on the ceiling or wall of the classroom based on the movement data. The collection unit also analyzes the temperature and humidity data and places the temperature sensor and humidity sensor in the center or corner of the classroom. In this way, the optimal sensor placement can be determined by analyzing the past sensor data.

[0071] When collecting sensor data, the collection unit can filter the data based on the student's current activity. For example, the collection unit prioritizes collecting data from the heart rate sensor and the motion sensor during class, and prioritizes collecting data from the temperature sensor and the humidity sensor during breaks. The collection unit can also filter the motion sensor data to measure the student's concentration level when the student is studying independently. The collection unit can also filter the heart rate sensor data to measure the student's stress level when the student is participating in a group activity. For example, the collection unit prioritizes collecting data from the heart rate sensor and the motion sensor during class to understand the student's concentration level and tension. The collection unit prioritizes collecting data from the temperature sensor and the humidity sensor during breaks to optimize the classroom environment. The collection unit can measure the student's concentration level by filtering the motion sensor data during self-study. This allows for more relevant data to be collected by filtering sensor data based on the student's current activity.

[0072] When collecting sensor data, the collection unit can select an appropriate collection means depending on the student's input method. For example, if the student uses voice input, the collection unit prioritizes collecting voice sensor data. Furthermore, if the student uses text input, the collection unit can also collect keyboard keystroke data. Furthermore, if the student uses motion input, the collection unit can also prioritize collecting motion sensor data. For example, the collection unit prioritizes collecting voice sensor data from students using voice input to understand the student's spoken content and emotions. Furthermore, the collection unit prioritizes collecting keyboard keystroke data from students using text input to evaluate the student's input speed and accuracy. Furthermore, the collection unit prioritizes collecting motion sensor data from students using motion input to understand the student's movement patterns and concentration levels. This allows for more effective data collection by selecting the optimal collection means depending on the student's input method.

[0073] The collection unit can estimate the student's emotions and prioritize the sensor data to be collected based on the estimated student's emotions. For example, if the student is nervous, the collection unit can prioritize collecting heart rate sensor data. Also, if the student is relaxed, the collection unit can prioritize collecting motion sensor data. Furthermore, if the student is concentrating, the collection unit can prioritize collecting temperature and humidity sensor data. For example, the collection unit prioritizes collecting heart rate sensor data to understand the student's state of tension in real time and take appropriate action. Also, the collection unit prioritizes collecting motion sensor data to understand the behavior patterns of relaxed students. Furthermore, the collection unit prioritizes collecting temperature and humidity sensor data to optimize the learning environment for concentrating students. Thus, by prioritizing the sensor data based on the student's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the student. For example, when a student is in a classroom, the collection unit prioritizes collecting data from the temperature sensor and humidity sensor in the classroom. Furthermore, when a student is in a schoolyard, the collection unit can also prioritize collecting data from the motion sensor. Furthermore, when a student is in a library, the collection unit can also prioritize collecting data from the heart rate sensor. For example, the collection unit optimizes the classroom environment by prioritizedly collecting data from the temperature sensor and humidity sensor of students in the classroom. Furthermore, the collection unit prioritizes collecting data from the motion sensor of students in the schoolyard to understand the student's exercise volume and activity level. Furthermore, the collection unit prioritizes collecting data from the heart rate sensor of students in the library to understand the student's concentration level and stress level. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the student.

[0075] The collection unit can analyze the student's social media activities and collect related data when collecting sensor data. For example, if a student feels stressed because of social media, the collection unit can prioritize collecting heart rate sensor data. Furthermore, if a student is relaxing because of social media, the collection unit can also prioritize collecting motion sensor data. Furthermore, if a student is concentrating on social media, the collection unit can prioritize collecting temperature and humidity sensor data. For example, the collection unit prioritizes collecting heart rate sensor data from a student who feels stressed because of social media to understand the student's stress level. Furthermore, the collection unit prioritizes collecting motion sensor data from a student who is relaxing because of social media to understand the student's state of relaxation. Furthermore, the collection unit prioritizes collecting temperature and humidity sensor data from a student who is concentrating on social media to understand the student's level of concentration. In this way, related data can be collected by analyzing the student's social media activities.

