System

Abstract

An object of the system according to the embodiment is to support effective learning by providing teaching materials and questions according to the level and purpose of the user.SOLUTION: A system according to an embodiment includes a teaching material providing unit, a question providing unit, a feedback providing unit, and a motivation providing unit. A teaching material providing part provides a teaching material corresponding to the level and purpose of the user. The question providing unit provides a question based on the teaching material provided by the teaching material providing unit. The feedback providing unit analyzes the answer to the question provided by the question providing unit and provides feedback. The motivation providing unit provides motivation on the basis of the feedback provided by the feedback providing 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 had the problem that it is difficult to provide teaching materials and questions that are suited to the user's level and purpose, and that the learning effect is not sufficient.

[0005] The system according to the embodiment aims to provide learning materials and questions that match the level and purpose of the user, and to support effective learning. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning material providing unit, a question providing unit, a feedback providing unit, and a motivation providing unit. The learning material providing unit provides learning material according to the user's level and purpose. The question providing unit provides questions based on the learning material provided by the learning material providing unit. The feedback providing unit analyzes answers to the questions provided by the question providing unit and provides feedback. The motivation providing unit provides motivation based on the feedback provided by the feedback providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide learning materials and questions that are suited to the level and purpose of the user, and can support effective learning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning tool according to an embodiment of the present invention is a system equipped with a generative AI that provides learning materials and questions tailored to the user's level and purpose, as well as feedback and motivation. This allows the learning tool to effectively support the user's learning and maintain their motivation to learn.

[0029] A learning tool according to an embodiment includes a learning material providing unit, a question providing unit, a feedback providing unit, and a motivation providing unit. The learning material providing unit provides learning materials according to the user's level and purpose. For example, the generation AI analyzes the user's knowledge level and learning purpose and provides optimal learning materials based on the analysis. The generation AI generates learning materials using technologies such as GPT-3 and BERT. The question providing unit provides questions based on the learning materials provided by the learning material providing unit. For example, the generation AI provides appropriate questions based on the user's learning progress and analyzes the answers. The feedback providing unit analyzes the answers to the questions provided by the question providing unit and provides feedback. For example, the generation AI analyzes the user's answers, determines whether they are correct or incorrect, and provides detailed explanations of the mistakes. The motivation providing unit provides motivation based on the feedback provided by the feedback providing unit. For example, the generation AI awards badges and points based on the user's learning progress, encouraging a sense of accomplishment. This allows the learning tool according to an embodiment to support learning according to the user's level and purpose, enabling effective learning.

[0030] The teaching material providing unit can analyze the user's past learning history and behavioral data to generate teaching materials optimized for the user's learning style. For example, the teaching material providing unit analyzes the user's past learning history to identify which teaching materials were most effective. For example, teaching materials that have received high ratings in the past are preferentially provided. The teaching material providing unit also analyzes the user's learning style based on the user's behavioral data to provide the optimal teaching material format. For example, a user for whom video teaching materials are effective is provided with more video teaching materials. The teaching material providing unit also combines the learning history and behavioral data to grasp the user's level of understanding and progress, and generates customized teaching materials based on that. For example, teaching materials specialized for areas where the user has a low level of understanding are provided. This makes it possible to maximize the learning effect by providing teaching materials optimized for the user's learning style.

[0031] The teaching material providing unit can provide customized teaching materials that reflect the user's interests and concerns, thereby enhancing motivation to learn. The teaching material providing unit, for example, analyzes the user's interests and concerns and provides customized teaching materials based on the analysis. For example, a user who is interested in a specific technical field can be provided with teaching materials specialized in that field. The teaching material providing unit can also identify topics of interest based on the user's past search history and browsing history, and provide related teaching materials. For example, teaching materials related to specific keywords can be provided preferentially. The teaching material providing unit can also provide quizzes and interactive teaching materials that reflect the user's interests and concerns, thereby enhancing motivation to learn. For example, a quiz related to a topic of interest can be provided. In this way, teaching materials based on the user's interests and concerns can be provided, thereby enhancing motivation to learn.

