system

Abstract

The system according to this embodiment aims to provide learning plans and emotional support tailored to the individual needs of each student. [Solution] The system according to the embodiment comprises a learning plan provision unit, a progress management unit, a community provision unit, a sponsorship unit, and a course sales unit. The learning plan provision unit provides an optimal learning plan for each individual student. The progress management unit provides feedback and progress management based on the learning plan provided by the learning plan provision unit. The community provision unit provides an online community where students and parents can interact with each other. The sponsorship unit partners with companies and educational institutions to obtain sponsorships. The course sales unit sells courses specializing in specific subjects or skills.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, in online education, it has not been fully possible to provide learning plans and mental support according to the needs of individual students, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a learning plan and mental support according to the needs of individual students.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning plan provision unit, a progress management unit, a community provision unit, a sponsorship unit, and a course sales unit. The learning plan provision unit provides an optimal learning plan for each individual student. The progress management unit provides feedback and progress management based on the learning plan provided by the learning plan provision unit. The community provision unit provides an online community where students and parents can interact with each other. The sponsorship unit partners with companies and educational institutions to obtain sponsorships. The course sales unit sells courses specializing in specific subjects or skills. [Effects of the Invention]

[0007] The system according to this embodiment can provide learning plans and emotional support tailored to the individual needs of each student. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An online school system according to an embodiment of the present invention is a mechanism for opening an innovative online school utilizing generative AI. This online school system provides basic lessons free of charge and offers seminars and workshops by special lecturers, online events and contests, and individual consultations with experts. Next, it uses generative AI to provide an optimal learning plan for each student and provides feedback and progress management. Furthermore, it provides an online community where students and parents can interact with each other, offering a place for support and information exchange. It also generates revenue by partnering with companies and educational institutions and obtaining sponsorships. Finally, it provides a model for selling courses specializing in specific subjects or skills. For example, the online school system provides basic lessons free of charge and holds seminars and workshops by special lecturers. It increases student motivation through online events and contests and supports student learning by providing individual consultations with experts. Next, it uses generative AI to provide an optimal learning plan for each student and provides feedback and progress management. For example, the generative AI analyzes the student's learning history and interests and automatically generates an optimal learning plan. Furthermore, it provides an online community where students and parents can interact with each other, offering a place for support and information exchange. For example, an online community equipped with chat and forum functions can be built to provide an environment where students and parents can freely interact. Furthermore, revenue can be generated by partnering with companies and educational institutions to secure sponsorships. For instance, sponsorships from companies and educational institutions can secure funding for the online school's operations. Finally, a model can be offered that sells courses specializing in specific subjects or skills. For example, specialized courses in programming, data science, or marketing can be sold to meet students' learning needs. This allows the online school system to improve the students' learning experience.

[0029] The online school system according to this embodiment comprises a learning plan provision unit, a progress management unit, a community provision unit, a sponsorship unit, and a course sales unit. The learning plan provision unit provides an optimal learning plan for each individual student. The learning plan provision unit analyzes the student's learning history and interests using, for example, a generative AI, and automatically generates an optimal learning plan. The generative AI analyzes the student's learning history using, for example, a text generation AI (e.g., LLM), and provides an optimal learning plan. The learning plan provision unit can also use the generative AI to monitor the student's learning progress in real time and adjust the learning plan as needed. For example, the generative AI monitors the student's learning progress, and if progress is behind, it adjusts the learning plan to promote progress. The progress management unit provides feedback and progress management based on the learning plan provided by the learning plan provision unit. The progress management unit monitors the student's learning progress in real time using, for example, a generative AI, and provides feedback as needed. For example, the generative AI monitors the student's learning progress, and if progress is behind, it provides feedback to promote progress. The progress management department can also use generative AI to analyze students' learning progress and provide optimal feedback. For example, the generative AI analyzes a student's learning progress and provides specific advice if they are falling behind. The community provision department provides online communities where students and parents can interact with each other. For example, the community provision department builds online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. For example, the community provision department promotes interaction between students and parents through online events and discussions. The community provision department can also use generative AI to suggest relevant community groups based on students' interests. For example, the generative AI analyzes students' interests and suggests relevant community groups. The sponsorship department partners with companies and educational institutions to obtain sponsorships. For example, the sponsorship department secures operating funds for the online school by receiving sponsorships from companies and educational institutions.For example, the Sponsorship Department partners with companies and educational institutions and concludes sponsorship agreements. The Sponsorship Department can also use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. For instance, the generative AI monitors the effectiveness of sponsorships and modifies proposals if they are unsuccessful. The Course Sales Department sells courses specializing in specific subjects or skills. For example, the Course Sales Department sells specialized courses in areas such as programming, data science, and marketing to meet students' learning needs. For instance, the Course Sales Department uses generative AI to suggest the most suitable courses based on students' learning history and interests. Furthermore, the Course Sales Department can use generative AI to analyze course sales history and suggest optimal sales strategies. For example, the generative AI analyzes course sales history and suggests improvements for underperforming courses. This allows the online school system according to this embodiment to enhance the students' learning experience.

[0030] The learning plan provision department provides each student with the most suitable learning plan. For example, it uses generative AI to analyze students' learning history and interests, automatically generating the optimal learning plan. The generative AI, for instance, uses text generation AI (e.g., LLM) to analyze students' learning history and provide the optimal learning plan. Specifically, the generative AI meticulously analyzes students' past learning data, test results, assignments, and course participation history to identify each student's strengths and weaknesses. Furthermore, it customizes the learning plan based on the student's areas of interest and future goals. For example, the generative AI prioritizes incorporating subjects in which the student has performed well in the past and topics they have shown particular interest in when constructing the learning plan. The generative AI can also flexibly adjust the learning plan according to the student's learning style and pace. For example, the generative AI determines whether a student is a short-term, intensive learner or a long-term, gradual learner, and proposes a learning schedule accordingly. Additionally, the generative AI can monitor students' learning progress in real time and adjust the learning plan as needed. For example, the generative AI monitors students' learning progress and adjusts the learning plan to accelerate progress if a student is falling behind. Specifically, if a student is struggling with a particular assignment, the generative AI provides supplementary materials and additional practice problems related to that assignment. Furthermore, if a student has lost interest in a particular topic, the generative AI suggests interesting articles and videos related to that topic to rekindle their motivation. This allows the learning plan provider to deliver the most suitable learning plan for each individual student, improving their learning experience.

[0031] The Progress Management Department provides feedback and progress management based on the learning plans provided by the Learning Plan Provision Department. For example, the Progress Management Department uses generative AI to monitor students' learning progress in real time and provide feedback as needed. The generative AI, for instance, monitors students' learning progress and provides feedback to accelerate progress if it is lagging behind. Specifically, the generative AI analyzes the results of assignments and tests submitted by students to identify areas where they are struggling. For example, if a student repeatedly makes mistakes on a particular math problem, the generative AI provides feedback that explains the solution to that problem in detail. Furthermore, if a student has a weak understanding of a particular topic, the generative AI suggests additional learning materials and practice problems related to that topic. In addition, the Progress Management Department can use the generative AI to analyze students' learning progress and provide optimal feedback. For example, the generative AI analyzes a student's learning progress and provides specific advice if it is lagging behind. Specifically, the generative AI suggests how students should proceed with their learning and which topics they should focus on. Furthermore, the generative AI can provide advice to help students maintain their motivation to learn and offer techniques to improve learning efficiency. For example, the generative AI can suggest time management methods for focused learning in short periods and effective review methods. This allows the progress management department to monitor students' learning progress in real time and provide appropriate feedback, thereby improving the students' learning experience.

[0032] The Community Provider Department provides online communities where students and parents can interact with each other. For example, the Community Provider Department builds online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. Specifically, the Community Provider Department provides chat rooms where students can share questions and opinions about their studies, and forums where they can discuss specific topics. For example, if a student posts a question about a particular assignment, other students and teachers can provide answers. The Community Provider Department also promotes interaction between students and parents through online events and discussions. For example, through regularly held online seminars and workshops, students can learn new knowledge and exchange opinions with other students. Furthermore, the Community Provider Department can use generative AI to suggest relevant community groups based on students' interests. For example, the generative AI analyzes students' interests and suggests relevant community groups. Specifically, the generative AI analyzes courses students have previously taken, assignments they have submitted, and comments they have posted to identify community groups that students are likely to be interested in. For example, the generative AI suggests programming-related community groups to students interested in programming. Furthermore, the generating AI can suggest forums or chat rooms related to a specific topic if a student wants to discuss it. This allows the community provider to create an environment where students and parents can freely interact with each other, thereby improving the learning experience.

