System

Abstract

An object of the system according to the embodiment is to specify a weak part based on a test result of an examinee and to effectively overcome the weak part.SOLUTION: A system according to an embodiment includes a test result analysis part, a weak part specification part, and a review question part. The test result analysis part analyzes the test result of the examinee. The difficult part specification unit specifies a difficult part of the examinee based on the result analyzed by the test result analysis unit. The review question setting unit sets a review and a retest for the weak part specified by the weak part 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 technology has the problem that it does not identify weak areas based on test takers' test results and does not provide sufficient support to help them overcome these areas effectively.

[0005] The system according to the embodiment aims to identify weak areas of the examinee based on the test results and to help them overcome them effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a test result analysis unit, a weak part identification unit, and a review question assignment unit. The test result analysis unit analyzes the test results of the examinee. The weak part identification unit identifies the examinee's weak part based on the results of the analysis by the test result analysis unit. The review question assignment unit assigns a review and a retest for the weak part identified by the weak part identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify weak points of the examinee based on the test results and help them overcome them effectively. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 support system for overcoming weaknesses according to an embodiment of the present invention is a system that analyzes test results of test takers, identifies weak areas, and analyzes the causes of those areas. This system supports test takers in overcoming their weaknesses by repeatedly reviewing and retesting until they understand the material. In this way, the support system for overcoming weaknesses can prevent test takers from making the same mistakes twice.

[0029] A support system for overcoming weaknesses according to an embodiment includes a test result analysis unit, a weak point identification unit, and a review question assignment unit. The test result analysis unit analyzes the test results of the test taker. For example, the test result analysis unit collects and analyzes the results of various confirmation tests and mock exams conducted by the test taker. For example, if the test taker makes many mistakes on a specific problem type (e.g., solving equations) in a mathematics test, the test result analysis unit identifies that problem type. The test result analysis unit also analyzes the reasons for the mistakes (e.g., calculation errors, lack of conceptual understanding). For example, the test result analysis unit performs analysis based on the test result data of the test taker. The weak point identification unit identifies the test taker's weak points based on the results of the analysis by the test result analysis unit. For example, if the test taker makes many mistakes on English grammar questions, the weak point identification unit identifies the grammatical items (e.g., tenses, articles) and analyzes the causes of the mistakes (e.g., misunderstanding of rules, lack of understanding of exceptions). The review question assignment unit assigns review questions and retests for the weak points identified by the weak point identification unit. For example, the review section provides explanations for questions that the examinee got wrong and asks additional questions to deepen understanding. The review section repeats this process until the examinee understands. In this way, the support system for overcoming weaknesses according to the embodiment allows the examinee to efficiently overcome their weak points. For example, the test taker repeatedly retests and monitors progress until they completely understand a particular question type. The review section adjusts the difficulty of the questions according to the examinee's level of understanding and provides an optimal study plan.

[0030] The test result analysis unit analyzes the test taker's response time, identifies trends where specific questions take too long, and can identify the cause. For example, the test result analysis unit uses AI to analyze in detail the test taker's response time, and if a test taker is taking an abnormally long time on a particular question, it will identify that question. For example, if the test taker takes significantly longer than average to answer a mathematical equation question, it will focus on analyzing that question. This allows the test taker's weak points to be identified in more detail by analyzing response time.

[0031] The test result analysis unit can analyze the test taker's answer patterns and evaluate the consistency of their answers to specific question formats. For example, the test result analysis unit uses AI to analyze the test taker's answer patterns and evaluate the consistency of their answers to specific question formats. For example, it evaluates the consistency of answers to multiple-choice questions and essay questions to identify the test taker's strengths and weaknesses. This makes it possible to identify the test taker's strengths and weaknesses in more detail by analyzing the answer patterns.

[0032] The test result analysis unit analyzes the examinee's handwritten answers and can identify the cause of errors from their handwriting and writing habits. For example, the test result analysis unit uses AI to analyze the examinee's handwritten answers and identify their handwriting and writing habits. For example, it analyzes the shape and size of the characters and changes in writing pressure to identify the cause of the error. This makes it possible to identify the cause of the examinee's errors in more detail by analyzing the handwritten answers.

