system

Abstract

The system according to this embodiment aims to enable students to efficiently participate in global problem-solving projects and receive feedback from experts. [Solution] The system according to the embodiment comprises a participation unit, a team formation unit, a theme selection unit, a support unit, and a feedback unit. The participation unit allows students to participate in an online platform. The team formation unit allows students who have participated through the participation unit to form teams. The theme selection unit allows the teams formed by the team formation unit to select a theme. The support unit allows AI to support research and project management based on the theme selected by the theme selection unit. The feedback unit allows experts to provide feedback on the projects supported by the support 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for students to efficiently participate in global problem-solving projects and receive feedback from experts.

[0005] The system according to the embodiment aims to enable students to efficiently participate in global problem-solving projects and receive feedback from experts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a participation unit, a team formation unit, a theme selection unit, a support unit, and a feedback unit. The participation unit allows students to join an online platform. The team formation unit forms teams with the students who have joined through the participation unit. The theme selection unit allows the teams formed by the team formation unit to select a theme. The support unit uses AI to support research and project management based on the theme selected by the theme selection unit. The feedback unit receives feedback from experts on the projects supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment allows students to efficiently participate in global problem-solving projects and receive feedback from experts. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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) An online platform according to an embodiment of the present invention is a system that allows students from all over the world to participate and advance global problem-solving projects using AI. This online platform allows students to participate, form teams, and work on themes such as environmental issues, poverty, and education. AI supports research and project management, and students can also receive feedback from experts. For example, students can participate in the online platform and form teams based on their interests and areas of expertise. For instance, a student interested in environmental issues can team up with other students who share the same interests. Next, the teams work on their chosen themes. For example, a team working on environmental issues can tackle specific challenges such as reducing plastic waste or promoting renewable energy. A team working on poverty can address challenges such as developing support measures for impoverished areas and providing educational opportunities. AI supports research and project management. For example, AI can collect information from the internet and provide relevant data. It can also manage project progress, assign tasks, and track progress. This allows students to efficiently advance projects. Furthermore, students can receive feedback from experts. For example, an environmental expert can provide advice on the team's proposals. This allows students to gain specialized knowledge and improve the quality of their projects. This system provides students with opportunities to develop practical solutions to global challenges. Furthermore, collaborating with students from different countries and cultures broadens their international perspectives. For example, Japanese and American students working together on environmental issues can learn different approaches from different viewpoints. Thus, AI-powered online platforms are an effective means for students to address global challenges. Students can acquire practical skills by receiving support for research and project management, and gaining feedback from experts. They can also broaden their international perspectives by collaborating with students from different countries and cultures.This allows online platforms to enable students to efficiently work on global problem-solving projects.

[0029] The online platform according to this embodiment comprises a participation unit, a team formation unit, a theme selection unit, a support unit, and a feedback unit. The participation unit enables students to participate in the online platform. For example, the participation unit allows students to participate in the platform by registering online. The participation unit can also employ an invitation system. For example, only students invited by a specific school or organization can participate. The team formation unit enables students who have joined through the participation unit to form teams. For example, the team formation unit allows students to form teams based on their interests and areas of expertise. The team formation unit can also perform random matching. For example, the system can automatically randomly match students and form teams. The theme selection unit enables the teams formed by the team formation unit to select a theme. For example, the theme selection unit provides themes such as environmental issues, poverty, and education, and allows students to select a theme by voting. The theme selection unit can also perform random selection. For example, the system can automatically randomly select a theme and assign it to a team. The support unit allows AI to support research and project management based on the theme selected by the theme selection unit. For example, the support department includes a research department that collects information from the internet and provides relevant data. The research department can collect information and provide it to students through methods such as web scraping and API usage. The support department also includes a management department that manages the progress of projects, assigns tasks, and tracks progress. The management department can manage the progress of projects and provide it to students through methods such as Gantt charts and progress reports. The feedback department enables projects supported by the support department to receive feedback from experts. For example, the feedback department includes an advice department where experts provide advice on the team's proposals. The advice department can provide expert feedback through methods such as online meetings and document reviews.This allows the online platform according to the embodiment to enable students to efficiently work on global problem-solving projects.

[0030] Participation functions enable students to participate in the online platform. For example, participation functions allow students to join the platform by registering online. Specifically, participation functions provide a registration form for students to enter their basic information, academic background, areas of interest, etc. This registration form has a user-friendly interface and is designed so that students can easily enter the necessary information. Participation functions can also include a verification and authentication process for registration information. For example, once a student completes registration, a confirmation email is sent, and registration is completed by clicking a link in the email. This process prevents unauthorized access to the platform and helps build a reliable user base. Furthermore, participation functions can also adopt an invitation system. For example, only students who have received an invitation from a specific school or organization can participate. In this case, participation functions issue and manage invitation codes, and invited students can join by entering the code. This promotes participation limited to specific communities or groups and allows for more focused activities. Participation functions play a crucial role in facilitating the initial steps for students to join the platform and can improve the user experience.

[0031] The Team Formation Department enables students who participate through the Participation Department to form teams. For example, the Team Formation Department allows students to form teams based on their interests and areas of expertise. Specifically, the Team Formation Department provides a function that allows students to register their interests and areas of expertise in their profiles and then matches them with other students based on that. For example, a student interested in environmental issues can form a team with other students who share the same interests. The Team Formation Department can also perform random matching. For example, the system can automatically match students randomly and form teams. In this case, the Team Formation Department can consider the diversity of students and create teams with more multifaceted perspectives by combining students with different backgrounds and areas of expertise. Furthermore, the Team Formation Department can flexibly set the size and composition of teams. For example, teams of 3 to 5 people can be formed depending on the scale and theme of the project. This allows the Team Formation Department to build optimal teams for students to cooperate efficiently and effectively and advance projects.

[0032] The theme selection section allows teams formed by the team formation section to select a theme. For example, the theme selection section provides themes such as environmental issues, poverty, and education, and allows students to select a theme by voting. Specifically, the theme selection section provides detailed explanations and background information on each theme, so that students can choose a theme after understanding its importance and interest. The theme selection section can also perform random selection. For example, the system can automatically select a theme randomly and assign it to the teams. In this case, the theme selection section ensures a fair distribution of themes, so that all teams can work on different themes. Furthermore, the theme selection section provides functions to make the theme selection process transparent and fair. For example, it can display the theme selection results in real time, allowing students to check the progress of the selection process. In this way, the theme selection section can support students in an important step of selecting a theme based on their interests and working on a project.

[0033] The support department utilizes AI to assist with research and project management based on themes selected by the theme selection department. For example, the support department includes a research department that collects information from the internet and provides relevant data. The research department can collect information and provide it to students through methods such as web scraping and API usage. Specifically, the research department uses AI to automatically collect and organize relevant academic papers, news articles, statistical data, etc. This allows students to quickly obtain the necessary information and use it as foundational material for their projects. The support department also includes a management department that manages project progress, assigns tasks, and tracks progress. The management department can manage project progress and provide it to students through methods such as Gantt charts and progress reports. Specifically, the management department provides functions to break down tasks for each team, assign responsibilities, and track progress in real time. This allows students to understand the project's progress and work on tasks efficiently. Furthermore, the support department can use AI to analyze project progress and suggest potential problems and areas for improvement. This allows the support department to provide crucial support for students to efficiently and effectively advance their projects.

[0034] The Feedback Department enables projects supported by the Support Department to receive feedback from experts. For example, the Feedback Department includes an Advice Department where experts provide advice on the team's proposals. The Advice Department can provide expert feedback through methods such as online meetings and document reviews. Specifically, the Advice Department schedules online meetings with experts, providing students with opportunities to ask questions and seek advice directly. Furthermore, through the document review function, experts can provide specific comments and revision suggestions on submitted project documents. In addition, the Feedback Department provides functions to organize the feedback so that students can easily understand and implement it. For example, by generating and providing students with a report summarizing the key points of the feedback, the feedback can be used effectively. This allows the Feedback Department to provide students with valuable advice from experts and crucial support for improving the quality of their projects.

[0035] The support department includes a research department that collects information from the internet and provides relevant data. The research department can automatically collect information from the internet using, for example, web scraping technology. For example, the research department can crawl websites based on specific keywords and collect relevant information. The research department can also obtain data using APIs. For example, the research department can use publicly available APIs to obtain information such as statistical data and research papers. Furthermore, the research department can organize the collected information and provide it to students. For example, the research department can organize the collected data by category to make it easily accessible to students. This streamlines project research by collecting information from the internet and providing relevant data. Some or all of the above processes in the research department may be performed using AI or not. For example, the research department can input the collected information into an AI, which can evaluate the relevance of the information and extract important data.

[0036] The support department includes a management department that manages the project's progress, assigns tasks, and tracks progress. The management department can visually manage the project's progress, for example, using a Gantt chart. For example, the management department can display each task in the project on a Gantt chart, allowing for an at-a-glance view of its progress. The management department can also provide regular progress reports. For example, the management department can request regular progress reports from students to understand the project's progress. Furthermore, the management department can assign tasks. For example, the management department can assign tasks based on each student's skills and role. This streamlines project management by managing the project's progress, assigning tasks, and tracking progress. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input project progress data into an AI, which can evaluate the progress and determine task priorities.

