system

Abstract

The system according to the embodiment aims to generate lectures and content based on the needs of learners and to promote interaction and collaborative research among learners. [Solution] A system according to an embodiment includes a generation unit, a provision unit, a communication unit, and a conference formation unit. The generation unit generates lectures or content based on learners' needs. The provision unit provides the content generated by the generation unit on an online platform. The communication unit allows learners to interact with each other and conduct discussions or collaborative research using the content provided by the provision unit. The conference formation unit aggregates activities carried out by the communication unit and ultimately forms a conference.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technology has not been able to adequately generate lectures and content that meet the needs of learners, and has had the problem of limiting opportunities for learners to interact with each other and conduct collaborative research.

[0005] The system according to the embodiment aims to generate lectures and content based on the needs of learners and to promote interaction and collaborative research among learners. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a provision unit, a communication unit, and a conference formation unit. The generation unit generates lectures or content based on the needs of learners. The provision unit provides the content generated by the generation unit on an online platform. The communication unit allows learners to interact with each other and conduct discussions or collaborative research using the content provided by the provision unit. The conference formation unit aggregates the activities carried out by the communication unit and ultimately forms an academic conference. [Effects of the Invention]

[0007] The system according to the embodiment can generate lectures and content based on the needs of learners, and can promote interaction and collaborative research among learners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention utilizes generative AI to provide lectures and content to support learning, ultimately forming a large academic society. This learning support system generates lectures and content based on learners' needs and provides them on an online platform. Learners use these to advance their studies, and learners interact with each other online, engaging in discussions and collaborative research, thereby sharing and deepening their knowledge. Ultimately, these activities are aggregated to form a large academic society. For example, generative AI generates lectures and content based on learners' needs. These may include lectures on specific fields, practical exercises, and review articles based on the latest research findings. The generative AI provides optimal content based on the learner's level and interests. The generated content is then provided on an online platform. Learners can browse the content at their own pace and progress through their studies. The platform also provides features that allow learners to interact with each other, engaging in discussions and collaborative research. Examples of these features include forums, chat functions, video conferencing, and collaborative editing. Furthermore, learners can interact with each other online, engage in discussions and collaborative research, thereby sharing and deepening their knowledge. For example, discussions on specific topics and the sharing of each individual's opinions and knowledge can lead to new discoveries and deeper understanding. It is also possible to work on actual projects through collaborative research. Specifically, weekly discussion sessions and progress management tools for collaborative projects are provided. Ultimately, these activities are consolidated into a large academic conference. At conferences, learners can present their research results and listen to presentations by other learners. Conferences also feature lectures and panel discussions on the latest research results and trends. This allows learners to always obtain the latest information and maintain their motivation to learn. The learning support system thus generates, provides, interacts with, and creates lectures and content based on learners' needs, enabling efficient learning and knowledge sharing.

[0029] A learning support system according to an embodiment includes a generation unit, a provision unit, a communication unit, and a creation unit. The generation unit generates lectures or content based on the learner's needs. For example, the generation unit generates lectures on a specific field, practical exercises, and review articles based on the latest research findings. The generation unit uses a generation AI to provide optimal content based on the learner's level and interests. For example, the generation AI can generate optimal lectures or content based on the learner's learning goals, areas of interest, and skill level. The provision unit provides the content generated by the generation unit on an online platform. For example, the provision unit allows learners to view the generated content at their own pace and progress through their studies. The provision unit also provides functions that allow learners to interact with each other. Examples of such functions include forums, chat functions, video conferencing functions, and collaborative editing functions. The communication unit uses the content provided by the provision unit to enable learners to interact with each other and engage in discussions and collaborative research. For example, the communication unit holds discussions on a specific topic, sharing opinions and knowledge to deepen new discoveries and understanding. The communication unit can also work on actual projects through collaborative research. For example, progress management tools for weekly discussion sessions and collaborative projects are provided. The Formative Section aggregates the activities carried out by the Exchange Section and ultimately forms an academic conference. For example, the Formative Section allows learners to present their own research results and listen to presentations by other learners. The Formative Section also holds lectures and panel discussions on the latest research results and trends. This allows learners to always obtain the latest information and maintain their motivation to learn. As a result, the learning support system according to the embodiment generates, provides, interacts with, and creates lectures and content based on the learners' needs, enabling efficient learning and knowledge sharing.

[0030] The system includes a functional unit that provides a forum or chat function. The forum or chat function provides a place where learners can interact with each other. For example, the forum function enables threaded discussions, allowing learners to exchange opinions on specific topics. The chat function enables real-time communication, allowing learners to instantly share questions and opinions. For example, the forum function provides a format in which learners can post questions and other learners or instructors can respond. The chat function also allows learners to create group chats and hold discussions on specific topics. This allows learners to interact with each other through the forum or chat.

[0031] It has a function section that provides video conferencing. The video conferencing function provides a place where learners can interact face-to-face. For example, the video conferencing function allows learners to hold online meetings and exchange opinions in real time. The video conferencing function also has a recording function, allowing the contents of the meeting to be reviewed later. For example, the video conferencing function provides a format in which a learner gives a presentation and other learners ask questions or offer their opinions. The video conferencing function also allows learners to hold group discussions and have in-depth discussions on specific topics. This allows learners to interact with each other through video conferencing.

[0032] It has a function section that provides collaborative editing. The collaborative editing function provides a space where learners can collaborate on editing documents. For example, the collaborative editing function allows learners to edit documents in real time and add comments. The collaborative editing function also has a version control function that allows them to check the history of past edits. For example, the collaborative editing function provides a format in which learners can collaborate on creating reports and reflect each other's opinions and knowledge. The collaborative editing function also allows learners to share the progress of projects and divide up tasks. This allows learners to interact with each other through collaborative editing.

[0033] The system has a function section that provides weekly discussions. The weekly discussion function provides a forum for learners to hold regular discussions with each other. For example, the weekly discussion function allows learners to discuss a specific topic each week and exchange opinions. The weekly discussion function also allows learners to set a facilitator and manage the progress of the discussion. For example, the weekly discussion function provides a format in which learners can set a topic and share their opinions and knowledge. The weekly discussion function also allows learners to summarize the results of the discussion and reflect them in the next discussion. This allows learners to interact with each other through weekly discussion sessions.

[0034] The system has a functional unit that provides a collaborative project management tool. The collaborative project management tool provides a place for learners to work together on a project. For example, the collaborative project management tool allows learners to allocate tasks and report progress. The collaborative project management tool also has a communication function that allows learners to exchange opinions about the progress of the project. For example, the collaborative project management tool provides a format for learners to set project goals and clarify each person's role. The collaborative project management tool also allows learners to report progress and share issues. This allows learners to interact with each other through collaborative projects.

[0035] The generation unit can analyze a learner's past learning history and generate lectures or content. For example, the generation unit uses a generation AI to generate new related lectures or content based on what the learner has learned in the past. The generation unit can also analyze a learner's past grades and generate lectures or content that focus on areas that need reinforcement. Furthermore, the generation unit can analyze a learner's past learning patterns and generate lectures or content in an optimal learning order. This improves the effectiveness of learning by providing optimal lectures and content based on the learner's past learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the learner's past learning history data into the generation AI and cause the generation AI to generate optimal lectures and content.

[0036] The generation unit can generate the next learning content based on the learner's current learning progress. For example, when the learner completes the current lecture, the generation unit causes the generation AI to automatically generate the next learning content. Furthermore, when the learner clears a specific challenge, the generation unit can also cause the generation AI to generate an appropriate lecture or content as the next step. Furthermore, when the learner reports their progress, the generation unit can cause the generation AI to generate the next learning content based on that progress. This improves the effectiveness of learning by dynamically generating the next learning content according to the learning progress. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the learner's current learning progress data into the generation AI and cause the generation AI to generate the next learning content.

[0037] The generation unit can generate region-specific examples and content based on the learner's geographic location information. For example, if the learner is in a specific region, the generation unit causes the generation AI to generate a lecture including examples related to that region. Furthermore, if the learner is overseas, the generation unit can also generate content related to the culture and history of that country. Furthermore, if the learner is in a specific city, the generation unit can also generate a lecture including business examples related to that city. This improves the effectiveness of learning by providing region-specific examples and content. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the learner's geographic location information to the generation AI and cause the generation AI to generate region-specific examples and content.

[0038] The generation unit can analyze the learner's social media activity and generate related topics or content. For example, the generation unit can have the generation AI generate related lectures based on topics the learner has shown interest in on social media. The generation unit can also analyze posts by experts the learner follows and generate related content. Furthermore, the generation unit can have the generation AI generate lectures or content based on topics in online communities in which the learner participates. This improves learning effectiveness by providing related topics and content based on social media activity. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the learner's social media activity data into the generation AI and cause the generation AI to generate related topics and content.

