System

Abstract

An object of the system according to the embodiment is to take a thorough measure for a field that a user is not good at and to generate unique qualification test questions of a company.SOLUTION: A system includes a problem generation part, a weak field countermeasure part, a company qualification test generation part, a progress management part, and a learning plan suggestion part. The problem generation unit infinitely generates problems. A difficult field countermeasure part specifies a difficult field of the user on the basis of the problem generated by the problem generation part, and intensively generates a problem to the specified difficult field. The company qualification test generator generates unique qualification test questions for the company. The progress manager manages the user's learning progress and provides appropriate feedback. The learning plan proposing section proposes a customized learning plan based on the learning objective and schedule of the user.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 do not adequately address users' weak areas or generate company-specific qualification exam questions, so there is room for improvement.

[0005] The system according to the embodiment aims to provide thorough solutions to the user's weak areas and to generate company-specific qualification examination questions. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, a weak area countermeasure unit, a company qualification exam generation unit, a progress management unit, and a study plan proposal unit. The question generation unit generates an infinite number of questions. The weak area countermeasure unit identifies the user's weak areas based on the questions generated by the question generation unit and generates questions that focus on the identified weak areas. The company qualification exam generation unit generates company-specific qualification exam questions. The progress management unit manages the user's learning progress and provides appropriate feedback. The study plan proposal unit proposes a customized study plan based on the user's learning goals and schedule. [Effects of the Invention]

[0007] The system according to the embodiment can provide thorough solutions to the user's weak areas and generate company-specific qualification exam questions. [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) A qualification support app according to an embodiment of the present invention is a system that uses generative AI to support studying for qualification exams. This system generates an infinite number of questions, thoroughly addresses weak areas, generates company-specific qualification exam questions, manages learning progress, and proposes customized study plans. This allows the qualification support app to efficiently and effectively advance users' qualification exam studies.

[0029] A qualification support app according to an embodiment includes a question generation unit, a weak area countermeasure unit, a corporate qualification exam generation unit, a progress management unit, and a study plan proposal unit. The question generation unit generates an infinite number of questions. For example, the generation AI generates an infinite number of questions based on the content of the qualification exam the user wants to study. For example, when the user inputs a prompt including an instruction such as "I want you to generate questions for a mathematics qualification exam," the generation AI generates various mathematics questions based on the instruction. The weak area countermeasure unit identifies the user's weak areas and generates questions focusing on those areas. For example, the generation AI analyzes the user's learning history and answer results to identify the user's weak areas. Then, it generates questions focusing on the identified weak areas. The corporate qualification exam generation unit generates company-specific qualification exam questions. For example, when materials for a qualification exam set by a company are input into the generation AI, the generation AI analyzes the materials and generates appropriate exam questions. The progress management unit manages the user's learning progress and provides appropriate feedback. For example, the generation AI records the accuracy rate and answer time of questions answered by the user and evaluates the user's learning progress based on this. The study plan suggestion unit proposes a customized study plan based on the user's learning goals and schedule. For example, when a user inputs information such as "I will take a qualification exam in one month," the generation AI creates an optimal study plan based on that information and proposes it to the user. As a result, the qualification support app according to the embodiment allows users to study for qualification exams efficiently and effectively.

[0030] The question generator dynamically adjusts the difficulty of questions based on the user's answer history, allowing it to always provide questions at an appropriate level. For example, the question generator uses a generation AI to analyze the user's past answer history and dynamically adjust the difficulty based on the correct answer rate and answer time. For example, if the correct answer rate is high, the difficulty level is increased, and if it is low, the difficulty level is decreased. This allows the user to always challenge questions at an appropriate level.

[0031] The question generation unit can generate simulation questions that are similar to the test format, allowing the user to become familiar with the test environment. For example, the question generation unit uses a generation AI to generate simulation questions based on the actual test format, allowing the user to become familiar with the test environment. For example, it provides a mock test that reproduces the test time and question format. This allows the user to become familiar with the test environment.

