System

Abstract

An object of a system according to an embodiment is to provide an optimal learning program to an individual learner and accurately determine the learning level of the learner.SOLUTION: A system includes a questionnaire execution part, an analysis part, a program generation part, and a skill level determination part. The questionnaire execution part executes a preliminary questionnaire. The analysis part analyzes the answer to the questionnaire executed by the questionnaire execution part. The program generation unit generates an optimal learning program on the basis of the result analyzed by the analysis unit. The proficiency level determination unit provides the learning program generated by the program generation unit, and determines the proficiency level through interaction with the generated AI after learning.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] Conventional techniques have had the problem that it is difficult to provide an optimal learning program for each individual learner and accurately assess their level of proficiency.

[0005] The system according to the embodiment aims to provide an optimal learning program for each individual learner and accurately assess their level of proficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a survey implementation unit, an analysis unit, a program generation unit, and a proficiency assessment unit. The survey implementation unit conducts a preliminary survey. The analysis unit analyzes responses to the survey conducted by the survey implementation unit. The program generation unit generates an optimal learning program based on the results of the analysis by the analysis unit. The proficiency assessment unit provides the learning program generated by the program generation unit and assesses proficiency through dialogue with the generation AI after learning. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal learning program for each individual learner and accurately assess their level of proficiency. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The recurrent education support system according to an embodiment of the present invention analyzes a user's strengths and weaknesses, and their suitability for the program, based on a preliminary questionnaire, and then generates and provides an optimal learning program. Furthermore, after learning, the user's proficiency level is assessed through dialogue with the generating AI, allowing the user to deepen their learning to a level where they can use the program in practice. This allows the recurrent education support system to provide the user with an optimal learning program and assess their proficiency level after learning, thereby deepening their learning to a level where they can use the program in practice.

[0029] A recurrent education support system according to an embodiment includes a questionnaire implementation unit, an analysis unit, a program generation unit, and a proficiency assessment unit. The questionnaire implementation unit conducts a pre-survey. For example, questions are asked about the user's past learning experience, current skill level, interests, and learning goals. The analysis unit analyzes the responses to the questionnaire implemented by the questionnaire implementation unit. For example, a generation AI analyzes the questionnaire responses and identifies the user's strengths and weaknesses, and their suitability for the program. The program generation unit generates an optimal learning program based on the results of the analysis by the analysis unit. For example, the generation AI proposes a customized learning program based on the user's interests and skill level. The proficiency assessment unit provides the learning program generated by the program generation unit and assesses the user's proficiency through dialogue with the generation AI after learning. For example, the generation AI asks the user questions about the learning content and analyzes the answers to evaluate the user's proficiency. As a result, the recurrent education support system according to an embodiment provides the user with an optimal learning program and assesses the user's proficiency after learning, thereby deepening learning to a level that can be used in practice.

[0030] The survey implementation unit can identify the user's learning style based on the survey responses and propose a learning program that matches that style. For example, the survey implementation unit can identify whether the user is a visual learner or an auditory learner based on the survey responses. For example, a program that makes heavy use of visual learning materials can be proposed to a visual learner, and a program that focuses on audio learning materials can be proposed to an auditory learner. This makes it possible to provide an optimal learning program that matches the user's learning style.

[0031] The analysis unit can automatically import the user's past learning history and work history and perform a detailed analysis. For example, the analysis unit automatically imports the user's past learning history and performs a detailed analysis based on that data. For example, it evaluates the current skill level based on the courses taken in the past and the qualifications obtained. The analysis unit also automatically imports the user's work history and performs a detailed analysis based on that data. For example, it evaluates the user's areas of expertise and experience based on the work history and job content. This makes it possible to perform a detailed analysis based on the user's past learning history and work history and provide an optimal learning program.

[0032] The survey implementation unit can customize the content of the survey questions to suit the cultural background and regional characteristics of the user. For example, the survey implementation unit customizes the content of the survey questions to suit the cultural background of the user. For example, for users from different cultural spheres, questions appropriate to that culture are asked. The survey implementation unit also customizes the content of the survey questions to suit the regional characteristics of the user. For example, questions are asked according to the language and climate of the region. This makes it possible to conduct a survey that suits the cultural background and regional characteristics of the user.

