system

Abstract

The system according to this embodiment aims to enable teachers to understand social and economic conditions and reflect them in educational programs. [Solution] The system according to the embodiment comprises a provisioning unit, an analysis unit, a tracking unit, and a feedback unit. The provisioning unit provides a virtual social training program. The analysis unit analyzes the teacher's learning data. The tracking unit grasps the progress based on the data analyzed by the analysis unit. The feedback unit provides feedback based on the progress grasped by the tracking unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult for teachers to understand social and economic situations and reflect them in educational programs, and there is room for improvement in terms of improving the value of education.

[0005] The system according to the embodiment aims to enable teachers to understand social and economic situations and reflect them in educational programs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a provisioning unit, an analysis unit, a tracking unit, and a feedback unit. The provisioning unit provides a virtual social training program. The analysis unit analyzes the teacher's learning data. The tracking unit grasps the progress based on the data analyzed by the analysis unit. The feedback unit provides feedback based on the progress grasped by the tracking unit. [Effects of the Invention]

[0007] The system according to this embodiment allows teachers to understand social and economic conditions and reflect them in educational programs. [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, and the like. 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 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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 educational support system according to an embodiment of the present invention is a mechanism to address the challenge of "declining value of education" in Japanese education. This educational support system provides a service to enhance teachers using an AI agent. First, the educational support system provides teachers with a virtual social training program to help them understand social and economic conditions. This program utilizes generative AI to automatically generate diverse business scenarios (e.g., project management, team building, problem solving) and provides training that closely resembles actual social experience. Next, the educational support system uses the AI ​​agent to analyze the teacher's learning data and grasp their progress in real time. This allows the system to provide feedback according to the level of achievement and identify which skills the teacher should strengthen. Furthermore, the educational support system recommends appropriate training and additional learning resources. This mechanism enables teachers to efficiently improve their skills even when busy, thereby increasing the value of education. This will improve the capabilities of Japanese people and, in the future, contribute to Japan's economic development. For example, the educational support system provides teachers with a virtual social training program to help them understand social and economic conditions. This program utilizes generative AI to automatically generate diverse business scenarios and provides training that closely resembles actual social experience. For example, in a project management scenario, teachers can learn project progress management and team-building skills. In a problem-solving scenario, teachers can receive training in problem identification and solution proposal. Furthermore, the educational support system analyzes teachers' learning data and monitors their progress in real time. This allows it to identify which skills teachers need to strengthen and provide feedback based on their achievements. For instance, if a teacher is taking a long time to acquire a particular skill, the educational support system will recommend additional training in that skill. Conversely, if a teacher quickly acquires a skill, the system will provide training to move on to the next step. This enables teachers to efficiently improve their skills, thereby enhancing the value of education. In short, the educational support system efficiently assists teachers in skill development and improves the value of education.

[0029] The educational support system according to this embodiment comprises a provisioning unit, an analysis unit, a tracking unit, and a feedback unit. The provisioning unit provides a virtual social training program. The provisioning unit automatically generates diverse business scenarios using, for example, generative AI. For example, the provisioning unit can generate a project management scenario, allowing teachers to learn project progress management and team building skills. The provisioning unit can also generate a problem-solving scenario, providing training for teachers to identify problems and propose solutions. Furthermore, the provisioning unit can use generative AI to customize scenarios based on the teacher's area of ​​expertise and interests. For example, the provisioning unit can provide scenarios related to the teacher's area of ​​expertise to promote practical learning. The analysis unit analyzes the teacher's learning data. For example, the analysis unit identifies which skills should be strengthened based on the teacher's learning data. For example, if the analysis unit is taking a long time to acquire a particular skill, it recommends additional training for that skill. The analysis unit can also provide training to move on to the next step if the teacher has quickly acquired a particular skill. The tracking unit grasps the progress based on the data analyzed by the analysis unit. The tracking unit tracks teachers' learning data in real time and understands their progress. For example, the tracking unit understands how long it takes teachers to acquire a particular skill and evaluates their progress. The feedback unit provides feedback based on the progress understood by the tracking unit. The feedback unit provides feedback according to the degree of achievement. For example, if a teacher is taking a long time to acquire a particular skill, the feedback unit recommends additional training for that skill. The feedback unit can also provide training to move on to the next step if a teacher has acquired a particular skill quickly. In this way, the educational support system according to the embodiment can efficiently support teachers' skill development and improve the value of education.

[0030] The service provider offers a virtual society training program. For example, the service provider automatically generates diverse business scenarios using generative AI. Specifically, the generative AI utilizes natural language processing technology to generate various business scenarios that instructors may encounter. For instance, in a project management scenario, the generative AI simulates the entire process from project start to finish, allowing instructors to practically learn project management and team-building skills. The scenario includes setting project goals, assigning tasks, monitoring progress, risk management, and communication among team members. In problem-solving scenarios, the generative AI sets up complex problems, providing training for instructors to identify problems and propose solutions. For example, it can generate scenarios for troubleshooting within a company or customer service, enabling instructors to develop problem-solving abilities relevant to real-world work. Furthermore, the service provider can use the generative AI to customize scenarios based on instructors' areas of expertise and interests. For example, if an instructor is an economics expert, an economic policy scenario can be generated, allowing them to practically learn policy-making and economic analysis skills. Furthermore, the service provider can adjust the difficulty level and content of scenarios based on teachers' past learning history and feedback, providing each teacher with an optimal learning experience. This allows the service provider to efficiently support teachers' skill development and enhance the value of education.

[0031] The analytics department analyzes teachers' learning data. For example, based on teachers' learning data, the analytics department identifies which skills should be strengthened. Specifically, the analytics department collects data acquired by teachers through virtual social training programs and analyzes it using AI algorithms. For example, if a teacher is taking a long time to acquire a particular skill, it recommends additional training for that skill. The analytics department analyzes teachers' learning patterns and progress in detail and provides an optimal training plan for each individual teacher. The analytics department can also provide training to move to the next step if a teacher has quickly acquired a particular skill. For example, if a teacher has quickly acquired project management skills, the analytics department may recommend leadership and strategic thinking training as the next step. Furthermore, based on teachers' learning data, the analytics department can also analyze long-term skill development trends and predict future training needs. This allows the analytics department to efficiently support teachers' skill development and improve the quality of education.