[0032] The question generation unit can add visual hints or explanatory videos to questions to make them easier for users to understand visually. For example, the question generation unit uses a generation AI to add visual hints to questions to make them easier for users to understand. For example, the question is explained using diagrams and graphs. This makes it easier for users to understand visually.

[0033] The question generation unit crosses over questions from different qualification exams to generate comprehensive questions that can be used for multiple qualification exams. For example, the question generation unit uses a generation AI to cross over questions from different qualification exams to generate comprehensive questions. For example, it provides a composite question that combines mathematics and physics questions. This allows users to take multiple qualification exams.

[0034] The weak area countermeasure unit can refer to the user's past test data and data from other users to identify weak areas with greater accuracy. For example, the weak area countermeasure unit uses a generation AI to analyze the user's past test data and identify weak areas. For example, it identifies areas with a low rate of correct answers based on past test results. This allows the user's weak areas to be identified with greater accuracy.

[0035] The weak area countermeasure unit can gradually increase the difficulty level when generating questions for weak areas, thereby enabling the user to gradually deepen their understanding. For example, the weak area countermeasure unit causes the generation AI to generate questions for weak areas with gradually increasing difficulty. For example, starting with basic questions and gradually increasing the difficulty level. This allows the user to gradually overcome weak areas.

[0036] The weak area countermeasure unit can incorporate a competitive element with other users when solving problems in weak areas, allowing users to progress with their studies in a game-like manner. For example, the weak area countermeasure unit incorporates a competitive element with other users when the generating AI solves problems in weak areas. For example, a ranking function can be added to allow users to compete with each other. This allows users to progress with their studies in a fun, game-like manner.

[0037] The weak area countermeasure unit can add an online tutor function that provides real-time explanations for problems in weak areas. For example, the weak area countermeasure unit adds an online tutor function that provides real-time explanations for problems in weak areas by the generation AI. For example, an explanatory video is displayed immediately after solving a problem. This allows the user to progress with their studies while receiving explanations in real time.

[0038] The corporate qualification exam generation unit can generate questions that reflect the business operations of a company and industry trends. For example, the generation AI analyzes the business operations of a company and generates qualification exam questions based on that analysis. For example, it provides questions related to the business processes of the company. This allows questions to be generated that reflect the business operations of a company and industry trends.

[0039] The corporate qualification test generation unit can evaluate practical skills by including questions that simulate actual business scenarios in the company's unique qualification test questions. For example, the corporate qualification test generation unit includes questions that simulate actual business scenarios in the company's unique qualification test questions using a generation AI. For example, it provides scenario questions that reproduce part of a business process. This makes it possible to generate questions that evaluate practical skills.

[0040] The corporate qualification test generation unit can add a function to compare and benchmark the company's unique qualification test questions with those of other companies or industries when generating the questions. For example, the generation AI in the corporate qualification test generation unit collects qualification test questions from other companies or industries and compares them with the company's unique qualification test questions. For example, the unit benchmarks the company's own test questions with those of other companies. This allows the quality of the company's unique qualification test questions to be improved.

[0041] The corporate qualification exam generation unit can introduce collaborative questions to be solved in teams into the company's unique qualification exam questions, thereby evaluating teamwork. For example, the generation AI in the corporate qualification exam generation unit introduces collaborative questions to be solved in teams into the company's unique qualification exam questions. For example, it provides questions that multiple examinees solve cooperatively. This makes it possible to generate questions that evaluate teamwork.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] Qualification support apps can also provide customization features according to the user's learning style. For example, for users who prefer visual learning, questions that make extensive use of diagrams and graphs can be generated, while for users who prefer auditory learning, questions with audio commentary can be provided. Furthermore, for users who prefer tactile learning, interactive simulation questions can be provided. This allows users to study efficiently using the learning method that best suits them.

[0044] Qualification support apps can also provide a dashboard function that visualizes learning progress based on the user's learning history. For example, data such as study time, correct answer rate, and answer speed can be displayed in graphs and charts, allowing users to understand their learning situation at a glance. They can also evaluate progress by comparing it with past learning data and display indicators showing the degree of goal achievement. This allows users to specifically understand their learning progress, making it easier to maintain motivation.

