system

Abstract

The system according to this embodiment aims to enable working adults to improve their skills and advance their careers while maintaining their current careers and living situations. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a learning unit, and a management unit. The reception unit receives input from working adults regarding the skills and knowledge they wish to learn. The generation unit generates an optimal learning program based on the information input by the reception unit. The learning unit proceeds with learning based on the learning program generated by the generation unit. The management unit manages the progress of learning carried out by the learning unit and makes adjustments as necessary.

Description

Technical Field

[0006] , , ,

[0005] , , ,

[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, the method including 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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for working people to improve their skills or advance their careers while maintaining their current careers and living bases, and there is room for improvement.

[0005] ​​​​​​​​The system according to this embodiment comprises a reception unit, a generation unit, a learning unit, and a management unit. The reception unit receives input from working adults regarding the skills and knowledge they wish to learn. The generation unit generates an optimal learning program based on the information entered by the reception unit. The learning unit proceeds with learning based on the learning program generated by the generation unit. The management unit manages the progress of learning carried out by the learning unit and makes adjustments as necessary. [Effects of the Invention]

[0007] The system according to this embodiment allows working adults to improve their skills and advance their careers while maintaining their current careers and living situations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, 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) The educational system according to an embodiment of the present invention is a completely new educational system that enables working adults to achieve "smooth" skill and career advancement while maintaining their current careers and living situations. This educational system is provided by a generating AI agent. Specifically, it comprises a reception unit for inputting the skills and knowledge that working adults wish to learn, a generation unit that generates an optimal learning program based on the input information, a learning unit that proceeds with learning based on the generated learning program, and a management unit that manages the progress of learning and makes adjustments as needed. Furthermore, it includes a knowledge provision unit for utilizing the expertise of senior university professors and a remote learning unit that provides learning in a fully remote environment. For example, the educational system inputs the skills and knowledge that working adults wish to learn. For example, the educational system's generating AI agent generates an optimal learning program based on that input. This learning program is flexibly adjusted to suit the working adult's current career and living situation. For example, the educational system is designed to allow efficient learning in limited time, such as during breaks at work or during commuting. The educational system also utilizes the expertise of senior university professors to improve the quality of learning content. Furthermore, the educational system provides learning in a fully remote environment to reduce tuition costs. This reduces facility fees and maintenance costs and improves accessibility to learning. This will enable the education system to efficiently improve skills and advance careers while maintaining current careers and living conditions. It is also expected to promote the growth of companies and Japanese society. This will enable the education system to efficiently improve skills and advance careers while maintaining current careers and living conditions.

[0029] The educational system according to this embodiment comprises a reception unit, a generation unit, a learning unit, and a management unit. The reception unit receives input from working adults regarding the skills and knowledge they wish to learn. These skills and knowledge may include, but are not limited to, business skills, technical skills, and specialized knowledge. The reception unit can, for example, store the skills and knowledge entered by the user in a database. The reception unit can also analyze the user's input and classify it into appropriate categories. Furthermore, the reception unit can suggest relevant skills and knowledge based on the user's input. For example, the reception unit can automatically search for and suggest information related to the skills and knowledge entered by the user. The generation unit uses a generation AI to generate an optimal learning program based on the information entered by the reception unit. The generation unit customizes the learning program according to the user's learning goals and learning style. For example, the generation unit analyzes the user's learning history and learning outcomes and suggests an optimal learning program. The generation unit can also automatically update the content of the learning program using a generation AI. For example, the generation unit adjusts the content of the learning program according to the user's learning progress. The learning unit advances learning based on the learning program generated by the generation unit. The learning unit provides support, for example, to help the user progress through the learning program. For example, the learning unit provides necessary learning materials and resources when the user progresses through the learning program. The learning unit can also monitor the user's learning progress and provide feedback as needed. For example, the learning unit evaluates the user's learning results and points out areas for improvement. The management unit manages the progress of learning advanced by the learning unit and makes adjustments as needed. For example, the management unit monitors the user's learning progress in real time. For example, the management unit collects user learning data and visualizes the progress. The management unit can also adjust the content of the learning program according to the user's learning progress. For example, the management unit adjusts the progress of the learning program to match the user's learning pace. As a result, the educational system according to this embodiment allows working adults to efficiently improve their skills and advance their careers while maintaining their current careers and living situations.

[0030] The reception desk allows working professionals to input the skills and knowledge they wish to learn. These skills and knowledge may include, but are not limited to, business skills, technical skills, and specialized knowledge. The reception desk, for example, stores the skills and knowledge entered by users in a database. Specifically, information entered by users through a web interface or mobile application is immediately recorded in the database and used for subsequent processing. The reception desk can also analyze user input and categorize it appropriately. For example, natural language processing technology can be used to analyze the entered text and automatically categorize it into categories such as business skills, technical skills, and specialized knowledge. This ensures that user input is accurately categorized, leading to smoother subsequent processing. Furthermore, the reception desk can suggest related skills and knowledge based on user input. For example, if a user enters "data science," the reception desk will suggest related skills such as "machine learning" and "statistics." This allows users to obtain information related to the skills and knowledge they wish to learn, broadening their learning scope. Additionally, the reception desk can provide more personalized suggestions by considering the user's past input and learning history. For example, if a user who has previously studied "programming" enters "data science," the system will also suggest information about programming languages ​​such as "Python" and "R." This allows users to create an optimal learning plan based on their learning history.

[0031] The generation unit uses a generation AI to generate an optimal learning program based on the information entered by the reception unit. For example, the generation unit customizes the learning program according to the user's learning goals and style. Specifically, the generation AI analyzes information such as the user's skills, knowledge, learning goals, and past learning history to automatically generate an optimal learning program. For example, if a user inputs "I want to learn data science" and has a history of learning "programming," the generation AI will generate a learning program covering data science from basic to advanced levels, and may include practical exercises that utilize programming knowledge. Furthermore, the generation unit can automatically update the content of the learning program using the generation AI. For example, it adjusts the program content according to the user's learning progress. If a user is struggling with a particular task, the generation AI can provide supplementary materials or additional practice problems related to that task. In addition, the generation unit customizes the learning program according to the user's learning style. For example, it provides a program with many video materials and infographics for users who prefer visual learning, and a program with many project-based exercises for users who prefer practical learning. This allows the generation unit to provide each user with an optimal learning program, supporting efficient learning.

[0032] The learning unit facilitates learning based on the learning program generated by the generation unit. For example, the learning unit provides support to help users progress through the learning program. Specifically, it provides necessary learning materials and resources as users progress through the program. For instance, it offers a variety of materials tailored to the user's learning needs, such as online materials, video lectures, interactive practice problems, and practical projects. The learning unit can also monitor the user's learning progress and provide feedback as needed. For example, if a user is struggling with a particular task, the learning unit provides hints and explanations to help them understand it better. Furthermore, the learning unit evaluates the user's learning outcomes and identifies areas for improvement. For example, it evaluates the results of assignments and tests submitted by the user and analyzes their accuracy and comprehension. This allows users to understand their learning progress and clearly identify areas for improvement. The learning unit can also adjust the content of the learning program according to the user's learning style and progress. For example, if a user has a particular interest in a specific field, it provides additional materials and tasks related to that field. Also, if a user sets new goals while progressing through the learning program, the learning unit restructures the learning program to accommodate those goals. This allows the learning unit to continuously support the user's learning, enabling efficient and effective learning.

[0033] The Management Department manages the progress of learning conducted by the Learning Department and makes adjustments as needed. For example, the Management Department monitors users' learning progress in real time. Specifically, the Management Department collects user learning data and visualizes the progress. For example, it collects information such as how much learning a user has completed, how much time they have spent on each task, and which areas they are particularly struggling with, and displays this information in a dashboard format. This allows users to grasp their learning status at a glance. The Management Department can also adjust the content of the learning program according to the user's learning progress. For example, if a user is struggling with a particular task, the Management Department will provide supplementary materials or additional practice problems for that task. Also, if a user sets a new goal while progressing through the learning program, the Management Department will restructure the learning program to match that goal. Furthermore, the Management Department adjusts the progress of the learning program to match the user's learning pace. For example, it can flexibly reduce the learning load when the user is busy and increase it when they have more time. This allows the Management Department to efficiently support user learning and promote continued learning. In addition, the Management Department can analyze user learning data and evaluate the effectiveness of the learning program. For example, based on users' learning outcomes and progress, areas for improvement in the learning program can be identified and incorporated into the next program. This allows the management department to continuously improve the quality of the learning program and maximize the learning effectiveness for users.

