System

Abstract

An object of a system according to an embodiment is to propose an optimal qualification on the basis of work content and a skill set of a user and support learning for efficiently acquiring a qualification.SOLUTION: A system includes a recommended qualification proposal part, a countermeasure problem preparation part, a weak field specification part, and an iterative learning part. The recommended qualification proposing section analyzes the work content and the skill set of the user and proposes an optimum qualification. The countermeasure problem preparation part prepares a countermeasure problem on the basis of the qualification proposed by the recommended qualification proposal part. A weak field specification part analyzes an answer result of the countermeasure problem created by the countermeasure problem creation part, and specifies a weak field of the user. The repetitive learning unit repeatedly sets a problem related to the weak field specified by the weak field specifying unit.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 technologies have had the problem of making it difficult for users to find the qualifications that are best suited to their work and to study efficiently to obtain the qualifications.

[0005] The system according to the embodiment aims to propose the most suitable qualification based on the user's job content and skill set, and to efficiently support learning for obtaining the qualification. [Means for solving the problem]

[0006] The system according to the embodiment includes a recommended qualification suggestion unit, a practice question creation unit, a weak area identification unit, and an iterative learning unit. The recommended qualification suggestion unit analyzes the user's work content and skill set and suggests the most suitable qualification. The practice question creation unit creates practice questions based on the qualifications suggested by the recommended qualification suggestion unit. The weak area identification unit analyzes the answers to the practice questions created by the practice question creation unit and identifies the user's weak areas. The iterative learning unit repeatedly presents questions related to the weak areas identified by the weak area identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable qualification based on the user's job content and skill set, and can efficiently support learning to obtain the qualification. [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 qualification acquisition support system according to an embodiment of the present invention analyzes a user's work content and skill set, uses a generation AI to propose the most suitable qualification, creates practice questions for obtaining that qualification, and identifies weak areas and encourages repeated learning. As a result, the qualification acquisition support system efficiently supports users in obtaining qualifications and improves work productivity.

[0029] A qualification acquisition support system according to an embodiment includes a recommended qualification suggestion unit, a practice question creation unit, a weak area identification unit, and an iterative learning unit. The recommended qualification suggestion unit analyzes a user's work content and skill set and suggests the most appropriate qualification. For example, if a user works in the IT industry, the recommended qualification suggestion unit suggests qualifications such as the "Information Processing Engineer Examination" or "Project Manager Examination." The recommended qualification suggestion unit also inputs prompts containing information about the user's work content and skill set to a generation AI, which then suggests the most appropriate qualification based on the prompts. The practice question creation unit creates practice questions based on the qualifications suggested by the recommended qualification suggestion unit. For example, the practice question creation unit generates questions related to algorithms and databases as practice questions for the "Information Processing Engineer Examination." The practice question creation unit also inputs prompts containing information about the qualification content and exam scope to the generation AI, which then creates practice questions based on the prompts. The weak area identification unit analyzes the answers to the practice questions created by the practice question creation unit and identifies the user's weak areas. For example, if a user makes many mistakes on questions related to algorithms, the weak area identification unit identifies that area as a weak area. The weak area identification unit also inputs prompts containing information about the user's answer results to the generation AI, and the generation AI identifies the weak area based on the prompts. The iterative learning unit repeatedly presents questions related to the weak area identified by the weak area identification unit. For example, if the user has difficulty with questions related to algorithms, the iterative learning unit repeatedly presents questions related to algorithms. The iterative learning unit also inputs prompts containing information about the identified weak area to the generation AI, and the generation AI creates questions for iterative learning based on the prompts. In this way, the qualification acquisition support system can efficiently support users in obtaining qualifications and improve work productivity.

[0030] The recommended qualification suggestion unit can analyze a user's past work history and project results and suggest the most suitable qualification based on that. For example, the recommended qualification suggestion unit can analyze in detail the content and results of projects that the user has been in charge of in the past and suggest the most suitable qualification based on that data. For example, a project manager exam can be suggested for a user with extensive experience in project management. This makes it possible to suggest the most suitable qualification based on the user's past work history and project results.

[0031] The recommended qualification suggestion unit can analyze the latest industry trends and technology trends related to the user's work and suggest qualifications based on that. The recommended qualification suggestion unit can, for example, analyze the latest industry trends and technology trends and suggest the most suitable qualifications for the user based on that. For example, if AI technology is rapidly evolving, it can suggest AI-related qualifications. This makes it possible to suggest qualifications based on the latest industry trends and technology trends.