[0076] When collecting sensor data, the collection unit can customize the collection method by reflecting the student's past feedback. For example, if a student has requested collection of heart rate sensor data in the past, the collection unit prioritizes collection of heart rate sensor data. Furthermore, if a student has requested collection of motion sensor data in the past, the collection unit can also prioritize collection of motion sensor data. Furthermore, if a student has requested collection of temperature and humidity sensor data in the past, the collection unit can also prioritize collection of temperature and humidity sensor data. For example, the collection unit prioritizes collection of heart rate sensor data from a student who requested collection of heart rate sensor data to understand the student's heart rate. Furthermore, the collection unit prioritizes collection of motion sensor data from a student who requested collection of motion sensor data to understand the student's motion patterns. Furthermore, the collection unit prioritizes collection of temperature and humidity sensor data from a student who requested collection of temperature and humidity sensor data to optimize the student's learning environment. This allows the collection method to be customized by reflecting the student's past feedback.

[0077] The analysis unit can estimate the student's emotions and adjust the way the analysis is presented based on the estimated student's emotions. For example, if the student is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the student is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the student is concentrating, the analysis unit can provide a summary of the key points. For example, the analysis unit can provide a simple, highly visible analysis result to a nervous student, making it easier for the student to understand the analysis result. Furthermore, the analysis unit can provide a detailed analysis result to a relaxed student, encouraging the student to deeply understand the analysis result. Furthermore, the analysis unit can provide a summary of the key points to a concentrating student, helping the student to study efficiently. This allows the analysis unit to adjust the way the analysis is presented based on the student's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the sensor data. For example, if data from a heart rate sensor is important, the analysis unit performs a detailed analysis. Also, if data from a motion sensor is important, the analysis unit can also perform a detailed analysis. Furthermore, if data from a temperature sensor and a humidity sensor is important, the analysis unit can also perform a detailed analysis. For example, if data from a heart rate sensor is important, the analysis unit performs a detailed analysis using a heart rate variability analysis algorithm. Also, if data from a motion sensor is important, the analysis unit performs a detailed analysis using a motion analysis algorithm. Furthermore, if data from a temperature sensor and a humidity sensor is important, the analysis unit performs a detailed analysis using an environmental analysis algorithm. In this way, by adjusting the level of detail of the analysis based on the importance of the sensor data, more detailed analysis can be performed on more important data.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of sensor data. For example, the analysis unit applies a heart rate variability analysis algorithm to data from a heart rate sensor. The analysis unit can also apply a motion analysis algorithm to data from a motion sensor. The analysis unit can also apply an environment analysis algorithm to data from a temperature sensor and a humidity sensor. For example, the analysis unit applies a heart rate variability analysis algorithm to data from a heart rate sensor to analyze heart rate fluctuations in detail. The analysis unit applies a motion analysis algorithm to data from a motion sensor to analyze students' motion patterns in detail. The analysis unit also applies an environment analysis algorithm to data from a temperature sensor and a humidity sensor to analyze the environment in the classroom in detail. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of sensor data.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. For example, the analysis unit can improve the accuracy of the current heart rate data analysis by referring to the student's past heart rate data analysis results. The analysis unit can also improve the accuracy of the current movement data analysis by referring to the student's past movement data analysis results. Furthermore, the analysis unit can improve the accuracy of the current environmental data analysis by referring to the student's past temperature and humidity data analysis results. For example, the analysis unit adjusts the algorithm for analyzing the current heart rate data based on the past heart rate data analysis results. The analysis unit can also adjust the algorithm for analyzing the current movement data based on the past movement data analysis results. Furthermore, the analysis unit adjusts the algorithm for analyzing the current environmental data based on the past temperature and humidity data analysis results. In this way, the accuracy of the current analysis can be improved by referring to the student's past analysis results.