[0037] The feedback department includes an advice department where experts provide advice on the team's proposals. The advice department can provide feedback from experts, for example, through online meetings. For example, the advice department can use a video conferencing system to allow experts and students to interact directly and receive advice on their proposals. The advice department can also conduct document reviews. For example, the advice department can have experts review submitted proposals and provide comments and suggestions for revisions. Furthermore, the advice department can provide feedback based on expert opinions. For example, the advice department can have experts evaluate proposals and provide feedback on the evaluation results to the students. This improves the quality of the project by providing expert advice on the team's proposals. Some or all of the above processes in the advice department may or may not be performed using AI. For example, the advice department can input expert feedback data into an AI, which can analyze the content of the feedback and provide it to the students.

[0038] The participation system allows students to form teams based on their interests and areas of expertise. For example, students can identify their interests and areas of expertise by answering questionnaires. For example, the system can suggest appropriate teams based on themes of interest and past project history entered by students. The system can also identify students' areas of expertise based on their past project history. For example, the system can analyze data from projects students have participated in in the past and suggest the optimal team. This improves students' motivation for projects by allowing them to form teams based on their interests and areas of expertise. Some or all of the processes described above in the participation system may be performed using AI or not. For example, the system can input student questionnaire data into an AI, which can then suggest the optimal team.

[0039] The theme selection section allows students to choose themes such as environmental issues, poverty, and education. For example, the theme selection section could allow students to select themes through voting. For example, the theme selection section could allow students to choose from multiple themes and select the one with the most votes. The theme selection section could also perform random selection. For example, the system could automatically select themes randomly and assign them to teams. Furthermore, the theme selection section could allow students to select themes based on their interests and areas of expertise. For example, the theme selection section could allow students to select a theme they are interested in and proceed with a project based on that theme. This allows students to work on a variety of themes. Some or all of the above processes in the theme selection section may be performed using AI or not. For example, the theme selection section could input student interest and area of ​​expertise data into an AI, which could then suggest the most suitable themes.

[0040] The participation department can analyze a student's past participation history and select the optimal participation method. For example, the participation department can suggest the optimal participation method based on the success rate of projects the student has participated in in the past. It can also suggest new related projects based on the themes of projects the student has participated in in the past. Furthermore, the participation department can suggest the most effective time slots for participation based on the student's past participation history. Thus, by analyzing a student's past participation history, the optimal participation method can be suggested. Some or all of the above processes in the participation department may be performed using AI or not. For example, the participation department can input student participation history data into an AI, which can then suggest the optimal participation method.

[0041] The participating component can filter students based on their current learning status and areas of interest when they participate. For example, it can analyze a student's current learning status and suggest the most suitable project. It can also prioritize displaying relevant projects based on the student's areas of interest. Furthermore, it can suggest projects of appropriate difficulty according to the student's learning progress. This allows for the suggestion of the most suitable project based on the student's current learning status and areas of interest. Some or all of the above processing in the participating component may be performed using AI or not. For example, the participating component can input student learning status data into an AI, which can then suggest the most suitable project.

[0042] The participating department can prioritize students' participation in highly relevant projects by considering their geographical location. For example, the department can propose projects related to local issues based on the student's geographical location. It can also propose projects that allow students to collaborate with nearby students based on their geographical location. Furthermore, the department can propose projects that allow students to receive feedback from local experts based on their geographical location. This ensures that students can participate in projects related to local issues by considering their geographical location. Some or all of the above processing by the participating department may be performed using AI or not. For example, the participating department can input students' geographical location data into an AI, which can then propose the most suitable project.

[0043] The participating department can analyze students' social media activity and propose relevant projects upon their participation. For example, it can analyze students' social media activity and propose projects related to topics of interest. It can also analyze students' social media activity and propose projects in which they can collaborate with other students who share similar interests. Furthermore, it can analyze students' social media activity and propose projects related to trends. In this way, by analyzing students' social media activity, projects related to topics of interest can be proposed. Some or all of the above processing by the participating department may be performed using AI or not. For example, the participating department can input students' social media data into an AI, which can then propose the most suitable project.

[0044] The team formation department can select the most suitable team members by analyzing students' past collaboration history during team formation. For example, the team formation department can select the most cooperative members based on students' past collaboration history. It can also select members from the most successful projects based on students' past collaboration history. Furthermore, the team formation department can select members who are the most compatible based on students' past collaboration history. In this way, the optimal team members can be selected by analyzing students' past collaboration history. Some or all of the above processes in the team formation department may be performed using AI or not. For example, the team formation department can input student collaboration history data into AI, and the AI ​​can suggest the most suitable team members.

[0045] The team formation department can customize teams based on students' areas of expertise and skill sets during team formation. For example, the team formation department can select members with the optimal skill sets based on students' areas of expertise. It can also select members with the skills necessary for a project based on students' skill sets. Furthermore, the team formation department can create a well-balanced team based on students' areas of expertise and skill sets. This allows for the creation of well-balanced teams by customizing teams based on students' areas of expertise and skill sets. Some or all of the above processes in the team formation department may be performed using AI, or not. For example, the team formation department can input student areas of expertise and skill set data into an AI, which can then propose the optimal team.

[0046] The theme selection unit can analyze a student's past project history to suggest the most suitable theme during the theme selection process. For example, it can suggest relevant themes based on the student's past project history. It can also suggest successful themes based on the student's past project history. Furthermore, it can suggest new themes based on the student's past project history. In this way, the system can suggest the most suitable theme by analyzing the student's past project history. Some or all of the above processes in the theme selection unit may be performed using AI, or they may not. For example, the theme selection unit can input the student's project history data into an AI, which can then suggest the most suitable theme.

[0047] The theme selection unit can customize themes based on students' areas of expertise and interests during the selection process. For example, the theme selection unit can suggest relevant themes based on the student's area of ​​expertise. It can also suggest themes that will pique the student's interest based on their areas of interest. Furthermore, the theme selection unit can suggest well-balanced themes based on both the student's area of ​​expertise and areas of interest. This allows for the selection of more appropriate themes by customizing them based on the student's area of ​​expertise and areas of interest. Some or all of the above processing in the theme selection unit may be performed using AI, or not. For example, the theme selection unit can input data on students' areas of expertise and areas of interest into an AI, which can then suggest the most suitable themes.

[0048] The support department can monitor the project's progress in real time and provide optimal support during support sessions. For example, the support department can monitor the project's progress in real time and issue alerts if delays occur. It can also monitor the project's progress in real time and suggest the next steps if progress is on track. Furthermore, it can monitor the project's progress in real time and suggest solutions if problems arise. This allows for the provision of optimal support by monitoring the project's progress in real time. Some or all of the above processes performed by the support department may or may not be performed using AI. For example, the support department can input project progress data into an AI, which can then suggest the optimal support method.

[0049] The support unit can apply different support algorithms depending on the characteristics of the project during support. For example, the support unit can apply a support algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply a support algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply a support algorithm based on educational data to projects addressing educational issues. This improves the accuracy of support by applying the most appropriate support algorithm for each project's characteristics. Some or all of the above-described processes in the support unit may be performed using AI, or they may not. For example, the support unit can input project characteristic data into an AI, which can then propose the most suitable support algorithm.

[0050] The support department can provide optimal support by considering the geographical distribution of the project. For example, the support department can provide support that utilizes local resources based on the project's geographical distribution. Furthermore, the support department can provide support that obtains feedback from local experts based on the project's geographical distribution. In addition, the support department can provide support tailored to local issues based on the project's geographical distribution. This allows for the provision of support that utilizes local resources by considering the project's geographical distribution. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input project geographical distribution data into AI, which can then propose the optimal support method.

[0051] The support department can improve the accuracy of its support by referring to relevant project literature during the support process. For example, the support department can refer to relevant project literature and provide support based on the latest research findings. It can also refer to relevant project literature and provide support based on past success stories. Furthermore, the support department can refer to relevant project literature and provide support based on expert opinions. This improves the accuracy of support by referring to relevant project literature. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input relevant literature data into AI, which can then suggest the optimal support method.

[0052] The feedback unit can provide optimal feedback by analyzing the expert's past feedback history. For example, the feedback unit can provide optimal feedback based on the expert's past feedback history. Furthermore, the feedback unit can provide feedback based on success stories, also based on the expert's past feedback history. In addition, the feedback unit can provide feedback that promotes student growth, based on the expert's past feedback history. Thus, by analyzing the expert's past feedback history, optimal feedback can be provided. Some or all of the above processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input expert feedback history data into AI, which can then propose the optimal feedback method.

[0053] The feedback unit can apply different feedback algorithms depending on the characteristics of the project during the feedback process. For example, the feedback unit can apply a feedback algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply a feedback algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply a feedback algorithm based on educational data to projects addressing educational issues. This improves the accuracy of feedback by applying the most appropriate feedback algorithm for each project's characteristics. Some or all of the above-described processes in the feedback unit may be performed using AI, or they may not. For example, the feedback unit can input project characteristic data into an AI, which can then propose the most appropriate feedback algorithm.