[0039] The provision unit can analyze the learner's past usage history and select a content provision method. For example, the provision unit can provide content in an optimal format based on the content format that the learner has previously preferred. The provision unit can also analyze the learner's past learning patterns and provide content at the optimal timing. Furthermore, the provision unit can prioritize providing content in areas that require reinforcement based on the learner's past grades. This improves the effectiveness of learning by selecting an optimal content provision method based on the learner's past usage history. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, AI, or can be performed without AI. For example, the provision unit can input the learner's past usage history data into a generation AI and cause the generation AI to select an optimal content provision method.

[0040] The providing unit can provide a content display format based on the learner's device information. For example, if the learner is using a smartphone, the providing unit can provide content optimized for mobile devices. Furthermore, if the learner is using a tablet, the providing unit can provide content optimized for large screens. Furthermore, if the learner is using a desktop computer, the providing unit can provide high-resolution content. This improves the effectiveness of learning by providing an optimal content display format based on device information. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the learner's device information into the generation AI and cause the generation AI to provide the optimal content display format.

[0041] The providing unit can provide information about region-specific events and seminars based on the learner's geographical location information. For example, if the learner is in a specific region, the providing unit can provide information about seminars held in that region. Furthermore, if the learner is overseas, the providing unit can provide information about events held in that country. Furthermore, if the learner is in a specific city, the providing unit can provide information about workshops held in that city. This improves the effectiveness of learning by providing information about region-specific events and seminars. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the learner's geographical location information into the generation AI and cause the generation AI to provide information about region-specific events and seminars.

[0042] The providing unit can analyze the learner's social media activity and provide relevant content preferentially. The providing unit can provide relevant content based on, for example, topics in which the learner has shown interest on social media. The providing unit can also analyze posts by experts followed by the learner and provide relevant content. Furthermore, the providing unit can provide relevant content based on topics in online communities in which the learner participates. This improves the effectiveness of learning by providing relevant content based on social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the learner's social media activity data into a generation AI and cause the generation AI to provide relevant content.

[0043] The communication unit can analyze the learner's past communication history and suggest a communication method. For example, the communication unit can suggest an optimal communication format based on the learner's preferred communication format in the past. The communication unit can also analyze the learner's past communication patterns and suggest communication at the optimal timing. Furthermore, the communication unit can suggest the optimal communication partner based on the learner's past communication history. In this way, the effectiveness of communication is improved by suggesting the optimal communication method based on the past communication history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the learner's past communication history data into the generation AI and cause the generation AI to suggest the optimal communication method.

[0044] The communication unit can recommend communication partners based on the learner's current learning progress. For example, if a learner completes a specific task, the communication unit can recommend other learners who have completed the same task. Also, if a learner shows interest in a specific field, the communication unit can recommend other learners who are interested in the same field. Furthermore, when a learner reports their progress, the communication unit can recommend other learners who have the same progress. This improves the effectiveness of communication by recommending appropriate communication partners according to the learner's learning progress. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the learner's current learning progress data into the generation AI and cause the generation AI to recommend appropriate communication partners.

[0045] The communication unit can suggest region-specific communication events based on the learner's geographical location information. For example, if the learner is in a specific region, the communication unit can suggest communication events held in that region. Furthermore, if the learner is overseas, the communication unit can suggest communication events held in that country. Furthermore, if the learner is in a specific city, the communication unit can suggest communication events held in that city. In this way, by suggesting region-specific communication events, the effectiveness of communication is improved. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the learner's geographical location information into the generation AI and cause the generation AI to suggest region-specific communication events.

[0046] The communication unit can analyze the learner's social media activity and recommend related social networking groups. For example, the communication unit can recommend related social networking groups based on topics in which the learner has shown interest on social media. The communication unit can also recommend related social networking groups by analyzing the content posted by experts followed by the learner. Furthermore, the communication unit can recommend related social networking groups based on topics in online communities in which the learner participates. This improves the effectiveness of communication by recommending related social networking groups based on social media activity. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the learner's social media activity data into a generation AI and cause the generation AI to recommend related social networking groups.

[0047] The formative unit can analyze the learner's past research results and propose a conference structure. For example, the formative unit can organize a conference on a related topic based on the learner's previously published research results. The formative unit can also analyze the learner's past research field and organize a conference specialized in that field. Furthermore, the formative unit can propose the optimal presentation order and session structure based on the learner's past research results. This improves the effectiveness of the conference by proposing the optimal conference structure based on the learner's past research results. Some or all of the above-mentioned processing in the formative unit may be performed using, for example, AI, or may be performed without using AI. For example, the formative unit can input the learner's past research result data into the generation AI and have the generation AI execute the optimal conference structure.

[0048] The formative unit can adjust the conference presentation schedule based on the learner's current research progress. For example, the formative unit can propose an optimal presentation schedule when the learner reports their current research progress. Furthermore, if the learner is at a specific research stage, the formative unit can also propose a presentation schedule appropriate for that stage. Furthermore, the formative unit can dynamically adjust the presentation schedule each time the learner reports their research progress. This improves the effectiveness of the conference by dynamically adjusting the presentation schedule according to the research progress. Some or all of the above-described processing in the formative unit may be performed, for example, using AI, or may be performed without using AI. For example, the formative unit can input the learner's current research progress data into the generation AI and have the generation AI adjust the optimal presentation schedule.

[0049] The formation unit can suggest region-specific academic events based on the learner's geographical location information. For example, if the learner is in a specific region, the formation unit can suggest academic events held in that region. Furthermore, if the learner is overseas, the formation unit can suggest academic events held in that country. Furthermore, if the learner is in a specific city, the formation unit can suggest academic events held in that city. This improves the effectiveness of academic conferences by suggesting region-specific academic events. Some or all of the above-described processing in the formation unit may be performed using AI, for example, or may be performed without using AI. For example, the formation unit can input the learner's geographical location information into the generation AI and cause the generation AI to suggest region-specific academic events.

[0050] The generation unit can analyze the learner's social media activity and recommend relevant conferences and events. For example, the generation unit can recommend relevant conferences based on topics in which the learner has shown interest on social media. The generation unit can also recommend relevant conferences based on the content of posts by experts followed by the learner. Furthermore, the generation unit can recommend relevant conferences based on topics in online communities in which the learner participates. This improves the effectiveness of conferences by recommending relevant conferences and events based on social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the learner's social media activity data into the generation AI and cause the generation AI to recommend relevant conferences and events.

[0051] The functional unit providing the forum or chat function can analyze a learner's past posting history and suggest a method for participating in a forum or chat. For example, the functional unit providing the forum or chat function can suggest an optimal forum based on topics that the learner has previously participated in. The functional unit providing the forum or chat function can also analyze a learner's past posting patterns and suggest an optimal method for participating in a chat. Furthermore, the functional unit providing the forum or chat function can also suggest an optimal timing for participation based on the learner's past posting history. This improves the effectiveness of interaction by suggesting an optimal method for participating in a forum or chat based on the learner's past posting history. Some or all of the above-mentioned processing in the functional unit providing the forum or chat function may be performed using, for example, AI, or may be performed without using AI. For example, the functional unit providing the forum or chat function can input a learner's past posting history data into a generation AI and cause the generation AI to suggest an optimal method for participation.

[0052] The functional unit providing the forum and chat function can suggest region-specific forums or chats based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit providing the forum and chat function can suggest forums held in that region. Furthermore, if the learner is overseas, the functional unit providing the forum and chat function can suggest chats held in that country. Furthermore, if the learner is in a specific city, the functional unit providing the forum and chat function can suggest forums held in that city. This improves the effectiveness of interaction by suggesting region-specific forums and chats. Some or all of the above-described processing in the functional unit providing the forum and chat function may be performed using, or without, AI, for example. For example, the functional unit providing the forum and chat function can input the learner's geographical location information to a generation AI and cause the generation AI to suggest region-specific forums and chats.

[0053] The functional unit providing the video conferencing function can analyze the learner's past video conferencing history and suggest a method for participating in the video conferencing. For example, the functional unit providing the video conferencing function can suggest an optimal participation method based on the learner's preferred video conferencing format in the past. The functional unit providing the video conferencing function can also analyze the learner's past video conferencing patterns and suggest the optimal timing for participation. Furthermore, the functional unit providing the video conferencing function can also suggest an optimal participation method based on the learner's past video conferencing history. This improves the effectiveness of interaction by suggesting an optimal participation method based on the past video conferencing history. Some or all of the above-described processing in the functional unit providing the video conferencing function may be performed using, or without, AI, for example. For example, the functional unit providing the video conferencing function can input the learner's past video conferencing history data into a generation AI and cause the generation AI to suggest an optimal participation method.

