System

Abstract

An object of the system according to the embodiment is to immediately provide an appropriate answer or explanation to a question or a task that a learner faces in programming or STEM education.SOLUTION: A system according to an embodiment includes an experimental tool, a tablet, and a generation AI. The experiment tool is a tool for the learner to perform an experiment. The tablet is a device for a learner to input a question or a task. The generated AI provides appropriate answers or commentary to the questions or challenges entered through the tablet.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] With conventional technology, it was difficult to provide immediate and appropriate answers and explanations to the questions and challenges students faced in programming and STEM education.

[0005] The system according to the embodiment aims to provide immediate and appropriate answers and explanations to questions and problems that learners face in programming and STEM education. [Means for solving the problem]

[0006] The system according to the embodiment includes an experimental tool, a tablet, and a generating AI. The experimental tool is a tool used by learners to conduct experiments. The tablet is a device used by learners to input questions and tasks. The generating AI provides appropriate answers or explanations for the questions or tasks input through the tablet. [Effects of the Invention]

[0007] The system according to the embodiment can instantly provide appropriate answers and explanations to questions and challenges faced by learners in programming and STEM education. [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 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) An educational support system according to an embodiment of the present invention is a system that supports programming and STEM education by integrating a generative AI into a mobile educational kit. This educational kit includes laboratory tools and a tablet, and the generative AI supports learning. This allows learners to effectively progress through their learning using the laboratory tools and tablet with the support of the generative AI. For example, this makes it easier for learners to understand the procedures of scientific experiments, and allows them to efficiently improve their programming skills. Furthermore, by managing and providing feedback on their learning progress, it is possible to provide an optimal learning environment tailored to each individual learner.

[0029] An educational support system according to an embodiment includes laboratory tools, a tablet, and a generating AI. The laboratory tools include tools and materials necessary for scientific experiments and engineering projects, such as beakers, reagents, and sensors. The tablet is installed with a generating AI and supports learners as they engage in programming and STEM education. For example, the tablet allows the generating AI to provide appropriate answers and explanations to questions or tasks entered by learners. The generating AI provides appropriate answers or explanations to questions or tasks entered by learners through the tablet. For example, if a learner enters, "Please tell me the steps for this experiment," the generating AI provides detailed explanations of the experimental steps. It also provides guidance on how to write code and correct errors in programming tasks. This allows learners to effectively progress through their learning using the laboratory tools and the tablet with the support of the generating AI.

[0030] When a learner types "Please tell me the steps for this experiment" into a tablet, the generative AI can provide a detailed explanation of the steps for that experiment. For example, when a learner types "Please tell me the steps for this experiment," the generative AI can provide a detailed explanation of the steps for that experiment. For example, it can explain the steps for a chemistry experiment step by step and provide a list of the tools and materials needed. The generative AI can also provide a video tutorial on the steps for the experiment, making it easier for learners to understand the steps for the experiment.

[0031] If a learner types "Why doesn't this program work?" through a tablet, the generative AI can analyze the error message and suggest specific ways to fix it. For example, if a learner types "Why doesn't this program work?", the generative AI can analyze the error message and suggest specific ways to fix it. For example, it can identify the cause of a compilation error or runtime error and provide step-by-step instructions on how to fix it. The generative AI can also provide examples of code corrections based on the error message, allowing learners to quickly fix program errors.

[0032] When a learner asks via tablet, "How should we interpret the results of this experiment?", the generative AI can explain how to interpret the results and the related theory. For example, when a learner asks, "How should we interpret the results of this experiment?", the generative AI can explain how to interpret the results and the related theory. For example, it can analyze the experimental result data and explain the meaning of that data. The generative AI can also provide related theory and background information to support the interpretation of experimental results. This allows learners to accurately understand the experimental results.