[0033] The survey unit can analyze survey responses in real time and provide instant feedback. The survey unit, for example, builds a system that analyzes survey responses in real time and provides instant feedback to users. For example, it immediately suggests a learning program based on the content of the responses. The survey unit also provides instant feedback to users based on the results of the real-time analysis. For example, it advises on how to proceed with learning based on the content of the responses. This makes it possible to provide instant feedback on survey responses.

[0034] The program generation unit can dynamically adjust the program content according to the user's learning progress and provide an optimal learning pace. The program generation unit, for example, monitors the user's learning progress in real time and builds a system that dynamically adjusts the program content. For example, if progress is slow, supplementary learning materials are provided. The program generation unit also dynamically adjusts the program content according to the user's learning progress and provides an optimal learning pace. For example, if progress is fast, learning materials for moving on to the next step are provided. This makes it possible to provide an optimal learning pace according to the user's learning progress.

[0035] The program generation unit can introduce an algorithm that continuously improves the learning program based on user feedback. The program generation unit, for example, collects feedback from users and develops an algorithm that continuously improves the learning program based on that data. For example, it updates the content of the program based on the feedback. The program generation unit also introduces an algorithm that continuously improves the learning program based on user feedback. For example, it uses a feedback loop to improve the program. This allows the learning program to be continuously improved based on user feedback.

[0036] The program generation unit can combine learning programs from different fields to provide a cross-disciplinary learning experience. For example, the program generation unit builds a system that combines learning programs from different fields to provide a cross-disciplinary learning experience. For example, it provides a program that allows students to learn both IT and business. The program generation unit also combines learning programs from different fields to provide a cross-disciplinary learning experience. For example, it integrates different fields to provide an interdisciplinary project. This makes it possible to provide a learning experience that combines learning programs from different fields.

[0037] The program generation unit can add a module that simulates an actual business scenario to the learning program. For example, the program generation unit adds a module that simulates an actual business scenario to the learning program, allowing the user to acquire practical skills. For example, a simulation of project management is provided. The program generation unit also adds a module that simulates an actual business scenario to the learning program. For example, a module that simulates a business process is provided to allow the user to acquire practical skills. This allows the module that simulates an actual business scenario to be added.

[0038] The proficiency assessment unit can provide individual feedback based on the user's answers and indicate specific areas for improvement. The proficiency assessment unit, for example, analyzes the user's answers and provides feedback indicating specific areas for improvement. For example, it points out weaknesses in specific skills and suggests ways to improve them. The proficiency assessment unit also provides individual feedback based on the user's answers and indicates specific areas for improvement. For example, it gives specific advice based on the content of the answers. This makes it possible to provide the user with individual feedback indicating specific areas for improvement.

[0039] The proficiency assessment unit can analyze the user's answers, identify user groups with common weaknesses, and suggest group learning. The proficiency assessment unit, for example, builds a system that analyzes the user's answers and identifies user groups with common weaknesses. For example, it groups users with a low level of understanding of a particular skill. The proficiency assessment unit also analyzes the user's answers, identifies user groups with common weaknesses, and suggests group learning. For example, it suggests how to proceed with the group learning. This makes it possible to identify user groups with common weaknesses and suggest group learning.

[0040] The proficiency assessment unit can link the results of the proficiency assessment with other learning platforms to perform a comprehensive skill assessment. The proficiency assessment unit, for example, links the results of the proficiency assessment with other learning platforms to build a system that performs a comprehensive skill assessment. For example, it integrates data from multiple platforms to perform an assessment. The proficiency assessment unit also links the results of the proficiency assessment with other learning platforms to perform a comprehensive skill assessment. For example, it uses an API to share data and perform an assessment. This makes it possible to perform a comprehensive skill assessment in collaboration with other learning platforms.

[0041] The proficiency assessment unit can reflect the results of the proficiency assessment in the user's career plan and suggest the next learning step. The proficiency assessment unit, for example, builds a system that reflects the results of the proficiency assessment in the user's career plan and suggests the next learning step. For example, it suggests a learning program according to the career goal. The proficiency assessment unit also reflects the results of the proficiency assessment in the user's career plan and suggests the next learning step. For example, it suggests the next skill to learn based on the career goal. This makes it possible to suggest the next learning step based on the user's career plan.