[0054] The functional unit providing the video conferencing function can suggest region-specific video conferences based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit providing the video conferencing function can suggest video conferences to be held in that region. Furthermore, if the learner is overseas, the functional unit providing the video conferencing function can suggest video conferences to be held in that country. Furthermore, if the learner is in a specific city, the functional unit providing the video conferencing function can suggest video conferences to be held in that city. This improves the effectiveness of interaction by suggesting region-specific video conferences. Some or all of the above-described processing in the functional unit providing the video conferencing function can be performed using AI, for example, or without AI. For example, the functional unit providing the video conferencing function can input the learner's geographical location information to a generation AI and cause the generation AI to suggest region-specific video conferences.

[0055] The functional unit providing the collaborative editing function can analyze the learner's past collaborative editing history and propose a collaborative editing method. The functional unit providing the collaborative editing function can, for example, propose an optimal method based on the learner's past preferred collaborative editing format. The functional unit providing the collaborative editing function can also analyze the learner's past collaborative editing patterns and propose collaborative editing at the optimal timing. Furthermore, the functional unit providing the collaborative editing function can also propose an optimal method based on the learner's past collaborative editing history. In this way, by proposing an optimal method based on the learner's past collaborative editing history, the effectiveness of collaborative editing is improved. Some or all of the above-mentioned processing in the functional unit providing the collaborative editing function may be performed using, for example, AI, or may be performed without using AI. For example, the functional unit providing the collaborative editing function can input the learner's past collaborative editing history data into a generation AI and have the generation AI execute a proposal for an optimal method.

[0056] The functional unit providing the collaborative editing function can suggest region-specific collaborative editing projects based on the learner's geographic location information. For example, if the learner is in a specific region, the functional unit providing the collaborative editing function can suggest collaborative editing projects to be held in that region. Furthermore, if the learner is overseas, the functional unit providing the collaborative editing function can suggest collaborative editing projects to be held in that country. Furthermore, if the learner is in a specific city, the functional unit providing the collaborative editing function can suggest collaborative editing projects to be held in that city. This improves the effectiveness of collaborative editing by suggesting region-specific collaborative editing projects. Some or all of the above-described processing in the functional unit providing the collaborative editing function may be performed using AI, for example, or may be performed without using AI. For example, the functional unit providing the collaborative editing function can input the learner's geographic location information to a generation AI and cause the generation AI to suggest region-specific collaborative editing projects.

[0057] The functional unit providing the weekly discussion session can analyze the learner's past discussion history and suggest a method for proceeding with the discussion. The functional unit providing the weekly discussion session can suggest an optimal method for proceeding with the discussion, for example, based on the discussion format that the learner has preferred to participate in in the past. The functional unit providing the weekly discussion session can also analyze the learner's past discussion patterns and suggest a method for proceeding with the discussion at the optimal timing. Furthermore, the functional unit providing the weekly discussion session can also suggest an optimal method for proceeding with the discussion based on the learner's past discussion history. In this way, by suggesting an optimal method for proceeding with the discussion based on the past discussion history, the effectiveness of the discussion is improved. Some or all of the above-mentioned processing in the functional unit providing the weekly discussion session can be performed using, for example, AI, or can be performed without using AI. For example, the functional unit providing the weekly discussion session can input the learner's past discussion history data into a generation AI and cause the generation AI to suggest an optimal method for proceeding with the discussion.

[0058] The functional unit for providing weekly discussion sessions can suggest region-specific discussion sessions based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit for providing weekly discussion sessions can suggest discussion sessions to be held in that region. Furthermore, if the learner is overseas, the functional unit for providing weekly discussion sessions can suggest discussion sessions to be held in that country. Furthermore, if the learner is in a specific city, the functional unit for providing weekly discussion sessions can suggest discussion sessions to be held in that city. In this way, suggesting region-specific discussion sessions improves the effectiveness of discussions. Some or all of the above-described processing in the functional unit for providing weekly discussion sessions can be performed using, for example, AI, or can be performed without using AI. For example, the functional unit for providing weekly discussion sessions can input the learner's geographical location information to a generation AI and cause the generation AI to suggest region-specific discussion sessions.

[0059] The functional unit providing the collaborative project progress management tool can analyze the learner's past project history and propose a project management method. The functional unit providing the collaborative project progress management tool can, for example, propose an optimal method based on the learner's preferred project management methods in the past. The functional unit providing the collaborative project progress management tool can also analyze the learner's past project patterns and propose a method for progressing the project at the optimal timing. Furthermore, the functional unit providing the collaborative project progress management tool can also propose an optimal management method based on the learner's past project history. In this way, by proposing an optimal management method based on the past project history, the effectiveness of the project is improved. Some or all of the above-mentioned processing in the functional unit providing the collaborative project progress management tool can be performed using, for example, AI, or can be performed without using AI. For example, the functional unit providing the collaborative project progress management tool can input the learner's past project history data into a generation AI and cause the generation AI to propose an optimal management method.

[0060] The functional unit for providing a collaborative project progress management tool can suggest region-specific projects based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit for providing a collaborative project progress management tool can suggest projects held in that region. Furthermore, if the learner is overseas, the functional unit for providing a collaborative project progress management tool can suggest projects held in that country. Furthermore, if the learner is in a specific city, the functional unit for providing a collaborative project progress management tool can suggest projects held in that city. This improves the effectiveness of the project by suggesting region-specific projects. Some or all of the above-described processing in the functional unit for providing a collaborative project progress management tool can be performed using AI, for example, or without AI. For example, the functional unit for providing a collaborative project progress management tool can input the learner's geographical location information into the generation AI and cause the generation AI to suggest region-specific projects.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The providing unit can analyze the learning style of the learner and adjust the display order of the content based on the analyzed learning style. For example, visual content can be displayed preferentially to a learner who prefers visual learning. Audio content can also be displayed preferentially to a learner who prefers auditory learning. Furthermore, exercises and simulations can be displayed preferentially to a learner who prefers practical learning. In this way, the effectiveness of learning can be improved by adjusting the display order of the content according to the learner's learning style.

[0063] The generation unit can analyze the learner's hobbies and interests and generate lectures or content based on the analyzed hobbies and interests. For example, if the learner is interested in music, a lecture including music-related examples can be generated. If the learner is interested in sports, content related to sports can be generated. Furthermore, if the learner is interested in art, a lecture related to art can be generated. This improves the effectiveness of learning by providing optimal lectures and content according to the learner's hobbies and interests.

[0064] The providing unit can analyze the learner's device usage history and adjust the content display format based on the analyzed device usage history. For example, if the learner frequently uses a smartphone, the providing unit can provide content optimized for mobile devices. Also, if the learner frequently uses a tablet, the providing unit can provide content optimized for large screens. Furthermore, if the learner frequently uses a desktop, the providing unit can provide high-resolution content. This improves the effectiveness of learning by providing the optimal content display format based on the device usage history.

[0065] The generation unit can analyze the learner's learning goals and generate lectures or content based on the analyzed learning goals. For example, if a learner wants to obtain a specific qualification, the generation unit can generate lectures related to that qualification. Also, if a learner wants to acquire a specific skill, the generation unit can generate content related to that skill. Furthermore, if a learner wants to complete a specific project, the generation unit can generate lectures related to that project. This improves the effectiveness of learning by providing optimal lectures and content according to the learner's learning goals.

[0066] The communication unit can analyze the learner's past communication history and suggest communication methods. For example, it can suggest the most appropriate communication format based on the communication format that the learner has preferred in the past. It can also analyze the learner's past communication patterns and suggest communication at the optimal timing. Furthermore, it can suggest the most appropriate communication partner based on the learner's past communication history. In this way, the effectiveness of communication can be improved by suggesting the optimal communication method based on the learner's past communication history.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The generator generates lectures or content based on the learner's needs. For example, the generator generates lectures on a specific field, practical exercises, and even review articles based on the latest research findings. The generator uses generative AI to provide optimal content based on the learner's level and interests. For example, the generator AI can generate optimal lectures or content based on the learner's learning goals, areas of interest, and skill level. Step 2: The providing unit provides the content generated by the generating unit on an online platform. For example, the providing unit allows learners to view the generated content at their own pace and progress through their studies. The providing unit also provides functions that allow learners to interact with each other. For example, the functions include forums, chat functions, video conferencing functions, and collaborative editing functions. Step 3: In the Communication Club, learners interact with each other and engage in discussions and collaborative research using the content provided by the Provider Club. For example, the Communication Club holds discussions on specific topics, sharing each learner's opinions and knowledge, leading to new discoveries and deeper understanding. The Communication Club can also be used to work on actual projects through collaborative research. For example, it provides weekly discussion sessions and progress management tools for collaborative projects. Step 4: The Formative Division brings together the activities carried out by the Exchange Division and ultimately forms an academic conference. For example, the Formative Division allows learners to present their own research results and listen to presentations by other learners. The Formative Division also holds lectures and panel discussions on the latest research results and trends. This allows learners to always have access to the latest information and maintain their motivation to study.