[0045] Certification support apps can also provide environment setting functions to optimize the user's study environment. For example, adding a function to select background music or environmental sounds while studying can create an environment where users can relax and concentrate on their studies. It can also add a study time management function and incorporate time management methods such as the Pomodoro Technique. This allows users to create a study environment that suits them and study efficiently.

[0046] Qualification support apps can also incorporate gamification elements to increase users' motivation to learn. For example, they can add a feature that allows users to earn badges and points according to their learning progress, giving them a sense of accomplishment. They can also incorporate elements of competition with other users, displaying ranking functions and leaderboards. This allows users to enjoy learning in a game-like manner.

[0047] Qualification support apps can also provide social features to form user learning communities. For example, they can add message boards and chat features that allow users to share their learning progress and results, encouraging each other as they study. They can also add group learning features that allow users to solve problems together. This allows users to study as part of a community, without feeling isolated.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The problem generator generates an infinite number of problems. For example, the generation AI generates an infinite number of problems based on the content of the qualification exam that the user wants to study. When the user inputs a prompt including the instruction "I want questions generated for a mathematics qualification exam," the AI ​​generates various mathematics problems based on the instruction. Step 2: The weak area countermeasure unit identifies the user's weak areas and generates questions that focus on those areas. For example, the generation AI analyzes the user's learning history and answer results to identify the user's weak areas. Then, it generates questions that focus on the identified weak areas. Step 3: The company qualification test generation unit generates company-specific qualification test questions. For example, when a company inputs its own qualification test materials into the generation AI, the generation AI analyzes the materials and generates appropriate test questions. Step 4: The progress management unit manages the user's learning progress and provides appropriate feedback. For example, the generation AI records the accuracy rate and answer time of the questions the user answers, and evaluates the learning progress based on that. Step 5: The study plan suggestion unit proposes a customized study plan based on the user's learning goals and schedule. For example, if a user inputs information such as "I'm taking a qualification exam in one month," the generation AI will create an optimal study plan based on that information and propose it to the user.

[0050] (Example 2) A qualification support app according to an embodiment of the present invention is a system that uses generative AI to support studying for qualification exams. This system generates an infinite number of questions, thoroughly addresses weak areas, generates company-specific qualification exam questions, manages learning progress, and proposes customized study plans. This allows the qualification support app to efficiently and effectively advance users' qualification exam studies.

[0051] A qualification support app according to an embodiment includes a question generation unit, a weak area countermeasure unit, a corporate qualification exam generation unit, a progress management unit, and a study plan proposal unit. The question generation unit generates an infinite number of questions. For example, the generation AI generates an infinite number of questions based on the content of the qualification exam the user wants to study. For example, when the user inputs a prompt including an instruction such as "I want you to generate questions for a mathematics qualification exam," the generation AI generates various mathematics questions based on the instruction. The weak area countermeasure unit identifies the user's weak areas and generates questions focusing on those areas. For example, the generation AI analyzes the user's learning history and answer results to identify the user's weak areas. Then, it generates questions focusing on the identified weak areas. The corporate qualification exam generation unit generates company-specific qualification exam questions. For example, when materials for a qualification exam set by a company are input into the generation AI, the generation AI analyzes the materials and generates appropriate exam questions. The progress management unit manages the user's learning progress and provides appropriate feedback. For example, the generation AI records the accuracy rate and answer time of questions answered by the user and evaluates the user's learning progress based on this. The study plan suggestion unit proposes a customized study plan based on the user's learning goals and schedule. For example, when a user inputs information such as "I will take a qualification exam in one month," the generation AI creates an optimal study plan based on that information and proposes it to the user. As a result, the qualification support app according to the embodiment allows users to study for qualification exams efficiently and effectively.

[0052] The question generator dynamically adjusts the difficulty of questions based on the user's answer history, allowing it to always provide questions at an appropriate level. For example, the question generator uses a generation AI to analyze the user's past answer history and dynamically adjust the difficulty based on the correct answer rate and answer time. For example, if the correct answer rate is high, the difficulty level is increased, and if it is low, the difficulty level is decreased. This allows the user to always challenge questions at an appropriate level.