[0034] The Knowledge Provision Department is a department dedicated to leveraging the expertise of senior university professors. For example, the Knowledge Provision Department enhances the quality of learning content by utilizing the senior university professors' specialized knowledge and teaching experience. For instance, it delivers lectures and seminars provided by senior university professors online. It can also incorporate teaching materials and papers written by senior university professors into learning programs. For example, it digitizes teaching materials written by senior university professors and provides them to learners. Furthermore, the Knowledge Provision Department can incorporate individual tutoring and consulting services provided by senior university professors into learning programs. For example, it provides opportunities for senior university professors to provide individual tutoring to learners. This allows for an improvement in the quality of learning content by leveraging the expertise of senior university professors.

[0035] The Remote Learning Department is a department that provides learning in a fully remote environment. For example, the Remote Learning Department provides learning programs through online platforms. For instance, it uses video conferencing tools to conduct real-time lectures and discussions. Furthermore, the Remote Learning Department can provide on-demand learning materials, allowing learners to progress at their own pace. For example, it can provide recorded lectures and materials online, allowing learners to access them at their convenience. In addition, the Remote Learning Department can provide learners with the resources and support they need online. For example, it can offer online question-and-answer sessions and tutoring. By providing learning in a fully remote environment, it is possible to reduce tuition costs and improve accessibility to learning.

[0036] The generation unit can flexibly adjust the learning program to match the current career and lifestyle of working adults. For example, the generation unit can adjust the learning time to match the user's working hours and daily rhythm. For example, the generation unit can provide modules that can be learned in short bursts so that users can learn during breaks at work or during their commute. The generation unit can also customize the learning content according to the user's learning style and learning goals. For example, the generation unit can provide the learning materials and resources necessary for the user to acquire specific skills. Furthermore, the generation unit can adjust the content of the learning program in real time according to the user's learning progress. For example, the generation unit can adjust the difficulty level or provide additional learning materials according to the user's learning progress. This allows for efficient learning by flexibly adjusting the learning program to match the current career and lifestyle of working adults. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's working hours and daily rhythm into the generation AI and have the generation AI perform the adjustment of the learning program.

[0037] The management department can monitor learning progress in real time and make adjustments as needed. For example, the management department can provide a dashboard for monitoring the user's learning progress. For example, the management department can collect user learning data in real time and visualize the progress. The management department can also adjust the content of the learning program according to the user's learning progress. For example, the management department can provide additional support and resources if the user falls behind in their learning progress. Furthermore, the management department can adjust the schedule of the learning program based on the user's learning progress. For example, the management department can adjust the learning pace according to the user's learning progress. This allows for real-time monitoring of learning progress and adjustments as needed, thereby improving learning efficiency. Some or all of the above processes in the management department may be performed using generative AI, or not. For example, the management department can input user learning data into a generative AI and have the generative AI perform adjustments to the learning program.

[0038] The reception desk can analyze the user's past learning history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the skills and knowledge the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest skills and knowledge the user might want to learn at a specific time based on their past learning history. For example, the reception desk can suggest skills and knowledge the user might want to learn at a similar time based on the skills and knowledge they have learned at a specific time in the past. In this way, the reception desk can suggest the optimal input method by analyzing the user's past learning history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's past learning history data into a generative AI and have the generative AI suggest the optimal input method.

[0039] The reception desk can filter the input of skills and knowledge that the user wishes to learn based on their current job responsibilities and areas of interest. For example, the reception desk may prioritize displaying skills and knowledge related to the user's job responsibilities. For example, the reception desk may suggest relevant skills and knowledge based on the user's areas of interest. The reception desk can also filter and display skills and knowledge related to both the user's job responsibilities and areas of interest. For example, the reception desk may suggest the most relevant skills and knowledge based on the user's job responsibilities and areas of interest. This allows the reception desk to suggest highly relevant skills and knowledge by filtering based on the user's job responsibilities and areas of interest. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk may input data about the user's job responsibilities and areas of interest into a generative AI and have the generative AI perform the filtering.

[0040] The reception desk can prioritize inputting highly relevant skills and knowledge when users input the skills and knowledge they wish to learn, taking into account the user's geographical location. For example, the reception desk can suggest region-specific skills and knowledge based on the user's geographical location. For example, the reception desk can suggest nearby learning opportunities based on the user's geographical location. The reception desk can also suggest skills and knowledge related to local industries based on the user's geographical location. For example, the reception desk can suggest skills and knowledge related to local industries based on the user's geographical location. In this way, by suggesting highly relevant skills and knowledge based on the user's geographical location, region-specific learning content can be provided. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI suggest highly relevant skills and knowledge.

[0041] The reception desk can analyze the user's social media activity when they input the skills and knowledge they want to learn, and suggest relevant skills and knowledge. For example, the reception desk can suggest skills and knowledge that the user is interested in based on their social media activity. For example, the reception desk can suggest skills and knowledge related to the experts and influencers the user follows based on their social media activity. The reception desk can also suggest skills and knowledge related to trends based on the user's social media activity. For example, the reception desk can suggest skills and knowledge related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to suggest skills and knowledge that the user is interested in. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without generative AI. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI suggest relevant skills and knowledge.

[0042] The generation unit can adjust the difficulty level of a learning program based on the user's past learning achievements when generating the program. For example, the generation unit can analyze the user's past learning achievements and provide a program of appropriate difficulty. For example, the generation unit can provide a program that gradually increases in difficulty based on the user's past learning achievements. The generation unit can also provide a program specialized in a specific field based on the user's past learning achievements. For example, the generation unit can provide a program specialized in a specific field based on the user's past learning achievements. By adjusting the difficulty level of the program based on the user's past learning achievements, the generation unit can provide a learning program of appropriate difficulty. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's past learning achievement data into a generation AI and have the generation AI perform the adjustment of the program's difficulty level.

[0043] The generation unit can apply different learning algorithms depending on the user's job duties when generating a learning program. For example, the generation unit can select the optimal learning algorithm according to the user's job duties. For example, the generation unit can apply a learning algorithm specialized for a specific skill according to the user's job duties. The generation unit can also adjust the parameters of the learning algorithm according to the user's job duties. For example, the generation unit can adjust the parameters of the learning algorithm according to the user's job duties. This enables efficient learning by applying the optimal learning algorithm according to the user's job duties. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's job duties data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0044] The generation unit can determine program priorities based on the user's learning history when generating learning programs. For example, the generation unit can prioritize providing important skills and knowledge based on the user's learning history. For example, the generation unit can prioritize providing unlearned skills and knowledge based on the user's learning history. The generation unit can also prioritize providing skills and knowledge that require review based on the user's learning history. For example, the generation unit can provide skills and knowledge that require review based on the user's learning history. In this way, by determining program priorities based on the user's learning history, important skills and knowledge can be prioritized. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's learning history data into a generation AI and have the generation AI perform the determination of program priorities.

[0045] The generation unit can adjust the program order based on user relevance when generating a learning program. For example, the generation unit may provide important skills and knowledge first based on user relevance. For example, the generation unit may provide related skills and knowledge sequentially based on user relevance. The generation unit can also adjust the program order according to the progress of learning based on user relevance. For example, the generation unit may adjust the program order according to the progress of learning based on user relevance. This allows for efficient learning by adjusting the program order based on user relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the program order.

[0046] The learning unit can analyze the user's past learning achievements during the learning process and select the optimal learning method. For example, the learning unit can analyze the user's past learning achievements and provide an appropriate learning method. For example, the learning unit can provide a learning method that gradually increases in difficulty based on the user's past learning achievements. The learning unit can also provide a learning method specialized for a specific field based on the user's past learning achievements. For example, the learning unit can provide a learning method specialized for a specific field based on the user's past learning achievements. This allows the learning unit to provide an appropriate learning method by analyzing the user's past learning achievements. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's past learning achievement data into a generative AI and have the generative AI select the optimal learning method.

[0047] The learning unit can customize the learning methods based on the user's current living situation as the learning progresses. For example, the learning unit can provide the optimal learning methods based on the user's living situation. For example, the learning unit can adjust the timing of learning based on the user's living situation. The learning unit can also customize the content of learning based on the user's living situation. For example, the learning unit customizes the content of learning based on the user's living situation. This allows for efficient learning by customizing the learning methods based on the user's living situation. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input user living situation data into a generative AI and have the generative AI perform the customization of the learning methods.

[0048] The learning unit can select the optimal learning method while considering the user's geographical location information during the learning process. For example, the learning unit can provide region-specific learning methods based on the user's geographical location information. For example, the learning unit can provide nearby learning opportunities based on the user's geographical location information. The learning unit can also provide learning methods related to local industries based on the user's geographical location information. For example, the learning unit can provide learning methods related to local industries based on the user's geographical location information. In this way, by providing the optimal learning method based on the user's geographical location information, region-specific learning content can be provided. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the learning unit can input the user's geographical location information data into a generative AI and have the generative AI select the optimal learning method.