[0069] (Example 2) A learning support system according to an embodiment of the present invention utilizes generative AI to provide lectures and content to support learning, ultimately forming a large academic society. This learning support system generates lectures and content based on learners' needs and provides them on an online platform. Learners use these to advance their studies, and learners interact with each other online, engaging in discussions and collaborative research, thereby sharing and deepening their knowledge. Ultimately, these activities are aggregated to form a large academic society. For example, generative AI generates lectures and content based on learners' needs. These may include lectures on specific fields, practical exercises, and review articles based on the latest research findings. The generative AI provides optimal content based on the learner's level and interests. The generated content is then provided on an online platform. Learners can browse the content at their own pace and progress through their studies. The platform also provides features that allow learners to interact with each other, engaging in discussions and collaborative research. Examples of these features include forums, chat functions, video conferencing, and collaborative editing. Furthermore, learners can interact with each other online, engage in discussions and collaborative research, thereby sharing and deepening their knowledge. For example, discussions on specific topics and the sharing of each individual's opinions and knowledge can lead to new discoveries and deeper understanding. It is also possible to work on actual projects through collaborative research. Specifically, weekly discussion sessions and progress management tools for collaborative projects are provided. Ultimately, these activities are consolidated into a large academic conference. At conferences, learners can present their research results and listen to presentations by other learners. Conferences also feature lectures and panel discussions on the latest research results and trends. This allows learners to always obtain the latest information and maintain their motivation to learn. The learning support system thus generates, provides, interacts with, and creates lectures and content based on learners' needs, enabling efficient learning and knowledge sharing.

[0070] A learning support system according to an embodiment includes a generation unit, a provision unit, a communication unit, and a creation unit. The generation unit generates lectures or content based on the learner's needs. For example, the generation unit generates lectures on a specific field, practical exercises, and review articles based on the latest research findings. The generation unit uses a generation AI to provide optimal content based on the learner's level and interests. For example, the generation AI can generate optimal lectures or content based on the learner's learning goals, areas of interest, and skill level. The provision unit provides the content generated by the generation unit on an online platform. For example, the provision unit allows learners to view the generated content at their own pace and progress through their studies. The provision unit also provides functions that allow learners to interact with each other. Examples of such functions include forums, chat functions, video conferencing functions, and collaborative editing functions. The communication unit uses the content provided by the provision unit to enable learners to interact with each other and engage in discussions and collaborative research. For example, the communication unit holds discussions on a specific topic, sharing opinions and knowledge to deepen new discoveries and understanding. The communication unit can also work on actual projects through collaborative research. For example, progress management tools for weekly discussion sessions and collaborative projects are provided. The Formative Section aggregates the activities carried out by the Exchange Section and ultimately forms an academic conference. For example, the Formative Section allows learners to present their own research results and listen to presentations by other learners. The Formative Section also holds lectures and panel discussions on the latest research results and trends. This allows learners to always obtain the latest information and maintain their motivation to learn. As a result, the learning support system according to the embodiment generates, provides, interacts with, and creates lectures and content based on the learners' needs, enabling efficient learning and knowledge sharing.

[0071] The system includes a functional unit that provides a forum or chat function. The forum or chat function provides a place where learners can interact with each other. For example, the forum function enables threaded discussions, allowing learners to exchange opinions on specific topics. The chat function enables real-time communication, allowing learners to instantly share questions and opinions. For example, the forum function provides a format in which learners can post questions and other learners or instructors can respond. The chat function also allows learners to create group chats and hold discussions on specific topics. This allows learners to interact with each other through the forum or chat.

[0072] It has a function section that provides video conferencing. The video conferencing function provides a place where learners can interact face-to-face. For example, the video conferencing function allows learners to hold online meetings and exchange opinions in real time. The video conferencing function also has a recording function, allowing the contents of the meeting to be reviewed later. For example, the video conferencing function provides a format in which a learner gives a presentation and other learners ask questions or offer their opinions. The video conferencing function also allows learners to hold group discussions and have in-depth discussions on specific topics. This allows learners to interact with each other through video conferencing.

[0073] It has a function section that provides collaborative editing. The collaborative editing function provides a space where learners can collaborate on editing documents. For example, the collaborative editing function allows learners to edit documents in real time and add comments. The collaborative editing function also has a version control function that allows them to check the history of past edits. For example, the collaborative editing function provides a format in which learners can collaborate on creating reports and reflect each other's opinions and knowledge. The collaborative editing function also allows learners to share the progress of projects and divide up tasks. This allows learners to interact with each other through collaborative editing.

[0074] The system has a function section that provides weekly discussions. The weekly discussion function provides a forum for learners to hold regular discussions with each other. For example, the weekly discussion function allows learners to discuss a specific topic each week and exchange opinions. The weekly discussion function also allows learners to set a facilitator and manage the progress of the discussion. For example, the weekly discussion function provides a format in which learners can set a topic and share their opinions and knowledge. The weekly discussion function also allows learners to summarize the results of the discussion and reflect them in the next discussion. This allows learners to interact with each other through weekly discussion sessions.

[0075] The system has a functional unit that provides a collaborative project management tool. The collaborative project management tool provides a place for learners to work together on a project. For example, the collaborative project management tool allows learners to allocate tasks and report progress. The collaborative project management tool also has a communication function that allows learners to exchange opinions about the progress of the project. For example, the collaborative project management tool provides a format for learners to set project goals and clarify each person's role. The collaborative project management tool also allows learners to report progress and share issues. This allows learners to interact with each other through collaborative projects.

[0076] The generation unit can analyze the learner's emotions and adjust the difficulty of the lecture or content based on the analyzed learner's emotions. For example, if the learner is feeling stressed, the generation unit can cause the generation AI to generate a lecture or content with a lower difficulty level. Furthermore, if the learner is relaxed, the generation unit can also generate a lecture or content with a higher difficulty level. Furthermore, if the learner is excited, the generation unit can also generate a lecture or content with a more challenging task. This improves learning effectiveness by adjusting the difficulty level of the lecture or content according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the learner's emotional data into the generation AI and cause the generation AI to generate a lecture or content based on the emotion.

[0077] The generation unit can analyze a learner's past learning history and generate lectures or content. For example, the generation unit uses a generation AI to generate new related lectures or content based on what the learner has learned in the past. The generation unit can also analyze a learner's past grades and generate lectures or content that focus on areas that need reinforcement. Furthermore, the generation unit can analyze a learner's past learning patterns and generate lectures or content in an optimal learning order. This improves the effectiveness of learning by providing optimal lectures and content based on the learner's past learning history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the learner's past learning history data into the generation AI and cause the generation AI to generate optimal lectures and content.

[0078] The generation unit can generate the next learning content based on the learner's current learning progress. For example, when the learner completes the current lecture, the generation unit causes the generation AI to automatically generate the next learning content. Furthermore, when the learner clears a specific challenge, the generation unit can also cause the generation AI to generate an appropriate lecture or content as the next step. Furthermore, when the learner reports their progress, the generation unit can cause the generation AI to generate the next learning content based on that progress. This improves the effectiveness of learning by dynamically generating the next learning content according to the learning progress. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the learner's current learning progress data into the generation AI and cause the generation AI to generate the next learning content.

[0079] The generation unit can analyze the learner's emotions and select the format of the lecture or content based on the analyzed learner's emotions. For example, if the learner is tired, the generation AI can generate an audio-format lecture. If the learner prefers visual learning, the generation unit can also generate video-format content. If the learner wants to improve their reading comprehension, the generation unit can also generate a text-format lecture. This improves learning effectiveness by selecting the format of the lecture or content based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the learner's emotional data into the generation AI and have the generation AI select the format of the lecture or content based on the emotion.

