system

Abstract

An object of the system according to the embodiment is to provide quick and accurate answers to questions and problems that students face during learning.SOLUTION: A system according to an embodiment includes an AIChatbot, a generation AI, and a notifier. AIChatbot accepts student questions. The generation AI generates an answer based on the question received by AIChatbot. The notifying unit notifies the student of the answer generated by the generating AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for students to obtain quick and accurate answers to the questions and problems they encounter while studying.

[0005] The system according to the embodiment aims to provide quick and accurate answers to questions and problems that students encounter during their studies. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI Chatbot, a generation AI, and a notification unit. The AI ​​Chatbot accepts questions from students. The generation AI generates answers based on the questions accepted by the AI ​​Chatbot. The notification unit notifies the students of the answers generated by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can provide quick and accurate answers to questions and problems that students encounter during their studies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A real-time learning support system according to an embodiment of the present invention uses an AI chatbot to provide quick and accurate answers to students' questions, thereby enabling the real-time learning support system to quickly resolve students' learning questions and problems and improve their learning effectiveness.

[0029] A real-time learning support system according to an embodiment includes an AI Chatbot, a generation AI, and a notification unit. The AI ​​Chatbot accepts questions from students. For example, the AI ​​Chatbot accepts questions input in text format. The AI ​​Chatbot can also accept questions input in voice format. The AI ​​Chatbot can select an appropriate dialogue flow depending on the type of question. The generation AI generates an answer based on the question accepted by the AI ​​Chatbot. For example, the generation AI analyzes the intent of the question using natural language processing technology and generates an appropriate answer. The generation AI can also learn from past data using a machine learning model to generate a more accurate answer. The generation AI can also search for related information depending on the content of the question and reflect it in the answer. The notification unit notifies the student of the answer generated by the generation AI. For example, the notification unit can notify the student of the answer as a text message. The notification unit can also notify the student of the answer as a push notification. The notification unit can also notify the student of the answer as a voice notification. This enables the real-time learning support system according to an embodiment to provide quick and accurate answers to students' questions. For example, if a student asks, "I can't solve this math problem. What should I do?", the AI ​​Chatbot's generation AI will suggest a specific solution and the notification section will notify the student of the answer. Similarly, if a student asks, "Please teach me about English grammar," the AI ​​Chatbot's generation AI will explain the exact grammar rules and the notification section will notify the student of the answer. Similarly, if a student asks, "Where are the new lecture materials?", the AI ​​Chatbot's generation AI will guide the student to the location of the latest lecture materials and the notification section will notify the student of that information.

[0030] Generative AI can automatically generate individually customized study plans based on a student's learning history and provide them in real time. For example, generative AI automatically generates individually customized study plans based on the problems a student has solved in the past and the content they have learned. For example, when solving math problems, it provides problems of an appropriate level of difficulty, taking into account past grades and solution time. Generative AI can also analyze a student's learning history and propose an optimal study plan for that student in real time. For example, when learning English grammar, it can provide a plan that focuses on reviewing points where students made mistakes in the past. Generative AI can also create individually customized study plans based on a student's learning history and provide them in real time. For example, when solving science experiment problems, it can propose appropriate experimental procedures, taking into account past experimental results and level of understanding. This allows it to provide the optimal study plan for each student.

[0031] Generative AI can monitor students' learning progress in real time and suggest review or additional learning at the appropriate time. For example, generative AI can monitor students' learning progress in real time and suggest review at the appropriate time. For example, it can automatically detect topics that have not been studied for a certain period of time and encourage review. Generative AI can also analyze students' learning progress and suggest additional learning at the appropriate time. For example, when solving math problems, it can provide additional questions to complement parts of the problem that are not fully understood. Generative AI can also monitor students' learning progress in real time and suggest review or additional learning at the appropriate time. For example, when memorizing English vocabulary, it can test students again after a certain period of time to check that their memory has been solidified. This makes it possible to provide appropriate support according to students' learning progress.