[0032] The tracking unit understands progress based on data analyzed by the analysis unit. For example, the tracking unit tracks teachers' learning data in real time to understand their progress. Specifically, the tracking unit monitors in real time how teachers are progressing through the virtual social training program. For example, it understands how long it takes teachers to acquire a particular skill and evaluates their progress. The tracking unit visualizes teachers' learning data and displays progress in graphs and charts, making it easier for teachers to understand their own learning status. The tracking unit can also provide advice to improve learning efficiency based on teachers' learning data. For example, if a teacher is taking a long time to acquire a particular skill, it will recommend additional training for that skill. Furthermore, the tracking unit regularly reports on learning progress based on teachers' learning data, providing support for teachers to achieve their learning goals. This allows the tracking unit to understand teachers' learning status in real time and support efficient learning.

[0033] The Feedback Department provides feedback based on the progress tracked by the Tracking Department. For example, the Feedback Department provides feedback tailored to the level of achievement. Specifically, if an instructor is taking a long time to acquire a particular skill, the Feedback Department will recommend additional training in that skill. For instance, if an instructor is taking a long time to acquire project management skills, the Feedback Department will provide additional training on the basic concepts and techniques of project management. The Feedback Department can also provide training to help instructors move to the next step if they have quickly acquired a particular skill. For example, if an instructor has quickly acquired problem-solving skills, the Feedback Department will provide training on more advanced problem-solving techniques and strategies. Furthermore, based on the instructor's learning data, the Feedback Department can provide individualized feedback. For example, it can adjust the content and format of feedback according to the instructor's learning style and progress, enabling them to learn effectively. This allows the Feedback Department to efficiently support instructor skill development and improve the quality of education.

[0034] The service provider automatically generates diverse business scenarios using generative AI. For example, it can use generative AI to generate project management scenarios, allowing instructors to learn project progress management and team-building skills. For example, it can also use generative AI to generate problem-solving scenarios, providing training in problem identification and solution proposals. Furthermore, the service provider can use generative AI to customize scenarios based on instructors' areas of expertise and interests. For example, it can provide scenarios related to instructors' areas of expertise to promote practical learning. By automatically generating diverse business scenarios, it can provide training that closely resembles real-world experiences.

[0035] The analysis unit analyzes the teacher's learning data to identify which skills need strengthening. For example, if the teacher is taking a long time to acquire a particular skill, the analysis unit may recommend additional training for that skill. The analysis unit can also provide training to help the teacher move on to the next step if they have quickly acquired a particular skill. In this way, by analyzing the teacher's learning data, it is possible to identify the skills that need strengthening. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the teacher's learning data into an AI and have the AI ​​identify the skills that need strengthening.

[0036] The feedback unit provides feedback tailored to the level of achievement. For example, if a teacher is taking a long time to acquire a particular skill, the feedback unit may recommend additional training for that skill. For example, if a teacher has quickly acquired a particular skill, the feedback unit may also provide training to help them move on to the next step. This helps support teacher skill development by providing feedback tailored to the level of achievement. Some or all of the processes described above in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit could input teacher achievement data into an AI and have the AI ​​provide the feedback.

[0037] The service provider recommends appropriate training and additional learning resources. For example, if a teacher is taking a long time to acquire a particular skill, the service provider will recommend additional training for that skill. For example, if a teacher has quickly acquired a particular skill, the service provider may also provide training to help them move on to the next step. This helps teachers improve their skills by recommending appropriate training and additional learning resources. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input teacher learning data into AI and have the AI ​​recommend appropriate training and additional learning resources.

[0038] The service provider analyzes the teacher's past training history when providing a virtual society training program and selects the optimal scenario. For example, the service provider provides a new, non-repeating scenario based on the training content the teacher has previously received. For example, the service provider can also provide a scenario that strengthens areas the teacher has struggled with in the past. Alternatively, the service provider can provide a scenario that further explores areas in which the teacher has received high marks in the past. In this way, the service provider can provide the optimal scenario by analyzing past training history. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the teacher's past training history data into an AI and have the AI ​​select the optimal scenario.

[0039] The service provider customizes scenarios based on the teacher's area of ​​expertise and interests when providing virtual society training programs. For example, the service provider can provide scenarios related to the teacher's area of ​​expertise to promote practical learning. For example, the service provider can also provide scenarios based on the teacher's interests to increase learning motivation. Furthermore, the service provider can provide scenarios that combine the teacher's area of ​​expertise and interests to achieve effective learning. In this way, effective learning can be provided by customizing scenarios based on area of ​​expertise and interests. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input teacher's area of ​​expertise and interest data into a generative AI and have the generative AI perform the scenario customization.

[0040] The service provider, when providing the virtual society training program, prioritizes providing highly relevant scenarios by considering the geographical location information of the instructors. For example, if the instructor is in an urban area, the service provider will provide urban business scenarios. For example, if the instructor is in a rural area, the service provider may also provide rural business scenarios. Furthermore, if the instructor is overseas, the service provider may also provide international business scenarios. In this way, highly relevant scenarios can be provided by considering geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without the use of a generative AI. For example, the service provider can input the instructor's geographical location information into a generative AI and have the generative AI perform the task of providing highly relevant scenarios.

[0041] The service provider analyzes teachers' social media activity and provides relevant scenarios when delivering the virtual society training program. For example, the service provider provides scenarios based on topics that teachers show interest in on social media. For example, the service provider can also provide scenarios that incorporate the opinions of experts that teachers follow on social media. Furthermore, the service provider can provide scenarios related to communities that teachers participate in on social media. In this way, relevant scenarios can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input teachers' social media activity data into generative AI and have the generative AI perform the task of providing relevant scenarios.

[0042] The analysis unit improves the accuracy of the analysis by referring to the teacher's past learning history when analyzing training data. For example, the analysis unit can improve the accuracy of the analysis based on the teacher's past learning history. For example, the analysis unit can focus on analyzing areas that the teacher has struggled with in the past. The analysis unit can also select an effective analysis method from the teacher's past learning history. In this way, the accuracy of the analysis is improved by referring to past learning history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the teacher's past learning history data into AI and have the AI ​​perform the analysis accuracy improvement.

[0043] The analysis unit customizes the analysis algorithm based on the teacher's area of ​​expertise and interests when analyzing training data. For example, the analysis unit may use an analysis algorithm specialized in the teacher's area of ​​expertise. For example, the analysis unit may also use an analysis algorithm based on the teacher's interests. Furthermore, the analysis unit may use an analysis algorithm that combines the teacher's area of ​​expertise and interests. This allows for effective data analysis by customizing the analysis algorithm based on the teacher's area of ​​expertise and interests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the teacher's area of ​​expertise and interest data into the AI ​​and have the AI ​​perform the customization of the analysis algorithm.