[0081] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated student's emotions. For example, if the student is nervous, the analysis unit can provide a short and concise analysis result. Furthermore, if the student is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the student is concentrating, the analysis unit can provide a more concise analysis result. For example, the analysis unit can provide a short and concise analysis result to a nervous student, making it easier for the student to understand the analysis result. Furthermore, the analysis unit can provide a detailed analysis result to a relaxed student, encouraging the student to deeply understand the analysis result. Furthermore, the analysis unit can provide a more concise analysis result to a concentrating student, helping the student to study efficiently. Thus, by adjusting the length of the analysis based on the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time when the sensor data was collected. For example, the analysis unit prioritizes analysis of the most recent heart rate data. The analysis unit can also prioritize analysis of the most recent movement data. Furthermore, the analysis unit can also prioritize analysis of the most recent temperature and humidity data. For example, the analysis unit prioritizes analysis of the most recent heart rate data to understand the current heart rate state of the student. The analysis unit prioritizes analysis of the most recent movement data to understand the current movement pattern of the student. Furthermore, the analysis unit prioritizes analysis of the most recent temperature and humidity data to understand the current environment in the classroom. In this way, by determining the priority of analysis based on the time when the sensor data was collected, the most recent data can be analyzed preferentially.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sensor data. For example, if there is a high relevance between heart rate data and movement data, the analysis unit prioritizes analyzing these data. Furthermore, if there is a high relevance between temperature data and humidity data, the analysis unit can also prioritize analyzing these data. For example, if there is a high relevance between heart rate data and movement data, the analysis unit prioritizes analyzing these data to comprehensively grasp the heart rate status and movement patterns of students. Furthermore, if there is a high relevance between temperature data and humidity data, the analysis unit prioritizes analyzing these data to comprehensively grasp the environment in the classroom. Furthermore, if there is a high relevance between heart rate data and temperature data, the analysis unit prioritizes analyzing these data to comprehensively grasp the heart rate status of students and the environment in the classroom. Thus, by adjusting the order of analysis based on the relevance of the sensor data, it is possible to prioritize analyzing more relevant data.

[0084] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. For example, if the student is a beginner, the analysis unit can provide the analysis results while avoiding technical terminology. Also, if the student is an intermediate learner, the analysis unit can provide the analysis results using appropriate technical terminology. Furthermore, if the student is an advanced learner, the analysis unit can provide the analysis results using detailed technical terminology. For example, the analysis unit can provide the analysis results to beginner students while avoiding technical terminology, making the analysis results easier for the student to understand. Also, the analysis unit can provide the analysis results to intermediate students using appropriate technical terminology, encouraging the student to deeply understand the analysis results. Furthermore, the analysis unit can provide the analysis results to advanced students using detailed technical terminology, helping the student deepen their technical knowledge. In this way, by adjusting the use of technical terminology in the analysis according to the student's level of expertise, it is possible to provide analysis results that are easier to understand.

[0085] The feedback unit can estimate the student's emotions and adjust the feedback method based on the estimated student's emotions. For example, if the student is nervous, the feedback unit can suggest ways to relax. Also, if the student is relaxed, the feedback unit can suggest ways to improve concentration. Furthermore, if the student is concentrating, the feedback unit can provide feedback praising the student's learning progress. For example, the feedback unit can help a nervous student relieve tension by suggesting ways to relax. Also, if a relaxed student is concentrating, the feedback unit can encourage the student to concentrate on their studies by suggesting ways to improve concentration. Furthermore, the feedback unit can increase the student's motivation by providing feedback praising the student's learning progress. This allows the provision of more appropriate feedback by adjusting the feedback method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When providing feedback, the feedback unit can select an appropriate feedback method by analyzing the student's past feedback history. For example, the feedback unit prioritizes the use of feedback methods that the student has previously preferred. The feedback unit can also select feedback methods that have been effective for the student in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided. For example, the feedback unit makes it easier for the student to accept feedback by preferentially using feedback methods that the student has previously preferred. Furthermore, the feedback unit enhances the student's learning effectiveness by selecting feedback methods that have been effective in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided, allowing the student to continue learning without feeling stressed. In this way, the optimal feedback method can be selected by analyzing the student's past feedback history.