[0032] The practice question creation unit can analyze past questions from qualification exams, learn question trends, and create practice questions. The practice question creation unit, for example, analyzes past questions from qualification exams, learns question trends, and creates practice questions. For example, it prioritizes questions that have appeared frequently in the past. This allows practice questions to be created based on the analysis of past questions.

[0033] The test preparation section can analyze the official guidelines and syllabus of the qualification exam and create test preparation questions based on them. The test preparation section can analyze the official guidelines and syllabus of the qualification exam and create test preparation questions based on them. For example, the test preparation section can include questions on important topics described in the official guidelines. This allows test preparation questions to be created based on the official guidelines and syllabus.

[0034] The weak area identification unit can analyze the user's answer history in detail and identify patterns of questions that were answered incorrectly. The weak area identification unit, for example, analyzes the user's answer history in detail and identifies patterns of questions that were answered incorrectly. For example, it extracts mistakes related to a specific theme or topic. This makes it possible to analyze the user's answer history and identify patterns of questions that were answered incorrectly.

[0035] The weak area identification unit can analyze the user's answering speed and time spent wondering to identify the weak area. The weak area identification unit can, for example, analyze the user's answering speed and time spent wondering to identify the weak area. For example, it can extract questions that take a long time to answer. This makes it possible to identify the weak area by analyzing the user's answering speed and time spent wondering.

[0036] The repetitive learning unit can monitor the user's learning progress in real time and present repetitive questions at the optimal timing. The repetitive learning unit, for example, monitors the user's learning progress in real time and presents repetitive questions at the optimal timing. For example, presenting repetitive questions when learning is stagnating. This makes it possible to monitor the user's learning progress in real time and present repetitive questions at the optimal timing.

[0037] The repetitive learning unit can create repetitive questions that match the user's learning style. The repetitive learning unit creates repetitive questions that match the user's learning style (visual, auditory, tactile), for example. For example, visual learners are given questions that use diagrams and graphs. This makes it possible to create repetitive questions that match the user's learning style.

[0038] The repetitive learning unit can set a combination of repetitive questions in different formats. The repetitive learning unit sets a combination of repetitive questions in different formats (multiple choice, written, practical). For example, multiple choice questions and written questions are set alternately. This allows a combination of repetitive questions in different formats to be set.

[0039] The repetitive learning unit can provide repetitive problems in a group learning format in cooperation with the user's colleagues or superiors. The repetitive learning unit, for example, provides repetitive problems in a group learning format in cooperation with the user's colleagues or superiors. For example, it provides problems to be solved as a team. This allows the user to provide repetitive problems in a group learning format in cooperation with the user's colleagues or superiors.

[0040] The system can periodically monitor work performance after qualification is obtained and provide feedback. The system can periodically monitor work performance after qualification is obtained and provide feedback. For example, the system can analyze the progress of work and point out areas for improvement. This makes it possible to monitor work performance after qualification is obtained and provide feedback.

[0041] The system can specifically suggest career paths after obtaining qualifications and support long-term goal setting. The system can specifically suggest career paths after obtaining qualifications and support long-term goal setting, for example, by proposing a career advancement plan that makes use of qualifications. This makes it possible to specifically suggest career paths after obtaining qualifications and support long-term goal setting.

[0042] The system can suggest tools and apps to improve work efficiency after obtaining a qualification and use them in actual work. The system can suggest tools and apps to improve work efficiency after obtaining a qualification and use them in actual work. For example, it can suggest project management tools and task management apps. This allows the system to suggest tools and apps to improve work efficiency after obtaining a qualification and use them in actual work.

[0043] The system can suggest training and seminars for improving skills after obtaining a qualification, and support continuous learning.The system can suggest training and seminars for improving skills after obtaining a qualification, and support continuous learning, for example.For example, it can suggest training on the latest technology and seminars on industry trends.This makes it possible to suggest training and seminars for improving skills after obtaining a qualification, and support continuous learning.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The qualification acquisition support system not only analyzes the user's work content and skill set, but can also suggest qualifications taking into account the user's hobbies and interests. For example, if the user programs as a hobby, it can suggest programming-related qualifications. It can also increase the user's motivation to learn by suggesting qualifications related to the user's field of interest. Furthermore, it can also suggest career paths after obtaining qualifications based on the user's hobbies and interests.

[0046] The certification support system can analyze a user's past learning history and test results and suggest optimal learning methods based on that information. For example, it can recommend online learning to a user who has previously achieved high grades in online courses. It can also identify a user's preferred learning style from past test results and create a study plan based on that. It can also suggest effective learning tools and resources based on the user's learning history.

