system

Abstract

The system according to this embodiment aims to provide a curriculum tailored to the learner's objectives and level, and to realize a real-time question-answering system and an environment where learning is possible 24 hours a day. [Solution] The system according to this embodiment comprises a generation unit, a lecture unit, a question response unit, and a learning unit. The generation unit automatically generates a curriculum tailored to the learner's objectives and level. The lecture unit conducts lectures based on the curriculum generated by the generation unit. The question response unit answers questions asked during the learning session in real time. The learning unit is accessible 24 hours a day.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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[0003] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there are problems that it is difficult to provide a curriculum tailored to the purposes and levels of learners, and it is also difficult to provide an environment for real-time question answering and 24-hour learning.

[0005] The system according to the embodiment aims to provide a curriculum tailored to the purposes and levels of learners and to realize an environment for real-time question answering and 24-hour learning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a lecture unit, a question-answering unit, and a learning unit. The generation unit automatically generates a curriculum tailored to the learner's objectives and level. The lecture unit conducts lectures based on the curriculum generated by the generation unit. The question-answering unit answers questions asked during the learning session in real time. The learning unit is accessible 24 hours a day. [Effects of the Invention]

[0007] The system according to this embodiment can provide a curriculum tailored to the learner's objectives and level, and can realize a real-time question-answering system and an environment where learning is possible 24 hours a day. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The online school system according to an embodiment of the present invention is a completely free online school utilizing AI for middle-aged people who are anxious about the future. This online school system provides lectures tailored to the student's purpose and level, allows questions anytime and as many times as needed during the course, and can be accessed anytime, anywhere. First, the student inputs their purpose and current skill level. For example, they set a specific goal such as wanting to learn the basics of programming or wanting to improve their data analysis skills. Next, the AI ​​analyzes the student's input information and automatically generates an optimal curriculum. This curriculum is customized to the student's purpose and level, allowing for efficient learning. During the course, the student can ask questions anytime and as many times as needed. An interactive AI acts as an instructor, answering the student's questions in real time. For example, it provides appropriate answers to specific problems the student has, such as questions about programming code or doubts about data analysis methods. Furthermore, this online school system is available 24 hours a day, allowing students to learn at their own convenience. For example, they can continue learning without being restricted by time or place, such as during work breaks, at night, or on weekends. Since the course can be taken with just a smartphone, learning is possible even while commuting or out and about. Thus, a completely free online school system utilizing AI can be an effective means for middle-aged people to alleviate anxieties about the future and improve their skills. Students can learn at their own pace and efficiently acquire skills with the support of interactive AI. As a result, the online school system can provide a curriculum tailored to the student's goals and level, answer questions in real time during lessons, and offer a system that can be accessed 24 hours a day.

[0029] The online school system according to this embodiment comprises a generation unit, a lecture unit, a question-answering unit, and a learning unit. The generation unit automatically generates a curriculum tailored to the learner's objectives and level. For example, if a learner inputs that they want to learn the basics of programming, the generation unit generates a curriculum that includes basic programming lectures. The generation unit can also generate a curriculum for learning data analysis methods if a learner inputs that they want to improve their data analysis skills. Furthermore, the generation unit can generate beginner, intermediate, and advanced curricula according to the learner's skill level. For example, the generation unit generates a curriculum for beginners that includes lectures explaining basic concepts, and a curriculum for intermediate learners that includes practical exercises. The lecture unit conducts lectures based on the curriculum generated by the generation unit. The lecture unit can, for example, provide video lectures that learners can view. The lecture unit can also provide text lectures that learners can read. Furthermore, the lecture unit can provide interactive lectures that learners can participate in. For example, the lecture unit can provide video lectures that learners can view. The lecture unit can also provide text lectures that learners can read. Furthermore, the lecture section can provide interactive lectures, allowing students to participate. The question support section provides real-time answers to questions asked during lectures. The question support section can, for example, use conversational AI to instantly answer students' questions. The question support section can also provide appropriate answers to questions entered by students using conversational AI. Furthermore, the question support section can provide appropriate answers to questions entered by students using conversational AI. Lectures are available 24 hours a day. The lecture section allows students to learn at their own pace. Furthermore, students can continue learning using their smartphones while traveling or away from home. Furthermore, students can continue learning using their smartphones while traveling or away from home.As a result, the online school system according to this embodiment can provide a curriculum tailored to the student's objectives and level, answer questions in real time during lessons, and offer a system that allows students to take lessons anytime, 24 hours a day.

[0030] The generation unit automatically generates a curriculum tailored to the learner's objectives and level. For example, if a learner inputs that they want to learn the basics of programming, the generation unit will generate a curriculum that includes basic programming lectures. Specifically, the generation unit uses AI to design the optimal curriculum based on the information input by the learner. The AI ​​analyzes the learner's past learning history, current skill level, and learning goals, and constructs the curriculum based on that. For example, if a learner inputs that they want to learn the basics of programming, the AI ​​will select lectures that include basic concepts such as variables, loops, and conditional branching, and combine them to generate a curriculum. Also, if a learner inputs that they want to improve their data analysis skills, the AI ​​will select lectures to learn data preprocessing, statistical analysis, and basic machine learning techniques, and combine them to generate a curriculum. Furthermore, the generation unit can also generate beginner, intermediate, and advanced curricula according to the learner's skill level. For example, for beginners, it will generate a curriculum that includes lectures explaining basic concepts, for intermediate learners, it will generate a curriculum that includes practical exercises, and for advanced learners, it will generate a curriculum that includes lectures to learn more advanced technologies and application examples. This allows the generation unit to respond to the diverse needs of learners and provide an optimal learning experience.

[0031] The lecture department conducts lectures based on the curriculum generated by the generation department. For example, the lecture department provides video lectures that students can view. Specifically, the lecture department provides video lectures recorded by professional instructors, allowing students to view them at their own pace. The lecture department can also provide text lectures that students can read. These text lectures are provided as teaching materials, including detailed explanations and diagrams, and can be used as supplementary material to deepen students' understanding. Furthermore, the lecture department can provide interactive lectures that students can participate in. For example, live lectures can be held, allowing students to ask questions and offer opinions in real time. Interactive quizzes and exercises can also allow students to practically confirm what they have learned. This allows the lecture department to offer students diverse learning methods and support effective learning. Additionally, the lecture department can monitor students' progress and provide feedback as needed. For example, if a student struggles with a particular lecture, additional supplementary materials or individual guidance can be provided to deepen their understanding. This allows the lecture department to maximize student learning effectiveness and improve the overall quality of the online school system.

[0032] The question support unit provides real-time answers to questions asked during the course. For example, it uses conversational AI to instantly respond to students' questions. Specifically, when a student inputs a question, the conversational AI analyzes it and provides an appropriate answer. The conversational AI uses natural language processing technology to understand the student's question, searches for relevant information, and generates an answer. For example, if a student asks, "I don't know how to use variables," the conversational AI provides information on variable definitions and usage. The conversational AI can also provide more appropriate answers based on past question history and the student's learning history. Furthermore, the question support unit can escalate complex questions that the conversational AI cannot answer to a specialist instructor. This ensures that students receive quick and appropriate answers to any question. The question support unit enhances learning effectiveness by supporting students' learning and resolving their doubts. Additionally, the question support unit can analyze students' questions to identify common questions and issues. This allows it to collaborate with the lecture and production units to improve the curriculum and lecture content, providing a learning environment better suited to students' needs.