[0044] The analysis unit considers the geographical location of teachers when analyzing training data. For example, if a teacher is in an urban area, the analysis unit will prioritize analyzing data from urban areas. For example, if a teacher is in a rural area, the analysis unit can also prioritize analyzing data from rural areas. Furthermore, if a teacher is overseas, the analysis unit can prioritize analyzing international data. This allows for the analysis of highly relevant data by considering geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the teacher's geographical location information into the AI ​​and have the AI ​​perform the analysis.

[0045] The analysis unit improves the accuracy of its analysis by referring to relevant literature of the instructor during the analysis of training data. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the instructor's field of expertise. For example, the analysis unit can also improve the accuracy of its analysis by referring to literature related to the instructor's interests. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to the instructor's past research results. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the instructor's relevant literature data into AI and have AI perform the improvement of the analysis accuracy.

[0046] The tracking unit improves tracking accuracy by referring to the teacher's past progress data when tracking progress. For example, the tracking unit can improve tracking accuracy based on the teacher's past progress data. For example, the tracking unit can focus on tracking areas where the teacher has struggled in the past. The tracking unit can also select an effective tracking method from the teacher's past progress data. This improves tracking accuracy by referring to past progress data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the teacher's past progress data into AI and have the AI ​​perform the tracking accuracy improvement.

[0047] The tracking unit customizes the tracking algorithm based on the teacher's area of ​​expertise and interests when tracking progress. For example, the tracking unit may use a tracking algorithm specialized in the teacher's area of ​​expertise. For example, the tracking unit may also use a tracking algorithm based on the teacher's interests. Furthermore, the tracking unit may use a tracking algorithm that combines the teacher's area of ​​expertise and interests. This allows for effective progress tracking by customizing the tracking algorithm based on the teacher's area of ​​expertise and interests. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input teacher's area of ​​expertise and interest data into AI and have the AI ​​perform the customization of the tracking algorithm.

[0048] The tracking unit considers the geographical location of teachers when tracking progress. For example, if a teacher is in an urban area, the tracking unit will prioritize tracking data from urban areas. For example, if a teacher is in a rural area, the tracking unit can also prioritize tracking data from rural areas. Furthermore, if a teacher is overseas, the tracking unit can prioritize tracking international data. This allows for more relevant data tracking by considering geographical location. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the teacher's geographical location information into AI and have the AI ​​perform the tracking.

[0049] The tracking unit improves the accuracy of tracking by referring to the faculty member's relevant literature when tracking progress. For example, the tracking unit can improve tracking accuracy by referring to literature related to the faculty member's field of expertise. For example, the tracking unit can also improve tracking accuracy by referring to literature related to the faculty member's interests. Furthermore, the tracking unit can improve tracking accuracy by referring to the faculty member's past research results. In this way, the accuracy of tracking is improved by referring to relevant literature. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the faculty member's relevant literature data into AI and have the AI ​​perform the tracking accuracy improvement.

[0050] The feedback unit provides optimal feedback by referring to the teacher's past feedback history when providing feedback. For example, the feedback unit provides new, non-repeating feedback based on the teacher's past feedback history. For example, the feedback unit can focus on providing feedback on areas where the teacher has struggled in the past. The feedback unit can also select an effective feedback method from the teacher's past feedback history. In this way, optimal feedback can be provided by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the teacher's past feedback history data into AI and have the AI ​​perform the task of providing optimal feedback.

[0051] The feedback unit customizes the feedback content based on the teacher's area of ​​expertise and interests when providing feedback. For example, the feedback unit can provide feedback that is specific to the teacher's area of ​​expertise. For example, the feedback unit can also provide feedback based on the teacher's interests. Furthermore, the feedback unit can provide feedback that combines the teacher's area of ​​expertise and interests. This allows for more effective feedback by customizing the content based on the teacher's area of ​​expertise and interests. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the teacher's area of ​​expertise and interest data into the AI ​​and have the AI ​​customize the feedback content.

[0052] The feedback unit provides optimal feedback by considering the teacher's geographical location information when providing feedback. For example, if the teacher is in an urban area, the feedback unit provides feedback based on urban data. For example, if the teacher is in a rural area, the feedback unit can also provide feedback based on rural data. Furthermore, if the teacher is overseas, the feedback unit can provide feedback based on international data. In this way, optimal feedback can be provided by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the teacher's geographical location information into AI and have the AI ​​perform the task of providing optimal feedback.

[0053] The feedback department analyzes the teacher's social media activity when providing feedback and provides relevant feedback. For example, the feedback department provides feedback based on topics the teacher shows interest in on social media. For example, the feedback department can also provide feedback that incorporates the opinions of experts the teacher follows on social media. Furthermore, the feedback department can provide feedback related to communities the teacher participates in on social media. In this way, relevant feedback can be provided by analyzing social media activity. Some or all of the above processing in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input the teacher's social media activity data into AI and have the AI ​​perform the provision of relevant feedback.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Educational support systems can improve the accuracy of teacher learning data by considering teachers' past learning patterns. For example, they can analyze which learning methods have been effective for teachers in the past and prioritize recommending those methods. They can also focus on analyzing areas where teachers have struggled in the past and identify areas for improvement. Furthermore, they can select and provide effective learning resources based on teachers' past learning history. By considering past learning patterns, they can improve the accuracy of analysis and efficiently support teachers' skill development.

[0056] Educational support systems can customize analysis algorithms based on teachers' areas of expertise and interests when analyzing teachers' learning data. For example, using an analysis algorithm specialized in a teacher's area of ​​expertise can yield more accurate results. Furthermore, using an analysis algorithm based on a teacher's interests can enhance their motivation to learn. Combining an analysis algorithm with a teacher's area of ​​expertise and interests enables more effective data analysis. In this way, customizing analysis algorithms based on areas of expertise and interests can efficiently support teachers' skill development.

[0057] Educational support systems can also analyze teachers' learning data while considering their geographical location. For example, if a teacher is located in an urban area, the system can prioritize analyzing urban data to obtain more relevant results. Similarly, if a teacher is located in a rural area, the system can prioritize analyzing rural data. Furthermore, if a teacher is overseas, the system can prioritize analyzing international data. By considering geographical location, this enables more relevant data analysis and efficiently supports teachers' skill development.

