System

Abstract

An object of a system according to an embodiment is to provide learning support customized in accordance with skills and interests of individual users.SOLUTION: A system includes a generation AI, a community, a progress management part, and a support part. The generative AI provides a customized interaction according to the skill, domain, interest, academic ability, and proficiency level of the user. In the community, in addition to the learning support by the generative AI, teachers and experts as real humans provide support through the community. The generation AI analyzes the training data of the user and manages a progressing situation. The support division supports teachers and experts whose generated AI belongs to the community.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to provide customized learning support tailored to the skills and interests of individual users.

[0005] The system according to the embodiment aims to provide learning support customized to the skills and interests of each individual user. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a community, a progress management unit, and a support unit. The generation AI provides customized dialogue according to the user's skills, field, interests, academic ability, and level of proficiency. In addition to learning support provided by the generation AI, the community also provides support through the community from real human teachers and experts. In the progress management unit, the generation AI analyzes the user's learning data and manages the progress. In the support unit, the generation AI supports the teachers and experts who belong to the community. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized learning support according to the skills and interests of each individual user. [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) The educational platform according to an embodiment of the present invention is a system that uses generative AI to provide customized dialogues according to the user's skills, field, interests, academic ability, and level of proficiency, and provides support from real human teachers and experts through a community. This enables the educational platform to provide advanced education to anyone, anywhere.

[0029] An educational platform according to an embodiment includes a generation AI, a community, teachers, and experts. The generation AI provides customized dialogue based on a user's skills, domain, interests, academic ability, and proficiency level. For example, the generation AI analyzes a user's prompts and provides appropriate learning content and advice. For example, if a junior high school student interested in mathematics inputs a prompt such as "I want to learn calculus," the generation AI provides learning materials and problems appropriate to the user's level and provides explanations. For example, the generation AI analyzes the user's past learning history and performance data to provide dialogue optimized for each individual learning style. For example, the generation AI analyzes the user's facial expressions and tone of voice in real time while studying and provides appropriate feedback and encouragement. In addition to learning support provided by the generation AI, the community provides support from real-life teachers and experts. For example, online forums and video conferences can be used to resolve questions and doubts about the learning content provided by the generation AI. The community can check the user's learning progress and provide additional guidance as needed. The progress management unit allows the generation AI to analyze the user's learning data and manage the user's progress. For example, the generative AI analyzes the user's level of understanding and which areas they are struggling with, and then suggests the next learning step based on that. The progress management section, for example, predicts future learning progress based on the user's learning data, detects problems early, and suggests solutions. In the support section, the generative AI supports teachers and experts who belong to the community. For example, the generative AI automatically generates reports to make it easier for teachers to understand the learning status of their students. In the support section, for example, when a teacher is creating a lesson plan, the generative AI suggests the most appropriate teaching materials and activities. This enables the educational platform to provide advanced education to anyone, anywhere.

[0030] Generative AI can analyze a user's past learning history and performance data to provide dialogue optimized for the user's learning style. For example, generative AI can analyze a user's past learning history to identify their strengths and weaknesses. For example, if a user has good grades in math but low grades in English, it can provide dialogue that focuses on learning English. Generative AI can identify a user's learning style based on their performance data. For example, it can provide learning materials that make extensive use of diagrams and graphs to a user who is effective at visual learning. Generative AI can integrate a user's learning history and performance data to propose an optimal study plan. For example, it can automatically select the next topic to study based on past test results. This makes it possible to provide dialogue optimized for the user's learning style.

[0031] Generative AI can accommodate different languages ​​and cultural backgrounds and provide customized dialogue for international learners. For example, generative AI provides learning content in an appropriate language based on the user's language settings. For example, it provides dialogue in English for users whose native language is English. Generative AI can provide appropriate learning content by taking into account the user's cultural background. For example, it can avoid culturally sensitive topics. Generative AI has a multilingual dialogue function, for example, and provides the same quality of learning support to users who speak different languages. For example, it can use a translation function to conduct dialogue in real time. This allows it to accommodate different languages ​​and cultural backgrounds and provide customized dialogue for international learners.

[0032] Generative AI can suggest learning content based on a user's hobbies and interests, increasing the user's motivation to learn. For example, generative AI can analyze a user's hobbies and interests and provide learning content based on them. For example, a user interested in sports can be provided with sports-related math problems. For example, generative AI can suggest related learning resources based on a user's interests. For example, a user interested in history can be introduced to historical documentaries. For example, generative AI can create a learning plan that reflects a user's hobbies and interests, increasing the user's motivation to learn. For example, a user interested in music can be provided with materials for learning music theory. This makes it possible to suggest learning content based on a user's hobbies and interests, increasing the user's motivation to learn.

[0033] The generative AI can coordinate group learning to promote cooperation between learners. For example, the generative AI may adjust learners' schedules and set times for group learning. For example, it may group learners studying the same topic. For example, the generative AI may organize optimal groups based on learners' interests and skill levels. For example, it may balance learners with different skill levels. For example, the generative AI may support the progress of group learning and suggest activities and discussion topics as needed. For example, it may provide themes for group discussions. This promotes cooperation between learners and enables effective coordination of group learning.