[0054] The feedback unit can provide optimal feedback by considering the geographical distribution of the project. For example, the feedback unit can provide feedback that utilizes local resources based on the project's geographical distribution. It can also provide feedback that obtains input from local experts based on the project's geographical distribution. Furthermore, the feedback unit can provide feedback tailored to local issues based on the project's geographical distribution. This allows for the provision of feedback that utilizes local resources by considering the project's geographical distribution. 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 project geographical distribution data into AI, which can then propose the optimal feedback method.

[0055] The feedback unit can improve the accuracy of its feedback by referring to relevant project literature during the feedback process. For example, the feedback unit can refer to relevant project literature and provide feedback based on the latest research findings. It can also refer to relevant project literature and provide feedback based on past success stories. Furthermore, the feedback unit can refer to relevant project literature and provide feedback based on expert opinions. This improves the accuracy of feedback by referring to relevant project literature. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input relevant literature data into AI, which can then propose the optimal feedback method.

[0056] The research department can collect information from the internet in real time during research and provide optimal data. For example, the research department can collect and provide the latest research findings from the internet in real time. It can also collect and provide the latest news from the internet in real time. Furthermore, the research department can collect and provide the latest statistical data from the internet in real time. This allows for the provision of optimal data by collecting information from the internet in real time. Some or all of the above processes in the research department may be performed using AI, or not. For example, the research department can input information from the internet into an AI, which can then extract and provide the optimal data.

[0057] The research department can apply different research algorithms depending on the characteristics of the project during the research process. For example, for projects addressing environmental issues, the research department can apply a research algorithm based on environmental data. Similarly, for projects addressing poverty issues, the research department can apply a research algorithm based on socioeconomic data. Furthermore, for projects addressing educational issues, the research department can apply a research algorithm based on educational data. This improves the accuracy of the research by applying the most appropriate research algorithm for each project. Some or all of the above-described processes in the research department may be performed using AI, or they may not. For example, the research department can input project characteristic data into an AI, which can then propose the most appropriate research algorithm.

[0058] The research department can provide optimal data during research, taking into account the geographical distribution of the project. For example, the research department can provide regional data based on the geographical distribution of the project. Furthermore, the research department can provide data from regional experts based on the geographical distribution of the project. In addition, the research department can provide data utilizing regional resources based on the geographical distribution of the project. This allows for the provision of regional data by considering the geographical distribution of the project. Some or all of the above processes in the research department may be performed using AI, or not. For example, the research department can input the geographical distribution data of the project into an AI, which can then suggest the optimal data.

[0059] The research department can improve the accuracy of its research by referring to relevant project literature during the research process. For example, the research department can refer to relevant project literature and provide research based on the latest research findings. It can also refer to relevant project literature and provide research based on past success stories. Furthermore, the research department can refer to relevant project literature and provide research based on expert opinions. This improves the accuracy of the research by referring to relevant project literature. Some or all of the above processes in the research department may be performed using AI, or not. For example, the research department can input relevant literature data into AI, which can then suggest the optimal research method.

[0060] The management department can monitor the project's progress in real time and provide optimal management during the management phase. For example, the management department can monitor the project's progress in real time and issue alerts if delays occur. Furthermore, the management department can monitor the project's progress in real time and suggest the next steps if progress is on track. In addition, the management department can monitor the project's progress in real time and suggest solutions if problems arise. This allows for optimal management by monitoring the project's progress in real time. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input project progress data into an AI, which can then suggest the optimal management method.

[0061] The management department can apply different management algorithms depending on the characteristics of the project during management. For example, the management department can apply a management algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply a management algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply a management algorithm based on educational data to projects addressing educational issues. This improves the accuracy of management by applying the most appropriate management algorithm for each project's characteristics. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input project characteristic data into an AI, which can then propose the most appropriate management algorithm.

[0062] The management department can provide optimal management by considering the geographical distribution of the project. For example, the management department can provide management that utilizes local resources based on the geographical distribution of the project. Furthermore, the management department can provide management that obtains feedback from local experts based on the geographical distribution of the project. In addition, the management department can provide management tailored to local issues based on the geographical distribution of the project. This allows for management that utilizes local resources by considering the geographical distribution of the project. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input geographical distribution data of the project into an AI, which can then propose the optimal management method.

[0063] The management department can improve the accuracy of its management by referring to relevant project literature during the management process. For example, the management department can refer to relevant project literature and provide management based on the latest research findings. It can also refer to relevant project literature and provide management based on past success stories. Furthermore, the management department can refer to relevant project literature and provide management based on expert opinions. This improves the accuracy of management by referring to relevant project literature. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input relevant literature data into AI, which can then propose the optimal management method.

[0064] The advice department can provide optimal advice by analyzing the expert's past advice history. For example, the advice department can provide optimal advice based on the expert's past advice history. Furthermore, the advice department can provide advice based on success stories, also based on the expert's past advice history. In addition, the advice department can provide advice that promotes student growth, based on the expert's past advice history. Thus, by analyzing the expert's past advice history, optimal advice can be provided. Some or all of the above processes in the advice department may be performed using AI, or not. For example, the advice department can input expert advice history data into AI, which can then propose the optimal advice method.

[0065] The advisory unit can apply different advisory algorithms depending on the characteristics of the project when providing advice. For example, the advisory unit can apply an advisory algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply an advisory algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply an advisory algorithm based on educational data to projects addressing educational issues. This improves the accuracy of advice by applying the most appropriate advisory algorithm for each project's characteristics. Some or all of the above-described processes in the advisory unit may be performed using AI, or they may not. For example, the advisory unit can input project characteristic data into an AI, which can then propose the most appropriate advisory algorithm.

[0066] The advisory unit can provide optimal advice by considering the geographical distribution of the project. For example, the advisory unit can provide advice that utilizes local resources based on the geographical distribution of the project. It can also provide advice that allows for obtaining advice from local experts based on the geographical distribution of the project. Furthermore, the advisory unit can provide advice tailored to specific local issues based on the geographical distribution of the project. This allows for the provision of advice that utilizes local resources by considering the geographical distribution of the project. Some or all of the above processes in the advisory unit may be performed using AI or not. For example, the advisory unit can input geographical distribution data of the project into an AI, which can then propose the optimal advice method.

[0067] The advisory unit can improve the accuracy of its advice by referring to relevant project literature during the advice-giving process. For example, the advisory unit can refer to relevant project literature and provide advice based on the latest research findings. It can also refer to relevant project literature and provide advice based on past success stories. Furthermore, the advisory unit can refer to relevant project literature and provide advice based on expert opinions. This improves the accuracy of the advice by referring to relevant project literature. Some or all of the above processes in the advisory unit may be performed using AI or not. For example, the advisory unit can input relevant literature data into AI, which can then propose the optimal advice method.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The participation department can analyze a student's past participation history and select the optimal participation method. For example, the participation department can suggest the optimal participation method based on the success rate of projects the student has participated in in the past. It can also suggest new related projects based on the themes of projects the student has participated in in the past. Furthermore, the participation department can suggest the most effective time slots for participation based on the student's past participation history. Thus, by analyzing a student's past participation history, the optimal participation method can be suggested. Some or all of the above processes in the participation department may be performed using AI or not. For example, the participation department can input student participation history data into an AI, which can then suggest the optimal participation method.

[0070] The participating component can filter students based on their current learning status and areas of interest when they participate. For example, it can analyze a student's current learning status and suggest the most suitable project. It can also prioritize displaying relevant projects based on the student's areas of interest. Furthermore, it can suggest projects of appropriate difficulty according to the student's learning progress. This allows for the suggestion of the most suitable project based on the student's current learning status and areas of interest. Some or all of the above processing in the participating component may be performed using AI or not. For example, the participating component can input student learning status data into an AI, which can then suggest the most suitable project.

[0071] The team formation department can select the most suitable team members by analyzing students' past collaboration history during team formation. For example, the team formation department can select the most cooperative members based on students' past collaboration history. It can also select members from the most successful projects based on students' past collaboration history. Furthermore, the team formation department can select members who are the most compatible based on students' past collaboration history. In this way, the optimal team members can be selected by analyzing students' past collaboration history. Some or all of the above processes in the team formation department may be performed using AI or not. For example, the team formation department can input student collaboration history data into AI, and the AI ​​can suggest the most suitable team members.

[0072] The theme selection unit can analyze a student's past project history to suggest the most suitable theme during the theme selection process. For example, it can suggest relevant themes based on the student's past project history. It can also suggest successful themes based on the student's past project history. Furthermore, it can suggest new themes based on the student's past project history. In this way, the system can suggest the most suitable theme by analyzing the student's past project history. Some or all of the above processes in the theme selection unit may be performed using AI, or they may not. For example, the theme selection unit can input the student's project history data into an AI, which can then suggest the most suitable theme.