[0058] Educational support systems can improve the accuracy of teacher learning data by referencing relevant literature. For example, referencing literature related to a teacher's area of ​​expertise can yield more accurate analysis results. Furthermore, referencing literature related to a teacher's interests can increase their motivation to learn. Additionally, referencing a teacher's past research findings can help select effective analysis methods. This allows for improved analysis accuracy and efficient support for teacher skill development through the use of relevant literature.

[0059] Educational support systems can improve the accuracy of analysis by referencing teachers' past learning history when analyzing their learning data. For example, the accuracy of the analysis can be improved based on the teacher's past learning history. It can also focus the analysis on areas where the teacher has struggled in the past. Furthermore, it can select effective analysis methods based on the teacher's past learning history. As a result, by referring to past learning history, the accuracy of the analysis can be improved, and teachers' skill development can be efficiently supported.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The service provider will offer a virtual society training program. Using generative AI, the service provider will automatically generate diverse business scenarios, allowing instructors to learn project management and problem-solving skills. The service provider will also customize scenarios based on instructors' areas of expertise and interests to facilitate practical learning. Step 2: The analysis unit analyzes the teachers' learning data. Based on the teachers' learning data, the analysis unit identifies which skills need strengthening and recommends additional training as needed. Step 3: The tracking unit grasps the progress based on the data analyzed by the analysis unit. The tracking unit tracks the teacher's learning data in real time and evaluates the progress. Step 4: The feedback department provides feedback based on the progress tracked by the tracking department. The feedback department provides feedback according to the level of achievement and recommends additional training as needed.

[0062] (Example of form 2) The educational support system according to an embodiment of the present invention is a mechanism to address the challenge of "declining value of education" in Japanese education. This educational support system provides a service to enhance teachers using an AI agent. First, the educational support system provides teachers with a virtual social training program to help them understand social and economic conditions. This program utilizes generative AI to automatically generate diverse business scenarios (e.g., project management, team building, problem solving) and provides training that closely resembles actual social experience. Next, the educational support system uses the AI ​​agent to analyze the teacher's learning data and grasp their progress in real time. This allows the system to provide feedback according to the level of achievement and identify which skills the teacher should strengthen. Furthermore, the educational support system recommends appropriate training and additional learning resources. This mechanism enables teachers to efficiently improve their skills even when busy, thereby increasing the value of education. This will improve the capabilities of Japanese people and, in the future, contribute to Japan's economic development. For example, the educational support system provides teachers with a virtual social training program to help them understand social and economic conditions. This program utilizes generative AI to automatically generate diverse business scenarios and provides training that closely resembles actual social experience. For example, in a project management scenario, teachers can learn project progress management and team-building skills. In a problem-solving scenario, teachers can receive training in problem identification and solution proposal. Furthermore, the educational support system analyzes teachers' learning data and monitors their progress in real time. This allows it to identify which skills teachers need to strengthen and provide feedback based on their achievements. For instance, if a teacher is taking a long time to acquire a particular skill, the educational support system will recommend additional training in that skill. Conversely, if a teacher quickly acquires a skill, the system will provide training to move on to the next step. This enables teachers to efficiently improve their skills, thereby enhancing the value of education. In short, the educational support system efficiently assists teachers in skill development and improves the value of education.

[0063] The educational support system according to this embodiment comprises a provisioning unit, an analysis unit, a tracking unit, and a feedback unit. The provisioning unit provides a virtual social training program. The provisioning unit automatically generates diverse business scenarios using, for example, generative AI. For example, the provisioning unit can generate a project management scenario, allowing teachers to learn project progress management and team building skills. The provisioning unit can also generate a problem-solving scenario, providing training for teachers to identify problems and propose solutions. Furthermore, the provisioning unit can use generative AI to customize scenarios based on the teacher's area of ​​expertise and interests. For example, the provisioning unit can provide scenarios related to the teacher's area of ​​expertise to promote practical learning. The analysis unit analyzes the teacher's learning data. For example, the analysis unit identifies which skills should be strengthened based on the teacher's learning data. For example, if the analysis unit is taking a long time to acquire a particular skill, it recommends additional training for that skill. The analysis unit can also provide training to move on to the next step if the teacher has quickly acquired a particular skill. The tracking unit grasps the progress based on the data analyzed by the analysis unit. The tracking unit tracks teachers' learning data in real time and understands their progress. For example, the tracking unit understands how long it takes teachers to acquire a particular skill and evaluates their progress. The feedback unit provides feedback based on the progress understood by the tracking unit. The feedback unit provides feedback according to the degree of achievement. For example, if a teacher is taking a long time to acquire a particular skill, the feedback unit recommends additional training for that skill. The feedback unit can also provide training to move on to the next step if a teacher has acquired a particular skill quickly. In this way, the educational support system according to the embodiment can efficiently support teachers' skill development and improve the value of education.

[0064] The service provider offers a virtual society training program. For example, the service provider automatically generates diverse business scenarios using generative AI. Specifically, the generative AI utilizes natural language processing technology to generate various business scenarios that instructors may encounter. For instance, in a project management scenario, the generative AI simulates the entire process from project start to finish, allowing instructors to practically learn project management and team-building skills. The scenario includes setting project goals, assigning tasks, monitoring progress, risk management, and communication among team members. In problem-solving scenarios, the generative AI sets up complex problems, providing training for instructors to identify problems and propose solutions. For example, it can generate scenarios for troubleshooting within a company or customer service, enabling instructors to develop problem-solving abilities relevant to real-world work. Furthermore, the service provider can use the generative AI to customize scenarios based on instructors' areas of expertise and interests. For example, if an instructor is an economics expert, an economic policy scenario can be generated, allowing them to practically learn policy-making and economic analysis skills. Furthermore, the service provider can adjust the difficulty level and content of scenarios based on teachers' past learning history and feedback, providing each teacher with an optimal learning experience. This allows the service provider to efficiently support teachers' skill development and enhance the value of education.

[0065] The analytics department analyzes teachers' learning data. For example, based on teachers' learning data, the analytics department identifies which skills should be strengthened. Specifically, the analytics department collects data acquired by teachers through virtual social training programs and analyzes it using AI algorithms. For example, if a teacher is taking a long time to acquire a particular skill, it recommends additional training for that skill. The analytics department analyzes teachers' learning patterns and progress in detail and provides an optimal training plan for each individual teacher. The analytics department can also provide training to move to the next step if a teacher has quickly acquired a particular skill. For example, if a teacher has quickly acquired project management skills, the analytics department may recommend leadership and strategic thinking training as the next step. Furthermore, based on teachers' learning data, the analytics department can also analyze long-term skill development trends and predict future training needs. This allows the analytics department to efficiently support teachers' skill development and improve the quality of education.