[0032] Generative AI can suggest related topics and additional materials based on a student's interests. For example, generative AI can analyze a student's interests and suggest related topics and additional materials. For example, it can provide additional materials about a period that a student became interested in in history class. Generative AI can also suggest related topics based on a student's learning history, based on the student's interests and suggest related topics and additional materials. For example, it can provide additional materials about an experiment that a student became interested in in science class. Generative AI can also analyze a student's interests and suggest related topics and additional materials. For example, it can provide additional materials about an author that a student became interested in in literature class. This makes it possible to provide learning materials that match a student's interests and concerns.

[0033] Generative AI can add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, generative AI can add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, it can help the discussion progress and suggest appropriate questions. Generative AI can also add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, it can summarize the content of the discussion and highlight important points. Generative AI can also add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, it can record the results of the discussion so that they can be referenced later. This supports discussions between students and improves learning effectiveness.

[0034] Generative AI can learn from the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, generative AI can learn from the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, it can provide accurate solutions to specialized mathematics problems. Generative AI can also analyze the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, it can provide easy-to-understand explanations for complex physics concepts. Generative AI can also learn from the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, it can provide accurate answers to detailed historical background information. This allows it to respond to more advanced questions.

[0035] The generative AI can automatically recommend relevant video lectures or tutorials based on the content of a student's question. For example, the generative AI analyzes the content of a student's question and automatically recommends relevant video lectures or tutorials. For example, it suggests relevant video lectures for math problems. The generative AI can also build a system that recommends relevant video lectures or tutorials based on the content of a student's question. For example, it suggests relevant tutorials for questions about English grammar. The generative AI can also analyze the content of a student's question and automatically recommend relevant video lectures or tutorials. For example, it suggests relevant video lectures for questions about science experiments. This makes it possible to provide relevant learning materials for students' questions.

[0036] Generative AI can automatically complement the intent of a question when a student inputs it, generating a more specific answer. For example, generative AI will build a system that automatically complements the intent of a question when a student inputs it, generating a more specific answer. For example, it will provide a specific answer to an ambiguous question. Generative AI can also analyze the content of a student's question and automatically complement the intent of the question. For example, it will provide a specific solution to a math problem. Generative AI can also develop a system that automatically complements the intent of a question when a student inputs it, generating a more specific answer. For example, it will provide specific example sentences for questions about English grammar. This will allow for more specific answers to be provided to students' questions.

[0037] The generative AI can automatically manage the update history of course content and notify students of past changes as well. For example, the generative AI will build a system that automatically manages the update history of course content and notifies students of past changes as well. For example, when new lecture materials are added, it will notify them of the differences from previous materials. The generative AI will also automatically manage the update history of course content and notify students of past changes as well. For example, when the content of an assignment is changed, it will compare the content before and after the change and notify them. The generative AI will also develop a system that automatically manages the update history of course content and notify students of past changes as well. For example, when the scope of an exam is changed, it will notify them of the scope before and after the change. This allows students to accurately notify them of changes to the course content.

[0038] The generative AI can provide individually optimized course content update information based on a student's learning history. For example, the generative AI analyzes a student's learning history and builds a system that provides individually optimized course content update information. For example, it may prioritize notifications of new materials on specific topics. The generative AI also provides individually optimized course content update information based on a student's learning history. For example, it may notify the student of new materials in areas that the student has previously struggled with. The generative AI also develops a system that provides individually optimized course content update information based on a student's learning history. For example, it may notify the student of new hints on specific assignments. This makes it possible to provide individually optimized course content update information to students.

[0039] The generative AI can provide related industry news or trend information in addition to course content update information. For example, the generative AI builds a system that automatically collects and provides related industry news and trend information to students in addition to course content update information. For example, it notifies them of the latest technological and market trends. The generative AI also provides related industry news and trend information in addition to course content update information. For example, it notifies them of information about new research results and technological innovations. The generative AI also develops a system that provides related industry news and trend information in addition to course content update information. For example, it notifies them of the latest industry news and event information. This makes it possible to provide students with the latest industry news and trend information.

[0040] Generative AI can automatically generate and provide quizzes or tests to help students understand new course content. For example, generative AI builds a system that automatically generates quizzes and tests based on new course content and provides them to students. For example, it provides quizzes related to new lecture content. Generative AI also provides automatically generated quizzes and tests to help students understand new course content. For example, it provides tests related to new assignments. Generative AI also develops a system that automatically generates quizzes and tests based on new course content and provides them to students. For example, it provides quizzes related to new experimental content. This makes it possible to provide quizzes and tests to help students understand new course content.

