system

Abstract

The system according to the embodiment aims to effectively demonstrate cybersecurity and threat detection in educational settings and to support composition in music education. [Solution] The system according to the embodiment comprises a checking unit, a performance unit, a suggestion unit, a composition unit, and a providing unit. The checking unit asks questions to the generation AI in advance and checks the answers. The performance unit uses the generation AI during class to demonstrate cybersecurity and threat detection based on the questions and answers checked by the checking unit. The suggestion unit suggests countermeasures to students based on the content demonstrated by the performance unit. The composition unit inputs composition instructions to the generation AI and supports composition. The providing unit provides the sheet music composed by the composition unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to effectively demonstrate cybersecurity and threat detection in educational settings, and there have also been issues with the lack of support for composition in music education.

[0005] The system according to the embodiment aims to effectively demonstrate cybersecurity and threat detection in educational settings and to support composition in music education. [Means for solving the problem]

[0006] The system according to the embodiment comprises a checking unit, a performance unit, a suggestion unit, a composition unit, and a providing unit. The checking unit asks questions to the generation AI in advance and checks the answers. The performance unit uses the generation AI during class to demonstrate cybersecurity and threat detection based on the questions and answers checked by the checking unit. The suggestion unit suggests countermeasures to students based on the content demonstrated by the performance unit. The composition unit inputs composition instructions to the generation AI and supports composition. The providing unit provides the musical score composed by the composition unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively demonstrate cybersecurity and threat detection in educational settings and can support composition in music 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 touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention uses generative AI to teach cybersecurity and threat detection in classrooms and develop children's self-defense capabilities. This educational support system uses generative AI to demonstrate cybersecurity and threat detection and suggest countermeasures to students. It can also be used as a tool for learning how to compose music in music classes. For example, a teacher can ask the generative AI questions in advance and check the answers. The generative AI generates appropriate answers to questions related to cybersecurity and threat detection. For example, in response to the question, "What is a phishing attack?", the generative AI might respond, "A phishing attack is a method of stealing personal information using fake websites or emails." The teacher then checks the answer and uses it in class. Next, the generative AI is used during class to demonstrate cybersecurity and threat detection. The generative AI detects threats and suggests countermeasures. For example, the generative AI might detect "This email may be a phishing attack" and suggest "Do not click on the link." This allows students to learn about security and how to deal with threats. Furthermore, the generative AI can assist students in composing music in music classes. For example, the generative AI can compose music based on instructions such as "Compose a string quartet piece in C major in under three minutes" or "Compose a four-minute lullaby in the Okinawan scale." This allows children to learn how to compose music. For example, the generative AI can compose a "string quartet piece in C major" and provide the sheet music. Children can then play the sheet music and learn the composition process. This allows the educational support system to develop children's self-defense capabilities and teach them how to deal with security and threats. It can also be used as a tool to teach composition in music classes.

[0029] An educational support system according to an embodiment includes a check unit, a performance unit, a suggestion unit, a composition unit, and a provision unit. The check unit asks the generation AI questions in advance and checks the answers. For example, the check unit asks the generation AI questions related to cybersecurity and threat detection and verifies that the generation AI generates appropriate answers. For example, the check unit asks the generation AI the question, "What is a phishing attack?" and verifies that the generation AI replies, "A phishing attack is a method of stealing personal information using fake websites or emails." The performance unit uses the generation AI to demonstrate cybersecurity and threat detection. For example, the performance unit demonstrates that the generation AI detects that "this email may be a phishing attack" and suggests a countermeasure, such as "do not click on the link." The suggestion unit provides the countermeasure proposed by the generation AI to a child. For example, the suggestion unit provides the countermeasure proposed by the generation AI, such as "do not click on the link," to a child. The composition unit inputs composition instructions to the generation AI, and the generation AI composes the music. For example, the composition unit gives instructions to the generation AI, such as "Compose a piece in C major for a string quartet in under three minutes" or "Compose a four-minute lullaby in the Okinawan scale," and the generation AI composes the music. The provision unit provides the sheet music composed by the generation AI to children. For example, the provision unit provides the sheet music for "a piece in C major for a string quartet" composed by the generation AI to children. In this way, the education support system according to the embodiment can develop children's self-defense capabilities and teach them how to deal with security and threats. It can also be used as a tool for learning how to compose music in music classes.

[0030] The checking unit can ask the generating AI questions related to cybersecurity and threat detection, and the generating AI can generate answers. For example, the checking unit can ask the generating AI, "What is a phishing attack?" and verify that the generating AI answers, "A phishing attack is a method of stealing personal information using fake websites or emails." The checking unit can also ask the generating AI, "What is malware?" and verify that the generating AI answers, "Malware is malicious software designed to damage computer systems." The checking unit can also ask the generating AI, "What is ransomware?" and verify that the generating AI answers, "Ransomware is malicious software that locks a computer system and demands a ransom to unlock it." This improves the quality of lessons by allowing the generating AI to generate appropriate answers to questions related to cybersecurity and threat detection.

[0031] The demonstration unit can use generative AI to detect threats and suggest countermeasures. For example, the demonstration unit demonstrates that the generative AI detects, "This email may be a phishing attack," and suggests the countermeasure, "Do not click on the link." The demonstration unit can also demonstrate that the generative AI detects, "This file may be malware," and suggests the countermeasure, "Do not open the file." The demonstration unit can also demonstrate that the generative AI detects, "This system is infected with ransomware," and suggests the countermeasure, "Shut down the system and consult an expert." In this way, children can learn about security and how to deal with threats by using the generative AI to detect threats and suggest countermeasures.