[0033] The learning program is available 24 hours a day. For example, it allows learners to study at their own pace. Specifically, the program provides an online platform that enables learners to access lectures anytime, anywhere. Learners can use devices such as PCs, tablets, and smartphones to watch lectures, read texts, and participate in interactive exercises. The program also allows learners to continue learning on the go or while traveling using their smartphones. For example, they can watch lectures or answer quizzes while commuting or traveling. Furthermore, the program can provide tools for learners to manage their progress and set goals. For instance, learners can set their own study schedules and track their progress. The program can also analyze learner data and provide personalized feedback and learning advice. This allows the program to provide an environment where learners can learn efficiently at their own pace, maximizing learning effectiveness.

[0034] The analysis unit can analyze the learner's input information. For example, the analysis unit can analyze the learner's input objectives and skill level and provide data to generate an optimal curriculum. For example, if a learner inputs that they want to learn the basics of programming, the analysis unit will analyze that information and provide it to the generation unit. The analysis unit can also analyze if a learner inputs that they want to improve their data analysis skills and provide that information to the generation unit. Furthermore, the analysis unit can analyze the learner's skill level and provide data to generate beginner, intermediate, and advanced curricula. For example, if the analysis unit determines that a learner is a beginner, it will provide data to generate a curriculum that includes lectures explaining basic concepts. In this way, the analysis unit can provide a more appropriate curriculum by analyzing the learner's input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's input information into AI and obtain the analysis results from AI.

[0035] The management department can manage the progress of students. For example, the management department can record which lectures students have attended, which exercises they have completed, and which tests they have taken. For example, if a student attends a basic programming lecture, the management department will record that information. The management department can also record if a student has completed data analysis exercises. Furthermore, if a student takes a test, the management department can record the results. For example, if a student takes a basic programming test, the management department will record the results. In this way, the management department can improve the effectiveness of learning by managing the progress of students. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input student progress information into AI and obtain progress management results from AI.

[0036] The generation unit can analyze a student's past learning history and select the optimal curriculum. For example, the generation unit can suggest what the student should learn next based on what they have learned in the past. The generation unit can also identify areas where the student struggles from their past learning history and create a curriculum that focuses on those areas. The generation unit can also analyze a student's past learning history and adjust the curriculum according to their learning progress. In this way, the generation unit can provide the optimal curriculum by analyzing a student's past learning history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's past learning history data into AI and obtain analysis results from the AI ​​to select the optimal curriculum.

[0037] The generation unit can filter the curriculum based on the learner's current occupation and areas of interest during the curriculum generation process. For example, the generation unit can provide a curriculum that strengthens skills related to the learner's occupation. The generation unit can also incorporate relevant topics into the curriculum based on the learner's areas of interest. For example, the generation unit can create a curriculum that includes practical projects based on the learner's occupation and areas of interest. This allows the generation unit to provide more relevant learning content by filtering the curriculum based on the learner's occupation and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on the learner's occupation and areas of interest into AI and obtain filtering results from AI.

[0038] The generation unit can prioritize incorporating highly relevant content by considering the geographical location information of the learners when generating the curriculum. For example, the generation unit can incorporate skills in high demand in the learners' region into the curriculum. For example, the generation unit can also provide content tailored to regional characteristics based on the learners' geographical location information. For example, the generation unit can create a curriculum that includes collaboration with local businesses and organizations, taking into account the learners' geographical location information. In this way, the generation unit can provide regionally relevant content by considering the learners' geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learners' geographical location information into AI and obtain analysis results from the AI ​​to prioritize the incorporation of highly relevant content.

[0039] The generation unit can analyze students' social media activity and incorporate relevant content when generating the curriculum. For example, the generation unit can incorporate topics that students have shown interest in on social media into the curriculum. The generation unit can also identify areas of interest from students' social media activity and provide content related to those areas. For example, the generation unit can analyze students' social media activity and create a curriculum that aligns with trends. In this way, the generation unit can provide content of interest by analyzing students' social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input students' social media activity data into AI and obtain analysis results from the AI ​​to incorporate relevant content.

[0040] The lecture department can evaluate the students' understanding in real time during the lecture and adjust the lecture content as needed. For example, if the students' understanding is low, the lecture department can revert to basic content and explain it again. For example, if the students' understanding is high, the lecture department can advance the lecture content and provide more advanced material. The lecture department can also evaluate the students' understanding in real time and adjust the pace of the lecture according to their understanding. In this way, the lecture department can enhance the effectiveness of learning by adjusting the lecture content according to the students' understanding. Some or all of the above processes in the lecture department may be performed using AI, for example, or without AI. For example, the lecture department can input student understanding data into AI and obtain understanding evaluation results from AI.

[0041] The lecture department may provide additional reference materials during lectures according to the students' areas of interest. For example, the lecture department may provide reference materials related to topics that students have shown interest in. The lecture department may also provide relevant papers or articles based on the students' areas of interest. The lecture department may also provide additional learning resources according to the students' areas of interest. In this way, the lecture department can enhance the effectiveness of learning by providing reference materials according to the students' areas of interest. Some or all of the above processing in the lecture department may be performed using AI, for example, or not using AI. For example, the lecture department may input data on students' areas of interest into AI and obtain analysis results from the AI ​​to provide additional reference materials.

[0042] The lecturer may introduce relevant case studies during the lecture, taking into account the geographical location information of the participants. For example, the lecturer may introduce case studies related to the participants' region during the lecture. For example, the lecturer may also provide case studies tailored to the characteristics of the region based on the participants' geographical location information. For example, the lecturer may also introduce case studies of collaboration with local companies and organizations, taking into account the participants' geographical location information. In this way, the lecturer can provide case studies relevant to the region by taking into account the participants' geographical location information. Some or all of the above processing in the lecturer may be performed using AI, for example, or not using AI. For example, the lecturer may input the participants' geographical location information into AI and obtain analysis results from the AI ​​to introduce relevant case studies.

[0043] The lecture department can analyze students' social media activity during lectures and address relevant topics. For example, the lecture department can address topics that students have shown interest in on social media. The lecture department can also identify areas of interest from students' social media activity and provide topics related to those areas. The lecture department can also analyze students' social media activity and address trending topics during lectures. In this way, the lecture department can provide topics of interest by analyzing students' social media activity. Some or all of the above processing in the lecture department may be performed using AI, for example, or not. For example, the lecture department can input students' social media activity data into an AI and obtain analysis results from the AI ​​to address relevant topics.

[0044] The question response unit can provide the best answer by referring to past question history when responding to a question. For example, the question response unit can provide the best answer based on the content of questions previously asked by the student. For example, the question response unit can also provide answers to similar questions from the student's past question history. For example, the question response unit can analyze the student's past question history and provide the most appropriate answer. In this way, the question response unit can provide the best answer by referring to past question history. Some or all of the above processing in the question response unit may be performed using AI, for example, or without AI. For example, the question response unit can input past question history data into AI and obtain analysis results from AI to provide the best answer.

[0045] The question-answering unit can adjust the level of detail in its answers based on the learner's current learning progress when answering questions. For example, if the learner is behind in their learning progress, the question-answering unit can provide answers that include basic information. For example, if the learner is on track, the question-answering unit can also provide answers that include detailed information. For example, the question-answering unit can evaluate the learner's learning progress in real time and adjust the level of detail in its answers according to the progress. This allows the question-answering unit to provide more appropriate answers by adjusting the level of detail in its answers according to the learner's learning progress. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the learner's learning progress data into the AI ​​and obtain analysis results from the AI ​​to adjust the level of detail in its answers.

[0046] The question-answering unit can provide relevant information while considering the participant's geographical location when answering questions. For example, the question-answering unit can provide information related to the participant's region when answering questions. For example, the question-answering unit can also provide information tailored to the characteristics of a region based on the participant's geographical location. For example, the question-answering unit can provide information on collaborations with local companies and organizations while considering the participant's geographical location. In this way, the question-answering unit can provide region-related information by considering the participant's geographical location. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the participant's geographical location into AI and obtain analysis results from AI to provide relevant information.