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

[0042] The real-time learning support system may further include an environmental adjustment unit for optimizing the student's learning environment. For example, the environmental adjustment unit may automatically adjust the brightness of the lighting during learning. The environmental adjustment unit may also select music or background sounds during learning to provide an audio environment that enhances concentration. The environmental adjustment unit may also monitor the temperature and humidity during learning and make adjustments to maintain a comfortable learning environment. This may optimize the student's learning environment and improve learning effectiveness.

[0043] The real-time learning support system can further include a customization function according to the student's learning style. For example, if a student prefers visual learning, the customization function can provide learning materials that make extensive use of diagrams and graphs. Alternatively, if a student prefers auditory learning, the customization function can provide learning materials in the form of audio commentary or podcasts. Alternatively, if a student prefers hands-on learning, the customization function can provide interactive simulations and experiments. This allows the system to provide an optimal learning experience according to the student's learning style.

[0044] The real-time learning support system can also be equipped with a dashboard function that visualizes students' learning progress. For example, the dashboard function can display students' learning progress in graphs and charts, allowing them to see at a glance how well they have achieved their goals. The dashboard function can also display students' learning history in a timeline format, allowing them to look back on their past learning content and achievements. The dashboard function can also provide feedback based on students' learning progress and suggest their next learning steps. This makes it possible to visualize students' learning progress and maintain their motivation.

[0045] The real-time learning support system can further include a predictive analysis function based on the student's learning history. For example, the predictive analysis function can analyze the student's past learning data and predict future learning outcomes. The predictive analysis function can also analyze the student's learning patterns and predict their learning progress. The predictive analysis function can also suggest what the student should learn next based on their learning history. This can optimize the student's learning plan and improve their learning effectiveness.

[0046] The real-time learning support system can also be equipped with a recommendation function based on a student's learning history. For example, the recommendation function can analyze a student's past learning content and suggest related learning materials or courses. The recommendation function can also suggest the next topic to study based on the student's learning history. The recommendation function can also suggest the optimal learning method based on the student's learning style. This can personalize the student's learning experience and improve learning effectiveness.

[0047] The real-time learning support system can further include a learning goal setting function based on the student's learning history. For example, the learning goal setting function can analyze the student's past learning data and suggest achievable learning goals. The learning goal setting function can also set short-term and long-term learning goals based on the student's learning history. The learning goal setting function can also adjust goals according to the student's learning progress. This can clarify the student's learning goals and improve learning effectiveness.

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

[0049] Step 1: The AI ​​Chatbot accepts questions from students. For example, the AI ​​Chatbot can accept questions entered in text or voice format. It can also select an appropriate dialogue flow depending on the type of question. Step 2: The generative AI generates an answer based on the question received by the AI ​​Chatbot. For example, the generative AI uses natural language processing technology to analyze the intent of the question and generate an appropriate answer. It can also use machine learning models to learn from past data and generate more accurate answers. It can also search for related information based on the content of the question and reflect it in the answer. Step 3: The notification unit notifies the student of the answer generated by the generation AI. For example, the notification unit can notify the student of the answer as a text message, push notification, or voice notification.

[0050] (Example 2) A real-time learning support system according to an embodiment of the present invention uses an AI chatbot to provide quick and accurate answers to students' questions, thereby enabling the real-time learning support system to quickly resolve students' learning questions and problems and improve their learning effectiveness.