[0049] The learning unit can analyze the user's social media activity during the learning process and suggest learning methods. For example, the learning unit can suggest learning methods that the user is interested in based on their social media activity. For example, the learning unit can suggest learning methods related to experts or influencers that the user follows based on their social media activity. The learning unit can also suggest learning methods related to trends based on the user's social media activity. For example, the learning unit can suggest learning methods related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to suggest learning methods that the user is interested in. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the suggestion of learning methods.

[0050] The management unit can provide the optimal management method by referring to the user's past learning history when managing learning progress. For example, the management unit can analyze the user's past learning history and provide an appropriate management method. For example, the management unit can provide a management method that gradually increases the difficulty level based on the user's past learning history. The management unit can also provide a management method specialized for a specific field based on the user's past learning history. For example, the management unit can provide a management method specialized for a specific field based on the user's past learning history. This allows the management unit to provide an appropriate management method by referring to the user's past learning history. Some or all of the above processing in the management unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the management unit can input the user's past learning history data into a generative AI and have the generative AI perform the task of providing the optimal management method.

[0051] The management unit can customize the means of management based on the user's current living situation when managing learning progress. For example, the management unit can provide the optimal means of management based on the user's living situation. For example, the management unit can adjust the timing of management based on the user's living situation. The management unit can also customize the content of management based on the user's living situation. For example, the management unit customizes the content of management based on the user's living situation. By customizing the means of management based on the user's living situation, efficient learning management can be achieved. Some or all of the above processes in the management unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the management unit can input user living situation data into a generative AI and have the generative AI perform the customization of the means of management.

[0052] The management unit can provide an optimal management method when managing learning progress, taking into account the user's geographical location information. For example, the management unit can provide region-specific management methods based on the user's geographical location information. For example, the management unit can provide nearby learning opportunities based on the user's geographical location information. The management unit can also provide management methods related to local industries based on the user's geographical location information. For example, the management unit can provide management methods related to local industries based on the user's geographical location information. In this way, by providing an optimal management method based on the user's geographical location information, region-specific learning management can be provided. Some or all of the above processing in the management unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the management unit can input the user's geographical location information data into a generative AI and have the generative AI perform the task of providing an optimal management method.

[0053] The management department can analyze a user's social media activity and propose management methods when managing learning progress. For example, the management department can propose management methods related to the user's interests based on their social media activity. For example, the management department can propose management methods related to the experts and influencers the user follows based on their social media activity. The management department can also propose management methods related to trends based on the user's social media activity. For example, the management department can propose management methods related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to propose management methods that the user is interested in. Some or all of the above processing in the management department may be performed using generative AI, or it may be performed without using generative AI. For example, the management department can input the user's social media activity data into a generative AI and have the generative AI execute the proposal of management methods.

[0054] The knowledge-providing unit can provide optimal knowledge by analyzing the user's past learning achievements when providing knowledge. For example, the knowledge-providing unit can analyze the user's past learning achievements and provide appropriate knowledge. For example, the knowledge-providing unit can provide knowledge that gradually increases in difficulty based on the user's past learning achievements. The knowledge-providing unit can also provide knowledge specialized in a specific field based on the user's past learning achievements. For example, the knowledge-providing unit can provide knowledge specialized in a specific field based on the user's past learning achievements. This allows the knowledge-providing unit to provide appropriate knowledge by analyzing the user's past learning achievements. Some or all of the above processing in the knowledge-providing unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the knowledge-providing unit can input the user's past learning achievement data into a generative AI and have the generative AI perform the provision of optimal knowledge.

[0055] The knowledge provision unit can customize the content of the knowledge based on the user's current job responsibilities when providing knowledge. For example, the knowledge provision unit can provide optimal knowledge based on the user's job responsibilities. For example, the knowledge provision unit can provide knowledge specialized in specific skills based on the user's job responsibilities. The knowledge provision unit can also customize the content of the knowledge based on the user's job responsibilities. For example, the knowledge provision unit customizes the content of the knowledge based on the user's job responsibilities. This enables efficient knowledge provision by customizing the content of the knowledge based on the user's job responsibilities. Some or all of the above processing in the knowledge provision unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the knowledge provision unit can input the user's job responsibilities data into a generation AI and have the generation AI perform the customization of the knowledge content.

[0056] The knowledge-providing unit can provide optimal knowledge by considering the user's geographical location information when providing knowledge. For example, the knowledge-providing unit can provide region-specific knowledge based on the user's geographical location information. For example, the knowledge-providing unit can provide nearby learning opportunities based on the user's geographical location information. The knowledge-providing unit can also provide knowledge related to local industries based on the user's geographical location information. For example, the knowledge-providing unit can provide knowledge related to local industries based on the user's geographical location information. In this way, by providing optimal knowledge based on the user's geographical location information, region-specific knowledge can be provided. Some or all of the above processing in the knowledge-providing unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the knowledge-providing unit can input the user's geographical location information data into a generative AI and have the generative AI perform the provision of optimal knowledge.

[0057] The knowledge provision unit can analyze the user's social media activity and propose the content of the knowledge when providing it. For example, the knowledge provision unit can propose knowledge that the user is interested in based on the user's social media activity. For example, the knowledge provision unit can propose knowledge related to the experts and influencers the user follows based on the user's social media activity. The knowledge provision unit can also propose knowledge related to trends based on the user's social media activity. For example, the knowledge provision unit can propose knowledge related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to propose knowledge that the user is interested in. Some or all of the above processing in the knowledge provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the knowledge provision unit can input the user's social media activity data into a generative AI and have the generative AI propose the content of the knowledge.

[0058] The remote learning unit can provide the optimal learning method by referring to the user's past learning history during remote learning. For example, the remote learning unit can analyze the user's past learning history and provide an appropriate remote learning method. For example, the remote learning unit can provide a remote learning method that gradually increases in difficulty based on the user's past learning history. The remote learning unit can also provide a remote learning method specialized for a specific field based on the user's past learning history. For example, the remote learning unit can provide a remote learning method specialized for a specific field based on the user's past learning history. This allows the remote learning unit to provide an appropriate remote learning method by referring to the user's past learning history. Some or all of the above processing in the remote learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the remote learning unit can input the user's past learning history data into a generative AI and have the generative AI perform the task of providing the optimal remote learning method.

[0059] The remote learning unit can customize the learning methods based on the user's current living situation during remote learning. For example, the remote learning unit can provide the optimal remote learning method based on the user's living situation. For example, the remote learning unit can adjust the timing of learning based on the user's living situation. The remote learning unit can also customize the learning content. For example, the remote learning unit can customize the learning content based on the user's living situation. This allows for efficient remote learning by customizing the learning methods based on the user's living situation. Some or all of the above processing in the remote learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the remote learning unit can input user living situation data into a generative AI and have the generative AI perform the customization of the learning methods.

[0060] The remote learning unit can provide the optimal learning method during remote learning, taking into account the user's geographical location information. For example, the remote learning unit can provide region-specific learning methods based on the user's geographical location information. For example, the remote learning unit can provide nearby learning opportunities based on the user's geographical location information. The remote learning unit can also provide learning methods related to local industries based on the user's geographical location information. For example, the remote learning unit can provide learning methods related to local industries based on the user's geographical location information. In this way, by providing the optimal learning method based on the user's geographical location information, region-specific learning content can be provided. Some or all of the above processing in the remote learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the remote learning unit can input the user's geographical location information data into a generative AI and have the generative AI perform the task of providing the optimal learning method.

[0061] The remote learning unit can analyze a user's social media activity during remote learning and suggest learning methods. For example, the remote learning unit can suggest learning methods that the user is interested in based on their social media activity. For example, the remote learning unit can suggest learning methods related to experts or influencers that the user follows based on their social media activity. The remote learning unit can also suggest learning methods related to trends based on the user's social media activity. For example, the remote learning unit can suggest learning methods related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to suggest learning methods that the user is interested in. Some or all of the above processing in the remote learning unit may be performed using generative AI, or it may be performed without using generative AI. For example, the remote learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the task of suggesting learning methods.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The reception desk can analyze a user's past learning history and suggest the most suitable input method. For example, it can automatically display skills and knowledge that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest skills and knowledge that the user may want to learn at a specific time based on their past learning history. In this way, by analyzing the user's past learning history, the system can suggest the most suitable input method.

[0064] The generation unit can adjust the difficulty level of a learning program based on the user's past learning achievements when generating the program. For example, it can analyze the user's past learning achievements and provide a program of appropriate difficulty. It can also provide a program that gradually increases in difficulty based on the user's past learning achievements. Furthermore, it can provide a program specialized in a specific field based on the user's past learning achievements. In this way, by adjusting the difficulty level of the program based on the user's past learning achievements, it is possible to provide a learning program of appropriate difficulty.