[0047] The qualification acquisition support system can make suggestions to optimize the user's learning environment. For example, if the user is studying at home, it can suggest environmental settings to improve concentration. Also, if the user is studying at a cafe, it can suggest appropriate learning tools and resources. Furthermore, by suggesting a learning environment that matches the user's learning style, it can improve learning efficiency.

[0048] The qualification acquisition support system can provide a dashboard to visualize the user's learning progress. For example, it can display the learning progress and achievement level in graphs and charts. It can also allow the user to check in real time how far they have progressed toward their goals. Furthermore, it can suggest the next tasks and goals to tackle according to the learning progress.

[0049] The qualification acquisition support system can customize the learning content according to the user's learning progress. For example, if the user has achieved high grades in a particular field, the learning content in that field can be made more advanced. Also, for areas in which the user is weak, the learning content can be adjusted so that the user can learn from the basics. Furthermore, the learning plan can be flexibly changed according to the user's learning style and progress.

[0050] The qualification acquisition support system can suggest learning priorities according to the user's learning progress. For example, if the user is falling behind in a particular field, it will suggest that they prioritize studying that field. It will also suggest further in-depth study in areas in which the user excels. Furthermore, it can flexibly change learning priorities according to the user's learning goals.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The recommended qualification suggestion unit analyzes the user's job content and skill set and suggests the most suitable qualifications. For example, if the user works in the IT industry, it will suggest qualifications such as the "Information Processing Engineer Examination" or "Project Manager Examination." The recommended qualification suggestion unit also inputs prompts containing information about the user's job content and skill set into the generation AI, which then suggests the most suitable qualifications based on the prompts. Step 2: The practice question creation unit creates practice questions based on the qualifications proposed by the recommended qualification proposal unit. For example, it generates questions related to algorithms and databases as practice questions for the "Information Processing Engineer Examination." The practice question creation unit also inputs prompts containing information about the qualification content and exam scope into the generation AI, which then creates practice questions based on the prompts. Step 3: The weak area identification unit analyzes the answers to the practice questions created by the practice question creation unit and identifies the user's weak areas. For example, if the user makes many mistakes on questions about algorithms, it will identify that area as a weak area. The weak area identification unit also inputs prompts containing information about the user's answers to the generation AI, and the generation AI identifies the weak areas based on the prompts. Step 4: The iterative learning unit repeatedly presents questions related to the weak areas identified by the weak area identification unit. For example, if the user has difficulty with problems related to algorithms, it repeatedly presents questions related to algorithms. The iterative learning unit also inputs prompts containing information about the identified weak areas to the generation AI, and the generation AI creates questions for iterative learning based on the prompts.

[0053] (Example 2) The qualification acquisition support system according to an embodiment of the present invention analyzes a user's work content and skill set, uses a generation AI to propose the most suitable qualification, creates practice questions for obtaining that qualification, and identifies weak areas and encourages repeated learning. As a result, the qualification acquisition support system efficiently supports users in obtaining qualifications and improves work productivity.

[0054] A qualification acquisition support system according to an embodiment includes a recommended qualification suggestion unit, a practice question creation unit, a weak area identification unit, and an iterative learning unit. The recommended qualification suggestion unit analyzes a user's work content and skill set and suggests the most appropriate qualification. For example, if a user works in the IT industry, the recommended qualification suggestion unit suggests qualifications such as the "Information Processing Engineer Examination" or "Project Manager Examination." The recommended qualification suggestion unit also inputs prompts containing information about the user's work content and skill set to a generation AI, which then suggests the most appropriate qualification based on the prompts. The practice question creation unit creates practice questions based on the qualifications suggested by the recommended qualification suggestion unit. For example, the practice question creation unit generates questions related to algorithms and databases as practice questions for the "Information Processing Engineer Examination." The practice question creation unit also inputs prompts containing information about the qualification content and exam scope to the generation AI, which then creates practice questions based on the prompts. The weak area identification unit analyzes the answers to the practice questions created by the practice question creation unit and identifies the user's weak areas. For example, if a user makes many mistakes on questions related to algorithms, the weak area identification unit identifies that area as a weak area. The weak area identification unit also inputs prompts containing information about the user's answer results to the generation AI, and the generation AI identifies the weak area based on the prompts. The iterative learning unit repeatedly presents questions related to the weak area identified by the weak area identification unit. For example, if the user has difficulty with questions related to algorithms, the iterative learning unit repeatedly presents questions related to algorithms. The iterative learning unit also inputs prompts containing information about the identified weak area to the generation AI, and the generation AI creates questions for iterative learning based on the prompts. In this way, the qualification acquisition support system can efficiently support users in obtaining qualifications and improve work productivity.