[0033] The Sponsorship Department partners with companies and educational institutions to secure sponsorships. For example, the Sponsorship Department secures operating funds for online schools by receiving sponsorships from companies and educational institutions. Specifically, the Sponsorship Department partners with companies and educational institutions and concludes sponsorship agreements. For example, the Sponsorship Department receives funding from companies in exchange for advertising their products or services on the online school platform. The Sponsorship Department can also use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. For example, the generative AI monitors the effectiveness of sponsorships and modifies proposals if they are ineffective. Specifically, the generative AI analyzes the click-through rate and conversion rate of sponsored advertisements and suggests changing the content or placement of advertisements if they are ineffective. The generative AI can also generate new proposals to maximize the effectiveness of sponsorships. For example, the generative AI suggests effective advertising campaigns for specific target audiences to improve the effectiveness of sponsorships. Furthermore, the Sponsorship Department regularly reports on the effectiveness of sponsorships and strengthens relationships with sponsoring companies and educational institutions. For example, the Sponsorship Department provides sponsoring companies with detailed reports demonstrating the effectiveness of their sponsorships and collects feedback for future partnerships. This allows the Sponsorship Department to build strong relationships with companies and educational institutions and secure a stable source of funding for the online school's operations.

[0034] The Course Sales Department sells courses specializing in specific subjects or skills. For example, it sells specialized courses in areas such as programming, data science, and marketing, meeting students' learning needs. Specifically, the Course Sales Department collaborates with experts in each field to develop high-quality courses. For instance, its programming courses provide materials that teach the latest programming languages ​​and frameworks, while its data science courses cover data analysis and machine learning from fundamentals to advanced applications. Furthermore, the Course Sales Department uses generative AI to suggest optimal courses based on students' learning history and interests. For example, the generative AI analyzes a student's learning history and suggests relevant courses based on subjects where they have performed well in the past and topics they have shown interest in. The generative AI can also customize the course progression according to the student's learning style and pace. For instance, it can determine whether a student is a short-term, intensive learner or a long-term, gradual learner and suggest a corresponding course schedule. The Course Sales Department can also use generative AI to analyze course sales history and propose optimal sales strategies. For example, the generative AI analyzes the sales history of courses and suggests improvements for underperforming courses. Specifically, the generative AI reviews the content, pricing, and marketing strategies of underperforming courses and proposes effective improvements. This allows the course sales department to provide high-quality courses that meet students' learning needs and improve the online school's revenue.

[0035] The learning plan provision unit can provide optimal learning plans to individual students using generative AI. For example, the unit can use generative AI to analyze a student's learning history and interests, and automatically generate an optimal learning plan. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze a student's learning history and provide an optimal learning plan. Furthermore, the learning plan provision unit can use generative AI to monitor students' learning progress in real time and adjust the learning plan as needed. For example, if the generative AI monitors a student's learning progress and is behind schedule, it adjusts the learning plan to accelerate progress. This allows for the provision of optimal learning plans to individual students using generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generative AI.

[0036] The progress management department can use generative AI to provide feedback on learning plans and manage progress. For example, the progress management department can use generative AI to monitor students' learning progress in real time and provide feedback as needed. The generative AI can, for example, monitor students' learning progress and provide feedback to promote progress if it is behind schedule. The progress management department can also use generative AI to analyze students' learning progress and provide optimal feedback. For example, the generative AI can analyze students' learning progress and provide specific advice if it is behind schedule. This improves the accuracy of feedback on learning plans and progress management by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0037] The Community Provider Department can provide online communities where students and parents can interact with each other. For example, the Department can build online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. For instance, the Department can facilitate interaction between students and parents through online events and discussions. Furthermore, the Department can use generative AI to suggest relevant community groups based on students' interests. For example, the generative AI analyzes students' interests and suggests relevant community groups. This facilitates interaction between students and parents by providing online communities.

[0038] The Sponsorship Department can partner with companies and educational institutions to secure sponsorships. For example, the Sponsorship Department can secure operating funds for online schools by receiving sponsorships from companies and educational institutions. For instance, the Sponsorship Department partners with companies and educational institutions and concludes sponsorship agreements. Furthermore, the Sponsorship Department can use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. For example, the generative AI monitors the effectiveness of sponsorships and modifies the proposals if the effectiveness is low. This allows the department to secure sponsorships by partnering with companies and educational institutions.

[0039] The course sales department can sell courses specializing in specific subjects or skills. For example, it can sell specialized courses in areas such as programming, data science, and marketing, meeting students' learning needs. For instance, it can use generative AI to suggest optimal courses based on students' learning history and interests. Furthermore, it can use generative AI to analyze course sales history and propose optimal sales strategies. For example, the generative AI can analyze course sales history and suggest improvements for underperforming courses. This allows the department to meet students' learning needs by selling courses specializing in specific subjects or skills.

[0040] The learning plan provision unit can analyze a student's past learning history and automatically generate an optimal learning plan. For example, the learning plan provision unit uses a generative AI to analyze a student's past learning history and provide an optimal learning plan. The generative AI, for example, uses a text generation AI (e.g., LLM) to analyze a student's past learning history and provide an optimal learning plan. Furthermore, the learning plan provision unit can also use the generative AI to provide a balanced learning plan based on a student's past learning history. For example, the generative AI provides a balanced learning plan based on a student's past performance data. This allows for the provision of an optimal learning plan by analyzing a student's past learning history. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generative AI, but is not limited to these examples.

[0041] The learning plan provider can provide additional resources related to the learning plan based on students' interests. For example, the learning plan provider can use generative AI to analyze students' interests and provide relevant additional resources. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' interests and provide relevant additional resources. The learning plan provider can also use generative AI to provide relevant articles and videos based on students' interests. For example, the generative AI provides articles and videos related to topics that students are interested in. This improves the quality of learning by providing additional resources based on students' interests. The generative AI is, for example, text generation AI (e.g., LLM) or multimodal generative AI, but is not limited to these examples.

[0042] The learning plan provider can provide region-specific learning plans based on students' geographical location information. For example, the learning plan provider can use generative AI to analyze students' geographical location information and provide region-specific learning plans. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' geographical location information and provide region-specific learning plans. Furthermore, the learning plan provider can use generative AI to provide learning plans related to local history and culture based on students' geographical location information. For example, the generative AI provides a learning plan related to the history and culture of the area where the student lives. This improves students' learning experience by providing region-specific learning plans. The generative AI is, for example, text generation AI (e.g., LLM) or multimodal generative AI, but is not limited to these examples.

[0043] The learning plan provision department can analyze students' social media activity and propose relevant learning plans. For example, it can use generative AI to analyze students' social media activity and provide relevant learning plans. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' social media activity and provide relevant learning plans. The learning plan provision department can also use generative AI to provide relevant articles and videos based on students' social media activity. For example, it can provide relevant articles and videos based on topics that the generative AI has shown interest in on social media. This allows the department to provide relevant learning plans to students by analyzing their social media activity. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generative AI.

[0044] The progress management department can monitor students' learning progress in real time and automatically issue alerts as needed. For example, the progress management department can use generative AI to monitor students' learning progress in real time and issue alerts as needed. The generative AI, for instance, monitors students' learning progress and issues alerts to encourage progress if progress is behind schedule. The progress management department can also use generative AI to analyze students' learning progress and provide optimal alerts. For example, the generative AI analyzes students' learning progress and provides alerts with specific advice if progress is behind schedule. This allows for timely alerts by monitoring learning progress in real time. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0045] The progress management department can analyze students' learning patterns and propose the optimal progress management method. For example, the progress management department can use generative AI to analyze students' learning patterns and provide the optimal progress management method. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' learning patterns and provide the optimal progress management method. Furthermore, the progress management department can also use generative AI to provide the optimal progress management schedule based on students' learning patterns. For example, the generative AI analyzes students' learning pace and provides the optimal progress management schedule. This allows for the provision of the optimal progress management method by analyzing learning patterns. The generative AI is, for example, text generation AI (e.g., LLM) or multimodal generative AI, but is not limited to these examples.