[0065] The learning unit can analyze the user's past learning achievements as they progress and select the optimal learning method. For example, it can analyze the user's past learning achievements and provide an appropriate learning method. It can also provide a learning method that gradually increases in difficulty based on the user's past learning achievements. Furthermore, it can provide a learning method specialized for a specific field based on the user's past learning achievements. In this way, by analyzing the user's past learning achievements, it can provide an appropriate learning method.

[0066] The management department can provide the optimal management method when managing learning progress by referring to the user's past learning history. For example, it can analyze the user's past learning history and provide an appropriate management method. It can also provide a management method that gradually increases the difficulty level based on the user's past learning history. Furthermore, it can provide a management method specialized for a specific field based on the user's past learning history. In this way, by referring to the user's past learning history, an appropriate management method can be provided.

[0067] The knowledge provision unit can provide optimal knowledge by analyzing the user's past learning achievements when providing knowledge. For example, it can analyze the user's past learning achievements and provide appropriate knowledge. It can also provide knowledge that gradually increases in difficulty based on the user's past learning achievements. Furthermore, it can provide knowledge specialized in a specific field based on the user's past learning achievements. In this way, appropriate knowledge can be provided by analyzing the user's past learning achievements.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives input from working professionals about the skills and knowledge they wish to learn. This includes, for example, business skills, technical skills, and specialized knowledge. The reception desk stores the skills and knowledge entered by users in a database, analyzes the input, and categorizes it appropriately. It can also suggest related skills and knowledge. Step 2: The generation unit generates an optimal learning program based on the information entered by the reception unit. The generation unit uses generation AI to customize the learning program according to the user's learning goals and learning style, and analyzes the learning history and learning results to propose the optimal learning program. The generation unit also automatically updates the content of the learning program according to the learning progress. Step 3: The learning unit proceeds with learning based on the learning program generated by the generation unit. The learning unit provides support for the user to proceed with learning according to the learning program and provides necessary learning materials and resources. The learning unit also monitors the user's learning progress and provides feedback as needed. Step 4: The management department manages the progress of learning conducted by the learning department and makes adjustments as needed. The management department monitors users' learning progress in real time, collects learning data, and visualizes the progress. It also adjusts the progress of the learning program to match the user's learning pace.

[0070] (Example of form 2) The educational system according to an embodiment of the present invention is a completely new educational system that enables working adults to achieve "smooth" skill and career advancement while maintaining their current careers and living situations. This educational system is provided by a generating AI agent. Specifically, it comprises a reception unit for inputting the skills and knowledge that working adults wish to learn, a generation unit that generates an optimal learning program based on the input information, a learning unit that proceeds with learning based on the generated learning program, and a management unit that manages the progress of learning and makes adjustments as needed. Furthermore, it includes a knowledge provision unit for utilizing the expertise of senior university professors and a remote learning unit that provides learning in a fully remote environment. For example, the educational system inputs the skills and knowledge that working adults wish to learn. For example, the educational system's generating AI agent generates an optimal learning program based on that input. This learning program is flexibly adjusted to suit the working adult's current career and living situation. For example, the educational system is designed to allow efficient learning in limited time, such as during breaks at work or during commuting. The educational system also utilizes the expertise of senior university professors to improve the quality of learning content. Furthermore, the educational system provides learning in a fully remote environment to reduce tuition costs. This reduces facility fees and maintenance costs and improves accessibility to learning. This will enable the education system to efficiently improve skills and advance careers while maintaining current careers and living conditions. It is also expected to promote the growth of companies and Japanese society. This will enable the education system to efficiently improve skills and advance careers while maintaining current careers and living conditions.

[0071] The educational system according to this embodiment comprises a reception unit, a generation unit, a learning unit, and a management unit. The reception unit receives input from working adults regarding the skills and knowledge they wish to learn. These skills and knowledge may include, but are not limited to, business skills, technical skills, and specialized knowledge. The reception unit can, for example, store the skills and knowledge entered by the user in a database. The reception unit can also analyze the user's input and classify it into appropriate categories. Furthermore, the reception unit can suggest relevant skills and knowledge based on the user's input. For example, the reception unit can automatically search for and suggest information related to the skills and knowledge entered by the user. The generation unit uses a generation AI to generate an optimal learning program based on the information entered by the reception unit. The generation unit customizes the learning program according to the user's learning goals and learning style. For example, the generation unit analyzes the user's learning history and learning outcomes and suggests an optimal learning program. The generation unit can also automatically update the content of the learning program using a generation AI. For example, the generation unit adjusts the content of the learning program according to the user's learning progress. The learning unit advances learning based on the learning program generated by the generation unit. The learning unit provides support, for example, to help the user progress through the learning program. For example, the learning unit provides necessary learning materials and resources when the user progresses through the learning program. The learning unit can also monitor the user's learning progress and provide feedback as needed. For example, the learning unit evaluates the user's learning results and points out areas for improvement. The management unit manages the progress of learning advanced by the learning unit and makes adjustments as needed. For example, the management unit monitors the user's learning progress in real time. For example, the management unit collects user learning data and visualizes the progress. The management unit can also adjust the content of the learning program according to the user's learning progress. For example, the management unit adjusts the progress of the learning program to match the user's learning pace. As a result, the educational system according to this embodiment allows working adults to efficiently improve their skills and advance their careers while maintaining their current careers and living situations.

[0072] The reception desk allows working professionals to input the skills and knowledge they wish to learn. These skills and knowledge may include, but are not limited to, business skills, technical skills, and specialized knowledge. The reception desk, for example, stores the skills and knowledge entered by users in a database. Specifically, information entered by users through a web interface or mobile application is immediately recorded in the database and used for subsequent processing. The reception desk can also analyze user input and categorize it appropriately. For example, natural language processing technology can be used to analyze the entered text and automatically categorize it into categories such as business skills, technical skills, and specialized knowledge. This ensures that user input is accurately categorized, leading to smoother subsequent processing. Furthermore, the reception desk can suggest related skills and knowledge based on user input. For example, if a user enters "data science," the reception desk will suggest related skills such as "machine learning" and "statistics." This allows users to obtain information related to the skills and knowledge they wish to learn, broadening their learning scope. Additionally, the reception desk can provide more personalized suggestions by considering the user's past input and learning history. For example, if a user who has previously studied "programming" enters "data science," the system will also suggest information about programming languages ​​such as "Python" and "R." This allows users to create an optimal learning plan based on their learning history.

[0073] The generation unit uses a generation AI to generate an optimal learning program based on the information entered by the reception unit. For example, the generation unit customizes the learning program according to the user's learning goals and style. Specifically, the generation AI analyzes information such as the user's skills, knowledge, learning goals, and past learning history to automatically generate an optimal learning program. For example, if a user inputs "I want to learn data science" and has a history of learning "programming," the generation AI will generate a learning program covering data science from basic to advanced levels, and may include practical exercises that utilize programming knowledge. Furthermore, the generation unit can automatically update the content of the learning program using the generation AI. For example, it adjusts the program content according to the user's learning progress. If a user is struggling with a particular task, the generation AI can provide supplementary materials or additional practice problems related to that task. In addition, the generation unit customizes the learning program according to the user's learning style. For example, it provides a program with many video materials and infographics for users who prefer visual learning, and a program with many project-based exercises for users who prefer practical learning. This allows the generation unit to provide each user with an optimal learning program, supporting efficient learning.

[0074] The learning unit facilitates learning based on the learning program generated by the generation unit. For example, the learning unit provides support to help users progress through the learning program. Specifically, it provides necessary learning materials and resources as users progress through the program. For instance, it offers a variety of materials tailored to the user's learning needs, such as online materials, video lectures, interactive practice problems, and practical projects. The learning unit can also monitor the user's learning progress and provide feedback as needed. For example, if a user is struggling with a particular task, the learning unit provides hints and explanations to help them understand it better. Furthermore, the learning unit evaluates the user's learning outcomes and identifies areas for improvement. For example, it evaluates the results of assignments and tests submitted by the user and analyzes their accuracy and comprehension. This allows users to understand their learning progress and clearly identify areas for improvement. The learning unit can also adjust the content of the learning program according to the user's learning style and progress. For example, if a user has a particular interest in a specific field, it provides additional materials and tasks related to that field. Also, if a user sets new goals while progressing through the learning program, the learning unit restructures the learning program to accommodate those goals. This allows the learning unit to continuously support the user's learning, enabling efficient and effective learning.