[0033] Generative AI can evaluate a learner's progress and suggest the next task to tackle. For example, generative AI can evaluate a learner's progress and suggest the next task to tackle. For example, it can evaluate how much progress a learner has made and list the next tasks to tackle. Generative AI can also provide additional practice problems and explanations for areas where the learner is weak. This allows the learner to study efficiently.

[0034] Sensors can be built into the lab tools, and data from the tools can be sent to a tablet in real time, where the generative AI can analyze the data and provide feedback. Sensors can be built into the lab tools, for example, using temperature or pressure sensors to send data during the experiment to a tablet in real time. The generative AI can analyze the data and provide feedback. For example, temperature changes in a chemistry experiment can be monitored in real time, and the data can be analyzed and feedback can be provided. This allows the experiment results to be analyzed in real time and feedback can be provided, deepening the learner's understanding.

[0035] By equipping tablets with AR functionality, it is possible to provide visual guidance on how to use laboratory tools and experimental procedures. By equipping tablets with AR functionality, it is possible to provide visual guidance on how to use laboratory tools, for example. For example, AR can be used to display how to handle beakers and reagents used in chemical experiments, allowing learners to operate them accurately. In addition, by providing visual guidance on experimental procedures, it becomes easier for learners to understand the experimental procedures. This allows learners to visually understand how to use laboratory tools and experimental procedures.

[0036] Experimental tools can be modularized so that they can be easily exchanged for different learning themes. Experimental tools can be modularized, for example, by providing a module for chemistry experiments and a module for physics experiments, allowing learners to choose depending on the theme. For example, the module for chemistry experiments includes beakers and reagents, while the module for physics experiments includes sensors and measuring instruments. This allows learners to easily exchange experimental tools for different learning themes.

[0037] It is possible to add a voice recognition function to the tablet, allowing experimental procedures and program instructions to be given by voice commands. Adding a voice recognition function to the tablet, for example, allows learners to give instructions for experimental procedures by voice commands. For example, they can say "tell me the next step," and the generating AI will explain the next step. It is also possible to give instructions for programs by voice commands. This allows learners to give instructions for experimental procedures and programs by voice commands.

[0038] Generative AI can analyze a learner's past question history and learning patterns to generate an individually customized learning plan. For example, generative AI can analyze a learner's past question history to generate an individually customized learning plan. For example, it can propose a plan that focuses on learning topics that the learner has asked about frequently in the past. It can also analyze a learner's learning patterns to propose an optimal learning schedule. This makes it possible to provide the learner with the optimal learning plan.

[0039] The generation AI automatically generates questions with adjusted difficulty according to the learner's level of understanding, allowing them to progress through learning in stages. The generation AI, for example, analyzes the learner's level of understanding and automatically generates questions with adjusted difficulty. For example, it can start with basic questions and provide questions with gradually increasing difficulty. It can also provide additional practice questions and explanations according to the learner's level of understanding. This allows learners to progress through learning in stages by providing questions that match their level of understanding.

[0040] The generative AI can add online collaboration features so that learners can work together with other learners in real time to solve problems. For example, the generative AI can add features that support online collaboration between learners. For example, it can provide documents and chat features that can be co-edited in real time. It can also add video conferencing features so that learners can work together face-to-face to solve problems. This allows learners to work together with other learners in real time to solve problems.

[0041] Generative AI can analyze a learner's code in real time and provide optimization suggestions and advice to improve performance. Generative AI can, for example, analyze a learner's code in real time and provide optimization suggestions. For example, it can reduce redundant code and suggest efficient algorithms. It can also provide advice to improve performance. This allows learners to improve their programming skills by analyzing their code in real time and providing optimization suggestions and advice to improve performance.

[0042] Generative AI can automatically detect bugs in code written by learners and provide step-by-step guidance on how to fix them. For example, generative AI can analyze a learner's code and automatically detect bugs. For example, it can identify syntax errors and logic errors and provide step-by-step guidance on how to fix them. It can also provide specific examples of how to fix them. This allows learners to improve their programming skills by automatically detecting bugs in code written by them and providing step-by-step guidance on how to fix them.