[0066] The tracking unit understands progress based on data analyzed by the analysis unit. For example, the tracking unit tracks teachers' learning data in real time to understand their progress. Specifically, the tracking unit monitors in real time how teachers are progressing through the virtual social training program. For example, it understands how long it takes teachers to acquire a particular skill and evaluates their progress. The tracking unit visualizes teachers' learning data and displays progress in graphs and charts, making it easier for teachers to understand their own learning status. The tracking unit can also provide advice to improve learning efficiency based on teachers' learning data. For example, if a teacher is taking a long time to acquire a particular skill, it will recommend additional training for that skill. Furthermore, the tracking unit regularly reports on learning progress based on teachers' learning data, providing support for teachers to achieve their learning goals. This allows the tracking unit to understand teachers' learning status in real time and support efficient learning.

[0067] The Feedback Department provides feedback based on the progress tracked by the Tracking Department. For example, the Feedback Department provides feedback tailored to the level of achievement. Specifically, if an instructor is taking a long time to acquire a particular skill, the Feedback Department will recommend additional training in that skill. For instance, if an instructor is taking a long time to acquire project management skills, the Feedback Department will provide additional training on the basic concepts and techniques of project management. The Feedback Department can also provide training to help instructors move to the next step if they have quickly acquired a particular skill. For example, if an instructor has quickly acquired problem-solving skills, the Feedback Department will provide training on more advanced problem-solving techniques and strategies. Furthermore, based on the instructor's learning data, the Feedback Department can provide individualized feedback. For example, it can adjust the content and format of feedback according to the instructor's learning style and progress, enabling them to learn effectively. This allows the Feedback Department to efficiently support instructor skill development and improve the quality of education.

[0068] The service provider automatically generates diverse business scenarios using generative AI. For example, it can use generative AI to generate project management scenarios, allowing instructors to learn project progress management and team-building skills. For example, it can also use generative AI to generate problem-solving scenarios, providing training in problem identification and solution proposals. Furthermore, the service provider can use generative AI to customize scenarios based on instructors' areas of expertise and interests. For example, it can provide scenarios related to instructors' areas of expertise to promote practical learning. By automatically generating diverse business scenarios, it can provide training that closely resembles real-world experiences.

[0069] The analysis unit analyzes the teacher's learning data to identify which skills need strengthening. For example, if the teacher is taking a long time to acquire a particular skill, the analysis unit may recommend additional training for that skill. The analysis unit can also provide training to help the teacher move on to the next step if they have quickly acquired a particular skill. In this way, by analyzing the teacher's learning data, it is possible to identify the skills that need strengthening. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the teacher's learning data into an AI and have the AI ​​identify the skills that need strengthening.

[0070] The feedback unit provides feedback tailored to the level of achievement. For example, if a teacher is taking a long time to acquire a particular skill, the feedback unit may recommend additional training for that skill. For example, if a teacher has quickly acquired a particular skill, the feedback unit may also provide training to help them move on to the next step. This helps support teacher skill development by providing feedback tailored to the level of achievement. Some or all of the processes described above in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit could input teacher achievement data into an AI and have the AI ​​provide the feedback.

[0071] The service provider recommends appropriate training and additional learning resources. For example, if a teacher is taking a long time to acquire a particular skill, the service provider will recommend additional training for that skill. For example, if a teacher has quickly acquired a particular skill, the service provider may also provide training to help them move on to the next step. This helps teachers improve their skills by recommending appropriate training and additional learning resources. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input teacher learning data into AI and have the AI ​​recommend appropriate training and additional learning resources.

[0072] The service provider estimates the teacher's emotions and adjusts the content of the virtual social training program based on the estimated emotions. For example, if the teacher is feeling stressed, the service provider may provide a relaxing scenario to reduce the burden of training. For example, if the teacher is excited, the service provider may provide a challenging scenario to maintain motivation. Also, if the teacher is tired, the service provider may provide a short and effective scenario to facilitate learning. In this way, effective learning can be provided by adjusting the content of the training program according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input teacher emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The service provider analyzes the teacher's past training history when providing a virtual society training program and selects the optimal scenario. For example, the service provider provides a new, non-repeating scenario based on the training content the teacher has previously received. For example, the service provider can also provide a scenario that strengthens areas the teacher has struggled with in the past. Alternatively, the service provider can provide a scenario that further explores areas in which the teacher has received high marks in the past. In this way, the service provider can provide the optimal scenario by analyzing past training history. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the teacher's past training history data into an AI and have the AI ​​select the optimal scenario.

[0074] The service provider customizes scenarios based on the teacher's area of ​​expertise and interests when providing virtual society training programs. For example, the service provider can provide scenarios related to the teacher's area of ​​expertise to promote practical learning. For example, the service provider can also provide scenarios based on the teacher's interests to increase learning motivation. Furthermore, the service provider can provide scenarios that combine the teacher's area of ​​expertise and interests to achieve effective learning. In this way, effective learning can be provided by customizing scenarios based on area of ​​expertise and interests. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input teacher's area of ​​expertise and interest data into a generative AI and have the generative AI perform the scenario customization.

[0075] The service provider estimates the teacher's emotions and determines the priority of scenarios to offer based on the estimated emotions. For example, if the teacher is stressed, the service provider will prioritize relaxing scenarios. For example, if the teacher is excited, the service provider may prioritize challenging scenarios. Also, if the teacher is tired, the service provider may prioritize short, effective scenarios. This allows for effective learning by prioritizing scenarios according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input teacher emotion data into a generative AI and have the generative AI determine the priority of scenarios.

[0076] The service provider, when providing the virtual society training program, prioritizes providing highly relevant scenarios by considering the geographical location information of the instructors. For example, if the instructor is in an urban area, the service provider will provide urban business scenarios. For example, if the instructor is in a rural area, the service provider may also provide rural business scenarios. Furthermore, if the instructor is overseas, the service provider may also provide international business scenarios. In this way, highly relevant scenarios can be provided by considering geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without the use of a generative AI. For example, the service provider can input the instructor's geographical location information into a generative AI and have the generative AI perform the task of providing highly relevant scenarios.

[0077] The service provider analyzes teachers' social media activity and provides relevant scenarios when delivering the virtual society training program. For example, the service provider provides scenarios based on topics that teachers show interest in on social media. For example, the service provider can also provide scenarios that incorporate the opinions of experts that teachers follow on social media. Furthermore, the service provider can provide scenarios related to communities that teachers participate in on social media. In this way, relevant scenarios can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input teachers' social media activity data into generative AI and have the generative AI perform the task of providing relevant scenarios.