[0075] The Management Department manages the progress of learning conducted by the Learning Department and makes adjustments as needed. For example, the Management Department monitors users' learning progress in real time. Specifically, the Management Department collects user learning data and visualizes the progress. For example, it collects information such as how much learning a user has completed, how much time they have spent on each task, and which areas they are particularly struggling with, and displays this information in a dashboard format. This allows users to grasp their learning status at a glance. The Management Department can also adjust the content of the learning program according to the user's learning progress. For example, if a user is struggling with a particular task, the Management Department will provide supplementary materials or additional practice problems for that task. Also, if a user sets a new goal while progressing through the learning program, the Management Department will restructure the learning program to match that goal. Furthermore, the Management Department adjusts the progress of the learning program to match the user's learning pace. For example, it can flexibly reduce the learning load when the user is busy and increase it when they have more time. This allows the Management Department to efficiently support user learning and promote continued learning. In addition, the Management Department can analyze user learning data and evaluate the effectiveness of the learning program. For example, based on users' learning outcomes and progress, areas for improvement in the learning program can be identified and incorporated into the next program. This allows the management department to continuously improve the quality of the learning program and maximize the learning effectiveness for users.

[0076] The Knowledge Provision Department is a department dedicated to leveraging the expertise of senior university professors. For example, the Knowledge Provision Department enhances the quality of learning content by utilizing the senior university professors' specialized knowledge and teaching experience. For instance, it delivers lectures and seminars provided by senior university professors online. It can also incorporate teaching materials and papers written by senior university professors into learning programs. For example, it digitizes teaching materials written by senior university professors and provides them to learners. Furthermore, the Knowledge Provision Department can incorporate individual tutoring and consulting services provided by senior university professors into learning programs. For example, it provides opportunities for senior university professors to provide individual tutoring to learners. This allows for an improvement in the quality of learning content by leveraging the expertise of senior university professors.

[0077] The Remote Learning Department is a department that provides learning in a fully remote environment. For example, the Remote Learning Department provides learning programs through online platforms. For instance, it uses video conferencing tools to conduct real-time lectures and discussions. Furthermore, the Remote Learning Department can provide on-demand learning materials, allowing learners to progress at their own pace. For example, it can provide recorded lectures and materials online, allowing learners to access them at their convenience. In addition, the Remote Learning Department can provide learners with the resources and support they need online. For example, it can offer online question-and-answer sessions and tutoring. By providing learning in a fully remote environment, it is possible to reduce tuition costs and improve accessibility to learning.

[0078] The generation unit can flexibly adjust the learning program to match the current career and lifestyle of working adults. For example, the generation unit can adjust the learning time to match the user's working hours and daily rhythm. For example, the generation unit can provide modules that can be learned in short bursts so that users can learn during breaks at work or during their commute. The generation unit can also customize the learning content according to the user's learning style and learning goals. For example, the generation unit can provide the learning materials and resources necessary for the user to acquire specific skills. Furthermore, the generation unit can adjust the content of the learning program in real time according to the user's learning progress. For example, the generation unit can adjust the difficulty level or provide additional learning materials according to the user's learning progress. This allows for efficient learning by flexibly adjusting the learning program to match the current career and lifestyle of working adults. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's working hours and daily rhythm into the generation AI and have the generation AI perform the adjustment of the learning program.

[0079] The management department can monitor learning progress in real time and make adjustments as needed. For example, the management department can provide a dashboard for monitoring the user's learning progress. For example, the management department can collect user learning data in real time and visualize the progress. The management department can also adjust the content of the learning program according to the user's learning progress. For example, the management department can provide additional support and resources if the user falls behind in their learning progress. Furthermore, the management department can adjust the schedule of the learning program based on the user's learning progress. For example, the management department can adjust the learning pace according to the user's learning progress. This allows for real-time monitoring of learning progress and adjustments as needed, thereby improving learning efficiency. Some or all of the above processes in the management department may be performed using generative AI, or not. For example, the management department can input user learning data into a generative AI and have the generative AI perform adjustments to the learning program.

[0080] The reception desk can estimate the user's emotions and adjust the input method for the skills and knowledge they wish to learn based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if the user is in a hurry, allowing them to quickly input the skills and knowledge they wish to learn. For example, the reception desk can provide a microphone for the user to input the skills and knowledge they wish to learn using voice input. This allows the user to comfortably input skills and knowledge by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI adjust the input method.

[0081] The reception desk can analyze the user's past learning history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the skills and knowledge the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest skills and knowledge the user might want to learn at a specific time based on their past learning history. For example, the reception desk can suggest skills and knowledge the user might want to learn at a similar time based on the skills and knowledge they have learned at a specific time in the past. In this way, the reception desk can suggest the optimal input method by analyzing the user's past learning history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's past learning history data into a generative AI and have the generative AI suggest the optimal input method.

[0082] The reception desk can filter the input of skills and knowledge that the user wishes to learn based on their current job responsibilities and areas of interest. For example, the reception desk may prioritize displaying skills and knowledge related to the user's job responsibilities. For example, the reception desk may suggest relevant skills and knowledge based on the user's areas of interest. The reception desk can also filter and display skills and knowledge related to both the user's job responsibilities and areas of interest. For example, the reception desk may suggest the most relevant skills and knowledge based on the user's job responsibilities and areas of interest. This allows the reception desk to suggest highly relevant skills and knowledge by filtering based on the user's job responsibilities and areas of interest. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk may input data about the user's job responsibilities and areas of interest into a generative AI and have the generative AI perform the filtering.

[0083] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of the skills and knowledge to be input. For example, if the user is stressed, the reception desk will prioritize suggesting relaxing skills and knowledge. For example, if the user is relaxed, the reception desk will prioritize suggesting challenging skills and knowledge. Also, if the user is in a hurry, the reception desk can prioritize suggesting skills and knowledge that can be learned in a short amount of time. For example, if the user is in a hurry, the reception desk will suggest skills and knowledge that can be learned in a short amount of time. In this way, by determining the priority of skills and knowledge according to the user's emotions, the system can provide the user with the most suitable learning content. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input user emotion data into a generative AI and have the AI ​​determine the priority of skills and knowledge.

[0084] The reception desk can prioritize inputting highly relevant skills and knowledge when users input the skills and knowledge they wish to learn, taking into account the user's geographical location. For example, the reception desk can suggest region-specific skills and knowledge based on the user's geographical location. For example, the reception desk can suggest nearby learning opportunities based on the user's geographical location. The reception desk can also suggest skills and knowledge related to local industries based on the user's geographical location. For example, the reception desk can suggest skills and knowledge related to local industries based on the user's geographical location. In this way, by suggesting highly relevant skills and knowledge based on the user's geographical location, region-specific learning content can be provided. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI suggest highly relevant skills and knowledge.

[0085] The reception desk can analyze the user's social media activity when they input the skills and knowledge they want to learn, and suggest relevant skills and knowledge. For example, the reception desk can suggest skills and knowledge that the user is interested in based on their social media activity. For example, the reception desk can suggest skills and knowledge related to the experts and influencers the user follows based on their social media activity. The reception desk can also suggest skills and knowledge related to trends based on the user's social media activity. For example, the reception desk can suggest skills and knowledge related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to suggest skills and knowledge that the user is interested in. Some or all of the above processing in the reception desk may be performed using generative AI, or it may be performed without generative AI. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI suggest relevant skills and knowledge.

[0086] The generation unit can estimate the user's emotions and adjust the content of the learning program based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide content that allows for learning in a relaxed state. For example, if the user is stressed, the generation unit can provide content that reduces stress. The generation unit can also provide content that maintains excitement if the user is excited. For example, if the generation unit is excited, the generation unit can provide content that maintains excitement. In this way, by adjusting the content of the learning program according to the user's emotions, the optimal learning content can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the learning program content.

[0087] The generation unit can adjust the difficulty level of a learning program based on the user's past learning achievements when generating the program. For example, the generation unit can analyze the user's past learning achievements and provide a program of appropriate difficulty. For example, the generation unit can provide a program that gradually increases in difficulty based on the user's past learning achievements. The generation unit can also provide a program specialized in a specific field based on the user's past learning achievements. For example, the generation unit can provide a program specialized in a specific field based on the user's past learning achievements. By adjusting the difficulty level of the program based on the user's past learning achievements, the generation unit can provide a learning program of appropriate difficulty. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's past learning achievement data into a generation AI and have the generation AI perform the adjustment of the program's difficulty level.

[0088] The generation unit can apply different learning algorithms depending on the user's job duties when generating a learning program. For example, the generation unit can select the optimal learning algorithm according to the user's job duties. For example, the generation unit can apply a learning algorithm specialized for a specific skill according to the user's job duties. The generation unit can also adjust the parameters of the learning algorithm according to the user's job duties. For example, the generation unit can adjust the parameters of the learning algorithm according to the user's job duties. This enables efficient learning by applying the optimal learning algorithm according to the user's job duties. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's job duties data into a generation AI and have the generation AI execute the application of the learning algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the learning program based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a longer learning program. For example, if the user is stressed, the generation unit can provide a shorter learning program. The generation unit can also provide a learning program that can be completed in a short amount of time if the user is in a hurry. For example, if the generation unit is in a hurry, the generation unit can provide a learning program that can be completed in a short amount of time. In this way, by adjusting the length of the learning program according to the user's emotions, the optimal learning time can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using the generative AI or not using the generative AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the learning program.