[0043] Generative AI can automatically convert code between different programming languages, making it easier for learners to learn multiple languages. Generative AI can automatically convert code between different programming languages, making it easier for learners to learn multiple languages. For example, it can convert Python code to Java. It can also use algorithms to improve the accuracy of code conversion. This allows learners to learn multiple programming languages ​​efficiently.

[0044] Generative AI can gamify programming tasks and provide interactive content that allows learners to improve their skills while having fun. For example, generative AI can gamify programming tasks and provide interactive content that allows learners to improve their skills while having fun. For example, it can create a game in which characters are controlled using code. It can also set up a point system and rewards to increase learners' motivation. This allows learners to improve their programming skills while having fun.

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

[0046] The educational support system allows learners to effectively progress through their studies using laboratory equipment and tablets with the support of generative AI. For example, it makes it easier to understand the procedures for scientific experiments, and allows them to efficiently improve their programming skills. Furthermore, by managing and providing feedback on learning progress, it can provide an optimal learning environment tailored to each individual learner. Furthermore, the educational support system can add online collaboration functions so that learners can work together with other learners in real time to solve problems. For example, it can provide documents and chat functions that can be co-edited in real time. It can also add video conferencing functions so that learners can work together face-to-face to solve problems. This allows learners to work together with other learners in real time to solve problems.

[0047] If a learner types "Please tell me the steps for this experiment" into a tablet, the generative AI can explain the steps in detail. For example, it can explain the steps of a chemistry experiment step by step and provide a list of the tools and materials needed. The generative AI can also provide video tutorials on the experimental steps, making it easier for learners to understand the steps. Furthermore, the generative AI can analyze the learner's past question history and learning patterns to generate individually customized learning plans. For example, it can suggest a plan that focuses on topics that the learner has asked about most in the past. It can also analyze the learner's learning patterns and suggest an optimal learning schedule. This allows it to provide the learner with the optimal learning plan.

[0048] If a learner types "Why isn't this program working?" into a tablet, the generative AI can analyze the error message and suggest specific fixes. For example, it can identify the cause of a compilation or runtime error and provide step-by-step instructions on how to fix it. The generative AI can also provide code fix examples based on the error message, allowing learners to quickly fix program errors. Furthermore, the generative AI can analyze the learner's code in real time and offer optimization suggestions and performance improvement advice. For example, it can reduce redundant code and suggest efficient algorithms. It can also offer performance improvement advice. This allows the learner to improve their programming skills by analyzing their code in real time and offering optimization suggestions and performance improvement advice.

[0049] When a learner asks via tablet, "How should I interpret the results of this experiment?", the generative AI can explain how to interpret the results and the relevant theory. For example, it can analyze the experimental result data and explain its meaning. The generative AI can also provide relevant theory and background information to support the interpretation of the experimental results. This allows the learner to accurately understand the experimental results. Furthermore, the generative AI can automatically generate problems with adjusted difficulty according to the learner's level of understanding, allowing for gradual learning. For example, it can start with basic problems and provide problems with gradually increasing difficulty. It can also provide additional practice problems and explanations according to the learner's level of understanding. This allows for gradual learning by providing problems that match the learner's level of understanding.

[0050] Generative AI can evaluate a learner's progress and suggest the next task they should tackle. For example, it can evaluate how much progress a learner has made and list the next tasks they should tackle. Generative AI can also provide additional practice problems and explanations for areas where the learner is weak. This allows learners to progress efficiently. Furthermore, generative AI can gamify programming tasks and provide interactive content that allows learners to improve their skills while having fun. For example, it can create a game in which a character is controlled using code. It can also set up a point system and rewards to increase learners' motivation. This allows learners to improve their programming skills while having fun.