[0033] The test result analysis unit analyzes the test taker's audio responses and can identify the cause of the error from pronunciation and intonation errors. For example, the test result analysis unit uses AI to analyze the test taker's audio responses and identify pronunciation and intonation errors. For example, if the pronunciation of a specific word or phrase is incorrect, the cause is analyzed in detail and feedback is provided to the test taker. This makes it possible to identify the cause of the test taker's error in more detail by analyzing the audio responses.

[0034] The weak area identification unit can analyze the test taker's past learning history and identify how specific learning content affects their weak areas. For example, the weak area identification unit uses AI to analyze the test taker's past learning history and identify how specific learning content affects their weak areas. For example, it can analyze the study time and grades for specific units or topics to identify their weak areas. This analysis of past learning history makes it possible to identify the causes of the test taker's weak areas in more detail.

[0035] The weak part identification unit can analyze the test taker's learning style and identify the cause of the weak part based on that. For example, the weak part identification unit uses AI to analyze the test taker's learning style and identify the cause of the weak part based on that. For example, if a visual test taker is studying with auditory learning materials, it will identify that the mismatch in learning style is the cause of the weak part. In this way, the analysis of learning style can identify the cause of the test taker's weak part in more detail.

[0036] The weak point identification unit can analyze the lifestyle habits of test takers and identify how they affect their learning outcomes. For example, the weak point identification unit uses AI to analyze the lifestyle habits of test takers and identify how they affect their learning outcomes. For example, it analyzes the impact of sleep duration and eating patterns on learning outcomes and provides feedback to the test taker. This makes it possible to identify in more detail the factors that affect the test taker's learning outcomes through the analysis of lifestyle habits.

[0037] The weak area identification unit can analyze the test taker's social media activity and identify the test taker's learning efficiency at specific times of the day and in specific situations. For example, the weak area identification unit uses AI to analyze the test taker's social media activity and identify the test taker's learning efficiency at specific times of the day and in specific situations. For example, it can analyze the impact of social media activity at night on learning efficiency and provide feedback to the test taker. This makes it possible to identify in more detail the factors that affect the test taker's learning efficiency through the analysis of social media activity.

[0038] The review section can analyze the test taker's learning pace and provide an individually customized study plan. For example, the review section can use AI to analyze the test taker's learning pace and provide an individually customized study plan. For example, advanced questions are provided to test takers who learn at a fast pace, and basic questions are provided to test takers who learn at a slower pace. In this way, the analysis of the learning pace can provide the test taker with the optimal study plan.

[0039] The reflection section can analyze the examinee's interactions with fellow learners and tutors and adjust the content of the retest based on that. For example, the reflection section can use AI to analyze the examinee's interactions with fellow learners and tutors and adjust the content of the retest based on that. For example, it can analyze the content of discussions with fellow learners and set retest questions related to that content. This allows the content of the retest to be optimized by analyzing interactions with fellow learners and tutors.

[0040] The review section can analyze the examinee's usage of learning apps and online resources and adjust the content of the retest based on that. For example, the review section can use AI to analyze the examinee's usage of learning apps and online resources and adjust the content of the retest based on that. For example, if a examinee uses a particular app or resource frequently, the review section can set retest questions related to that content. This allows the content of the retest to be optimized by analyzing the usage of learning apps and online resources.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The support system for overcoming weaknesses can further include an environment analysis unit that monitors the test taker's learning environment. The environment analysis unit, for example, analyzes the noise level and lighting brightness in the test taker's study area to identify how this affects the test taker's learning outcomes. For example, if the test taker is studying in a noisy environment, it can identify that the environment may be hindering concentration. Also, if the lighting is dim, it can identify that visual fatigue may be reducing learning efficiency. This analysis of the study environment can identify in more detail the factors that affect the test taker's learning efficiency.