[0051] A real-time learning support system according to an embodiment includes an AI Chatbot, a generation AI, and a notification unit. The AI ​​Chatbot accepts questions from students. For example, the AI ​​Chatbot accepts questions input in text format. The AI ​​Chatbot can also accept questions input in voice format. The AI ​​Chatbot can select an appropriate dialogue flow depending on the type of question. The generation AI generates an answer based on the question accepted by the AI ​​Chatbot. For example, the generation AI analyzes the intent of the question using natural language processing technology and generates an appropriate answer. The generation AI can also learn from past data using a machine learning model to generate a more accurate answer. The generation AI can also search for related information depending on the content of the question and reflect it in the answer. The notification unit notifies the student of the answer generated by the generation AI. For example, the notification unit can notify the student of the answer as a text message. The notification unit can also notify the student of the answer as a push notification. The notification unit can also notify the student of the answer as a voice notification. This enables the real-time learning support system according to an embodiment to provide quick and accurate answers to students' questions. For example, if a student asks, "I can't solve this math problem. What should I do?", the AI ​​Chatbot's generation AI will suggest a specific solution and the notification section will notify the student of the answer. Similarly, if a student asks, "Please teach me about English grammar," the AI ​​Chatbot's generation AI will explain the exact grammar rules and the notification section will notify the student of the answer. Similarly, if a student asks, "Where are the new lecture materials?", the AI ​​Chatbot's generation AI will guide the student to the location of the latest lecture materials and the notification section will notify the student of that information.

[0052] Generative AI can automatically generate individually customized study plans based on a student's learning history and provide them in real time. For example, generative AI automatically generates individually customized study plans based on the problems a student has solved in the past and the content they have learned. For example, when solving math problems, it provides problems of an appropriate level of difficulty, taking into account past grades and solution time. Generative AI can also analyze a student's learning history and propose an optimal study plan for that student in real time. For example, when learning English grammar, it can provide a plan that focuses on reviewing points where students made mistakes in the past. Generative AI can also create individually customized study plans based on a student's learning history and provide them in real time. For example, when solving science experiment problems, it can propose appropriate experimental procedures, taking into account past experimental results and level of understanding. This allows it to provide the optimal study plan for each student.

[0053] Generative AI can monitor students' learning progress in real time and suggest review or additional learning at the appropriate time. For example, generative AI can monitor students' learning progress in real time and suggest review at the appropriate time. For example, it can automatically detect topics that have not been studied for a certain period of time and encourage review. Generative AI can also analyze students' learning progress and suggest additional learning at the appropriate time. For example, when solving math problems, it can provide additional questions to complement parts of the problem that are not fully understood. Generative AI can also monitor students' learning progress in real time and suggest review or additional learning at the appropriate time. For example, when memorizing English vocabulary, it can test students again after a certain period of time to check that their memory has been solidified. This makes it possible to provide appropriate support according to students' learning progress.

[0054] The generative AI can analyze students' emotions while studying and suggest ways to relax or take a break if they feel stressed or tired. For example, using an emotion estimation function, the generative AI can analyze students' emotions while studying in real time and suggest ways to relax if they feel stressed. For example, it could recommend deep breathing or light exercise. The generative AI can also analyze students' emotions while studying and suggest a break if they feel tired. For example, it could encourage them to take a short break after studying for a certain period of time. The generative AI can also use an emotion estimation function to analyze students' emotions while studying and suggest ways to relax or take a break if they feel stressed or tired. For example, it could recommend listening to relaxing music. This can help reduce stress and fatigue for students while studying.

[0055] Generative AI can suggest related topics and additional materials based on a student's interests. For example, generative AI can analyze a student's interests and suggest related topics and additional materials. For example, it can provide additional materials about a period that a student became interested in in history class. Generative AI can also suggest related topics based on a student's learning history, based on the student's interests and suggest related topics and additional materials. For example, it can provide additional materials about an experiment that a student became interested in in science class. Generative AI can also analyze a student's interests and suggest related topics and additional materials. For example, it can provide additional materials about an author that a student became interested in in literature class. This makes it possible to provide learning materials that match a student's interests and concerns.

[0056] Generative AI can add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, generative AI can add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, it can help the discussion progress and suggest appropriate questions. Generative AI can also add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, it can summarize the content of the discussion and highlight important points. Generative AI can also add a feature that allows students to have real-time discussions with other students while they are studying, and support those discussions. For example, it can record the results of the discussion so that they can be referenced later. This supports discussions between students and improves learning effectiveness.

[0057] The generative AI can analyze fluctuations in students' motivation and provide advice to help them maintain their motivation. For example, using an emotion estimation function, the generative AI can analyze fluctuations in students' motivation while they are studying in real time and provide advice to help them maintain their motivation. For example, it can suggest ways to set goals and feel a sense of accomplishment. The generative AI can also analyze fluctuations in students' motivation and provide specific advice to help them maintain their motivation. For example, it can visualize their learning progress and help them feel a sense of accomplishment. The generative AI can also use an emotion estimation function to analyze fluctuations in students' motivation while they are studying and provide advice to help them maintain their motivation. For example, it can suggest ways to refresh themselves between studies. This helps to maintain students' motivation and improves their learning effectiveness.