[0042] The proficiency assessment unit provides a simulation environment for evaluating a user's practical skills and can provide feedback in real time. The proficiency assessment unit, for example, provides a simulation environment for evaluating a user's practical skills and builds a system for providing feedback in real time. For example, skills are evaluated through a virtual project. The proficiency assessment unit also provides a simulation environment for evaluating a user's practical skills and provides feedback in real time. For example, feedback is provided based on performance in the simulation environment. This allows the user's practical skills to be evaluated and feedback to be provided in real time.

[0043] The proficiency assessment unit provides project-based assignments for evaluating the user's practical skills, allowing the user to study in an environment similar to actual work. The proficiency assessment unit, for example, provides project-based assignments for evaluating the user's practical skills, building a system for studying in an environment similar to actual work. For example, it provides assignments that simulate actual projects. The proficiency assessment unit also provides project-based assignments for evaluating the user's practical skills, allowing the user to study in an environment similar to actual work. For example, it evaluates skills through a work simulation. This allows the user's practical skills to be evaluated, and the user to study in an environment similar to actual work.

[0044] The proficiency assessment unit can provide opportunities for internships and on-the-job training to evaluate the user's practical skills. The proficiency assessment unit, for example, builds a system that provides opportunities for internships and on-the-job training to evaluate the user's practical skills. For example, on-the-job training is provided through collaboration with companies. The proficiency assessment unit also provides opportunities for internships and on-the-job training to evaluate the user's practical skills. For example, it provides a program for on-the-job training. This makes it possible to provide opportunities for internships and on-the-job training to evaluate the user's practical skills.

[0045] The proficiency assessment unit can introduce a peer review system for evaluating practical skills and receive feedback from other users. The proficiency assessment unit, for example, introduces a peer review system for evaluating practical skills and builds a system for receiving feedback from other users. For example, it peer reviews the results of a collaborative project. The proficiency assessment unit also introduces a peer review system for evaluating practical skills and receives feedback from other users. For example, it sets a format for the peer review and performs an evaluation. This allows the peer review system for evaluating practical skills to be introduced and receive feedback from other users.

[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] The survey implementation unit can identify the user's motivations and goals for learning based on the user's survey responses and suggest a learning program that matches them. For example, a user aiming to advance their career can be suggested a program that strengthens their professional skills, while a user looking to deepen their hobbies or interests can be suggested a program that allows them to learn while relaxing. The survey implementation unit can also identify the user's motivations and goals for learning and suggest a learning program that matches them. For example, a user aiming for self-improvement can be offered a program that promotes personal growth. This makes it possible to provide the user with the optimal learning program that matches their motivations and goals for learning.

[0048] The analysis unit can provide a dashboard that visualizes the user's learning progress based on the user's learning history and questionnaire responses. For example, the learning progress can be displayed in graphs and charts, allowing the user to grasp their learning status at a glance. The analysis unit can also provide a dashboard that visualizes the user's learning progress based on the user's learning history and questionnaire responses. For example, the learning progress can be displayed in a timeline format, allowing the user to look back on their past learning history. This allows the user to visualize their learning status and progress effectively in their studies.

[0049] The program generation unit can customize the format of the learning program according to the user's learning style. For example, a visual learner can be provided with a program that makes extensive use of visual learning materials, and an auditory learner can be provided with a program that mainly uses audio learning materials. The program generation unit also customizes the format of the learning program according to the user's learning style. For example, a practical learner can be provided with a program that includes many hands-on exercises. This makes it possible to provide the user with the optimal learning program according to their learning style.

[0050] The proficiency assessment unit can provide individual feedback based on the user's answers and suggest specific areas for improvement. For example, it can point out weaknesses in specific skills and suggest ways to improve them. The proficiency assessment unit can also provide individual feedback based on the user's answers and suggest specific areas for improvement. For example, it can provide specific advice based on the content of the answers. This makes it possible to provide the user with individual feedback that suggests specific areas for improvement.