[0047] The question-answering unit can analyze the participant's social media activity and provide relevant information when answering questions. For example, the question-answering unit can provide information related to topics the participant has shown interest in on social media. For example, the question-answering unit can also provide information related to areas of interest from the participant's social media activity. For example, the question-answering unit can analyze the participant's social media activity and provide information aligned with trends. In this way, the question-answering unit can provide information of interest by analyzing the participant's social media activity. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or not using AI. For example, the question-answering unit can input the participant's social media activity data into AI and obtain analysis results from AI to provide relevant information.

[0048] The learning unit can evaluate the learner's level of concentration in real time during the lesson and adjust the learning environment as needed. For example, if the learner's level of concentration is low, the learning unit can suggest reducing ambient noise. For example, if the learner's level of concentration is high, the learning unit can also suggest an optimal environment for continuing learning. The learning unit can also evaluate the learner's level of concentration in real time and adjust the learning environment according to that level of concentration. In this way, the learning unit can enhance the effectiveness of learning by adjusting the learning environment according to the learner's level of concentration. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input learner concentration data into AI and obtain analysis results from AI to adjust the learning environment.

[0049] The learning department can provide additional learning resources during the course according to the learner's areas of interest. For example, the learning department can provide learning resources related to topics the learner has shown interest in. The learning department can also provide relevant papers and articles based on the learner's areas of interest. The learning department can also provide additional learning resources according to the learner's areas of interest. This allows the learning department to enhance the effectiveness of learning by providing learning resources according to the learner's areas of interest. Some or all of the above processing in the learning department may be performed using AI, for example, or without AI. For example, the learning department can input the learner's areas of interest data into AI and obtain analysis results from AI to provide additional learning resources.

[0050] The training section can introduce relevant case studies during the training session, taking into account the geographical location of the participants. For example, the training section can introduce case studies related to the participants' regions during the training session. For example, the training section can also provide case studies tailored to the characteristics of a region based on the participants' geographical location. For example, the training section can introduce case studies of collaboration with local companies and organizations, taking into account the participants' geographical location. In this way, the training section can provide case studies relevant to a region by taking into account the participants' geographical location. Some or all of the above processing in the training section may be performed using AI, for example, or without using AI. For example, the training section can input the participants' geographical location into AI and obtain analysis results from AI to introduce relevant case studies.

[0051] The instructor may analyze participants' social media activity during the course and address relevant topics. For example, the instructor may address topics that participants have shown interest in on social media. The instructor may also identify areas of interest from participants' social media activity and provide topics related to those areas. The instructor may also analyze participants' social media activity and address trending topics during the course. In this way, the instructor can provide topics of interest by analyzing participants' social media activity. Some or all of the above processing in the instructor may be performed using AI, for example, or not using AI. For example, the instructor may input participants' social media activity data into AI and obtain analysis results from the AI ​​to address relevant topics.

[0052] The analysis unit can select the optimal analysis method by referring to the learner's past learning history during the analysis. For example, the analysis unit selects the optimal analysis method based on the learner's past learning history. The analysis unit can also identify areas of weakness from the learner's past learning history and provide an analysis method suitable for those areas. The analysis unit can also analyze the learner's past learning history and adjust the analysis method according to the learning progress. In this way, the analysis unit can provide the optimal analysis method by referring to the learner's past learning history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the learner's past learning history data into AI and obtain analysis results from AI to select the optimal analysis method.

[0053] The analysis unit can filter the data based on the learner's current occupation and areas of interest during the analysis. For example, the analysis unit can provide analysis methods that enhance skills related to the learner's occupation. The analysis unit can also analyze relevant topics based on the learner's areas of interest. Furthermore, the analysis unit can provide analysis methods that include practical projects based on the learner's occupation and areas of interest. This allows the analysis unit to provide more relevant analysis by filtering based on the learner's occupation and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the learner's occupation and areas of interest into an AI and obtain filtering results from the AI.

[0054] The analysis unit can prioritize the analysis of highly relevant data by considering the geographical location information of the participants during the analysis. For example, the analysis unit can prioritize the analysis of data related to the participants' region. The analysis unit can also analyze data tailored to regional characteristics based on the participants' geographical location information. For example, the analysis unit can prioritize the analysis of collaborative data with local companies and organizations by considering the participants' geographical location information. In this way, the analysis unit can provide region-related data by considering the participants' geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the participants' geographical location information into AI and obtain analysis results from AI to prioritize the analysis of highly relevant data.

[0055] The analysis unit can analyze the learner's social media activity and analyze related data during the analysis process. For example, the analysis unit can analyze data related to topics that the learner has shown interest in on social media. For example, the analysis unit can also analyze data related to areas of interest from the learner's social media activity. For example, the analysis unit can analyze the learner's social media activity and analyze data aligned with trends. In this way, the analysis unit can provide data of interest by analyzing the learner's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's social media activity data into AI and obtain analysis results from AI for analyzing related data.

[0056] The management department can select the optimal management method by referring to the learner's past learning history when managing progress. For example, the management department can select the optimal progress management method based on the learner's past learning history. The management department can also identify areas of weakness from the learner's past learning history and provide a progress management method suitable for those areas. The management department can also analyze the learner's past learning history and adjust the progress management method according to the learning progress. In this way, the management department can provide the optimal progress management method by referring to the learner's past learning history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the learner's past learning history data into AI and obtain analysis results from AI to select the optimal progress management method.

[0057] The management department can adjust the level of detail in progress management based on the learner's current learning progress. For example, if a learner is behind schedule, the management department can provide detailed progress management. For example, if a learner is on track, the management department can provide concise progress management. The management department can also evaluate the learner's learning progress in real time and adjust the level of detail in management according to the progress. This allows the management department to improve the effectiveness of learning by adjusting the level of detail in management according to the learner's learning progress. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input learner learning progress data into AI and obtain analysis results from the AI ​​to adjust the level of detail in management.

[0058] The management department can provide relevant information while considering the geographical location of participants during progress management. For example, the management department can provide information related to the participant's region during progress management. For example, the management department can also provide information tailored to the characteristics of a region based on the participant's geographical location. For example, the management department can provide information on collaborations with local companies and organizations while considering the participant's geographical location. In this way, the management department can provide region-related information by considering the participant's geographical location. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the participant's geographical location into AI and obtain analysis results from AI to provide relevant information.

[0059] The management department can analyze participants' social media activity and provide relevant information during progress management. For example, the management department can provide information related to topics that participants have shown interest in on social media. For example, the management department can also provide information related to areas of interest from participants' social media activity. For example, the management department can analyze participants' social media activity and provide information aligned with trends. In this way, the management department can provide information of interest by analyzing participants' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input participants' social media activity data into AI and obtain analysis results from the AI ​​to provide relevant information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] Online school systems can also include a feedback function. This function provides personalized feedback based on the student's learning progress and understanding. For example, if a student struggles with a particular task, the feedback function can analyze the cause and suggest specific areas for improvement. It can also offer advice tailored to the student's learning style. For instance, it might recommend diagrams or videos to students who prefer visual learning. Furthermore, the feedback function can send encouraging messages to maintain student motivation. In this way, the feedback function can provide support to enhance the student's learning effectiveness.

[0062] Online school systems can also include a community section. This community section provides a platform for students to interact and share information. For example, it can offer a forum where students can post questions and opinions. It can also provide features that allow students to create groups and work on projects collaboratively. Furthermore, it can provide features that allow students to check the progress of other students and encourage each other. This allows the community section to increase students' motivation and improve the effectiveness of their learning.