[0058] Generative AI can learn from the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, generative AI can learn from the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, it can provide accurate solutions to specialized mathematics problems. Generative AI can also analyze the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, it can provide easy-to-understand explanations for complex physics concepts. Generative AI can also learn from the instructor's past answer data and evolve so that it can respond to more advanced questions. For example, it can provide accurate answers to detailed historical background information. This allows it to respond to more advanced questions.

[0059] The generative AI can automatically recommend relevant video lectures or tutorials based on the content of a student's question. For example, the generative AI analyzes the content of a student's question and automatically recommends relevant video lectures or tutorials. For example, it suggests relevant video lectures for math problems. The generative AI can also build a system that recommends relevant video lectures or tutorials based on the content of a student's question. For example, it suggests relevant tutorials for questions about English grammar. The generative AI can also analyze the content of a student's question and automatically recommend relevant video lectures or tutorials. For example, it suggests relevant video lectures for questions about science experiments. This makes it possible to provide relevant learning materials for students' questions.

[0060] Generative AI can analyze students' emotional responses to questions and generate more empathetic answers. For example, using an emotion estimation function, generative AI can analyze students' emotional responses to questions in real time and generate empathetic answers. For example, by adding words of encouragement to the question. Generative AI can also build a system that analyzes students' emotional responses and provides empathetic answers. For example, by adding words of gratitude to the question. Generative AI can also use an emotion estimation function to analyze students' emotional responses to questions and generate more empathetic answers. For example, by adding words that show understanding to the question. This makes it possible to provide answers that take students' emotions into consideration.

[0061] Generative AI can automatically complement the intent of a question when a student inputs it, generating a more specific answer. For example, generative AI will build a system that automatically complements the intent of a question when a student inputs it, generating a more specific answer. For example, it will provide a specific answer to an ambiguous question. Generative AI can also analyze the content of a student's question and automatically complement the intent of the question. For example, it will provide a specific solution to a math problem. Generative AI can also develop a system that automatically complements the intent of a question when a student inputs it, generating a more specific answer. For example, it will provide specific example sentences for questions about English grammar. This will allow for more specific answers to be provided to students' questions.

[0062] The generation AI can analyze the emotions of students when they ask questions and make suggestions to adjust the difficulty or content of the questions. For example, the generation AI uses an emotion estimation function to analyze the emotions of students when they ask questions in real time and make suggestions to adjust the difficulty or content of the questions. For example, it can suggest easy questions to students who are nervous. The generation AI can also build a system that analyzes students' emotions and makes suggestions to adjust the difficulty and content of questions. For example, it can suggest relaxing questions to students who are feeling stressed. The generation AI can also use the emotion estimation function to analyze the emotions of students when they ask questions and make suggestions to adjust the difficulty or content of the questions. For example, it can suggest challenging questions to students who are excited. This makes it possible to adjust questions according to students' emotions.

[0063] The generative AI can automatically manage the update history of course content and notify students of past changes as well. For example, the generative AI will build a system that automatically manages the update history of course content and notifies students of past changes as well. For example, when new lecture materials are added, it will notify them of the differences from previous materials. The generative AI will also automatically manage the update history of course content and notify students of past changes as well. For example, when the content of an assignment is changed, it will compare the content before and after the change and notify them. The generative AI will also develop a system that automatically manages the update history of course content and notify students of past changes as well. For example, when the scope of an exam is changed, it will notify them of the scope before and after the change. This allows students to accurately notify them of changes to the course content.

[0064] The generative AI can provide individually optimized course content update information based on a student's learning history. For example, the generative AI analyzes a student's learning history and builds a system that provides individually optimized course content update information. For example, it may prioritize notifications of new materials on specific topics. The generative AI also provides individually optimized course content update information based on a student's learning history. For example, it may notify the student of new materials in areas that the student has previously struggled with. The generative AI also develops a system that provides individually optimized course content update information based on a student's learning history. For example, it may notify the student of new hints on specific assignments. This makes it possible to provide individually optimized course content update information to students.

