System

Abstract

An object of a system according to an embodiment is to deepen the understanding of AI and foster human resources that can be effectively utilized.SOLUTION: A system includes an online university providing part, a curriculum providing part, a personnel training part, and a qualification support part. The online university providing unit provides an online university for deepening the understanding of the AI. The curriculum providing section provides a curriculum using examples. The human resource development unit performs human resource development from basic learning to verification of prompt design. The qualification support department provides a qualification support system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a lack of education and human resource development to deepen understanding of AI, making it difficult for companies and individuals to effectively utilize AI.

[0005] The system of the embodiment aims to deepen understanding of AI and develop human resources who can utilize it effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes an online university providing unit, a curriculum providing unit, a human resource development unit, and a qualification support unit. The online university providing unit provides an online university for deepening understanding of AI. The curriculum providing unit provides a curriculum using real-world examples. The human resource development unit provides human resource development from basic learning to prompt design verification. The qualification support unit provides a qualification support system. [Effects of the Invention]

[0007] The system according to the embodiment can deepen understanding of AI and cultivate human resources who can utilize it effectively. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online university system according to an embodiment of the present invention provides an online university for deepening understanding and utilization of AI technology. This online university system specializes in generative AI and machine learning models, and supports companies and individuals in effectively utilizing AI. As a result, the online university system allows companies and individuals to gain a deeper understanding of AI technology and effectively utilize it in actual business situations.

[0029] An online university system according to an embodiment includes an online university provider, a curriculum provider, a human resources development provider, and a qualification support provider. The online university provider provides an online university designed to deepen understanding of AI. For example, participants can learn about basic AI concepts and technologies, as well as the latest research results. The curriculum provider provides a curriculum using real-world examples. For example, participants can learn about how specific companies have implemented AI and the benefits they have achieved. The human resources development department provides human resources development, from basic learning to prompt design verification. For example, participants can design appropriate prompts for AI models and actually verify how those prompts work. The qualification support department provides a qualification support system. For example, the qualification support system can be provided in conjunction with exam preparation courses for the G-Test and Python 3 Engineer Certification Exam. This allows the online university system according to an embodiment to provide a comprehensive educational platform for deepening understanding and utilization of AI technology. For example, companies can strengthen their AI utilization strategies, and individuals can build new careers using AI technology.

[0030] The online university provider can use the generative AI to analyze the student's learning progress and level of comprehension in real time and provide an individually optimized learning plan. For example, the online university provider monitors the student's learning progress in real time, and the generative AI automatically generates an individually optimized learning plan. For example, additional learning materials and practice problems are provided for areas where understanding is low. The generative AI can also be used to analyze the student's learning history and test results and provide an individually customized learning plan. For example, if the student's understanding of a particular topic is low, supplementary materials related to that topic are provided. The generative AI can also analyze the student's learning progress in real time and propose an optimal learning plan. For example, the learning content can be adjusted according to the student's learning speed and level of comprehension to support efficient learning. This maximizes the student's learning efficiency.

[0031] The online university provider can automatically generate customized learning courses based on the student's background information. For example, the online university provider collects the student's work history, educational background, and areas of interest as input data, and the generation AI automatically generates a customized learning course based on that. For example, it provides advanced technical courses for engineers and basic courses for beginners. It also analyzes the student's background information and builds a system that provides individually optimized learning courses. For example, a student with experience in a specific industry can be provided with an AI-applied course specialized for that industry. The generation AI also suggests customized learning courses based on the student's areas of interest and past learning history. For example, it provides teaching materials and exercises related to topics of interest. This makes it possible to provide learning courses that meet the student's needs.

[0032] The curriculum department can incorporate guest lectures by experts from different industries to provide learning from a wide range of perspectives. For example, the curriculum department can incorporate guest lectures by experts from different industries into the online university curriculum to provide students with learning from a wide range of perspectives. For example, an expert in the medical field can give a lecture on AI application cases. In addition, guest lectures can be held regularly to provide opportunities to learn about the latest trends and technological developments in different industries. For example, an expert in the financial industry can give a lecture on AI use cases. In addition, guest lectures by experts from different industries can be incorporated into the online university curriculum to allow students to learn about AI technology from a variety of perspectives. For example, an expert in the manufacturing industry can give a lecture on AI implementation cases. This allows students to learn from a variety of perspectives.

[0033] The curriculum offering department can provide an internship program that allows participants to try out what they have learned in an actual project. For example, the curriculum offering department can provide an internship program that allows participants to try out what they have learned in an actual project, thereby providing an opportunity to acquire practical skills. For example, by participating in a collaborative project with a company. In addition, through the internship program, participants can experience applying the AI ​​technology they have learned in an actual business situation. For example, by participating in a corporate AI project and gaining practical experience. In addition, the curriculum offering department can provide an internship program that allows participants to try out what they have learned in an actual project, thereby providing an opportunity to acquire practical skills. For example, by participating in a collaborative project with a startup company. In this way, participants can be provided with an opportunity to acquire practical skills.