[0034] Generative AI can classify and prioritize questions to improve the quality of questions and answers within a community. For example, generative AI analyzes questions within a community and classifies them by related topic. For example, it classifies math questions into the math category and science questions into the science category. Generative AI prioritizes questions based on their importance and urgency. For example, it displays questions with high urgency first. Generative AI analyzes the content of questions and automatically recommends appropriate respondents. For example, it recommends experts who are knowledgeable in a particular field. This can improve the quality of questions and answers within a community.

[0035] The generation AI can add a function that allows learners to share their progress within the community and encourage each other. For example, the generation AI automatically records learners' progress and shares it within the community. For example, it displays learning achievement and progress in graphs. For example, the generation AI automatically generates messages of encouragement for each other based on learners' progress. For example, it sends congratulatory messages to learners who achieve their goals. For example, the generation AI analyzes learners' progress data and sends encouraging messages to learners who are falling behind. For example, it provides advice to increase motivation. This allows learners to share their progress within the community and encourage each other.

[0036] Generative AI can introduce gamification elements that evaluate learners' achievements within a community and award badges and points. Generative AI, for example, builds a system that automatically evaluates learners' achievements and awards badges and points. For example, badges are awarded to learners who complete specific challenges. Generative AI, for example, introduces gamification elements based on learners' progress. For example, points are awarded according to the level of learning achievement and a ranking is displayed. Generative AI, for example, builds a system that evaluates learners' achievements and provides rewards based on badges and points. For example, benefits are provided to learners who have earned a certain number of points. In this way, learners' achievements within a community can be evaluated and badges and points awarded, thereby increasing their motivation to learn.

[0037] The generation AI can analyze a user's learning data, identify the user's learning patterns, and propose an optimal learning plan. The generation AI, for example, analyzes a user's learning data and identifies their learning patterns. For example, for a user who often studies at night, the generation AI proposes a study plan suitable for that time. The generation AI, for example, creates an optimal study plan based on the user's learning history. For example, it suggests the next topic to study based on past grades. The generation AI, for example, analyzes a user's learning data and monitors their learning progress in real time. For example, if progress is lagging, the generation AI adjusts the study plan. This makes it possible to identify the user's learning patterns and propose an optimal study plan.

[0038] Generative AI can predict future learning progress based on a user's learning data, detect problems early, and suggest countermeasures. Generative AI, for example, analyzes a user's learning data and predicts future learning progress. For example, it predicts the next test score based on past learning history. Generative AI, for example, builds a system that detects problems early based on learning data. For example, if a user is struggling with a particular topic, it suggests additional learning materials. Generative AI, for example, analyzes a user's learning data and predicts future learning progress and suggests countermeasures. For example, if learning progress is lagging, it adjusts the learning plan. This makes it possible to predict future learning progress, detect problems early, and suggest countermeasures.

[0039] Generative AI can integrate data from different learning areas and manage overall learning progress. For example, generative AI can integrate data from different learning areas and build a system to manage overall learning progress. For example, it can centrally manage progress in mathematics, science, and English. For example, generative AI can integrate learning data and create an overall learning plan. For example, it can suggest a balanced learning plan based on progress in each area. For example, generative AI can analyze data from different learning areas and monitor overall learning progress in real time. For example, it can provide additional support for areas where progress is lagging behind. This makes it possible to integrate data from different learning areas and manage overall learning progress.

[0040] The generating AI can compare the user's learning data with other learners and provide a relative progress status. The generating AI, for example, builds a system that compares the user's learning data with other learners and provides a relative progress status. For example, it displays progress compared with learners of the same age group. The generating AI, for example, evaluates the user's relative progress based on the learning data. For example, it evaluates the level of understanding in comparison with other learners studying the same topic. The generating AI, for example, compares the user's learning data with other learners and provides a relative progress status in real time. For example, if progress is lagging, it suggests additional support. This makes it possible to compare the user's learning data with other learners and provide a relative progress status.

[0041] Generative AI can analyze the past teaching data of teachers and experts and propose the optimal teaching method. Generative AI, for example, analyzes the past teaching data of teachers and experts and builds a system that proposes the optimal teaching method. For example, it proposes a teaching method based on past success stories. Generative AI, for example, proposes the optimal teaching method for each student based on the teaching data. For example, it proposes a teaching method that suits a specific learning style. Generative AI, for example, analyzes the teaching data of teachers and experts and evaluates the effectiveness of the teaching. For example, it selects the optimal teaching method based on past teaching results. This makes it possible to analyze the past teaching data of teachers and experts and propose the optimal teaching method.