[0063] Online school systems can also incorporate gamification features. Gamification features incorporate game elements to make the learning experience more enjoyable for students. For example, a gamification feature could provide a system where students earn points each time they complete an assignment. It could also offer features that allow students to earn badges or titles for achieving specific goals. Furthermore, a ranking system could be provided where students can compete with other students. This allows gamification features to increase students' motivation and improve the effectiveness of their learning.

[0064] Online school systems can also include a reminder function. This function provides reminders to help students adhere to their study schedules. For example, it can send notifications at study times set by the student. It can also provide a function to remind students not to forget deadlines for specific assignments or tests. Furthermore, it can suggest what students should learn next based on their progress. In this way, the reminder function can support students in planning their studies effectively.

[0065] Online school systems can also include a personalized testing section. This section generates individual tests based on each student's learning progress and understanding. For example, it can provide tests that focus on areas where students struggle. It can also offer test formats tailored to each student's learning style. For instance, it can present questions using diagrams and videos for students who prefer visual learning. Furthermore, the personalized testing section can adjust the difficulty level of the tests according to the student's learning progress. This allows the personalized testing section to provide support that enhances students' learning effectiveness.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The generation unit automatically generates a curriculum tailored to the learner's objectives and level. For example, if the learner inputs that they want to learn the basics of programming, it will generate a curriculum that includes basic programming lectures. It can also generate a curriculum for learning data analysis techniques if the learner inputs that they want to improve their data analysis skills. Furthermore, it can generate beginner, intermediate, and advanced curricula depending on the learner's skill level. Step 2: The lecture department delivers lectures based on the curriculum generated by the generation department. For example, it provides video lectures, text lectures, and interactive lectures that students can watch, read, and participate in. Step 3: The question support team answers questions in real time during the course. For example, they use interactive AI to provide immediate answers to participants' questions. Step 4: The course is available 24 hours a day. For example, students can study at their own convenience and continue learning while on the go or out and about using their smartphones.

[0068] (Example of form 2) The online school system according to an embodiment of the present invention is a completely free online school utilizing AI for middle-aged people who are anxious about the future. This online school system provides lectures tailored to the student's purpose and level, allows questions anytime and as many times as needed during the course, and can be accessed anytime, anywhere. First, the student inputs their purpose and current skill level. For example, they set a specific goal such as wanting to learn the basics of programming or wanting to improve their data analysis skills. Next, the AI ​​analyzes the student's input information and automatically generates an optimal curriculum. This curriculum is customized to the student's purpose and level, allowing for efficient learning. During the course, the student can ask questions anytime and as many times as needed. An interactive AI acts as an instructor, answering the student's questions in real time. For example, it provides appropriate answers to specific problems the student has, such as questions about programming code or doubts about data analysis methods. Furthermore, this online school system is available 24 hours a day, allowing students to learn at their own convenience. For example, they can continue learning without being restricted by time or place, such as during work breaks, at night, or on weekends. Since the course can be taken with just a smartphone, learning is possible even while commuting or out and about. Thus, a completely free online school system utilizing AI can be an effective means for middle-aged people to alleviate anxieties about the future and improve their skills. Students can learn at their own pace and efficiently acquire skills with the support of interactive AI. As a result, the online school system can provide a curriculum tailored to the student's goals and level, answer questions in real time during lessons, and offer a system that can be accessed 24 hours a day.

[0069] The online school system according to this embodiment comprises a generation unit, a lecture unit, a question-answering unit, and a learning unit. The generation unit automatically generates a curriculum tailored to the learner's objectives and level. For example, if a learner inputs that they want to learn the basics of programming, the generation unit generates a curriculum that includes basic programming lectures. The generation unit can also generate a curriculum for learning data analysis methods if a learner inputs that they want to improve their data analysis skills. Furthermore, the generation unit can generate beginner, intermediate, and advanced curricula according to the learner's skill level. For example, the generation unit generates a curriculum for beginners that includes lectures explaining basic concepts, and a curriculum for intermediate learners that includes practical exercises. The lecture unit conducts lectures based on the curriculum generated by the generation unit. The lecture unit can, for example, provide video lectures that learners can view. The lecture unit can also provide text lectures that learners can read. Furthermore, the lecture unit can provide interactive lectures that learners can participate in. For example, the lecture unit can provide video lectures that learners can view. The lecture unit can also provide text lectures that learners can read. Furthermore, the lecture section can provide interactive lectures, allowing students to participate. The question support section provides real-time answers to questions asked during lectures. The question support section can, for example, use conversational AI to instantly answer students' questions. The question support section can also provide appropriate answers to questions entered by students using conversational AI. Furthermore, the question support section can provide appropriate answers to questions entered by students using conversational AI. Lectures are available 24 hours a day. The lecture section allows students to learn at their own pace. Furthermore, students can continue learning using their smartphones while traveling or away from home. Furthermore, students can continue learning using their smartphones while traveling or away from home.As a result, the online school system according to this embodiment can provide a curriculum tailored to the student's objectives and level, answer questions in real time during lessons, and offer a system that allows students to take lessons anytime, 24 hours a day.

[0070] The generation unit automatically generates a curriculum tailored to the learner's objectives and level. For example, if a learner inputs that they want to learn the basics of programming, the generation unit will generate a curriculum that includes basic programming lectures. Specifically, the generation unit uses AI to design the optimal curriculum based on the information input by the learner. The AI ​​analyzes the learner's past learning history, current skill level, and learning goals, and constructs the curriculum based on that. For example, if a learner inputs that they want to learn the basics of programming, the AI ​​will select lectures that include basic concepts such as variables, loops, and conditional branching, and combine them to generate a curriculum. Also, if a learner inputs that they want to improve their data analysis skills, the AI ​​will select lectures to learn data preprocessing, statistical analysis, and basic machine learning techniques, and combine them to generate a curriculum. Furthermore, the generation unit can also generate beginner, intermediate, and advanced curricula according to the learner's skill level. For example, for beginners, it will generate a curriculum that includes lectures explaining basic concepts, for intermediate learners, it will generate a curriculum that includes practical exercises, and for advanced learners, it will generate a curriculum that includes lectures to learn more advanced technologies and application examples. This allows the generation unit to respond to the diverse needs of learners and provide an optimal learning experience.

[0071] The lecture department conducts lectures based on the curriculum generated by the generation department. For example, the lecture department provides video lectures that students can view. Specifically, the lecture department provides video lectures recorded by professional instructors, allowing students to view them at their own pace. The lecture department can also provide text lectures that students can read. These text lectures are provided as teaching materials, including detailed explanations and diagrams, and can be used as supplementary material to deepen students' understanding. Furthermore, the lecture department can provide interactive lectures that students can participate in. For example, live lectures can be held, allowing students to ask questions and offer opinions in real time. Interactive quizzes and exercises can also allow students to practically confirm what they have learned. This allows the lecture department to offer students diverse learning methods and support effective learning. Additionally, the lecture department can monitor students' progress and provide feedback as needed. For example, if a student struggles with a particular lecture, additional supplementary materials or individual guidance can be provided to deepen their understanding. This allows the lecture department to maximize student learning effectiveness and improve the overall quality of the online school system.

[0072] The question support unit provides real-time answers to questions asked during the course. For example, it uses conversational AI to instantly respond to students' questions. Specifically, when a student inputs a question, the conversational AI analyzes it and provides an appropriate answer. The conversational AI uses natural language processing technology to understand the student's question, searches for relevant information, and generates an answer. For example, if a student asks, "I don't know how to use variables," the conversational AI provides information on variable definitions and usage. The conversational AI can also provide more appropriate answers based on past question history and the student's learning history. Furthermore, the question support unit can escalate complex questions that the conversational AI cannot answer to a specialist instructor. This ensures that students receive quick and appropriate answers to any question. The question support unit enhances learning effectiveness by supporting students' learning and resolving their doubts. Additionally, the question support unit can analyze students' questions to identify common questions and issues. This allows it to collaborate with the lecture and production units to improve the curriculum and lecture content, providing a learning environment better suited to students' needs.