[0046] The progress management unit can compare students' learning progress with other students and provide a relative progress report. For example, the progress management unit can use generative AI to compare students' learning progress with other students and provide a relative progress report. The generative AI, for example, compares a student's learning progress with other students and shows how far along they are. The progress management unit can also use generative AI to show how much progress a student has made compared to their past self. For example, the generative AI provides progress based on the student's past performance data. This allows students to understand their relative progress by comparing them with other students. The generative AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0047] The progress management unit can automatically provide additional learning resources based on students' learning progress. For example, the progress management unit can use generative AI to analyze students' learning progress and provide additional learning resources. The generative AI, for example, analyzes students' learning progress and provides supplementary materials if they are struggling with a particular topic. The progress management unit can also use generative AI to provide additional practice problems based on students' learning progress. For example, the generative AI analyzes students' learning progress and provides additional practice problems to improve specific skills. This improves the quality of learning by providing additional learning resources based on learning progress. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0048] The community provisioning unit can automatically suggest relevant community groups based on students' interests. For example, it can use generative AI to analyze students' interests and suggest relevant community groups. The generative AI, for example, analyzes students' interests and provides relevant community groups. The community provisioning unit can also use generative AI to suggest relevant events and discussions based on students' interests. For example, the generative AI suggests relevant events and discussions based on topics that students are interested in. This increases student participation by suggesting community groups based on interests. The generative AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0049] The community provider can analyze activity history within a community and propose the most suitable interaction methods. For example, the community provider can use generative AI to analyze activity history within a community and provide the most suitable interaction methods. The generative AI, for example, can analyze a student's past activity history and provide the most suitable interaction methods. Furthermore, the community provider can use generative AI to analyze a student's past posts and propose interactions on relevant topics. For example, the generative AI analyzes a student's past posts and proposes interactions on relevant topics. This allows for the provision of the most suitable interaction methods by analyzing activity history. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0050] The community provision department can provide region-specific community groups based on students' geographical location information. For example, the community provision department can use generative AI to analyze students' geographical location information and provide region-specific community groups. The generative AI can, for example, analyze students' geographical location information and provide region-specific community groups. Furthermore, the community provision department can use generative AI to provide community groups related to local events and activities based on students' geographical location information. For example, the generative AI can provide community groups related to events and activities in the area where the student lives. This promotes student interaction by providing region-specific community groups. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0051] The community provision department can analyze students' social media activity and suggest relevant community groups. For example, the community provision department can use generative AI to analyze students' social media activity and provide relevant community groups. The generative AI, for example, analyzes students' social media activity and provides relevant community groups. Furthermore, the community provision department can use generative AI to suggest relevant events and discussions based on students' social media activity. For example, the generative AI suggests relevant events and discussions based on topics that students have shown interest in on social media. This allows for the provision of relevant community groups to students by analyzing their social media activity. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0052] The Sponsorship Department can analyze students' learning histories and propose the most suitable sponsorships. For example, the Sponsorship Department can use generative AI to analyze students' learning histories and provide optimal sponsorships. The generative AI, for instance, analyzes a student's learning history and, if they excel in a particular field, provides sponsorships related to that field. The Sponsorship Department can also use generative AI to propose optimal sponsorships based on students' learning histories. For example, if the generative AI analyzes a student's learning history and they are acquiring a particular skill, it proposes sponsorships related to that skill. This allows for the provision of optimal sponsorships by analyzing learning histories. Generative AI can include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0053] The Sponsorship Department can monitor the effectiveness of sponsorships in real time and modify the proposals as needed. For example, the Sponsorship Department can use generative AI to monitor the effectiveness of sponsorships in real time and modify the proposals as necessary. The generative AI, for instance, will modify the proposal if the sponsorship is ineffective. Furthermore, the Sponsorship Department can use the generative AI to continue with similar proposals if the sponsorship is highly effective. For example, the generative AI monitors the effectiveness of the sponsorship and continues with similar proposals if the effectiveness is high. This allows for appropriate modification of proposals by monitoring the effectiveness of sponsorships in real time. The generative AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0054] The Sponsorship Department can provide region-specific sponsorships based on students' geographical location information. For example, the Sponsorship Department can use generative AI to analyze students' geographical location information and provide region-specific sponsorships. The generative AI can, for example, analyze students' geographical location information and provide region-specific sponsorships. Furthermore, the Sponsorship Department can use generative AI to provide sponsorships from local companies and organizations based on students' geographical location information. For example, the generative AI can provide sponsorships from companies and organizations in the area where the student lives. This allows the department to meet students' needs by providing region-specific sponsorships. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0055] The Sponsorship Department can analyze students' social media activities and propose relevant sponsorships. For example, the Sponsorship Department can use generative AI to analyze students' social media activities and provide relevant sponsorships. The generative AI, for example, analyzes students' social media activities and provides relevant sponsorships. Furthermore, the Sponsorship Department can use generative AI to propose sponsorships from relevant companies and organizations based on students' social media activities. For example, the generative AI could propose sponsorships from relevant companies and organizations based on topics that students have shown interest in on social media. This allows for the provision of relevant sponsorships to students by analyzing their social media activities. Generative AI can include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0056] The course sales department can analyze students' learning history and suggest the most suitable courses. For example, the course sales department can use generative AI to analyze students' learning history and provide the most suitable courses. The generative AI, for example, analyzes students' learning history and suggests relevant courses based on courses they have previously taken. The course sales department can also use generative AI to suggest the most suitable courses based on students' learning history. For example, if the generative AI analyzes a student's learning history and they are acquiring a specific skill, it will suggest courses related to that skill. This allows for the provision of optimal courses by analyzing learning history. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0057] The course sales department can analyze the sales history of courses and propose the optimal sales strategy. For example, the course sales department can use generative AI to analyze the sales history of courses and provide the optimal sales strategy. The generative AI can, for example, analyze the sales strategies of popular courses in the past and propose similar strategies. The course sales department can also use generative AI to analyze the sales strategies of underperforming courses and propose improvements. For example, the generative AI analyzes the sales history of courses and proposes improvements for underperforming courses. In this way, the optimal sales strategy can be provided by analyzing the sales history. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0058] The course sales department can offer region-specific courses based on students' geographical location information. For example, the course sales department can use generative AI to analyze students' geographical location information and offer region-specific courses. The generative AI can, for example, analyze students' geographical location information and offer region-specific courses. Furthermore, the course sales department can use generative AI to offer courses on local culture and history based on students' geographical location information. For example, the generative AI can offer courses on the culture and history of the area where the student lives. This improves the students' learning experience by providing region-specific courses. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0059] The course sales department can analyze students' social media activity and suggest relevant courses. For example, the course sales department can use generative AI to analyze students' social media activity and provide relevant courses. The generative AI, for example, analyzes students' social media activity and provides relevant courses. The course sales department can also use generative AI to suggest courses recommended by relevant experts and influencers based on students' social media activity. For example, the generative AI suggests courses recommended by relevant experts and influencers based on topics students have shown interest in on social media. This allows for the provision of relevant courses to students by analyzing their social media activity. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[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] The learning plan provision department can analyze students' learning styles and propose the most suitable learning methods. For example, it can use generative AI to analyze students' learning styles (visual, auditory, tactile, etc.) and provide the most suitable learning methods based on that analysis. For students with a visual learning style, it can provide learning plans that include a lot of visual content. For students with an auditory learning style, it can provide learning plans that include a lot of audio content. For students with a tactile learning style, it can provide learning plans that include a lot of practical exercises. By providing the most suitable learning methods tailored to each student's learning style, it is possible to improve learning effectiveness.

[0062] The progress management department can provide incentives to maintain student motivation based on their learning progress. For example, it can use generative AI to analyze students' learning progress and award badges or points when certain goals are achieved. Furthermore, the generative AI can monitor students' learning progress and provide encouraging messages to boost motivation if progress is falling behind. It can also analyze students' learning progress and offer special rewards when specific goals are achieved. This helps maintain student motivation and improve learning effectiveness.

[0063] The sponsorship department can customize sponsorship proposals based on students' learning progress. For example, it can use generative AI to analyze students' learning progress and propose sponsorships related to specific areas for students who are excelling in those areas. Furthermore, the generative AI can monitor students' learning progress and propose sponsorships to support their learning if they are falling behind. It can also analyze students' learning progress and propose sponsorships related to specific skills if they are acquiring those skills. This allows for the provision of optimal sponsorships based on students' learning progress, thereby improving the students' learning experience.