[0042] Generative AI can manage the schedules of teachers and experts and create efficient teaching plans. Generative AI, for example, builds a system that automatically manages the schedules of teachers and experts and creates efficient teaching plans. For example, it can provide instruction using free time. Generative AI, for example, suggests optimal teaching times based on schedule data. For example, it can set teaching times that suit the convenience of students. Generative AI, for example, manages the schedules of teachers and experts in real time and creates efficient teaching plans. For example, it can adjust teaching plans in response to schedule changes. This makes it possible to manage the schedules of teachers and experts and create efficient teaching plans.

[0043] Generative AI can automatically record the teaching content of teachers and experts, making it possible to refer to it later. Generative AI, for example, builds a system that automatically records the teaching content of teachers and experts, making it possible to refer to it later. For example, it saves the teaching content as text or audio data. Generative AI, for example, manages the teaching history based on the teaching content. For example, it can search and refer to past teaching content. Generative AI, for example, records the teaching content of teachers and experts in real time, making it possible to refer to it later. For example, it automatically organizes and saves the teaching content. This makes it possible to automatically record the teaching content of teachers and experts, making it possible to refer to it later.

[0044] Generative AI can share the teaching content of teachers and experts with other teachers and experts, spreading best practices. For example, generative AI could build a system that automatically shares the teaching content of teachers and experts and spreads best practices. For example, it could introduce excellent teaching methods to other teachers. For example, generative AI could share information with other teachers and experts based on the teaching content. For example, it could share successful teaching examples so that other teachers can use them as reference. For example, generative AI could share the teaching content of teachers and experts in real time and spread best practices. For example, it could publish the teaching content on an online platform. This would allow teachers and experts to share their teaching content with other teachers and experts, spreading best practices.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] In addition to the generative AI, the educational platform may include an environmental adjustment unit for optimizing the user's learning environment. The environmental adjustment unit, for example, adjusts the lighting and sound in the user's learning environment. For example, it can change the color temperature of the lighting to improve concentration. The environmental adjustment unit, for example, adjusts the temperature and humidity in the user's learning environment. For example, it can automatically control an air conditioner or humidifier to maintain a comfortable learning environment. The environmental adjustment unit, for example, adjusts the noise level in the user's learning environment. For example, it can provide a noise canceling function to block out external noise. This optimizes the user's learning environment and improves learning efficiency.

[0047] In addition to the generation AI, the educational platform can be equipped with an outcome display unit that visually displays the user's learning results. The outcome display unit, for example, displays the user's learning progress in graphs or charts. For example, the user can visually check the level of learning achievement and understanding. The outcome display unit, for example, displays the user's learning history in a timeline format. For example, the user can check past learning content and test results at a glance. The outcome display unit, for example, sets the user's learning goals and displays the progress of those goals in real time. For example, the user can check the progress towards goal achievement. This visually displays the user's learning results and increases motivation to learn.

[0048] In addition to the generation AI, the educational platform can include a learning style suggestion unit that suggests a learning method according to the user's learning style. The learning style suggestion unit, for example, analyzes the user's learning history and performance data and suggests an optimal learning method. For example, a user who is effective at visual learning can be provided with learning materials that make extensive use of diagrams and graphs. The learning style suggestion unit, for example, creates a learning plan according to the user's learning style. For example, a user who is effective at short, concentrated learning can be suggested a short learning session. The learning style suggestion unit, for example, provides feedback based on the user's learning style. For example, it can send appropriate advice or encouraging messages according to the user's learning progress. This makes it possible to suggest a learning method according to the user's learning style and maximize the learning effect.

[0049] In addition to generative AI, the educational platform can introduce gamification elements to increase users' motivation to learn. The gamification elements, for example, create a system that awards badges and points according to the level of learning achievement. For example, badges can be awarded to learners who complete specific challenges. The gamification elements, for example, display rankings according to the progress of learning. For example, points can be awarded according to the level of learning achievement and the rankings can be displayed. The gamification elements, for example, create a system that evaluates learning outcomes and provides rewards based on badges and points. For example, learners who have earned a certain number of points can be provided with special benefits. This can increase users' motivation to learn and make learning more enjoyable.

[0050] In addition to generative AI, the education platform can compare a user's learning data with other learners and provide a relative progress report. For example, a system can be built that compares a user's learning data with other learners and provides a relative progress report. For example, progress can be displayed compared with learners in the same age group. Based on the learning data, the user's relative progress can be evaluated. For example, comprehension can be evaluated compared with other learners studying the same topic. The user's learning data can be compared with other learners and a relative progress report can be provided in real time. For example, if progress is lagging, additional support can be suggested. This makes it possible to compare a user's learning data with other learners and provide a relative progress report.