[0051] The program generation department can combine learning programs from different fields to provide a cross-disciplinary learning experience. For example, it can provide a program where students can study both IT and business. The program generation department can also combine learning programs from different fields to provide a cross-disciplinary learning experience. For example, it can integrate different fields and provide an interdisciplinary project. This allows it to provide a learning experience where students can combine learning programs from different fields.

[0052] The proficiency assessment unit provides a simulation environment for assessing a user's practical skills and can provide feedback in real time. For example, the skills are assessed through a virtual project. The proficiency assessment unit also provides a simulation environment for assessing a user's practical skills and can provide feedback in real time. For example, the feedback is provided based on performance in the simulation environment. This allows the user's practical skills to be assessed and feedback to be provided in real time.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The survey implementation unit conducts a pre-survey, asking questions about the user's past learning experience, current skill level, interests, learning goals, etc. Step 2: The analysis unit analyzes the responses to the survey conducted by the survey implementation unit. For example, the generation AI analyzes the survey responses and identifies the user's strengths and weaknesses, and what they are good at and what they are not good at. Step 3: The program generation unit generates an optimal learning program based on the results of the analysis by the analysis unit. For example, the generation AI may propose a customized learning program based on the user's interests and skill level. Step 4: The proficiency assessment unit provides the learning program generated by the program generation unit and assesses the user's proficiency through dialogue with the generation AI after learning. For example, the generation AI may ask the user questions about the learning content and analyze the answers to assess the user's proficiency.

[0055] (Example 2) The recurrent education support system according to an embodiment of the present invention analyzes a user's strengths and weaknesses, and their suitability for the program, based on a preliminary questionnaire, and then generates and provides an optimal learning program. Furthermore, after learning, the user's proficiency level is assessed through dialogue with the generating AI, allowing the user to deepen their learning to a level where they can use the program in practice. This allows the recurrent education support system to provide the user with an optimal learning program and assess their proficiency level after learning, thereby deepening their learning to a level where they can use the program in practice.

[0056] A recurrent education support system according to an embodiment includes a questionnaire implementation unit, an analysis unit, a program generation unit, and a proficiency assessment unit. The questionnaire implementation unit conducts a pre-survey. For example, questions are asked about the user's past learning experience, current skill level, interests, and learning goals. The analysis unit analyzes the responses to the questionnaire implemented by the questionnaire implementation unit. For example, a generation AI analyzes the questionnaire responses and identifies the user's strengths and weaknesses, and their suitability for the program. The program generation unit generates an optimal learning program based on the results of the analysis by the analysis unit. For example, the generation AI proposes a customized learning program based on the user's interests and skill level. The proficiency assessment unit provides the learning program generated by the program generation unit and assesses the user's proficiency through dialogue with the generation AI after learning. For example, the generation AI asks the user questions about the learning content and analyzes the answers to evaluate the user's proficiency. As a result, the recurrent education support system according to an embodiment provides the user with an optimal learning program and assesses the user's proficiency after learning, thereby deepening learning to a level that can be used in practice.

[0057] The survey implementation unit can identify the user's learning style based on the survey responses and propose a learning program that matches that style. For example, the survey implementation unit can identify whether the user is a visual learner or an auditory learner based on the survey responses. For example, a program that makes heavy use of visual learning materials can be proposed to a visual learner, and a program that focuses on audio learning materials can be proposed to an auditory learner. This makes it possible to provide an optimal learning program that matches the user's learning style.

[0058] The analysis unit can automatically import the user's past learning history and work history and perform a detailed analysis. For example, the analysis unit automatically imports the user's past learning history and performs a detailed analysis based on that data. For example, it evaluates the current skill level based on the courses taken in the past and the qualifications obtained. The analysis unit also automatically imports the user's work history and performs a detailed analysis based on that data. For example, it evaluates the user's areas of expertise and experience based on the work history and job content. This makes it possible to perform a detailed analysis based on the user's past learning history and work history and provide an optimal learning program.

[0059] The analysis unit can use the emotion estimation function to analyze the user's emotions when answering a questionnaire and suggest a learning program to reduce stress and anxiety. For example, the analysis unit can analyze the user's emotions in real time when answering a questionnaire and suggest a learning program that will help them relax if they are feeling stressed or anxious. For example, it can provide a program that incorporates music with a relaxing effect. The analysis unit can also use the emotion estimation function to analyze the user's emotions and suggest a learning program to reduce stress and anxiety. For example, it can provide a program that incorporates relaxation techniques. This makes it possible to provide a learning program that takes the user's emotions into consideration.

