system

Abstract

The system according to this embodiment aims to comprehensively grasp students' learning progress, level of understanding, and mental health status, and to provide individualized instruction. [Solution] The system according to the embodiment comprises an analysis unit, a guidance unit, a dashboard generation unit, and a mental health analysis unit. The analysis unit analyzes the student's learning progress or level of understanding. The guidance unit provides individualized instruction based on the data analyzed by the analysis unit. The dashboard generation unit visually displays the progress of the lesson or learning outcomes. The mental health analysis unit analyzes the student's life log to understand their mental health status.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to comprehensively grasp the learning progress, understanding level, and mental health status of students and provide individual guidance, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively grasp the learning progress, understanding level, and mental health status of students and provide individual guidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a guidance unit, a dashboard generation unit, and a mental health analysis unit. The analysis unit analyzes students' learning progress or level of understanding. The guidance unit provides individualized instruction based on the data analyzed by the analysis unit. The dashboard generation unit visually displays the progress of the lesson or learning outcomes. The mental health analysis unit analyzes students' life logs to understand their mental health status. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively grasp students' learning progress, level of understanding, and mental health status, and provide individualized instruction. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 3'4. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The comprehensive teacher support system according to an embodiment of the present invention is a system that uses generative AI to support children's learning in school education. This system analyzes each student's learning progress and level of understanding in real time and provides individually optimized instruction. It also analyzes the progress of the lesson and the students' learning outcomes and automatically generates a dashboard for teachers. Furthermore, it analyzes students' life logs to understand their mental health status. For example, the generative AI analyzes students' answer data to identify areas where understanding is insufficient and provides additional teaching materials and practice problems for those areas. This enables individualized instruction tailored to each student. Next, the generative AI analyzes the progress of the lesson and the students' learning outcomes and automatically generates a dashboard for teachers. For example, it analyzes students' test results and assignment submission status and visually displays the overall level of understanding and progress of the class. This allows teachers to grasp the effectiveness of the lesson at a glance and quickly identify necessary improvements. Furthermore, the generative AI analyzes students' life logs to understand their mental health status. For example, it analyzes students' daily actions and statements to detect signs of stress and anxiety. The system supports students' mental health by issuing alerts to encourage consultation with specialists as needed. These features provide a comprehensive teacher support service that combines study logs and life logs to support children's learning. As a result, the comprehensive teacher support system analyzes students' learning progress and understanding, provides individualized instruction, visually displays the progress of lessons and learning outcomes, and allows teachers to understand the students' mental health status.

[0029] The comprehensive teacher support system according to this embodiment comprises an analysis unit, an instruction unit, a dashboard generation unit, and a mental health analysis unit. The analysis unit analyzes students' learning progress or level of understanding. For example, the analysis unit analyzes students' answer data and identifies areas where understanding is insufficient. The analysis unit can also use a generating AI to analyze students' answer data and identify areas where understanding is insufficient. The instruction unit provides individualized instruction based on the data analyzed by the analysis unit. For example, the instruction unit provides additional teaching materials or practice problems for areas where understanding is insufficient, as identified by the analysis unit. The instruction unit can also use a generating AI to provide additional teaching materials or practice problems for areas where understanding is insufficient, as identified by the analysis unit. The dashboard generation unit visually displays the progress of the lesson or learning outcomes. For example, the dashboard generation unit analyzes students' test results and assignment submission status and visually displays the overall understanding and progress of the class. The dashboard generation unit can also use a generating AI to analyze students' test results and assignment submission status and visually display the overall understanding and progress of the class. The Mental Health Analysis Department analyzes students' life logs to understand their mental health status. For example, the Mental Health Analysis Department analyzes students' daily behavior and statements to detect signs of stress and anxiety. The Mental Health Analysis Department can also use generative AI to analyze students' daily behavior and statements and detect signs of stress and anxiety. As a result, the comprehensive teacher support system according to this embodiment can analyze students' learning progress and understanding, provide individualized instruction, visually display the progress of lessons and learning outcomes, and understand students' mental health status.

[0030] The analysis unit can analyze students' answer data and identify areas where understanding is insufficient. For example, the analysis unit can analyze students' answer data and identify areas where understanding is insufficient. The analysis unit can also use a generative AI to analyze students' answer data and identify areas where understanding is insufficient. This makes individualized instruction more effective by analyzing students' answer data and identifying areas where understanding is insufficient. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input students' answer data into a generative AI and have the generative AI identify areas where understanding is insufficient.

[0031] The instructional unit can provide additional teaching materials and practice problems for areas where understanding is insufficient, as identified by the analysis unit. For example, the instructional unit can provide additional teaching materials and practice problems for areas where understanding is insufficient, as identified by the analysis unit. The instructional unit can also use generative AI to provide additional teaching materials and practice problems for areas where understanding is insufficient. This improves students' understanding by providing additional teaching materials and practice problems for areas where understanding is insufficient. Some or all of the above processing in the instructional unit may be performed using generative AI or not. For example, the instructional unit can have the generative AI execute additional teaching materials and practice problems for areas where understanding is insufficient, as identified by the generative AI.

[0032] The dashboard generation unit can analyze students' test results or assignment submission status and visually display the overall understanding or progress of the class. For example, the dashboard generation unit can analyze students' test results or assignment submission status and visually display the overall understanding or progress of the class. The dashboard generation unit can also use a generation AI to analyze students' test results or assignment submission status and visually display the overall understanding or progress of the class. This allows teachers to quickly grasp the effectiveness of their lessons and identify necessary improvements by visually displaying the overall understanding or progress of the class. Some or all of the above-described processes in the dashboard generation unit may be performed using a generation AI, or they may not. For example, the dashboard generation unit can generate a dashboard based on data analyzed by a generation AI and display it visually.