[0032] The suggestion unit can provide the child with the countermeasures suggested by the generation AI. For example, the suggestion unit can provide the child with the countermeasure suggested by the generation AI, such as "Do not click on links." The suggestion unit can also provide the child with the countermeasure suggested by the generation AI, such as "Do not open files." Furthermore, the suggestion unit can also provide the child with the countermeasure suggested by the generation AI, such as "Shut down the system and consult an expert." In this way, by providing the child with the countermeasures suggested by the generation AI, the child can learn specific countermeasures.

[0033] The composition unit can input instructions to the generation AI, such as "Compose a piece in C major for a string quartet in less than three minutes" or "Compose a four-minute lullaby in the Okinawan scale," and the generation AI can compose the piece. For example, the composition unit can input instructions to the generation AI, such as "Compose a piece in C major for a string quartet in less than three minutes," and the generation AI can compose the piece. The composition unit can also input instructions to the generation AI, such as "Compose a four-minute lullaby in the Okinawan scale," and the generation AI can compose the piece. Furthermore, the composition unit can input instructions to the generation AI, such as "Compose a piece in a minor key for a piano solo in less than five minutes," and the generation AI can compose the piece. In this way, children can learn how to compose music by having the generation AI compose the piece.

[0034] The providing unit can provide the child with the sheet music composed by the generation AI. For example, the providing unit can provide the child with the sheet music for a "string quartet piece in C major" composed by the generation AI. The providing unit can also provide the child with the sheet music for an "Okinawa scale lullaby" composed by the generation AI. Furthermore, the providing unit can also provide the child with the sheet music for a "piano solo piece in a minor key" composed by the generation AI. In this way, by providing the child with the sheet music composed by the generation AI, the child can learn the process of composition.

[0035] When asking a question, the checking unit can refer to the content of past lessons to increase the relevance of the question. For example, the checking unit generates relevant questions based on topics that the generation AI covered in past lessons. The checking unit can also generate questions of an appropriate level of difficulty by taking into account the child's level of understanding in past lessons. Furthermore, the checking unit can generate questions that focus on areas in which the generation AI is weak based on feedback from past lessons. In this way, by referring to the content of past lessons, the relevance of the question increases and the child's understanding deepens.

[0036] The checking unit can adjust the level of detail of the question based on the child's level of understanding when asking the question. For example, if the generation AI determines that the child has a high level of understanding, the checking unit generates a detailed and complex question. Also, if the generation AI determines that the child has a low level of understanding, the checking unit can generate a basic question to deepen the child's understanding. Furthermore, the checking unit can also cause the generation AI to generate questions of gradually increasing difficulty according to the child's level of understanding. In this way, adjusting the level of detail of the question according to the child's level of understanding deepens the child's understanding.

[0037] When asking questions, the checking unit can customize the content of the questions based on the teacher's area of ​​expertise. For example, the checking unit allows the generation AI to prioritize generating questions related to the teacher's area of ​​expertise. The checking unit can also allow the generation AI to generate detailed questions that utilize the teacher's specialized knowledge. Furthermore, the checking unit can allow the generation AI to generate questions that are likely to interest students based on the teacher's area of ​​expertise. This improves the quality of lessons by customizing the content of questions based on the teacher's area of ​​expertise.

[0038] When asking a question, the checking unit can adjust the difficulty of the question by referring to the child's past grades. For example, the generation AI generates questions of an appropriate level of difficulty based on the child's past grades. The checking unit can also generate more difficult questions for children with good grades and basic questions for children with poor grades. Furthermore, the checking unit can also generate questions of gradually increasing difficulty according to the generation AI's grades. This makes it possible to generate questions of an appropriate level of difficulty by referring to the child's past grades.

[0039] During the demonstration, the demonstration department can refer to past threat data to enhance the realism of the demonstration. For example, in the demonstration department, the generating AI generates realistic scenarios based on actual threat data from the past. The demonstration department can also have the generating AI analyze past threat data and propose countermeasures that are in line with reality. Furthermore, the demonstration department can have the generating AI refer to past threat data to make the content of the demonstration more specific and detailed. In this way, by referencing past threat data, the realism of the demonstration is enhanced and children's understanding is deepened.

[0040] During the demonstration, the demonstration unit can adjust the level of detail of the demonstration based on the child's level of understanding. For example, if the generating AI determines that the child has a high level of understanding, the demonstration unit will perform a detailed and complex demonstration. In addition, if the generating AI determines that the child has a low level of understanding, the demonstration unit can perform a basic demonstration to deepen understanding. Furthermore, the demonstration unit can also perform demonstrations in which the generating AI gradually increases the level of detail depending on the child's level of understanding. In this way, adjusting the level of detail of the demonstration according to the child's level of understanding deepens the child's understanding.

[0041] The demonstration unit can customize the content of the demonstration based on the child's interests during the demonstration. For example, the generation AI prioritizes demonstrations related to the child's interests. The demonstration unit can also provide visually appealing demonstrations to attract the child's attention. Furthermore, the generation AI can make the content of the demonstration specific and detailed based on the child's areas of interest. This allows the content of the demonstration to be customized based on the child's interests, thereby deepening the child's understanding.