[0060] The survey implementation unit can customize the content of the survey questions to suit the cultural background and regional characteristics of the user. For example, the survey implementation unit customizes the content of the survey questions to suit the cultural background of the user. For example, for users from different cultural spheres, questions appropriate to that culture are asked. The survey implementation unit also customizes the content of the survey questions to suit the regional characteristics of the user. For example, questions are asked according to the language and climate of the region. This makes it possible to conduct a survey that suits the cultural background and regional characteristics of the user.

[0061] The survey unit can analyze survey responses in real time and provide instant feedback. The survey unit, for example, builds a system that analyzes survey responses in real time and provides instant feedback to users. For example, it immediately suggests a learning program based on the content of the responses. The survey unit also provides instant feedback to users based on the results of the real-time analysis. For example, it advises on how to proceed with learning based on the content of the responses. This makes it possible to provide instant feedback on survey responses.

[0062] The analysis unit can use the emotion estimation function to monitor the user's emotions in real time when answering a questionnaire and add questions that elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when answering a questionnaire and add questions that elicit positive emotions. For example, questions about successful experiences are asked. The analysis unit also uses the emotion estimation function to monitor the user's emotions in real time and add questions that elicit positive emotions. For example, questions that elicit positive emotions are asked. This makes it possible to add questions that elicit positive emotions from the user.

[0063] The program generation unit can dynamically adjust the program content according to the user's learning progress and provide an optimal learning pace. The program generation unit, for example, monitors the user's learning progress in real time and builds a system that dynamically adjusts the program content. For example, if progress is slow, supplementary learning materials are provided. The program generation unit also dynamically adjusts the program content according to the user's learning progress and provides an optimal learning pace. For example, if progress is fast, learning materials for moving on to the next step are provided. This makes it possible to provide an optimal learning pace according to the user's learning progress.

[0064] The program generation unit can introduce an algorithm that continuously improves the learning program based on user feedback. The program generation unit, for example, collects feedback from users and develops an algorithm that continuously improves the learning program based on that data. For example, it updates the content of the program based on the feedback. The program generation unit also introduces an algorithm that continuously improves the learning program based on user feedback. For example, it uses a feedback loop to improve the program. This allows the learning program to be continuously improved based on user feedback.

[0065] The program generation unit can use the emotion estimation function to monitor the user's emotions during learning and add interactive elements to maintain motivation. For example, the program generation unit can use the emotion estimation function to monitor the user's emotions during learning in real time and add interactive elements to maintain motivation. For example, the program generation unit can display encouraging messages. The program generation unit can also use the emotion estimation function to monitor the user's emotions during learning and add interactive elements to maintain motivation. For example, the program generation unit can provide an interactive quiz. This can add interactive elements to maintain user motivation.

[0066] The program generation unit can combine learning programs from different fields to provide a cross-disciplinary learning experience. For example, the program generation unit builds a system that combines learning programs from different fields to provide a cross-disciplinary learning experience. For example, it provides a program that allows students to learn both IT and business. The program generation unit also combines learning programs from different fields to provide a cross-disciplinary learning experience. For example, it integrates different fields to provide an interdisciplinary project. This makes it possible to provide a learning experience that combines learning programs from different fields.

[0067] The program generation unit can add a module that simulates an actual business scenario to the learning program. For example, the program generation unit adds a module that simulates an actual business scenario to the learning program, allowing the user to acquire practical skills. For example, a simulation of project management is provided. The program generation unit also adds a module that simulates an actual business scenario to the learning program. For example, a module that simulates a business process is provided to allow the user to acquire practical skills. This allows the module that simulates an actual business scenario to be added.

[0068] The program generation unit can use the emotion estimation function to monitor the user's emotions during learning in real time and add interactive elements to elicit positive emotions. The program generation unit, for example, uses the emotion estimation function to monitor the user's emotions during learning in real time and add interactive elements to elicit positive emotions. For example, a message emphasizing a successful experience can be displayed. The program generation unit can also use the emotion estimation function to monitor the user's emotions during learning in real time and add interactive elements to elicit positive emotions. For example, an interactive quiz can be provided. This allows the program generation unit to add interactive elements to elicit positive emotions from the user.