[0033] The Mental Health Analysis Department can analyze students' daily behavior and statements to detect signs of stress or anxiety. For example, the Mental Health Analysis Department can analyze students' daily behavior and statements to detect signs of stress or anxiety. The Mental Health Analysis Department can also use generative AI to analyze students' daily behavior and statements to detect signs of stress or anxiety. This allows the department to support students' mental health by analyzing their daily behavior and statements and detecting signs of stress and anxiety. Some or all of the above-described processes in the Mental Health Analysis Department may be performed using generative AI, or not. For example, the Mental Health Analysis Department can detect signs of stress or anxiety based on data analyzed by generative AI and issue alerts prompting consultation with a professional as needed.

[0034] The Mental Health Analysis Department can issue alerts prompting consultation with a professional as needed. For example, if it detects signs of stress or anxiety, it will issue an alert prompting consultation with a professional. The Mental Health Analysis Department can also use generative AI to detect signs of stress or anxiety and issue alerts prompting consultation with a professional. This allows for more effective support of students' mental health by issuing alerts prompting consultation with a professional. Some or all of the above processing in the Mental Health Analysis Department may be performed using generative AI or not. For example, the Mental Health Analysis Department can support students' mental health by issuing alerts based on data analyzed by generative AI.

[0035] The analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. The analysis unit can also use generative AI to analyze a student's past learning history and select the optimal analysis algorithm. This allows the optimal analysis algorithm to be selected by analyzing a student's past learning history. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can select the optimal analysis algorithm based on past learning history data analyzed by generative AI.

[0036] The analysis unit can filter learning progress based on the student's current learning environment and time of day. For example, the analysis unit can filter learning progress based on the student's current learning environment and time of day. The analysis unit can also use generative AI to filter learning progress based on the student's current learning environment and time of day. This allows for more appropriate analysis by filtering based on the student's current learning environment and time of day. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can filter based on learning environment and time of day data analyzed by generative AI.

[0037] The analysis unit can prioritize the analysis of highly relevant data by considering the students' geographical location information when analyzing learning progress. For example, the analysis unit prioritizes the analysis of highly relevant data by considering the students' geographical location information when analyzing learning progress. The analysis unit can also use generative AI to prioritize the analysis of highly relevant data by considering the students' geographical location information when analyzing learning progress. This enables more appropriate analysis by prioritizing the analysis of highly relevant data by considering the students' geographical location information. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can prioritize the analysis of highly relevant data based on geographical location information analyzed by generative AI.

[0038] The analysis unit can analyze students' social media activities and related data when analyzing learning progress. For example, the analysis unit can analyze students' social media activities and related data when analyzing learning progress. The analysis unit can also use generative AI to analyze students' social media activities and related data when analyzing learning progress. This allows for the priority analysis of related data by analyzing students' social media activities. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can prioritize the analysis of related data based on social media activity data analyzed by generative AI.

[0039] The instructional department can adjust the level of detail in instruction based on the importance of the learning content. For example, the instructional department can adjust the level of detail in instruction based on the importance of the learning content. The instructional department can also use generative AI to adjust the level of detail in instruction based on the importance of the learning content. By adjusting the level of detail in instruction based on the importance of the learning content, more effective instruction becomes possible. Some or all of the above processing in the instructional department may be performed using generative AI, or it may be performed without using generative AI. For example, the instructional department can adjust the level of detail in instruction based on learning content importance data analyzed by generative AI.

[0040] The instruction unit can apply different instructional algorithms depending on the category of learning content during instruction. For example, the instruction unit can apply different instructional algorithms depending on the category of learning content during instruction. The instruction unit can also use generative AI to apply different instructional algorithms depending on the category of learning content during instruction. This makes it possible to provide more appropriate instruction by applying different instructional algorithms depending on the category of learning content. Some or all of the above processing in the instruction unit may be performed using generative AI or not. For example, the instruction unit can apply different instructional algorithms based on the category data of learning content analyzed by generative AI.

[0041] The instruction department can determine the priority of instruction based on the submission timing of learning materials during instruction. For example, the instruction department can determine the priority of instruction based on the submission timing of learning materials during instruction. The instruction department can also use generative AI to determine the priority of instruction based on the submission timing of learning materials during instruction. This makes instruction more effective by determining the priority of instruction based on the submission timing of learning materials. Some or all of the above processing in the instruction department may be performed using generative AI or not. For example, the instruction department can determine the priority of instruction based on submission timing data analyzed by generative AI.

[0042] The instruction unit can adjust the order of instruction based on the relevance of the learning content during instruction. For example, the instruction unit can adjust the order of instruction based on the relevance of the learning content during instruction. The instruction unit can also use generative AI to adjust the order of instruction based on the relevance of the learning content during instruction. This makes it possible to provide more effective instruction by adjusting the order of instruction based on the relevance of the learning content. Some or all of the above processing in the instruction unit may be performed using generative AI or not. For example, the instruction unit can adjust the order of instruction based on the relevance data of the learning content analyzed by generative AI.

[0043] The dashboard generation unit can adjust the level of detail displayed based on the importance of the learning outcomes when generating the dashboard. For example, the dashboard generation unit adjusts the level of detail displayed based on the importance of the learning outcomes when generating the dashboard. The dashboard generation unit can also use a generation AI to adjust the level of detail displayed based on the importance of the learning outcomes when generating the dashboard. This makes it possible to provide more effective information by adjusting the level of detail displayed based on the importance of the learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the dashboard generation unit can adjust the level of detail displayed based on the importance data of the learning outcomes analyzed by the generation AI.