[0078] The analysis unit estimates the teacher's emotions and adjusts the analysis method of the learning data based on the estimated teacher's emotions. For example, if the teacher is stressed, the analysis unit may use a simplified analysis method to reduce the burden. For example, if the teacher is excited, the analysis unit may use a more detailed analysis method to enhance the learning effect. Also, if the teacher is tired, the analysis unit may use a method that completes the analysis in a short time. This allows for effective data analysis by adjusting the analysis method according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the teacher's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method.

[0079] The analysis unit improves the accuracy of the analysis by referring to the teacher's past learning history when analyzing training data. For example, the analysis unit can improve the accuracy of the analysis based on the teacher's past learning history. For example, the analysis unit can focus on analyzing areas that the teacher has struggled with in the past. The analysis unit can also select an effective analysis method from the teacher's past learning history. In this way, the accuracy of the analysis is improved by referring to past learning history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the teacher's past learning history data into AI and have the AI ​​perform the analysis accuracy improvement.

[0080] The analysis unit customizes the analysis algorithm based on the teacher's area of ​​expertise and interests when analyzing training data. For example, the analysis unit may use an analysis algorithm specialized in the teacher's area of ​​expertise. For example, the analysis unit may also use an analysis algorithm based on the teacher's interests. Furthermore, the analysis unit may use an analysis algorithm that combines the teacher's area of ​​expertise and interests. This allows for effective data analysis by customizing the analysis algorithm based on the teacher's area of ​​expertise and interests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the teacher's area of ​​expertise and interest data into the AI ​​and have the AI ​​perform the customization of the analysis algorithm.

[0081] The analysis unit estimates the teacher's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the teacher is stressed, the analysis unit provides a simple and highly visible display method. For example, if the teacher is excited, the analysis unit may provide a display method that includes detailed information. Furthermore, if the teacher is tired, the analysis unit may provide a display method that focuses on the key points. This allows for effective data display by adjusting the display method according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the teacher's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0082] The analysis unit considers the geographical location of teachers when analyzing training data. For example, if a teacher is in an urban area, the analysis unit will prioritize analyzing data from urban areas. For example, if a teacher is in a rural area, the analysis unit can also prioritize analyzing data from rural areas. Furthermore, if a teacher is overseas, the analysis unit can prioritize analyzing international data. This allows for the analysis of highly relevant data by considering geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the teacher's geographical location information into the AI ​​and have the AI ​​perform the analysis.

[0083] The analysis unit improves the accuracy of its analysis by referring to relevant literature of the instructor during the analysis of training data. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the instructor's field of expertise. For example, the analysis unit can also improve the accuracy of its analysis by referring to literature related to the instructor's interests. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to the instructor's past research results. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the instructor's relevant literature data into AI and have AI perform the improvement of the analysis accuracy.

[0084] The tracking unit estimates the teacher's emotions and adjusts the progress tracking method based on the estimated emotions. For example, if the teacher is stressed, the tracking unit may use a simplified tracking method to reduce the burden. For example, if the teacher is excited, the tracking unit may use a more detailed tracking method to enhance learning effectiveness. Also, if the teacher is tired, the tracking unit may use a method that completes tracking in a short time. By adjusting the tracking method according to the teacher's emotions, it becomes possible to effectively grasp progress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the teacher's emotion data into the generative AI and have the generative AI perform the adjustment of the tracking method.

[0085] The tracking unit improves tracking accuracy by referring to the teacher's past progress data when tracking progress. For example, the tracking unit can improve tracking accuracy based on the teacher's past progress data. For example, the tracking unit can focus on tracking areas where the teacher has struggled in the past. The tracking unit can also select an effective tracking method from the teacher's past progress data. This improves tracking accuracy by referring to past progress data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the teacher's past progress data into AI and have the AI ​​perform the tracking accuracy improvement.

[0086] The tracking unit customizes the tracking algorithm based on the teacher's area of ​​expertise and interests when tracking progress. For example, the tracking unit may use a tracking algorithm specialized in the teacher's area of ​​expertise. For example, the tracking unit may also use a tracking algorithm based on the teacher's interests. Furthermore, the tracking unit may use a tracking algorithm that combines the teacher's area of ​​expertise and interests. This allows for effective progress tracking by customizing the tracking algorithm based on the teacher's area of ​​expertise and interests. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input teacher's area of ​​expertise and interest data into AI and have the AI ​​perform the customization of the tracking algorithm.

[0087] The tracking unit estimates the teacher's emotions and adjusts the display method of the tracking results based on the estimated emotions. For example, if the teacher is stressed, the tracking unit provides a simple and highly visible display method. For example, if the teacher is excited, the tracking unit can also provide a display method that includes detailed information. Furthermore, if the teacher is tired, the tracking unit can provide a concise display method. This allows for effective data display by adjusting the display method according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the teacher's emotion data into the generative AI and have the generative AI adjust the display method.

[0088] The tracking unit considers the geographical location of teachers when tracking progress. For example, if a teacher is in an urban area, the tracking unit will prioritize tracking data from urban areas. For example, if a teacher is in a rural area, the tracking unit can also prioritize tracking data from rural areas. Furthermore, if a teacher is overseas, the tracking unit can prioritize tracking international data. This allows for more relevant data tracking by considering geographical location. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the teacher's geographical location information into AI and have the AI ​​perform the tracking.

[0089] The tracking unit improves the accuracy of tracking by referring to the faculty member's relevant literature when tracking progress. For example, the tracking unit can improve tracking accuracy by referring to literature related to the faculty member's field of expertise. For example, the tracking unit can also improve tracking accuracy by referring to literature related to the faculty member's interests. Furthermore, the tracking unit can improve tracking accuracy by referring to the faculty member's past research results. In this way, the accuracy of tracking is improved by referring to relevant literature. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the faculty member's relevant literature data into AI and have the AI ​​perform the tracking accuracy improvement.

[0090] The feedback unit estimates the teacher's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the teacher is stressed, the feedback unit may provide positive feedback to increase motivation. For example, if the teacher is excited, the feedback unit may provide challenging feedback to encourage further growth. Also, if the teacher is tired, the feedback unit may provide concise and specific feedback to efficiently communicate areas for improvement. This allows for effective feedback by adjusting the content of the feedback according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit may input the teacher's emotion data into the generative AI and have the generative AI adjust the content of the feedback.