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

[0052] Step 1: The generative AI provides customized dialogue based on the user's skills, domain, interests, academic ability, and level of proficiency. For example, the generative AI analyzes the user's prompts and provides appropriate learning content and advice. The generative AI analyzes the user's past learning history and performance data to provide dialogue optimized for each individual learning style. It also analyzes the user's facial expressions and tone of voice in real time while they are studying to provide appropriate feedback and encouragement. Step 2: In addition to the learning support provided by the generative AI, the community will provide support from real human teachers and experts. For example, through online forums and video conferences, they will answer questions and clarify doubts about the learning content provided by the generative AI. The community will monitor learning progress and provide additional guidance as needed. Step 3: In the progress management section, the generation AI analyzes the user's learning data and manages their progress. For example, the generation AI analyzes the user's level of understanding and which areas they are struggling with, and based on that, suggests the next learning step. The progress management section predicts future learning progress based on the user's learning data, detects problems early, and suggests solutions. Step 4: In the support section, the generative AI supports teachers and experts in the community. For example, the generative AI automatically generates reports to help teachers understand their students' learning progress. In the support section, the generative AI suggests optimal teaching materials and activities when teachers create lesson plans.

[0053] (Example 2) The educational platform according to an embodiment of the present invention is a system that uses generative AI to provide customized dialogues according to the user's skills, field, interests, academic ability, and level of proficiency, and provides support from real human teachers and experts through a community. This enables the educational platform to provide advanced education to anyone, anywhere.

[0054] An educational platform according to an embodiment includes a generation AI, a community, teachers, and experts. The generation AI provides customized dialogue based on a user's skills, domain, interests, academic ability, and proficiency level. For example, the generation AI analyzes a user's prompts and provides appropriate learning content and advice. For example, if a junior high school student interested in mathematics inputs a prompt such as "I want to learn calculus," the generation AI provides learning materials and problems appropriate to the user's level and provides explanations. For example, the generation AI analyzes the user's past learning history and performance data to provide dialogue optimized for each individual learning style. For example, the generation AI analyzes the user's facial expressions and tone of voice in real time while studying and provides appropriate feedback and encouragement. In addition to learning support provided by the generation AI, the community provides support from real-life teachers and experts. For example, online forums and video conferences can be used to resolve questions and doubts about the learning content provided by the generation AI. The community can check the user's learning progress and provide additional guidance as needed. The progress management unit allows the generation AI to analyze the user's learning data and manage the user's progress. For example, the generative AI analyzes the user's level of understanding and which areas they are struggling with, and then suggests the next learning step based on that. The progress management section, for example, predicts future learning progress based on the user's learning data, detects problems early, and suggests solutions. In the support section, the generative AI supports teachers and experts who belong to the community. For example, the generative AI automatically generates reports to make it easier for teachers to understand the learning status of their students. In the support section, for example, when a teacher is creating a lesson plan, the generative AI suggests the most appropriate teaching materials and activities. This enables the educational platform to provide advanced education to anyone, anywhere.

[0055] Generative AI can analyze a user's past learning history and performance data to provide dialogue optimized for the user's learning style. For example, generative AI can analyze a user's past learning history to identify their strengths and weaknesses. For example, if a user has good grades in math but low grades in English, it can provide dialogue that focuses on learning English. Generative AI can identify a user's learning style based on their performance data. For example, it can provide learning materials that make extensive use of diagrams and graphs to a user who is effective at visual learning. Generative AI can integrate a user's learning history and performance data to propose an optimal study plan. For example, it can automatically select the next topic to study based on past test results. This makes it possible to provide dialogue optimized for the user's learning style.

[0056] The generative AI can analyze the user's facial expressions and voice tone in real time while they are studying, and provide appropriate feedback and encouragement. For example, the generative AI can analyze the user's facial expressions with a camera to determine whether they are concentrating. For example, if their concentration is declining, it can suggest taking a break. For example, the generative AI can analyze the user's voice tone to detect stress and fatigue. For example, if they are tired, it can suggest relaxing music. For example, the generative AI can comprehensively analyze the user's facial expressions and voice tone to provide appropriate feedback. For example, if their studies are progressing smoothly, it can give them words of praise. This makes it possible to analyze the user's facial expressions and voice tone while they are studying, and provide appropriate feedback and encouragement.

[0057] The generation AI can use its emotion estimation function to estimate the user's emotional state and provide content to help them relax when they feel stressed or fatigued. For example, the generation AI can analyze the user's emotional state in real time and, if they are feeling stressed, provide a meditation guide to help them relax. For example, the generation AI can suggest videos or music to help them relax based on the user's emotional state. For example, it can play natural scenery or relaxation music. For example, the generation AI can monitor the user's emotional state and, if they feel fatigued, suggest taking a short break. For example, it can recommend stretching or light exercise. This makes it possible to estimate the user's emotional state and provide content to help them relax when they feel stressed or fatigued.

[0058] Generative AI can accommodate different languages ​​and cultural backgrounds and provide customized dialogue for international learners. For example, generative AI provides learning content in an appropriate language based on the user's language settings. For example, it provides dialogue in English for users whose native language is English. Generative AI can provide appropriate learning content by taking into account the user's cultural background. For example, it can avoid culturally sensitive topics. Generative AI has a multilingual dialogue function, for example, and provides the same quality of learning support to users who speak different languages. For example, it can use a translation function to conduct dialogue in real time. This allows it to accommodate different languages ​​and cultural backgrounds and provide customized dialogue for international learners.