[0069] The proficiency assessment unit can provide individual feedback based on the user's answers and indicate specific areas for improvement. The proficiency assessment unit, for example, analyzes the user's answers and provides feedback indicating specific areas for improvement. For example, it points out weaknesses in specific skills and suggests ways to improve them. The proficiency assessment unit also provides individual feedback based on the user's answers and indicates specific areas for improvement. For example, it gives specific advice based on the content of the answers. This makes it possible to provide the user with individual feedback indicating specific areas for improvement.

[0070] The proficiency assessment unit can analyze the user's answers, identify user groups with common weaknesses, and suggest group learning. The proficiency assessment unit, for example, builds a system that analyzes the user's answers and identifies user groups with common weaknesses. For example, it groups users with a low level of understanding of a particular skill. The proficiency assessment unit also analyzes the user's answers, identifies user groups with common weaknesses, and suggests group learning. For example, it suggests how to proceed with the group learning. This makes it possible to identify user groups with common weaknesses and suggest group learning.

[0071] The proficiency determination unit can use the emotion estimation function to analyze the emotion of the user when answering a question and provide feedback to reduce stress and anxiety. The proficiency determination unit can, for example, use the emotion estimation function to analyze the emotion of the user when answering a question and provide feedback to reduce stress and anxiety. For example, it can provide advice to help the user relax. The proficiency determination unit can also use the emotion estimation function to analyze the emotion of the user when answering a question and provide feedback to reduce stress and anxiety. For example, it can display a positive message. This can provide feedback to reduce the user's stress and anxiety.

[0072] The proficiency assessment unit can link the results of the proficiency assessment with other learning platforms to perform a comprehensive skill assessment. The proficiency assessment unit, for example, links the results of the proficiency assessment with other learning platforms to build a system that performs a comprehensive skill assessment. For example, it integrates data from multiple platforms to perform an assessment. The proficiency assessment unit also links the results of the proficiency assessment with other learning platforms to perform a comprehensive skill assessment. For example, it uses an API to share data and perform an assessment. This makes it possible to perform a comprehensive skill assessment in collaboration with other learning platforms.

[0073] The proficiency assessment unit can reflect the results of the proficiency assessment in the user's career plan and suggest the next learning step. The proficiency assessment unit, for example, builds a system that reflects the results of the proficiency assessment in the user's career plan and suggests the next learning step. For example, it suggests a learning program according to the career goal. The proficiency assessment unit also reflects the results of the proficiency assessment in the user's career plan and suggests the next learning step. For example, it suggests the next skill to learn based on the career goal. This makes it possible to suggest the next learning step based on the user's career plan.

[0074] The proficiency determination unit can use the emotion estimation function to monitor the user's emotion when answering in real time and provide feedback to elicit positive emotions. The proficiency determination unit can, for example, use the emotion estimation function to monitor the user's emotion when answering in real time and provide feedback to elicit positive emotions. For example, a message emphasizing a successful experience can be displayed. The proficiency determination unit can also use the emotion estimation function to monitor the user's emotion when answering in real time and provide feedback to elicit positive emotions. For example, an encouraging message can be displayed. This makes it possible to provide feedback to elicit positive emotions from the user.

[0075] The proficiency assessment unit provides a simulation environment for evaluating a user's practical skills and can provide feedback in real time. The proficiency assessment unit, for example, provides a simulation environment for evaluating a user's practical skills and builds a system for providing feedback in real time. For example, skills are evaluated through a virtual project. The proficiency assessment unit also provides a simulation environment for evaluating a user's practical skills and provides feedback in real time. For example, feedback is provided based on performance in the simulation environment. This allows the user's practical skills to be evaluated and feedback to be provided in real time.

[0076] The proficiency assessment unit provides project-based assignments for evaluating the user's practical skills, allowing the user to study in an environment similar to actual work. The proficiency assessment unit, for example, provides project-based assignments for evaluating the user's practical skills, building a system for studying in an environment similar to actual work. For example, it provides assignments that simulate actual projects. The proficiency assessment unit also provides project-based assignments for evaluating the user's practical skills, allowing the user to study in an environment similar to actual work. For example, it evaluates skills through a work simulation. This allows the user's practical skills to be evaluated, and the user to study in an environment similar to actual work.