[0044] The dashboard generation unit can apply different display algorithms depending on the category of learning outcomes when generating a dashboard. For example, the dashboard generation unit applies different display algorithms depending on the category of learning outcomes when generating a dashboard. The dashboard generation unit can also use a generation AI to apply different display algorithms depending on the category of learning outcomes when generating a dashboard. This makes it possible to provide more appropriate information by applying different display algorithms depending on the category of learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can apply different display algorithms based on the category data of learning outcomes analyzed by the generation AI.

[0045] The dashboard generation unit can determine the display priority based on the submission timing of learning outcomes when generating the dashboard. For example, the dashboard generation unit can determine the display priority based on the submission timing of learning outcomes when generating the dashboard. The dashboard generation unit can also use a generation AI to determine the display priority based on the submission timing of learning outcomes when generating the dashboard. This makes it possible to provide more effective information by determining the display priority based on the submission timing of learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can determine the display priority based on submission timing data analyzed by a generation AI.

[0046] The dashboard generation unit can adjust the display order based on the relevance of learning outcomes when generating the dashboard. For example, the dashboard generation unit adjusts the display order based on the relevance of learning outcomes when generating the dashboard. The dashboard generation unit can also use a generation AI to adjust the display order based on the relevance of learning outcomes when generating the dashboard. This makes it possible to provide more appropriate information by adjusting the display order based on the relevance of learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can adjust the display order based on the relevance data of learning outcomes analyzed by a generation AI.

[0047] The mental health analysis unit can analyze a student's past behavioral history and select the optimal analysis algorithm during mental health analysis. For example, the mental health analysis unit can analyze a student's past behavioral history and select the optimal analysis algorithm during mental health analysis. The mental health analysis unit can also use generative AI to analyze a student's past behavioral history and select the optimal analysis algorithm during mental health analysis. This allows for the selection of the optimal analysis algorithm by analyzing a student's past behavioral history. Some or all of the above-described processes in the mental health analysis unit may be performed using generative AI or not. For example, the mental health analysis unit can select the optimal analysis algorithm based on past behavioral history data analyzed by generative AI.

[0048] The mental health analysis unit can perform filtering based on the student's current living situation and time of day during mental health analysis. For example, the mental health analysis unit can perform filtering based on the student's current living situation and time of day during mental health analysis. The mental health analysis unit can also use generative AI to perform filtering based on the student's current living situation and time of day during mental health analysis. This allows for more appropriate analysis by filtering based on the student's current living situation and time of day. Some or all of the above processing in the mental health analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the mental health analysis unit can perform filtering based on living situation and time of day data analyzed by generative AI.

[0049] The mental health analysis unit can prioritize the analysis of highly relevant data by considering the geographical location information of students during mental health analysis. For example, the mental health analysis unit prioritizes the analysis of highly relevant data by considering the geographical location information of students during mental health analysis. The mental health analysis unit can also use generative AI to prioritize the analysis of highly relevant data by considering the geographical location information of students during mental health analysis. This enables more appropriate analysis by prioritizing the analysis of highly relevant data by considering the geographical location information of students. Some or all of the above processing in the mental health analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the mental health analysis unit can prioritize the analysis of highly relevant data based on geographical location information analyzed by generative AI.

[0050] The Mental Health Analysis Department can analyze students' social media activity and related data during mental health analysis. For example, the Mental Health Analysis Department can analyze students' social media activity and related data during mental health analysis. The Mental Health Analysis Department can also use generative AI to analyze students' social media activity and related data during mental health analysis. This allows for the priority analysis of relevant data by analyzing students' social media activity. Some or all of the above processing in the Mental Health Analysis Department may be performed using generative AI or not. For example, the Mental Health Analysis Department can prioritize the analysis of relevant data based on social media activity data analyzed by generative AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The analysis unit can adjust its analysis method when analyzing students' learning progress, taking into account the difficulty level of the learning content. For example, it can perform detailed analysis on difficult problems and simplified analysis on easy problems. Furthermore, the analysis unit can prioritize the analysis results based on the difficulty level of the learning content. This allows for more effective individualized instruction by prioritizing the provision of analysis results for difficult problems. In addition, the analysis unit can select an analysis algorithm according to the difficulty level of the learning content. For example, it can apply an advanced analysis algorithm to difficult problems and a simpler one to easy problems.

[0053] The instruction department can adjust teaching methods based on students' learning styles. For example, students who prefer visual learning can be provided with materials that heavily utilize diagrams and graphs, while students who prefer auditory learning can be provided with materials that include audio explanations. The instruction department can also adjust the pace of instruction according to students' learning styles. This enables optimal instruction tailored to each individual student. Furthermore, the instruction department can select the format of feedback based on students' learning styles. For example, students who prefer visual learning can be provided with visual feedback, while students who prefer auditory learning can be provided with audio feedback.

[0054] The dashboard generation unit can adjust how the dashboard is displayed based on the teacher's teaching style. For example, it can provide a dashboard with many graphs and charts for teachers who prioritize visual information, and a detailed text report for teachers who prioritize text information. The dashboard generation unit can also determine the priority of the information displayed according to the teacher's teaching style. This allows teachers to quickly grasp the information they need most. Furthermore, the dashboard generation unit can adjust the dashboard layout based on the teacher's teaching style. For example, it can center the dashboard with large graphs and charts for teachers who prioritize visual information, and center the dashboard with detailed text reports for teachers who prioritize text information.

[0055] The Mental Health Analysis Department can adjust its mental health analysis methods to take into account students' physical health status. For example, the department can analyze students' sleep patterns and exercise levels and evaluate their mental health status based on this data. The Mental Health Analysis Department can also prioritize analysis results based on students' physical health status. This allows for analysis that considers the impact of physical health on mental health. Furthermore, the Mental Health Analysis Department can select the format of feedback according to students' physical health status. For example, it can provide detailed feedback to students with good physical health and simplified feedback to students with poor health.