[0032] The question providing unit can analyze the user's answer patterns, identify the cause of an incorrect answer, and provide individualized feedback. For example, the question providing unit analyzes the user's answer patterns and develops an algorithm that identifies the cause of an incorrect answer. For example, it identifies a lack of understanding of a specific concept. After identifying the cause of an incorrect answer, the question providing unit provides individually customized feedback. For example, it provides additional learning materials or explanations related to the cause of the incorrect answer. The question providing unit can also continuously monitor the user's answer patterns, identify the cause of an incorrect answer in real time, and provide immediate feedback. For example, it can provide a detailed explanation for a question that was answered incorrectly. In this way, the learning effect can be improved by identifying the cause of the user's incorrect answer and providing individualized feedback.

[0033] The question providing unit can dynamically adjust the difficulty of questions and provide optimal questions according to the user's level of understanding. The question providing unit, for example, develops an algorithm that dynamically adjusts the difficulty of questions based on the user's answer data. For example, the difficulty is increased when the correct answer rate is high and decreased when the correct answer rate is low. The question providing unit also builds a system that provides optimal questions according to the user's level of understanding. For example, questions specialized for areas with low levels of understanding are provided. The question providing unit also adjusts the difficulty of questions in real time to maximize the user's learning effect. For example, the unit analyzes the user's answer patterns and provides questions of the optimal difficulty. This makes it possible to maximize the learning effect by providing optimal questions according to the user's level of understanding.

[0034] The feedback providing unit can analyze the user's learning data in detail and provide individually customized feedback. The feedback providing unit, for example, analyzes the user's learning data in detail and builds a system that provides individually customized feedback. For example, it provides feedback based on the level of understanding and progress. The feedback providing unit also identifies the user's strengths and weaknesses based on the learning data and provides customized feedback based on that. For example, it provides additional learning materials and questions to strengthen the weak points. The feedback providing unit also analyzes the user's learning data in real time and develops a system that provides instantly customized feedback. For example, it provides detailed explanations for mistakes made during learning. In this way, by analyzing the user's learning data in detail and providing individually customized feedback, it is possible to improve the learning effect.

[0035] The feedback providing unit can adjust the content of the feedback to suit the user's learning style and preferences. For example, the feedback providing unit builds a system that adjusts the content of the feedback according to the user's learning style and preferences. For example, feedback using graphs and diagrams is provided to a user who prefers visual feedback. The feedback providing unit also customizes the format of the feedback based on the learning style. For example, detailed written feedback is provided to a user who prefers text-based feedback. The feedback providing unit also adjusts the timing and frequency of feedback according to the user's preferences. For example, instant feedback is provided to a user who prefers real-time feedback. In this way, feedback tailored to the user's learning style and preferences can be provided, thereby improving the learning effect.

[0036] The motivation providing unit can analyze a user's learning history and behavioral data and generate an individually customized motivation providing method. The motivation providing unit, for example, analyzes a user's learning history and behavioral data and builds a system that generates an individually customized motivation providing method. For example, it provides encouraging messages based on past learning results. The motivation providing unit also provides customized incentives to increase the user's motivation based on the learning history and behavioral data. For example, it awards badges or points when a specific goal is achieved. The motivation providing unit also develops a system that analyzes a user's behavioral data in real time and instantly generates a customized motivation providing method. For example, it provides feedback to make the user feel a sense of accomplishment while studying. In this way, by generating a customized motivation providing method based on the user's learning history and behavioral data, it is possible to increase motivation to study.