[0073] The support department can monitor the project's progress in real time and provide optimal support during support sessions. For example, the support department can monitor the project's progress in real time and issue alerts if delays occur. It can also monitor the project's progress in real time and suggest the next steps if progress is on track. Furthermore, it can monitor the project's progress in real time and suggest solutions if problems arise. This allows for the provision of optimal support by monitoring the project's progress in real time. Some or all of the above processes performed by the support department may or may not be performed using AI. For example, the support department can input project progress data into an AI, which can then suggest the optimal support method.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The participating organization enables students to join the online platform. For example, students can join the platform by registering online. Alternatively, the organization can restrict participation to students who have received an invitation from a specific school or organization. Step 2: The team formation section enables students who have participated through the participation section to form teams. For example, students can form teams based on their interests or areas of expertise. Alternatively, the system can automatically randomly match students to form teams. Step 3: The theme selection section allows the teams formed by the team formation section to select a theme. For example, themes such as environmental issues, poverty, and education can be provided, and students can choose a theme by voting. Alternatively, the system can automatically select a theme randomly and assign it to the teams. Step 4: The support unit uses AI to assist with research and project management based on the theme selected by the theme selection unit. For example, it includes a research unit that collects information from the internet and provides relevant data. The research unit can collect information and provide it to students using methods such as web scraping and API usage. It also includes a management unit that manages the progress of the project, assigns tasks, and tracks progress. The management unit can manage the progress of the project and provide it to students using methods such as Gantt charts and progress reports. Step 5: The Feedback Department enables the project, supported by the Support Department, to receive feedback from experts. For example, it may include an Advice Department where experts provide advice on the team's proposals. The Advice Department can provide expert feedback through methods such as online meetings or document reviews.

[0076] (Example of form 2) An online platform according to an embodiment of the present invention is a system that allows students from all over the world to participate and advance global problem-solving projects using AI. This online platform allows students to participate, form teams, and work on themes such as environmental issues, poverty, and education. AI supports research and project management, and students can also receive feedback from experts. For example, students can participate in the online platform and form teams based on their interests and areas of expertise. For instance, a student interested in environmental issues can team up with other students who share the same interests. Next, the teams work on their chosen themes. For example, a team working on environmental issues can tackle specific challenges such as reducing plastic waste or promoting renewable energy. A team working on poverty can address challenges such as developing support measures for impoverished areas and providing educational opportunities. AI supports research and project management. For example, AI can collect information from the internet and provide relevant data. It can also manage project progress, assign tasks, and track progress. This allows students to efficiently advance projects. Furthermore, students can receive feedback from experts. For example, an environmental expert can provide advice on the team's proposals. This allows students to gain specialized knowledge and improve the quality of their projects. This system provides students with opportunities to develop practical solutions to global challenges. Furthermore, collaborating with students from different countries and cultures broadens their international perspectives. For example, Japanese and American students working together on environmental issues can learn different approaches from different viewpoints. Thus, AI-powered online platforms are an effective means for students to address global challenges. Students can acquire practical skills by receiving support for research and project management, and gaining feedback from experts. They can also broaden their international perspectives by collaborating with students from different countries and cultures.This allows online platforms to enable students to efficiently work on global problem-solving projects.

[0077] The online platform according to this embodiment comprises a participation unit, a team formation unit, a theme selection unit, a support unit, and a feedback unit. The participation unit enables students to participate in the online platform. For example, the participation unit allows students to participate in the platform by registering online. The participation unit can also employ an invitation system. For example, only students invited by a specific school or organization can participate. The team formation unit enables students who have joined through the participation unit to form teams. For example, the team formation unit allows students to form teams based on their interests and areas of expertise. The team formation unit can also perform random matching. For example, the system can automatically randomly match students and form teams. The theme selection unit enables the teams formed by the team formation unit to select a theme. For example, the theme selection unit provides themes such as environmental issues, poverty, and education, and allows students to select a theme by voting. The theme selection unit can also perform random selection. For example, the system can automatically randomly select a theme and assign it to a team. The support unit allows AI to support research and project management based on the theme selected by the theme selection unit. For example, the support department includes a research department that collects information from the internet and provides relevant data. The research department can collect information and provide it to students through methods such as web scraping and API usage. The support department also includes a management department that manages the progress of projects, assigns tasks, and tracks progress. The management department can manage the progress of projects and provide it to students through methods such as Gantt charts and progress reports. The feedback department enables projects supported by the support department to receive feedback from experts. For example, the feedback department includes an advice department where experts provide advice on the team's proposals. The advice department can provide expert feedback through methods such as online meetings and document reviews.This allows the online platform according to the embodiment to enable students to efficiently work on global problem-solving projects.

[0078] Participation functions enable students to participate in the online platform. For example, participation functions allow students to join the platform by registering online. Specifically, participation functions provide a registration form for students to enter their basic information, academic background, areas of interest, etc. This registration form has a user-friendly interface and is designed so that students can easily enter the necessary information. Participation functions can also include a verification and authentication process for registration information. For example, once a student completes registration, a confirmation email is sent, and registration is completed by clicking a link in the email. This process prevents unauthorized access to the platform and helps build a reliable user base. Furthermore, participation functions can also adopt an invitation system. For example, only students who have received an invitation from a specific school or organization can participate. In this case, participation functions issue and manage invitation codes, and invited students can join by entering the code. This promotes participation limited to specific communities or groups and allows for more focused activities. Participation functions play a crucial role in facilitating the initial steps for students to join the platform and can improve the user experience.

[0079] The Team Formation Department enables students who participate through the Participation Department to form teams. For example, the Team Formation Department allows students to form teams based on their interests and areas of expertise. Specifically, the Team Formation Department provides a function that allows students to register their interests and areas of expertise in their profiles and then matches them with other students based on that. For example, a student interested in environmental issues can form a team with other students who share the same interests. The Team Formation Department can also perform random matching. For example, the system can automatically match students randomly and form teams. In this case, the Team Formation Department can consider the diversity of students and create teams with more multifaceted perspectives by combining students with different backgrounds and areas of expertise. Furthermore, the Team Formation Department can flexibly set the size and composition of teams. For example, teams of 3 to 5 people can be formed depending on the scale and theme of the project. This allows the Team Formation Department to build optimal teams for students to cooperate efficiently and effectively and advance projects.

[0080] The theme selection section allows teams formed by the team formation section to select a theme. For example, the theme selection section provides themes such as environmental issues, poverty, and education, and allows students to select a theme by voting. Specifically, the theme selection section provides detailed explanations and background information on each theme, so that students can choose a theme after understanding its importance and interest. The theme selection section can also perform random selection. For example, the system can automatically select a theme randomly and assign it to the teams. In this case, the theme selection section ensures a fair distribution of themes, so that all teams can work on different themes. Furthermore, the theme selection section provides functions to make the theme selection process transparent and fair. For example, it can display the theme selection results in real time, allowing students to check the progress of the selection process. In this way, the theme selection section can support students in an important step of selecting a theme based on their interests and working on a project.

[0081] The support department utilizes AI to assist with research and project management based on themes selected by the theme selection department. For example, the support department includes a research department that collects information from the internet and provides relevant data. The research department can collect information and provide it to students through methods such as web scraping and API usage. Specifically, the research department uses AI to automatically collect and organize relevant academic papers, news articles, statistical data, etc. This allows students to quickly obtain the necessary information and use it as foundational material for their projects. The support department also includes a management department that manages project progress, assigns tasks, and tracks progress. The management department can manage project progress and provide it to students through methods such as Gantt charts and progress reports. Specifically, the management department provides functions to break down tasks for each team, assign responsibilities, and track progress in real time. This allows students to understand the project's progress and work on tasks efficiently. Furthermore, the support department can use AI to analyze project progress and suggest potential problems and areas for improvement. This allows the support department to provide crucial support for students to efficiently and effectively advance their projects.

[0082] The Feedback Department enables projects supported by the Support Department to receive feedback from experts. For example, the Feedback Department includes an Advice Department where experts provide advice on the team's proposals. The Advice Department can provide expert feedback through methods such as online meetings and document reviews. Specifically, the Advice Department schedules online meetings with experts, providing students with opportunities to ask questions and seek advice directly. Furthermore, through the document review function, experts can provide specific comments and revision suggestions on submitted project documents. In addition, the Feedback Department provides functions to organize the feedback so that students can easily understand and implement it. For example, by generating and providing students with a report summarizing the key points of the feedback, the feedback can be used effectively. This allows the Feedback Department to provide students with valuable advice from experts and crucial support for improving the quality of their projects.

[0083] The support department includes a research department that collects information from the internet and provides relevant data. The research department can automatically collect information from the internet using, for example, web scraping technology. For example, the research department can crawl websites based on specific keywords and collect relevant information. The research department can also obtain data using APIs. For example, the research department can use publicly available APIs to obtain information such as statistical data and research papers. Furthermore, the research department can organize the collected information and provide it to students. For example, the research department can organize the collected data by category to make it easily accessible to students. This streamlines project research by collecting information from the internet and providing relevant data. Some or all of the above processes in the research department may be performed using AI or not. For example, the research department can input the collected information into an AI, which can evaluate the relevance of the information and extract important data.