[0056] The analysis unit can adjust its analysis method when analyzing students' learning progress, taking into account the relevance of the learning content. For example, the analysis unit can perform a detailed analysis for highly relevant learning content and a simplified analysis for less relevant content. Furthermore, the analysis unit can prioritize the analysis results based on the relevance of the learning content. This allows for more effective individualized instruction by prioritizing the provision of analysis results for highly relevant learning content. In addition, the analysis unit can select an analysis algorithm according to the relevance of the learning content. For example, it can apply a sophisticated analysis algorithm to highly relevant learning content and a simpler one to less relevant content.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis unit analyzes the students' learning progress or level of understanding. For example, it analyzes the students' answer data to identify areas where understanding is insufficient. Generative AI can also be used to analyze the students' answer data and identify areas where understanding is insufficient. Step 2: The instruction department provides individualized instruction based on the data analyzed by the analysis department. For example, it provides additional learning materials and practice problems for areas where understanding is insufficient, as identified by the analysis department. Generative AI can also be used to provide additional learning materials and practice problems for areas where understanding is insufficient, as identified by the analysis department. Step 3: The dashboard generation unit visually displays the progress of the lesson or learning outcomes. For example, it analyzes students' test results and assignment submission status to visually display the overall understanding and progress of the class. It is also possible to use the generation AI to analyze students' test results and assignment submission status to visually display the overall understanding and progress of the class. Step 4: The Mental Health Analysis Department analyzes students' life logs to understand their mental health status. For example, it analyzes students' daily behavior and statements to detect signs of stress and anxiety. Generative AI can also be used to analyze students' daily behavior and statements and detect signs of stress and anxiety.

[0059] (Example of form 2) The comprehensive teacher support system according to an embodiment of the present invention is a system that uses generative AI to support children's learning in school education. This system analyzes each student's learning progress and level of understanding in real time and provides individually optimized instruction. It also analyzes the progress of the lesson and the students' learning outcomes and automatically generates a dashboard for teachers. Furthermore, it analyzes students' life logs to understand their mental health status. For example, the generative AI analyzes students' answer data to identify areas where understanding is insufficient and provides additional teaching materials and practice problems for those areas. This enables individualized instruction tailored to each student. Next, the generative AI analyzes the progress of the lesson and the students' learning outcomes and automatically generates a dashboard for teachers. For example, it analyzes students' test results and assignment submission status and visually displays the overall level of understanding and progress of the class. This allows teachers to grasp the effectiveness of the lesson at a glance and quickly identify necessary improvements. Furthermore, the generative AI analyzes students' life logs to understand their mental health status. For example, it analyzes students' daily actions and statements to detect signs of stress and anxiety. The system supports students' mental health by issuing alerts to encourage consultation with specialists as needed. These features provide a comprehensive teacher support service that combines study logs and life logs to support children's learning. As a result, the comprehensive teacher support system analyzes students' learning progress and understanding, provides individualized instruction, visually displays the progress of lessons and learning outcomes, and allows teachers to understand the students' mental health status.

[0060] The comprehensive teacher support system according to this embodiment comprises an analysis unit, an instruction unit, a dashboard generation unit, and a mental health analysis unit. The analysis unit analyzes students' learning progress or level of understanding. For example, the analysis unit analyzes students' answer data and identifies areas where understanding is insufficient. The analysis unit can also use a generating AI to analyze students' answer data and identify areas where understanding is insufficient. The instruction unit provides individualized instruction based on the data analyzed by the analysis unit. For example, the instruction unit provides additional teaching materials or practice problems for areas where understanding is insufficient, as identified by the analysis unit. The instruction unit can also use a generating AI to provide additional teaching materials or practice problems for areas where understanding is insufficient, as identified by the analysis unit. The dashboard generation unit visually displays the progress of the lesson or learning outcomes. For example, the dashboard generation unit analyzes students' test results and assignment submission status and visually displays the overall understanding and progress of the class. The dashboard generation unit can also use a generating AI to analyze students' test results and assignment submission status and visually display the overall understanding and progress of the class. The Mental Health Analysis Department analyzes students' life logs to understand their mental health status. For example, the Mental Health Analysis Department analyzes students' daily behavior and statements to detect signs of stress and anxiety. The Mental Health Analysis Department can also use generative AI to analyze students' daily behavior and statements and detect signs of stress and anxiety. As a result, the comprehensive teacher support system according to this embodiment can analyze students' learning progress and understanding, provide individualized instruction, visually display the progress of lessons and learning outcomes, and understand students' mental health status.

[0061] The analysis unit can analyze students' answer data and identify areas where understanding is insufficient. For example, the analysis unit can analyze students' answer data and identify areas where understanding is insufficient. The analysis unit can also use a generative AI to analyze students' answer data and identify areas where understanding is insufficient. This makes individualized instruction more effective by analyzing students' answer data and identifying areas where understanding is insufficient. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input students' answer data into a generative AI and have the generative AI identify areas where understanding is insufficient.

[0062] The instructional unit can provide additional teaching materials and practice problems for areas where understanding is insufficient, as identified by the analysis unit. For example, the instructional unit can provide additional teaching materials and practice problems for areas where understanding is insufficient, as identified by the analysis unit. The instructional unit can also use generative AI to provide additional teaching materials and practice problems for areas where understanding is insufficient. This improves students' understanding by providing additional teaching materials and practice problems for areas where understanding is insufficient. Some or all of the above processing in the instructional unit may be performed using generative AI or not. For example, the instructional unit can have the generative AI execute additional teaching materials and practice problems for areas where understanding is insufficient, as identified by the generative AI.