[0037] The motivation providing unit can visualize the user's degree of goal achievement and display the progress in real time. The motivation providing unit, for example, builds a system that visualizes the user's degree of goal achievement and displays the progress in real time. For example, the goal achievement level is displayed in dashboard format. Furthermore, by displaying the progress in real time, the motivation providing unit enables the user to grasp the results of their learning at a glance. For example, the progress is displayed using a progress bar or graph. Furthermore, the motivation providing unit develops an interface for visualizing the user's degree of goal achievement and allows the user to easily access it. For example, the user can check the progress using a mobile app. In this way, the user's motivation to learn can be increased by visualizing the user's degree of goal achievement and displaying the progress in real time.

[0038] The motivation providing unit can provide not only badges and points but also actual rewards (e.g., coupons and gifts) according to the progress of learning. For example, the motivation providing unit builds a system that provides not only badges and points but also actual rewards (e.g., coupons and gifts) according to the progress of learning. For example, a coupon is awarded when a specific goal is achieved. The motivation providing unit also increases the user's motivation to learn by providing actual rewards. For example, a gift card is provided according to the progress of learning. The motivation providing unit also develops a system that monitors the progress of learning in real time and immediately provides rewards. For example, a reward is provided to give the user a sense of accomplishment while learning. In this way, the user's motivation to learn can be increased by providing actual rewards according to the progress of learning.

[0039] The motivation providing unit can introduce a ranking function to encourage competition and cooperation with other users. The motivation providing unit, for example, introduces a ranking function to encourage competition and cooperation with other users to increase users' motivation to learn. For example, it displays rankings based on learning results. The motivation providing unit also uses the ranking function to encourage competition and cooperation between users. For example, it displays group rankings and individual rankings. The motivation providing unit also updates rankings in real time based on users' learning data, building a system that encourages competition and cooperation. For example, it updates rankings according to learning progress. In this way, by introducing a ranking function that encourages competition and cooperation with other users, motivation to learn can be increased.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The learning tool may include an environment adjustment unit for optimizing the user's learning environment. For example, the environment adjustment unit may monitor the user's learning environment (lighting, volume, temperature, etc.) and provide an optimal learning environment. Specifically, the environment adjustment unit may adjust the brightness of the lighting or provide background music. The environment adjustment unit may also collect feedback on the user's learning environment and customize the environment according to the user's preferences. This allows the user to continue learning in a comfortable environment and improves learning effectiveness.

[0042] The learning tool may include a progress display unit that visually displays the user's learning progress. For example, the progress display unit may display the user's learning progress in graphs or charts, allowing the user to grasp their progress at a glance. The progress display unit may also display the user's goal achievement level in real time, allowing the user to feel a sense of accomplishment. Furthermore, the progress display unit may display the user's learning history, allowing the user to review past learning results. This allows the user to visually check their learning progress and maintain their motivation.

[0043] The learning tool may include a plan generation unit that automatically generates a study plan according to the user's learning style. For example, the plan generation unit may analyze the user's past study data and propose an optimal study plan. Specifically, the plan generation unit may adjust the study content and schedule based on the user's learning pace and level of understanding. The plan generation unit may also provide a customized study plan according to the user's goals and indicate specific steps toward achieving the goals. This allows the user to create an effective study plan and improve the efficiency of their study.

[0044] The learning tool can be equipped with a prediction unit that predicts future learning content based on the user's learning history. For example, the prediction unit can analyze the user's past learning data and suggest what content the user should study next. Specifically, the prediction unit can provide learning materials specialized for areas in which the user has a low level of understanding, or encourage the user to prepare for the next step. The prediction unit can also dynamically adjust the learning plan according to the user's learning pace. This allows the user to study effectively and maximize the learning effect.

[0045] The learning tool can be equipped with a monitoring unit that monitors the progress of learning in real time based on the user's learning data. For example, the monitoring unit can analyze the user's learning progress and level of understanding in real time and adjust the learning content as needed. The monitoring unit can also evaluate the effectiveness of learning based on the user's learning data and suggest areas for improvement. This allows the user to check the progress of their learning in real time and study effectively.