[0042] During the demonstration, the content of the demonstration can be linked to other subjects. For example, the generative AI can demonstrate cybersecurity by linking it to an information technology class. Alternatively, the generative AI can demonstrate threat detection by linking it to a social studies class. Furthermore, the generative AI can link the content of the demonstration to topics in other subjects to promote students' comprehensive understanding. This allows students to deepen their comprehensive understanding by linking the content of the demonstration to other subjects.

[0043] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past countermeasure data. For example, the proposal unit allows the generation AI to propose optimal countermeasures based on past countermeasure data. The proposal unit can also allow the generation AI to analyze past countermeasure data and propose effective countermeasures. Furthermore, the proposal unit can allow the generation AI to propose specific and detailed countermeasures by referring to past countermeasure data. In this way, the accuracy of the proposal is improved by referring to past countermeasure data.

[0044] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the child's level of understanding. For example, if the generating AI determines that the child has a high level of understanding, the suggestion unit can suggest detailed and complex measures. In addition, if the generating AI determines that the child has a low level of understanding, the suggestion unit can also suggest basic measures to deepen the child's understanding. Furthermore, the suggestion unit can also suggest measures that gradually increase in detail depending on the generating AI's level of understanding. In this way, adjusting the level of detail of the proposal according to the child's level of understanding deepens the child's understanding.

[0045] When proposing, the suggestion unit can customize the content of the measures based on the child's living environment. For example, the suggestion unit allows the generation AI to suggest feasible measures based on the child's home environment. The suggestion unit can also allow the generation AI to suggest appropriate measures based on the child's school environment. Furthermore, the suggestion unit can allow the generation AI to suggest specific and detailed measures based on the child's local environment. In this way, by customizing the content of the measures based on the child's living environment, more feasible measures can be proposed.

[0046] When proposing, the suggestion unit can associate the content of the countermeasures with other subjects. For example, the suggestion unit's generation AI can propose cybersecurity countermeasures by associating them with information technology lessons. The suggestion unit can also propose threat detection countermeasures by associating them with social studies lessons. Furthermore, the suggestion unit's generation AI can associate the content of the countermeasures with topics in other subjects to encourage students' comprehensive understanding. In this way, by associating the content of the countermeasures with other subjects, students' comprehensive understanding will be deepened.

[0047] When composing, the composition unit can improve the accuracy of the composition by referring to past composition data. For example, the composition unit allows the generation AI to generate an optimal melody based on past composition data. The composition unit can also allow the generation AI to analyze past composition data and generate effective chords. Furthermore, the composition unit can allow the generation AI to generate specific and detailed musical scores by referring to past composition data. In this way, the accuracy of the composition is improved by referring to past composition data.

[0048] When composing, the composition unit can adjust the level of detail of the composition based on the child's musical background. For example, the composition unit generates detailed and complex scores for children with a rich musical background through the generation AI. The composition unit can also generate basic scores for children with little musical background to deepen their understanding. Furthermore, the composition unit can generate scores with gradually increasing levels of detail according to the child's musical background through the generation AI. This allows the child's understanding to be deepened by adjusting the level of detail of the composition according to their musical background.

[0049] The composition unit can customize the content of the composition based on the child's musical preferences when composing. For example, the composition unit uses a generation AI to compose music based on the child's favorite musical genre. The composition unit can also use the generation AI to compose a song using the child's favorite instrument. Furthermore, the composition unit can also use the generation AI to generate specific and detailed sheet music based on the child's musical preferences. This allows the composition to be tailored to the content of the composition based on the child's musical preferences, making it possible to compose a song that is likely to attract the child's interest.

[0050] When composing, the composition department can associate the content of the composition with other subjects. For example, the composition department's generative AI can compose music by associating it with a history class. The composition department's generative AI can also compose music by associating it with a literature class. Furthermore, the composition department's generative AI can associate the content of the composition with topics in other subjects to encourage children's comprehensive understanding. In this way, by associating the content of the composition with other subjects, children's comprehensive understanding will be deepened.

[0051] When providing the musical score, the providing unit can improve the accuracy of the provision by referring to the previously provided data. For example, the providing unit allows the generation AI to provide an optimal musical score based on the previously provided data. The providing unit can also allow the generation AI to analyze the previously provided data and provide an effective musical score. Furthermore, the providing unit can allow the generation AI to provide a specific and detailed musical score by referring to the previously provided data. In this way, the accuracy of the musical score provision is improved by referring to the previously provided data.

[0052] When providing the sheet music, the providing unit can adjust the level of detail provided based on the child's musical background. For example, the providing unit may provide detailed and complex sheet music to children with a rich musical background. The providing unit may also provide basic sheet music to children with little musical background to deepen their understanding. Furthermore, the providing unit may provide sheet music with gradually increasing levels of detail to the generating AI according to the child's musical background. This allows the child's understanding to be deepened by adjusting the level of detail provided in the sheet music according to the child's musical background.

[0053] When providing the sheet music, the providing unit can customize the content of the sheet music based on the child's musical preferences. For example, the providing unit can provide sheet music based on the child's favorite musical genre using the generation AI. The providing unit can also provide sheet music using the child's favorite instrument using the generation AI. Furthermore, the providing unit can provide specific and detailed sheet music based on the child's musical preferences using the generation AI. In this way, by customizing the content of the sheet music based on the child's musical preferences, it is possible to provide sheet music that is likely to attract the child's interest.