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

[0052] Step 1: Lab supplies include the tools and materials needed for science experiments and engineering projects, such as beakers, reagents, and sensors. Step 2: The tablet is equipped with a generative AI that supports students as they engage in programming and STEM education. For example, the generative AI provides appropriate answers and explanations to questions and assignments entered by students. Step 3: The generative AI provides appropriate answers or explanations for questions or tasks entered by the learner through the tablet. For example, if a learner enters, "Please tell me the steps for this experiment," the generative AI will provide a detailed explanation of the experiment's steps. It will also provide guidance on how to write code and correct errors in programming tasks. Step 4: The educational support system allows learners to effectively learn using experimental tools and tablets with the support of generative AI.

[0053] (Example 2) An educational support system according to an embodiment of the present invention is a system that supports programming and STEM education by integrating a generative AI into a mobile educational kit. This educational kit includes laboratory tools and a tablet, and the generative AI supports learning. This allows learners to effectively progress through their learning using the laboratory tools and tablet with the support of the generative AI. For example, this makes it easier for learners to understand the procedures of scientific experiments, and allows them to efficiently improve their programming skills. Furthermore, by managing and providing feedback on their learning progress, it is possible to provide an optimal learning environment tailored to each individual learner.

[0054] An educational support system according to an embodiment includes laboratory tools, a tablet, and a generating AI. The laboratory tools include tools and materials necessary for scientific experiments and engineering projects, such as beakers, reagents, and sensors. The tablet is installed with a generating AI and supports learners as they engage in programming and STEM education. For example, the tablet allows the generating AI to provide appropriate answers and explanations to questions or tasks entered by learners. The generating AI provides appropriate answers or explanations to questions or tasks entered by learners through the tablet. For example, if a learner enters, "Please tell me the steps for this experiment," the generating AI provides detailed explanations of the experimental steps. It also provides guidance on how to write code and correct errors in programming tasks. This allows learners to effectively progress through their learning using the laboratory tools and the tablet with the support of the generating AI.

[0055] When a learner types "Please tell me the steps for this experiment" into a tablet, the generative AI can provide a detailed explanation of the steps for that experiment. For example, when a learner types "Please tell me the steps for this experiment," the generative AI can provide a detailed explanation of the steps for that experiment. For example, it can explain the steps for a chemistry experiment step by step and provide a list of the tools and materials needed. The generative AI can also provide a video tutorial on the steps for the experiment, making it easier for learners to understand the steps for the experiment.

[0056] If a learner types "Why doesn't this program work?" through a tablet, the generative AI can analyze the error message and suggest specific ways to fix it. For example, if a learner types "Why doesn't this program work?", the generative AI can analyze the error message and suggest specific ways to fix it. For example, it can identify the cause of a compilation error or runtime error and provide step-by-step instructions on how to fix it. The generative AI can also provide examples of code corrections based on the error message, allowing learners to quickly fix program errors.

[0057] When a learner asks via tablet, "How should we interpret the results of this experiment?", the generative AI can explain how to interpret the results and the related theory. For example, when a learner asks, "How should we interpret the results of this experiment?", the generative AI can explain how to interpret the results and the related theory. For example, it can analyze the experimental result data and explain the meaning of that data. The generative AI can also provide related theory and background information to support the interpretation of experimental results. This allows learners to accurately understand the experimental results.

[0058] Generative AI can evaluate a learner's progress and suggest the next task to tackle. For example, generative AI can evaluate a learner's progress and suggest the next task to tackle. For example, it can evaluate how much progress a learner has made and list the next tasks to tackle. Generative AI can also provide additional practice problems and explanations for areas where the learner is weak. This allows the learner to study efficiently.

[0059] Sensors can be built into the lab tools, and data from the tools can be sent to a tablet in real time, where the generative AI can analyze the data and provide feedback. Sensors can be built into the lab tools, for example, using temperature or pressure sensors to send data during the experiment to a tablet in real time. The generative AI can analyze the data and provide feedback. For example, temperature changes in a chemistry experiment can be monitored in real time, and the data can be analyzed and feedback can be provided. This allows the experiment results to be analyzed in real time and feedback can be provided, deepening the learner's understanding.