[0063] The dashboard generation unit can analyze students' test results or assignment submission status and visually display the overall understanding or progress of the class. For example, the dashboard generation unit can analyze students' test results or assignment submission status and visually display the overall understanding or progress of the class. The dashboard generation unit can also use a generation AI to analyze students' test results or assignment submission status and visually display the overall understanding or progress of the class. This allows teachers to quickly grasp the effectiveness of their lessons and identify necessary improvements by visually displaying the overall understanding or progress of the class. Some or all of the above-described processes in the dashboard generation unit may be performed using a generation AI, or they may not. For example, the dashboard generation unit can generate a dashboard based on data analyzed by a generation AI and display it visually.

[0064] The Mental Health Analysis Department can analyze students' daily behavior and statements to detect signs of stress or anxiety. For example, the Mental Health Analysis Department can analyze students' daily behavior and statements to detect signs of stress or anxiety. The Mental Health Analysis Department can also use generative AI to analyze students' daily behavior and statements to detect signs of stress or anxiety. This allows the department to support students' mental health by analyzing their daily behavior and statements and detecting signs of stress and anxiety. Some or all of the above-described processes in the Mental Health Analysis Department may be performed using generative AI, or not. For example, the Mental Health Analysis Department can detect signs of stress or anxiety based on data analyzed by generative AI and issue alerts prompting consultation with a professional as needed.

[0065] The Mental Health Analysis Department can issue alerts prompting consultation with a professional as needed. For example, if it detects signs of stress or anxiety, it will issue an alert prompting consultation with a professional. The Mental Health Analysis Department can also use generative AI to detect signs of stress or anxiety and issue alerts prompting consultation with a professional. This allows for more effective support of students' mental health by issuing alerts prompting consultation with a professional. Some or all of the above processing in the Mental Health Analysis Department may be performed using generative AI or not. For example, the Mental Health Analysis Department can support students' mental health by issuing alerts based on data analyzed by generative AI.

[0066] The analysis unit can estimate students' emotions and adjust the learning progress analysis method based on the estimated emotions. For example, the analysis unit can estimate students' emotions and adjust the learning progress analysis method based on the estimated emotions. The analysis unit can also use generative AI to estimate students' emotions and adjust the learning progress analysis method based on the estimated emotions. This allows for more appropriate analysis by adjusting the learning progress analysis method based on students' emotions. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can adjust the learning progress analysis method based on student emotion data estimated by generative AI.

[0067] The analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. The analysis unit can also use generative AI to analyze a student's past learning history and select the optimal analysis algorithm. This allows the optimal analysis algorithm to be selected by analyzing a student's past learning history. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can select the optimal analysis algorithm based on past learning history data analyzed by generative AI.

[0068] The analysis unit can filter learning progress based on the student's current learning environment and time of day. For example, the analysis unit can filter learning progress based on the student's current learning environment and time of day. The analysis unit can also use generative AI to filter learning progress based on the student's current learning environment and time of day. This allows for more appropriate analysis by filtering based on the student's current learning environment and time of day. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can filter based on learning environment and time of day data analyzed by generative AI.

[0069] The analysis unit can estimate students' emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit can estimate students' emotions and determine the priority of analysis results based on the estimated emotions. The analysis unit can also use generative AI to estimate students' emotions and determine the priority of analysis results based on the estimated emotions. This allows for the provision of more appropriate analysis results by determining the priority of analysis results based on students' emotions. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can determine the priority of analysis results based on student emotion data estimated by generative AI.

[0070] The analysis unit can prioritize the analysis of highly relevant data by considering the students' geographical location information when analyzing learning progress. For example, the analysis unit prioritizes the analysis of highly relevant data by considering the students' geographical location information when analyzing learning progress. The analysis unit can also use generative AI to prioritize the analysis of highly relevant data by considering the students' geographical location information when analyzing learning progress. This enables more appropriate analysis by prioritizing the analysis of highly relevant data by considering the students' geographical location information. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can prioritize the analysis of highly relevant data based on geographical location information analyzed by generative AI.

[0071] The analysis unit can analyze students' social media activities and related data when analyzing learning progress. For example, the analysis unit can analyze students' social media activities and related data when analyzing learning progress. The analysis unit can also use generative AI to analyze students' social media activities and related data when analyzing learning progress. This allows for the priority analysis of related data by analyzing students' social media activities. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can prioritize the analysis of related data based on social media activity data analyzed by generative AI.

[0072] The instruction department can estimate students' emotions and adjust the way instruction is delivered based on those estimated emotions. For example, the instruction department can estimate students' emotions and adjust the way instruction is delivered based on those estimated emotions. The instruction department can also use generative AI to estimate students' emotions and adjust the way instruction is delivered based on those estimated emotions. This allows for more appropriate instruction by adjusting the way instruction is delivered based on students' emotions. Some or all of the above processing in the instruction department may be performed using generative AI or not. For example, the instruction department can adjust the way instruction is delivered based on student emotion data estimated by generative AI.

[0073] The instructional department can adjust the level of detail in instruction based on the importance of the learning content. For example, the instructional department can adjust the level of detail in instruction based on the importance of the learning content. The instructional department can also use generative AI to adjust the level of detail in instruction based on the importance of the learning content. By adjusting the level of detail in instruction based on the importance of the learning content, more effective instruction becomes possible. Some or all of the above processing in the instructional department may be performed using generative AI, or it may be performed without using generative AI. For example, the instructional department can adjust the level of detail in instruction based on learning content importance data analyzed by generative AI.