[0034] The curriculum provision department can use the generative AI to automatically analyze AI implementation cases of specific companies and extract success factors and challenges. For example, the curriculum provision department uses the generative AI to build a system that automatically analyzes AI implementation cases of specific companies and extracts success factors and challenges. For example, the department analyzes the company's project data and identifies success factors. The generative AI also automatically analyzes the AI ​​implementation cases of specific companies and extracts success factors and challenges. For example, the department analyzes the project progress and results and identifies success factors. The generative AI also develops a system that automatically analyzes AI implementation cases of specific companies and extracts success factors and challenges. For example, the department analyzes the company's project data and identifies challenges. This makes it possible to learn from the AI ​​implementation cases of specific companies and clarify success factors and challenges.

[0035] The curriculum provider can create a simulation environment based on corporate AI implementation cases, allowing trainees to experience the actual implementation process in a virtual environment. For example, the curriculum provider can create a simulation environment based on corporate AI implementation cases, allowing trainees to experience the actual implementation process in a virtual environment. For example, they can learn the steps of AI implementation through a virtual project. They can also create a simulation environment, allowing trainees to experience the corporate AI implementation process in a virtual environment. For example, they can solve AI implementation challenges through a virtual project. They can also create a simulation environment based on corporate AI implementation cases, allowing trainees to experience the actual implementation process in a virtual environment. For example, they can learn the factors for success in AI implementation through a virtual project. This allows trainees to experience the actual implementation process in a virtual environment.

[0036] The Curriculum Provider will be able to compare and analyze AI implementation cases in different industries to identify common success patterns and different approaches. For example, the Curriculum Provider will build a system that compares and analyzes AI implementation cases in different industries to identify common success patterns and different approaches. For example, compare AI implementation cases in the medical industry and the manufacturing industry. Also, analyze AI implementation cases in different industries to identify common success patterns and different approaches. For example, compare AI implementation cases in the financial industry and the retail industry. Also, compare and analyze AI implementation cases in different industries to identify common success patterns and different approaches. For example, compare AI implementation cases in the energy industry and the logistics industry. This will make it possible to clarify success patterns and approaches in different industries.

[0037] The curriculum providing department can create case studies based on real-life examples and provide opportunities for participants to deepen their learning through group discussions. For example, the curriculum providing department can create case studies based on real-life examples and provide opportunities for participants to deepen their learning through group discussions. For example, a discussion can be held on the topic of AI implementation cases in a specific company. In addition, the curriculum providing department can create case studies and provide opportunities for participants to learn from real-life examples through group discussions. For example, a discussion can be held on the topic of AI implementation cases in a specific industry. In addition, a case study can be created based on real-life examples and provide opportunities for participants to deepen their learning through group discussions. For example, a discussion can be held on the topic of AI implementation cases in a specific project. This allows participants to deepen their learning through group discussions.

[0038] The Human Resources Development Department can use generation AI to evaluate the effectiveness of prompts designed by students in real time and provide feedback on areas for improvement. The Human Resources Development Department, for example, will use generation AI to build a system that evaluates the effectiveness of prompts designed by students in real time and provides feedback on areas for improvement. For example, the accuracy of the prompts and response speed will be evaluated. The generation AI will also evaluate prompts designed by students in real time and provide feedback on areas for improvement. For example, the appropriateness and effectiveness of the prompts will be evaluated and specific improvement suggestions will be made. The Human Resources Development Department will also develop a system that uses generation AI to evaluate the effectiveness of prompts designed by students in real time and provide feedback on areas for improvement. For example, the response content of the prompts and user satisfaction will be evaluated. This will enable the effectiveness of prompts designed by students to be evaluated in real time and provide feedback on areas for improvement.

[0039] The Human Resources Development Department can automatically record the prompt design process so that trainees can review it later to learn. The Human Resources Development Department, for example, builds a system that automatically records the prompt design process so that trainees can review it later to learn. For example, it saves a log of the design process and a change history. It also automatically records the process of prompts designed by trainees so that trainees can review it later to learn. For example, it saves screenshots and notes for each step of the design process. It also develops a system that automatically records the prompt design process so that trainees can review it later to learn. For example, it saves video recordings and audio memos of the design process. This allows trainees to review the prompt design process later to learn.

[0040] The Human Resources Development Department will apply the same prompt to different AI models and compare and analyze the results to understand the characteristics of the models. The Human Resources Development Department will, for example, build a system that applies the same prompt to different AI models and compare and analyze the results. For example, it will compare the response content and accuracy of each model. It will also apply the same prompt to different AI models and compare and analyze the results to understand the characteristics of the models. For example, it will compare response speed and user satisfaction. It will also develop a system that applies the same prompt to different AI models and compare and analyze the results. For example, it will identify the strengths and weaknesses of each model. This will enable it to understand the characteristics of different AI models.