[0087] The feedback unit can customize the content of the feedback based on the student's current learning situation when providing feedback. For example, if the student has a high level of understanding, the feedback unit can provide feedback with more advanced content. Also, if the student has a low level of understanding, the feedback unit can provide feedback with basic content. Furthermore, if the student's progress is lagging, the feedback unit can provide feedback to encourage progress. For example, the feedback unit can increase the student's motivation to learn by providing feedback with more advanced content to a student with a high level of understanding. Also, the feedback unit can deepen the student's understanding by providing feedback with basic content to a student with a low level of understanding. Furthermore, the feedback unit can improve the student's learning pace by providing feedback to encourage progress to a student who is lagging behind. In this way, more effective feedback can be provided by customizing the content of the feedback based on the student's current learning situation.

[0088] The feedback unit can improve the feedback method by reflecting the student's feedback when providing feedback. For example, if the student has a positive reaction to the feedback, the feedback unit continues that method. Also, if the student has a negative reaction to the feedback, the feedback unit can improve that method. Furthermore, if the student does not react to the feedback, the feedback unit can try a different method. For example, the feedback unit can maintain the student's motivation to learn by continuing the feedback method that has shown a positive reaction. Also, the feedback unit can reduce the student's stress by improving the feedback method that has shown a negative reaction. Furthermore, the feedback unit can find a feedback method that is suitable for the student by changing the feedback method that has shown no reaction. In this way, the feedback method can be improved by reflecting the student's feedback.

[0089] The feedback unit can estimate the student's emotions and prioritize feedback based on the estimated student's emotions. For example, if the student is nervous, the feedback unit prioritizes feedback on how to relax. Furthermore, if the student is relaxed, the feedback unit can also prioritize feedback on how to improve concentration. Furthermore, if the student is concentrating, the feedback unit can prioritize feedback praising the student's progress in learning. For example, the feedback unit can prioritize feedback on how to relax for a nervous student, thereby helping the student to relieve tension. Furthermore, if a student is relaxed, the feedback unit can prioritize feedback on how to improve concentration, thereby encouraging the student to concentrate on their studies. Furthermore, if a student is concentrating, the feedback unit can prioritize feedback praising the student's progress in learning, thereby increasing the student's motivation. Thus, by prioritizing feedback based on the student's emotions, more important feedback can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The feedback unit can select an appropriate feedback method by taking into account the geographical location information of the student when providing feedback. For example, the feedback unit provides direct feedback when the student is in a classroom. The feedback unit can also provide online feedback when the student is at home. Furthermore, the feedback unit can provide feedback via a mobile device when the student is on the move. For example, the feedback unit can provide direct feedback to a student in a classroom, allowing the student to respond immediately. The feedback unit can also provide online feedback to a student at home, helping the student to continue learning at home. Furthermore, the feedback unit can provide feedback via a mobile device to a student on the move, allowing the student to continue learning anywhere. In this way, the optimal feedback method can be selected by taking into account the geographical location information of the student.

[0091] When providing feedback, the feedback unit can analyze the student's social media activity and suggest a means of providing feedback. For example, if the student is active on social media, the feedback unit can provide feedback through social media. Also, if the student is feeling stressed because of social media, the feedback unit can provide feedback on ways to relax. Furthermore, if the student is concentrating on social media, the feedback unit can provide feedback praising the student's progress in learning. For example, the feedback unit makes the student more receptive to feedback by providing feedback through social media to a student who is active on social media. Also, the feedback unit reduces the student's stress by providing feedback on ways to relax to a student who is feeling stressed because of social media. Furthermore, the feedback unit increases the student's motivation by providing feedback praising the student's progress in learning to a student who is concentrating on social media. In this way, by analyzing the student's social media activity, more appropriate means of feedback can be suggested.