[0059] Generative AI can suggest learning content based on a user's hobbies and interests, increasing the user's motivation to learn. For example, generative AI can analyze a user's hobbies and interests and provide learning content based on them. For example, a user interested in sports can be provided with sports-related math problems. For example, generative AI can suggest related learning resources based on a user's interests. For example, a user interested in history can be introduced to historical documentaries. For example, generative AI can create a learning plan that reflects a user's hobbies and interests, increasing the user's motivation to learn. For example, a user interested in music can be provided with materials for learning music theory. This makes it possible to suggest learning content based on a user's hobbies and interests, increasing the user's motivation to learn.

[0060] The generative AI can use its emotion estimation function to identify the topics in which a user is most interested and provide learning content related to those topics. For example, the generative AI analyzes the user's emotional responses to identify the topics in which the user is most interested. For example, it prioritizes providing learning content related to topics in which the user has shown interest. For example, the generative AI identifies topics in which the user is interested based on the user's emotional data and suggests learning materials and resources related to those topics. For example, it provides the latest science news to a user who is interested in science. For example, the generative AI monitors the user's emotional state and suggests learning activities related to topics in which the user is interested. For example, it suggests projects based on topics in which the user is interested. This allows the generative AI to identify the topics in which the user is most interested and provide learning content related to those topics.

[0061] The generative AI can coordinate group learning to promote cooperation between learners. For example, the generative AI may adjust learners' schedules and set times for group learning. For example, it may group learners studying the same topic. For example, the generative AI may organize optimal groups based on learners' interests and skill levels. For example, it may balance learners with different skill levels. For example, the generative AI may support the progress of group learning and suggest activities and discussion topics as needed. For example, it may provide themes for group discussions. This promotes cooperation between learners and enables effective coordination of group learning.

[0062] Generative AI can classify and prioritize questions to improve the quality of questions and answers within a community. For example, generative AI analyzes questions within a community and classifies them by related topic. For example, it classifies math questions into the math category and science questions into the science category. Generative AI prioritizes questions based on their importance and urgency. For example, it displays questions with high urgency first. Generative AI analyzes the content of questions and automatically recommends appropriate respondents. For example, it recommends experts who are knowledgeable in a particular field. This can improve the quality of questions and answers within a community.

[0063] The generative AI can use its emotion estimation function to monitor the emotional state of learners within the community and make suggestions to maintain a positive learning environment. For example, the generative AI can analyze the emotional state of learners within the community in real time and make relaxation suggestions to learners who are feeling stressed or anxious. For example, the generative AI can suggest activities to maintain a positive learning environment based on learners' emotional data. For example, it can suggest team building activities. For example, the generative AI can monitor the emotional state within the community and take measures to prevent negative emotions from spreading. For example, it can make suggestions to increase positive feedback. This makes it possible to monitor the emotional state of learners within the community and make suggestions to maintain a positive learning environment.

[0064] The generation AI can add a function that allows learners to share their progress within the community and encourage each other. For example, the generation AI automatically records learners' progress and shares it within the community. For example, it displays learning achievement and progress in graphs. For example, the generation AI automatically generates messages of encouragement for each other based on learners' progress. For example, it sends congratulatory messages to learners who achieve their goals. For example, the generation AI analyzes learners' progress data and sends encouraging messages to learners who are falling behind. For example, it provides advice to increase motivation. This allows learners to share their progress within the community and encourage each other.

[0065] Generative AI can introduce gamification elements that evaluate learners' achievements within a community and award badges and points. Generative AI, for example, builds a system that automatically evaluates learners' achievements and awards badges and points. For example, badges are awarded to learners who complete specific challenges. Generative AI, for example, introduces gamification elements based on learners' progress. For example, points are awarded according to the level of learning achievement and a ranking is displayed. Generative AI, for example, builds a system that evaluates learners' achievements and provides rewards based on badges and points. For example, benefits are provided to learners who have earned a certain number of points. In this way, learners' achievements within a community can be evaluated and badges and points awarded, thereby increasing their motivation to learn.

[0066] The generative AI can use its emotion estimation function to analyze the emotional responses of learners within a community and match them with the most suitable learning partners. For example, the generative AI analyzes learners' emotional responses and matches learners with positive emotions with each other. For example, it pairs learners who are highly motivated to learn. For example, the generative AI recommends the most suitable learning partners based on learners' emotional data. For example, it matches learners with skills that complement each other. For example, the generative AI monitors learners' emotional states and recommends partners who will have a positive influence on learners with negative emotions. For example, it pairs learners who are highly motivated. In this way, the generative AI can analyze learners' emotional responses within a community and match them with the most suitable learning partners.