[0090] The generation unit can determine program priorities based on the user's learning history when generating learning programs. For example, the generation unit can prioritize providing important skills and knowledge based on the user's learning history. For example, the generation unit can prioritize providing unlearned skills and knowledge based on the user's learning history. The generation unit can also prioritize providing skills and knowledge that require review based on the user's learning history. For example, the generation unit can provide skills and knowledge that require review based on the user's learning history. In this way, by determining program priorities based on the user's learning history, important skills and knowledge can be prioritized. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's learning history data into a generation AI and have the generation AI perform the determination of program priorities.

[0091] The generation unit can adjust the program order based on user relevance when generating a learning program. For example, the generation unit may provide important skills and knowledge first based on user relevance. For example, the generation unit may provide related skills and knowledge sequentially based on user relevance. The generation unit can also adjust the program order according to the progress of learning based on user relevance. For example, the generation unit may adjust the program order according to the progress of learning based on user relevance. This allows for efficient learning by adjusting the program order based on user relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the program order.

[0092] The learning unit can estimate the user's emotions and adjust the learning process based on those emotions. For example, if the user is relaxed, the learning unit can provide a learning process that allows for a relaxed learning state. For example, if the user is stressed, the learning unit can provide a learning process that reduces stress. The learning unit can also provide a learning process that maintains excitement if the user is excited. For example, if the learning unit is excited, the learning unit can provide a learning process that maintains excitement. By adjusting the learning process according to the user's emotions, the learning unit can provide the user with an optimal learning experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using or without a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning process.

[0093] The learning unit can analyze the user's past learning achievements during the learning process and select the optimal learning method. For example, the learning unit can analyze the user's past learning achievements and provide an appropriate learning method. For example, the learning unit can provide a learning method that gradually increases in difficulty based on the user's past learning achievements. The learning unit can also provide a learning method specialized for a specific field based on the user's past learning achievements. For example, the learning unit can provide a learning method specialized for a specific field based on the user's past learning achievements. This allows the learning unit to provide an appropriate learning method by analyzing the user's past learning achievements. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's past learning achievement data into a generative AI and have the generative AI select the optimal learning method.

[0094] The learning unit can customize the learning methods based on the user's current living situation as the learning progresses. For example, the learning unit can provide the optimal learning methods based on the user's living situation. For example, the learning unit can adjust the timing of learning based on the user's living situation. The learning unit can also customize the content of learning based on the user's living situation. For example, the learning unit customizes the content of learning based on the user's living situation. This allows for efficient learning by customizing the learning methods based on the user's living situation. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input user living situation data into a generative AI and have the generative AI perform the customization of the learning methods.

[0095] The learning unit can estimate the user's emotions and determine learning priorities based on those emotions. For example, if the user is relaxed, the learning unit will prioritize providing learning content that promotes relaxation. For example, if the user is stressed, the learning unit will prioritize providing learning content that reduces stress. The learning unit can also prioritize providing learning content that can be learned in a short amount of time if the user is in a hurry. For example, if the learning unit is in a hurry, the learning unit will prioritize providing learning content that can be learned in a short amount of time. In this way, by determining learning priorities according to the user's emotions, the learning unit can provide the user with the most optimal learning content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI determine the learning priorities.

[0096] The learning unit can select the optimal learning method while considering the user's geographical location information during the learning process. For example, the learning unit can provide region-specific learning methods based on the user's geographical location information. For example, the learning unit can provide nearby learning opportunities based on the user's geographical location information. The learning unit can also provide learning methods related to local industries based on the user's geographical location information. For example, the learning unit can provide learning methods related to local industries based on the user's geographical location information. In this way, by providing the optimal learning method based on the user's geographical location information, region-specific learning content can be provided. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the learning unit can input the user's geographical location information data into a generative AI and have the generative AI select the optimal learning method.

[0097] The learning unit can analyze the user's social media activity during the learning process and suggest learning methods. For example, the learning unit can suggest learning methods that the user is interested in based on their social media activity. For example, the learning unit can suggest learning methods related to experts or influencers that the user follows based on their social media activity. The learning unit can also suggest learning methods related to trends based on the user's social media activity. For example, the learning unit can suggest learning methods related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to suggest learning methods that the user is interested in. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the suggestion of learning methods.

[0098] The management unit can estimate the user's emotions and adjust the learning progress management method based on the estimated user emotions. For example, if the user is relaxed, the management unit can provide a method of managing learning progress in a relaxed state. For example, if the user is stressed, the management unit can provide a method of managing learning progress that reduces stress. Furthermore, if the user is excited, the management unit can also provide a method of managing learning progress that maintains excitement. For example, if the user is excited, the management unit can provide a method of managing learning progress that maintains excitement. In this way, by adjusting the learning progress management method according to the user's emotions, optimal learning management can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using a generative AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the learning progress management method.

[0099] The management unit can provide the optimal management method by referring to the user's past learning history when managing learning progress. For example, the management unit can analyze the user's past learning history and provide an appropriate management method. For example, the management unit can provide a management method that gradually increases the difficulty level based on the user's past learning history. The management unit can also provide a management method specialized for a specific field based on the user's past learning history. For example, the management unit can provide a management method specialized for a specific field based on the user's past learning history. This allows the management unit to provide an appropriate management method by referring to the user's past learning history. Some or all of the above processing in the management unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the management unit can input the user's past learning history data into a generative AI and have the generative AI perform the task of providing the optimal management method.

[0100] The management unit can customize the means of management based on the user's current living situation when managing learning progress. For example, the management unit can provide the optimal means of management based on the user's living situation. For example, the management unit can adjust the timing of management based on the user's living situation. The management unit can also customize the content of management based on the user's living situation. For example, the management unit customizes the content of management based on the user's living situation. By customizing the means of management based on the user's living situation, efficient learning management can be achieved. Some or all of the above processes in the management unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the management unit can input user living situation data into a generative AI and have the generative AI perform the customization of the means of management.

[0101] The management unit can estimate the user's emotions and prioritize learning progress based on those emotions. For example, if the user is relaxed, the management unit will prioritize learning content that promotes relaxation. For example, if the user is stressed, the management unit will prioritize learning content that reduces stress. The management unit can also prioritize learning content that can be learned in a short amount of time if the user is in a hurry. For example, if the management unit is in a hurry, the management unit will prioritize learning content that can be learned in a short amount of time. This allows for optimal learning management for the user by prioritizing learning progress according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using or without generative AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the priority of learning progress.

[0102] The management unit can provide an optimal management method when managing learning progress, taking into account the user's geographical location information. For example, the management unit can provide region-specific management methods based on the user's geographical location information. For example, the management unit can provide nearby learning opportunities based on the user's geographical location information. The management unit can also provide management methods related to local industries based on the user's geographical location information. For example, the management unit can provide management methods related to local industries based on the user's geographical location information. In this way, by providing an optimal management method based on the user's geographical location information, region-specific learning management can be provided. Some or all of the above processing in the management unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the management unit can input the user's geographical location information data into a generative AI and have the generative AI perform the task of providing an optimal management method.

[0103] The management department can analyze a user's social media activity and propose management methods when managing learning progress. For example, the management department can propose management methods related to the user's interests based on their social media activity. For example, the management department can propose management methods related to the experts or influencers the user follows based on their social media activity. The management department can also propose management methods related to trends based on the user's social media activity. For example, the management department can propose management methods related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to propose management methods that the user is interested in. Some or all of the above processing in the management department may be performed using generative AI, or not. For example, the management department can input the user's social media activity data into a generative AI and have the generative AI execute the proposal of management methods.

[0104] The insight-providing unit can estimate the user's emotions and adjust the method of providing insights based on the estimated user emotions. For example, if the user is relaxed, the insight-providing unit can provide insights in a relaxed state. For example, if the user is stressed, the insight-providing unit can provide insights that reduce stress. Furthermore, if the user is excited, the insight-providing unit can also provide insights that maintain excitement. For example, if the insight-providing unit is excited, it can provide insights that maintain excitement. In this way, by adjusting the method of providing insights according to the user's emotions, the optimal insights for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the insight-providing unit may be performed using a generative AI or not. For example, the insight-providing unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the method of providing insights.