[0064] The learning plan provision department can analyze students' learning history and dynamically adjust learning plans according to their learning progress. For example, it can use generative AI to analyze a student's learning history and simplify the learning plan if progress is slow. If progress is on track, it can change the learning plan to include more difficult content. Furthermore, if the generative AI analyzes a student's learning history and finds a lack of understanding in a particular area, it can provide a learning plan tailored to that area. In this way, by providing the optimal learning plan according to the student's learning progress, learning effectiveness can be improved.

[0065] The community provisioning department can analyze students' learning histories and suggest relevant community groups. For example, it can use generative AI to analyze students' learning histories and suggest community groups related to specific fields for students with a learning history in those areas. Furthermore, the generative AI can monitor students' learning histories and suggest community groups related to skills if they are currently acquiring those skills. It can also analyze students' learning histories and suggest relevant community groups based on topics they are interested in. This allows for the provision of optimal community groups based on students' learning histories, thereby promoting student interaction.

[0066] The course sales department can analyze students' learning progress and propose the most suitable course sales methods. For example, it can use generative AI to analyze students' learning progress and suggest easier courses if they are falling behind. If they are progressing well, it can suggest more challenging courses. Furthermore, if the generative AI analyzes students' learning progress and finds a lack of understanding in a particular area, it can suggest courses specializing in that area. In this way, by providing the most suitable courses based on students' learning progress, learning effectiveness can be improved.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The learning plan provision department provides each student with the most suitable learning plan. For example, it uses a generation AI to analyze the student's learning history and interests and automatically generates the optimal learning plan. It can also use the generation AI to monitor the student's learning progress in real time and adjust the learning plan as needed. Step 2: The progress management department provides feedback and progress management based on the learning plans provided by the learning plan provision department. For example, it can use generative AI to monitor students' learning progress in real time and provide feedback as needed. It can also use generative AI to analyze students' learning progress and provide optimal feedback. Step 3: The community provision department provides online communities where students and parents can interact with each other. For example, they build online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. They can also use generative AI to suggest relevant community groups based on students' interests. Step 4: The Sponsorship Department partners with companies and educational institutions to secure sponsorships. For example, they can secure operating funds for the online school by receiving sponsorships from companies and educational institutions. They can also use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. Step 5: The course sales department sells courses specializing in specific subjects or skills. For example, they sell specialized courses in programming, data science, marketing, etc., to meet students' learning needs. They can also use generative AI to suggest the most suitable courses based on students' learning history and interests.

[0069] (Example of form 2) An online school system according to an embodiment of the present invention is a mechanism for opening an innovative online school utilizing generative AI. This online school system provides basic lessons free of charge and offers seminars and workshops by special lecturers, online events and contests, and individual consultations with experts. Next, it uses generative AI to provide an optimal learning plan for each student and provides feedback and progress management. Furthermore, it provides an online community where students and parents can interact with each other, offering a place for support and information exchange. It also generates revenue by partnering with companies and educational institutions and obtaining sponsorships. Finally, it provides a model for selling courses specializing in specific subjects or skills. For example, the online school system provides basic lessons free of charge and holds seminars and workshops by special lecturers. It increases student motivation through online events and contests and supports student learning by providing individual consultations with experts. Next, it uses generative AI to provide an optimal learning plan for each student and provides feedback and progress management. For example, the generative AI analyzes the student's learning history and interests and automatically generates an optimal learning plan. Furthermore, it provides an online community where students and parents can interact with each other, offering a place for support and information exchange. For example, an online community equipped with chat and forum functions can be built to provide an environment where students and parents can freely interact. Furthermore, revenue can be generated by partnering with companies and educational institutions to secure sponsorships. For instance, sponsorships from companies and educational institutions can secure funding for the online school's operations. Finally, a model can be offered that sells courses specializing in specific subjects or skills. For example, specialized courses in programming, data science, or marketing can be sold to meet students' learning needs. This allows the online school system to improve the students' learning experience.

[0070] The online school system according to this embodiment comprises a learning plan provision unit, a progress management unit, a community provision unit, a sponsorship unit, and a course sales unit. The learning plan provision unit provides an optimal learning plan for each individual student. The learning plan provision unit analyzes the student's learning history and interests using, for example, a generative AI, and automatically generates an optimal learning plan. The generative AI analyzes the student's learning history using, for example, a text generation AI (e.g., LLM), and provides an optimal learning plan. The learning plan provision unit can also use the generative AI to monitor the student's learning progress in real time and adjust the learning plan as needed. For example, the generative AI monitors the student's learning progress, and if progress is behind, it adjusts the learning plan to promote progress. The progress management unit provides feedback and progress management based on the learning plan provided by the learning plan provision unit. The progress management unit monitors the student's learning progress in real time using, for example, a generative AI, and provides feedback as needed. For example, the generative AI monitors the student's learning progress, and if progress is behind, it provides feedback to promote progress. The progress management department can also use generative AI to analyze students' learning progress and provide optimal feedback. For example, the generative AI analyzes a student's learning progress and provides specific advice if they are falling behind. The community provision department provides online communities where students and parents can interact with each other. For example, the community provision department builds online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. For example, the community provision department promotes interaction between students and parents through online events and discussions. The community provision department can also use generative AI to suggest relevant community groups based on students' interests. For example, the generative AI analyzes students' interests and suggests relevant community groups. The sponsorship department partners with companies and educational institutions to obtain sponsorships. For example, the sponsorship department secures operating funds for the online school by receiving sponsorships from companies and educational institutions.For example, the Sponsorship Department partners with companies and educational institutions and concludes sponsorship agreements. The Sponsorship Department can also use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. For instance, the generative AI monitors the effectiveness of sponsorships and modifies proposals if they are unsuccessful. The Course Sales Department sells courses specializing in specific subjects or skills. For example, the Course Sales Department sells specialized courses in areas such as programming, data science, and marketing to meet students' learning needs. For instance, the Course Sales Department uses generative AI to suggest the most suitable courses based on students' learning history and interests. Furthermore, the Course Sales Department can use generative AI to analyze course sales history and suggest optimal sales strategies. For example, the generative AI analyzes course sales history and suggests improvements for underperforming courses. This allows the online school system according to this embodiment to enhance the students' learning experience.

[0071] The learning plan provision department provides each student with the most suitable learning plan. For example, it uses generative AI to analyze students' learning history and interests, automatically generating the optimal learning plan. The generative AI, for instance, uses text generation AI (e.g., LLM) to analyze students' learning history and provide the optimal learning plan. Specifically, the generative AI meticulously analyzes students' past learning data, test results, assignments, and course participation history to identify each student's strengths and weaknesses. Furthermore, it customizes the learning plan based on the student's areas of interest and future goals. For example, the generative AI prioritizes incorporating subjects in which the student has performed well in the past and topics they have shown particular interest in when constructing the learning plan. The generative AI can also flexibly adjust the learning plan according to the student's learning style and pace. For example, the generative AI determines whether a student is a short-term, intensive learner or a long-term, gradual learner, and proposes a learning schedule accordingly. Additionally, the generative AI can monitor students' learning progress in real time and adjust the learning plan as needed. For example, the generative AI monitors students' learning progress and adjusts the learning plan to accelerate progress if a student is falling behind. Specifically, if a student is struggling with a particular assignment, the generative AI provides supplementary materials and additional practice problems related to that assignment. Furthermore, if a student has lost interest in a particular topic, the generative AI suggests interesting articles and videos related to that topic to rekindle their motivation. This allows the learning plan provider to deliver the most suitable learning plan for each individual student, improving their learning experience.