[0060] By equipping tablets with AR functionality, it is possible to provide visual guidance on how to use laboratory tools and experimental procedures. By equipping tablets with AR functionality, it is possible to provide visual guidance on how to use laboratory tools, for example. For example, AR can be used to display how to handle beakers and reagents used in chemical experiments, allowing learners to operate them accurately. In addition, by providing visual guidance on experimental procedures, it becomes easier for learners to understand the experimental procedures. This allows learners to visually understand how to use laboratory tools and experimental procedures.

[0061] By equipping the tablet with an emotion estimation function, it is possible to monitor the learner's interest and concentration in real time and suggest breaks or additional explanations at appropriate times. By equipping the tablet with an emotion estimation function, it is possible to monitor the learner's interest and concentration in real time, for example by analyzing the learner's facial expressions and voice. For example, it can suggest a break if the learner loses concentration. Also, if the learner shows interest, it can provide additional explanations. In this way, learning efficiency can be improved by monitoring the learner's interest and concentration in real time and suggesting breaks or additional explanations at appropriate times.

[0062] Experimental tools can be modularized so that they can be easily exchanged for different learning themes. Experimental tools can be modularized, for example, by providing a module for chemistry experiments and a module for physics experiments, allowing learners to choose depending on the theme. For example, the module for chemistry experiments includes beakers and reagents, while the module for physics experiments includes sensors and measuring instruments. This allows learners to easily exchange experimental tools for different learning themes.

[0063] It is possible to add a voice recognition function to the tablet, allowing experimental procedures and program instructions to be given by voice commands. Adding a voice recognition function to the tablet, for example, allows learners to give instructions for experimental procedures by voice commands. For example, they can say "tell me the next step," and the generating AI will explain the next step. It is also possible to give instructions for programs by voice commands. This allows learners to give instructions for experimental procedures and programs by voice commands.

[0064] Equipping the tablet with an emotion estimation function makes it possible to provide relaxation content to reduce the stress and anxiety that learners feel during experiments. Equipping the tablet with an emotion estimation function can, for example, monitor the stress and anxiety that learners feel during experiments and provide relaxation content. For example, it can display relaxing music or videos. It can also provide meditation guides and relaxation exercises. This can reduce the stress and anxiety that learners feel during experiments.

[0065] Generative AI can analyze a learner's past question history and learning patterns to generate an individually customized learning plan. For example, generative AI can analyze a learner's past question history to generate an individually customized learning plan. For example, it can propose a plan that focuses on learning topics that the learner has asked about frequently in the past. It can also analyze a learner's learning patterns to propose an optimal learning schedule. This makes it possible to provide the learner with the optimal learning plan.

[0066] The generation AI automatically generates questions with adjusted difficulty according to the learner's level of understanding, allowing them to progress through learning in stages. The generation AI, for example, analyzes the learner's level of understanding and automatically generates questions with adjusted difficulty. For example, it can start with basic questions and provide questions with gradually increasing difficulty. It can also provide additional practice questions and explanations according to the learner's level of understanding. This allows learners to progress through learning in stages by providing questions that match their level of understanding.

[0067] The generative AI can use its emotion estimation function to provide encouragement and advice according to the learner's emotional state, thereby increasing their motivation to learn. For example, the generative AI can use its emotion estimation function to monitor the learner's emotional state and provide encouragement and advice. For example, if the learner is feeling down, it can display an encouraging message. It can also display words of praise if the learner is successful. This allows the generative AI to provide encouragement and advice according to the learner's emotional state, increasing their motivation to learn.

[0068] The generative AI can add online collaboration features so that learners can work together with other learners in real time to solve problems. For example, the generative AI can add features that support online collaboration between learners. For example, it can provide documents and chat features that can be co-edited in real time. It can also add video conferencing features so that learners can work together face-to-face to solve problems. This allows learners to work together with other learners in real time to solve problems.