[0041] The Human Resources Development Department can hold prompt design workshops and provide a forum where participants can share their ideas with each other. The Human Resources Development Department, for example, can hold prompt design workshops on a regular basis and provide a forum where participants can share their ideas with each other. For example, they can hold group discussions and brainstorming sessions. The workshops can also provide opportunities for participants to share their prompt design ideas with each other. For example, they can share success stories and failure stories. The Human Resources Development Department can also hold prompt design workshops and provide a forum where participants can share their ideas with each other. For example, they can design actual prompts and exchange feedback. This can provide a forum where participants can share their ideas with each other.

[0042] The qualification support department can use the generation AI to monitor the learning progress of students in real time and provide individual study plans for qualification exams. For example, the qualification support department uses the generation AI to build a system that monitors the learning progress of students in real time and provides individual study plans for qualification exams. For example, it provides test preparation questions based on the level of understanding. In addition, the generation AI analyzes the student's learning history and automatically generates an individual study plan for the qualification exam. For example, it provides additional learning materials for weak areas. In addition, it uses the generation AI to develop a system that monitors the learning progress of students in real time and provides individual study plans for qualification exams. For example, it provides mock exams based on the learning progress. This makes it possible to monitor the learning progress of students in real time and provide individual study plans for qualification exams.

[0043] The Qualification Support Department can analyze past qualification exam questions and provide tools that automatically extract frequently asked questions and important points. The Qualification Support Department, for example, builds a system that analyzes past qualification exam questions and provides tools that automatically extract frequently asked questions and important points. For example, it identifies important questions based on past exam data. It also develops tools that analyze past questions and automatically extract frequently asked questions and important points. For example, it analyzes exam trends and identifies important topics. It also develops a system that analyzes past qualification exam questions and provides tools that automatically extract frequently asked questions and important points. For example, it identifies frequently asked questions based on past exam data. This makes it possible to provide tools that automatically extract frequently asked questions and important points from past qualification exam questions.

[0044] The qualification support department may provide online mock exams for qualification exam preparation, allowing students to experience a real exam environment. For example, the qualification support department may provide online mock exams for qualification exam preparation, and build a system that allows students to experience a real exam environment. For example, the department may provide real-time feedback on mock exam results. Furthermore, the department may enable students to experience a real exam environment through online mock exams. For example, the department may reproduce the format and time limits of exams. Furthermore, the department may develop a system that provides online mock exams for qualification exam preparation, allowing students to experience a real exam environment. For example, the department may provide individual study plans based on the results of the mock exams, allowing students to experience a real exam environment.

[0045] The qualification support department can provide a matching service with companies as career support after qualification acquisition. The qualification support department, for example, builds a system that provides a matching service with companies as career support after qualification acquisition. For example, it recommends appropriate companies based on the skills and qualifications of the student. Also, it provides a matching service with companies as career support after qualification acquisition. For example, it matches with companies based on the student's resume and job history. Also, it develops a system that provides a matching service with companies as career support after qualification acquisition. For example, it provides appropriate job information based on the student's skills and qualifications. This makes it possible to provide a matching service with companies as career support after qualification acquisition.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] An online university system can also have a community building section. The community building section provides online forums and discussion boards where students can interact with each other and share information. For example, by providing a place where students can share what they have learned, they can deepen their mutual learning. Students can also exchange opinions through discussions on specific topics. Furthermore, the community building section can provide a Q&A section where students can post questions and receive answers from other students and instructors. This can promote interaction between students and increase their motivation to learn.

[0048] Online university providers can also incorporate gamification elements. For example, students can earn points for completing certain tasks and use them to purchase virtual items. They can also motivate students by awarding badges and titles based on their progress. They can also introduce a ranking system where students can compete with each other, offering rewards to top performers. This helps students stay motivated and makes learning fun.

[0049] Online university providers can also use virtual reality (VR) technology to allow students to experience actual AI technology in a virtual environment. For example, VR can be used to visually experience the training process of an AI model. Also, AI simulations can be performed in a virtual environment, allowing students to learn how to use AI in real business situations. Furthermore, interactive learning content can be provided using VR, allowing students to gain a deeper understanding of AI technology. This can improve students' learning experience.

[0050] The curriculum provider can also be equipped with an automatic translation function using AI technology. For example, learning materials provided in different languages ​​can be automatically translated, allowing students to study in their native language. The automatic translation function can also be used to remove language barriers during real-time lectures and discussions. Furthermore, assignments and reports submitted by students can be automatically translated, allowing instructors to easily evaluate them. This allows students who speak different languages ​​to enjoy the same learning opportunities.