[0072] The Progress Management Department provides feedback and progress management based on the learning plans provided by the Learning Plan Provision Department. For example, the Progress Management Department uses generative AI to monitor students' learning progress in real time and provide feedback as needed. The generative AI, for instance, monitors students' learning progress and provides feedback to accelerate progress if it is lagging behind. Specifically, the generative AI analyzes the results of assignments and tests submitted by students to identify areas where they are struggling. For example, if a student repeatedly makes mistakes on a particular math problem, the generative AI provides feedback that explains the solution to that problem in detail. Furthermore, if a student has a weak understanding of a particular topic, the generative AI suggests additional learning materials and practice problems related to that topic. In addition, the Progress Management Department can use the generative AI to analyze students' learning progress and provide optimal feedback. For example, the generative AI analyzes a student's learning progress and provides specific advice if it is lagging behind. Specifically, the generative AI suggests how students should proceed with their learning and which topics they should focus on. Furthermore, the generative AI can provide advice to help students maintain their motivation to learn and offer techniques to improve learning efficiency. For example, the generative AI can suggest time management methods for focused learning in short periods and effective review methods. This allows the progress management department to monitor students' learning progress in real time and provide appropriate feedback, thereby improving the students' learning experience.

[0073] The Community Provider Department provides online communities where students and parents can interact with each other. For example, the Community Provider Department builds online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. Specifically, the Community Provider Department provides chat rooms where students can share questions and opinions about their studies, and forums where they can discuss specific topics. For example, if a student posts a question about a particular assignment, other students and teachers can provide answers. The Community Provider Department also promotes interaction between students and parents through online events and discussions. For example, through regularly held online seminars and workshops, students can learn new knowledge and exchange opinions with other students. Furthermore, the Community Provider Department can use generative AI to suggest relevant community groups based on students' interests. For example, the generative AI analyzes students' interests and suggests relevant community groups. Specifically, the generative AI analyzes courses students have previously taken, assignments they have submitted, and comments they have posted to identify community groups that students are likely to be interested in. For example, the generative AI suggests programming-related community groups to students interested in programming. Furthermore, the generating AI can suggest forums or chat rooms related to a specific topic if a student wants to discuss it. This allows the community provider to create an environment where students and parents can freely interact with each other, thereby improving the learning experience.

[0074] The Sponsorship Department partners with companies and educational institutions to secure sponsorships. For example, the Sponsorship Department secures operating funds for online schools by receiving sponsorships from companies and educational institutions. Specifically, the Sponsorship Department partners with companies and educational institutions and concludes sponsorship agreements. For example, the Sponsorship Department receives funding from companies in exchange for advertising their products or services on the online school platform. The Sponsorship Department can also use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. For example, the generative AI monitors the effectiveness of sponsorships and modifies proposals if they are ineffective. Specifically, the generative AI analyzes the click-through rate and conversion rate of sponsored advertisements and suggests changing the content or placement of advertisements if they are ineffective. The generative AI can also generate new proposals to maximize the effectiveness of sponsorships. For example, the generative AI suggests effective advertising campaigns for specific target audiences to improve the effectiveness of sponsorships. Furthermore, the Sponsorship Department regularly reports on the effectiveness of sponsorships and strengthens relationships with sponsoring companies and educational institutions. For example, the Sponsorship Department provides sponsoring companies with detailed reports demonstrating the effectiveness of their sponsorships and collects feedback for future partnerships. This allows the Sponsorship Department to build strong relationships with companies and educational institutions and secure a stable source of funding for the online school's operations.

[0075] The Course Sales Department sells courses specializing in specific subjects or skills. For example, it sells specialized courses in areas such as programming, data science, and marketing, meeting students' learning needs. Specifically, the Course Sales Department collaborates with experts in each field to develop high-quality courses. For instance, its programming courses provide materials that teach the latest programming languages ​​and frameworks, while its data science courses cover data analysis and machine learning from fundamentals to advanced applications. Furthermore, the Course Sales Department uses generative AI to suggest optimal courses based on students' learning history and interests. For example, the generative AI analyzes a student's learning history and suggests relevant courses based on subjects where they have performed well in the past and topics they have shown interest in. The generative AI can also customize the course progression according to the student's learning style and pace. For instance, it can determine whether a student is a short-term, intensive learner or a long-term, gradual learner and suggest a corresponding course schedule. The Course Sales Department can also use generative AI to analyze course sales history and propose optimal sales strategies. For example, the generative AI analyzes the sales history of courses and suggests improvements for underperforming courses. Specifically, the generative AI reviews the content, pricing, and marketing strategies of underperforming courses and proposes effective improvements. This allows the course sales department to provide high-quality courses that meet students' learning needs and improve the online school's revenue.

[0076] The learning plan provision unit can provide optimal learning plans to individual students using generative AI. For example, the unit can use generative AI to analyze a student's learning history and interests, and automatically generate an optimal learning plan. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze a student's learning history and provide an optimal learning plan. Furthermore, the learning plan provision unit can use generative AI to monitor students' learning progress in real time and adjust the learning plan as needed. For example, if the generative AI monitors a student's learning progress and is behind schedule, it adjusts the learning plan to accelerate progress. This allows for the provision of optimal learning plans to individual students using generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generative AI.

[0077] The progress management department can use generative AI to provide feedback on learning plans and manage progress. For example, the progress management department can use generative AI to monitor students' learning progress in real time and provide feedback as needed. The generative AI can, for example, monitor students' learning progress and provide feedback to promote progress if it is behind schedule. The progress management department can also use generative AI to analyze students' learning progress and provide optimal feedback. For example, the generative AI can analyze students' learning progress and provide specific advice if it is behind schedule. This improves the accuracy of feedback on learning plans and progress management by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The Community Provider Department can provide online communities where students and parents can interact with each other. For example, the Department can build online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. For instance, the Department can facilitate interaction between students and parents through online events and discussions. Furthermore, the Department can use generative AI to suggest relevant community groups based on students' interests. For example, the generative AI analyzes students' interests and suggests relevant community groups. This facilitates interaction between students and parents by providing online communities.

[0079] The Sponsorship Department can partner with companies and educational institutions to secure sponsorships. For example, the Sponsorship Department can secure operating funds for online schools by receiving sponsorships from companies and educational institutions. For instance, the Sponsorship Department partners with companies and educational institutions and concludes sponsorship agreements. Furthermore, the Sponsorship Department can use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. For example, the generative AI monitors the effectiveness of sponsorships and modifies the proposals if the effectiveness is low. This allows the department to secure sponsorships by partnering with companies and educational institutions.

[0080] The course sales department can sell courses specializing in specific subjects or skills. For example, it can sell specialized courses in areas such as programming, data science, and marketing, meeting students' learning needs. For instance, it can use generative AI to suggest optimal courses based on students' learning history and interests. Furthermore, it can use generative AI to analyze course sales history and propose optimal sales strategies. For example, the generative AI can analyze course sales history and suggest improvements for underperforming courses. This allows the department to meet students' learning needs by selling courses specializing in specific subjects or skills.

[0081] The learning plan provider can estimate a student's emotions and adjust the difficulty level of the learning plan based on those emotions. For example, if a student is stressed, the learning plan provider can use a generative AI to provide a learning plan with a lower difficulty level. If a student is relaxed, the learning plan provider can use a generative AI to provide a learning plan with a higher difficulty level. If a student is excited, the learning plan provider can use a generative AI to provide a learning plan that includes challenging tasks. By adjusting the difficulty level of the learning plan based on the student's emotions, a more appropriate learning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The learning plan provision unit can analyze a student's past learning history and automatically generate an optimal learning plan. For example, the learning plan provision unit uses a generative AI to analyze a student's past learning history and provide an optimal learning plan. The generative AI, for example, uses a text generation AI (e.g., LLM) to analyze a student's past learning history and provide an optimal learning plan. Furthermore, the learning plan provision unit can also use the generative AI to provide a balanced learning plan based on a student's past learning history. For example, the generative AI provides a balanced learning plan based on a student's past performance data. This allows for the provision of an optimal learning plan by analyzing a student's past learning history. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generative AI, but is not limited to these examples.

[0083] The learning plan provider can provide additional resources related to the learning plan based on students' interests. For example, the learning plan provider can use generative AI to analyze students' interests and provide relevant additional resources. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' interests and provide relevant additional resources. The learning plan provider can also use generative AI to provide relevant articles and videos based on students' interests. For example, the generative AI provides articles and videos related to topics that students are interested in. This improves the quality of learning by providing additional resources based on students' interests. The generative AI is, for example, text generation AI (e.g., LLM) or multimodal generative AI, but is not limited to these examples.