[0080] The generation unit can generate region-specific examples and content based on the learner's geographic location information. For example, if the learner is in a specific region, the generation unit causes the generation AI to generate a lecture including examples related to that region. Furthermore, if the learner is overseas, the generation unit can also generate content related to the culture and history of that country. Furthermore, if the learner is in a specific city, the generation unit can also generate a lecture including business examples related to that city. This improves the effectiveness of learning by providing region-specific examples and content. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the learner's geographic location information to the generation AI and cause the generation AI to generate region-specific examples and content.

[0081] The generation unit can analyze the learner's social media activity and generate related topics or content. For example, the generation unit can have the generation AI generate related lectures based on topics the learner has shown interest in on social media. The generation unit can also analyze posts by experts the learner follows and generate related content. Furthermore, the generation unit can have the generation AI generate lectures or content based on topics in online communities in which the learner participates. This improves learning effectiveness by providing related topics and content based on social media activity. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the learner's social media activity data into the generation AI and cause the generation AI to generate related topics and content.

[0082] The providing unit can analyze the learner's emotions and adjust the display order of the content based on the analyzed learner's emotions. For example, if the learner is stressed, the providing unit can prioritize displaying relaxing content. Furthermore, if the learner is excited, the providing unit can prioritize displaying challenging content. Furthermore, if the learner is tired, the providing unit can prioritize displaying easy content. This improves learning effectiveness by adjusting the content display order according to the learner's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the learner's emotional data into the generation AI and cause the generation AI to adjust the content display order based on the emotion.

[0083] The provision unit can analyze the learner's past usage history and select a content provision method. For example, the provision unit can provide content in an optimal format based on the content format that the learner has previously preferred. The provision unit can also analyze the learner's past learning patterns and provide content at the optimal timing. Furthermore, the provision unit can prioritize providing content in areas that require reinforcement based on the learner's past grades. This improves the effectiveness of learning by selecting an optimal content provision method based on the learner's past usage history. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, AI, or can be performed without AI. For example, the provision unit can input the learner's past usage history data into a generation AI and cause the generation AI to select an optimal content provision method.

[0084] The providing unit can provide a content display format based on the learner's device information. For example, if the learner is using a smartphone, the providing unit can provide content optimized for mobile devices. Furthermore, if the learner is using a tablet, the providing unit can provide content optimized for large screens. Furthermore, if the learner is using a desktop computer, the providing unit can provide high-resolution content. This improves the effectiveness of learning by providing an optimal content display format based on device information. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the learner's device information into the generation AI and cause the generation AI to provide the optimal content display format.

[0085] The providing unit can analyze the learner's emotions and adjust the timing of content notifications based on the analyzed learner's emotions. For example, if the learner is relaxed, the providing unit can send a notification to notify the learner of new content. Furthermore, if the learner is concentrating, the providing unit can refrain from sending notifications to avoid disrupting the learner's learning. Furthermore, if the learner is excited, the providing unit can immediately send a notification to notify the learner of new content. This improves learning effectiveness by adjusting the timing of content notifications according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the learner's emotion data into the generation AI and cause the generation AI to adjust the notification timing based on the emotion.

[0086] The providing unit can provide information about region-specific events and seminars based on the learner's geographical location information. For example, if the learner is in a specific region, the providing unit can provide information about seminars held in that region. Furthermore, if the learner is overseas, the providing unit can provide information about events held in that country. Furthermore, if the learner is in a specific city, the providing unit can provide information about workshops held in that city. This improves the effectiveness of learning by providing information about region-specific events and seminars. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the learner's geographical location information into the generation AI and cause the generation AI to provide information about region-specific events and seminars.

[0087] The providing unit can analyze the learner's social media activity and provide relevant content preferentially. The providing unit can provide relevant content based on, for example, topics in which the learner has shown interest on social media. The providing unit can also analyze posts by experts followed by the learner and provide relevant content. Furthermore, the providing unit can provide relevant content based on topics in online communities in which the learner participates. This improves the effectiveness of learning by providing relevant content based on social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the learner's social media activity data into a generation AI and cause the generation AI to provide relevant content.

[0088] The communication unit can analyze the learner's emotions and select a form of communication based on the analyzed learner's emotions. For example, if the learner is nervous, the communication unit can suggest a text-based communication. Furthermore, if the learner is relaxed, the communication unit can also suggest a video-based communication. Furthermore, if the learner is excited, the communication unit can also suggest an audio-based communication. By selecting a form of communication according to the learner's emotions, the effectiveness of the communication can be improved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the communication unit can be performed using, for example, AI, or without AI. For example, the communication unit can input the learner's emotional data into the generation AI and cause the generation AI to select a form of communication based on the emotion.

[0089] The communication unit can analyze the learner's past communication history and suggest a communication method. For example, the communication unit can suggest an optimal communication format based on the learner's preferred communication format in the past. The communication unit can also analyze the learner's past communication patterns and suggest communication at the optimal timing. Furthermore, the communication unit can suggest the optimal communication partner based on the learner's past communication history. In this way, the effectiveness of communication is improved by suggesting the optimal communication method based on the past communication history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the learner's past communication history data into the generation AI and cause the generation AI to suggest the optimal communication method.

[0090] The communication unit can recommend communication partners based on the learner's current learning progress. For example, if a learner completes a specific task, the communication unit can recommend other learners who have completed the same task. Also, if a learner shows interest in a specific field, the communication unit can recommend other learners who are interested in the same field. Furthermore, when a learner reports their progress, the communication unit can recommend other learners who have the same progress. This improves the effectiveness of communication by recommending appropriate communication partners according to the learner's learning progress. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the learner's current learning progress data into the generation AI and cause the generation AI to recommend appropriate communication partners.

[0091] The communication unit can analyze the learner's emotions and adjust the frequency of interactions based on the analyzed learner's emotions. For example, the communication unit can reduce the frequency of interactions if the learner is stressed. The communication unit can also increase the frequency of interactions if the learner is relaxed. Furthermore, the communication unit can immediately suggest interactions if the learner is excited. This improves the effectiveness of interactions by adjusting the frequency of interactions according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or without AI. For example, the communication unit can input the learner's emotional data into the generation AI and cause the generation AI to adjust the frequency of interactions based on the emotion.

[0092] The communication unit can suggest region-specific communication events based on the learner's geographical location information. For example, if the learner is in a specific region, the communication unit can suggest communication events held in that region. Furthermore, if the learner is overseas, the communication unit can suggest communication events held in that country. Furthermore, if the learner is in a specific city, the communication unit can suggest communication events held in that city. In this way, by suggesting region-specific communication events, the effectiveness of communication is improved. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the learner's geographical location information into the generation AI and cause the generation AI to suggest region-specific communication events.

[0093] The communication unit can analyze the learner's social media activity and recommend related social networking groups. For example, the communication unit can recommend related social networking groups based on topics in which the learner has shown interest on social media. The communication unit can also recommend related social networking groups by analyzing the content posted by experts followed by the learner. Furthermore, the communication unit can recommend related social networking groups based on topics in online communities in which the learner participates. This improves the effectiveness of communication by recommending related social networking groups based on social media activity. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the learner's social media activity data into a generation AI and cause the generation AI to recommend related social networking groups.

[0094] The generation unit can analyze the learner's emotions and select a theme or topic for the conference based on the analyzed learner's emotions. For example, if the learner is relaxed, the generation unit can select a conference with a relaxing theme. Furthermore, if the learner is excited, the generation unit can select a conference with a challenging theme. Furthermore, if the learner is stressed, the generation unit can select a conference with a stress-relieving theme. This improves the effectiveness of the conference by selecting a theme or topic for the conference based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the learner's emotion data into the generation AI and cause the generation AI to select a theme or topic for the conference based on the emotion.

[0095] The formative unit can analyze the learner's past research results and propose a conference structure. For example, the formative unit can organize a conference on a related topic based on the learner's previously published research results. The formative unit can also analyze the learner's past research field and organize a conference specialized in that field. Furthermore, the formative unit can propose the optimal presentation order and session structure based on the learner's past research results. This improves the effectiveness of the conference by proposing the optimal conference structure based on the learner's past research results. Some or all of the above-mentioned processing in the formative unit may be performed using, for example, AI, or may be performed without using AI. For example, the formative unit can input the learner's past research result data into the generation AI and have the generation AI execute the optimal conference structure.

[0096] The formative unit can adjust the conference presentation schedule based on the learner's current research progress. For example, the formative unit can propose an optimal presentation schedule when the learner reports their current research progress. Furthermore, if the learner is at a specific research stage, the formative unit can also propose a presentation schedule appropriate for that stage. Furthermore, the formative unit can dynamically adjust the presentation schedule each time the learner reports their research progress. This improves the effectiveness of the conference by dynamically adjusting the presentation schedule according to the research progress. Some or all of the above-described processing in the formative unit may be performed, for example, using AI, or may be performed without using AI. For example, the formative unit can input the learner's current research progress data into the generation AI and have the generation AI adjust the optimal presentation schedule.