[0092] When providing feedback, the feedback unit can customize the feedback method by reflecting the student's past feedback. For example, the feedback unit prioritizes the use of feedback methods that the student has previously preferred. The feedback unit can also select feedback methods that have been effective for the student in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided. For example, the feedback unit makes it easier for the student to accept feedback by preferentially using feedback methods that the student has previously preferred. Furthermore, the feedback unit enhances the student's learning effectiveness by selecting feedback methods that have been effective in the past. Furthermore, the feedback unit can eliminate feedback methods that the student has previously avoided, allowing the student to continue learning without feeling stressed. In this way, the feedback method can be customized by reflecting the student's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data such as temperature, humidity, heart rate, and movement using sensors in the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using LLM to grasp the student's situation. The feedback unit is realized, for example, by the control unit 46A of the smart device 14, and feeds back the analysis results to the agent or xR to provide the student with appropriate advice and guidance. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data such as temperature, humidity, heart rate, and movement using sensors in the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using LLM to grasp the student's situation. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and feeds back the analysis results to an agent or xR to provide the student with appropriate advice and guidance. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data such as temperature, humidity, heart rate, and movement using sensors in the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using LLM to grasp the student's situation. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314, and feeds back the analysis results to an agent or xR to provide the student with appropriate advice and guidance. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data such as temperature, humidity, heart rate, and movement using sensors in the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using LLM to grasp the student's situation. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and feeds back the analysis results to the agent or xR to provide the student with appropriate advice and guidance.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can also refer to weather data when interpreting sensor data. For example, the analysis unit can collect external weather data and predict fluctuations in temperature and humidity in the classroom. The analysis unit can also identify factors that affect students' physical condition and concentration based on the weather data. Furthermore, the analysis unit can use the weather data to make suggestions for optimizing the classroom environment. This allows for more accurate analysis by referring to weather data.

[0095] When collecting sensor data, the collection unit can adjust the placement of sensors based on the individual learning style of each student. For example, sensors that collect visual information can be placed more prominently for students with a visual learning style. Audio sensors can also be placed more prominently for students with an auditory learning style. Furthermore, movement sensors can be placed more prominently for students with a learning style that involves movement. This allows for sensor placement according to each student's learning style, resulting in more effective data collection.

[0096] The analysis unit can use AI pattern recognition technology when interpreting sensor data. For example, the analysis unit can use AI to learn students' behavioral patterns and detect abnormal behavior. The analysis unit can also use AI to analyze students' learning patterns and suggest effective learning methods. Furthermore, the analysis unit can use AI to analyze students' emotional patterns and predict emotional fluctuations. This makes it possible to perform more advanced analysis using AI pattern recognition technology.

[0097] The analysis unit can analyze learning trends when referring to past learning records. For example, the analysis unit can graph the student's learning progress based on past learning records and visually display the trends. The analysis unit can also identify areas in the student's learning that need strengthening based on the learning trends. Furthermore, the analysis unit can propose a learning plan for the student based on the learning trends. In this way, analyzing learning trends can more effectively support student learning.

[0098] The feedback unit can customize the character settings of the agent when feeding back the analysis results to the agent. For example, the feedback unit can change the agent's appearance or voice according to the student's preferences. The feedback unit can also adjust the agent's teaching method according to the student's learning style. Furthermore, the feedback unit can change the agent's facial expression or tone according to the student's emotions. In this way, by customizing the agent's character settings, it is possible to provide more friendly feedback to students.

[0099] The feedback unit can add interactive elements when displaying virtual classrooms or virtual teaching materials on xR. For example, the feedback unit can provide a function for students to collaborate with other students in the virtual classroom to solve assignments. The feedback unit can also incorporate quizzes and mini-games into the virtual teaching materials. Furthermore, the feedback unit can provide a function for students to communicate with teachers in real time in the virtual classroom. This can increase students' motivation to learn by adding interactive elements.

[0100] The collection unit can estimate the student's emotions and adjust the frequency of sensor data collection based on the estimated student's emotions. For example, if the student is nervous, the collection unit can increase the frequency of data collection from the heart rate sensor and monitor heart rate fluctuations in real time. Also, if the student is relaxed, the collection unit can decrease the frequency of data collection from the motion sensor and collect only the minimum amount of data necessary. Furthermore, if the student is concentrating, the collection unit can adjust the frequency of data collection from the temperature sensor and humidity sensor to maintain a comfortable learning environment. This allows for more appropriate data collection by adjusting the frequency of sensor data collection based on the student's emotions.