[0053] The question generation unit can generate simulation questions that are similar to the test format, allowing the user to become familiar with the test environment. For example, the question generation unit uses a generation AI to generate simulation questions based on the actual test format, allowing the user to become familiar with the test environment. For example, it provides a mock test that reproduces the test time and question format. This allows the user to become familiar with the test environment.

[0054] The question generator uses the emotion estimation function to measure the stress level of the user when solving the questions, and can adjust the questions so that the user can proceed with their studies in a low-stress state. The question generator, for example, uses the emotion estimation function to measure the stress level of the user when solving the questions in real time. For example, it analyzes facial expressions and voice tone to quantify the stress level. This allows the user to proceed with their studies in a low-stress state.

[0055] The question generation unit can add visual hints or explanatory videos to questions to make them easier for users to understand visually. For example, the question generation unit uses a generation AI to add visual hints to questions to make them easier for users to understand. For example, the question is explained using diagrams and graphs. This makes it easier for users to understand visually.

[0056] The question generation unit crosses over questions from different qualification exams to generate comprehensive questions that can be used for multiple qualification exams. For example, the question generation unit uses a generation AI to cross over questions from different qualification exams to generate comprehensive questions. For example, it provides a composite question that combines mathematics and physics questions. This allows users to take multiple qualification exams.

[0057] The question generation unit uses the emotion estimation function to preferentially generate questions in the field in which the user is most interested, thereby increasing the user's motivation to learn. For example, the question generation unit uses the emotion estimation function to identify the field in which the user is most interested and preferentially generate questions in that field. For example, it provides many questions in fields in which the user is most interested. This increases the user's motivation to learn.

[0058] The weak area countermeasure unit can refer to the user's past test data and data from other users to identify weak areas with greater accuracy. For example, the weak area countermeasure unit uses a generation AI to analyze the user's past test data and identify weak areas. For example, it identifies areas with a low rate of correct answers based on past test results. This allows the user's weak areas to be identified with greater accuracy.

[0059] The weak area countermeasure unit can gradually increase the difficulty level when generating questions for weak areas, thereby enabling the user to gradually deepen their understanding. For example, the weak area countermeasure unit causes the generation AI to generate questions for weak areas with gradually increasing difficulty. For example, starting with basic questions and gradually increasing the difficulty level. This allows the user to gradually overcome weak areas.

[0060] The weak area countermeasure unit can use the emotion estimation function to generate relaxation questions to reduce the anxiety and stress the user feels about the weak area. The weak area countermeasure unit, for example, uses the emotion estimation function to measure the anxiety and stress the user feels about the weak area. For example, it analyzes facial expressions and voice tone to quantify the stress level. This allows the user to proceed with their studies while reducing anxiety and stress.

[0061] The weak area countermeasure unit can incorporate a competitive element with other users when solving problems in weak areas, allowing users to progress with their studies in a game-like manner. For example, the weak area countermeasure unit incorporates a competitive element with other users when the generating AI solves problems in weak areas. For example, a ranking function can be added to allow users to compete with each other. This allows users to progress with their studies in a fun, game-like manner.

[0062] The weak area countermeasure unit can add an online tutor function that provides real-time explanations for problems in weak areas. For example, the weak area countermeasure unit adds an online tutor function that provides real-time explanations for problems in weak areas by the generation AI. For example, an explanatory video is displayed immediately after solving a problem. This allows the user to progress with their studies while receiving explanations in real time.

[0063] The weak area countermeasure unit can use the emotion estimation function to generate questions that allow the user to accumulate successful experiences so that the user can have positive feelings about the weak area. The weak area countermeasure unit, for example, uses the emotion estimation function to allow the user to have positive feelings about the weak area. For example, it provides easy questions that allow the user to accumulate successful experiences. This allows the user to proceed with their studies while having positive feelings.

[0064] The corporate qualification exam generation unit can generate questions that reflect the business operations of a company and industry trends. For example, the generation AI analyzes the business operations of a company and generates qualification exam questions based on that analysis. For example, it provides questions related to the business processes of the company. This allows questions to be generated that reflect the business operations of a company and industry trends.