[0097] The creation unit can analyze the learner's emotions and adjust the conference participant list based on the analyzed learner's emotions. For example, if the learner is relaxed, the creation unit can prioritize adding participants who can relax to the list. Furthermore, if the learner is excited, the creation unit can add participants who can engage in challenging discussions to the list. Furthermore, if the learner is stressed, the creation unit can add participants who can reduce stress to the list. This improves the effectiveness of the conference by adjusting the participant list according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the creation unit can be performed using AI, for example, or without AI. For example, the creation unit can input the learner's emotional data into the generation AI and cause the generation AI to adjust the participant list based on the emotions.

[0098] The formation unit can suggest region-specific academic events based on the learner's geographical location information. For example, if the learner is in a specific region, the formation unit can suggest academic events held in that region. Furthermore, if the learner is overseas, the formation unit can suggest academic events held in that country. Furthermore, if the learner is in a specific city, the formation unit can suggest academic events held in that city. This improves the effectiveness of academic conferences by suggesting region-specific academic events. Some or all of the above-described processing in the formation unit may be performed using AI, for example, or may be performed without using AI. For example, the formation unit can input the learner's geographical location information into the generation AI and cause the generation AI to suggest region-specific academic events.

[0099] The generation unit can analyze the learner's social media activity and recommend relevant conferences and events. For example, the generation unit can recommend relevant conferences based on topics in which the learner has shown interest on social media. The generation unit can also recommend relevant conferences based on the content of posts by experts followed by the learner. Furthermore, the generation unit can recommend relevant conferences based on topics in online communities in which the learner participates. This improves the effectiveness of conferences by recommending relevant conferences and events based on social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the learner's social media activity data into the generation AI and cause the generation AI to recommend relevant conferences and events.

[0100] The functional unit providing the forum or chat function can analyze the learner's emotions and suggest forum or chat topics based on the analyzed learner's emotions. For example, if the learner is relaxed, the functional unit providing the forum or chat function can suggest relaxing topics. Furthermore, if the learner is excited, the functional unit providing the forum or chat function can suggest challenging topics. Furthermore, if the learner is stressed, the functional unit providing the forum or chat function can suggest topics that will reduce stress. This improves the effectiveness of interactions by suggesting forum or chat topics based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the functional unit providing the forum or chat function can be performed using AI, for example, or without AI. For example, the functional unit providing the forum or chat function can input the learner's emotional data into the generation AI and cause the generation AI to suggest topics based on the emotions.

[0101] The functional unit providing the forum or chat function can analyze a learner's past posting history and suggest a method for participating in a forum or chat. For example, the functional unit providing the forum or chat function can suggest an optimal forum based on topics that the learner has previously participated in. The functional unit providing the forum or chat function can also analyze a learner's past posting patterns and suggest an optimal method for participating in a chat. Furthermore, the functional unit providing the forum or chat function can also suggest an optimal timing for participation based on the learner's past posting history. This improves the effectiveness of interaction by suggesting an optimal method for participating in a forum or chat based on the learner's past posting history. Some or all of the above-mentioned processing in the functional unit providing the forum or chat function may be performed using, for example, AI, or may be performed without using AI. For example, the functional unit providing the forum or chat function can input a learner's past posting history data into a generation AI and cause the generation AI to suggest an optimal method for participation.

[0102] The functional unit providing the forum or chat function can analyze the learner's emotions and adjust the timing of forum or chat notifications based on the analyzed learner's emotions. For example, the functional unit providing the forum or chat function can send a notification to notify the learner of a new topic when the learner is relaxed. The functional unit providing the forum or chat function can also refrain from sending notifications to the learner when the learner is concentrating so as not to disrupt their learning. Furthermore, the functional unit providing the forum or chat function can immediately send a notification to notify the learner of a new topic when the learner is excited. This improves the effectiveness of interactions by adjusting the timing of forum or chat notifications according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the functional unit providing the forum or chat function can be performed, for example, using AI or without AI. For example, a functional unit that provides forum and chat functions can input learner emotional data into the generation AI and have the generation AI adjust the timing of notifications based on the learner's emotions.

[0103] The functional unit providing the forum and chat function can suggest region-specific forums or chats based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit providing the forum and chat function can suggest forums held in that region. Furthermore, if the learner is overseas, the functional unit providing the forum and chat function can suggest chats held in that country. Furthermore, if the learner is in a specific city, the functional unit providing the forum and chat function can suggest forums held in that city. This improves the effectiveness of interaction by suggesting region-specific forums and chats. Some or all of the above-described processing in the functional unit providing the forum and chat function may be performed using, or without, AI, for example. For example, the functional unit providing the forum and chat function can input the learner's geographical location information to a generation AI and cause the generation AI to suggest region-specific forums and chats.

[0104] The functional unit providing the video conferencing function can analyze the learner's emotions and adjust the video conference schedule based on the analyzed learner's emotions. For example, if the learner is relaxed, the functional unit providing the video conferencing function can schedule the video conference for a relaxing time period. Furthermore, if the learner is excited, the functional unit providing the video conferencing function can schedule the video conference for a challenging time period. Furthermore, if the learner is stressed, the functional unit providing the video conferencing function can schedule the video conference for a time period that will reduce stress. This improves the effectiveness of interaction by adjusting the video conference schedule according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the functional unit providing the video conferencing function can be performed using AI, for example, or without AI. For example, the functional unit providing the video conferencing function can input the learner's emotional data into the generation AI and cause the generation AI to adjust the schedule based on the emotion.

[0105] The functional unit providing the video conferencing function can analyze the learner's past video conferencing history and suggest a method for participating in the video conferencing. For example, the functional unit providing the video conferencing function can suggest an optimal participation method based on the learner's preferred video conferencing format in the past. The functional unit providing the video conferencing function can also analyze the learner's past video conferencing patterns and suggest the optimal timing for participation. Furthermore, the functional unit providing the video conferencing function can also suggest an optimal participation method based on the learner's past video conferencing history. This improves the effectiveness of interaction by suggesting an optimal participation method based on the past video conferencing history. Some or all of the above-described processing in the functional unit providing the video conferencing function may be performed using, or without, AI, for example. For example, the functional unit providing the video conferencing function can input the learner's past video conferencing history data into a generation AI and cause the generation AI to suggest an optimal participation method.

[0106] The functional unit providing the video conferencing function can analyze the learner's emotions and adjust the timing of the video conferencing notification based on the analyzed learner's emotions. For example, the functional unit providing the video conferencing function can send a notification to notify the learner of a video conferencing if the learner is relaxed. The functional unit providing the video conferencing function can also refrain from sending notifications to the learner if the learner is concentrating so as not to disrupt their learning. Furthermore, the functional unit providing the video conferencing function can immediately send a notification to notify the learner of a video conferencing if the learner is excited. This improves the effectiveness of interaction by adjusting the timing of the video conferencing notification according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the functional unit providing the video conferencing function can be performed using, for example, AI, or without AI. For example, the functional unit providing the video conferencing function can input the learner's emotional data into the generation AI and cause the generation AI to adjust the timing of the notification based on the learner's emotions.

[0107] The functional unit providing the video conferencing function can suggest region-specific video conferences based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit providing the video conferencing function can suggest video conferences to be held in that region. Furthermore, if the learner is overseas, the functional unit providing the video conferencing function can suggest video conferences to be held in that country. Furthermore, if the learner is in a specific city, the functional unit providing the video conferencing function can suggest video conferences to be held in that city. This improves the effectiveness of interaction by suggesting region-specific video conferences. Some or all of the above-described processing in the functional unit providing the video conferencing function can be performed using AI, for example, or without AI. For example, the functional unit providing the video conferencing function can input the learner's geographical location information to a generation AI and cause the generation AI to suggest region-specific video conferences.

[0108] The functional unit providing the collaborative editing function can analyze the learner's emotions and adjust the timing of collaborative editing based on the analyzed learner's emotions. For example, if the learner is relaxed, the functional unit providing the collaborative editing function can schedule collaborative editing for a relaxing time period. Furthermore, if the learner is excited, the functional unit providing the collaborative editing function can schedule collaborative editing for a challenging time period. Furthermore, if the learner is stressed, the functional unit providing the collaborative editing function can schedule collaborative editing for a time period that reduces stress. This improves the effectiveness of interaction by adjusting the timing of collaborative editing according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the functional unit providing the collaborative editing function can be performed using AI, for example, or without AI. For example, the functional unit providing the collaborative editing function can input the learner's emotional data into the generation AI and cause the generation AI to adjust the timing based on the emotion.