[0043] The support system for overcoming weaknesses can further include a health analysis unit that monitors the physical condition of the test taker. The health analysis unit, for example, analyzes the test taker's heart rate and body temperature to identify how this affects their learning outcomes. For example, if the heart rate is high, it can identify that stress or tension may be reducing their learning efficiency. Also, if the body temperature is high, it can identify that poor health may be affecting their learning. In this way, by analyzing the physical condition, it is possible to identify in more detail the factors that affect the test taker's learning efficiency.

[0044] The support system for overcoming weaknesses can further include a progress analysis unit that analyzes the test taker's learning history and visualizes their learning progress. The progress analysis unit, for example, analyzes the test taker's past learning content and test results, and displays their learning progress in graphs and charts. For example, it can display changes in the test taker's understanding of a specific subject or unit in chronological order, allowing the test taker to visually grasp where their progress is. The progress analysis unit can also evaluate the extent to which the test taker has achieved their goals, which can be used to adjust the test taker's study plan. This visualization of the test taker's learning progress can improve the test taker's learning efficiency.

[0045] The support system for overcoming weaknesses can further include a reward system to increase test takers' motivation to study. The reward system, for example, can award badges or points when test takers achieve specific goals, thereby increasing test takers' motivation to study. For example, it can provide rewards when test takers fully understand a specific unit or when they achieve a high score on a retest. The reward system can also incorporate an element where test takers can compete with other test takers, displaying rankings and leaderboards. In this way, the introduction of a reward system can increase test takers' motivation to study.

[0046] The support system for overcoming weaknesses can further include a teaching material providing unit that provides teaching materials customized according to the test taker's learning style. For example, if the test taker is a visual learner, the teaching material providing unit provides teaching materials that make extensive use of diagrams and graphs. For example, if the test taker is an auditory learner, the teaching material providing unit provides teaching materials in the form of audio commentary or podcasts. Furthermore, if the test taker is a tactile learner, the teaching material providing unit provides teaching materials that include interactive simulations and experiments. In this way, the test taker's learning efficiency can be improved by providing teaching materials customized according to their learning style.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The test result analysis unit analyzes the test results of test takers. For example, the test result analysis unit collects and analyzes the results of various confirmation tests and mock exams taken by test takers. Specifically, if test takers make many mistakes on a particular type of problem in a mathematics test (e.g., solving equations), the unit identifies that type of problem and analyzes the reasons for the mistakes (e.g., calculation errors, lack of understanding of the concept). Step 2: The weak point identification unit identifies the test-taker's weak points based on the results analyzed by the test result analysis unit. For example, if the test-taker makes many mistakes in English grammar questions, it identifies the grammar items (e.g., tenses, articles) and analyzes the causes of the mistakes (e.g., misunderstanding of rules, not understanding exceptions). Step 3: The Review Section provides review and retest questions for the weak areas identified by the Weak Area Identification Section. For example, the Review Section provides explanations for questions that the examinee got wrong and asks additional questions to deepen understanding. This process is repeated until the examinee understands, and progress is monitored. Furthermore, the difficulty of the questions is adjusted according to the examinee's level of understanding, providing an optimal study plan.

[0049] (Example 2) The support system for overcoming weaknesses according to an embodiment of the present invention is a system that analyzes test results of test takers, identifies weak areas, and analyzes the causes of those areas. This system supports test takers in overcoming their weaknesses by repeatedly reviewing and retesting until they understand the material. In this way, the support system for overcoming weaknesses can prevent test takers from making the same mistakes twice.