[0105] The knowledge-providing unit can provide optimal knowledge by analyzing the user's past learning achievements when providing knowledge. For example, the knowledge-providing unit can analyze the user's past learning achievements and provide appropriate knowledge. For example, the knowledge-providing unit can provide knowledge that gradually increases in difficulty based on the user's past learning achievements. The knowledge-providing unit can also provide knowledge specialized in a specific field based on the user's past learning achievements. For example, the knowledge-providing unit can provide knowledge specialized in a specific field based on the user's past learning achievements. This allows the knowledge-providing unit to provide appropriate knowledge by analyzing the user's past learning achievements. Some or all of the above processing in the knowledge-providing unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the knowledge-providing unit can input the user's past learning achievement data into a generative AI and have the generative AI perform the provision of optimal knowledge.

[0106] The knowledge provision unit can customize the content of the knowledge based on the user's current job responsibilities when providing knowledge. For example, the knowledge provision unit can provide optimal knowledge based on the user's job responsibilities. For example, the knowledge provision unit can provide knowledge specialized in specific skills based on the user's job responsibilities. The knowledge provision unit can also customize the content of the knowledge based on the user's job responsibilities. For example, the knowledge provision unit customizes the content of the knowledge based on the user's job responsibilities. This enables efficient knowledge provision by customizing the content of the knowledge based on the user's job responsibilities. Some or all of the above processing in the knowledge provision unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the knowledge provision unit can input the user's job responsibilities data into a generation AI and have the generation AI perform the customization of the knowledge content.

[0107] The insight provider can estimate the user's emotions and prioritize insights based on those emotions. For example, if the user is relaxed, the insight provider will prioritize providing insights that promote relaxation. For example, if the user is stressed, the insight provider will prioritize providing insights that reduce stress. Furthermore, if the user is in a hurry, the insight provider can prioritize providing insights that can be learned in a short amount of time. In this way, by prioritizing insights according to the user's emotions, the insight provider can provide the user with the most optimal insights. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the insight provider may be performed using or without a generative AI. For example, the insight provider can input user emotion data into a generative AI and have the generative AI determine the priority of insights.

[0108] The knowledge-providing unit can provide optimal knowledge by considering the user's geographical location information when providing knowledge. For example, the knowledge-providing unit can provide region-specific knowledge based on the user's geographical location information. For example, the knowledge-providing unit can provide nearby learning opportunities based on the user's geographical location information. The knowledge-providing unit can also provide knowledge related to local industries based on the user's geographical location information. For example, the knowledge-providing unit can provide knowledge related to local industries based on the user's geographical location information. In this way, by providing optimal knowledge based on the user's geographical location information, region-specific knowledge can be provided. Some or all of the above processing in the knowledge-providing unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the knowledge-providing unit can input the user's geographical location information data into a generative AI and have the generative AI perform the provision of optimal knowledge.

[0109] The knowledge provision unit can analyze the user's social media activity and propose the content of the knowledge when providing it. For example, the knowledge provision unit can propose knowledge that the user is interested in based on the user's social media activity. For example, the knowledge provision unit can propose knowledge related to the experts and influencers the user follows based on the user's social media activity. The knowledge provision unit can also propose knowledge related to trends based on the user's social media activity. For example, the knowledge provision unit can propose knowledge related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to propose knowledge that the user is interested in. Some or all of the above processing in the knowledge provision unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the knowledge provision unit can input the user's social media activity data into a generative AI and have the generative AI propose the content of the knowledge.

[0110] The remote learning unit can estimate the user's emotions and adjust the remote learning method based on the estimated emotions. For example, if the user is relaxed, the remote learning unit can provide a remote learning method that allows for relaxed learning. For example, if the user is stressed, the remote learning unit can provide a remote learning method that reduces stress. Furthermore, if the user is excited, the remote learning unit can also provide a remote learning method that maintains excitement. For example, if the remote learning unit is excited, the remote learning unit can provide a remote learning method that maintains excitement. By adjusting the remote learning method according to the user's emotions, the system can provide the user with the optimal learning experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the remote learning unit may be performed using the generative AI or not. For example, the remote learning unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the remote learning method.

[0111] The remote learning unit can provide the optimal learning method by referring to the user's past learning history during remote learning. For example, the remote learning unit can analyze the user's past learning history and provide an appropriate remote learning method. For example, the remote learning unit can provide a remote learning method that gradually increases in difficulty based on the user's past learning history. The remote learning unit can also provide a remote learning method specialized for a specific field based on the user's past learning history. For example, the remote learning unit can provide a remote learning method specialized for a specific field based on the user's past learning history. This allows the remote learning unit to provide an appropriate remote learning method by referring to the user's past learning history. Some or all of the above processing in the remote learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the remote learning unit can input the user's past learning history data into a generative AI and have the generative AI perform the task of providing the optimal remote learning method.

[0112] The remote learning unit can customize the learning methods based on the user's current living situation during remote learning. For example, the remote learning unit can provide the optimal remote learning method based on the user's living situation. For example, the remote learning unit can adjust the timing of learning based on the user's living situation. The remote learning unit can also customize the learning content. For example, the remote learning unit can customize the learning content based on the user's living situation. This allows for efficient remote learning by customizing the learning methods based on the user's living situation. Some or all of the above processing in the remote learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the remote learning unit can input user living situation data into a generative AI and have the generative AI perform the customization of the learning methods.

[0113] The remote learning unit can estimate the user's emotions and determine the priority of remote learning based on those emotions. For example, if the user is relaxed, the remote learning unit will prioritize providing relaxing learning content. For example, if the user is stressed, the remote learning unit will prioritize providing stress-reducing learning content. Furthermore, if the user is in a hurry, the remote learning unit can prioritize providing learning content that can be learned in a short amount of time. For example, if the remote learning unit is in a hurry, the remote learning unit will prioritize providing learning content that can be learned in a short amount of time. In this way, by determining the priority of remote learning according to the user's emotions, the system can provide the user with the most suitable learning content. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the remote learning unit may be performed using a generative AI or not. For example, the remote learning unit can input user emotion data into a generative AI and have the generative AI determine the priority of remote learning.

[0114] The remote learning unit can provide the optimal learning method during remote learning, taking into account the user's geographical location information. For example, the remote learning unit can provide region-specific learning methods based on the user's geographical location information. For example, the remote learning unit can provide nearby learning opportunities based on the user's geographical location information. The remote learning unit can also provide learning methods related to local industries based on the user's geographical location information. For example, the remote learning unit can provide learning methods related to local industries based on the user's geographical location information. In this way, by providing the optimal learning method based on the user's geographical location information, region-specific learning content can be provided. Some or all of the above processing in the remote learning unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the remote learning unit can input the user's geographical location information data into a generative AI and have the generative AI perform the task of providing the optimal learning method.

[0115] The remote learning unit can analyze a user's social media activity during remote learning and suggest learning methods. For example, the remote learning unit can suggest learning methods that the user is interested in based on their social media activity. For example, the remote learning unit can suggest learning methods related to experts or influencers that the user follows based on their social media activity. The remote learning unit can also suggest learning methods related to trends based on the user's social media activity. For example, the remote learning unit can suggest learning methods related to trends based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to suggest learning methods that the user is interested in. Some or all of the above processing in the remote learning unit may be performed using generative AI, or it may be performed without using generative AI. For example, the remote learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the task of suggesting learning methods.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The reception system can estimate the user's emotions and adjust the input method for the skills and knowledge they want to learn based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow them to quickly input the skills and knowledge they want to learn. In this way, by adjusting the input method according to the user's emotions, the system allows users to comfortably input skills and knowledge.

[0118] The generation unit can estimate the user's emotions and adjust the content of the learning program based on those emotions. For example, if the user is relaxed, it can provide content that allows them to learn in a relaxed state. If the user is stressed, it can provide content that reduces stress. Furthermore, if the user is excited, it can provide content that maintains that excitement. In this way, by adjusting the content of the learning program according to the user's emotions, it is possible to provide the user with the most optimal learning content.

[0119] The learning unit can estimate the user's emotions and adjust the learning process based on those emotions. For example, if the user is relaxed, it can provide a learning process that promotes relaxation. If the user is stressed, it can provide a learning process that reduces stress. Furthermore, if the user is excited, it can provide a learning process that maintains that excitement. By adjusting the learning process according to the user's emotions, it can provide the user with the optimal learning experience.

[0120] The management unit can estimate the user's emotions and adjust the learning progress management method based on the estimated emotions. For example, if the user is relaxed, it can provide a learning progress management method that maintains a relaxed state. If the user is stressed, it can provide a learning progress management method that reduces stress. Furthermore, if the user is excited, it can provide a learning progress management method that maintains excitement. In this way, by adjusting the learning progress management method according to the user's emotions, it is possible to provide optimal learning management for the user.

[0121] The remote learning system can estimate the user's emotions and adjust the remote learning method based on those emotions. For example, if the user is relaxed, it can provide a remote learning method that allows them to learn in a relaxed state. If the user is stressed, it can provide a remote learning method that reduces stress. Furthermore, if the user is excited, it can provide a remote learning method that maintains that excitement. By adjusting the remote learning method according to the user's emotions, the system can provide the user with the optimal learning experience.