[0084] The learning plan provider can estimate a student's emotions and adjust the pace of the learning plan based on those emotions. For example, if a student is stressed, the generating AI can slow down the pace. If a student is relaxed, the generating AI can speed up the pace. If a student is excited, the generating AI can provide challenging tasks while adjusting the pace. This allows for the provision of more appropriate learning plans by adjusting the pace of the learning plan based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The learning plan provider can provide region-specific learning plans based on students' geographical location information. For example, the learning plan provider can use generative AI to analyze students' geographical location information and provide region-specific learning plans. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' geographical location information and provide region-specific learning plans. Furthermore, the learning plan provider can use generative AI to provide learning plans related to local history and culture based on students' geographical location information. For example, the generative AI provides a learning plan related to the history and culture of the area where the student lives. This improves students' learning experience by providing region-specific learning plans. The generative AI is, for example, text generation AI (e.g., LLM) or multimodal generative AI, but is not limited to these examples.

[0086] The learning plan provision department can analyze students' social media activity and propose relevant learning plans. For example, it can use generative AI to analyze students' social media activity and provide relevant learning plans. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' social media activity and provide relevant learning plans. The learning plan provision department can also use generative AI to provide relevant articles and videos based on students' social media activity. For example, it can provide relevant articles and videos based on topics that the generative AI has shown interest in on social media. This allows the department to provide relevant learning plans to students by analyzing their social media activity. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generative AI.

[0087] The progress management unit can estimate a student's emotions and adjust the content of feedback based on the estimated emotions. For example, if a student is stressed, the progress management unit can use a generative AI to provide feedback that includes words of encouragement. For example, if a student is relaxed, the progress management unit can use a generative AI to provide detailed feedback. For example, if a student is excited, the progress management unit can use a generative AI to provide challenging feedback. This allows for more appropriate feedback to be provided by adjusting the content of feedback based on the student'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.

[0088] The progress management department can monitor students' learning progress in real time and automatically issue alerts as needed. For example, the progress management department can use generative AI to monitor students' learning progress in real time and issue alerts as needed. The generative AI, for instance, monitors students' learning progress and issues alerts to encourage progress if progress is behind schedule. The progress management department can also use generative AI to analyze students' learning progress and provide optimal alerts. For example, the generative AI analyzes students' learning progress and provides alerts with specific advice if progress is behind schedule. This allows for timely alerts by monitoring learning progress in real time. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The progress management department can analyze students' learning patterns and propose the optimal progress management method. For example, the progress management department can use generative AI to analyze students' learning patterns and provide the optimal progress management method. The generative AI, for example, uses text generation AI (e.g., LLM) to analyze students' learning patterns and provide the optimal progress management method. Furthermore, the progress management department can also use generative AI to provide the optimal progress management schedule based on students' learning patterns. For example, the generative AI analyzes students' learning pace and provides the optimal progress management schedule. This allows for the provision of the optimal progress management method by analyzing learning patterns. The generative AI is, for example, text generation AI (e.g., LLM) or multimodal generative AI, but is not limited to these examples.

[0090] The progress management unit can estimate students' emotions and adjust the timing of feedback based on those emotions. For example, if a student is stressed, the progress management unit can use the generative AI to reduce the frequency of feedback. If a student is relaxed, the progress management unit can use the generative AI to increase the frequency of feedback. If a student is excited, the progress management unit can use the generative AI to provide timely feedback. This allows for more appropriate feedback to be provided by adjusting the timing of feedback based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The progress management unit can compare students' learning progress with other students and provide a relative progress report. For example, the progress management unit can use generative AI to compare students' learning progress with other students and provide a relative progress report. The generative AI, for example, compares a student's learning progress with other students and shows how far along they are. The progress management unit can also use generative AI to show how much progress a student has made compared to their past self. For example, the generative AI provides progress based on the student's past performance data. This allows students to understand their relative progress by comparing them with other students. The generative AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The progress management unit can automatically provide additional learning resources based on students' learning progress. For example, the progress management unit can use generative AI to analyze students' learning progress and provide additional learning resources. The generative AI, for example, analyzes students' learning progress and provides supplementary materials if they are struggling with a particular topic. The progress management unit can also use generative AI to provide additional practice problems based on students' learning progress. For example, the generative AI analyzes students' learning progress and provides additional practice problems to improve specific skills. This improves the quality of learning by providing additional learning resources based on learning progress. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The community provider can estimate students' emotions and facilitate interaction within the community based on those estimated emotions. For example, if a student is feeling lonely, the community provider's generative AI can suggest events to promote interaction. If a student is relaxed, the community provider's generative AI can also provide a casual space for interaction. If a student is excited, the community provider's generative AI can also suggest a challenging discussion. This revitalizes community activity by facilitating interaction based on students' emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The community provisioning unit can automatically suggest relevant community groups based on students' interests. For example, it can use generative AI to analyze students' interests and suggest relevant community groups. The generative AI, for example, analyzes students' interests and provides relevant community groups. The community provisioning unit can also use generative AI to suggest relevant events and discussions based on students' interests. For example, the generative AI suggests relevant events and discussions based on topics that students are interested in. This increases student participation by suggesting community groups based on interests. The generative AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The community provider can analyze activity history within a community and propose the most suitable interaction methods. For example, the community provider can use generative AI to analyze activity history within a community and provide the most suitable interaction methods. The generative AI, for example, can analyze a student's past activity history and provide the most suitable interaction methods. Furthermore, the community provider can use generative AI to analyze a student's past posts and propose interactions on relevant topics. For example, the generative AI analyzes a student's past posts and proposes interactions on relevant topics. This allows for the provision of the most suitable interaction methods by analyzing activity history. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The community delivery unit can estimate students' emotions and adjust notifications within the community based on the estimated emotions. For example, if a student is stressed, the community delivery unit's generative AI can reduce the frequency of notifications. For example, if a student is relaxed, the community delivery unit's generative AI can increase the frequency of notifications. For example, if a student is excited, the community delivery unit's generative AI can provide timely notifications. This allows for notifications to be delivered at a more appropriate time by adjusting them based on students' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The community provision department can provide region-specific community groups based on students' geographical location information. For example, the community provision department can use generative AI to analyze students' geographical location information and provide region-specific community groups. The generative AI can, for example, analyze students' geographical location information and provide region-specific community groups. Furthermore, the community provision department can use generative AI to provide community groups related to local events and activities based on students' geographical location information. For example, the generative AI can provide community groups related to events and activities in the area where the student lives. This promotes student interaction by providing region-specific community groups. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The community provision department can analyze students' social media activity and suggest relevant community groups. For example, the community provision department can use generative AI to analyze students' social media activity and provide relevant community groups. The generative AI, for example, analyzes students' social media activity and provides relevant community groups. Furthermore, the community provision department can use generative AI to suggest relevant events and discussions based on students' social media activity. For example, the generative AI suggests relevant events and discussions based on topics that students have shown interest in on social media. This allows for the provision of relevant community groups to students by analyzing their social media activity. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The sponsorship department can estimate students' emotions and adjust sponsorship proposals based on those estimates. For example, if a student is stressed, the sponsorship department's generative AI can suggest relaxing sponsorships. If a student is relaxed, the sponsorship department's generative AI can suggest challenging sponsorships. If a student is excited, the sponsorship department's generative AI can suggest stimulating sponsorships. By adjusting sponsorship proposals based on students' emotions, the department can provide more appropriate suggestions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The Sponsorship Department can analyze students' learning histories and propose the most suitable sponsorships. For example, the Sponsorship Department can use generative AI to analyze students' learning histories and provide optimal sponsorships. The generative AI, for instance, analyzes a student's learning history and, if they excel in a particular field, provides sponsorships related to that field. The Sponsorship Department can also use generative AI to propose optimal sponsorships based on students' learning histories. For example, if the generative AI analyzes a student's learning history and they are acquiring a particular skill, it proposes sponsorships related to that skill. This allows for the provision of optimal sponsorships by analyzing learning histories. Generative AI can include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The Sponsorship Department can monitor the effectiveness of sponsorships in real time and modify the proposals as needed. For example, the Sponsorship Department can use generative AI to monitor the effectiveness of sponsorships in real time and modify the proposals as necessary. The generative AI, for instance, will modify the proposal if the sponsorship is ineffective. Furthermore, the Sponsorship Department can use the generative AI to continue with similar proposals if the sponsorship is highly effective. For example, the generative AI monitors the effectiveness of the sponsorship and continues with similar proposals if the effectiveness is high. This allows for appropriate modification of proposals by monitoring the effectiveness of sponsorships in real time. The generative AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The sponsorship department can estimate students' emotions and adjust the timing of sponsorships based on those emotions. For example, if a student is stressed, the sponsorship department's generative AI may delay offering sponsorships. If a student is relaxed, the sponsorship department may also expedite offering sponsorships. If a student is excited, the sponsorship department may also offer sponsorships in a timely manner. By adjusting the timing of sponsorships based on students' emotions, offers can be provided at a more appropriate time. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The Sponsorship Department can provide region-specific sponsorships based on students' geographical location information. For example, the Sponsorship Department can use generative AI to analyze students' geographical location information and provide region-specific sponsorships. The generative AI can, for example, analyze students' geographical location information and provide region-specific sponsorships. Furthermore, the Sponsorship Department can use generative AI to provide sponsorships from local companies and organizations based on students' geographical location information. For example, the generative AI can provide sponsorships from companies and organizations in the area where the student lives. This allows the department to meet students' needs by providing region-specific sponsorships. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0104] The Sponsorship Department can analyze students' social media activities and propose relevant sponsorships. For example, the Sponsorship Department can use generative AI to analyze students' social media activities and provide relevant sponsorships. The generative AI, for example, analyzes students' social media activities and provides relevant sponsorships. Furthermore, the Sponsorship Department can use generative AI to propose sponsorships from relevant companies and organizations based on students' social media activities. For example, the generative AI could propose sponsorships from relevant companies and organizations based on topics that students have shown interest in on social media. This allows for the provision of relevant sponsorships to students by analyzing their social media activities. Generative AI can include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The course sales department can estimate students' emotions and adjust the course sales method based on the estimated emotions. For example, if a student is stressed, the course sales department can use a generative AI to suggest a relaxing course sales method. If a student is relaxed, the course sales department can use a generative AI to suggest a challenging course sales method. If a student is excited, the course sales department can use a generative AI to suggest an exciting course sales method. This allows for more appropriate sales methods to be provided by adjusting the course sales method based on students' 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.