[0101] When interpreting the sensor data, the analysis unit can estimate the student's emotions and adjust the way the analysis is presented based on the estimated student's emotions. For example, if the student is nervous, the analysis unit can provide a simple, highly visible analysis result. If the student is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the student is concentrating, the analysis unit can also provide a summary analysis result. This makes it possible to provide more appropriate analysis results by adjusting the way the analysis is presented based on the student's emotions.

[0102] The feedback unit can estimate the student's emotions and adjust the feedback method based on the estimated student's emotions. For example, if the student is tense, the feedback unit can suggest ways to relax. Also, if the student is relaxed, the feedback unit can suggest ways to improve concentration. Furthermore, if the student is concentrating, the feedback unit can provide feedback praising the student's progress in learning. In this way, by adjusting the feedback method based on the student's emotions, it is possible to provide more appropriate feedback.

[0103] The feedback unit can estimate the student's emotions and determine the priority of feedback based on the estimated student's emotions. For example, if the student is tense, the feedback unit can prioritize feedback on how to relax. Also, if the student is relaxed, the feedback unit can prioritize feedback on how to improve concentration. Furthermore, if the student is concentrating, the feedback unit can prioritize feedback praising the student's learning progress. In this way, by determining the priority of feedback based on the student's emotions, it is possible to provide more important feedback preferentially.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects sensor data. The sensor data includes temperature data, humidity data, heart rate data, and movement data. For example, the collection unit measures the temperature in the classroom using a temperature sensor and the humidity in the classroom using a humidity sensor. It also measures the students' heart rates using a heart rate sensor and detects the students' movements using a movement sensor. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is based on the data interpretation method and algorithm used. For example, the analysis unit uses LLM to interpret sensor data and understand the student's current situation. It can also refer to past learning records to evaluate the student's learning progress and level of understanding, and even estimate the student's emotions. Step 3: The feedback unit provides feedback to the agent and xR based on the analysis results obtained by the analysis unit. For example, the feedback unit may provide the agent with feedback on the analysis results and provide appropriate advice and guidance to the student. It may also be possible to display virtual classrooms and virtual teaching materials on the xR screen, allowing students to progress through their studies in a way that is easier for them to understand.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0107] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and 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.

[0113] 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.

[0114] 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 audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] 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 carried out in a secure state.

[0117] Fig. 4 shows an example of the 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.

[0118] 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.

[0119] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0123] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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, RAM 30, and 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.

[0129] 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.

[0130] 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 audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] 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 carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0134] 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.

[0135] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0137] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0139] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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, RAM 30, and 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.

[0145] 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.

[0146] 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 audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] 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 carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0150] 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.

[0151] 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.

[0152] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0154] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] 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 voice indicating a user input regarding 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.

[0156] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] 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.

[0167] 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 USB (Universal Serial Bus) 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.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0172] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 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. a collection unit that collects sensor data; an analysis unit that analyzes the data collected by the collection unit; a feedback unit that provides feedback to an agent or xR based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit Collects sensor data from temperature, humidity, heart rate, and motion sensors 2. The system of claim 1.

3. The analysis unit Interpreting sensor data to understand students' current situation 2. The system of claim 1.

4. The analysis unit Refer to past learning records to assess students' learning progress and understanding 2. The system of claim 1.

5. The feedback unit The analysis results are fed back to the agent, and appropriate advice and guidance is given to the student.

2. The system of claim 1.

6. The feedback unit Displaying virtual classrooms or virtual teaching materials on xR allows students to learn in a way that is easier to understand.

2. The system of claim 1.

7. The collecting unit Estimate the student's emotions and adjust the frequency of sensor data collection based on the estimated student emotions.

2. The system of claim 1.

8. The collecting unit Analyze students' past sensor data and determine appropriate sensor placement 2. The system of claim 1.

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