[0122] The reception desk can analyze a user's past learning history and suggest the most suitable input method. For example, it can automatically display skills and knowledge that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest skills and knowledge that the user may want to learn at a specific time based on their past learning history. In this way, by analyzing the user's past learning history, the system can suggest the most suitable input method.

[0123] The generation unit can adjust the difficulty level of a learning program based on the user's past learning achievements when generating the program. For example, it can analyze the user's past learning achievements and provide a program of appropriate difficulty. It can also provide a program that gradually increases in difficulty based on the user's past learning achievements. Furthermore, it can provide a program specialized in a specific field based on the user's past learning achievements. In this way, by adjusting the difficulty level of the program based on the user's past learning achievements, it is possible to provide a learning program of appropriate difficulty.

[0124] The learning unit can analyze the user's past learning achievements as they progress and select the optimal learning method. For example, it can analyze the user's past learning achievements and provide an appropriate learning method. It can also provide a learning method that gradually increases in difficulty based on the user's past learning achievements. Furthermore, it can provide a learning method specialized for a specific field based on the user's past learning achievements. In this way, by analyzing the user's past learning achievements, it can provide an appropriate learning method.

[0125] The management department can provide the optimal management method when managing learning progress by referring to the user's past learning history. For example, it can analyze the user's past learning history and provide an appropriate management method. It can also provide a management method that gradually increases the difficulty level based on the user's past learning history. Furthermore, it can provide a management method specialized for a specific field based on the user's past learning history. In this way, by referring to the user's past learning history, an appropriate management method can be provided.

[0126] The knowledge provision unit can provide optimal knowledge by analyzing the user's past learning achievements when providing knowledge. For example, it can analyze the user's past learning achievements and provide appropriate knowledge. It can also provide knowledge that gradually increases in difficulty based on the user's past learning achievements. Furthermore, it can provide knowledge specialized in a specific field based on the user's past learning achievements. In this way, appropriate knowledge can be provided by analyzing the user's past learning achievements.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk receives input from working professionals about the skills and knowledge they wish to learn. This includes, for example, business skills, technical skills, and specialized knowledge. The reception desk stores the skills and knowledge entered by users in a database, analyzes the input, and categorizes it appropriately. It can also suggest related skills and knowledge. Step 2: The generation unit generates an optimal learning program based on the information entered by the reception unit. The generation unit uses generation AI to customize the learning program according to the user's learning goals and learning style, and analyzes the learning history and learning results to propose the optimal learning program. The generation unit also automatically updates the content of the learning program according to the learning progress. Step 3: The learning unit proceeds with learning based on the learning program generated by the generation unit. The learning unit provides support for the user to proceed with learning according to the learning program and provides necessary learning materials and resources. The learning unit also monitors the user's learning progress and provides feedback as needed. Step 4: The management department manages the progress of learning conducted by the learning department and makes adjustments as needed. The management department monitors the user's learning progress in real time, collects learning data, and visualizes the progress. It also adjusts the progress of the learning program to match the user's learning pace.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, management unit, knowledge provision unit, and remote learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs the skills and knowledge they wish to learn. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an optimal learning program is generated based on the input information. The learning unit is implemented by the control unit 46A of the smart device 14, where learning proceeds based on the generated learning program. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, where the progress of learning is managed and adjustments are made as needed. The knowledge provision unit is implemented by the specific processing unit 290 of the data processing unit 12, where the knowledge of senior university professors is utilized. The remote learning unit is implemented by the control unit 46A of the smart device 14, where learning is provided in a fully remote environment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, management unit, knowledge provision unit, and remote learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs the skills and knowledge they wish to learn. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an optimal learning program is generated based on the input information. The learning unit is implemented by the control unit 46A of the smart glasses 214, where learning proceeds based on the generated learning program. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, where the progress of learning is managed and adjustments are made as needed. The knowledge provision unit is implemented by the specific processing unit 290 of the data processing unit 12, where the knowledge of senior university professors is utilized. The remote learning unit is implemented by the control unit 46A of the smart glasses 214, where learning is provided in a fully remote environment. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, management unit, knowledge provision unit, and remote learning unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs the skills and knowledge they wish to learn. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where an optimal learning program is generated based on the input information. The learning unit is implemented by, for example, the control unit 46A of the headset terminal 314, where learning proceeds based on the generated learning program. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the progress of learning is managed and adjustments are made as needed. The knowledge provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the knowledge of senior university professors is utilized. The remote learning unit is implemented by, for example, the control unit 46A of the headset terminal 314, where learning in a fully remote environment is provided. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the reception unit, generation unit, learning unit, management unit, knowledge provision unit, and remote learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs the skills and knowledge they wish to learn. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an optimal learning program is generated based on the input information. The learning unit is implemented by the control unit 46A of the robot 414, where learning proceeds based on the generated learning program. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, where the progress of learning is managed and adjustments are made as needed. The knowledge provision unit is implemented by the specific processing unit 290 of the data processing unit 12, where the knowledge of senior university professors is utilized. The remote learning unit is implemented by the control unit 46A of the robot 414, where learning is provided in a fully remote environment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A reception desk where working adults input the skills and knowledge they want to learn, A generation unit that generates an optimal learning program based on the information input by the reception unit, A learning unit that proceeds with learning based on the learning program generated by the generation unit, The system includes a management unit that manages the progress of learning conducted by the aforementioned learning unit and makes adjustments as necessary. A system characterized by the following features. (Note 2) It includes a knowledge-sharing department to leverage the expertise of senior university professors. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a remote learning department that provides learning in a fully remote environment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The learning program is flexibly adjusted to suit the current career and lifestyle of working adults. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Monitor learning progress in real time and make adjustments as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for the skills and knowledge they want to learn based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past learning history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users input the skills and knowledge they want to learn, the system filters the results based on their current job responsibilities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input skills and knowledge based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input the skills and knowledge they wish to learn, the system prioritizes inputting highly relevant skills and knowledge by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When you input the skills and knowledge you want to learn, the system analyzes your social media activity and suggests related skills and knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the content of the learning program based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a learning program, the program's difficulty level is adjusted based on the user's past learning performance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating the learning program, different learning algorithms are applied depending on the user's job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the learning program based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a learning program, the program's priority is determined based on the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a learning program, the program order is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During the learning process, the system analyzes the user's past learning achievements to select the optimal learning method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, As the learning process progresses, the learning methods are customized based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, During the learning process, the system selects the optimal learning method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During the learning process, the system analyzes the user's social media activity and suggests learning methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, It estimates the user's emotions and adjusts the learning progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing learning progress, the system provides the optimal management method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing learning progress, customize the management methods based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It estimates the user's emotions and prioritizes learning progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing learning progress, we provide the optimal management method by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing learning progress, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned knowledge-providing unit, We estimate user emotions and adjust how we deliver insights based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned knowledge-providing unit, When providing insights, we analyze the user's past learning outcomes to deliver the most optimal insights. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned knowledge-providing unit, When providing insights, customize the content of the insights based on the user's current job responsibilities. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned knowledge-providing unit, We estimate user emotions and prioritize insights based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned knowledge-providing unit, When providing insights, we take the user's geographical location into consideration to provide the most optimal insights. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned knowledge-providing unit, When providing insights, we analyze users' social media activity and propose content based on that analysis. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned remote learning unit, It estimates the user's emotions and adjusts the remote learning method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned remote learning unit, During remote learning, the system provides the optimal learning method by referring to the user's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned remote learning unit, During remote learning, customize learning methods based on the user's current living situation. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned remote learning unit, It estimates user emotions and prioritizes remote learning based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned remote learning unit, During remote learning, the system provides the optimal learning method by taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned remote learning unit, During remote learning, we analyze users' social media activity to suggest learning methods. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where working adults input the skills and knowledge they want to learn, A generation unit that generates an optimal learning program based on the information input by the reception unit, A learning unit that proceeds with learning based on the learning program generated by the generation unit, The system includes a management unit that manages the progress of learning conducted by the aforementioned learning unit and makes adjustments as necessary. A system characterized by the following features.

2. It includes a knowledge-sharing department to leverage the expertise of senior university professors. The system according to feature 1.

3. It has a remote learning department that provides learning in a fully remote environment. The system according to feature 1.

4. The generating unit is The learning program is flexibly adjusted to suit the current career and lifestyle of working adults. The system according to feature 1.

5. The aforementioned management department, Monitor learning progress in real time and make adjustments as needed. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for the skills and knowledge they want to learn based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past learning history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When users input the skills and knowledge they want to learn, the system filters the results based on their current job responsibilities and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input skills and knowledge based on the estimated user emotions. The system according to feature 1.

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