[0054] When providing the sheet music, the providing unit can associate the content of the sheet music with other subjects. For example, the providing unit can have the generating AI provide a music score by associating it with a history lesson. The providing unit can also have the generating AI provide a music score by associating it with a literature lesson. Furthermore, the providing unit can have the generating AI associate the content of the sheet music with topics in other subjects to encourage children's comprehensive understanding. In this way, associating the content of the sheet music with other subjects deepens children's comprehensive understanding.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The education support system can further include a progress management unit. The progress management unit records the learning progress of students and reports it to teachers and parents. For example, the progress management unit visualizes the level of understanding of each topic by the student and displays it in graphs and charts. The progress management unit can also identify areas in which the student is struggling with a particular topic and provide additional learning resources. Furthermore, the progress management unit can suggest the next topic to study based on the student's learning progress. This makes it possible to efficiently manage students' learning progress and respond to individual learning needs.

[0057] The educational support system may further include a collaborative learning unit. The collaborative learning unit provides a function for students to cooperate with each other in their learning. For example, the collaborative learning unit may provide a forum for group discussions, encouraging students to work together to solve cybersecurity problems. The collaborative learning unit may also provide a platform for students to provide feedback to each other. Furthermore, the collaborative learning unit may provide tools for students to work together on projects, fostering teamwork. This allows students to learn collaboratively and gain a deeper understanding.

[0058] The educational support system may further include a customization unit. The customization unit customizes the learning content based on the individual learning style and interests of each child. For example, if a child prefers visual learning, the customization unit may provide learning materials that make extensive use of visual aids. If a child prefers auditory learning, the customization unit may also provide audio learning materials. Furthermore, the customization unit may provide learning materials that incorporate relevant topics and examples based on the child's interests. This allows each child to progress in their learning in a way that suits them best.

[0059] The educational support system may further include a resource management unit. The resource management unit efficiently manages and provides resources necessary for learning. For example, the resource management unit automatically collects and organizes teaching materials and reference materials needed by students. The resource management unit may also provide appropriate resources when a student requests additional learning resources on a specific topic. Furthermore, the resource management unit may manage devices and software used by students for learning and optimize the learning environment. This allows students to efficiently use the resources they need.

[0060] The educational support system can further include an evaluation unit. The evaluation unit evaluates the learning outcomes of students and provides feedback. For example, the evaluation unit analyzes the results of tests and assignments taken by students and assigns grades. The evaluation unit can also provide individual feedback based on the students' learning progress. Furthermore, the evaluation unit can identify the students' strengths and weaknesses and propose future learning plans. This allows students to objectively understand their learning outcomes and obtain guidelines for moving on to the next step.

[0061] The educational support system may further include an interactive section. The interactive section helps students learn through interactive experiences during their studies. For example, the interactive section may enable students to learn through cybersecurity simulation games. The interactive section may also enable students to experience real cyberattack scenarios using virtual reality (VR). Furthermore, the interactive section may provide an interactive platform for students to collaborate with other students to solve problems. This allows students to learn through practical experiences.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The checking unit asks the generating AI questions in advance and checks the answers. For example, the checking unit asks the generating AI questions related to cybersecurity and threat detection, and verifies that the generating AI generates appropriate answers. Specifically, the checking unit asks the question, "What is a phishing attack?" and verifies that the generating AI answers, "A phishing attack is a method of stealing personal information using fake websites or emails." Step 2: The demonstration team will demonstrate cybersecurity and threat detection using generative AI. For example, they will demonstrate how generative AI can detect "this email may be a phishing attack" and suggest countermeasures such as "do not click on the link." Step 3: The suggestion unit provides the child with the countermeasures proposed by the generation AI. For example, the suggestion unit provides the child with the countermeasure "Do not click on the link." Step 4: The composition department inputs composition instructions into the generation AI, which then composes the music. For example, instructions might be given such as "Compose a string quartet piece in C major in under three minutes" or "Compose a four-minute lullaby in the Okinawan scale," and the generation AI then composes the music. Step 5: The providing unit provides the child with the musical score composed by the generation AI. For example, the score for a "string quartet piece in C major" is provided to the child.

[0064] (Example 2) An educational support system according to an embodiment of the present invention uses generative AI to teach cybersecurity and threat detection in classrooms and develop children's self-defense capabilities. This educational support system uses generative AI to demonstrate cybersecurity and threat detection and suggest countermeasures to students. It can also be used as a tool for learning how to compose music in music classes. For example, a teacher can ask the generative AI questions in advance and check the answers. The generative AI generates appropriate answers to questions related to cybersecurity and threat detection. For example, in response to the question, "What is a phishing attack?", the generative AI might respond, "A phishing attack is a method of stealing personal information using fake websites or emails." The teacher then checks the answer and uses it in class. Next, the generative AI is used during class to demonstrate cybersecurity and threat detection. The generative AI detects threats and suggests countermeasures. For example, the generative AI might detect "This email may be a phishing attack" and suggest "Do not click on the link." This allows students to learn about security and how to deal with threats. Furthermore, the generative AI can assist students in composing music in music classes. For example, the generative AI can compose music based on instructions such as "Compose a string quartet piece in C major in under three minutes" or "Compose a four-minute lullaby in the Okinawan scale." This allows children to learn how to compose music. For example, the generative AI can compose a "string quartet piece in C major" and provide the sheet music. Children can then play the sheet music and learn the composition process. This allows the educational support system to develop children's self-defense capabilities and teach them how to deal with security and threats. It can also be used as a tool to teach composition in music classes.