[0074] The instruction unit can apply different instructional algorithms depending on the category of learning content during instruction. For example, the instruction unit can apply different instructional algorithms depending on the category of learning content during instruction. The instruction unit can also use generative AI to apply different instructional algorithms depending on the category of learning content during instruction. This makes it possible to provide more appropriate instruction by applying different instructional algorithms depending on the category of learning content. Some or all of the above processing in the instruction unit may be performed using generative AI or not. For example, the instruction unit can apply different instructional algorithms based on the category data of learning content analyzed by generative AI.

[0075] The instruction department can estimate students' emotions and adjust the length of instruction based on the estimated emotions. For example, the instruction department can estimate students' emotions and adjust the length of instruction based on the estimated emotions. The instruction department can also use generative AI to estimate students' emotions and adjust the length of instruction based on the estimated emotions. This allows for more appropriate instruction by adjusting the length of instruction based on students' emotions. Some or all of the above processing in the instruction department may be performed using generative AI or not. For example, the instruction department can adjust the length of instruction based on student emotion data estimated by generative AI.

[0076] The instruction department can determine the priority of instruction based on the submission timing of learning materials during instruction. For example, the instruction department can determine the priority of instruction based on the submission timing of learning materials during instruction. The instruction department can also use generative AI to determine the priority of instruction based on the submission timing of learning materials during instruction. This makes instruction more effective by determining the priority of instruction based on the submission timing of learning materials. Some or all of the above processing in the instruction department may be performed using generative AI or not. For example, the instruction department can determine the priority of instruction based on submission timing data analyzed by generative AI.

[0077] The instruction unit can adjust the order of instruction based on the relevance of the learning content during instruction. For example, the instruction unit can adjust the order of instruction based on the relevance of the learning content during instruction. The instruction unit can also use generative AI to adjust the order of instruction based on the relevance of the learning content during instruction. This makes it possible to provide more effective instruction by adjusting the order of instruction based on the relevance of the learning content. Some or all of the above processing in the instruction unit may be performed using generative AI or not. For example, the instruction unit can adjust the order of instruction based on the relevance data of the learning content analyzed by generative AI.

[0078] The dashboard generation unit can estimate students' emotions and adjust the dashboard display method based on the estimated emotions. For example, the dashboard generation unit can estimate students' emotions and adjust the dashboard display method based on the estimated emotions. The dashboard generation unit can also use a generation AI to estimate students' emotions and adjust the dashboard display method based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the dashboard display method based on students' emotions. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can adjust the dashboard display method based on student emotion data estimated by a generation AI.

[0079] The dashboard generation unit can adjust the level of detail displayed based on the importance of the learning outcomes when generating the dashboard. For example, the dashboard generation unit adjusts the level of detail displayed based on the importance of the learning outcomes when generating the dashboard. The dashboard generation unit can also use a generation AI to adjust the level of detail displayed based on the importance of the learning outcomes when generating the dashboard. This makes it possible to provide more effective information by adjusting the level of detail displayed based on the importance of the learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the dashboard generation unit can adjust the level of detail displayed based on the importance data of the learning outcomes analyzed by the generation AI.

[0080] The dashboard generation unit can apply different display algorithms depending on the category of learning outcomes when generating a dashboard. For example, the dashboard generation unit applies different display algorithms depending on the category of learning outcomes when generating a dashboard. The dashboard generation unit can also use a generation AI to apply different display algorithms depending on the category of learning outcomes when generating a dashboard. This makes it possible to provide more appropriate information by applying different display algorithms depending on the category of learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can apply different display algorithms based on the category data of learning outcomes analyzed by the generation AI.

[0081] The dashboard generation unit can estimate students' emotions and adjust the display order of the dashboard based on the estimated emotions. For example, the dashboard generation unit estimates students' emotions and adjusts the display order of the dashboard based on the estimated emotions. The dashboard generation unit can also use a generation AI to estimate students' emotions and adjust the display order of the dashboard based on the estimated emotions. This makes it possible to provide more appropriate information by adjusting the display order of the dashboard based on students' emotions. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can adjust the display order of the dashboard based on student emotion data estimated by a generation AI.

[0082] The dashboard generation unit can determine the display priority based on the submission timing of learning outcomes when generating the dashboard. For example, the dashboard generation unit can determine the display priority based on the submission timing of learning outcomes when generating the dashboard. The dashboard generation unit can also use a generation AI to determine the display priority based on the submission timing of learning outcomes when generating the dashboard. This makes it possible to provide more effective information by determining the display priority based on the submission timing of learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can determine the display priority based on submission timing data analyzed by a generation AI.

[0083] The dashboard generation unit can adjust the display order based on the relevance of learning outcomes when generating the dashboard. For example, the dashboard generation unit adjusts the display order based on the relevance of learning outcomes when generating the dashboard. The dashboard generation unit can also use a generation AI to adjust the display order based on the relevance of learning outcomes when generating the dashboard. This makes it possible to provide more appropriate information by adjusting the display order based on the relevance of learning outcomes. Some or all of the above processing in the dashboard generation unit may be performed using a generation AI or not. For example, the dashboard generation unit can adjust the display order based on the relevance data of learning outcomes analyzed by a generation AI.

[0084] The mental health analysis unit can estimate students' emotions and adjust the mental health analysis method based on the estimated emotions. For example, the mental health analysis unit can estimate students' emotions and adjust the mental health analysis method based on the estimated emotions. The mental health analysis unit can also use generative AI to estimate students' emotions and adjust the mental health analysis method based on the estimated emotions. By adjusting the mental health analysis method based on students' emotions, a more appropriate analysis becomes possible. Some or all of the above-described processes in the mental health analysis unit may be performed using generative AI or not. For example, the mental health analysis unit can adjust the mental health analysis method based on student emotion data estimated by generative AI.