[0055] The recommended qualification suggestion unit can analyze a user's past work history and project results and suggest the most suitable qualification based on that. For example, the recommended qualification suggestion unit can analyze in detail the content and results of projects that the user has been in charge of in the past and suggest the most suitable qualification based on that data. For example, a project manager exam can be suggested for a user with extensive experience in project management. This makes it possible to suggest the most suitable qualification based on the user's past work history and project results.

[0056] The recommended qualification suggestion unit can analyze the latest industry trends and technology trends related to the user's work and suggest qualifications based on that. The recommended qualification suggestion unit can, for example, analyze the latest industry trends and technology trends and suggest the most suitable qualifications for the user based on that. For example, if AI technology is rapidly evolving, it can suggest AI-related qualifications. This makes it possible to suggest qualifications based on the latest industry trends and technology trends.

[0057] The recommended qualification suggestion unit can use the emotion estimation function to suggest qualifications that will most interest and motivate the user. The recommended qualification suggestion unit, for example, uses the emotion estimation function to suggest qualifications that will most likely interest the user. For example, it analyzes the user's past behavioral data and feedback to identify qualifications that will pique the user's interest. This makes it possible to suggest qualifications based on the user's interests and motivation.

[0058] The practice question creation unit can analyze past questions from qualification exams, learn question trends, and create practice questions. The practice question creation unit, for example, analyzes past questions from qualification exams, learns question trends, and creates practice questions. For example, it prioritizes questions that have appeared frequently in the past. This allows practice questions to be created based on the analysis of past questions.

[0059] The test preparation section can analyze the official guidelines and syllabus of the qualification exam and create test preparation questions based on them. The test preparation section can analyze the official guidelines and syllabus of the qualification exam and create test preparation questions based on them. For example, the test preparation section can include questions on important topics described in the official guidelines. This allows test preparation questions to be created based on the official guidelines and syllabus.

[0060] The practice question creation unit can use the emotion estimation function to propose a question format that reduces the stress the user feels when answering a question. The practice question creation unit, for example, uses the emotion estimation function to propose a question format that reduces the stress the user feels when answering a question. For example, it selects a question format that allows the user to relax. This makes it possible to propose a question format that reduces the user's stress.

[0061] The weak area identification unit can analyze the user's answer history in detail and identify patterns of questions that were answered incorrectly. The weak area identification unit, for example, analyzes the user's answer history in detail and identifies patterns of questions that were answered incorrectly. For example, it extracts mistakes related to a specific theme or topic. This makes it possible to analyze the user's answer history and identify patterns of questions that were answered incorrectly.

[0062] The weak area identification unit can analyze the user's answering speed and time spent wondering to identify the weak area. The weak area identification unit can, for example, analyze the user's answering speed and time spent wondering to identify the weak area. For example, it can extract questions that take a long time to answer. This makes it possible to identify the weak area by analyzing the user's answering speed and time spent wondering.

[0063] The weak area identification unit uses the emotion estimation function to identify the area in which the user feels the most stress and allows the user to focus on studying that area. The weak area identification unit, for example, uses the emotion estimation function to identify the area in which the user feels the most stress. For example, it extracts areas in which negative emotions are strongly expressed when answering questions. This allows the user to identify the area in which the user feels the most stress and allows the user to focus on studying that area.

[0064] The repetitive learning unit can monitor the user's learning progress in real time and present repetitive questions at the optimal timing. The repetitive learning unit, for example, monitors the user's learning progress in real time and presents repetitive questions at the optimal timing. For example, presenting repetitive questions when learning is stagnating. This makes it possible to monitor the user's learning progress in real time and present repetitive questions at the optimal timing.

[0065] The repetitive learning unit can create repetitive questions that match the user's learning style. The repetitive learning unit creates repetitive questions that match the user's learning style (visual, auditory, tactile), for example. For example, visual learners are given questions that use diagrams and graphs. This makes it possible to create repetitive questions that match the user's learning style.

[0066] The repetitive learning unit can use the emotion estimation function to present repetitive questions at a timing when the user feels most motivated. The repetitive learning unit, for example, uses the emotion estimation function to present repetitive questions at a timing when the user feels most motivated. For example, the optimal timing is selected based on the user's emotion data. This allows repetitive questions to be presented at a timing when the user feels most motivated.