[0073] The learning program is available 24 hours a day. For example, it allows learners to study at their own pace. Specifically, the program provides an online platform that enables learners to access lectures anytime, anywhere. Learners can use devices such as PCs, tablets, and smartphones to watch lectures, read texts, and participate in interactive exercises. The program also allows learners to continue learning on the go or while traveling using their smartphones. For example, they can watch lectures or answer quizzes while commuting or traveling. Furthermore, the program can provide tools for learners to manage their progress and set goals. For instance, learners can set their own study schedules and track their progress. The program can also analyze learner data and provide personalized feedback and learning advice. This allows the program to provide an environment where learners can learn efficiently at their own pace, maximizing learning effectiveness.

[0074] The analysis unit can analyze the learner's input information. For example, the analysis unit can analyze the learner's input objectives and skill level and provide data to generate an optimal curriculum. For example, if a learner inputs that they want to learn the basics of programming, the analysis unit will analyze that information and provide it to the generation unit. The analysis unit can also analyze if a learner inputs that they want to improve their data analysis skills and provide that information to the generation unit. Furthermore, the analysis unit can analyze the learner's skill level and provide data to generate beginner, intermediate, and advanced curricula. For example, if the analysis unit determines that a learner is a beginner, it will provide data to generate a curriculum that includes lectures explaining basic concepts. In this way, the analysis unit can provide a more appropriate curriculum by analyzing the learner's input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's input information into AI and obtain the analysis results from AI.

[0075] The management department can manage the progress of students. For example, the management department can record which lectures students have attended, which exercises they have completed, and which tests they have taken. For example, if a student attends a basic programming lecture, the management department will record that information. The management department can also record if a student has completed data analysis exercises. Furthermore, if a student takes a test, the management department can record the results. For example, if a student takes a basic programming test, the management department will record the results. In this way, the management department can improve the effectiveness of learning by managing the progress of students. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input student progress information into AI and obtain progress management results from AI.

[0076] The generation unit can estimate the learner's emotions and adjust the difficulty level of the curriculum based on the estimated emotions. For example, if the learner is stressed, the generation unit can lower the difficulty level of the curriculum and start with basic content. For example, if the learner is relaxed, the generation unit can raise the difficulty level of the curriculum and provide challenging content. For example, if the learner is excited, the generation unit can add interactive elements to the curriculum to increase motivation to learn. In this way, the generation unit can enhance the effectiveness of learning by adjusting the difficulty level of the curriculum according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input learner emotion data into an AI and obtain emotion estimation results from the AI.

[0077] The generation unit can analyze a student's past learning history and select the optimal curriculum. For example, the generation unit can suggest what the student should learn next based on what they have learned in the past. The generation unit can also identify areas where the student struggles from their past learning history and create a curriculum that focuses on those areas. The generation unit can also analyze a student's past learning history and adjust the curriculum according to their learning progress. In this way, the generation unit can provide the optimal curriculum by analyzing a student's past learning history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's past learning history data into AI and obtain analysis results from the AI ​​to select the optimal curriculum.

[0078] The generation unit can filter the curriculum based on the learner's current occupation and areas of interest during the curriculum generation process. For example, the generation unit can provide a curriculum that strengthens skills related to the learner's occupation. The generation unit can also incorporate relevant topics into the curriculum based on the learner's areas of interest. For example, the generation unit can create a curriculum that includes practical projects based on the learner's occupation and areas of interest. This allows the generation unit to provide more relevant learning content by filtering the curriculum based on the learner's occupation and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on the learner's occupation and areas of interest into AI and obtain filtering results from AI.

[0079] The generation unit can estimate the learner's emotions and prioritize the curriculum based on the estimated emotions. For example, if the learner is stressed, the generation unit will prioritize providing relaxing content. For example, if the learner is relaxed, the generation unit may also prioritize providing more challenging content. For example, if the learner is excited, the generation unit may also prioritize providing interactive content. In this way, the generation unit can enhance the effectiveness of learning by prioritizing the curriculum according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learner emotion data into an AI and obtain emotion estimation results from the AI.

[0080] The generation unit can prioritize incorporating highly relevant content by considering the geographical location information of the learners when generating the curriculum. For example, the generation unit can incorporate skills in high demand in the learners' region into the curriculum. For example, the generation unit can also provide content tailored to regional characteristics based on the learners' geographical location information. For example, the generation unit can create a curriculum that includes collaboration with local businesses and organizations, taking into account the learners' geographical location information. In this way, the generation unit can provide regionally relevant content by considering the learners' geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learners' geographical location information into AI and obtain analysis results from the AI ​​to prioritize the incorporation of highly relevant content.

[0081] The generation unit can analyze students' social media activity and incorporate relevant content when generating the curriculum. For example, the generation unit can incorporate topics that students have shown interest in on social media into the curriculum. The generation unit can also identify areas of interest from students' social media activity and provide content related to those areas. For example, the generation unit can analyze students' social media activity and create a curriculum that aligns with trends. In this way, the generation unit can provide content of interest by analyzing students' social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input students' social media activity data into AI and obtain analysis results from the AI ​​to incorporate relevant content.

[0082] The lecture system can estimate the emotions of the students and adjust the pace of the lecture based on the estimated emotions. For example, if a student is stressed, the lecture system can slow down the pace of the lecture to make it easier to understand. For example, if a student is relaxed, the lecture system can speed up the pace of the lecture to facilitate efficient learning. For example, if a student is excited, the lecture system can add interactive elements and adjust the pace of the lecture. In this way, the lecture system can enhance the effectiveness of learning by adjusting the pace of the lecture according to the emotions of the students. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lecture system may be performed using AI or not using AI. For example, the lecture system can input student emotion data into AI and obtain emotion estimation results from AI.

[0083] The lecture department can evaluate the students' understanding in real time during the lecture and adjust the lecture content as needed. For example, if the students' understanding is low, the lecture department can revert to basic content and explain it again. For example, if the students' understanding is high, the lecture department can advance the lecture content and provide more advanced material. The lecture department can also evaluate the students' understanding in real time and adjust the pace of the lecture according to their understanding. In this way, the lecture department can enhance the effectiveness of learning by adjusting the lecture content according to the students' understanding. Some or all of the above processes in the lecture department may be performed using AI, for example, or without AI. For example, the lecture department can input student understanding data into AI and obtain understanding evaluation results from AI.

[0084] The lecture department may provide additional reference materials during lectures according to the students' areas of interest. For example, the lecture department may provide reference materials related to topics that students have shown interest in. The lecture department may also provide relevant papers or articles based on the students' areas of interest. The lecture department may also provide additional learning resources according to the students' areas of interest. In this way, the lecture department can enhance the effectiveness of learning by providing reference materials according to the students' areas of interest. Some or all of the above processing in the lecture department may be performed using AI, for example, or not using AI. For example, the lecture department may input data on students' areas of interest into AI and obtain analysis results from the AI ​​to provide additional reference materials.

[0085] The lecture system can estimate the emotions of the students and adjust the order of the lectures based on the estimated emotions. For example, if a student is feeling stressed, the lecture system can provide relaxing content first. For example, if a student is relaxed, the lecture system can provide more difficult content first. For example, if a student is excited, the lecture system can provide interactive content first. In this way, the lecture system can enhance the effectiveness of learning by adjusting the order of lectures according to the emotions of the students. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lecture system may be performed using AI or not using AI. For example, the lecture system can input student emotion data into AI and obtain emotion estimation results from AI.