[0051] The curriculum provider can also be equipped with an automated assessment system using AI technology. For example, AI can automatically assess assignments and reports submitted by students and provide feedback. It can also analyze test and quiz results in real time to assess students' understanding. Furthermore, AI can be used to analyze students' learning histories and provide individually optimized feedback. This can improve students' learning efficiency.

[0052] The curriculum provider can also be equipped with an automated tutoring system that uses AI technology. For example, if a student has a question while studying, AI can provide an answer in real time. It can also analyze the student's learning history and provide individually optimized tutoring. Furthermore, AI can be used to monitor the student's learning progress and provide additional support as needed. This allows students to study efficiently.

[0053] The curriculum provider can also be equipped with an automatic feedback system using AI technology. For example, AI can automatically evaluate assignments and reports submitted by students and provide feedback. It can also analyze test and quiz results in real time to assess students' understanding. Furthermore, AI can be used to analyze students' learning history and provide individually optimized feedback. This can improve students' learning efficiency.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The Online University Provider will provide an online university to deepen understanding of AI, including learning about basic AI concepts and technologies, as well as the latest research results. Step 2: The Curriculum Department will provide a curriculum using real-world examples, such as how a specific company implemented AI and what benefits it achieved. Step 3: The Human Resources Development Department will provide training from basic learning to prompt design verification. For example, trainees will be able to design appropriate prompts for AI models and actually verify how the prompts work. Step 4: The Qualification Support Department provides a qualification support system. For example, it provides a qualification support system in conjunction with exam preparation courses for the G-Test and Python 3 Engineer Certification Exam.

[0056] (Example 2) An online university system according to an embodiment of the present invention provides an online university for deepening understanding and utilization of AI technology. This online university system specializes in generative AI and machine learning models, and supports companies and individuals in effectively utilizing AI. As a result, the online university system allows companies and individuals to gain a deeper understanding of AI technology and effectively utilize it in actual business situations.

[0057] An online university system according to an embodiment includes an online university provider, a curriculum provider, a human resources development provider, and a qualification support provider. The online university provider provides an online university designed to deepen understanding of AI. For example, participants can learn about basic AI concepts and technologies, as well as the latest research results. The curriculum provider provides a curriculum using real-world examples. For example, participants can learn about how specific companies have implemented AI and the benefits they have achieved. The human resources development department provides human resources development, from basic learning to prompt design verification. For example, participants can design appropriate prompts for AI models and actually verify how those prompts work. The qualification support department provides a qualification support system. For example, the qualification support system can be provided in conjunction with exam preparation courses for the G-Test and Python 3 Engineer Certification Exam. This allows the online university system according to an embodiment to provide a comprehensive educational platform for deepening understanding and utilization of AI technology. For example, companies can strengthen their AI utilization strategies, and individuals can build new careers using AI technology.

[0058] The online university provider can use the generative AI to analyze the student's learning progress and level of comprehension in real time and provide an individually optimized learning plan. For example, the online university provider monitors the student's learning progress in real time, and the generative AI automatically generates an individually optimized learning plan. For example, additional learning materials and practice problems are provided for areas where understanding is low. The generative AI can also be used to analyze the student's learning history and test results and provide an individually customized learning plan. For example, if the student's understanding of a particular topic is low, supplementary materials related to that topic are provided. The generative AI can also analyze the student's learning progress in real time and propose an optimal learning plan. For example, the learning content can be adjusted according to the student's learning speed and level of comprehension to support efficient learning. This maximizes the student's learning efficiency.

[0059] The online university provider can automatically generate customized learning courses based on the student's background information. For example, the online university provider collects the student's work history, educational background, and areas of interest as input data, and the generation AI automatically generates a customized learning course based on that. For example, it provides advanced technical courses for engineers and basic courses for beginners. It also analyzes the student's background information and builds a system that provides individually optimized learning courses. For example, a student with experience in a specific industry can be provided with an AI-applied course specialized for that industry. The generation AI also suggests customized learning courses based on the student's areas of interest and past learning history. For example, it provides teaching materials and exercises related to topics of interest. This makes it possible to provide learning courses that meet the student's needs.

[0060] The online university providing unit can use the emotion estimation function to monitor the emotions of students while they are studying and provide feedback to maintain their motivation. For example, the online university providing unit uses the emotion estimation function to monitor the emotions of students while they are studying in real time and provide feedback to maintain their motivation. For example, if a student is feeling stressed while studying, advice to relax is displayed. The online university providing unit also analyzes the student's emotion data and provides customized feedback to maintain their motivation while studying. For example, an encouraging message is displayed if the student has strong positive emotions. The emotion estimation function also monitors changes in the student's emotions while they are studying and provides feedback at an appropriate time. For example, if their motivation to study is declining, specific actions to increase their motivation are suggested. This allows the student's motivation to study to be maintained.