[0106] The course sales department can analyze students' learning history and suggest the most suitable courses. For example, the course sales department can use generative AI to analyze students' learning history and provide the most suitable courses. The generative AI, for example, analyzes students' learning history and suggests relevant courses based on courses they have previously taken. The course sales department can also use generative AI to suggest the most suitable courses based on students' learning history. For example, if the generative AI analyzes a student's learning history and they are acquiring a specific skill, it will suggest courses related to that skill. This allows for the provision of optimal courses by analyzing learning history. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The course sales department can analyze the sales history of courses and propose the optimal sales strategy. For example, the course sales department can use generative AI to analyze the sales history of courses and provide the optimal sales strategy. The generative AI can, for example, analyze the sales strategies of popular courses in the past and propose similar strategies. The course sales department can also use generative AI to analyze the sales strategies of underperforming courses and propose improvements. For example, the generative AI analyzes the sales history of courses and proposes improvements for underperforming courses. In this way, the optimal sales strategy can be provided by analyzing the sales history. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] The course sales department can estimate students' emotions and adjust the timing of course sales based on those estimated emotions. For example, if a student is stressed, the generative AI can delay the course sales timing. If a student is relaxed, the generative AI can also advance the course sales timing. If a student is excited, the generative AI can suggest a timely course sale. This allows courses to be delivered at a more appropriate time by adjusting the timing of course sales based on students' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The course sales department can offer region-specific courses based on students' geographical location information. For example, the course sales department can use generative AI to analyze students' geographical location information and offer region-specific courses. The generative AI can, for example, analyze students' geographical location information and offer region-specific courses. Furthermore, the course sales department can use generative AI to offer courses on local culture and history based on students' geographical location information. For example, the generative AI can offer courses on the culture and history of the area where the student lives. This improves the students' learning experience by providing region-specific courses. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The course sales department can analyze students' social media activity and suggest relevant courses. For example, the course sales department can use generative AI to analyze students' social media activity and provide relevant courses. The generative AI, for example, analyzes students' social media activity and provides relevant courses. The course sales department can also use generative AI to suggest courses recommended by relevant experts and influencers based on students' social media activity. For example, the generative AI suggests courses recommended by relevant experts and influencers based on topics students have shown interest in on social media. This allows for the provision of relevant courses to students by analyzing their social media activity. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The learning plan provision department can analyze students' learning styles and propose the most suitable learning methods. For example, it can use generative AI to analyze students' learning styles (visual, auditory, tactile, etc.) and provide the most suitable learning methods based on that analysis. For students with a visual learning style, it can provide learning plans that include a lot of visual content. For students with an auditory learning style, it can provide learning plans that include a lot of audio content. For students with a tactile learning style, it can provide learning plans that include a lot of practical exercises. By providing the most suitable learning methods tailored to each student's learning style, it is possible to improve learning effectiveness.

[0113] The progress management department can provide incentives to maintain student motivation based on their learning progress. For example, it can use generative AI to analyze students' learning progress and award badges or points when certain goals are achieved. Furthermore, the generative AI can monitor students' learning progress and provide encouraging messages to boost motivation if progress is falling behind. It can also analyze students' learning progress and offer special rewards when specific goals are achieved. This helps maintain student motivation and improve learning effectiveness.

[0114] The community provisioning department can estimate students' emotions and provide support within the community based on those estimated emotions. For example, if a student is feeling lonely, the generative AI can suggest a support group. If a student is feeling stressed, the generative AI can also suggest relaxing activities. If a student is feeling agitated, the generative AI can also suggest challenging discussions. By providing appropriate support based on students' emotions, community activities become more active and student satisfaction improves.

[0115] The sponsorship department can customize sponsorship proposals based on students' learning progress. For example, it can use generative AI to analyze students' learning progress and propose sponsorships related to specific areas for students who are excelling in those areas. Furthermore, the generative AI can monitor students' learning progress and propose sponsorships to support their learning if they are falling behind. It can also analyze students' learning progress and propose sponsorships related to specific skills if they are acquiring those skills. This allows for the provision of optimal sponsorships based on students' learning progress, thereby improving the students' learning experience.

[0116] The course sales department can estimate students' emotions and customize course content based on those estimates. For example, if a student is stressed, the generative AI will suggest a course with relaxing content. If the student is relaxed, the generative AI can suggest a course with challenging content. If the student is excited, the generative AI can suggest a course with stimulating content. This allows for a more appropriate learning experience by customizing course content based on students' emotions.

[0117] The learning plan provision department can analyze students' learning history and dynamically adjust learning plans according to their learning progress. For example, it can use generative AI to analyze a student's learning history and simplify the learning plan if progress is slow. If progress is on track, it can change the learning plan to include more difficult content. Furthermore, if the generative AI analyzes a student's learning history and finds a lack of understanding in a particular area, it can provide a learning plan tailored to that area. In this way, by providing the optimal learning plan according to the student's learning progress, learning effectiveness can be improved.

[0118] The progress management department can estimate students' emotions and adjust how learning progress is reported based on those emotions. For example, if a student is stressed, the generating AI will report progress using gentle language. If a student is relaxed, the generating AI can provide a detailed progress report. If a student is excited, the generating AI can provide challenging feedback. This allows for more appropriate feedback to be provided by adjusting the progress reporting method based on the student's emotions.

[0119] The community provisioning department can analyze students' learning histories and suggest relevant community groups. For example, it can use generative AI to analyze students' learning histories and suggest community groups related to specific fields for students with a learning history in those areas. Furthermore, the generative AI can monitor students' learning histories and suggest community groups related to skills if they are currently acquiring those skills. It can also analyze students' learning histories and suggest relevant community groups based on topics they are interested in. This allows for the provision of optimal community groups based on students' learning histories, thereby promoting student interaction.

[0120] The sponsorship department can estimate students' emotions and adjust the content of sponsorships based on those estimates. For example, if a student is stressed, the generative AI will suggest a relaxing sponsorship. If the student is relaxed, the generative AI may suggest a challenging sponsorship. If the student is excited, the generative AI may suggest an exciting sponsorship. By adjusting the sponsorship content based on students' emotions, more appropriate suggestions can be provided.