[0086] The lecturer may introduce relevant case studies during the lecture, taking into account the geographical location information of the participants. For example, the lecturer may introduce case studies related to the participants' region during the lecture. For example, the lecturer may also provide case studies tailored to the characteristics of the region based on the participants' geographical location information. For example, the lecturer may also introduce case studies of collaboration with local companies and organizations, taking into account the participants' geographical location information. In this way, the lecturer can provide case studies relevant to the region by taking into account the participants' geographical location information. Some or all of the above processing in the lecturer may be performed using AI, for example, or not using AI. For example, the lecturer may input the participants' geographical location information into AI and obtain analysis results from the AI ​​to introduce relevant case studies.

[0087] The lecture department can analyze students' social media activity during lectures and address relevant topics. For example, the lecture department can address topics that students have shown interest in on social media. The lecture department can also identify areas of interest from students' social media activity and provide topics related to those areas. The lecture department can also analyze students' social media activity and address trending topics during lectures. In this way, the lecture department can provide topics of interest by analyzing students' social media activity. Some or all of the above processing in the lecture department may be performed using AI, for example, or not. For example, the lecture department can input students' social media activity data into an AI and obtain analysis results from the AI ​​to address relevant topics.

[0088] The question-answering unit can estimate the learner's emotions and adjust the way it expresses its answers based on the estimated emotions. For example, if the learner is stressed, the question-answering unit will provide a concise and easy-to-understand answer. If the learner is relaxed, the question-answering unit may also provide an answer that includes detailed explanations. If the learner is excited, the question-answering unit may also provide an answer that includes interactive elements. In this way, the question-answering unit can provide more appropriate answers by adjusting the way it expresses its answers according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or not using AI. For example, the question-answering unit can input learner emotion data into AI and obtain emotion estimation results from AI.

[0089] The question response unit can provide the best answer by referring to past question history when responding to a question. For example, the question response unit can provide the best answer based on the content of questions previously asked by the student. For example, the question response unit can also provide answers to similar questions from the student's past question history. For example, the question response unit can analyze the student's past question history and provide the most appropriate answer. In this way, the question response unit can provide the best answer by referring to past question history. Some or all of the above processing in the question response unit may be performed using AI, for example, or without AI. For example, the question response unit can input past question history data into AI and obtain analysis results from AI to provide the best answer.

[0090] The question-answering unit can adjust the level of detail in its answers based on the learner's current learning progress when answering questions. For example, if the learner is behind in their learning progress, the question-answering unit can provide answers that include basic information. For example, if the learner is on track, the question-answering unit can also provide answers that include detailed information. For example, the question-answering unit can evaluate the learner's learning progress in real time and adjust the level of detail in its answers according to the progress. This allows the question-answering unit to provide more appropriate answers by adjusting the level of detail in its answers according to the learner's learning progress. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the learner's learning progress data into the AI ​​and obtain analysis results from the AI ​​to adjust the level of detail in its answers.

[0091] The question-answering unit can estimate the learner's emotions and prioritize answers based on the estimated emotions. For example, if the learner is stressed, the question-answering unit will prioritize answering urgent questions. For example, if the learner is relaxed, the question-answering unit may prioritize answering questions that include detailed explanations. For example, if the learner is excited, the question-answering unit may prioritize answering questions that include interactive elements. In this way, the question-answering unit can provide more appropriate answers by prioritizing answers according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question-answering unit may be performed using AI or not using AI. For example, the question-answering unit can input learner emotion data into AI and obtain emotion estimation results from AI.

[0092] The question-answering unit can provide relevant information while considering the participant's geographical location when answering questions. For example, the question-answering unit can provide information related to the participant's region when answering questions. For example, the question-answering unit can also provide information tailored to the characteristics of a region based on the participant's geographical location. For example, the question-answering unit can provide information on collaborations with local companies and organizations while considering the participant's geographical location. In this way, the question-answering unit can provide region-related information by considering the participant's geographical location. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the participant's geographical location into AI and obtain analysis results from AI to provide relevant information.

[0093] The question-answering unit can analyze the participant's social media activity and provide relevant information when answering questions. For example, the question-answering unit can provide information related to topics the participant has shown interest in on social media. For example, the question-answering unit can also provide information related to areas of interest from the participant's social media activity. For example, the question-answering unit can analyze the participant's social media activity and provide information aligned with trends. In this way, the question-answering unit can provide information of interest by analyzing the participant's social media activity. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or not using AI. For example, the question-answering unit can input the participant's social media activity data into AI and obtain analysis results from AI to provide relevant information.

[0094] The learning unit can estimate the learner's emotions and adjust the timing of lessons based on the estimated emotions. For example, if the learner is feeling stressed, the unit can suggest lessons during times when they can relax. If the learner is relaxed, the unit can also suggest lessons during times when they can concentrate easily. If the learner is excited, the unit can also suggest lessons during times that include interactive content. In this way, the learning unit can enhance the effectiveness of learning by adjusting the timing of lessons according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the learner's emotion data into the AI ​​and obtain emotion estimation results from the AI.

[0095] The learning unit can evaluate the learner's level of concentration in real time during the lesson and adjust the learning environment as needed. For example, if the learner's level of concentration is low, the learning unit can suggest reducing ambient noise. For example, if the learner's level of concentration is high, the learning unit can also suggest an optimal environment for continuing learning. The learning unit can also evaluate the learner's level of concentration in real time and adjust the learning environment according to that level of concentration. In this way, the learning unit can enhance the effectiveness of learning by adjusting the learning environment according to the learner's level of concentration. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input learner concentration data into AI and obtain analysis results from AI to adjust the learning environment.

[0096] The learning department can provide additional learning resources during the course according to the learner's areas of interest. For example, the learning department can provide learning resources related to topics the learner has shown interest in. The learning department can also provide relevant papers and articles based on the learner's areas of interest. The learning department can also provide additional learning resources according to the learner's areas of interest. This allows the learning department to enhance the effectiveness of learning by providing learning resources according to the learner's areas of interest. Some or all of the above processing in the learning department may be performed using AI, for example, or without AI. For example, the learning department can input the learner's areas of interest data into AI and obtain analysis results from AI to provide additional learning resources.

[0097] The learning unit can estimate the learner's emotions and determine the priority of the lessons based on the estimated emotions. For example, if the learner is stressed, the learning unit can prioritize providing relaxing content. For example, if the learner is relaxed, the learning unit can also prioritize providing more challenging content. For example, if the learner is excited, the learning unit can also prioritize providing interactive content. In this way, the learning unit can enhance the effectiveness of learning by determining the priority of lessons according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the learner's emotion data into the AI ​​and obtain emotion estimation results from the AI.

[0098] The training section can introduce relevant case studies during the training session, taking into account the geographical location of the participants. For example, the training section can introduce case studies related to the participants' regions during the training session. For example, the training section can also provide case studies tailored to the characteristics of a region based on the participants' geographical location. For example, the training section can introduce case studies of collaboration with local companies and organizations, taking into account the participants' geographical location. In this way, the training section can provide case studies relevant to a region by taking into account the participants' geographical location. Some or all of the above processing in the training section may be performed using AI, for example, or without using AI. For example, the training section can input the participants' geographical location into AI and obtain analysis results from AI to introduce relevant case studies.

[0099] The instructor may analyze participants' social media activity during the course and address relevant topics. For example, the instructor may address topics that participants have shown interest in on social media. The instructor may also identify areas of interest from participants' social media activity and provide topics related to those areas. The instructor may also analyze participants' social media activity and address trending topics during the course. In this way, the instructor can provide topics of interest by analyzing participants' social media activity. Some or all of the above processing in the instructor may be performed using AI, for example, or not using AI. For example, the instructor may input participants' social media activity data into AI and obtain analysis results from the AI ​​to address relevant topics.