[0065] The generative AI can analyze the anxieties and questions that students have about new course content and provide appropriate support. For example, using an emotion estimation function, the generative AI can analyze the anxieties and questions that students have about new course content in real time and provide appropriate support. For example, it can provide additional explanations to students who are feeling anxious. The generative AI can also analyze students' emotions and build a system that provides support to resolve their anxieties and questions about new course content. For example, it can provide related materials to students who have questions. The generative AI can also use the emotion estimation function to analyze the anxieties and questions that students have about new course content and provide appropriate support. For example, it can suggest relaxation methods for students who are feeling stressed. This makes it possible to provide appropriate support to students for their anxieties and questions.

[0066] The generative AI can provide related industry news or trend information in addition to course content update information. For example, the generative AI builds a system that automatically collects and provides related industry news and trend information to students in addition to course content update information. For example, it notifies them of the latest technological and market trends. The generative AI also provides related industry news and trend information in addition to course content update information. For example, it notifies them of information about new research results and technological innovations. The generative AI also develops a system that provides related industry news and trend information in addition to course content update information. For example, it notifies them of the latest industry news and event information. This makes it possible to provide students with the latest industry news and trend information.

[0067] Generative AI can automatically generate and provide quizzes or tests to help students understand new course content. For example, generative AI builds a system that automatically generates quizzes and tests based on new course content and provides them to students. For example, it provides quizzes related to new lecture content. Generative AI also provides automatically generated quizzes and tests to help students understand new course content. For example, it provides tests related to new assignments. Generative AI also develops a system that automatically generates quizzes and tests based on new course content and provides them to students. For example, it provides quizzes related to new experimental content. This makes it possible to provide quizzes and tests to help students understand new course content.

[0068] The generative AI can analyze students' interest in new course content and provide them with more in-depth learning materials. For example, the generative AI can use an emotion estimation function to analyze students' interest in new course content in real time and provide them with more in-depth learning materials. For example, detailed materials on topics of interest can be provided. The generative AI can also analyze students' emotions and build a system that provides more in-depth learning materials based on their interest in the new course content. For example, additional materials on areas of interest can be provided. The generative AI can also use an emotion estimation function to analyze students' interest in new course content and provide them with more in-depth learning materials. For example, detailed research materials on a specific topic can be provided. This makes it possible to provide more in-depth learning materials that match students' interests.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The real-time learning support system may further include an environmental adjustment unit for optimizing the student's learning environment. For example, the environmental adjustment unit may automatically adjust the brightness of the lighting during learning. The environmental adjustment unit may also select music or background sounds during learning to provide an audio environment that enhances concentration. The environmental adjustment unit may also monitor the temperature and humidity during learning and make adjustments to maintain a comfortable learning environment. This may optimize the student's learning environment and improve learning effectiveness.

[0071] The real-time learning support system can further include a customization function according to the student's learning style. For example, if a student prefers visual learning, the customization function can provide learning materials that make extensive use of diagrams and graphs. Alternatively, if a student prefers auditory learning, the customization function can provide learning materials in the form of audio commentary or podcasts. Alternatively, if a student prefers hands-on learning, the customization function can provide interactive simulations and experiments. This allows the system to provide an optimal learning experience according to the student's learning style.

[0072] The real-time learning support system can also be equipped with a dashboard function that visualizes students' learning progress. For example, the dashboard function can display students' learning progress in graphs and charts, allowing them to see at a glance how well they have achieved their goals. The dashboard function can also display students' learning history in a timeline format, allowing them to look back on their past learning content and achievements. The dashboard function can also provide feedback based on students' learning progress and suggest their next learning steps. This makes it possible to visualize students' learning progress and maintain their motivation.

[0073] The real-time learning support system can further analyze students' emotions while studying and suggest an appropriate learning pace. For example, if a student is feeling impatient or anxious, the emotion estimation function can be used to suggest that the student slow down their learning pace. Also, if a student is concentrating, the emotion estimation function can be used to suggest that the student maintain their learning pace. Also, if a student is feeling tired, the emotion estimation function can be used to suggest that the student take a break. In this way, it is possible to provide an optimal learning pace according to the student's emotions.