[0061] The curriculum department can incorporate guest lectures by experts from different industries to provide learning from a wide range of perspectives. For example, the curriculum department can incorporate guest lectures by experts from different industries into the online university curriculum to provide students with learning from a wide range of perspectives. For example, an expert in the medical field can give a lecture on AI application cases. In addition, guest lectures can be held regularly to provide opportunities to learn about the latest trends and technological developments in different industries. For example, an expert in the financial industry can give a lecture on AI use cases. In addition, guest lectures by experts from different industries can be incorporated into the online university curriculum to allow students to learn about AI technology from a variety of perspectives. For example, an expert in the manufacturing industry can give a lecture on AI implementation cases. This allows students to learn from a variety of perspectives.

[0062] The curriculum offering department can provide an internship program that allows participants to try out what they have learned in an actual project. For example, the curriculum offering department can provide an internship program that allows participants to try out what they have learned in an actual project, thereby providing an opportunity to acquire practical skills. For example, by participating in a collaborative project with a company. In addition, through the internship program, participants can experience applying the AI ​​technology they have learned in an actual business situation. For example, by participating in a corporate AI project and gaining practical experience. In addition, the curriculum offering department can provide an internship program that allows participants to try out what they have learned in an actual project, thereby providing an opportunity to acquire practical skills. For example, by participating in a collaborative project with a startup company. In this way, participants can be provided with an opportunity to acquire practical skills.

[0063] The curriculum providing unit can use the emotion estimation function to identify topics in which students are most interested and provide additional learning materials related to those topics. The curriculum providing unit, for example, uses the emotion estimation function to identify topics in which students are most interested and provide additional learning materials related to those topics. For example, it provides supplementary materials to deepen understanding of the topic of interest. It also analyzes students' emotion data and builds a system to identify topics in which students are most interested. For example, it provides video lectures and exercises related to the topic of interest. It also uses the emotion estimation function to identify topics in which students are most interested and provide additional learning materials related to those topics. For example, it provides online discussions to deepen understanding of the topic of interest. This makes it possible to provide learning that suits the students' interests.

[0064] The curriculum provision department can use the generative AI to automatically analyze AI implementation cases of specific companies and extract success factors and challenges. For example, the curriculum provision department uses the generative AI to build a system that automatically analyzes AI implementation cases of specific companies and extracts success factors and challenges. For example, the department analyzes the company's project data and identifies success factors. The generative AI also automatically analyzes the AI ​​implementation cases of specific companies and extracts success factors and challenges. For example, the department analyzes the project progress and results and identifies success factors. The generative AI also develops a system that automatically analyzes AI implementation cases of specific companies and extracts success factors and challenges. For example, the department analyzes the company's project data and identifies challenges. This makes it possible to learn from the AI ​​implementation cases of specific companies and clarify success factors and challenges.

[0065] The curriculum provider can create a simulation environment based on corporate AI implementation cases, allowing trainees to experience the actual implementation process in a virtual environment. For example, the curriculum provider can create a simulation environment based on corporate AI implementation cases, allowing trainees to experience the actual implementation process in a virtual environment. For example, they can learn the steps of AI implementation through a virtual project. They can also create a simulation environment, allowing trainees to experience the corporate AI implementation process in a virtual environment. For example, they can solve AI implementation challenges through a virtual project. They can also create a simulation environment based on corporate AI implementation cases, allowing trainees to experience the actual implementation process in a virtual environment. For example, they can learn the factors for success in AI implementation through a virtual project. This allows trainees to experience the actual implementation process in a virtual environment.

[0066] The curriculum providing unit can use the emotion estimation function to analyze the emotions that students have toward the examples and provide supplemental explanations to improve their understanding. The curriculum providing unit, for example, uses the emotion estimation function to analyze the emotions that students have toward the examples in real time and provide supplemental explanations to improve their understanding. For example, additional explanations are provided for parts that are difficult to understand. The curriculum providing unit also analyzes students' emotion data and builds a system that provides supplemental explanations to improve their understanding of the examples. For example, supplemental materials are provided for parts with low emotion scores. The emotion estimation function also analyzes the emotions that students have toward the examples and provides supplemental explanations to improve their understanding. For example, additional video lectures are provided for parts that are difficult to understand. This makes it possible to provide supplemental explanations to improve the students' understanding.

[0067] The Curriculum Provider will be able to compare and analyze AI implementation cases in different industries to identify common success patterns and different approaches. For example, the Curriculum Provider will build a system that compares and analyzes AI implementation cases in different industries to identify common success patterns and different approaches. For example, compare AI implementation cases in the medical industry and the manufacturing industry. Also, analyze AI implementation cases in different industries to identify common success patterns and different approaches. For example, compare AI implementation cases in the financial industry and the retail industry. Also, compare and analyze AI implementation cases in different industries to identify common success patterns and different approaches. For example, compare AI implementation cases in the energy industry and the logistics industry. This will make it possible to clarify success patterns and approaches in different industries.