[0065] An educational support system according to an embodiment includes a check unit, a performance unit, a suggestion unit, a composition unit, and a provision unit. The check unit asks the generation AI questions in advance and checks the answers. For example, the check unit asks the generation AI questions related to cybersecurity and threat detection and verifies that the generation AI generates appropriate answers. For example, the check unit asks the generation AI the question, "What is a phishing attack?" and verifies that the generation AI replies, "A phishing attack is a method of stealing personal information using fake websites or emails." The performance unit uses the generation AI to demonstrate cybersecurity and threat detection. For example, the performance unit demonstrates that the generation AI detects that "this email may be a phishing attack" and suggests a countermeasure, such as "do not click on the link." The suggestion unit provides the countermeasure proposed by the generation AI to a child. For example, the suggestion unit provides the countermeasure proposed by the generation AI, such as "do not click on the link," to a child. The composition unit inputs composition instructions to the generation AI, and the generation AI composes the music. For example, the composition unit gives instructions to the generation AI, such as "Compose a piece in C major for a string quartet in under three minutes" or "Compose a four-minute lullaby in the Okinawan scale," and the generation AI composes the music. The provision unit provides the sheet music composed by the generation AI to children. For example, the provision unit provides the sheet music for "a piece in C major for a string quartet" composed by the generation AI to children. In this way, the education support system according to the embodiment can develop children's self-defense capabilities and teach them how to deal with security and threats. It can also be used as a tool for learning how to compose music in music classes.

[0066] The checking unit can ask the generating AI questions related to cybersecurity and threat detection, and the generating AI can generate answers. For example, the checking unit can ask the generating AI, "What is a phishing attack?" and verify that the generating AI answers, "A phishing attack is a method of stealing personal information using fake websites or emails." The checking unit can also ask the generating AI, "What is malware?" and verify that the generating AI answers, "Malware is malicious software designed to damage computer systems." The checking unit can also ask the generating AI, "What is ransomware?" and verify that the generating AI answers, "Ransomware is malicious software that locks a computer system and demands a ransom to unlock it." This improves the quality of lessons by allowing the generating AI to generate appropriate answers to questions related to cybersecurity and threat detection.

[0067] The demonstration unit can use generative AI to detect threats and suggest countermeasures. For example, the demonstration unit demonstrates that the generative AI detects, "This email may be a phishing attack," and suggests the countermeasure, "Do not click on the link." The demonstration unit can also demonstrate that the generative AI detects, "This file may be malware," and suggests the countermeasure, "Do not open the file." The demonstration unit can also demonstrate that the generative AI detects, "This system is infected with ransomware," and suggests the countermeasure, "Shut down the system and consult an expert." In this way, children can learn about security and how to deal with threats by using the generative AI to detect threats and suggest countermeasures.

[0068] The suggestion unit can provide the child with the countermeasures suggested by the generation AI. For example, the suggestion unit can provide the child with the countermeasure suggested by the generation AI, such as "Do not click on links." The suggestion unit can also provide the child with the countermeasure suggested by the generation AI, such as "Do not open files." Furthermore, the suggestion unit can also provide the child with the countermeasure suggested by the generation AI, such as "Shut down the system and consult an expert." In this way, by providing the child with the countermeasures suggested by the generation AI, the child can learn specific countermeasures.

[0069] The composition unit can input instructions to the generation AI, such as "Compose a piece in C major for a string quartet in less than three minutes" or "Compose a four-minute lullaby in the Okinawan scale," and the generation AI can compose the piece. For example, the composition unit can input instructions to the generation AI, such as "Compose a piece in C major for a string quartet in less than three minutes," and the generation AI can compose the piece. The composition unit can also input instructions to the generation AI, such as "Compose a four-minute lullaby in the Okinawan scale," and the generation AI can compose the piece. Furthermore, the composition unit can input instructions to the generation AI, such as "Compose a piece in a minor key for a piano solo in less than five minutes," and the generation AI can compose the piece. In this way, children can learn how to compose music by having the generation AI compose the piece.

[0070] The providing unit can provide the child with the sheet music composed by the generation AI. For example, the providing unit can provide the child with the sheet music for a "string quartet piece in C major" composed by the generation AI. The providing unit can also provide the child with the sheet music for an "Okinawa scale lullaby" composed by the generation AI. Furthermore, the providing unit can also provide the child with the sheet music for a "piano solo piece in a minor key" composed by the generation AI. In this way, by providing the child with the sheet music composed by the generation AI, the child can learn the process of composition.

[0071] The checking unit can estimate the teacher's emotions and adjust the content and difficulty of questions based on the estimated teacher's emotions. For example, if the teacher is nervous, the generation AI will prioritize generating simple questions, allowing the lesson to proceed smoothly. Alternatively, if the teacher is relaxed, the checking unit can generate detailed and complex questions to encourage deeper understanding. Furthermore, if the teacher is tired, the generation AI can generate short and to-the-point questions, increasing the efficiency of the lesson. This allows the content and difficulty of questions to be adjusted according to the teacher's emotions, allowing the lesson to proceed smoothly.