[0065] The corporate qualification test generation unit can evaluate practical skills by including questions that simulate actual business scenarios in the company's unique qualification test questions. For example, the corporate qualification test generation unit includes questions that simulate actual business scenarios in the company's unique qualification test questions using a generation AI. For example, it provides scenario questions that reproduce part of a business process. This makes it possible to generate questions that evaluate practical skills.

[0066] The corporate qualification exam generation unit can use the emotion estimation function to generate relaxation questions to reduce the pressure that examinees feel about the company's unique qualification exam. For example, the corporate qualification exam generation unit uses the emotion estimation function to measure the pressure that examinees feel about the company's unique qualification exam. For example, the corporate qualification exam generation unit analyzes facial expressions and tone of voice to quantify the pressure level. This allows examinees to study while reducing their pressure.

[0067] The corporate qualification test generation unit can add a function to compare and benchmark the company's unique qualification test questions with those of other companies or industries when generating the questions. For example, the generation AI in the corporate qualification test generation unit collects qualification test questions from other companies or industries and compares them with the company's unique qualification test questions. For example, the unit benchmarks the company's own test questions with those of other companies. This allows the quality of the company's unique qualification test questions to be improved.

[0068] The corporate qualification exam generation unit can introduce collaborative questions to be solved in teams into the company's unique qualification exam questions, thereby evaluating teamwork. For example, the generation AI in the corporate qualification exam generation unit introduces collaborative questions to be solved in teams into the company's unique qualification exam questions. For example, it provides questions that multiple examinees solve cooperatively. This makes it possible to generate questions that evaluate teamwork.

[0069] The corporate qualification exam generation unit can use the emotion estimation function to generate questions that increase motivation so that examinees can have positive emotions about the company's unique qualification exam. The corporate qualification exam generation unit, for example, uses the emotion estimation function to enable examinees to have positive emotions about the company's unique qualification exam. For example, it provides simple questions to increase motivation. This makes it possible to generate questions that increase the examinees' motivation.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] Qualification support apps can also provide customization features according to the user's learning style. For example, for users who prefer visual learning, questions that make extensive use of diagrams and graphs can be generated, while for users who prefer auditory learning, questions with audio commentary can be provided. Furthermore, for users who prefer tactile learning, interactive simulation questions can be provided. This allows users to study efficiently using the learning method that best suits them.

[0072] Qualification support apps can also provide a dashboard function that visualizes learning progress based on the user's learning history. For example, data such as study time, correct answer rate, and answer speed can be displayed in graphs and charts, allowing users to understand their learning situation at a glance. They can also evaluate progress by comparing it with past learning data and display indicators showing the degree of goal achievement. This allows users to specifically understand their learning progress, making it easier to maintain motivation.

[0073] Certification support apps can also provide environment setting functions to optimize the user's study environment. For example, adding a function to select background music or environmental sounds while studying can create an environment where users can relax and concentrate on their studies. It can also add a study time management function and incorporate time management methods such as the Pomodoro Technique. This allows users to create a study environment that suits them and study efficiently.

[0074] Qualification support apps can also incorporate gamification elements to increase users' motivation to learn. For example, they can add a feature that allows users to earn badges and points according to their learning progress, giving them a sense of accomplishment. They can also incorporate elements of competition with other users, displaying ranking functions and leaderboards. This allows users to enjoy learning in a game-like manner.

[0075] The qualification support app can also use the emotion estimation function to provide a feedback function to maintain the user's motivation to study. For example, the emotion estimation function can detect when the user's motivation decreases while studying and provide encouraging messages or content to increase motivation at the appropriate time. In addition, when the user achieves a goal, the emotion estimation function can be used to display a message sharing the user's joy and sense of accomplishment. This allows the user to continue studying while always maintaining high motivation.

[0076] The qualification support app can also use the emotion estimation function to provide a relaxation function to reduce the user's learning stress. For example, the emotion estimation function can detect the stress the user feels while studying and provide relaxation music or guided meditation at the appropriate time. It can also add a reminder function to encourage short breaks between studies, allowing the user to refresh themselves appropriately. This allows the user to study effectively while reducing stress.