[0109] The functional unit providing the collaborative editing function can analyze the learner's past collaborative editing history and propose a collaborative editing method. The functional unit providing the collaborative editing function can, for example, propose an optimal method based on the learner's past preferred collaborative editing format. The functional unit providing the collaborative editing function can also analyze the learner's past collaborative editing patterns and propose collaborative editing at the optimal timing. Furthermore, the functional unit providing the collaborative editing function can also propose an optimal method based on the learner's past collaborative editing history. In this way, by proposing an optimal method based on the learner's past collaborative editing history, the effectiveness of collaborative editing is improved. Some or all of the above-mentioned processing in the functional unit providing the collaborative editing function may be performed using, for example, AI, or may be performed without using AI. For example, the functional unit providing the collaborative editing function can input the learner's past collaborative editing history data into a generation AI and have the generation AI execute a proposal for an optimal method.

[0110] The functional unit providing the collaborative editing function can analyze the learner's emotions and adjust the timing of the collaborative editing notification based on the analyzed learner's emotions. For example, the functional unit providing the collaborative editing function can send a notification to notify the learner of the collaborative editing if the learner is relaxed. The functional unit providing the collaborative editing function can also refrain from sending notifications if the learner is concentrating so as not to disrupt their learning. Furthermore, the functional unit providing the collaborative editing function can immediately send a notification to notify the learner of the collaborative editing if the learner is excited. This improves the effectiveness of the collaborative editing by adjusting the timing of the collaborative editing notification according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the functional unit providing the collaborative editing function can be performed using, for example, AI, or without AI. For example, the functional unit providing the collaborative editing function can input the learner's emotional data into the generation AI and cause the generation AI to adjust the notification timing based on the learner's emotions.

[0111] The functional unit providing the collaborative editing function can suggest region-specific collaborative editing projects based on the learner's geographic location information. For example, if the learner is in a specific region, the functional unit providing the collaborative editing function can suggest collaborative editing projects to be held in that region. Furthermore, if the learner is overseas, the functional unit providing the collaborative editing function can suggest collaborative editing projects to be held in that country. Furthermore, if the learner is in a specific city, the functional unit providing the collaborative editing function can suggest collaborative editing projects to be held in that city. This improves the effectiveness of collaborative editing by suggesting region-specific collaborative editing projects. Some or all of the above-described processing in the functional unit providing the collaborative editing function may be performed using AI, for example, or may be performed without using AI. For example, the functional unit providing the collaborative editing function can input the learner's geographic location information to a generation AI and cause the generation AI to suggest region-specific collaborative editing projects.

[0112] The functional unit for providing the weekly discussion session can analyze the learner's emotions and select a discussion topic based on the analyzed learner's emotions. For example, if the learner is relaxed, the functional unit for providing the weekly discussion session can select a discussion topic with a relaxing theme. Furthermore, if the learner is excited, the functional unit for providing the weekly discussion session can select a discussion topic with a challenging theme. Furthermore, if the learner is stressed, the functional unit for providing the weekly discussion session can select a discussion topic with a stress-relieving theme. Thus, by selecting a discussion topic based on the learner's emotions, the effectiveness of the discussion is improved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the functional unit for providing the weekly discussion session can be performed using, for example, AI, or without AI. For example, the functional unit for providing the weekly discussion session can input the learner's emotion data into the generation AI and cause the generation AI to select a topic based on the emotion.

[0113] The functional unit providing the weekly discussion session can analyze the learner's past discussion history and suggest a method for proceeding with the discussion. The functional unit providing the weekly discussion session can suggest an optimal method for proceeding with the discussion, for example, based on the discussion format that the learner has preferred to participate in in the past. The functional unit providing the weekly discussion session can also analyze the learner's past discussion patterns and suggest a method for proceeding with the discussion at the optimal timing. Furthermore, the functional unit providing the weekly discussion session can also suggest an optimal method for proceeding with the discussion based on the learner's past discussion history. In this way, by suggesting an optimal method for proceeding with the discussion based on the past discussion history, the effectiveness of the discussion is improved. Some or all of the above-mentioned processing in the functional unit providing the weekly discussion session can be performed using, for example, AI, or can be performed without using AI. For example, the functional unit providing the weekly discussion session can input the learner's past discussion history data into a generation AI and cause the generation AI to suggest an optimal method for proceeding with the discussion.

[0114] The functional unit for providing the weekly discussion session can analyze the learner's emotions and adjust the timing of the discussion notification based on the analyzed learner's emotions. For example, the functional unit for providing the weekly discussion session can send a notification to notify the learner of the discussion if the learner is relaxed. The functional unit for providing the weekly discussion session can also refrain from sending notifications to notify the learner of the discussion if the learner is concentrating so as not to disrupt their learning. Furthermore, the functional unit for providing the weekly discussion session can immediately send a notification to notify the learner of the discussion if the learner is excited. This improves the effectiveness of the discussion by adjusting the timing of the discussion notification according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the functional unit for providing the weekly discussion session can be performed using, for example, an AI, or without an AI. For example, the functional unit for providing the weekly discussion session can input learner's emotion data into the generation AI and cause the generation AI to adjust the timing of the notification based on the learner's emotions.

[0115] The functional unit for providing weekly discussion sessions can suggest region-specific discussion sessions based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit for providing weekly discussion sessions can suggest discussion sessions to be held in that region. Furthermore, if the learner is overseas, the functional unit for providing weekly discussion sessions can suggest discussion sessions to be held in that country. Furthermore, if the learner is in a specific city, the functional unit for providing weekly discussion sessions can suggest discussion sessions to be held in that city. In this way, by suggesting region-specific discussion sessions, the effectiveness of the discussions is improved. Some or all of the above-described processing in the functional unit for providing weekly discussion sessions can be performed using, for example, AI, or can be performed without using AI. For example, the functional unit for providing weekly discussion sessions can input the learner's geographical location information to a generation AI and cause the generation AI to suggest region-specific discussion sessions.

[0116] The functional unit providing the collaborative project progress management tool can analyze the learner's emotions and adjust the project progress schedule based on the analyzed learner's emotions. For example, if the learner is relaxed, the functional unit providing the collaborative project progress management tool can adjust the project progress schedule to a relaxing time period. Furthermore, if the learner is excited, the functional unit providing the collaborative project progress management tool can adjust the project progress schedule to a challenging time period. Furthermore, if the learner is stressed, the functional unit providing the collaborative project progress management tool can adjust the project progress schedule to a time period that reduces stress. This improves the effectiveness of the project by adjusting the project progress schedule according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the functional unit providing the collaborative project progress management tool can be performed, for example, using AI or without AI. For example, a functional unit that provides a progress management tool for a collaborative project can input learner emotional data into a generation AI and have the generation AI adjust the progress schedule based on the emotions.

[0117] The functional unit providing the collaborative project progress management tool can analyze the learner's past project history and propose a project management method. The functional unit providing the collaborative project progress management tool can, for example, propose an optimal method based on the learner's preferred project management methods in the past. The functional unit providing the collaborative project progress management tool can also analyze the learner's past project patterns and propose a method for progressing the project at the optimal timing. Furthermore, the functional unit providing the collaborative project progress management tool can also propose an optimal management method based on the learner's past project history. In this way, by proposing an optimal management method based on the past project history, the effectiveness of the project is improved. Some or all of the above-mentioned processing in the functional unit providing the collaborative project progress management tool can be performed using, for example, AI, or can be performed without using AI. For example, the functional unit providing the collaborative project progress management tool can input the learner's past project history data into a generation AI and cause the generation AI to propose an optimal management method.

[0118] The functional unit providing the collaborative project progress management tool can analyze the learner's emotions and adjust the timing of project notifications based on the analyzed learner's emotions. For example, the functional unit providing the collaborative project progress management tool can send notifications to notify the learner of the progress of the project when the learner is relaxed. The functional unit providing the collaborative project progress management tool can also refrain from sending notifications to notify the learner of the progress of the project when the learner is concentrating, so as not to disrupt the learner's learning. Furthermore, the functional unit providing the collaborative project progress management tool can immediately send notifications to notify the learner of the progress of the project when the learner is excited. This improves the effectiveness of the project by adjusting the timing of project notifications according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the functional unit providing the collaborative project progress management tool can be performed, for example, using AI, or without AI. For example, a functional unit that provides a progress management tool for a collaborative project can input learner emotional data into a generation AI and have the generation AI adjust the timing of notifications based on the learner's emotions.