[0069] The generative AI can use the emotion estimation function to provide hints and support to reduce the difficulty a learner feels about a particular task. For example, the generative AI can use the emotion estimation function to monitor the difficulty a learner feels about a particular task and provide hints and support. For example, it can display specific hints when a learner is having difficulty. It can also provide additional learning materials and explanations. This improves learning efficiency by providing hints and support to reduce the difficulty a learner feels about a particular task.

[0070] Generative AI can analyze a learner's code in real time and provide optimization suggestions and advice to improve performance. Generative AI can, for example, analyze a learner's code in real time and provide optimization suggestions. For example, it can reduce redundant code and suggest efficient algorithms. It can also provide advice to improve performance. This allows learners to improve their programming skills by analyzing their code in real time and providing optimization suggestions and advice to improve performance.

[0071] Generative AI can automatically detect bugs in code written by learners and provide step-by-step guidance on how to fix them. For example, generative AI can analyze a learner's code and automatically detect bugs. For example, it can identify syntax errors and logic errors and provide step-by-step guidance on how to fix them. It can also provide specific examples of how to fix them. This allows learners to improve their programming skills by automatically detecting bugs in code written by them and providing step-by-step guidance on how to fix them.

[0072] The generative AI can use its emotion estimation function to make refreshing suggestions to reduce the frustration a learner feels while programming. For example, the generative AI can use its emotion estimation function to monitor the frustration a learner feels while programming and make refreshing suggestions. For example, it can suggest short breaks or relaxation exercises. It can also provide music or videos for refreshing. This improves learning efficiency by making refreshing suggestions to reduce the frustration a learner feels while programming.

[0073] Generative AI can automatically convert code between different programming languages, making it easier for learners to learn multiple languages. Generative AI can automatically convert code between different programming languages, making it easier for learners to learn multiple languages. For example, it can convert Python code to Java. It can also use algorithms to improve the accuracy of code conversion. This allows learners to learn multiple programming languages ​​efficiently.

[0074] Generative AI can gamify programming tasks and provide interactive content that allows learners to improve their skills while having fun. For example, generative AI can gamify programming tasks and provide interactive content that allows learners to improve their skills while having fun. For example, it can create a game in which characters are controlled using code. It can also set up a point system and rewards to increase learners' motivation. This allows learners to improve their programming skills while having fun.

[0075] The generative AI can use its emotion estimation function to provide positive feedback to reinforce the sense of accomplishment a learner feels for a specific programming task. For example, the generative AI can use its emotion estimation function to monitor the sense of accomplishment a learner feels for a specific programming task and provide positive feedback. For example, it can display words of praise when a task is completed. It can also provide advice on the next step. In this way, providing positive feedback to reinforce the sense of accomplishment a learner feels for a specific programming task can increase their motivation to learn.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The educational support system allows learners to effectively progress through their studies using laboratory equipment and tablets with the support of generative AI. For example, it makes it easier to understand the procedures for scientific experiments, and allows them to efficiently improve their programming skills. Furthermore, by managing and providing feedback on learning progress, it can provide an optimal learning environment tailored to each individual learner. Furthermore, the educational support system can add online collaboration functions so that learners can work together with other learners in real time to solve problems. For example, it can provide documents and chat functions that can be co-edited in real time. It can also add video conferencing functions so that learners can work together face-to-face to solve problems. This allows learners to work together with other learners in real time to solve problems.

[0078] If a learner types "Please tell me the steps for this experiment" into a tablet, the generative AI can explain the steps in detail. For example, it can explain the steps of a chemistry experiment step by step and provide a list of the tools and materials needed. The generative AI can also provide video tutorials on the experimental steps, making it easier for learners to understand the steps. Furthermore, the generative AI can analyze the learner's past question history and learning patterns to generate individually customized learning plans. For example, it can suggest a plan that focuses on topics that the learner has asked about most in the past. It can also analyze the learner's learning patterns and suggest an optimal learning schedule. This allows it to provide the learner with the optimal learning plan.