[0084] The support department includes a management department that manages the project's progress, assigns tasks, and tracks progress. The management department can visually manage the project's progress, for example, using a Gantt chart. For example, the management department can display each task in the project on a Gantt chart, allowing for an at-a-glance view of its progress. The management department can also provide regular progress reports. For example, the management department can request regular progress reports from students to understand the project's progress. Furthermore, the management department can assign tasks. For example, the management department can assign tasks based on each student's skills and role. This streamlines project management by managing the project's progress, assigning tasks, and tracking progress. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input project progress data into an AI, which can evaluate the progress and determine task priorities.

[0085] The feedback department includes an advice department where experts provide advice on the team's proposals. The advice department can provide feedback from experts, for example, through online meetings. For example, the advice department can use a video conferencing system to allow experts and students to interact directly and receive advice on their proposals. The advice department can also conduct document reviews. For example, the advice department can have experts review submitted proposals and provide comments and suggestions for revisions. Furthermore, the advice department can provide feedback based on expert opinions. For example, the advice department can have experts evaluate proposals and provide feedback on the evaluation results to the students. This improves the quality of the project by providing expert advice on the team's proposals. Some or all of the above processes in the advice department may or may not be performed using AI. For example, the advice department can input expert feedback data into an AI, which can analyze the content of the feedback and provide it to the students.

[0086] The participation system allows students to form teams based on their interests and areas of expertise. For example, students can identify their interests and areas of expertise by answering questionnaires. For example, the system can suggest appropriate teams based on themes of interest and past project history entered by students. The system can also identify students' areas of expertise based on their past project history. For example, the system can analyze data from projects students have participated in in the past and suggest the optimal team. This improves students' motivation for projects by allowing them to form teams based on their interests and areas of expertise. Some or all of the processes described above in the participation system may be performed using AI or not. For example, the system can input student questionnaire data into an AI, which can then suggest the optimal team.

[0087] The theme selection section allows students to choose themes such as environmental issues, poverty, and education. For example, the theme selection section could allow students to select themes through voting. For example, the theme selection section could allow students to choose from multiple themes and select the one with the most votes. The theme selection section could also perform random selection. For example, the system could automatically select themes randomly and assign them to teams. Furthermore, the theme selection section could allow students to select themes based on their interests and areas of expertise. For example, the theme selection section could allow students to select a theme they are interested in and proceed with a project based on that theme. This allows students to work on a variety of themes. Some or all of the above processes in the theme selection section may be performed using AI or not. For example, the theme selection section could input student interest and area of ​​expertise data into an AI, which could then suggest the most suitable themes.

[0088] The participation unit can estimate students' emotions and adjust the timing of their participation based on those emotions. For example, if a student is feeling stressed, the participation unit can encourage them to participate during a time when they can relax. If a student is excited, the participation unit can encourage them to participate immediately to maintain their motivation. Furthermore, if a student is tired, the participation unit can encourage them to rest before participating. This allows for more appropriate participation by adjusting the timing of participation according to the student's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the participation unit may be performed using AI or not. For example, the participation unit can input student emotion data into an AI, which can then suggest the optimal timing for participation.

[0089] The participation department can analyze a student's past participation history and select the optimal participation method. For example, the participation department can suggest the optimal participation method based on the success rate of projects the student has participated in in the past. It can also suggest new related projects based on the themes of projects the student has participated in in the past. Furthermore, the participation department can suggest the most effective time slots for participation based on the student's past participation history. Thus, by analyzing a student's past participation history, the optimal participation method can be suggested. Some or all of the above processes in the participation department may be performed using AI or not. For example, the participation department can input student participation history data into an AI, which can then suggest the optimal participation method.

[0090] The participating component can filter students based on their current learning status and areas of interest when they participate. For example, it can analyze a student's current learning status and suggest the most suitable project. It can also prioritize displaying relevant projects based on the student's areas of interest. Furthermore, it can suggest projects of appropriate difficulty according to the student's learning progress. This allows for the suggestion of the most suitable project based on the student's current learning status and areas of interest. Some or all of the above processing in the participating component may be performed using AI or not. For example, the participating component can input student learning status data into an AI, which can then suggest the most suitable project.

[0091] The participant team can estimate students' emotions and prioritize projects based on those emotions. For example, if a student is excited, the team can prioritize challenging projects. If a student is relaxed, the team can prioritize relaxing projects. Furthermore, if a student is stressed, the team can prioritize projects that can reduce stress. This allows students to participate in more appropriate projects by prioritizing them according to their 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 participant team may be performed using AI or not. For example, the participant team can input student emotion data into an AI, which can then suggest the optimal project priorities.

[0092] The participating department can prioritize students' participation in highly relevant projects by considering their geographical location. For example, the department can propose projects related to local issues based on the student's geographical location. It can also propose projects that allow students to collaborate with nearby students based on their geographical location. Furthermore, the department can propose projects that allow students to receive feedback from local experts based on their geographical location. This ensures that students can participate in projects related to local issues by considering their geographical location. Some or all of the above processing by the participating department may be performed using AI or not. For example, the participating department can input students' geographical location data into an AI, which can then propose the most suitable project.

[0093] The participating department can analyze students' social media activity and propose relevant projects upon their participation. For example, it can analyze students' social media activity and propose projects related to topics of interest. It can also analyze students' social media activity and propose projects in which they can collaborate with other students who share similar interests. Furthermore, it can analyze students' social media activity and propose projects related to trends. In this way, by analyzing students' social media activity, projects related to topics of interest can be proposed. Some or all of the above processing by the participating department may be performed using AI or not. For example, the participating department can input students' social media data into an AI, which can then propose the most suitable project.

[0094] The team formation unit can estimate students' emotions and adjust the team formation method based on the estimated emotions. For example, if a student is nervous, the team formation unit can adjust the team to match them with members who can help them relax. Similarly, if a student is excited, the team formation unit can match them with members who are also excited. Furthermore, if a student is stressed, the team formation unit can adjust the team to match them with members who can help reduce their stress. This allows for the formation of more appropriate teams by adjusting the team formation method according to students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the team formation unit may be performed using AI or not. For example, the team formation unit can input student emotion data into an AI, which can then propose the optimal team formation method.

[0095] The team formation department can select the most suitable team members by analyzing students' past collaboration history during team formation. For example, the team formation department can select the most cooperative members based on students' past collaboration history. It can also select members from the most successful projects based on students' past collaboration history. Furthermore, the team formation department can select members who are the most compatible based on students' past collaboration history. In this way, the optimal team members can be selected by analyzing students' past collaboration history. Some or all of the above processes in the team formation department may be performed using AI or not. For example, the team formation department can input student collaboration history data into AI, and the AI ​​can suggest the most suitable team members.

[0096] The team formation department can customize teams based on students' areas of expertise and skill sets during team formation. For example, the team formation department can select members with the optimal skill sets based on students' areas of expertise. It can also select members with the skills necessary for a project based on students' skill sets. Furthermore, the team formation department can create a well-balanced team based on students' areas of expertise and skill sets. This allows for the creation of well-balanced teams by customizing teams based on students' areas of expertise and skill sets. Some or all of the above processes in the team formation department may be performed using AI, or not. For example, the team formation department can input student areas of expertise and skill set data into an AI, which can then propose the optimal team.

[0097] The theme selection unit can estimate the student's emotions and adjust the theme selection method based on the estimated emotions. For example, if the student is excited, the theme selection unit can suggest a challenging theme. If the student is relaxed, the theme selection unit can suggest a relaxing theme. Furthermore, if the student is stressed, the theme selection unit can suggest a theme that can reduce stress. In this way, by adjusting the theme selection method according to the student's emotions, a more appropriate theme can be selected. Emotion estimation is achieved using an emotion estimation function, such as 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 theme selection unit may be performed using AI or not. For example, the theme selection unit can input student emotion data into an AI, and the AI ​​can suggest the most suitable theme.

[0098] The theme selection unit can analyze a student's past project history to suggest the most suitable theme during the theme selection process. For example, it can suggest relevant themes based on the student's past project history. It can also suggest successful themes based on the student's past project history. Furthermore, it can suggest new themes based on the student's past project history. In this way, the system can suggest the most suitable theme by analyzing the student's past project history. Some or all of the above processes in the theme selection unit may be performed using AI, or they may not. For example, the theme selection unit can input the student's project history data into an AI, which can then suggest the most suitable theme.

[0099] The theme selection unit can customize themes based on students' areas of expertise and interests during the selection process. For example, the theme selection unit can suggest relevant themes based on the student's area of ​​expertise. It can also suggest themes that will pique the student's interest based on their areas of interest. Furthermore, the theme selection unit can suggest well-balanced themes based on both the student's area of ​​expertise and areas of interest. This allows for the selection of more appropriate themes by customizing them based on the student's area of ​​expertise and areas of interest. Some or all of the above processing in the theme selection unit may be performed using AI, or not. For example, the theme selection unit can input data on students' areas of expertise and areas of interest into an AI, which can then suggest the most suitable themes.