[0091] The feedback unit provides optimal feedback by referring to the teacher's past feedback history when providing feedback. For example, the feedback unit provides new, non-repeating feedback based on the teacher's past feedback history. For example, the feedback unit can focus on providing feedback on areas where the teacher has struggled in the past. The feedback unit can also select an effective feedback method from the teacher's past feedback history. In this way, optimal feedback can be provided by referring to past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the teacher's past feedback history data into AI and have the AI ​​perform the task of providing optimal feedback.

[0092] The feedback unit customizes the feedback content based on the teacher's area of ​​expertise and interests when providing feedback. For example, the feedback unit can provide feedback that is specific to the teacher's area of ​​expertise. For example, the feedback unit can also provide feedback based on the teacher's interests. Furthermore, the feedback unit can provide feedback that combines the teacher's area of ​​expertise and interests. This allows for more effective feedback by customizing the content based on the teacher's area of ​​expertise and interests. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the teacher's area of ​​expertise and interest data into the AI ​​and have the AI ​​customize the feedback content.

[0093] The feedback unit estimates the teacher's emotions and determines the priority of feedback based on the estimated emotions. For example, if the teacher is stressed, the feedback unit will prioritize providing positive feedback. For example, if the teacher is excited, the feedback unit may also prioritize providing challenging feedback. Furthermore, if the teacher is tired, the feedback unit may also prioritize providing concise and specific feedback. This allows for effective feedback by prioritizing feedback according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the teacher's emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0094] The feedback unit provides optimal feedback by considering the teacher's geographical location information when providing feedback. For example, if the teacher is in an urban area, the feedback unit provides feedback based on urban data. For example, if the teacher is in a rural area, the feedback unit can also provide feedback based on rural data. Furthermore, if the teacher is overseas, the feedback unit can provide feedback based on international data. In this way, optimal feedback can be provided by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the teacher's geographical location information into AI and have the AI ​​perform the task of providing optimal feedback.

[0095] The feedback department analyzes the teacher's social media activity when providing feedback and provides relevant feedback. For example, the feedback department provides feedback based on topics the teacher shows interest in on social media. For example, the feedback department can also provide feedback that incorporates the opinions of experts the teacher follows on social media. Furthermore, the feedback department can provide feedback related to communities the teacher participates in on social media. In this way, relevant feedback can be provided by analyzing social media activity. Some or all of the above processing in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input the teacher's social media activity data into AI and have the AI ​​perform the provision of relevant feedback.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Educational support systems can also estimate teachers' emotions and provide incentives to maintain their motivation based on those estimated emotions. For example, if a teacher is feeling stressed, it can recommend relaxing breaks or refreshing activities. If a teacher is feeling excited, it can provide challenging tasks or projects to boost their motivation. Furthermore, if a teacher is feeling tired, it can provide effective learning resources in a short amount of time to help them learn efficiently. In this way, by providing incentives tailored to teachers' emotions, it is possible to maintain their motivation and support effective learning.

[0098] Educational support systems can improve the accuracy of teacher learning data by considering teachers' past learning patterns. For example, they can analyze which learning methods have been effective for teachers in the past and prioritize recommending those methods. They can also focus on analyzing areas where teachers have struggled in the past and identify areas for improvement. Furthermore, they can select and provide effective learning resources based on teachers' past learning history. By considering past learning patterns, they can improve the accuracy of analysis and efficiently support teachers' skill development.

[0099] The educational support system can also estimate teachers' emotions and adjust how learning resources are delivered based on those estimates. For example, if a teacher is stressed, it can provide learning resources with a visually relaxing design. If a teacher is agitated, it can provide interactive learning resources to increase their motivation. Furthermore, if a teacher is tired, it can provide concise learning resources that can be understood in a short amount of time. In this way, by adjusting how learning resources are delivered according to the teacher's emotions, effective learning can be supported.

[0100] Educational support systems can customize analysis algorithms based on teachers' areas of expertise and interests when analyzing teachers' learning data. For example, using an analysis algorithm specialized in a teacher's area of ​​expertise can yield more accurate results. Furthermore, using an analysis algorithm based on a teacher's interests can enhance their motivation to learn. Combining an analysis algorithm with a teacher's area of ​​expertise and interests enables more effective data analysis. In this way, customizing analysis algorithms based on areas of expertise and interests can efficiently support teachers' skill development.

[0101] The educational support system can also estimate the teacher's emotions and adjust the content of feedback based on those emotions. For example, if the teacher is stressed, positive feedback can be provided to boost their motivation. If the teacher is excited, challenging feedback can be provided to encourage further growth. Furthermore, if the teacher is tired, concise and specific feedback can be provided to efficiently communicate areas for improvement. In this way, by adjusting the content of feedback according to the teacher's emotions, effective feedback becomes possible.

[0102] Educational support systems can also analyze teachers' learning data while considering their geographical location. For example, if a teacher is located in an urban area, the system can prioritize analyzing urban data to obtain more relevant results. Similarly, if a teacher is located in a rural area, the system can prioritize analyzing rural data. Furthermore, if a teacher is overseas, the system can prioritize analyzing international data. By considering geographical location, this enables more relevant data analysis and efficiently supports teachers' skill development.

[0103] The educational support system can also estimate teachers' emotions and adjust progress tracking methods based on those estimates. For example, if a teacher is stressed, a simplified tracking method can be used to reduce their burden. Conversely, if a teacher is agitated, a more detailed tracking method can be used to enhance learning effectiveness. Furthermore, if a teacher is tired, a method that allows for quick tracking can be used. By adjusting tracking methods according to teachers' emotions, it becomes possible to effectively understand their progress.

[0104] Educational support systems can improve the accuracy of teacher learning data by referencing relevant literature. For example, referencing literature related to a teacher's area of ​​expertise can yield more accurate analysis results. Furthermore, referencing literature related to a teacher's interests can increase their motivation to learn. Additionally, referencing a teacher's past research findings can help select effective analysis methods. This allows for improved analysis accuracy and efficient support for teacher skill development through the use of relevant literature.

[0105] The educational support system can also estimate teachers' emotions and adjust how tracking results are displayed based on those estimates. For example, if a teacher is stressed, a simple and highly visible display can be provided. If a teacher is agitated, a display with more detailed information can be provided. Furthermore, if a teacher is tired, a concise display can be provided. This allows for more effective data presentation by adjusting the display method according to the teacher's emotions.