[0072] When asking a question, the checking unit can refer to the content of past lessons to increase the relevance of the question. For example, the checking unit generates relevant questions based on topics that the generation AI covered in past lessons. The checking unit can also generate questions of an appropriate level of difficulty by taking into account the child's level of understanding in past lessons. Furthermore, the checking unit can generate questions that focus on areas in which the generation AI is weak based on feedback from past lessons. In this way, by referring to the content of past lessons, the relevance of the question increases and the child's understanding deepens.

[0073] The checking unit can adjust the level of detail of the question based on the child's level of understanding when asking the question. For example, if the generation AI determines that the child has a high level of understanding, the checking unit generates a detailed and complex question. Also, if the generation AI determines that the child has a low level of understanding, the checking unit can generate a basic question to deepen the child's understanding. Furthermore, the checking unit can also cause the generation AI to generate questions of gradually increasing difficulty according to the child's level of understanding. In this way, adjusting the level of detail of the question according to the child's level of understanding deepens the child's understanding.

[0074] The checking unit can estimate the teacher's emotions and determine the order of questions based on the estimated teacher's emotions. For example, if the teacher is nervous, the generation AI will start with easy questions and gradually increase the difficulty. Alternatively, if the teacher is relaxed, the checking unit can start with more difficult questions to encourage deeper understanding. Furthermore, if the teacher is tired, the generation AI can prioritize short, to-the-point questions. This allows the lesson to proceed smoothly by determining the order of questions according to the teacher's emotions.

[0075] When asking questions, the checking unit can customize the content of the questions based on the teacher's area of ​​expertise. For example, the checking unit allows the generation AI to prioritize generating questions related to the teacher's area of ​​expertise. The checking unit can also allow the generation AI to generate detailed questions that utilize the teacher's specialized knowledge. Furthermore, the checking unit can allow the generation AI to generate questions that are likely to interest students based on the teacher's area of ​​expertise. This improves the quality of lessons by customizing the content of questions based on the teacher's area of ​​expertise.

[0076] When asking a question, the checking unit can adjust the difficulty of the question by referring to the child's past grades. For example, the generation AI generates questions of an appropriate level of difficulty based on the child's past grades. The checking unit can also generate more difficult questions for children with good grades and basic questions for children with poor grades. Furthermore, the checking unit can also generate questions of gradually increasing difficulty according to the generation AI's grades. This makes it possible to generate questions of an appropriate level of difficulty by referring to the child's past grades.

[0077] The demonstration section can estimate the child's emotions and adjust the speed of the demonstration based on the estimated child's emotions. For example, if the child is nervous, the generation AI can proceed with the demonstration at a slow pace. Alternatively, if the child is relaxed, the generation AI can proceed with the demonstration at a normal pace. Furthermore, if the child is excited, the generation AI can perform a fast-paced demonstration to keep the child interested. In this way, adjusting the speed of the demonstration according to the child's emotions deepens the child's understanding.

[0078] During the demonstration, the demonstration department can refer to past threat data to enhance the realism of the demonstration. For example, in the demonstration department, the generating AI generates realistic scenarios based on actual threat data from the past. The demonstration department can also have the generating AI analyze past threat data and propose countermeasures that are in line with reality. Furthermore, the demonstration department can have the generating AI refer to past threat data to make the content of the demonstration more specific and detailed. In this way, by referencing past threat data, the realism of the demonstration is enhanced and children's understanding is deepened.

[0079] During the demonstration, the demonstration unit can adjust the level of detail of the demonstration based on the child's level of understanding. For example, if the generating AI determines that the child has a high level of understanding, the demonstration unit will perform a detailed and complex demonstration. In addition, if the generating AI determines that the child has a low level of understanding, the demonstration unit can perform a basic demonstration to deepen understanding. Furthermore, the demonstration unit can also perform demonstrations in which the generating AI gradually increases the level of detail depending on the child's level of understanding. In this way, adjusting the level of detail of the demonstration according to the child's level of understanding deepens the child's understanding.

[0080] The demonstration section can estimate the child's emotions and determine the order of demonstrations based on the estimated child's emotions. For example, if the child is nervous, the generation AI can start with an easy demonstration and gradually increase the difficulty. Also, if the child is relaxed, the generation AI can start with a more difficult demonstration to encourage deeper understanding. Furthermore, if the child is excited, the generation AI can prioritize visually stimulating demonstrations. In this way, determining the order of demonstrations according to the child's emotions deepens the child's understanding.

[0081] The demonstration unit can customize the content of the demonstration based on the child's interests during the demonstration. For example, the generation AI prioritizes demonstrations related to the child's interests. The demonstration unit can also provide visually appealing demonstrations to attract the child's attention. Furthermore, the generation AI can make the content of the demonstration specific and detailed based on the child's areas of interest. This allows the content of the demonstration to be customized based on the child's interests, thereby deepening the child's understanding.

[0082] During the demonstration, the content of the demonstration can be linked to other subjects. For example, the generative AI can demonstrate cybersecurity by linking it to an information technology class. Alternatively, the generative AI can demonstrate threat detection by linking it to a social studies class. Furthermore, the generative AI can link the content of the demonstration to topics in other subjects to promote students' comprehensive understanding. This allows students to deepen their comprehensive understanding by linking the content of the demonstration to other subjects.