[0067] The generation AI can analyze a user's learning data, identify the user's learning patterns, and propose an optimal learning plan. The generation AI, for example, analyzes a user's learning data and identifies their learning patterns. For example, for a user who often studies at night, the generation AI proposes a study plan suitable for that time. The generation AI, for example, creates an optimal study plan based on the user's learning history. For example, it suggests the next topic to study based on past grades. The generation AI, for example, analyzes a user's learning data and monitors their learning progress in real time. For example, if progress is lagging, the generation AI adjusts the study plan. This makes it possible to identify the user's learning patterns and propose an optimal study plan.

[0068] Generative AI can predict future learning progress based on a user's learning data, detect problems early, and suggest countermeasures. Generative AI, for example, analyzes a user's learning data and predicts future learning progress. For example, it predicts the next test score based on past learning history. Generative AI, for example, builds a system that detects problems early based on learning data. For example, if a user is struggling with a particular topic, it suggests additional learning materials. Generative AI, for example, analyzes a user's learning data and predicts future learning progress and suggests countermeasures. For example, if learning progress is lagging, it adjusts the learning plan. This makes it possible to predict future learning progress, detect problems early, and suggest countermeasures.

[0069] The generative AI can use its emotion estimation function to monitor the user's emotional state while studying and provide feedback to maintain motivation. The generative AI, for example, analyzes the user's emotional state in real time and provides feedback to maintain motivation. For example, if studying is progressing smoothly, it may offer words of praise. The generative AI, for example, suggests activities to maintain motivation based on the user's emotional data. For example, it may suggest activities to relax during study breaks. The generative AI, for example, monitors the user's emotional state and, if motivation is declining, sends an encouraging message. For example, it provides advice on how to achieve goals. This makes it possible to monitor the user's emotional state while studying and provide feedback to maintain motivation.

[0070] Generative AI can integrate data from different learning areas and manage overall learning progress. For example, generative AI can integrate data from different learning areas and build a system to manage overall learning progress. For example, it can centrally manage progress in mathematics, science, and English. For example, generative AI can integrate learning data and create an overall learning plan. For example, it can suggest a balanced learning plan based on progress in each area. For example, generative AI can analyze data from different learning areas and monitor overall learning progress in real time. For example, it can provide additional support for areas where progress is lagging behind. This makes it possible to integrate data from different learning areas and manage overall learning progress.

[0071] The generating AI can compare the user's learning data with other learners and provide a relative progress status. The generating AI, for example, builds a system that compares the user's learning data with other learners and provides a relative progress status. For example, it displays progress compared with learners of the same age group. The generating AI, for example, evaluates the user's relative progress based on the learning data. For example, it evaluates the level of understanding in comparison with other learners studying the same topic. The generating AI, for example, compares the user's learning data with other learners and provides a relative progress status in real time. For example, if progress is lagging, it suggests additional support. This makes it possible to compare the user's learning data with other learners and provide a relative progress status.

[0072] The generation AI can use its emotion estimation function to analyze changes in a user's emotions while they are studying and suggest the optimal timing for studying. For example, the generation AI can analyze a user's emotional state in real time and suggest the optimal timing for studying. For example, it can recommend studying during times when concentration is high. The generation AI can adjust the timing of studying based on the user's emotional data, for example. For example, it can suggest taking a break if stress is high. The generation AI can monitor a user's emotional state and suggest the optimal timing for studying in real time. For example, it can recommend studying during times when emotions are stable. This makes it possible to analyze changes in a user's emotions while they are studying and suggest the optimal timing for studying.

[0073] Generative AI can analyze the past teaching data of teachers and experts and propose the optimal teaching method. Generative AI, for example, analyzes the past teaching data of teachers and experts and builds a system that proposes the optimal teaching method. For example, it proposes a teaching method based on past success stories. Generative AI, for example, proposes the optimal teaching method for each student based on the teaching data. For example, it proposes a teaching method that suits a specific learning style. Generative AI, for example, analyzes the teaching data of teachers and experts and evaluates the effectiveness of the teaching. For example, it selects the optimal teaching method based on past teaching results. This makes it possible to analyze the past teaching data of teachers and experts and propose the optimal teaching method.

[0074] Generative AI can manage the schedules of teachers and experts and create efficient teaching plans. Generative AI, for example, builds a system that automatically manages the schedules of teachers and experts and creates efficient teaching plans. For example, it can provide instruction using free time. Generative AI, for example, suggests optimal teaching times based on schedule data. For example, it can set teaching times that suit the convenience of students. Generative AI, for example, manages the schedules of teachers and experts in real time and creates efficient teaching plans. For example, it can adjust teaching plans in response to schedule changes. This makes it possible to manage the schedules of teachers and experts and create efficient teaching plans.

[0075] Using its emotion estimation function, the generative AI can monitor the emotional state of teachers and experts and make suggestions for stress management and maintaining motivation. For example, the generative AI can analyze the emotional state of teachers and experts in real time and make suggestions for stress management. For example, if stress is rising, it can suggest activities to help them relax. For example, the generative AI can provide advice for maintaining motivation based on emotional data. For example, if motivation is declining, it can send an encouraging message. For example, the generative AI can monitor the emotional state of teachers and experts and provide feedback for stress management and maintaining motivation. For example, it can suggest providing instruction during times when emotions are stable. This makes it possible to monitor the emotional state of teachers and experts and make suggestions for stress management and maintaining motivation.