[0068] The curriculum providing department can create case studies based on real-life examples and provide opportunities for participants to deepen their learning through group discussions. For example, the curriculum providing department can create case studies based on real-life examples and provide opportunities for participants to deepen their learning through group discussions. For example, a discussion can be held on the topic of AI implementation cases in a specific company. In addition, the curriculum providing department can create case studies and provide opportunities for participants to learn from real-life examples through group discussions. For example, a discussion can be held on the topic of AI implementation cases in a specific industry. In addition, a case study can be created based on real-life examples and provide opportunities for participants to deepen their learning through group discussions. For example, a discussion can be held on the topic of AI implementation cases in a specific project. This allows participants to deepen their learning through group discussions.

[0069] The curriculum providing unit can use the emotion estimation function to identify examples that interest students most and provide detailed materials related to those examples. The curriculum providing unit, for example, uses the emotion estimation function to identify examples that interest students most and provide detailed materials related to those examples. For example, it provides supplementary materials or video lectures for examples of interesting examples. It can also analyze students' emotion data and build a system to identify examples that interest students most. For example, it can provide detailed reports or research papers related to examples of interesting examples. It can also use the emotion estimation function to identify examples that interest students most and provide detailed materials related to those examples. For example, it can provide interactive simulations for examples of interesting examples. This makes it possible to provide detailed materials according to the students' interests.

[0070] The Human Resources Development Department can use generation AI to evaluate the effectiveness of prompts designed by students in real time and provide feedback on areas for improvement. The Human Resources Development Department, for example, will use generation AI to build a system that evaluates the effectiveness of prompts designed by students in real time and provides feedback on areas for improvement. For example, the accuracy of the prompts and response speed will be evaluated. The generation AI will also evaluate prompts designed by students in real time and provide feedback on areas for improvement. For example, the appropriateness and effectiveness of the prompts will be evaluated and specific improvement suggestions will be made. The Human Resources Development Department will also develop a system that uses generation AI to evaluate the effectiveness of prompts designed by students in real time and provide feedback on areas for improvement. For example, the response content of the prompts and user satisfaction will be evaluated. This will enable the effectiveness of prompts designed by students to be evaluated in real time and provide feedback on areas for improvement.

[0071] The Human Resources Development Department can automatically record the prompt design process so that trainees can review it later to learn. The Human Resources Development Department, for example, builds a system that automatically records the prompt design process so that trainees can review it later to learn. For example, it saves a log of the design process and a change history. It also automatically records the process of prompts designed by trainees so that trainees can review it later to learn. For example, it saves screenshots and notes for each step of the design process. It also develops a system that automatically records the prompt design process so that trainees can review it later to learn. For example, it saves video recordings and audio memos of the design process. This allows trainees to review the prompt design process later to learn.

[0072] The human resource development department can use the emotion estimation function to identify stress and difficulty felt by students while designing prompts and provide appropriate support. For example, the human resource development department uses the emotion estimation function to identify stress and difficulty felt by students while designing prompts in real time and provide appropriate support. For example, if students are feeling stressed, the department suggests relaxation methods. The human resource development department also builds a system that analyzes students' emotion data and identifies stress and difficulty felt by students while designing prompts. For example, the department provides additional support if the emotion score is low. The human resource development department also uses the emotion estimation function to identify stress and difficulty felt by students while designing prompts and provide appropriate support. For example, the department suggests specific solutions if students are experiencing difficulties. This makes it possible to identify stress and difficulty felt by students while designing prompts and provide appropriate support.

[0073] The Human Resources Development Department will apply the same prompt to different AI models and compare and analyze the results to understand the characteristics of the models. The Human Resources Development Department will, for example, build a system that applies the same prompt to different AI models and compare and analyze the results. For example, it will compare the response content and accuracy of each model. It will also apply the same prompt to different AI models and compare and analyze the results to understand the characteristics of the models. For example, it will compare response speed and user satisfaction. It will also develop a system that applies the same prompt to different AI models and compare and analyze the results. For example, it will identify the strengths and weaknesses of each model. This will enable it to understand the characteristics of different AI models.

[0074] The Human Resources Development Department can hold prompt design workshops and provide a forum where participants can share their ideas with each other. The Human Resources Development Department, for example, can hold prompt design workshops on a regular basis and provide a forum where participants can share their ideas with each other. For example, they can hold group discussions and brainstorming sessions. The workshops can also provide opportunities for participants to share their prompt design ideas with each other. For example, they can share success stories and failure stories. The Human Resources Development Department can also hold prompt design workshops and provide a forum where participants can share their ideas with each other. For example, they can design actual prompts and exchange feedback. This can provide a forum where participants can share their ideas with each other.