[0050] A support system for overcoming weaknesses according to an embodiment includes a test result analysis unit, a weak point identification unit, and a review question assignment unit. The test result analysis unit analyzes the test results of the test taker. For example, the test result analysis unit collects and analyzes the results of various confirmation tests and mock exams conducted by the test taker. For example, if the test taker makes many mistakes on a specific problem type (e.g., solving equations) in a mathematics test, the test result analysis unit identifies that problem type. The test result analysis unit also analyzes the reasons for the mistakes (e.g., calculation errors, lack of conceptual understanding). For example, the test result analysis unit performs analysis based on the test result data of the test taker. The weak point identification unit identifies the test taker's weak points based on the results of the analysis by the test result analysis unit. For example, if the test taker makes many mistakes on English grammar questions, the weak point identification unit identifies the grammatical items (e.g., tenses, articles) and analyzes the causes of the mistakes (e.g., misunderstanding of rules, lack of understanding of exceptions). The review question assignment unit assigns review questions and retests for the weak points identified by the weak point identification unit. For example, the review section provides explanations for questions that the examinee got wrong and asks additional questions to deepen understanding. The review section repeats this process until the examinee understands. In this way, the support system for overcoming weaknesses according to the embodiment allows the examinee to efficiently overcome their weak points. For example, the test taker repeatedly retests and monitors progress until they completely understand a particular question type. The review section adjusts the difficulty of the questions according to the examinee's level of understanding and provides an optimal study plan.

[0051] The test result analysis unit analyzes the test taker's response time, identifies trends where specific questions take too long, and can identify the cause. For example, the test result analysis unit uses AI to analyze in detail the test taker's response time, and if a test taker is taking an abnormally long time on a particular question, it will identify that question. For example, if the test taker takes significantly longer than average to answer a mathematical equation question, it will focus on analyzing that question. This allows the test taker's weak points to be identified in more detail by analyzing response time.

[0052] The test result analysis unit can analyze the test taker's answer patterns and evaluate the consistency of their answers to specific question formats. For example, the test result analysis unit uses AI to analyze the test taker's answer patterns and evaluate the consistency of their answers to specific question formats. For example, it evaluates the consistency of answers to multiple-choice questions and essay questions to identify the test taker's strengths and weaknesses. This makes it possible to identify the test taker's strengths and weaknesses in more detail by analyzing the answer patterns.

[0053] The test result analysis unit uses the emotion estimation function to analyze the stress and anxiety felt by the test taker while answering questions, and can identify how those emotions influenced the test taker's errors. The test result analysis unit, for example, uses the emotion estimation function to analyze the stress and anxiety felt by the test taker while answering questions. For example, it analyzes facial expressions and tone of voice to quantify the level of stress and anxiety. This allows the analysis of emotions to identify the cause of the test taker's errors in more detail.

[0054] The test result analysis unit analyzes the examinee's handwritten answers and can identify the cause of errors from their handwriting and writing habits. For example, the test result analysis unit uses AI to analyze the examinee's handwritten answers and identify their handwriting and writing habits. For example, it analyzes the shape and size of the characters and changes in writing pressure to identify the cause of the error. This makes it possible to identify the cause of the examinee's errors in more detail by analyzing the handwritten answers.

[0055] The test result analysis unit analyzes the test taker's audio responses and can identify the cause of the error from pronunciation and intonation errors. For example, the test result analysis unit uses AI to analyze the test taker's audio responses and identify pronunciation and intonation errors. For example, if the pronunciation of a specific word or phrase is incorrect, the cause is analyzed in detail and feedback is provided to the test taker. This makes it possible to identify the cause of the test taker's error in more detail by analyzing the audio responses.

[0056] The weak area identification unit can analyze the test taker's past learning history and identify how specific learning content affects their weak areas. For example, the weak area identification unit uses AI to analyze the test taker's past learning history and identify how specific learning content affects their weak areas. For example, it can analyze the study time and grades for specific units or topics to identify their weak areas. This analysis of past learning history makes it possible to identify the causes of the test taker's weak areas in more detail.

[0057] The weak part identification unit can analyze the test taker's learning style and identify the cause of the weak part based on that. For example, the weak part identification unit uses AI to analyze the test taker's learning style and identify the cause of the weak part based on that. For example, if a visual test taker is studying with auditory learning materials, it will identify that the mismatch in learning style is the cause of the weak part. In this way, the analysis of learning style can identify the cause of the test taker's weak part in more detail.