[0100] The support unit can estimate a student's emotions and adjust its support methods based on those estimates. For example, if a student is nervous, the support unit can provide relaxing support. If a student is excited, the support unit can provide challenging support. Furthermore, if a student is stressed, the support unit can provide stress-reducing support. By adjusting the support methods according to the student's emotions, more appropriate support can be provided. 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 support unit may be performed using AI or not. For example, the support unit can input student emotion data into an AI, which can then suggest the optimal support method.

[0101] The support department can monitor the project's progress in real time and provide optimal support during support sessions. For example, the support department can monitor the project's progress in real time and issue alerts if delays occur. It can also monitor the project's progress in real time and suggest the next steps if progress is on track. Furthermore, it can monitor the project's progress in real time and suggest solutions if problems arise. This allows for the provision of optimal support by monitoring the project's progress in real time. Some or all of the above processes performed by the support department may or may not be performed using AI. For example, the support department can input project progress data into an AI, which can then suggest the optimal support method.

[0102] The support unit can apply different support algorithms depending on the characteristics of the project during support. For example, the support unit can apply a support algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply a support algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply a support algorithm based on educational data to projects addressing educational issues. This improves the accuracy of support by applying the most appropriate support algorithm for each project's characteristics. Some or all of the above-described processes in the support unit may be performed using AI, or they may not. For example, the support unit can input project characteristic data into an AI, which can then propose the most suitable support algorithm.

[0103] The support unit can estimate a student's emotions and prioritize support based on those emotions. For example, if a student is nervous, the support unit can prioritize providing relaxing support. If a student is excited, the support unit can prioritize providing challenging support. Furthermore, if a student is stressed, the support unit can prioritize providing stress-reducing support. This allows for more appropriate support to be provided by prioritizing support according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input student emotion data into an AI, which can then suggest the optimal support priorities.

[0104] The support department can provide optimal support by considering the geographical distribution of the project. For example, the support department can provide support that utilizes local resources based on the project's geographical distribution. Furthermore, the support department can provide support that obtains feedback from local experts based on the project's geographical distribution. In addition, the support department can provide support tailored to local issues based on the project's geographical distribution. This allows for the provision of support that utilizes local resources by considering the project's geographical distribution. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input project geographical distribution data into AI, which can then propose the optimal support method.

[0105] The support department can improve the accuracy of its support by referring to relevant project literature during the support process. For example, the support department can refer to relevant project literature and provide support based on the latest research findings. It can also refer to relevant project literature and provide support based on past success stories. Furthermore, the support department can refer to relevant project literature and provide support based on expert opinions. This improves the accuracy of support by referring to relevant project literature. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input relevant literature data into AI, which can then suggest the optimal support method.

[0106] The feedback unit can estimate a student's emotions and adjust the feedback method based on the estimated emotions. For example, if a student is nervous, the feedback unit can provide relaxing feedback. If a student is excited, the feedback unit can provide challenging feedback. Furthermore, if a student is stressed, the feedback unit can provide stress-reducing feedback. This allows for more appropriate feedback by adjusting the feedback method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input student emotion data into an AI, which can then suggest the optimal feedback method.

[0107] The feedback unit can provide optimal feedback by analyzing the expert's past feedback history. For example, the feedback unit can provide optimal feedback based on the expert's past feedback history. Furthermore, the feedback unit can provide feedback based on success stories, also based on the expert's past feedback history. In addition, the feedback unit can provide feedback that promotes student growth, based on the expert's past feedback history. Thus, by analyzing the expert's past feedback history, optimal feedback can be provided. Some or all of the above processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input expert feedback history data into AI, which can then propose the optimal feedback method.

[0108] The feedback unit can apply different feedback algorithms depending on the characteristics of the project during the feedback process. For example, the feedback unit can apply a feedback algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply a feedback algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply a feedback algorithm based on educational data to projects addressing educational issues. This improves the accuracy of feedback by applying the most appropriate feedback algorithm for each project's characteristics. Some or all of the above-described processes in the feedback unit may be performed using AI, or they may not. For example, the feedback unit can input project characteristic data into an AI, which can then propose the most appropriate feedback algorithm.

[0109] The feedback unit can estimate a student's emotions and prioritize feedback based on those emotions. For example, if a student is nervous, the feedback unit can prioritize providing relaxing feedback. If a student is excited, the feedback unit can prioritize providing challenging feedback. Furthermore, if a student is stressed, the feedback unit can prioritize providing stress-reducing feedback. This allows for more appropriate feedback to be provided by prioritizing feedback according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input student emotion data into an AI, which can then suggest the optimal priority of feedback.

[0110] The feedback unit can provide optimal feedback by considering the geographical distribution of the project. For example, the feedback unit can provide feedback that utilizes local resources based on the project's geographical distribution. It can also provide feedback that obtains input from local experts based on the project's geographical distribution. Furthermore, the feedback unit can provide feedback tailored to local issues based on the project's geographical distribution. This allows for the provision of feedback that utilizes local resources by considering the project's geographical distribution. 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 project geographical distribution data into AI, which can then propose the optimal feedback method.

[0111] The feedback unit can improve the accuracy of its feedback by referring to relevant project literature during the feedback process. For example, the feedback unit can refer to relevant project literature and provide feedback based on the latest research findings. It can also refer to relevant project literature and provide feedback based on past success stories. Furthermore, the feedback unit can refer to relevant project literature and provide feedback based on expert opinions. This improves the accuracy of feedback by referring to relevant project literature. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input relevant literature data into AI, which can then propose the optimal feedback method.

[0112] The research department can estimate students' emotions and adjust research methods based on those estimated emotions. For example, if a student is nervous, the research department can provide a relaxing research method. If a student is excited, the research department can provide a challenging research method. Furthermore, if a student is stressed, the research department can provide a research method that reduces stress. By adjusting research methods according to students' emotions, more appropriate research can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the research department may be performed using AI or not. For example, the research department can input student emotion data into an AI, which can then suggest the optimal research method.

[0113] The research department can collect information from the internet in real time during research and provide optimal data. For example, the research department can collect and provide the latest research findings from the internet in real time. It can also collect and provide the latest news from the internet in real time. Furthermore, the research department can collect and provide the latest statistical data from the internet in real time. This allows for the provision of optimal data by collecting information from the internet in real time. Some or all of the above processes in the research department may be performed using AI, or not. For example, the research department can input information from the internet into an AI, which can then extract and provide the optimal data.

[0114] The research department can apply different research algorithms depending on the characteristics of the project during the research process. For example, for projects addressing environmental issues, the research department can apply a research algorithm based on environmental data. Similarly, for projects addressing poverty issues, the research department can apply a research algorithm based on socioeconomic data. Furthermore, for projects addressing educational issues, the research department can apply a research algorithm based on educational data. This improves the accuracy of the research by applying the most appropriate research algorithm for each project. Some or all of the above-described processes in the research department may be performed using AI, or they may not. For example, the research department can input project characteristic data into an AI, which can then propose the most appropriate research algorithm.

[0115] The research department can estimate students' emotions and prioritize research based on those estimated emotions. For example, if a student is feeling nervous, the research department can prioritize providing research that helps them relax. If a student is feeling excited, the research department can prioritize providing challenging research. Furthermore, if a student is feeling stressed, the research department can prioritize providing research that helps reduce stress. By prioritizing research according to students' emotions, more appropriate research can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the research department may be performed using AI or not. For example, the research department can input student emotion data into an AI, which can then suggest the optimal research priorities.

[0116] The research department can provide optimal data during research, taking into account the geographical distribution of the project. For example, the research department can provide regional data based on the geographical distribution of the project. Furthermore, the research department can provide data from regional experts based on the geographical distribution of the project. In addition, the research department can provide data utilizing regional resources based on the geographical distribution of the project. This allows for the provision of regional data by considering the geographical distribution of the project. Some or all of the above processes in the research department may be performed using AI, or not. For example, the research department can input the geographical distribution data of the project into an AI, which can then suggest the optimal data.

[0117] The research department can improve the accuracy of its research by referring to relevant project literature during the research process. For example, the research department can refer to relevant project literature and provide research based on the latest research findings. It can also refer to relevant project literature and provide research based on past success stories. Furthermore, the research department can refer to relevant project literature and provide research based on expert opinions. This improves the accuracy of the research by referring to relevant project literature. Some or all of the above processes in the research department may be performed using AI, or not. For example, the research department can input relevant literature data into AI, which can then suggest the optimal research method.

[0118] The management department can estimate students' emotions and adjust management methods based on the estimated emotions. For example, if a student is tense, the management department can provide a relaxing management method. If a student is excited, the management department can provide a challenging management method. Furthermore, if a student is stressed, the management department can provide a stress-reducing management method. This allows for more appropriate management by adjusting management methods according to students' emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 management department may be performed using AI or not. For example, the management department can input student emotion data into an AI, which can then suggest the optimal management method.

[0119] The management department can monitor the project's progress in real time and provide optimal management during the management phase. For example, the management department can monitor the project's progress in real time and issue alerts if delays occur. Furthermore, the management department can monitor the project's progress in real time and suggest the next steps if progress is on track. In addition, the management department can monitor the project's progress in real time and suggest solutions if problems arise. This allows for optimal management by monitoring the project's progress in real time. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input project progress data into an AI, which can then suggest the optimal management method.