[0121] The course sales department can analyze students' learning progress and propose the most suitable course sales methods. For example, it can use generative AI to analyze students' learning progress and suggest easier courses if they are falling behind. If they are progressing well, it can suggest more challenging courses. Furthermore, if the generative AI analyzes students' learning progress and finds a lack of understanding in a particular area, it can suggest courses specializing in that area. In this way, by providing the most suitable courses based on students' learning progress, learning effectiveness can be improved.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The learning plan provision department provides each student with the most suitable learning plan. For example, it uses a generation AI to analyze the student's learning history and interests and automatically generates the optimal learning plan. It can also use the generation AI to monitor the student's learning progress in real time and adjust the learning plan as needed. Step 2: The progress management department provides feedback and progress management based on the learning plans provided by the learning plan provision department. For example, it can use generative AI to monitor students' learning progress in real time and provide feedback as needed. It can also use generative AI to analyze students' learning progress and provide optimal feedback. Step 3: The community provision department provides online communities where students and parents can interact with each other. For example, they build online communities equipped with chat and forum functions, providing an environment where students and parents can freely interact. They can also use generative AI to suggest relevant community groups based on students' interests. Step 4: The Sponsorship Department partners with companies and educational institutions to secure sponsorships. For example, they can secure operating funds for the online school by receiving sponsorships from companies and educational institutions. They can also use generative AI to monitor the effectiveness of sponsorships in real time and modify proposals as needed. Step 5: The course sales department sells courses specializing in specific subjects or skills. For example, they sell specialized courses in programming, data science, marketing, etc., to meet students' learning needs. They can also use generative AI to suggest the most suitable courses based on students' learning history and interests.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the learning plan provision unit, progress management unit, community provision unit, sponsorship unit, and course sales unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the learning plan provision unit is implemented by the control unit 46A of the smart device 14, which uses generating AI to analyze the student's learning history and interests and provides an optimal learning plan. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the student's learning progress in real time and provides feedback. The community provision unit is implemented by the control unit 46A of the smart device 14, which provides an online community with chat and forum functions. The sponsorship unit is implemented by the specific processing unit 290 of the data processing unit 12, which receives sponsorship from companies and educational institutions. The course sales unit is implemented by the control unit 46A of the smart device 14, which sells courses specializing in specific subjects or skills. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the learning plan provision unit, progress management unit, community provision unit, sponsorship unit, and course sales unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the learning plan provision unit is implemented by the control unit 46A of the smart glasses 214, which uses generating AI to analyze the student's learning history and interests and provides an optimal learning plan. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the student's learning progress in real time and provides feedback. The community provision unit is implemented by the control unit 46A of the smart glasses 214, which provides an online community with chat and forum functions. The sponsorship unit is implemented by the specific processing unit 290 of the data processing unit 12, which receives sponsorship from companies and educational institutions. The course sales unit is implemented by the control unit 46A of the smart glasses 214, which sells courses specializing in specific subjects or skills. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the learning plan provision unit, progress management unit, community provision unit, sponsorship unit, and course sales unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the learning plan provision unit is implemented by the control unit 46A of the headset terminal 314, which uses generating AI to analyze the student's learning history and interests and provides an optimal learning plan. The progress management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the student's learning progress in real time and provides feedback. The community provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides an online community with chat and forum functions. The sponsorship unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which receives sponsorship from companies and educational institutions. The course sales unit is implemented by, for example, the control unit 46A of the headset terminal 314, which sells courses specializing in specific subjects or skills. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the learning plan provision unit, progress management unit, community provision unit, sponsorship unit, and course sales unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning plan provision unit is implemented by the control unit 46A of the robot 414, which uses generative AI to analyze students' learning history and interests and provides an optimal learning plan. The progress management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors students' learning progress in real time and provides feedback. The community provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides an online community with chat and forum functions. The sponsorship unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which receives sponsorship from companies and educational institutions. The course sales unit is implemented by, for example, the control unit 46A of the robot 414, which sells courses specializing in specific subjects or skills. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The Learning Plan Provision Department provides learning plans that are optimal for each individual student, A progress management unit provides feedback and manages progress based on the learning plan provided by the aforementioned learning plan provision unit, The Community Provision Department provides online communities where students and parents can interact with each other, The Sponsorship Department partners with companies and educational institutions to secure sponsorships, It includes a course sales department that sells courses specializing in specific subjects or skills. A system characterized by the following features. (Note 2) The aforementioned learning plan provision unit, We use generative AI to provide personalized learning plans for each student. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned progress management unit, Use generative AI to provide feedback on learning plans and manage progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned community provision unit is We provide an online community where students and parents can interact with each other. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Sponsorship Department, Partnering with companies and educational institutions to secure sponsorships The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned course sales department, Sell ​​courses that specialize in specific subjects or skills. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning plan provision unit, The system estimates students' emotions and adjusts the difficulty level of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning plan provision unit, Analyzes students' past learning history and automatically generates the optimal learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning plan provision unit, Based on students' interests and concerns, we provide additional resources related to their learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning plan provision unit, The system estimates students' emotions and adjusts the pace of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning plan provision unit, Based on students' geographical location information, we provide region-specific learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning plan provision unit, Analyze students' social media activity and propose relevant learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned progress management unit, The system estimates the student's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned progress management unit, Monitor students' learning progress in real time and automatically issue alerts as needed. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned progress management unit, We analyze students' learning patterns and propose the optimal progress management method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned progress management unit, The system estimates the student's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned progress management unit, This system compares students' learning progress with that of other students and provides a relative progress report. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress management unit, Automatically provide additional learning resources based on students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned community provision unit is Estimate students' emotions and promote interaction within the community based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned community provision unit is Based on students' interests, the system automatically suggests relevant community groups. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned community provision unit is We analyze activity history within the community and propose the most suitable methods of interaction. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned community provision unit is It estimates students' emotions and adjusts notifications within the community based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned community provision unit is Based on students' geographical location information, we provide community groups tailored to their specific region. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned community provision unit is Analyze students' social media activity and suggest relevant community groups. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Sponsorship Department, We estimate students' emotions and adjust sponsorship proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Sponsorship Department, We analyze students' learning histories and propose the most suitable sponsorships. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Sponsorship Department, Monitor the effectiveness of the sponsorship in real time and revise the proposal as needed. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Sponsorship Department, We estimate students' emotions and adjust the timing of sponsorships based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Sponsorship Department, We provide region-specific sponsorships based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Sponsorship Department, Analyze students' social media activity and propose relevant sponsorships. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned course sales department, We estimate students' emotions and adjust the course sales method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned course sales department, We analyze students' learning histories and suggest the most suitable courses. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned course sales department, We analyze the sales history of the course and propose the optimal sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned course sales department, We estimate students' emotions and adjust the timing of course sales based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned course sales department, We offer region-specific courses based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned course sales department, Analyze students' social media activity and propose relevant courses. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Learning Plan Provision Department provides learning plans that are optimal for each individual student, A progress management unit provides feedback and manages progress based on the learning plan provided by the aforementioned learning plan provision unit, The Community Provision Department provides online communities where students and parents can interact with each other, The Sponsorship Department partners with companies and educational institutions to secure sponsorships, It includes a course sales department that sells courses specializing in specific subjects or skills. A system characterized by the following features.

2. The aforementioned learning plan provision unit, Using generative AI, we provide personalized learning plans for each student. The system according to feature 1.

3. The aforementioned progress management unit, Use generative AI to provide feedback on learning plans and manage progress. The system according to feature 1.

4. The aforementioned community provision unit, We provide an online community where students and parents can interact with each other. The system according to feature 1.

5. The aforementioned Sponsorship Department, Partnering with companies and educational institutions to secure sponsorships The system according to feature 1.

6. The aforementioned course sales department, Sell ​​courses that specialize in specific subjects or skills. The system according to feature 1.

7. The aforementioned learning plan provision unit, The system estimates students' emotions and adjusts the difficulty level of the learning plan based on those estimated emotions. The system according to feature 1.

8. The aforementioned learning plan provision unit, Analyzes students' past learning history and automatically generates the optimal learning plan. The system according to feature 1.

9. The aforementioned learning plan provision unit, Based on students' interests and concerns, we provide additional resources related to their learning plans. The system according to feature 1.

10. The aforementioned learning plan provision unit, The system estimates students' emotions and adjusts the pace of the learning plan based on those estimated emotions. The system according to feature 1.

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