[0058] The weak part identification unit can use the emotion estimation function to analyze the sense of frustration or helplessness felt by the test taker in response to a specific question, and identify how that emotion affects the weak part. The weak part identification unit, for example, uses the emotion estimation function to analyze the sense of frustration or helplessness felt by the test taker in response to a specific question. For example, it analyzes facial expressions and tone of voice to quantify the level of frustration or helplessness. In this way, the analysis of frustration or helplessness can identify the cause of the test taker's weak part in more detail.

[0059] The weak point identification unit can analyze the lifestyle habits of test takers and identify how they affect their learning outcomes. For example, the weak point identification unit uses AI to analyze the lifestyle habits of test takers and identify how they affect their learning outcomes. For example, it analyzes the impact of sleep duration and eating patterns on learning outcomes and provides feedback to the test taker. This makes it possible to identify in more detail the factors that affect the test taker's learning outcomes through the analysis of lifestyle habits.

[0060] The weak area identification unit can analyze the test taker's social media activity and identify the test taker's learning efficiency at specific times of the day and in specific situations. For example, the weak area identification unit uses AI to analyze the test taker's social media activity and identify the test taker's learning efficiency at specific times of the day and in specific situations. For example, it can analyze the impact of social media activity at night on learning efficiency and provide feedback to the test taker. This makes it possible to identify in more detail the factors that affect the test taker's learning efficiency through the analysis of social media activity.

[0061] The weak area identification unit uses the emotion estimation function to analyze the emotions felt by the test taker in a specific learning environment, and can identify how that environment affects the test taker's ability to overcome their weak areas. The weak area identification unit, for example, uses the emotion estimation function to analyze the emotions felt by the test taker in a specific learning environment. For example, it quantifies emotions felt when studying in a quiet place or while listening to music, and analyzes the impact of that environment on learning outcomes. In this way, the analysis of the learning environment can identify in more detail the factors that affect the test taker's ability to overcome their weak areas.

[0062] The review section can analyze the test taker's learning pace and provide an individually customized study plan. For example, the review section can use AI to analyze the test taker's learning pace and provide an individually customized study plan. For example, advanced questions are provided to test takers who learn at a fast pace, and basic questions are provided to test takers who learn at a slower pace. In this way, the analysis of the learning pace can provide the test taker with the optimal study plan.

[0063] The review question section uses the emotion estimation function to analyze the motivation and concentration felt by the examinee during the retest, and can adjust the content of the retest based on that. The review question section, for example, uses the emotion estimation function to analyze the motivation and concentration felt by the examinee during the retest. For example, it analyzes facial expressions and tone of voice to quantify the level of motivation and concentration. This allows the content of the retest to be optimized based on the analysis of motivation and concentration.

[0064] The reflection section can analyze the examinee's interactions with fellow learners and tutors and adjust the content of the retest based on that. For example, the reflection section can use AI to analyze the examinee's interactions with fellow learners and tutors and adjust the content of the retest based on that. For example, it can analyze the content of discussions with fellow learners and set retest questions related to that content. This allows the content of the retest to be optimized by analyzing interactions with fellow learners and tutors.

[0065] The review section can analyze the examinee's usage of learning apps and online resources and adjust the content of the retest based on that. For example, the review section can use AI to analyze the examinee's usage of learning apps and online resources and adjust the content of the retest based on that. For example, if a examinee uses a particular app or resource frequently, the review section can set retest questions related to that content. This allows the content of the retest to be optimized by analyzing the usage of learning apps and online resources.

[0066] The review question section can use the emotion estimation function to analyze the sense of accomplishment and satisfaction felt by the examinee during the retest and provide retest feedback based on that. The review question section, for example, uses the emotion estimation function to analyze the sense of accomplishment and satisfaction felt by the examinee during the retest. For example, it can analyze facial expressions and tone of voice to quantify the level of the sense of accomplishment and satisfaction. This allows the retest feedback to be optimized by analyzing the sense of accomplishment and satisfaction.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The support system for overcoming weaknesses can further include an environment analysis unit that monitors the test taker's learning environment. The environment analysis unit, for example, analyzes the noise level and lighting brightness in the test taker's study area to identify how this affects the test taker's learning outcomes. For example, if the test taker is studying in a noisy environment, it can identify that the environment may be hindering concentration. Also, if the lighting is dim, it can identify that visual fatigue may be reducing learning efficiency. This analysis of the study environment can identify in more detail the factors that affect the test taker's learning efficiency.