[0100] The analysis unit can estimate the learner's emotions and adjust the analysis method based on the estimated learner's emotions. For example, if the learner is stressed, the analysis unit can provide a concise and easy-to-understand analysis method. For example, if the learner is relaxed, the analysis unit can also provide a detailed analysis method. For example, if the learner is excited, the analysis unit can also provide an analysis method that includes interactive elements. In this way, the analysis unit can enhance the effectiveness of the analysis by adjusting the analysis method according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input learner emotion data into AI and obtain emotion estimation results from AI.

[0101] The analysis unit can select the optimal analysis method by referring to the learner's past learning history during the analysis. For example, the analysis unit selects the optimal analysis method based on the learner's past learning history. The analysis unit can also identify areas of weakness from the learner's past learning history and provide an analysis method suitable for those areas. The analysis unit can also analyze the learner's past learning history and adjust the analysis method according to the learning progress. In this way, the analysis unit can provide the optimal analysis method by referring to the learner's past learning history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the learner's past learning history data into AI and obtain analysis results from AI to select the optimal analysis method.

[0102] The analysis unit can filter the data based on the learner's current occupation and areas of interest during the analysis. For example, the analysis unit can provide analysis methods that enhance skills related to the learner's occupation. The analysis unit can also analyze relevant topics based on the learner's areas of interest. Furthermore, the analysis unit can provide analysis methods that include practical projects based on the learner's occupation and areas of interest. This allows the analysis unit to provide more relevant analysis by filtering based on the learner's occupation and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the learner's occupation and areas of interest into an AI and obtain filtering results from the AI.

[0103] The analysis unit can estimate the learner's emotions and determine the priority of analysis based on the estimated learner's emotions. For example, if the learner is stressed, the analysis unit may prioritize high-urgency analyses. For example, if the learner is relaxed, the analysis unit may prioritize detailed analyses. For example, if the learner is excited, the analysis unit may prioritize analyses that include interactive elements. In this way, the analysis unit can enhance the effectiveness of the analysis by determining the priority of analysis according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input learner emotion data into AI and obtain emotion estimation results from AI.

[0104] The analysis unit can prioritize the analysis of highly relevant data by considering the geographical location information of the participants during the analysis. For example, the analysis unit can prioritize the analysis of data related to the participants' region. The analysis unit can also analyze data tailored to regional characteristics based on the participants' geographical location information. For example, the analysis unit can prioritize the analysis of collaborative data with local companies and organizations by considering the participants' geographical location information. In this way, the analysis unit can provide region-related data by considering the participants' geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the participants' geographical location information into AI and obtain analysis results from AI to prioritize the analysis of highly relevant data.

[0105] The analysis unit can analyze the learner's social media activity and analyze related data during the analysis process. For example, the analysis unit can analyze data related to topics that the learner has shown interest in on social media. For example, the analysis unit can also analyze data related to areas of interest from the learner's social media activity. For example, the analysis unit can analyze the learner's social media activity and analyze data aligned with trends. In this way, the analysis unit can provide data of interest by analyzing the learner's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's social media activity data into AI and obtain analysis results from AI for analyzing related data.

[0106] The management department can estimate the emotions of learners and adjust the progress management method based on the estimated emotions. For example, if a learner is stressed, the management department can reduce the frequency of progress management to alleviate the burden. For example, if a learner is relaxed, the management department can increase the frequency of progress management to fine-tune the learning progress. For example, if a learner is excited, the management department can provide a progress management method that includes interactive elements. In this way, the management department can enhance the effectiveness of learning by adjusting the progress management method according to the emotions of learners. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not using AI. For example, the management department can input learner emotion data into AI and obtain emotion estimation results from AI.

[0107] The management department can select the optimal management method by referring to the learner's past learning history when managing progress. For example, the management department can select the optimal progress management method based on the learner's past learning history. The management department can also identify areas of weakness from the learner's past learning history and provide a progress management method suitable for those areas. The management department can also analyze the learner's past learning history and adjust the progress management method according to the learning progress. In this way, the management department can provide the optimal progress management method by referring to the learner's past learning history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the learner's past learning history data into AI and obtain analysis results from AI to select the optimal progress management method.

[0108] The management department can adjust the level of detail in progress management based on the learner's current learning progress. For example, if a learner is behind schedule, the management department can provide detailed progress management. For example, if a learner is on track, the management department can provide concise progress management. The management department can also evaluate the learner's learning progress in real time and adjust the level of detail in management according to the progress. This allows the management department to improve the effectiveness of learning by adjusting the level of detail in management according to the learner's learning progress. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input learner learning progress data into AI and obtain analysis results from the AI ​​to adjust the level of detail in management.

[0109] The management department can estimate the emotions of learners and determine the priority of progress management based on the estimated emotions. For example, if a learner is stressed, the management department may prioritize urgent progress management. For example, if a learner is relaxed, the management department may prioritize detailed progress management. For example, if a learner is excited, the management department may prioritize progress management that includes interactive elements. In this way, the management department can enhance the effectiveness of learning by determining the priority of progress management according to the emotions of learners. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not using AI. For example, the management department can input learner emotion data into AI and obtain emotion estimation results from AI.

[0110] The management department can provide relevant information while considering the geographical location of participants during progress management. For example, the management department can provide information related to the participant's region during progress management. For example, the management department can also provide information tailored to the characteristics of a region based on the participant's geographical location. For example, the management department can provide information on collaborations with local companies and organizations while considering the participant's geographical location. In this way, the management department can provide region-related information by considering the participant's geographical location. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the participant's geographical location into AI and obtain analysis results from AI to provide relevant information.

[0111] The management department can analyze participants' social media activity and provide relevant information during progress management. For example, the management department can provide information related to topics that participants have shown interest in on social media. For example, the management department can also provide information related to areas of interest from participants' social media activity. For example, the management department can analyze participants' social media activity and provide information aligned with trends. In this way, the management department can provide information of interest by analyzing participants' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input participants' social media activity data into AI and obtain analysis results from the AI ​​to provide relevant information.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] Online school systems can also include a feedback function. This function provides personalized feedback based on the student's learning progress and understanding. For example, if a student struggles with a particular task, the feedback function can analyze the cause and suggest specific areas for improvement. It can also offer advice tailored to the student's learning style. For instance, it might recommend diagrams or videos to students who prefer visual learning. Furthermore, the feedback function can send encouraging messages to maintain student motivation. In this way, the feedback function can provide support to enhance the student's learning effectiveness.

[0114] Online school systems can also include a community section. This community section provides a platform for students to interact and share information. For example, it can offer a forum where students can post questions and opinions. It can also provide features that allow students to create groups and work on projects collaboratively. Furthermore, it can provide features that allow students to check the progress of other students and encourage each other. This allows the community section to increase students' motivation and improve the effectiveness of their learning.

[0115] Online school systems can also incorporate gamification features. Gamification features incorporate game elements to make the learning experience more enjoyable for students. For example, a gamification feature could provide a system where students earn points each time they complete an assignment. It could also offer features that allow students to earn badges or titles for achieving specific goals. Furthermore, a ranking system could be provided where students can compete with other students. This allows gamification features to increase students' motivation and improve the effectiveness of their learning.

[0116] Online school systems can also include a reminder function. This function provides reminders to help students adhere to their study schedules. For example, it can send notifications at study times set by the student. It can also provide a function to remind students not to forget deadlines for specific assignments or tests. Furthermore, it can suggest what students should learn next based on their progress. In this way, the reminder function can support students in planning their studies effectively.