[0077] The qualification support app can also adjust the user's learning pace using an emotion estimation function. For example, the emotion estimation function can detect when the user feels fatigue or a lack of concentration while studying, and display a message encouraging them to take a break at the appropriate time. The app can also adjust the learning content according to the user's emotional state, providing easier questions when fatigue is building up and more difficult questions when concentration is high. This allows users to study at their own pace without strain.

[0078] The qualification support app can also use an emotion estimation function to evaluate a user's learning outcomes. For example, the emotion estimation function can detect the sense of accomplishment or satisfaction a user feels while studying, and evaluate their learning outcomes based on that data. It can also provide feedback based on the user's emotional state, encouraging users with positive emotions to take on more challenges and displaying encouraging messages to users with negative emotions. This allows users to progress through their studies while feeling a sense of accomplishment.

[0079] The qualification support app can also use emotion estimation to optimize the user's learning style. For example, the emotion estimation function can detect the interest and attention the user feels while studying and adjust the learning style based on that data. For example, it can provide many questions related to areas that the user is interested in, and add visual hints and explanatory videos for areas that the user is less interested in. This allows the user to study efficiently in a style that suits them best.

[0080] Qualification support apps can also provide social features to form user learning communities. For example, they can add message boards and chat features that allow users to share their learning progress and results, encouraging each other as they study. They can also add group learning features that allow users to solve problems together. This allows users to study as part of a community, without feeling isolated.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The problem generator generates an infinite number of problems. For example, the generation AI generates an infinite number of problems based on the content of the qualification exam that the user wants to study. When the user inputs a prompt including the instruction "I want questions generated for a mathematics qualification exam," the AI ​​generates various mathematics problems based on the instruction. Step 2: The weak area countermeasure unit identifies the user's weak areas and generates questions that focus on those areas. For example, the generation AI analyzes the user's learning history and answer results to identify the user's weak areas. Then, it generates questions that focus on the identified weak areas. Step 3: The company qualification test generation unit generates company-specific qualification test questions. For example, when a company inputs its own qualification test materials into the generation AI, the generation AI analyzes the materials and generates appropriate test questions. Step 4: The progress management unit manages the user's learning progress and provides appropriate feedback. For example, the generation AI records the accuracy rate and answer time of the questions the user answers, and evaluates the learning progress based on that. Step 5: The study plan suggestion unit proposes a customized study plan based on the user's learning goals and schedule. For example, if a user inputs information such as "I'm taking a qualification exam in one month," the generation AI will create an optimal study plan based on that information and propose it to the user.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 problem generator that generates problems infinitely; a weak area countermeasure unit that identifies a weak area of ​​the user based on the questions generated by the question generation unit; a weak area countermeasure unit that generates questions focusing on the weak area identified by the weak area countermeasure unit; a company qualification test generation unit that generates company-specific qualification test questions; a progress management unit that manages the user's learning progress and provides appropriate feedback; A learning plan suggestion unit that suggests a customized learning plan based on the user's learning goals and schedule. A system characterized by:

2. The question generator Dynamically adjust the difficulty of the questions based on the answer history of the user, and always provide questions at an appropriate level 2. The system of claim 1.

3. The weak area countermeasure department By referring to the user's past test data and data of other users, the weak areas are identified with higher accuracy.

2. The system of claim 1.

4. The enterprise qualification test generation unit Generate the problem by reflecting the company's business operations and industry trends.

2. The system of claim 1.

5. The question generator Measure the stress level of the user when solving the problem, and adjust the problem so that the user can study under low stress.

2. The system of claim 1.

6. The weak area countermeasure department Generate relaxation questions to reduce the anxiety and stress the user feels about the weak areas 2. The system of claim 1.

7. The enterprise qualification test generation unit Generate relaxed questions to reduce the pressure test takers feel when taking company-specific certification exams 2. The system of claim 1.

8. The question generator To generate questions in the field in which the user is most interested preferentially, thereby increasing motivation to learn 2. The system of claim 1.

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