[0076] Generative AI can automatically record the teaching content of teachers and experts, making it possible to refer to it later. Generative AI, for example, builds a system that automatically records the teaching content of teachers and experts, making it possible to refer to it later. For example, it saves the teaching content as text or audio data. Generative AI, for example, manages the teaching history based on the teaching content. For example, it can search and refer to past teaching content. Generative AI, for example, records the teaching content of teachers and experts in real time, making it possible to refer to it later. For example, it automatically organizes and saves the teaching content. This makes it possible to automatically record the teaching content of teachers and experts, making it possible to refer to it later.

[0077] Generative AI can share the teaching content of teachers and experts with other teachers and experts, spreading best practices. For example, generative AI could build a system that automatically shares the teaching content of teachers and experts and spreads best practices. For example, it could introduce excellent teaching methods to other teachers. For example, generative AI could share information with other teachers and experts based on the teaching content. For example, it could share successful teaching examples so that other teachers can use them as reference. For example, generative AI could share the teaching content of teachers and experts in real time and spread best practices. For example, it could publish the teaching content on an online platform. This would allow teachers and experts to share their teaching content with other teachers and experts, spreading best practices.

[0078] Generative AI can use its emotion estimation function to analyze the emotional reactions of teachers and experts while they are teaching, and continuously improve optimal teaching methods. For example, generative AI can analyze the emotional reactions of teachers and experts while they are teaching in real time and suggest optimal teaching methods. For example, it can recommend teaching during times when emotions are stable. Generative AI can build a system that continuously improves teaching methods based on emotional data. For example, it can adjust teaching methods when emotions are high. Generative AI can monitor the emotional state of teachers and experts, and provide feedback to continuously improve teaching methods. For example, it can suggest teaching during times when emotions are stable. This makes it possible to analyze the emotional reactions of teachers and experts while they are teaching, and continuously improve optimal teaching methods.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] In addition to the generative AI, the educational platform may include an environmental adjustment unit for optimizing the user's learning environment. The environmental adjustment unit, for example, adjusts the lighting and sound in the user's learning environment. For example, it can change the color temperature of the lighting to improve concentration. The environmental adjustment unit, for example, adjusts the temperature and humidity in the user's learning environment. For example, it can automatically control an air conditioner or humidifier to maintain a comfortable learning environment. The environmental adjustment unit, for example, adjusts the noise level in the user's learning environment. For example, it can provide a noise canceling function to block out external noise. This optimizes the user's learning environment and improves learning efficiency.

[0081] In addition to the generative AI, the educational platform can be equipped with a health management unit that monitors the user's health condition. The health management unit, for example, monitors the user's heart rate and blood pressure. For example, if stress levels are high, it can suggest activities to help them relax. For example, the health management unit analyzes the user's sleep patterns and suggests optimal study times. For example, it can recommend starting study after getting enough sleep. For example, the health management unit suggests healthy lifestyle habits based on the user's diet and exercise data. For example, it can recommend a balanced diet and regular exercise. This makes it possible to monitor the user's health condition and provide a healthy learning environment.

[0082] In addition to the generation AI, the educational platform can be equipped with an outcome display unit that visually displays the user's learning results. The outcome display unit, for example, displays the user's learning progress in graphs or charts. For example, the user can visually check the level of learning achievement and understanding. The outcome display unit, for example, displays the user's learning history in a timeline format. For example, the user can check past learning content and test results at a glance. The outcome display unit, for example, sets the user's learning goals and displays the progress of those goals in real time. For example, the user can check the progress towards goal achievement. This visually displays the user's learning results and increases motivation to learn.

[0083] In addition to the generation AI, the educational platform can include a learning style suggestion unit that suggests a learning method according to the user's learning style. The learning style suggestion unit, for example, analyzes the user's learning history and performance data and suggests an optimal learning method. For example, a user who is effective at visual learning can be provided with learning materials that make extensive use of diagrams and graphs. The learning style suggestion unit, for example, creates a learning plan according to the user's learning style. For example, a user who is effective at short, concentrated learning can be suggested a short learning session. The learning style suggestion unit, for example, provides feedback based on the user's learning style. For example, it can send appropriate advice or encouraging messages according to the user's learning progress. This makes it possible to suggest a learning method according to the user's learning style and maximize the learning effect.

[0084] In addition to generative AI, the educational platform can introduce gamification elements to increase users' motivation to learn. The gamification elements, for example, create a system that awards badges and points according to the level of learning achievement. For example, badges can be awarded to learners who complete specific challenges. The gamification elements, for example, display rankings according to the progress of learning. For example, points can be awarded according to the level of learning achievement and the rankings can be displayed. The gamification elements, for example, create a system that evaluates learning outcomes and provides rewards based on badges and points. For example, learners who have earned a certain number of points can be provided with special benefits. This can increase users' motivation to learn and make learning more enjoyable.