[0069] The support system for overcoming weaknesses can further include a health analysis unit that monitors the physical condition of the test taker. The health analysis unit, for example, analyzes the test taker's heart rate and body temperature to identify how this affects their learning outcomes. For example, if the heart rate is high, it can identify that stress or tension may be reducing their learning efficiency. Also, if the body temperature is high, it can identify that poor health may be affecting their learning. In this way, by analyzing the physical condition, it is possible to identify in more detail the factors that affect the test taker's learning efficiency.

[0070] The support system for overcoming weaknesses can further include a progress analysis unit that analyzes the test taker's learning history and visualizes their learning progress. The progress analysis unit, for example, analyzes the test taker's past learning content and test results, and displays their learning progress in graphs and charts. For example, it can display changes in the test taker's understanding of a specific subject or unit in chronological order, allowing the test taker to visually grasp where their progress is. The progress analysis unit can also evaluate the extent to which the test taker has achieved their goals, which can be used to adjust the test taker's study plan. This visualization of the test taker's learning progress can improve the test taker's learning efficiency.

[0071] The support system for overcoming weaknesses can further include a reward system to increase test takers' motivation to study. The reward system, for example, can award badges or points when test takers achieve specific goals, thereby increasing test takers' motivation to study. For example, it can provide rewards when test takers fully understand a specific unit or when they achieve a high score on a retest. The reward system can also incorporate an element where test takers can compete with other test takers, displaying rankings and leaderboards. In this way, the introduction of a reward system can increase test takers' motivation to study.

[0072] The support system for overcoming weaknesses can further include a teaching material providing unit that provides teaching materials customized according to the test taker's learning style. For example, if the test taker is a visual learner, the teaching material providing unit provides teaching materials that make extensive use of diagrams and graphs. For example, if the test taker is an auditory learner, the teaching material providing unit provides teaching materials in the form of audio commentary or podcasts. Furthermore, if the test taker is a tactile learner, the teaching material providing unit provides teaching materials that include interactive simulations and experiments. In this way, the test taker's learning efficiency can be improved by providing teaching materials customized according to their learning style.

[0073] The support system for overcoming weaknesses can further include an emotion analysis unit that estimates the emotions of the test-taker and adjusts the study plan based on those emotions. The emotion analysis unit, for example, analyzes the stress and anxiety the test-taker feels while studying and adjusts the study plan based on those emotions. For example, if stress is high, it provides a relaxing study environment, and if anxiety is high, it provides feedback that gives a sense of security. The emotion analysis unit can also provide rewards that reinforce a sense of accomplishment when the test-taker feels that they have achieved something. In this way, the analysis of emotions can improve the test-taker's learning efficiency.

[0074] The support system for overcoming weaknesses can further include an emotional feedback unit that estimates the examinee's emotions and adjusts the study content based on those emotions. The emotional feedback unit, for example, analyzes the joy or excitement the examinee feels while studying and adjusts the study content based on those emotions. For example, if the examinee feels joy, it provides additional questions related to that content, and if the examinee feels excited, it provides challenging questions that maintain that excitement. The emotional feedback unit can also provide support to ease the examinee's feelings of frustration when the examinee feels frustrated. In this way, the analysis of emotions can increase the examinee's motivation to study.