[0077] The proficiency assessment unit can use the emotion estimation function to monitor the user's emotions during the practical task and provide support to reduce stress and anxiety. The proficiency assessment unit can, for example, use the emotion estimation function to monitor the user's emotions during the practical task and provide support to reduce stress and anxiety. For example, it can provide advice to help the user relax. The proficiency assessment unit can also use the emotion estimation function to monitor the user's emotions during the practical task and provide support to reduce stress and anxiety. For example, it can display a positive message. This can provide support to reduce the user's stress and anxiety during the practical task.

[0078] The proficiency assessment unit can provide opportunities for internships and on-the-job training to evaluate the user's practical skills. The proficiency assessment unit, for example, builds a system that provides opportunities for internships and on-the-job training to evaluate the user's practical skills. For example, on-the-job training is provided through collaboration with companies. The proficiency assessment unit also provides opportunities for internships and on-the-job training to evaluate the user's practical skills. For example, it provides a program for on-the-job training. This makes it possible to provide opportunities for internships and on-the-job training to evaluate the user's practical skills.

[0079] The proficiency assessment unit can introduce a peer review system for evaluating practical skills and receive feedback from other users. The proficiency assessment unit, for example, introduces a peer review system for evaluating practical skills and builds a system for receiving feedback from other users. For example, it peer reviews the results of a collaborative project. The proficiency assessment unit also introduces a peer review system for evaluating practical skills and receives feedback from other users. For example, it sets a format for the peer review and performs an evaluation. This allows the peer review system for evaluating practical skills to be introduced and receive feedback from other users.

[0080] The proficiency assessment unit can use the emotion estimation function to monitor the user's emotions during the practical task in real time and provide support for eliciting positive emotions. The proficiency assessment unit can, for example, use the emotion estimation function to monitor the user's emotions during the practical task in real time and provide support for eliciting positive emotions. For example, a message emphasizing successful experiences can be displayed. The proficiency assessment unit can also use the emotion estimation function to monitor the user's emotions during the practical task in real time and provide support for eliciting positive emotions. For example, an encouraging message can be displayed. This can provide support for eliciting positive emotions during the practical task.

[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] The survey implementation unit can identify the user's motivations and goals for learning based on the user's survey responses and suggest a learning program that matches them. For example, a user aiming to advance their career can be suggested a program that strengthens their professional skills, while a user looking to deepen their hobbies or interests can be suggested a program that allows them to learn while relaxing. The survey implementation unit can also identify the user's motivations and goals for learning and suggest a learning program that matches them. For example, a user aiming for self-improvement can be offered a program that promotes personal growth. This makes it possible to provide the user with the optimal learning program that matches their motivations and goals for learning.

[0083] The analysis unit can provide a dashboard that visualizes the user's learning progress based on the user's learning history and questionnaire responses. For example, the learning progress can be displayed in graphs and charts, allowing the user to grasp their learning status at a glance. The analysis unit can also provide a dashboard that visualizes the user's learning progress based on the user's learning history and questionnaire responses. For example, the learning progress can be displayed in a timeline format, allowing the user to look back on their past learning history. This allows the user to visualize their learning status and progress effectively in their studies.

[0084] The program generation unit can customize the format of the learning program according to the user's learning style. For example, a visual learner can be provided with a program that makes extensive use of visual learning materials, and an auditory learner can be provided with a program that mainly uses audio learning materials. The program generation unit also customizes the format of the learning program according to the user's learning style. For example, a practical learner can be provided with a program that includes many hands-on exercises. This makes it possible to provide the user with the optimal learning program according to their learning style.

[0085] The program generation unit can use the emotion estimation function to monitor the user's emotions during learning and add interactive elements to maintain motivation. For example, encouraging messages can be displayed according to the user's learning progress, thereby increasing the user's motivation. The program generation unit can also use the emotion estimation function to monitor the user's emotions during learning and add interactive elements to maintain motivation. For example, rewards can be provided according to the user's learning progress. This can provide interactive elements to maintain the user's motivation and encourage them to continue learning.