[0074] The real-time learning support system can further analyze students' emotions while they are studying and be equipped with a reward system to maintain their motivation. For example, the emotion estimation function can be used to award badges or points when students achieve their goals. The emotion estimation function can also be used to provide incentives for students to continue studying. The emotion estimation function can also be used to provide additional rewards when students have positive emotions about studying. This can maintain students' motivation and improve their learning effectiveness.

[0075] The real-time learning support system can further analyze students' emotions while they are learning and provide appropriate feedback. For example, if a student is confused, the emotion estimation function can be used to provide additional explanations or hints. If a student is confident, the emotion estimation function can be used to suggest the next challenge. If a student is feeling down, the emotion estimation function can be used to provide an encouraging message. In this way, appropriate feedback can be provided according to the student's emotions.

[0076] The real-time learning support system can further analyze students' emotions during learning and personalize the learning content. For example, the emotion estimation function can be used to provide learning materials related to topics that interest students. If a student is bored, the emotion estimation function can be used to suggest more interesting content. If a student is stressed, the emotion estimation function can be used to suggest a learning method that will help them relax. This makes it possible to provide a personalized learning experience that corresponds to the student's emotions.

[0077] The real-time learning support system can further include a predictive analysis function based on the student's learning history. For example, the predictive analysis function can analyze the student's past learning data and predict future learning outcomes. The predictive analysis function can also analyze the student's learning patterns and predict their learning progress. The predictive analysis function can also suggest what the student should learn next based on their learning history. This can optimize the student's learning plan and improve their learning effectiveness.

[0078] The real-time learning support system can also be equipped with a recommendation function based on a student's learning history. For example, the recommendation function can analyze a student's past learning content and suggest related learning materials or courses. The recommendation function can also suggest the next topic to study based on the student's learning history. The recommendation function can also suggest the optimal learning method based on the student's learning style. This can personalize the student's learning experience and improve learning effectiveness.

[0079] The real-time learning support system can further include a learning goal setting function based on the student's learning history. For example, the learning goal setting function can analyze the student's past learning data and suggest achievable learning goals. The learning goal setting function can also set short-term and long-term learning goals based on the student's learning history. The learning goal setting function can also adjust goals according to the student's learning progress. This can clarify the student's learning goals and improve learning effectiveness.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The AI ​​Chatbot accepts questions from students. For example, the AI ​​Chatbot can accept questions entered in text or voice format. It can also select an appropriate dialogue flow depending on the type of question. Step 2: The generative AI generates an answer based on the question received by the AI ​​Chatbot. For example, the generative AI uses natural language processing technology to analyze the intent of the question and generate an appropriate answer. It can also use machine learning models to learn from past data and generate more accurate answers. It can also search for related information based on the content of the question and reflect it in the answer. Step 3: The notification unit notifies the student of the answer generated by the generation AI. For example, the notification unit can notify the student of the answer as a text message, push notification, or voice notification.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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, in order to avoid confusion and to 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.

[0148] 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]

[0149] 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. Equipped with AI Chatbot, The AI ​​Chatbot accepts students' questions and A generation AI that generates an answer based on the question accepted by the AI ​​Chatbot; a notification unit that notifies the student of the answer generated by the generation AI; A system characterized by:

2. The generated AI is Analyze the student's emotions while studying and suggest relaxation methods or breaks when they feel stressed or tired 2. The system of claim 1.

3. The generated AI is Suggest related topics and additional resources based on the student's interests 2. The system of claim 1.

4. The generated AI is The system will learn from the instructor's past response data and evolve to be able to respond to more advanced questions.

2. The system of claim 1.

5. The generated AI is Automatically manages the update history of course content and notifies students of past changes 2. The system of claim 1.

6. The generated AI is Analyze fluctuations in the student's motivation and provide advice on how to maintain that motivation 2. The system of claim 1.

7. The generated AI is Analyze the student's emotional response to the question and generate a more empathetic response 2. The system of claim 1.

8. The generated AI is Analyze any concerns or questions the student may have about the new course content and provide appropriate support 2. The system of claim 1.

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