[0075] The human resource development department can use the emotion estimation function to collect examples of prompt design that students found most successful and share their success stories. The human resource development department, for example, uses the emotion estimation function to collect examples of prompt design that students found most successful and build a system for sharing their success stories. For example, the successful stories are stored in a database and shared with other students. Furthermore, the emotion data of students is analyzed to identify the examples of prompt design that students found most successful. For example, stories with high emotion scores are collected and shared. Furthermore, the emotion estimation function is used to collect examples of prompt design that students found most successful and share their success stories. For example, the successful stories are shared in an online forum. In this way, students' success stories can be shared.

[0076] The qualification support department can use the generation AI to monitor the learning progress of students in real time and provide individual study plans for qualification exams. For example, the qualification support department uses the generation AI to build a system that monitors the learning progress of students in real time and provides individual study plans for qualification exams. For example, it provides test preparation questions based on the level of understanding. In addition, the generation AI analyzes the student's learning history and automatically generates an individual study plan for the qualification exam. For example, it provides additional learning materials for weak areas. In addition, it uses the generation AI to develop a system that monitors the learning progress of students in real time and provides individual study plans for qualification exams. For example, it provides mock exams based on the learning progress. This makes it possible to monitor the learning progress of students in real time and provide individual study plans for qualification exams.

[0077] The Qualification Support Department can analyze past qualification exam questions and provide tools that automatically extract frequently asked questions and important points. The Qualification Support Department, for example, builds a system that analyzes past qualification exam questions and provides tools that automatically extract frequently asked questions and important points. For example, it identifies important questions based on past exam data. It also develops tools that analyze past questions and automatically extract frequently asked questions and important points. For example, it analyzes exam trends and identifies important topics. It also develops a system that analyzes past qualification exam questions and provides tools that automatically extract frequently asked questions and important points. For example, it identifies frequently asked questions based on past exam data. This makes it possible to provide tools that automatically extract frequently asked questions and important points from past qualification exam questions.

[0078] The qualification support department may provide online mock exams for qualification exam preparation, allowing students to experience a real exam environment. For example, the qualification support department may provide online mock exams for qualification exam preparation, and build a system that allows students to experience a real exam environment. For example, the department may provide real-time feedback on mock exam results. Furthermore, the department may enable students to experience a real exam environment through online mock exams. For example, the department may reproduce the format and time limits of exams. Furthermore, the department may develop a system that provides online mock exams for qualification exam preparation, allowing students to experience a real exam environment. For example, the department may provide individual study plans based on the results of the mock exams, allowing students to experience a real exam environment.

[0079] The qualification support department can provide a matching service with companies as career support after qualification acquisition. The qualification support department, for example, builds a system that provides a matching service with companies as career support after qualification acquisition. For example, it recommends appropriate companies based on the skills and qualifications of the student. Also, it provides a matching service with companies as career support after qualification acquisition. For example, it matches with companies based on the student's resume and job history. Also, it develops a system that provides a matching service with companies as career support after qualification acquisition. For example, it provides appropriate job information based on the student's skills and qualifications. This makes it possible to provide a matching service with companies as career support after qualification acquisition.

[0080] The qualification support unit can use the emotion estimation function to identify the areas in which the student is most confident and provide additional learning resources related to those areas. For example, the qualification support unit can use the emotion estimation function to identify the areas in which the student is most confident and provide additional learning resources related to those areas. For example, detailed lectures and exercises for the areas in which the student is confident can be provided. A system can also be built that analyzes the student's emotion data and identifies the areas in which the student is most confident. For example, research papers and case studies related to the areas in which the student is confident can be provided. The emotion estimation function can also be used to identify the areas in which the student is most confident and provide additional learning resources related to those areas. For example, interactive simulations for the areas in which the student is confident can be provided. This makes it possible to provide additional learning resources related to the areas in which the student is most confident.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] An online university system can also have a community building section. The community building section provides online forums and discussion boards where students can interact with each other and share information. For example, by providing a place where students can share what they have learned, they can deepen their mutual learning. Students can also exchange opinions through discussions on specific topics. Furthermore, the community building section can provide a Q&A section where students can post questions and receive answers from other students and instructors. This can promote interaction between students and increase their motivation to learn.

[0083] Online university providers can also incorporate gamification elements. For example, students can earn points for completing certain tasks and use them to purchase virtual items. They can also motivate students by awarding badges and titles based on their progress. They can also introduce a ranking system where students can compete with each other, offering rewards to top performers. This helps students stay motivated and makes learning fun.

[0084] Online university providers can also use virtual reality (VR) technology to allow students to experience actual AI technology in a virtual environment. For example, VR can be used to visually experience the training process of an AI model. Also, AI simulations can be performed in a virtual environment, allowing students to learn how to use AI in real business situations. Furthermore, interactive learning content can be provided using VR, allowing students to gain a deeper understanding of AI technology. This can improve students' learning experience.