[0046] The learning tool may include an evaluation unit that evaluates the effectiveness of learning based on the user's learning data. For example, the evaluation unit may analyze the user's learning progress and level of understanding and quantitatively evaluate the effectiveness of the learning. The evaluation unit may also suggest areas for improvement based on the user's learning data. Specifically, the evaluation unit may provide learning materials specialized for areas in which the user has low understanding, or suggest improvements to the learning method. This allows the user to confirm the effectiveness of their learning and progress with their studies more effectively.

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

[0048] Step 1: The teaching material provider provides learning materials tailored to the user's level and purpose. The generation AI analyzes the user's knowledge level and learning goals and provides optimal learning materials based on those. For example, it generates learning materials using technologies such as GPT-3 and BERT. Step 2: The question provider provides questions based on the learning materials provided by the learning material provider. The generation AI provides appropriate questions according to the user's learning progress and analyzes the answers. Step 3: The feedback provider analyzes the answers to the questions provided by the question provider and provides feedback. The generation AI analyzes the user's answers, determines whether they are correct or incorrect, and provides detailed explanations of the mistakes. Step 4: The motivation provider provides motivation based on the feedback provided by the feedback provider. The generation AI awards badges and points based on the user's learning progress, giving them a sense of accomplishment.

[0049] (Example 2) A learning tool according to an embodiment of the present invention is a system equipped with a generative AI that provides learning materials and questions tailored to the user's level and purpose, as well as feedback and motivation. This allows the learning tool to effectively support the user's learning and maintain their motivation to learn.

[0050] A learning tool according to an embodiment includes a learning material providing unit, a question providing unit, a feedback providing unit, and a motivation providing unit. The learning material providing unit provides learning materials according to the user's level and purpose. For example, the generation AI analyzes the user's knowledge level and learning purpose and provides optimal learning materials based on the analysis. The generation AI generates learning materials using technologies such as GPT-3 and BERT. The question providing unit provides questions based on the learning materials provided by the learning material providing unit. For example, the generation AI provides appropriate questions based on the user's learning progress and analyzes the answers. The feedback providing unit analyzes the answers to the questions provided by the question providing unit and provides feedback. For example, the generation AI analyzes the user's answers, determines whether they are correct or incorrect, and provides detailed explanations of the mistakes. The motivation providing unit provides motivation based on the feedback provided by the feedback providing unit. For example, the generation AI awards badges and points based on the user's learning progress, encouraging a sense of accomplishment. This allows the learning tool according to an embodiment to support learning according to the user's level and purpose, enabling effective learning.

[0051] The teaching material providing unit can analyze the user's past learning history and behavioral data to generate teaching materials optimized for the user's learning style. For example, the teaching material providing unit analyzes the user's past learning history to identify which teaching materials were most effective. For example, teaching materials that have received high ratings in the past are preferentially provided. The teaching material providing unit also analyzes the user's learning style based on the user's behavioral data to provide the optimal teaching material format. For example, a user for whom video teaching materials are effective is provided with more video teaching materials. The teaching material providing unit also combines the learning history and behavioral data to grasp the user's level of understanding and progress, and generates customized teaching materials based on that. For example, teaching materials specialized for areas where the user has a low level of understanding are provided. This makes it possible to maximize the learning effect by providing teaching materials optimized for the user's learning style.

[0052] The teaching material providing unit can provide customized teaching materials that reflect the user's interests and concerns, thereby enhancing motivation to learn. The teaching material providing unit, for example, analyzes the user's interests and concerns and provides customized teaching materials based on the analysis. For example, a user who is interested in a specific technical field can be provided with teaching materials specialized in that field. The teaching material providing unit can also identify topics of interest based on the user's past search history and browsing history, and provide related teaching materials. For example, teaching materials related to specific keywords can be provided preferentially. The teaching material providing unit can also provide quizzes and interactive teaching materials that reflect the user's interests and concerns, thereby enhancing motivation to learn. For example, a quiz related to a topic of interest can be provided. In this way, teaching materials based on the user's interests and concerns can be provided, thereby enhancing motivation to learn.