[0079] If a learner types "Why isn't this program working?" into a tablet, the generative AI can analyze the error message and suggest specific fixes. For example, it can identify the cause of a compilation or runtime error and provide step-by-step instructions on how to fix it. The generative AI can also provide code fix examples based on the error message, allowing learners to quickly fix program errors. Furthermore, the generative AI can analyze the learner's code in real time and offer optimization suggestions and performance improvement advice. For example, it can reduce redundant code and suggest efficient algorithms. It can also offer performance improvement advice. This allows the learner to improve their programming skills by analyzing their code in real time and offering optimization suggestions and performance improvement advice.

[0080] When a learner asks via tablet, "How should I interpret the results of this experiment?", the generative AI can explain how to interpret the results and the relevant theory. For example, it can analyze the experimental result data and explain its meaning. The generative AI can also provide relevant theory and background information to support the interpretation of the experimental results. This allows the learner to accurately understand the experimental results. Furthermore, the generative AI can automatically generate problems with adjusted difficulty according to the learner's level of understanding, allowing for gradual learning. For example, it can start with basic problems and provide problems with gradually increasing difficulty. It can also provide additional practice problems and explanations according to the learner's level of understanding. This allows for gradual learning by providing problems that match the learner's level of understanding.

[0081] Generative AI can evaluate a learner's progress and suggest the next task they should tackle. For example, it can evaluate how much progress a learner has made and list the next tasks they should tackle. Generative AI can also provide additional practice problems and explanations for areas where the learner is weak. This allows learners to progress efficiently. Furthermore, generative AI can gamify programming tasks and provide interactive content that allows learners to improve their skills while having fun. For example, it can create a game in which a character is controlled using code. It can also set up a point system and rewards to increase learners' motivation. This allows learners to improve their programming skills while having fun.

[0082] Sensors can be incorporated into experimental tools, sending data from the tools to a tablet in real time, where the generative AI can analyze the data and provide feedback. For example, temperature and pressure sensors can be used to send data during an experiment to a tablet in real time. The generative AI can then analyze the data and provide feedback. For example, temperature changes in a chemistry experiment can be monitored in real time, and the data can be analyzed and provided as feedback. This allows for real-time analysis of experimental results and feedback, deepening learners' understanding. Furthermore, the tablet can be equipped with emotion estimation functionality to monitor learners' interest and concentration in real time and suggest breaks or additional explanations at appropriate times. For example, learners' facial expressions and voices can be analyzed to monitor interest and concentration in real time. For example, if a learner loses concentration, the system can suggest a break. If a learner shows interest, the system can provide additional explanations. This allows learners' interest and concentration to be monitored in real time, and appropriate timing can be suggested for breaks or additional explanations, improving learning efficiency.

[0083] Equipping tablets with AR functionality can provide visual guidance on how to use laboratory tools and experimental procedures. For example, visual guidance on how to use laboratory tools can be provided. For example, AR can be used to display how to handle beakers and reagents used in chemical experiments, allowing learners to operate them accurately. Visual guidance of experimental procedures can also make it easier for learners to understand the procedures. This allows learners to visually understand how to use laboratory tools and experimental procedures. Furthermore, by equipping tablets with emotion estimation functionality, it is possible to provide relaxation content to reduce the stress and anxiety that learners feel during experiments. For example, the tablet can monitor the stress and anxiety that learners feel during experiments and provide relaxation content. For example, it can display relaxing music or videos. It can also provide meditation guides and relaxation exercises. This can reduce the stress and anxiety that learners feel during experiments.