[0117] Online school systems can further enhance the learning environment based on students' emotions using emotion estimation capabilities. For example, if a student is stressed, relaxing music or videos can be provided. If a student is relaxed, ambient sounds can be provided to improve concentration. Furthermore, if a student is agitated, interactive learning content can be offered. By adjusting the learning environment according to students' emotions, the effectiveness of learning can be improved.

[0118] Online school systems can further utilize emotion estimation features to adjust the pace of learning based on the learner's emotions. For example, if a learner is stressed, the pace can be slowed down to make it easier to understand. Conversely, if a learner is relaxed, the pace can be sped up to promote efficient learning. Furthermore, if a learner is excited, interactive elements can be added to adjust the pace of learning. By adjusting the pace of learning according to the learner's emotions, the effectiveness of learning can be enhanced.

[0119] Online school systems can further utilize emotion estimation features to prioritize learning based on the student's emotions. For example, if a student is stressed, relaxing content can be prioritized. Conversely, if a student is relaxed, more challenging content can be prioritized. Furthermore, if a student is excited, interactive content can be prioritized. By prioritizing learning according to the student's emotions, the effectiveness of learning can be enhanced.

[0120] The online school system can further utilize emotion estimation capabilities to adjust learning timing based on the student's emotions. For example, if a student is feeling stressed, learning can be suggested during times when they can relax. Similarly, if a student is relaxed, learning can be suggested during times when they can concentrate better. Furthermore, if a student is excited, learning can be suggested during times that include interactive content. By adjusting learning timing according to the student's emotions, the effectiveness of learning can be enhanced.

[0121] Online school systems can further enhance learning by using emotion estimation capabilities to tailor learning feedback based on the student's emotions. For example, if a student is stressed, concise and easy-to-understand feedback can be provided. If the student is relaxed, more detailed feedback can be offered. Furthermore, if the student is excited, feedback with interactive elements can be provided. This allows for improved learning effectiveness by tailoring learning feedback according to the student's emotions.

[0122] Online school systems can also include a personalized testing section. This section generates individual tests based on each student's learning progress and understanding. For example, it can provide tests that focus on areas where students struggle. It can also offer test formats tailored to each student's learning style. For instance, it can present questions using diagrams and videos for students who prefer visual learning. Furthermore, the personalized testing section can adjust the difficulty level of the tests according to the student's learning progress. This allows the personalized testing section to provide support that enhances students' learning effectiveness.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The generation unit automatically generates a curriculum tailored to the learner's objectives and level. For example, if the learner inputs that they want to learn the basics of programming, it will generate a curriculum that includes basic programming lectures. It can also generate a curriculum for learning data analysis techniques if the learner inputs that they want to improve their data analysis skills. Furthermore, it can generate beginner, intermediate, and advanced curricula depending on the learner's skill level. Step 2: The lecture department delivers lectures based on the curriculum generated by the generation department. For example, it provides video lectures, text lectures, and interactive lectures that students can watch, read, and participate in. Step 3: The question support team answers questions in real time during the course. For example, they use interactive AI to provide immediate answers to participants' questions. Step 4: The course is available 24 hours a day. For example, students can study at their own convenience and continue learning while on the go or out and about using their smartphones.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the generation unit, lecture unit, question handling unit, student learning unit, analysis unit, and management unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The question handling unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The student learning unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the generation unit, lecture unit, question handling unit, student learning unit, analysis unit, and management unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The question handling unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The student learning unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the generation unit, lecture unit, question handling unit, student learning unit, analysis unit, and management unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The question handling unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The student learning unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the generation unit, lecture unit, question handling unit, student learning unit, analysis unit, and management unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The question handling unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The student learning unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A generation unit that automatically generates a curriculum tailored to the learner's objectives and level, A lecture section that conducts lectures based on the curriculum generated by the aforementioned generation section, A question support department that answers questions in real time during the course, It includes a learning center that allows students to take courses 24 hours a day. A system characterized by the following features. (Note 2) It includes an analysis unit that analyzes the input information of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has a management department that manages the progress of its students. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The system estimates the emotions of the participants and adjusts the difficulty level of the curriculum based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We analyze the student's past learning history and select the most suitable curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is When generating the curriculum, filtering is performed based on the participants' current occupation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The system estimates the emotions of the participants and prioritizes the curriculum based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When generating the curriculum, the geographical location of the participants is taken into consideration, and highly relevant content is prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating the curriculum, analyze the social media activity of participants and incorporate relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned lecture section, The system estimates the emotions of the participants and adjusts the pace of the lecture based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned lecture section, During the lecture, the instructor will evaluate the students' level of understanding in real time and adjust the lecture content as needed. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned lecture section, During the lecture, additional reference materials will be provided according to the students' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned lecture section, The system estimates the emotions of the participants and adjusts the order of the lectures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned lecture section, During the lecture, relevant case studies will be presented while taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned lecture section, During the lecture, we will analyze the social media activity of the students and address related topics. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question handling unit is: The system estimates the emotions of the participants and adjusts the way they express their responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question handling unit is: When answering questions, we refer to past question history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question handling unit is: When answering questions, adjust the level of detail in the answer based on the student's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question handling unit is: The system estimates the emotions of the participants and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned question handling unit is: When answering questions, provide relevant information while taking into account the participant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question handling unit is: When answering questions, analyze the participants' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned training section is, The system estimates the emotions of the participants and adjusts the timing of the lessons based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned training section is, During the lesson, the level of concentration of the participants is evaluated in real time, and the learning environment is adjusted as needed. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned training section is, During the course, additional learning resources will be provided according to the participants' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned training section is, The system estimates the emotions of the participants and determines the priority of the courses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned training section is, During the course, relevant case studies will be presented, taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned training section is, During the course, we will analyze participants' social media activity and address related topics. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, The system estimates the emotions of the participants and adjusts the analysis method based on the estimated emotions of the participants. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned analysis unit, During analysis, the optimal analysis method is selected by referring to the student's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During the analysis, filtering is performed based on the participants' current occupation and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned analysis unit, The system estimates the emotions of the participants and determines the priority of analysis based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit, During analysis, the system prioritizes analyzing data with high relevance, taking into account the geographical location of the participants. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned analysis unit, During the analysis, we will analyze the participants' social media activity and analyze the relevant data. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned management department, The system estimates the emotions of the participants and adjusts the progress management method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned management department, When managing progress, the optimal management method is selected by referring to the student's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned management department, When managing progress, adjust the level of detail based on the learner's current learning progress. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned management department, The system estimates the emotions of the participants and determines the priority of progress management based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned management department, When managing progress, provide relevant information while considering the geographical location of the participants. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned management department, During progress management, analyze participants' social media activity and provide relevant information. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that automatically generates a curriculum tailored to the learner's objectives and level, A lecture section that conducts lectures based on the curriculum generated by the aforementioned generation section, A question support department that answers questions in real time during the course, It includes a learning center that allows students to take courses 24 hours a day. A system characterized by the following features.

2. It includes an analysis unit that analyzes the input information of the participants. The system according to feature 1.

3. The company has a management department that manages the progress of its students. The system according to feature 1.

4. The generating unit is The system estimates the emotions of the participants and adjusts the difficulty level of the curriculum based on those estimated emotions. The system according to feature 1.

5. The generating unit is We analyze the student's past learning history and select the most suitable curriculum. The system according to feature 1.

6. The generating unit is When generating the curriculum, filtering is performed based on the participants' current occupation and areas of interest. The system according to feature 1.

7. The generating unit is The system estimates the emotions of the participants and prioritizes the curriculum based on those estimated emotions. The system according to feature 1.

8. The generating unit is When generating the curriculum, the geographical location of the participants is taken into consideration, and highly relevant content is prioritized. The system according to feature 1.

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