[0053] The teaching material providing unit can use the emotion estimation function to analyze the user's emotions during study in real time and provide teaching materials for reducing stress and fatigue. For example, the teaching material providing unit uses the emotion estimation function to analyze the user's emotions during study in real time and, if stress or fatigue is detected, provides refreshing teaching materials. For example, it provides short break videos or relaxation music. The teaching material providing unit also adjusts the progress of study based on the user's emotion data and provides interactive teaching materials for reducing stress and fatigue. For example, it provides game-style teaching materials. The teaching material providing unit also adjusts the user's study pace based on the emotion estimation data and provides customized teaching materials for reducing stress and fatigue. For example, it provides teaching materials with adjusted difficulty. In this way, it is possible to support the user in continuing their studies by providing teaching materials for reducing stress and fatigue.

[0054] The question providing unit can analyze the user's answer patterns, identify the cause of an incorrect answer, and provide individualized feedback. For example, the question providing unit analyzes the user's answer patterns and develops an algorithm that identifies the cause of an incorrect answer. For example, it identifies a lack of understanding of a specific concept. After identifying the cause of an incorrect answer, the question providing unit provides individually customized feedback. For example, it provides additional learning materials or explanations related to the cause of the incorrect answer. The question providing unit can also continuously monitor the user's answer patterns, identify the cause of an incorrect answer in real time, and provide immediate feedback. For example, it can provide a detailed explanation for a question that was answered incorrectly. In this way, the learning effect can be improved by identifying the cause of the user's incorrect answer and providing individualized feedback.

[0055] The question providing unit can dynamically adjust the difficulty of questions and provide optimal questions according to the user's level of understanding. The question providing unit, for example, develops an algorithm that dynamically adjusts the difficulty of questions based on the user's answer data. For example, the difficulty is increased when the correct answer rate is high and decreased when the correct answer rate is low. The question providing unit also builds a system that provides optimal questions according to the user's level of understanding. For example, questions specialized for areas with low levels of understanding are provided. The question providing unit also adjusts the difficulty of questions in real time to maximize the user's learning effect. For example, the unit analyzes the user's answer patterns and provides questions of the optimal difficulty. This makes it possible to maximize the learning effect by providing optimal questions according to the user's level of understanding.

[0056] The question providing unit can use the emotion estimation function to provide support to reduce the stress and anxiety the user feels about the questions. For example, the question providing unit uses the emotion estimation function to analyze the stress and anxiety the user feels about the questions in real time and provide support. For example, it provides advice on relaxation and refreshing content. The question providing unit also provides customized support to reduce stress and anxiety based on the user's emotion data. For example, it provides questions with adjusted difficulty or relaxing music. The question providing unit also provides interactive support to reduce the user's stress and anxiety based on the emotion estimation data. For example, it provides refreshing games and quizzes according to the user's preferences. In this way, it is possible to support the user in continuing their studies by providing support to reduce their stress and anxiety.

[0057] The feedback providing unit can analyze the user's learning data in detail and provide individually customized feedback. The feedback providing unit, for example, analyzes the user's learning data in detail and builds a system that provides individually customized feedback. For example, it provides feedback based on the level of understanding and progress. The feedback providing unit also identifies the user's strengths and weaknesses based on the learning data and provides customized feedback based on that. For example, it provides additional learning materials and questions to strengthen the weak points. The feedback providing unit also analyzes the user's learning data in real time and develops a system that provides instantly customized feedback. For example, it provides detailed explanations for mistakes made during learning. In this way, by analyzing the user's learning data in detail and providing individually customized feedback, it is possible to improve the learning effect.