[0085] In addition to generative AI, the educational platform can monitor the user's emotional state and provide feedback to maintain motivation. For example, it can analyze the user's emotional state in real time and provide feedback to maintain motivation. For example, it can praise the user if their studies are progressing well. Based on the user's emotional data, it can suggest activities to maintain motivation. For example, it can suggest activities to relax in between studies. If the user's emotional state is monitored and their motivation is declining, it can send an encouraging message. For example, it can provide advice on how to achieve goals. This makes it possible to monitor the user's emotional state and provide feedback to maintain motivation.

[0086] In addition to generative AI, the educational platform can analyze changes in a user's emotions while they are studying and suggest the optimal timing for studying. For example, it can analyze a user's emotional state in real time and suggest the optimal timing for studying. For example, it can recommend studying during times when concentration is at its highest. It can adjust the timing of studying based on the user's emotional data. For example, it can suggest taking a break if stress is high. It can monitor a user's emotional state and suggest the optimal timing for studying in real time. For example, it can recommend studying during times when emotions are stable. This makes it possible to analyze changes in a user's emotions while they are studying and suggest the optimal timing for studying.

[0087] In addition to generative AI, the educational platform can monitor the user's emotional state and make suggestions for stress management and maintaining motivation. For example, it can analyze the user's emotional state in real time and make suggestions for stress management. For example, if stress is rising, it can suggest activities to help them relax. It provides advice for maintaining motivation based on emotional data. For example, if motivation is declining, it can send an encouraging message. It monitors the user's emotional state and provides feedback for stress management and maintaining motivation. For example, it can suggest studying during times when emotions are stable. This makes it possible to monitor the user's emotional state and make suggestions for stress management and maintaining motivation.

[0088] In addition to generative AI, the educational platform can analyze the user's emotional responses and match the optimal learning partner. For example, it can analyze the user's emotional responses and match learners with positive emotions. For example, it can pair learners who are highly motivated to learn. Based on the user's emotional data, it can recommend the optimal learning partner. For example, it can match learners with complementary skills. It can monitor the user's emotional state and recommend partners who will have a positive influence on learners with negative emotions. For example, it can pair learners with highly motivated learners. This makes it possible to analyze the user's emotional responses and match the optimal learning partner.

[0089] In addition to generative AI, the education platform can compare a user's learning data with other learners and provide a relative progress report. For example, a system can be built that compares a user's learning data with other learners and provides a relative progress report. For example, progress can be displayed compared with learners in the same age group. Based on the learning data, the user's relative progress can be evaluated. For example, comprehension can be evaluated compared with other learners studying the same topic. The user's learning data can be compared with other learners and a relative progress report can be provided in real time. For example, if progress is lagging, additional support can be suggested. This makes it possible to compare a user's learning data with other learners and provide a relative progress report.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The generative AI provides customized dialogue based on the user's skills, domain, interests, academic ability, and level of proficiency. For example, the generative AI analyzes the user's prompts and provides appropriate learning content and advice. The generative AI analyzes the user's past learning history and performance data to provide dialogue optimized for each individual learning style. It also analyzes the user's facial expressions and tone of voice in real time while they are studying to provide appropriate feedback and encouragement. Step 2: In addition to the learning support provided by the generative AI, the community will provide support from real human teachers and experts. For example, through online forums and video conferences, they will answer questions and clarify doubts about the learning content provided by the generative AI. The community will monitor learning progress and provide additional guidance as needed. Step 3: In the progress management section, the generation AI analyzes the user's learning data and manages their progress. For example, the generation AI analyzes the user's level of understanding and which areas they are struggling with, and based on that, suggests the next learning step. The progress management section predicts future learning progress based on the user's learning data, detects problems early, and suggests solutions. Step 4: In the support section, the generative AI supports teachers and experts in the community. For example, the generative AI automatically generates reports to help teachers understand their students' learning progress. In the support section, the generative AI suggests optimal teaching materials and activities when teachers create lesson plans.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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. Generative AI provides customized dialogue based on the user's skills, domain, interests, academic ability, and level of proficiency. In addition to the learning support provided by the generative AI, there is also a community where real human teachers and experts provide support through the community. a progress management unit in which the generation AI analyzes the user's learning data and manages the progress; A support unit that supports teachers and experts who belong to the generation AI community. A system characterized by:

2. The generated AI is Analyzing the user's past learning history and performance data and providing dialogue optimized for the user's learning style 2. The system of claim 1.

3. The generated AI is Analyze the user's facial expressions and tone of voice in real time while they are studying, and provide appropriate feedback and encouragement.

2. The system of claim 1.

4. The generated AI is Estimate the emotional state of the user and provide content to help the user relax when they feel stressed or tired.

2. The system of claim 1.

5. The generated AI is Accommodating different languages ​​and cultural backgrounds, providing customized dialogue for international learners 2. The system of claim 1.

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