[0085] Online university providers can use emotion estimation to monitor students' emotions while they are studying and adjust their learning content accordingly. For example, if a student is feeling stressed, they can temporarily reduce the learning content and provide content to help them relax. If a student is excited, they can provide challenging tasks to maintain their excitement. Furthermore, the emotion estimation function can be used to provide real-time feedback based on the student's emotions to maintain their motivation to study. This allows for individually optimizing the learning experience for each student.

[0086] The curriculum provider can also be equipped with an automatic translation function using AI technology. For example, learning materials provided in different languages ​​can be automatically translated, allowing students to study in their native language. The automatic translation function can also be used to remove language barriers during real-time lectures and discussions. Furthermore, assignments and reports submitted by students can be automatically translated, allowing instructors to easily evaluate them. This allows students who speak different languages ​​to enjoy the same learning opportunities.

[0087] The curriculum provider can also be equipped with an automated assessment system using AI technology. For example, AI can automatically assess assignments and reports submitted by students and provide feedback. It can also analyze test and quiz results in real time to assess students' understanding. Furthermore, AI can be used to analyze students' learning histories and provide individually optimized feedback. This can improve students' learning efficiency.

[0088] The curriculum provider can use the emotion estimation function to identify topics that students find most interesting and provide additional learning materials related to those topics. For example, it can provide supplementary materials to deepen understanding of topics of interest. It can also build a system that analyzes students' emotion data and identifies topics that students find most interesting. For example, it can provide video lectures and exercises related to topics of interest. It can also use the emotion estimation function to identify topics that students find most interesting and provide additional learning materials related to those topics. For example, it can provide online discussions to deepen understanding of topics of interest. This makes it possible to provide learning that suits students' interests.

[0089] The curriculum provider can also be equipped with an automated tutoring system that uses AI technology. For example, if a student has a question while studying, AI can provide an answer in real time. It can also analyze the student's learning history and provide individually optimized tutoring. Furthermore, AI can be used to monitor the student's learning progress and provide additional support as needed. This allows students to study efficiently.

[0090] The curriculum provider can also be equipped with an automatic feedback system using AI technology. For example, AI can automatically evaluate assignments and reports submitted by students and provide feedback. It can also analyze test and quiz results in real time to assess students' understanding. Furthermore, AI can be used to analyze students' learning history and provide individually optimized feedback. This can improve students' learning efficiency.

[0091] The curriculum provider can use the emotion estimation function to analyze the emotions students have toward the examples and provide supplemental explanations to improve their understanding. For example, additional explanations can be provided for parts that are difficult to understand. The curriculum provider can also build a system that analyzes students' emotion data and provides supplemental explanations to improve their understanding of the examples. For example, it can provide supplemental materials for parts with low emotion scores. The curriculum provider can also use the emotion estimation function to analyze the emotions students have toward the examples and provide supplemental explanations to improve their understanding. For example, it can provide additional video lectures for parts that are difficult to understand. This makes it possible to provide supplemental explanations to improve students' understanding.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The Online University Provider will provide an online university to deepen understanding of AI, including learning about basic AI concepts and technologies, as well as the latest research results. Step 2: The Curriculum Department will provide a curriculum using real-world examples, such as how a specific company implemented AI and what benefits it achieved. Step 3: The Human Resources Development Department will provide training from basic learning to prompt design verification. For example, trainees will be able to design appropriate prompts for AI models and actually verify how the prompts work. Step 4: The Qualification Support Department provides a qualification support system. For example, it provides a qualification support system in conjunction with exam preparation courses for the G-Test and Python 3 Engineer Certification Exam.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Online University Department provides an online university to deepen understanding of AI, A curriculum department that provides a curriculum using real-life examples; The Human Resources Development Department, which provides training from basic learning to prompt design verification, A qualification support department that provides a qualification support system; A system characterized by:

2. The online university provider: Generative AI is used to analyze students' learning progress and comprehension in real time, providing individually optimized learning plans.

2. The system of claim 1.

3. The online university provider: Automatically generate customized learning paths based on learner background information 2. The system of claim 1.

4. The online university provider: Monitor students' emotions during learning and provide feedback to keep them motivated 2. The system of claim 1.

5. The curriculum providing department Incorporating guest lectures by experts from different industries to provide learning from a wide range of perspectives 2. The system of claim 1.

6. The curriculum providing department Offering an internship program for students to put what they have learned into practice in real projects 2. The system of claim 1.

7. The curriculum providing department Identify topics that most interest students and provide additional educational materials related to those topics 2. The system of claim 1.

8. The curriculum providing department Using generative AI, we automatically analyze the AI ​​implementation cases of specific companies and extract success factors and challenges.

2. The system of claim 1.

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