[0083] The suggestion unit can estimate the child's emotions and adjust the method of proposing countermeasures based on the estimated child's emotions. For example, if the child is nervous, the generation AI can suggest simple, highly visible countermeasures. Also, if the child is relaxed, the suggestion unit can suggest countermeasures that include detailed information. Furthermore, if the child is in a hurry, the suggestion unit can suggest countermeasures that focus on the main points. This makes it possible to suggest more appropriate countermeasures by adjusting the method of proposing countermeasures according to the child's emotions.

[0084] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past countermeasure data. For example, the proposal unit allows the generation AI to propose optimal countermeasures based on past countermeasure data. The proposal unit can also allow the generation AI to analyze past countermeasure data and propose effective countermeasures. Furthermore, the proposal unit can allow the generation AI to propose specific and detailed countermeasures by referring to past countermeasure data. In this way, the accuracy of the proposal is improved by referring to past countermeasure data.

[0085] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the child's level of understanding. For example, if the generating AI determines that the child has a high level of understanding, the suggestion unit can suggest detailed and complex measures. In addition, if the generating AI determines that the child has a low level of understanding, the suggestion unit can also suggest basic measures to deepen the child's understanding. Furthermore, the suggestion unit can also suggest measures that gradually increase in detail depending on the generating AI's level of understanding. In this way, adjusting the level of detail of the proposal according to the child's level of understanding deepens the child's understanding.

[0086] The suggestion unit can estimate the child's emotions and prioritize countermeasures based on the estimated child's emotions. For example, if the child is nervous, the suggestion unit's generation AI can prioritize suggesting simple and easy-to-implement countermeasures. In addition, if the child is relaxed, the suggestion unit's generation AI can prioritize suggesting detailed and complex countermeasures. Furthermore, if the child is in a hurry, the suggestion unit's generation AI can prioritize suggesting countermeasures that focus on the main points. In this way, by prioritizing countermeasures according to the child's emotions, more appropriate countermeasures can be proposed.

[0087] When proposing, the suggestion unit can customize the content of the measures based on the child's living environment. For example, the suggestion unit allows the generation AI to suggest feasible measures based on the child's home environment. The suggestion unit can also allow the generation AI to suggest appropriate measures based on the child's school environment. Furthermore, the suggestion unit can allow the generation AI to suggest specific and detailed measures based on the child's local environment. In this way, by customizing the content of the measures based on the child's living environment, more feasible measures can be proposed.

[0088] When proposing, the suggestion unit can associate the content of the countermeasures with other subjects. For example, the suggestion unit's generation AI can propose cybersecurity countermeasures by associating them with information technology lessons. The suggestion unit can also propose threat detection countermeasures by associating them with social studies lessons. Furthermore, the suggestion unit's generation AI can associate the content of the countermeasures with topics in other subjects to encourage students' comprehensive understanding. In this way, by associating the content of the countermeasures with other subjects, students' comprehensive understanding will be deepened.

[0089] The composition unit can estimate the child's emotions and adjust the style of the composition based on the estimated child's emotions. For example, if the child is relaxed, the generation AI can compose a song with a calm melody. If the child is excited, the composition unit can also compose a song with a fast tempo. Furthermore, if the child is sad, the generation AI can compose a song that expresses that emotion. In this way, by adjusting the composition style according to the child's emotions, it is possible to compose a song that matches the child's emotions.

[0090] When composing, the composition unit can improve the accuracy of the composition by referring to past composition data. For example, the composition unit allows the generation AI to generate an optimal melody based on past composition data. The composition unit can also allow the generation AI to analyze past composition data and generate effective chords. Furthermore, the composition unit can allow the generation AI to generate specific and detailed musical scores by referring to past composition data. In this way, the accuracy of the composition is improved by referring to past composition data.

[0091] When composing, the composition unit can adjust the level of detail of the composition based on the child's musical background. For example, the composition unit generates detailed and complex scores for children with a rich musical background through the generation AI. The composition unit can also generate basic scores for children with little musical background to deepen their understanding. Furthermore, the composition unit can generate scores with gradually increasing levels of detail according to the child's musical background through the generation AI. This allows the child's understanding to be deepened by adjusting the level of detail of the composition according to their musical background.

[0092] The composition unit can estimate the child's emotions and determine the priority of composition based on the estimated child's emotions. For example, if the child is relaxed, the generation AI can prioritize composing songs with calm melodies. Also, if the child is excited, the composition unit can also prioritize composing songs with fast tempos. Furthermore, if the child is sad, the generation AI can also prioritize composing songs that express emotions. In this way, by determining the priority of composition according to the child's emotions, it is possible to prioritize composing songs that match the child's emotions.

[0093] The composition unit can customize the content of the composition based on the child's musical preferences when composing. For example, the composition unit uses a generation AI to compose music based on the child's favorite musical genre. The composition unit can also use the generation AI to compose a song using the child's favorite instrument. Furthermore, the composition unit can also use the generation AI to generate specific and detailed sheet music based on the child's musical preferences. This allows the composition to be tailored to the content of the composition based on the child's musical preferences, making it possible to compose a song that is likely to attract the child's interest.