[0086] The proficiency assessment unit can provide individual feedback based on the user's answers and suggest specific areas for improvement. For example, it can point out weaknesses in specific skills and suggest ways to improve them. The proficiency assessment unit can also provide individual feedback based on the user's answers and suggest specific areas for improvement. For example, it can provide specific advice based on the content of the answers. This makes it possible to provide the user with individual feedback that suggests specific areas for improvement.

[0087] The proficiency assessment unit can use the emotion estimation function to analyze the emotion of the user when answering a question and provide feedback to reduce stress and anxiety. For example, it can provide advice to help the user relax. The proficiency assessment unit can also use the emotion estimation function to analyze the emotion of the user when answering a question and provide feedback to reduce stress and anxiety. For example, it can display a positive message. This makes it possible to provide feedback to reduce the user's stress and anxiety.

[0088] The program generation department can combine learning programs from different fields to provide a cross-disciplinary learning experience. For example, it can provide a program where students can study both IT and business. The program generation department can also combine learning programs from different fields to provide a cross-disciplinary learning experience. For example, it can integrate different fields and provide an interdisciplinary project. This allows it to provide a learning experience where students can combine learning programs from different fields.

[0089] The proficiency assessment unit can use the emotion estimation function to monitor the user's emotions during the practical task and provide support to reduce stress and anxiety. For example, it can provide advice to help the user relax. The proficiency assessment unit can also use the emotion estimation function to monitor the user's emotions during the practical task and provide support to reduce stress and anxiety. For example, it can display positive messages. This can provide support to reduce the user's stress and anxiety during the practical task.

[0090] The proficiency assessment unit provides a simulation environment for assessing a user's practical skills and can provide feedback in real time. For example, the skills are assessed through a virtual project. The proficiency assessment unit also provides a simulation environment for assessing a user's practical skills and can provide feedback in real time. For example, the feedback is provided based on performance in the simulation environment. This allows the user's practical skills to be assessed and feedback to be provided in real time.

[0091] The program generation unit can use the emotion estimation function to monitor the user's emotions in real time while studying and add interactive elements to elicit positive emotions. For example, a message emphasizing successful experiences can be displayed. The program generation unit can also use the emotion estimation function to monitor the user's emotions in real time while studying and add interactive elements to elicit positive emotions. For example, an interactive quiz can be provided. This allows the program generation unit to add interactive elements to elicit positive emotions in the user.

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

[0093] Step 1: The survey implementation unit conducts a pre-survey, asking questions about the user's past learning experience, current skill level, interests, learning goals, etc. Step 2: The analysis unit analyzes the responses to the survey conducted by the survey implementation unit. For example, the generation AI analyzes the survey responses and identifies the user's strengths and weaknesses, and what they are good at and what they are not good at. Step 3: The program generation unit generates an optimal learning program based on the results of the analysis by the analysis unit. For example, the generation AI may propose a customized learning program based on the user's interests and skill level. Step 4: The proficiency assessment unit provides the learning program generated by the program generation unit and assesses the user's proficiency through dialogue with the generation AI after learning. For example, the generation AI may ask the user questions about the learning content and analyze the answers to assess the user's proficiency.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 type 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. A survey implementation department that conducts a preliminary survey; an analysis unit that analyzes responses to the questionnaire conducted by the questionnaire conducting unit; a program generation unit that generates an optimal learning program based on the results of the analysis by the analysis unit; a proficiency assessment unit that provides the learning program generated by the program generation unit and assesses the proficiency level through dialogue with the generation AI after learning; A system characterized by:

2. The analysis unit Automatically import users' past learning history and work experience for detailed analysis 2. The system of claim 1.

3. The survey implementation unit: Customize survey questions to fit the user's cultural background and regional characteristics 2. The system of claim 1.

4. The program generation unit Dynamically adjusts program content according to the user's learning progress, providing an optimal learning pace 2. The system of claim 1.

5. The proficiency determination unit Provide personalized feedback and specific areas for improvement based on your responses 2. The system of claim 1.

6. The analysis unit Analyze users' emotions when answering a questionnaire and suggest learning programs to reduce stress and anxiety 2. The system of claim 1.

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