[0085] The mental health analysis unit can analyze a student's past behavioral history and select the optimal analysis algorithm during mental health analysis. For example, the mental health analysis unit can analyze a student's past behavioral history and select the optimal analysis algorithm during mental health analysis. The mental health analysis unit can also use generative AI to analyze a student's past behavioral history and select the optimal analysis algorithm during mental health analysis. This allows for the selection of the optimal analysis algorithm by analyzing a student's past behavioral history. Some or all of the above-described processes in the mental health analysis unit may be performed using generative AI or not. For example, the mental health analysis unit can select the optimal analysis algorithm based on past behavioral history data analyzed by generative AI.

[0086] The mental health analysis unit can perform filtering based on the student's current living situation and time of day during mental health analysis. For example, the mental health analysis unit can perform filtering based on the student's current living situation and time of day during mental health analysis. The mental health analysis unit can also use generative AI to perform filtering based on the student's current living situation and time of day during mental health analysis. This allows for more appropriate analysis by filtering based on the student's current living situation and time of day. Some or all of the above processing in the mental health analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the mental health analysis unit can perform filtering based on living situation and time of day data analyzed by generative AI.

[0087] The mental health analysis unit can estimate students' emotions and determine the priority of mental health analysis results based on the estimated emotions. For example, the mental health analysis unit can estimate students' emotions and determine the priority of mental health analysis results based on the estimated emotions. The mental health analysis unit can also use generative AI to estimate students' emotions and determine the priority of mental health analysis results based on the estimated emotions. This makes it possible to provide more appropriate information by determining the priority of mental health analysis results based on students' emotions. Some or all of the above processing in the mental health analysis unit may be performed using generative AI or not. For example, the mental health analysis unit can determine the priority of mental health analysis results based on student emotion data estimated by generative AI.

[0088] The mental health analysis unit can prioritize the analysis of highly relevant data by considering the geographical location information of students during mental health analysis. For example, the mental health analysis unit prioritizes the analysis of highly relevant data by considering the geographical location information of students during mental health analysis. The mental health analysis unit can also use generative AI to prioritize the analysis of highly relevant data by considering the geographical location information of students during mental health analysis. This enables more appropriate analysis by prioritizing the analysis of highly relevant data by considering the geographical location information of students. Some or all of the above processing in the mental health analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the mental health analysis unit can prioritize the analysis of highly relevant data based on geographical location information analyzed by generative AI.

[0089] The Mental Health Analysis Department can analyze students' social media activity and related data during mental health analysis. For example, the Mental Health Analysis Department can analyze students' social media activity and related data during mental health analysis. The Mental Health Analysis Department can also use generative AI to analyze students' social media activity and related data during mental health analysis. This allows for the priority analysis of relevant data by analyzing students' social media activity. Some or all of the above processing in the Mental Health Analysis Department may be performed using generative AI or not. For example, the Mental Health Analysis Department can prioritize the analysis of relevant data based on social media activity data analyzed by generative AI. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, guidance unit, dashboard generation unit, and mental health analysis unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The guidance unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The dashboard generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the generated dashboard is displayed on the display 40A of the smart device 14. The mental health analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the analysis results are provided through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, guidance unit, dashboard generation unit, and mental health analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The guidance unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The dashboard generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the generated dashboard is displayed on the display of the smart glasses 214. The mental health analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the analysis results are provided through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, guidance unit, dashboard generation unit, and mental health analysis unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The guidance unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The dashboard generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the generated dashboard is displayed on the display 343 of the headset terminal 314. The mental health analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the analysis results are provided through the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, guidance unit, dashboard generation unit, and mental health analysis unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The guidance unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The dashboard generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the generated dashboard is displayed on the display device of the robot 414. The mental health analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and the analysis results are provided through the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The analysis unit can adjust its analysis method when analyzing students' learning progress, taking into account the difficulty level of the learning content. For example, it can perform detailed analysis on difficult problems and simplified analysis on easy problems. Furthermore, the analysis unit can prioritize the analysis results based on the difficulty level of the learning content. This allows for more effective individualized instruction by prioritizing the provision of analysis results for difficult problems. In addition, the analysis unit can select an analysis algorithm according to the difficulty level of the learning content. For example, it can apply an advanced analysis algorithm to difficult problems and a simpler one to easy problems.

[0092] The instruction department can adjust teaching methods based on students' learning styles. For example, students who prefer visual learning can be provided with materials that heavily utilize diagrams and graphs, while students who prefer auditory learning can be provided with materials that include audio explanations. The instruction department can also adjust the pace of instruction according to students' learning styles. This enables optimal instruction tailored to each individual student. Furthermore, the instruction department can select the format of feedback based on students' learning styles. For example, students who prefer visual learning can be provided with visual feedback, while students who prefer auditory learning can be provided with audio feedback.

[0093] The dashboard generation unit can adjust how the dashboard is displayed based on the teacher's teaching style. For example, it can provide a dashboard with many graphs and charts for teachers who prioritize visual information, and a detailed text report for teachers who prioritize text information. The dashboard generation unit can also determine the priority of the information displayed according to the teacher's teaching style. This allows teachers to quickly grasp the information they need most. Furthermore, the dashboard generation unit can adjust the dashboard layout based on the teacher's teaching style. For example, it can center the dashboard with large graphs and charts for teachers who prioritize visual information, and center the dashboard with detailed text reports for teachers who prioritize text information.