[0067] The repetitive learning unit can set a combination of repetitive questions in different formats. The repetitive learning unit sets a combination of repetitive questions in different formats (multiple choice, written, practical). For example, multiple choice questions and written questions are set alternately. This allows a combination of repetitive questions in different formats to be set.

[0068] The repetitive learning unit can provide repetitive problems in a group learning format in cooperation with the user's colleagues or superiors. The repetitive learning unit, for example, provides repetitive problems in a group learning format in cooperation with the user's colleagues or superiors. For example, it provides problems to be solved as a team. This allows the user to provide repetitive problems in a group learning format in cooperation with the user's colleagues or superiors.

[0069] The repetitive learning unit can use the emotion estimation function to suggest an environment in which the user can study in the most relaxed manner. The repetitive learning unit, for example, uses the emotion estimation function to suggest an environment in which the user can study in the most relaxed manner. For example, the repetitive learning unit selects an optimal learning environment based on the user's emotion data. This makes it possible to suggest an environment in which the user can study in the most relaxed manner.

[0070] The system can periodically monitor work performance after qualification is obtained and provide feedback. The system can periodically monitor work performance after qualification is obtained and provide feedback. For example, the system can analyze the progress of work and point out areas for improvement. This makes it possible to monitor work performance after qualification is obtained and provide feedback.

[0071] The system can specifically suggest career paths after obtaining qualifications and support long-term goal setting. The system can specifically suggest career paths after obtaining qualifications and support long-term goal setting, for example, by proposing a career advancement plan that makes use of qualifications. This makes it possible to specifically suggest career paths after obtaining qualifications and support long-term goal setting.

[0072] The system can use the emotion estimation function to propose measures to maintain motivation after obtaining a qualification. The system can, for example, use the emotion estimation function to propose measures to maintain motivation after obtaining a qualification. For example, the system can select optimal measures to maintain motivation based on the user's emotion data. This makes it possible to propose measures to maintain motivation after obtaining a qualification.

[0073] The system can suggest tools and apps to improve work efficiency after obtaining a qualification and use them in actual work. The system can suggest tools and apps to improve work efficiency after obtaining a qualification and use them in actual work. For example, it can suggest project management tools and task management apps. This allows the system to suggest tools and apps to improve work efficiency after obtaining a qualification and use them in actual work.

[0074] The system can suggest training and seminars for improving skills after obtaining a qualification, and support continuous learning.The system can suggest training and seminars for improving skills after obtaining a qualification, and support continuous learning, for example.For example, it can suggest training on the latest technology and seminars on industry trends.This makes it possible to suggest training and seminars for improving skills after obtaining a qualification, and support continuous learning.

[0075] The system can use the emotion estimation function to suggest stress management measures after qualification is obtained. The system can, for example, use the emotion estimation function to suggest stress management measures after qualification is obtained. For example, the system can select optimal stress management measures based on the user's emotion data. This makes it possible to suggest stress management measures after qualification is obtained.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The qualification acquisition support system not only analyzes the user's work content and skill set, but can also suggest qualifications taking into account the user's hobbies and interests. For example, if the user programs as a hobby, it can suggest programming-related qualifications. It can also increase the user's motivation to learn by suggesting qualifications related to the user's field of interest. Furthermore, it can also suggest career paths after obtaining qualifications based on the user's hobbies and interests.

[0078] The certification support system can analyze a user's past learning history and test results and suggest optimal learning methods based on that information. For example, it can recommend online learning to a user who has previously achieved high grades in online courses. It can also identify a user's preferred learning style from past test results and create a study plan based on that. It can also suggest effective learning tools and resources based on the user's learning history.

[0079] The qualification acquisition support system can estimate the user's emotions and provide feedback to maintain motivation according to the user's learning progress. For example, if the user has negative feelings about learning, it can send an encouraging message. If the user has positive feelings about learning, it can provide feedback encouraging further challenges. This makes it possible to maintain motivation for learning based on the user's emotions.

[0080] The qualification acquisition support system can make suggestions to optimize the user's learning environment. For example, if the user is studying at home, it can suggest environmental settings to improve concentration. Also, if the user is studying at a cafe, it can suggest appropriate learning tools and resources. Furthermore, by suggesting a learning environment that matches the user's learning style, it can improve learning efficiency.