[0075] The support system for overcoming weaknesses can further include an emotion evaluation unit that estimates the emotions of the test-taker and evaluates the test-taker's learning progress based on those emotions. The emotion evaluation unit, for example, analyzes the sense of accomplishment or satisfaction the test-taker feels while studying and evaluates the test-taker's learning progress based on those emotions. For example, if the test-taker feels a sense of accomplishment, it evaluates the test-taker's progress positively, and if the test-taker feels satisfied, it provides feedback to reinforce the test-taker's progress. The emotion evaluation unit can also provide advice to improve the test-taker's emotions when the test-taker feels dissatisfied. In this way, the test-taker's learning outcomes can be improved by analyzing emotions.

[0076] The support system for overcoming weaknesses can further include an emotion timing unit that estimates the examinee's emotions and adjusts the timing of study based on those emotions. The emotion timing unit, for example, analyzes the fatigue or loss of concentration felt by the examinee while studying and adjusts the timing of study based on those emotions. For example, if the examinee feels tired, it suggests taking a break, and if the examinee's concentration decreases, it provides a study plan that includes short breaks. The emotion timing unit can also take advantage of the timing when the examinee is maintaining high concentration to provide more difficult questions. In this way, the analysis of emotions can improve the examinee's learning efficiency.

[0077] The support system for overcoming weaknesses can further include an emotion feedback unit that estimates the emotions of the test-taker and provides learning feedback based on those emotions. The emotion feedback unit, for example, analyzes the anxiety or stress felt by the test-taker while studying and provides feedback based on those emotions. For example, if the test-taker feels anxious, it provides an encouraging message to ease those emotions, and if the test-taker feels stressed, it suggests relaxation methods to alleviate those emotions. The emotion feedback unit can also provide positive feedback to reinforce emotions when the test-taker feels joy or a sense of accomplishment. This makes it possible to increase the test-taker's motivation to study by analyzing emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The test result analysis unit analyzes the test results of test takers. For example, the test result analysis unit collects and analyzes the results of various confirmation tests and mock exams taken by test takers. Specifically, if test takers make many mistakes on a particular type of problem in a mathematics test (e.g., solving equations), the unit identifies that type of problem and analyzes the reasons for the mistakes (e.g., calculation errors, lack of understanding of the concept). Step 2: The weak point identification unit identifies the test-taker's weak points based on the results analyzed by the test result analysis unit. For example, if the test-taker makes many mistakes in English grammar questions, it identifies the grammar items (e.g., tenses, articles) and analyzes the causes of the mistakes (e.g., misunderstanding of rules, not understanding exceptions). Step 3: The Review Section provides review and retest questions for the weak areas identified by the Weak Area Identification Section. For example, the Review Section provides explanations for questions that the examinee got wrong and asks additional questions to deepen understanding. This process is repeated until the examinee understands, and progress is monitored. Furthermore, the difficulty of the questions is adjusted according to the examinee's level of understanding, providing an optimal study plan.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 test result analysis unit that analyzes the test results of the examinees; a weak point identification unit that identifies weak points of the examinee based on the results analyzed by the test result analysis unit; a review question setting unit that sets reviews and retests for the weak parts identified by the weak part identification unit. A system characterized by:

2. The test result analysis unit Analyzing the answering time of the examinee, finding a tendency for the examinee to take too much time on the specific question, and identifying the cause thereof 2. The system of claim 1.

3. The test result analysis unit Analyzing the examinee's handwritten answers and identifying the cause of the error based on the handwriting and writing habits 2. The system of claim 1.

4. The weak part identification unit Using the emotion estimation function, the examinee's feelings of frustration or helplessness regarding a specific problem are analyzed, and the effect of these feelings on the weak points is identified.

2. The system of claim 1.

5. The review question section Using an emotion estimation function, the motivation and concentration felt by the test taker during the retest are analyzed, and the content of the retest is adjusted based on the analysis.

2. The system of claim 1.

6. The test result analysis unit Analyzing the test taker's answer patterns and evaluating the consistency of the answers to the particular question type.

2. The system of claim 1.

7. The weak part identification unit Analyzing the learning style of the examinee and identifying the cause of the weak point based on the analysis 2. The system of claim 1.

8. The review question section Analyzing the test-taker's interactions with peers and tutors and adjusting the content of the retest accordingly 2. The system of claim 1.

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