[0119] The functional unit for providing a collaborative project progress management tool can suggest region-specific projects based on the learner's geographical location information. For example, if the learner is in a specific region, the functional unit for providing a collaborative project progress management tool can suggest projects held in that region. Furthermore, if the learner is overseas, the functional unit for providing a collaborative project progress management tool can suggest projects held in that country. Furthermore, if the learner is in a specific city, the functional unit for providing a collaborative project progress management tool can suggest projects held in that city. This improves the effectiveness of the project by suggesting region-specific projects. Some or all of the above-described processing in the functional unit for providing a collaborative project progress management tool can be performed using AI, for example, or without AI. For example, the functional unit for providing a collaborative project progress management tool can input the learner's geographical location information into the generation AI and cause the generation AI to suggest region-specific projects. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned generation unit, provision unit, communication unit, and formation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates lectures and content based on learners' needs using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated content on an online platform. The communication unit is realized, for example, by the control unit 46A of the smart device 14 and provides functions for learners to interact with each other, hold discussions, and conduct collaborative research. The formation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and aggregates learners' activities, ultimately forming an academic conference. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned generation unit, provision unit, communication unit, and formation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates lectures and content based on learners' needs using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated content on an online platform. The communication unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides functions for learners to interact with each other, hold discussions, and conduct collaborative research. The formation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and aggregates learners' activities, ultimately forming an academic conference. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned generation unit, provision unit, communication unit, and formation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates lectures and content based on learners' needs using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated content on an online platform. The communication unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides functions for learners to interact with each other and engage in discussions and collaborative research. The formation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and aggregates learners' activities, ultimately forming an academic conference. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned generation unit, provision unit, communication unit, and formation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates lectures and content based on learners' needs using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated content on an online platform. The communication unit is realized, for example, by the control unit 46A of the robot 414 and provides functions for learners to interact with each other and engage in discussions and collaborative research. The formation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and aggregates learners' activities, ultimately forming an academic conference.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The generation unit can analyze the learner's physiological data and adjust the format of the lecture or content based on the analyzed physiological data. For example, by monitoring the learner's heart rate and electrodermal activity, the system can provide a relaxing audio lecture if the learner is highly stressed. It can also provide video content if the learner is highly focused. Furthermore, it can provide a text-based lecture that can be learned in a short time if the learner is highly fatigued. This improves the effectiveness of learning by providing the optimal format of lectures and content according to the learner's physiological state.

[0122] The providing unit can analyze the learning style of the learner and adjust the display order of the content based on the analyzed learning style. For example, visual content can be displayed preferentially to a learner who prefers visual learning. Audio content can also be displayed preferentially to a learner who prefers auditory learning. Furthermore, exercises and simulations can be displayed preferentially to a learner who prefers practical learning. In this way, the effectiveness of learning can be improved by adjusting the display order of the content according to the learner's learning style.

[0123] The communication unit can analyze the learner's emotions and adjust the timing of interactions based on the analyzed learner's emotions. For example, if the learner is relaxed, the timing of interactions can be increased. Also, if the learner is stressed, the timing of interactions can be decreased. Furthermore, if the learner is excited, the communication unit can immediately suggest interactions. In this way, the effectiveness of interactions can be improved by adjusting the timing of interactions according to the learner's emotions.

[0124] The generation unit can analyze the learner's hobbies and interests and generate lectures or content based on the analyzed hobbies and interests. For example, if the learner is interested in music, a lecture including music-related examples can be generated. If the learner is interested in sports, content related to sports can be generated. Furthermore, if the learner is interested in art, a lecture related to art can be generated. This improves the effectiveness of learning by providing optimal lectures and content according to the learner's hobbies and interests.

[0125] The providing unit can analyze the learner's device usage history and adjust the content display format based on the analyzed device usage history. For example, if the learner frequently uses a smartphone, the providing unit can provide content optimized for mobile devices. Also, if the learner frequently uses a tablet, the providing unit can provide content optimized for large screens. Furthermore, if the learner frequently uses a desktop, the providing unit can provide high-resolution content. This improves the effectiveness of learning by providing the optimal content display format based on the device usage history.

[0126] The communication unit can analyze the learner's emotions and select the communication format based on the analyzed learner's emotions. For example, if the learner is nervous, it can suggest a text-based communication. If the learner is relaxed, it can suggest a video-based communication. Furthermore, if the learner is excited, it can suggest an audio-based communication. In this way, the effectiveness of communication can be improved by selecting the communication format according to the learner's emotions.

[0127] The generation unit can analyze the learner's learning goals and generate lectures or content based on the analyzed learning goals. For example, if a learner wants to obtain a specific qualification, the generation unit can generate lectures related to that qualification. Also, if a learner wants to acquire a specific skill, the generation unit can generate content related to that skill. Furthermore, if a learner wants to complete a specific project, the generation unit can generate lectures related to that project. This improves the effectiveness of learning by providing optimal lectures and content according to the learner's learning goals.

[0128] The providing unit can analyze the learner's emotions and adjust the timing of content notifications based on the analyzed learner's emotions. For example, if the learner is relaxed, notifications can be sent to inform the learner of new content. Also, if the learner is concentrating, notifications can be refrained from so as not to interrupt the learner's learning. Furthermore, if the learner is excited, notifications can be sent immediately to inform the learner of new content. In this way, the effectiveness of learning can be improved by adjusting the timing of content notifications according to the learner's emotions.

[0129] The communication unit can analyze the learner's past communication history and suggest communication methods. For example, it can suggest the most appropriate communication format based on the communication format that the learner has preferred in the past. It can also analyze the learner's past communication patterns and suggest communication at the optimal timing. Furthermore, it can suggest the most appropriate communication partner based on the learner's past communication history. In this way, the effectiveness of communication can be improved by suggesting the optimal communication method based on the learner's past communication history.

[0130] The providing unit can analyze the learner's emotions and adjust the display order of the content based on the analyzed learner's emotions. For example, if the learner is feeling stressed, relaxing content can be displayed preferentially. Also, if the learner is excited, challenging content can be displayed preferentially. Furthermore, if the learner is tired, easy content can be displayed preferentially. In this way, adjusting the display order of the content according to the learner's emotions improves the effectiveness of learning.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The generator generates lectures or content based on the learner's needs. For example, the generator generates lectures on a specific field, practical exercises, and even review articles based on the latest research findings. The generator uses generative AI to provide optimal content based on the learner's level and interests. For example, the generator AI can generate optimal lectures or content based on the learner's learning goals, areas of interest, and skill level. Step 2: The providing unit provides the content generated by the generating unit on an online platform. For example, the providing unit allows learners to view the generated content at their own pace and progress through their studies. The providing unit also provides functions that allow learners to interact with each other. For example, the functions include forums, chat functions, video conferencing functions, and collaborative editing functions. Step 3: In the Communication Club, learners interact with each other and engage in discussions and collaborative research using the content provided by the Provider Club. For example, the Communication Club holds discussions on specific topics, sharing each learner's opinions and knowledge, leading to new discoveries and deeper understanding. The Communication Club can also be used to work on actual projects through collaborative research. For example, it provides weekly discussion sessions and progress management tools for collaborative projects. Step 4: The Formative Division brings together the activities carried out by the Exchange Division and ultimately forms an academic conference. For example, the Formative Division allows learners to present their own research results and listen to presentations by other learners. The Formative Division also holds lectures and panel discussions on the latest research results and trends. This allows learners to always have access to the latest information and maintain their motivation to study.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates lectures or content based on learner needs; a providing unit that provides the content generated by the generating unit on an online platform; an exchange section where learners can interact with each other and hold discussions or collaborative research using the content provided by the provision section; The activities carried out by the above-mentioned exchange department will be consolidated and eventually formed into an academic society. Equipped with A system characterized by:

2. Have a functional section that provides forum or chat functionality 2. The system of claim 1.

3. Equipped with a function section that provides video conferencing 2. The system of claim 1.

4. Equipped with a function that provides collaborative editing 2. The system of claim 1.

5. Have a functional department that provides weekly discussions 2. The system of claim 1.

6. Establish a functional department that provides collaborative project management tools 2. The system of claim 1.

7. The generation unit Analyzing learner emotions and adjusting the difficulty level of lectures or content based on the analyzed learner emotions 2. The system of claim 1.

8. The generation unit Analyzing learners' past learning history and generating lectures or content 2. The system of claim 1.

9. The generation unit Generate next learning content based on the learner's current learning progress 2. The system of claim 1.

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