[0058] The feedback providing unit can adjust the content of the feedback to suit the user's learning style and preferences. For example, the feedback providing unit builds a system that adjusts the content of the feedback according to the user's learning style and preferences. For example, feedback using graphs and diagrams is provided to a user who prefers visual feedback. The feedback providing unit also customizes the format of the feedback based on the learning style. For example, detailed written feedback is provided to a user who prefers text-based feedback. The feedback providing unit also adjusts the timing and frequency of feedback according to the user's preferences. For example, instant feedback is provided to a user who prefers real-time feedback. In this way, feedback tailored to the user's learning style and preferences can be provided, thereby improving the learning effect.

[0059] The feedback providing unit uses the emotion estimation function to provide feedback according to the user's emotional state, thereby maintaining motivation to learn. The feedback providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide feedback according to the analysis. For example, if the user is feeling stressed, it provides an encouraging message. The feedback providing unit also provides customized feedback to maintain motivation to learn based on the user's emotional data. For example, it provides compliments or success stories to elicit positive emotions. The feedback providing unit also provides interactive feedback according to the user's emotional state based on the emotion estimation data. For example, if the user is tired, it provides refreshing content. In this way, by providing feedback according to the user's emotional state, it is possible to maintain motivation to learn.

[0060] The motivation providing unit can analyze a user's learning history and behavioral data and generate an individually customized motivation providing method. The motivation providing unit, for example, analyzes a user's learning history and behavioral data and builds a system that generates an individually customized motivation providing method. For example, it provides encouraging messages based on past learning results. The motivation providing unit also provides customized incentives to increase the user's motivation based on the learning history and behavioral data. For example, it awards badges or points when a specific goal is achieved. The motivation providing unit also develops a system that analyzes a user's behavioral data in real time and instantly generates a customized motivation providing method. For example, it provides feedback to make the user feel a sense of accomplishment while studying. In this way, by generating a customized motivation providing method based on the user's learning history and behavioral data, it is possible to increase motivation to study.

[0061] The motivation providing unit can visualize the user's degree of goal achievement and display the progress in real time. The motivation providing unit, for example, builds a system that visualizes the user's degree of goal achievement and displays the progress in real time. For example, the goal achievement level is displayed in dashboard format. Furthermore, by displaying the progress in real time, the motivation providing unit enables the user to grasp the results of their learning at a glance. For example, the progress is displayed using a progress bar or graph. Furthermore, the motivation providing unit develops an interface for visualizing the user's degree of goal achievement and allows the user to easily access it. For example, the user can check the progress using a mobile app. In this way, the user's motivation to learn can be increased by visualizing the user's degree of goal achievement and displaying the progress in real time.

[0062] The motivation providing unit can use the emotion estimation function to provide incentives to increase the user's motivation when their motivation to learn decreases. For example, the motivation providing unit uses the emotion estimation function to build a system that provides incentives to increase the user's motivation when their motivation to learn decreases. For example, the motivation providing unit provides encouraging messages and success stories. The motivation providing unit also provides customized incentives to increase the user's motivation to learn based on the user's emotion data. For example, badges and points are awarded when a specific goal is achieved. The motivation providing unit also provides interactive incentives to maintain the user's motivation to learn based on the emotion estimation data. For example, refreshing content is provided according to the user's preferences. In this way, the user's motivation to learn can be maintained by providing incentives to increase motivation when their motivation to learn decreases.

[0063] The motivation providing unit can provide not only badges and points but also actual rewards (e.g., coupons and gifts) according to the progress of learning. For example, the motivation providing unit builds a system that provides not only badges and points but also actual rewards (e.g., coupons and gifts) according to the progress of learning. For example, a coupon is awarded when a specific goal is achieved. The motivation providing unit also increases the user's motivation to learn by providing actual rewards. For example, a gift card is provided according to the progress of learning. The motivation providing unit also develops a system that monitors the progress of learning in real time and immediately provides rewards. For example, a reward is provided to give the user a sense of accomplishment while learning. In this way, the user's motivation to learn can be increased by providing actual rewards according to the progress of learning.