[0094] The Mental Health Analysis Department can adjust its mental health analysis methods to take into account students' physical health status. For example, the department can analyze students' sleep patterns and exercise levels and evaluate their mental health status based on this data. The Mental Health Analysis Department can also prioritize analysis results based on students' physical health status. This allows for analysis that considers the impact of physical health on mental health. Furthermore, the Mental Health Analysis Department can select the format of feedback according to students' physical health status. For example, it can provide detailed feedback to students with good physical health and simplified feedback to students with poor health.

[0095] The analysis unit can adjust its analysis method when analyzing students' learning progress, taking into account the relevance of the learning content. For example, the analysis unit can perform a detailed analysis for highly relevant learning content and a simplified analysis for less relevant content. Furthermore, the analysis unit can prioritize the analysis results based on the relevance of the learning content. This allows for more effective individualized instruction by prioritizing the provision of analysis results for highly relevant learning content. In addition, the analysis unit can select an analysis algorithm according to the relevance of the learning content. For example, it can apply a sophisticated analysis algorithm to highly relevant learning content and a simpler one to less relevant content.

[0096] The analysis unit can estimate students' emotions and adjust the method of analyzing learning progress based on the estimated emotions. For example, the analysis unit can reduce the level of detail in the analysis if a student is stressed and perform a detailed analysis if a student is relaxed. The analysis unit can also prioritize the analysis results based on the students' emotions. This makes it possible to provide appropriate analysis results that match the students' emotions. Furthermore, the analysis unit can select the format of feedback based on the students' emotions. For example, it can provide simplified feedback to stressed students and detailed feedback to relaxed students.

[0097] The instruction team can estimate students' emotions and adjust their teaching methods based on those estimates. For example, they can use gentle language when a student is feeling anxious and normal language when a student is relaxed. They can also adjust the pace of instruction based on the student's emotions. This allows for appropriate instruction tailored to the student's feelings. Furthermore, the instruction team can select the format of feedback based on the student's emotions. For example, they can provide positive feedback to anxious students and detailed feedback to relaxed students.

[0098] The dashboard generation unit can estimate students' emotions and adjust the dashboard display based on the estimated emotions. For example, it can display a simplified version if a student is stressed and a more detailed version if a student is relaxed. The dashboard generation unit can also prioritize the information displayed based on the student's emotions, enabling the provision of appropriate information according to the student's feelings. Furthermore, the dashboard generation unit can adjust the dashboard layout based on the student's emotions. For example, it can provide a simple layout to stressed students and a detailed layout to relaxed students.

[0099] The mental health analysis department can estimate students' emotions and adjust the mental health analysis method based on the estimated emotions. For example, the mental health analysis department can perform a detailed analysis if a student is stressed and a simplified analysis if a student is relaxed. The mental health analysis department can also prioritize the analysis results based on the student's emotions. This makes it possible to provide appropriate analysis results according to the student's emotions. Furthermore, the mental health analysis department can select the format of feedback based on the student's emotions. For example, it can provide simplified feedback to stressed students and detailed feedback to relaxed students.

[0100] The mental health analysis unit can estimate students' emotions and prioritize the results of mental health analysis based on those estimated emotions. For example, if a student is stressed, the unit can prioritize analyzing stress-related data, and if a student is relaxed, it can prioritize analyzing other mental health-related data. The mental health analysis unit can also select the format of feedback based on the student's emotions, enabling the provision of appropriate feedback tailored to the student's feelings. Furthermore, the mental health analysis unit can select the analysis algorithm based on the student's emotions. For example, it can apply a stress-specific algorithm to stressed students and a general mental health analysis algorithm to relaxed students.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The analysis unit analyzes the students' learning progress or level of understanding. For example, it analyzes the students' answer data to identify areas where understanding is insufficient. Generative AI can also be used to analyze the students' answer data and identify areas where understanding is insufficient. Step 2: The instruction department provides individualized instruction based on the data analyzed by the analysis department. For example, it provides additional learning materials and practice problems for areas where understanding is insufficient, as identified by the analysis department. Generative AI can also be used to provide additional learning materials and practice problems for areas where understanding is insufficient, as identified by the analysis department. Step 3: The dashboard generation unit visually displays the progress of the lesson or learning outcomes. For example, it analyzes students' test results and assignment submission status to visually display the overall understanding and progress of the class. It is also possible to use the generation AI to analyze students' test results and assignment submission status to visually display the overall understanding and progress of the class. Step 4: The Mental Health Analysis Department analyzes students' life logs to understand their mental health status. For example, it analyzes students' daily behavior and statements to detect signs of stress and anxiety. Generative AI can also be used to analyze students' daily behavior and statements and detect signs of stress and anxiety.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes students' learning progress or level of understanding, A guidance unit provides individualized instruction based on the data analyzed by the aforementioned analysis unit, A dashboard generation unit that visually displays the progress of the lesson or learning outcomes, It includes a mental health analysis unit that analyzes students' life logs to understand their mental health status. A system characterized by the following features.

2. The aforementioned analysis unit, We analyze students' answer data to identify areas where their understanding is insufficient. The system according to feature 1.

3. The aforementioned leadership, The analysis unit provides additional learning materials and practice problems for areas where understanding is insufficient. The system according to feature 1.

4. The dashboard generation unit, Analyze students' test results or assignment submission status to visually display the overall understanding or progress of the class. The system according to feature 1.

5. The aforementioned mental health analysis unit, Analyze students' daily behavior and statements to detect signs of stress or anxiety. The system according to feature 1.

6. The aforementioned mental health analysis unit, An alert will be issued prompting users to consult with experts as needed. The system according to feature 1.

7. The aforementioned analysis unit, We estimate students' emotions and adjust the method of analyzing learning progress based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, Analyze students' past learning history and select the optimal analysis algorithm. The system according to feature 1.

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