[0106] Educational support systems can improve the accuracy of analysis by referencing teachers' past learning history when analyzing their learning data. For example, the accuracy of the analysis can be improved based on the teacher's past learning history. It can also focus the analysis on areas where the teacher has struggled in the past. Furthermore, it can select effective analysis methods based on the teacher's past learning history. As a result, by referring to past learning history, the accuracy of the analysis can be improved, and teachers' skill development can be efficiently supported.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The service provider will offer a virtual society training program. Using generative AI, the service provider will automatically generate diverse business scenarios, allowing instructors to learn project management and problem-solving skills. The service provider will also customize scenarios based on instructors' areas of expertise and interests to facilitate practical learning. Step 2: The analysis unit analyzes the teachers' learning data. Based on the teachers' learning data, the analysis unit identifies which skills need strengthening and recommends additional training as needed. Step 3: The tracking unit grasps the progress based on the data analyzed by the analysis unit. The tracking unit tracks the teacher's learning data in real time and evaluates the progress. Step 4: The feedback department provides feedback based on the progress tracked by the tracking department. The feedback department provides feedback according to the level of achievement and recommends additional training as needed.

[0109] 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] 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.

[0112] For example, each of the multiple elements, including the provision unit, analysis unit, tracking unit, and feedback unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For instance, the provision unit provides a virtual social training program via the control unit 46A of the smart device 14 and automatically generates diverse business scenarios using generative AI. The analysis unit analyzes the teacher's learning data via the identification processing unit 290 of the data processing unit 12 to identify which skills should be strengthened. The tracking unit tracks progress in real time via the control unit 46A of the smart device 14, and the feedback unit provides feedback according to the level of achievement via the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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.

[0128] For example, each of the multiple elements, including the provision unit, analysis unit, tracking unit, and feedback unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the provision unit provides a virtual social training program via the control unit 46A of the smart glasses 214 and automatically generates diverse business scenarios using generative AI. The analysis unit analyzes the teacher's learning data via the identification processing unit 290 of the data processing unit 12 and identifies which skills should be strengthened. The tracking unit tracks progress in real time via the control unit 46A of the smart glasses 214, and the feedback unit provides feedback according to the level of achievement via the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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.

[0144] For example, each of the multiple elements, including the provision unit, analysis unit, tracking unit, and feedback unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the provision unit provides a virtual social training program via the control unit 46A of the headset terminal 314 and automatically generates diverse business scenarios using generative AI. The analysis unit analyzes the teacher's learning data via the identification processing unit 290 of the data processing unit 12 and identifies which skills should be strengthened. The tracking unit tracks progress in real time via the control unit 46A of the headset terminal 314, and the feedback unit provides feedback according to the level of achievement via the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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.

[0161] For example, each of the multiple elements, including the provision unit, analysis unit, tracking unit, and feedback unit, is implemented by at least one of the robot 414 and the data processing unit 12. For instance, the provision unit provides a virtual social training program via the control unit 46A of the robot 414 and automatically generates diverse business scenarios using generative AI. The analysis unit analyzes the teacher's learning data via the specific processing unit 290 of the data processing unit 12 to identify which skills should be strengthened. The tracking unit tracks progress in real time via the control unit 46A of the robot 414, and the feedback unit provides feedback according to the level of achievement via the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The provision department provides virtual society training programs, The analysis unit analyzes the teacher's learning data, A tracking unit that grasps the progress status based on the data analyzed by the aforementioned analysis unit, The system includes a feedback unit that provides feedback based on the progress status grasped by the tracking unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Generative AI is used to automatically generate diverse business scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze teachers' learning data to identify which skills need strengthening. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Provide feedback based on the level of achievement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Recommended appropriate training and additional learning resources. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The system estimates the emotions of the teachers and adjusts the content of the virtual social training program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, When providing a virtual society training program, we analyze the past training history of instructors and select the optimal scenario. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, When providing virtual society training programs, the scenarios are customized based on the instructors' areas of expertise and interests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, The system estimates the teachers' emotions and prioritizes the scenarios to be presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, When providing virtual society training programs, the program prioritizes providing highly relevant scenarios by taking into account the geographical location of the instructors. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, When providing a virtual society training program, we analyze the social media activities of instructors and provide relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the teachers' emotions and adjusts the analysis method of the training data based on the estimated teachers' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing training data, referencing the instructor's past learning history improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing training data, the analysis algorithm is customized based on the instructor's area of ​​expertise and interests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the teachers' emotions and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing training data, the geographical location information of the instructors is taken into consideration during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing training data, we improve the accuracy of the analysis by referring to relevant literature recommended by instructors. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned tracking unit is Estimate teachers' emotions and adjust progress tracking methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned tracking unit is When tracking progress, refer to the instructor's past progress data to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tracking unit is When tracking progress, customize the tracking algorithm based on the instructor's area of ​​expertise and interests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tracking unit is The system estimates the teachers' emotions and adjusts how tracking results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is When tracking progress, the tracking should take into account the geographical location of the instructors. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is When tracking progress, refer to relevant literature by instructors to improve the accuracy of tracking. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is The system estimates the teacher's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, refer to the instructor's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, customize the content based on the instructor's area of ​​expertise and interests. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is The system estimates the teacher's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, we take into account the instructor's geographical location to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, analyze the instructor's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The provision department provides virtual society training programs, The analysis unit analyzes the teacher's learning data, A tracking unit that grasps the progress status based on the data analyzed by the aforementioned analysis unit, The system includes a feedback unit that provides feedback based on the progress status grasped by the tracking unit. A system characterized by the following features.

2. The aforementioned supply unit is, Generative AI is used to automatically generate diverse business scenarios. The system according to feature 1.

3. The aforementioned analysis unit, Analyze teachers' learning data to identify which skills need strengthening. The system according to feature 1.

4. The aforementioned feedback unit is Provide feedback based on the level of achievement. The system according to feature 1.

5. The aforementioned supply unit is, Recommended appropriate training and additional learning resources. The system according to feature 1.

6. The aforementioned supply unit is, The system estimates the emotions of the teachers and adjusts the content of the virtual social training program based on those estimated emotions. The system according to feature 1.

7. The aforementioned supply unit is, When providing a virtual society training program, we analyze the past training history of instructors and select the optimal scenario. The system according to feature 1.

8. The aforementioned supply unit is, When providing virtual society training programs, the scenarios are customized based on the instructors' areas of expertise and interests. The system according to feature 1.

Images