[0064] The motivation providing unit can introduce a ranking function to encourage competition and cooperation with other users. The motivation providing unit, for example, introduces a ranking function to encourage competition and cooperation with other users to increase users' motivation to learn. For example, it displays rankings based on learning results. The motivation providing unit also uses the ranking function to encourage competition and cooperation between users. For example, it displays group rankings and individual rankings. The motivation providing unit also updates rankings in real time based on users' learning data, building a system that encourages competition and cooperation. For example, it updates rankings according to learning progress. In this way, by introducing a ranking function that encourages competition and cooperation with other users, motivation to learn can be increased.

[0065] The motivation providing unit can use the emotion estimation function to provide feedback to enhance the sense of accomplishment and satisfaction the user feels with regard to learning. The motivation providing unit, for example, uses the emotion estimation function to build a system that provides feedback to enhance the sense of accomplishment and satisfaction the user feels with regard to learning. For example, it provides compliments or success stories to elicit positive emotions. The motivation providing unit also provides customized feedback to enhance the sense of accomplishment and satisfaction based on the user's emotion data. For example, it provides an encouraging message when a specific goal is achieved. The motivation providing unit also provides interactive feedback to enhance the user's sense of accomplishment and satisfaction based on the emotion estimation data. For example, it provides refreshing content according to the user's preferences. In this way, by providing feedback that enhances the sense of accomplishment and satisfaction the user feels with regard to learning, it is possible to maintain motivation to learn.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The learning tool may include an environment adjustment unit for optimizing the user's learning environment. For example, the environment adjustment unit may monitor the user's learning environment (lighting, volume, temperature, etc.) and provide an optimal learning environment. Specifically, the environment adjustment unit may adjust the brightness of the lighting or provide background music. The environment adjustment unit may also collect feedback on the user's learning environment and customize the environment according to the user's preferences. This allows the user to continue learning in a comfortable environment and improves learning effectiveness.

[0068] The learning tool may include a progress display unit that visually displays the user's learning progress. For example, the progress display unit may display the user's learning progress in graphs or charts, allowing the user to grasp their progress at a glance. The progress display unit may also display the user's goal achievement level in real time, allowing the user to feel a sense of accomplishment. Furthermore, the progress display unit may display the user's learning history, allowing the user to review past learning results. This allows the user to visually check their learning progress and maintain their motivation.

[0069] The learning tool may include a plan generation unit that automatically generates a study plan according to the user's learning style. For example, the plan generation unit may analyze the user's past study data and propose an optimal study plan. Specifically, the plan generation unit may adjust the study content and schedule based on the user's learning pace and level of understanding. The plan generation unit may also provide a customized study plan according to the user's goals and indicate specific steps toward achieving the goals. This allows the user to create an effective study plan and improve the efficiency of their study.

[0070] The learning tool may include an emotion adaptation unit that analyzes the user's emotions during learning and provides learning content that corresponds to the emotions. For example, the emotion adaptation unit may provide relaxing content if the user is feeling stressed, or provide encouraging messages if the user's motivation is low. The emotion adaptation unit may also adjust the learning progress based on the user's emotion data, allowing the user to continue learning in an optimal state. This allows the tool to support the user's continued learning by providing learning content that corresponds to the user's emotions.

[0071] The learning tool can be equipped with a prediction unit that predicts future learning content based on the user's learning history. For example, the prediction unit can analyze the user's past learning data and suggest what content the user should study next. Specifically, the prediction unit can provide learning materials specialized for areas in which the user has a low level of understanding, or encourage the user to prepare for the next step. The prediction unit can also dynamically adjust the learning plan according to the user's learning pace. This allows the user to study effectively and maximize the learning effect.

[0072] The learning tool may include an emotional feedback unit that analyzes the user's emotions during learning and provides feedback according to the emotions. For example, the emotional feedback unit may provide praise if the user has positive emotions toward learning, or provide encouraging messages if the user has negative emotions. The emotional feedback unit may also adjust the learning progress based on the user's emotional data, allowing the user to continue learning in an optimal state. This allows the user to maintain their motivation to learn by providing feedback according to their emotions.