[0120] The management department can apply different management algorithms depending on the characteristics of the project during management. For example, the management department can apply a management algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply a management algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply a management algorithm based on educational data to projects addressing educational issues. This improves the accuracy of management by applying the most appropriate management algorithm for each project's characteristics. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input project characteristic data into an AI, which can then propose the most appropriate management algorithm.

[0121] The management department can estimate students' emotions and determine management priorities based on those estimated emotions. For example, if a student is tense, the management department can prioritize providing relaxing management. If a student is excited, the management department can prioritize providing challenging management. Furthermore, if a student is stressed, the management department can prioritize providing stress-reducing management. This allows for more appropriate management by prioritizing management according to students' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input student emotion data into an AI, which can then suggest the optimal management priorities.

[0122] The management department can provide optimal management by considering the geographical distribution of the project. For example, the management department can provide management that utilizes local resources based on the geographical distribution of the project. Furthermore, the management department can provide management that obtains feedback from local experts based on the geographical distribution of the project. In addition, the management department can provide management tailored to local issues based on the geographical distribution of the project. This allows for management that utilizes local resources by considering the geographical distribution of the project. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input geographical distribution data of the project into an AI, which can then propose the optimal management method.

[0123] The management department can improve the accuracy of its management by referring to relevant project literature during the management process. For example, the management department can refer to relevant project literature and provide management based on the latest research findings. It can also refer to relevant project literature and provide management based on past success stories. Furthermore, the management department can refer to relevant project literature and provide management based on expert opinions. This improves the accuracy of management by referring to relevant project literature. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input relevant literature data into AI, which can then propose the optimal management method.

[0124] The advice unit can estimate a student's emotions and adjust its advice based on those emotions. For example, if a student is nervous, the advice unit can offer relaxing advice. If a student is excited, it can offer challenging advice. Furthermore, if a student is stressed, it can offer stress-reducing advice. By adjusting the advice method according to the student's emotions, more appropriate advice can be provided. 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 advice unit may be performed using AI or not. For example, the advice unit can input student emotion data into an AI, which can then suggest the most appropriate advice method.

[0125] The advice department can provide optimal advice by analyzing the expert's past advice history. For example, the advice department can provide optimal advice based on the expert's past advice history. Furthermore, the advice department can provide advice based on success stories, also based on the expert's past advice history. In addition, the advice department can provide advice that promotes student growth, based on the expert's past advice history. Thus, by analyzing the expert's past advice history, optimal advice can be provided. Some or all of the above processes in the advice department may be performed using AI, or not. For example, the advice department can input expert advice history data into AI, which can then propose the optimal advice method.

[0126] The advisory unit can apply different advisory algorithms depending on the characteristics of the project when providing advice. For example, the advisory unit can apply an advisory algorithm based on environmental data to projects addressing environmental issues. Similarly, it can apply an advisory algorithm based on socioeconomic data to projects addressing poverty issues. Furthermore, it can apply an advisory algorithm based on educational data to projects addressing educational issues. This improves the accuracy of advice by applying the most appropriate advisory algorithm for each project's characteristics. Some or all of the above-described processes in the advisory unit may be performed using AI, or they may not. For example, the advisory unit can input project characteristic data into an AI, which can then propose the most appropriate advisory algorithm.

[0127] The advice unit can estimate a student's emotions and prioritize advice based on those emotions. For example, if a student is nervous, the advice unit can prioritize relaxing advice. If a student is excited, the advice unit can prioritize challenging advice. Furthermore, if a student is stressed, the advice unit can prioritize stress-reducing advice. This allows for more appropriate advice to be provided by prioritizing advice according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input student emotion data into an AI, which can then suggest the optimal priority of advice.

[0128] The advisory unit can provide optimal advice by considering the geographical distribution of the project. For example, the advisory unit can provide advice that utilizes local resources based on the geographical distribution of the project. It can also provide advice that allows for obtaining advice from local experts based on the geographical distribution of the project. Furthermore, the advisory unit can provide advice tailored to specific local issues based on the geographical distribution of the project. This allows for the provision of advice that utilizes local resources by considering the geographical distribution of the project. Some or all of the above processes in the advisory unit may be performed using AI or not. For example, the advisory unit can input geographical distribution data of the project into an AI, which can then propose the optimal advice method.

[0129] The advisory unit can improve the accuracy of its advice by referring to relevant project literature during the advice-giving process. For example, the advisory unit can refer to relevant project literature and provide advice based on the latest research findings. It can also refer to relevant project literature and provide advice based on past success stories. Furthermore, the advisory unit can refer to relevant project literature and provide advice based on expert opinions. This improves the accuracy of the advice by referring to relevant project literature. Some or all of the above processes in the advisory unit may be performed using AI or not. For example, the advisory unit can input relevant literature data into AI, which can then propose the optimal advice method.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The participation unit can estimate students' emotions and adjust the timing of their participation based on those emotions. For example, if a student is feeling stressed, the participation unit can encourage them to participate during a time when they can relax. If a student is excited, it can encourage them to participate immediately to maintain their motivation. Furthermore, if a student is tired, it can encourage them to participate after they have rested. This allows students to participate at a more appropriate time by adjusting the timing of their participation according to their 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 participation unit may be performed using AI or not. For example, the participation unit can input student emotion data into an AI, which can then suggest the optimal timing for participation.

[0132] The team formation unit can estimate students' emotions and adjust the team formation method based on the estimated emotions. For example, if a student is nervous, the team formation unit can adjust the team to match them with members who can help them relax. If a student is excited, it can adjust the team to match them with members who are also excited. Furthermore, if a student is stressed, it can adjust the team to match them with members who can help reduce their stress. In this way, by adjusting the team formation method according to students' emotions, more appropriate teams can be formed. Emotion estimation is achieved using an emotion estimation function, such as 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 team formation unit may be performed using AI or not. For example, the team formation unit can input student emotion data into an AI, which can then propose the optimal team formation method.

[0133] The theme selection unit can estimate the student's emotions and adjust the theme selection method based on the estimated emotions. For example, if the student is excited, the theme selection unit can suggest a challenging theme. If the student is relaxed, it can suggest a relaxing theme. Furthermore, if the student is stressed, it can suggest a theme that can reduce stress. In this way, by adjusting the theme selection method according to the student's emotions, a more appropriate theme can be selected. Emotion estimation is achieved using an emotion estimation function, such as 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 theme selection unit may be performed using AI or not. For example, the theme selection unit can input student emotion data into an AI, and the AI ​​can suggest the most suitable theme.

[0134] The support unit can estimate a student's emotions and adjust its support methods based on those estimates. For example, if a student is nervous, the support unit can provide relaxing support. If a student is excited, it can provide challenging support. Furthermore, if a student is stressed, it can provide stress-reducing support. By adjusting support methods according to the student's emotions, more appropriate support can be provided. 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 support unit may be performed using AI or not. For example, the support unit can input student emotion data into an AI, which can then suggest the optimal support method.

[0135] The feedback unit can estimate a student's emotions and adjust the feedback method based on the estimated emotions. For example, if a student is nervous, the feedback unit can provide relaxing feedback. If a student is excited, it can provide challenging feedback. Furthermore, if a student is stressed, it can provide stress-reducing feedback. In this way, by adjusting the feedback method according to the student's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 feedback unit may be performed using AI or not. For example, the feedback unit can input student emotion data into an AI, which can then suggest the optimal feedback method.

[0136] The participation department can analyze a student's past participation history and select the optimal participation method. For example, the participation department can suggest the optimal participation method based on the success rate of projects the student has participated in in the past. It can also suggest new related projects based on the themes of projects the student has participated in in the past. Furthermore, the participation department can suggest the most effective time slots for participation based on the student's past participation history. Thus, by analyzing a student's past participation history, the optimal participation method can be suggested. Some or all of the above processes in the participation department may be performed using AI or not. For example, the participation department can input student participation history data into an AI, which can then suggest the optimal participation method.

[0137] The participating component can filter students based on their current learning status and areas of interest when they participate. For example, it can analyze a student's current learning status and suggest the most suitable project. It can also prioritize displaying relevant projects based on the student's areas of interest. Furthermore, it can suggest projects of appropriate difficulty according to the student's learning progress. This allows for the suggestion of the most suitable project based on the student's current learning status and areas of interest. Some or all of the above processing in the participating component may be performed using AI or not. For example, the participating component can input student learning status data into an AI, which can then suggest the most suitable project.

[0138] The team formation department can select the most suitable team members by analyzing students' past collaboration history during team formation. For example, the team formation department can select the most cooperative members based on students' past collaboration history. It can also select members from the most successful projects based on students' past collaboration history. Furthermore, the team formation department can select members who are the most compatible based on students' past collaboration history. In this way, the optimal team members can be selected by analyzing students' past collaboration history. Some or all of the above processes in the team formation department may be performed using AI or not. For example, the team formation department can input student collaboration history data into AI, and the AI ​​can suggest the most suitable team members.