[0084] Equipping the tablet with an emotion estimation function can monitor a learner's interest and concentration in real time and suggest breaks or additional explanations at appropriate times. For example, the tablet can analyze a learner's facial expressions and voice to monitor their interest and concentration in real time. For example, if a learner loses concentration, the tablet can suggest a break. If a learner shows interest, the tablet can provide additional explanations. This improves learning efficiency by monitoring a learner's interest and concentration in real time and suggesting breaks or additional explanations at appropriate times. Furthermore, the generative AI can use the emotion estimation function to provide encouragement and advice according to the learner's emotional state, thereby increasing their motivation to learn. For example, the tablet can use the emotion estimation function to monitor a learner's emotional state and provide encouragement and advice. For example, if a learner is feeling down, the tablet can display an encouraging message. Also, if a learner succeeds, the tablet can display words of praise. This allows the tablet to provide encouragement and advice according to the learner's emotional state, increasing their motivation to learn.

[0085] Experimental tools can be modularized so that they can be easily interchanged for different learning themes. For example, a module for chemistry experiments and a module for physics experiments can be prepared, allowing learners to choose based on the theme. For example, the chemistry experiment module includes beakers and reagents, while the physics experiment module includes sensors and measuring instruments. This allows learners to easily exchange experimental tools for different learning themes. Furthermore, the generative AI can use its emotion estimation function to provide hints and support to reduce the difficulty a learner feels with a specific task. For example, the emotion estimation function can be used to monitor the difficulty a learner feels with a specific task and provide hints and support. For example, specific hints can be displayed if the learner is having difficulty. Additional learning materials and explanations can also be provided. This improves learning efficiency by providing hints and support to reduce the difficulty a learner feels with a specific task.

[0086] By adding a voice recognition function to the tablet, it is possible to allow experimental procedures and programming instructions to be given via voice commands. For example, learners can give voice commands to instruct experimental procedures. For example, they can say, "Tell me the next step," and the generation AI will explain the next step. Programming instructions can also be given via voice commands. This allows learners to instruct experimental procedures and programming via voice commands. Furthermore, the generation AI can use its emotion estimation function to make refreshment suggestions to reduce the frustration the learner feels while programming. For example, the emotion estimation function can be used to monitor the frustration the learner feels while programming and make refreshment suggestions. For example, it can suggest short breaks or relaxation exercises. It can also provide music or videos for refreshment. This improves learning efficiency by making refreshment suggestions to reduce the frustration the learner feels while programming.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: Lab supplies include the tools and materials needed for science experiments and engineering projects, such as beakers, reagents, and sensors. Step 2: The tablet is equipped with a generative AI that supports students as they engage in programming and STEM education. For example, the generative AI provides appropriate answers and explanations to questions and assignments entered by students. Step 3: The generative AI provides appropriate answers or explanations for questions or tasks entered by the learner through the tablet. For example, if a learner enters, "Please tell me the steps for this experiment," the generative AI will provide a detailed explanation of the experiment's steps. It will also provide guidance on how to write code and correct errors in programming tasks. Step 4: The educational support system allows learners to effectively learn using experimental tools and tablets with the support of generative AI.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, a 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] 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]

[0156] 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. Experimental tools and Tablet and Equipped with a generative AI, The generated AI is Providing appropriate answers or explanations to questions or tasks entered through the tablet A system characterized by:

2. The generated AI is Evaluate learners' progress and suggest next steps The system of claim 1 .

3. Adding a voice recognition function to the tablet; To enable experimental procedures and program instructions via voice commands. The system of claim 1 .

4. The generated AI is Analyze learners' past question history and learning patterns, Generate personalized learning plans The system of claim 1 .

5. The generated AI is Analyze learners' code in real time, Providing optimization suggestions and performance improvement advice The system of claim 1 .

6. The tablet is equipped with an emotion estimation function, Monitor learners' interest and concentration in real time, Suggest breaks or further explanations at appropriate times The system of claim 1 .

7. The generated AI is Using emotion estimation function, Providing encouragement and advice according to the learner's emotional state; Increasing motivation to learn The system of claim 1 .

8. The generated AI is Using emotion estimation function, Providing refresher suggestions to reduce frustrations students may experience while programming The system of claim 1 .

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