[0073] The learning tool can be equipped with a monitoring unit that monitors the progress of learning in real time based on the user's learning data. For example, the monitoring unit can analyze the user's learning progress and level of understanding in real time and adjust the learning content as needed. The monitoring unit can also evaluate the effectiveness of learning based on the user's learning data and suggest areas for improvement. This allows the user to check the progress of their learning in real time and study effectively.

[0074] The learning tool may include an emotion motivation unit that analyzes the user's emotions during learning and provides motivation according to the emotions. For example, the emotion motivation unit may provide additional challenges if the user has positive emotions toward learning, or provide encouraging messages or refreshing content if the user has negative emotions. The emotion motivation unit may also adjust the learning progress based on the user's emotion data, allowing the user to continue learning in an optimal state. This allows the user to maintain their motivation to learn by providing motivation according to their emotions.

[0075] The learning tool may include an evaluation unit that evaluates the effectiveness of learning based on the user's learning data. For example, the evaluation unit may analyze the user's learning progress and level of understanding and quantitatively evaluate the effectiveness of the learning. The evaluation unit may also suggest areas for improvement based on the user's learning data. Specifically, the evaluation unit may provide learning materials specialized for areas in which the user has low understanding, or suggest improvements to the learning method. This allows the user to confirm the effectiveness of their learning and progress with their studies more effectively.

[0076] The learning tool may include an emotion planning unit that analyzes the user's emotions during learning and provides a learning plan according to the emotions. For example, the emotion planning unit increases the amount of learning if the user has positive emotions toward learning, and decreases the amount of learning if the user has negative emotions. The emotion planning unit also adjusts the learning progress based on the user's emotion data, allowing the user to continue learning in an optimal state. In this way, providing a learning plan according to the user's emotions can maintain motivation to learn.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The teaching material provider provides learning materials tailored to the user's level and purpose. The generation AI analyzes the user's knowledge level and learning goals and provides optimal learning materials based on those. For example, it generates learning materials using technologies such as GPT-3 and BERT. Step 2: The question provider provides questions based on the learning materials provided by the learning material provider. The generation AI provides appropriate questions according to the user's learning progress and analyzes the answers. Step 3: The feedback provider analyzes the answers to the questions provided by the question provider and provides feedback. The generation AI analyzes the user's answers, determines whether they are correct or incorrect, and provides detailed explanations of the mistakes. Step 4: The motivation provider provides motivation based on the feedback provided by the feedback provider. The generation AI awards badges and points based on the user's learning progress, giving them a sense of accomplishment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

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

[0100] The 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.

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

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

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

[0104] Fig. 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.

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

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

[0107] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0109] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0111] The data processing system 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

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

[0115] The 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.

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

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 learning tool equipped with generative AI, a teaching material providing unit that provides teaching materials according to the level and purpose of the user; a question providing unit that provides questions based on the teaching materials provided by the teaching material providing unit; a feedback providing unit that analyzes the answers to the questions provided by the question providing unit and provides feedback; a motivation providing unit that provides motivation based on the feedback provided by the feedback providing unit. A system characterized by:

2. The teaching material providing unit Analyzing the user's past learning history and behavioral data, and generating the learning materials optimized for the user's learning style 2. The system of claim 1.

3. The teaching material providing unit To provide customized learning materials that reflect the user's interests and concerns, thereby enhancing motivation to learn.

2. The system of claim 1.

4. The teaching material providing unit Analyzing the user's emotions during learning in real time and providing the learning material to reduce stress and fatigue 2. The system of claim 1.

5. The question providing unit: Analyzing the answer pattern of the user, identifying the cause of the incorrect answer, and providing the feedback individually 2. The system of claim 1.

6. The question providing unit: Dynamically adjust the difficulty of the questions to provide the most appropriate questions according to the user's level of understanding.

2. The system of claim 1.

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