[0139] The theme selection unit can analyze a student's past project history to suggest the most suitable theme during the theme selection process. For example, it can suggest relevant themes based on the student's past project history. It can also suggest successful themes based on the student's past project history. Furthermore, it can suggest new themes based on the student's past project history. In this way, the system can suggest the most suitable theme by analyzing the student's past project history. Some or all of the above processes in the theme selection unit may be performed using AI, or they may not. For example, the theme selection unit can input the student's project history data into an AI, which can then suggest the most suitable theme.

[0140] The support department can monitor the project's progress in real time and provide optimal support during support sessions. For example, the support department can monitor the project's progress in real time and issue alerts if delays occur. It can also monitor the project's progress in real time and suggest the next steps if progress is on track. Furthermore, it can monitor the project's progress in real time and suggest solutions if problems arise. This allows for the provision of optimal support by monitoring the project's progress in real time. Some or all of the above processes performed by the support department may or may not be performed using AI. For example, the support department can input project progress data into an AI, which can then suggest the optimal support method.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The participating organization enables students to join the online platform. For example, students can join the platform by registering online. Alternatively, the organization can restrict participation to students who have received an invitation from a specific school or organization. Step 2: The team formation section enables students who have participated through the participation section to form teams. For example, students can form teams based on their interests or areas of expertise. Alternatively, the system can automatically randomly match students to form teams. Step 3: The theme selection section allows the teams formed by the team formation section to select a theme. For example, themes such as environmental issues, poverty, and education can be provided, and students can choose a theme by voting. Alternatively, the system can automatically select a theme randomly and assign it to the teams. Step 4: The support unit uses AI to assist with research and project management based on the theme selected by the theme selection unit. For example, it includes a research unit that collects information from the internet and provides relevant data. The research unit can collect information and provide it to students using methods such as web scraping and API usage. It also includes a management unit that manages the progress of the project, assigns tasks, and tracks progress. The management unit can manage the progress of the project and provide it to students using methods such as Gantt charts and progress reports. Step 5: The Feedback Department enables the project, supported by the Support Department, to receive feedback from experts. For example, it may include an Advice Department where experts provide advice on the team's proposals. The Advice Department can provide expert feedback through methods such as online meetings or document reviews.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the participation unit, team formation unit, theme selection unit, support unit, and feedback unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the participation unit is implemented by the control unit 46A of the smart device 14, allowing students to participate in the platform by registering online. The team formation unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing students to form teams based on their interests and areas of expertise. The theme selection unit is implemented by the control unit 46A of the smart device 14, allowing students to select a theme by voting. The support unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing AI to support research and project management. The feedback unit is implemented by the control unit 46A of the smart device 14, allowing users to receive feedback from experts. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 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.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the 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.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 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.

[0162] Each of the multiple elements described above, including the participation unit, team formation unit, theme selection unit, support unit, and feedback unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the participation unit is implemented by the control unit 46A of the smart glasses 214, allowing students to participate in the platform by registering online. The team formation unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing students to form teams based on their interests and areas of expertise. The theme selection unit is implemented by the control unit 46A of the smart glasses 214, allowing students to select a theme by voting. The support unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing AI to support research and project management. The feedback unit is implemented by the control unit 46A of the smart glasses 214, allowing users to receive feedback from experts. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] 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.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The 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.

[0167] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the participation unit, team formation unit, theme selection unit, support unit, and feedback unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the participation unit is implemented by the control unit 46A of the headset terminal 314, allowing students to participate in the platform by registering online. The team formation unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing students to form teams based on their interests and areas of expertise. The theme selection unit is implemented by the control unit 46A of the headset terminal 314, allowing students to select a theme by voting. The support unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing AI to support research and project management. The feedback unit is implemented by the control unit 46A of the headset terminal 314, allowing users to receive feedback from experts. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] Each of the multiple elements described above, including the participation unit, team formation unit, theme selection unit, support unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the participation unit is implemented by the control unit 46A of the robot 414, allowing students to participate in the platform by registering online. The team formation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, allowing students to form teams based on their interests and areas of expertise. The theme selection unit is implemented by, for example, the control unit 46A of the robot 414, allowing students to select a theme by voting. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where AI supports research and project management. The feedback unit is implemented by, for example, the control unit 46A of the robot 414, allowing for feedback from experts. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] (Note 1) Students participate in online platforms, The aforementioned participation section will form teams with participating students, The team formed by the team formation unit selects a theme from the theme selection unit, A support unit in which AI supports research and project management based on the theme selected by the aforementioned theme selection unit, The system includes a feedback unit that receives feedback from experts on projects supported by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned support unit is It has a research department that collects information from the internet and provides related data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is The project includes a management department that oversees project progress, assigns tasks, and tracks progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is An advisory department is prepared to provide expert advice on the team's proposals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned participating section is, This allows students to form teams based on their interests and areas of expertise. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned theme selection unit is It will be possible to select themes such as environmental issues, poverty, and education. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned participating section is, We estimate students' emotions and adjust the timing of their participation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned participating section is, We will analyze students' past participation history and select the most suitable method of participation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned participating section is, During the application process, participants will be filtered based on their current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned participating section is, Estimate students' emotions and prioritize projects they participate in based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned participating section is, When students participate, their geographical location will be taken into consideration to prioritize participation in highly relevant projects. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned participating section is, During participation, students will analyze their social media activity and propose related projects. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned team formation unit, Estimate students' emotions and adjust team formation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned team formation unit, When forming a team, we analyze students' past collaboration history to select the most suitable team members. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned team formation unit, When forming teams, customize them based on the students' areas of expertise and skill sets. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned theme selection unit is We estimate students' emotions and adjust the theme selection method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned theme selection unit is When selecting a theme, we analyze students' past project history to suggest the most suitable theme. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned theme selection unit is When selecting a theme, customize the theme based on the student's area of ​​expertise and interests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is We estimate students' emotions and adjust our support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is During support, we monitor the project's progress in real time and provide optimal support. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is During support, different support algorithms are applied depending on the characteristics of the project. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit is The system estimates students' emotions and prioritizes support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is When providing support, we take into account the geographical distribution of projects to offer the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is When providing support, we refer to relevant project documentation to improve the accuracy of the support. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is Estimate students' emotions and adjust the feedback method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, we analyze the expert's past feedback history to provide the most optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the characteristics of the project. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is The system estimates students' emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, consider the geographical distribution of the project to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, refer to relevant project literature to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned research department, We estimate students' emotions and adjust our research methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned research department, During research, we collect information from the internet in real time and provide the most relevant data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned research department, During research, different research algorithms are applied depending on the characteristics of the project. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned research department, We estimate students' emotions and prioritize research based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned research department, During research, we provide optimal data considering the geographical distribution of the project. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned research department, During research, refer to relevant literature for the project to improve the accuracy of the research. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned management department, Estimate students' emotions and adjust management methods based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned management department, During management, the project progress is monitored in real time, providing optimal management. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned management department, During management, different management algorithms are applied depending on the characteristics of the project. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned management department, Estimate students' emotions and determine management priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned management department, During management, we provide optimal management by considering the geographical distribution of projects. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned management department, During management, refer to relevant project literature to improve the accuracy of management. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned advice section, The system estimates students' emotions and adjusts its advice methods based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned advice section, When providing advice, we analyze the expert's past advice history to provide the most suitable advice. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the characteristics of the project. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned advice section, The system estimates students' emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned advice section, When providing advice, we take into account the geographical distribution of the project to offer the most appropriate advice. The system described in Appendix 4, characterized by the features described herein. (Note 48) The advice section When giving advice, refer to relevant documents of the project to improve the accuracy of the advice The system according to supplementary note 4, characterized by the above.

Explanation of symbols

[0215] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims

1. Students participate in online platforms, The aforementioned participation section will form teams with participating students, The team formed by the team formation unit selects a theme from the theme selection unit, A support unit in which AI supports research and project management based on the theme selected by the aforementioned theme selection unit, The system includes a feedback unit that receives feedback from experts on projects supported by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned support unit is It has a research department that collects information from the internet and provides related data. The system according to feature 1.

3. The aforementioned support unit is The project includes a management department that oversees project progress, assigns tasks, and tracks progress. The system according to feature 1.

4. The aforementioned feedback unit is An advisory department is prepared to provide expert advice on the team's proposals. The system according to feature 1.

5. The aforementioned participating section is, This allows students to form teams based on their interests and areas of expertise. The system according to feature 1.

6. The aforementioned theme selection unit is It will be possible to select themes such as environmental issues, poverty, and education. The system according to feature 1.

7. The aforementioned participating section is, We estimate students' emotions and adjust the timing of their participation based on those estimated emotions. The system according to feature 1.

8. The aforementioned participating section is, We will analyze students' past participation history and select the most suitable method of participation. The system according to feature 1.

9. The aforementioned participating section is, During the application process, participants will be filtered based on their current learning situation and areas of interest. The system according to feature 1.

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