[0081] The qualification acquisition support system can estimate the user's emotions and suggest appropriate break times based on the progress of their studies. For example, if the user feels tired from studying, it will suggest taking a break. Also, if the user is maintaining their concentration, it will provide feedback encouraging them to continue studying. This makes it possible to suggest appropriate break times based on the user's emotions and improve learning efficiency.

[0082] The qualification acquisition support system can provide a dashboard to visualize the user's learning progress. For example, it can display the learning progress and achievement level in graphs and charts. It can also allow the user to check in real time how far they have progressed toward their goals. Furthermore, it can suggest the next tasks and goals to tackle according to the learning progress.

[0083] The qualification acquisition support system can estimate the user's emotions and provide appropriate rewards according to the user's progress in learning. For example, if the user has positive emotions about learning, badges or points can be awarded as rewards. On the other hand, if the user has negative emotions about learning, encouraging messages or special rewards can be provided. This allows the system to provide appropriate rewards based on the user's emotions and maintain motivation to learn.

[0084] The qualification acquisition support system can customize the learning content according to the user's learning progress. For example, if the user has achieved high grades in a particular field, the learning content in that field can be made more advanced. Also, for areas in which the user is weak, the learning content can be adjusted so that the user can learn from the basics. Furthermore, the learning plan can be flexibly changed according to the user's learning style and progress.

[0085] The qualification acquisition support system can estimate the user's emotions and suggest appropriate learning resources according to the user's learning progress. For example, if the user has positive emotions about learning, it will suggest more difficult learning resources. On the other hand, if the user has negative emotions about learning, it will suggest basic learning resources. This makes it possible to suggest appropriate learning resources based on the user's emotions and improve learning efficiency.

[0086] The qualification acquisition support system can suggest learning priorities according to the user's learning progress. For example, if the user is falling behind in a particular field, it will suggest that they prioritize studying that field. It will also suggest further in-depth study in areas in which the user excels. Furthermore, it can flexibly change learning priorities according to the user's learning goals.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The recommended qualification suggestion unit analyzes the user's job content and skill set and suggests the most suitable qualifications. For example, if the user works in the IT industry, it will suggest qualifications such as the "Information Processing Engineer Examination" or "Project Manager Examination." The recommended qualification suggestion unit also inputs prompts containing information about the user's job content and skill set into the generation AI, which then suggests the most suitable qualifications based on the prompts. Step 2: The practice question creation unit creates practice questions based on the qualifications proposed by the recommended qualification proposal unit. For example, it generates questions related to algorithms and databases as practice questions for the "Information Processing Engineer Examination." The practice question creation unit also inputs prompts containing information about the qualification content and exam scope into the generation AI, which then creates practice questions based on the prompts. Step 3: The weak area identification unit analyzes the answers to the practice questions created by the practice question creation unit and identifies the user's weak areas. For example, if the user makes many mistakes on questions about algorithms, it will identify that area as a weak area. The weak area identification unit also inputs prompts containing information about the user's answers to the generation AI, and the generation AI identifies the weak areas based on the prompts. Step 4: The iterative learning unit repeatedly presents questions related to the weak areas identified by the weak area identification unit. For example, if the user has difficulty with problems related to algorithms, it repeatedly presents questions related to algorithms. The iterative learning unit also inputs prompts containing information about the identified weak areas to the generation AI, and the generation AI creates questions for iterative learning based on the prompts.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 qualification recommendation department analyzes the user's work content and skill set and recommends the most suitable qualifications; a test preparation section for preparing test questions based on the qualifications proposed by the recommended qualification proposal section; a weak area identification unit that analyzes the answers to the practice questions created by the practice question creation unit and identifies the weak areas of the user; a repetitive learning unit that repeatedly presents questions related to the weak field identified by the weak field identifying unit, A system characterized by:

2. The recommended qualification suggestion unit Analyzes the user's past work history and project results, and suggests the most suitable qualifications based on that.

2. The system of claim 1.

3. The preparation question creation unit Analyze past exam questions, learn exam trends, and create practice questions 2. The system of claim 1.

4. The weak field identification unit Analyze the user's answer history in detail to identify patterns of incorrect questions 2. The system of claim 1.

5. The iterative learning unit Monitor users' learning progress in real time and present repeated questions at the optimal time 2. The system of claim 1.

6. The recommended qualification suggestion unit Suggest the qualifications that interest and motivate users the most 2. The system of claim 1.

7. The preparation question creation unit Propose a question format that reduces the stress users feel when answering questions 2. The system of claim 1.

8. The weak field identification unit Identify areas that cause users the most stress and focus learning on those areas 2. The system of claim 1.

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