[0094] When composing, the composition department can associate the content of the composition with other subjects. For example, the composition department's generative AI can compose music by associating it with a history class. The composition department's generative AI can also compose music by associating it with a literature class. Furthermore, the composition department's generative AI can associate the content of the composition with topics in other subjects to encourage children's comprehensive understanding. In this way, by associating the content of the composition with other subjects, children's comprehensive understanding will be deepened.

[0095] The providing unit can estimate the child's emotions and adjust the way the score is provided based on the estimated child's emotions. For example, if the child is relaxed, the generating AI can provide detailed score. If the child is nervous, the providing unit can also provide simple, highly visible score. Furthermore, if the child is in a hurry, the providing unit can also provide score that focuses on the main points. In this way, adjusting the way the score is provided based on the child's emotions makes it easier for the child to understand the score.

[0096] When providing the musical score, the providing unit can improve the accuracy of the provision by referring to the previously provided data. For example, the providing unit allows the generation AI to provide an optimal musical score based on the previously provided data. The providing unit can also allow the generation AI to analyze the previously provided data and provide an effective musical score. Furthermore, the providing unit can allow the generation AI to provide a specific and detailed musical score by referring to the previously provided data. In this way, the accuracy of the musical score provision is improved by referring to the previously provided data.

[0097] When providing the sheet music, the providing unit can adjust the level of detail provided based on the child's musical background. For example, the providing unit may provide detailed and complex sheet music to children with a rich musical background. The providing unit may also provide basic sheet music to children with little musical background to deepen their understanding. Furthermore, the providing unit may provide sheet music with gradually increasing levels of detail to the generating AI according to the child's musical background. This allows the child's understanding to be deepened by adjusting the level of detail provided in the sheet music according to the child's musical background.

[0098] The providing unit can estimate the child's emotions and determine the priority of musical scores based on the estimated child's emotions. For example, if the child is relaxed, the providing unit can cause the generation AI to preferentially provide musical scores with calm melodies. Also, if the child is excited, the providing unit can cause the generation AI to preferentially provide musical scores with fast tempos. Furthermore, if the child is sad, the providing unit can cause the generation AI to preferentially provide musical scores that express emotions. In this way, by determining the priority of musical scores according to the child's emotions, musical scores that match the child's emotions can be preferentially provided.

[0099] When providing the sheet music, the providing unit can customize the content of the sheet music based on the child's musical preferences. For example, the providing unit can provide sheet music based on the child's favorite musical genre using the generation AI. The providing unit can also provide sheet music using the child's favorite instrument using the generation AI. Furthermore, the providing unit can provide specific and detailed sheet music based on the child's musical preferences using the generation AI. In this way, by customizing the content of the sheet music based on the child's musical preferences, it is possible to provide sheet music that is likely to attract the child's interest.

[0100] When providing the sheet music, the providing unit can associate the content of the sheet music with other subjects. For example, the providing unit can have the generating AI provide a music score by associating it with a history lesson. The providing unit can also have the generating AI provide a music score by associating it with a literature lesson. Furthermore, the providing unit can have the generating AI associate the content of the sheet music with topics in other subjects to encourage children's comprehensive understanding. In this way, associating the content of the sheet music with other subjects deepens children's comprehensive understanding. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned checking unit, performance unit, suggestion unit, composition unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the checking unit is realized by the control unit 46A of the smart device 14 and asks questions to the generation AI in advance and checks the answers. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses the generation AI to demonstrate cybersecurity and threat detection. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and provides the child with countermeasures proposed by the generation AI. The composition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inputs composition instructions to the generation AI, which then composes the music. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the child with the musical score composed by the generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned checking unit, performance unit, suggestion unit, composition unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the checking unit is realized by the control unit 46A of the smart glasses 214 and asks questions to the generation AI in advance and checks the answers. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses the generation AI to demonstrate cybersecurity and threat detection. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the child with countermeasures proposed by the generation AI. The composition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inputs composition instructions to the generation AI, which then composes the music. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the child with the musical score composed by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned checking unit, performance unit, suggestion unit, composition unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the checking unit is realized by the control unit 46A of the headset-type terminal 314 and asks questions to the generation AI in advance and checks the answers. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses the generation AI to demonstrate cybersecurity and threat detection. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the child with countermeasures proposed by the generation AI. The composition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inputs composition instructions to the generation AI, which then composes the music. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the child with the musical score composed by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned checking unit, performance unit, suggestion unit, composition unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the checking unit is realized by the control unit 46A of the robot 414 and asks questions to the generation AI in advance and checks the answers. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses the generation AI to demonstrate cybersecurity and threat detection. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and provides the child with countermeasures proposed by the generation AI. The composition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inputs composition instructions to the generation AI, which then composes the music. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the child with the musical score composed by the generation AI.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The educational support system can further include a feedback unit. The feedback unit provides real-time feedback to students based on their answers and actions during class. For example, if a student correctly answers a question about phishing attacks, the feedback unit can provide positive feedback such as, "That's right! Well done!". If a student gives an incorrect answer, the feedback unit can also provide constructive feedback such as, "That was close. Please try again." Furthermore, the feedback unit can analyze the student's answer history and provide advice to improve their understanding in specific areas. This allows students to check their level of understanding in real time and increase their motivation to learn.

[0103] The education support system can further include a progress management unit. The progress management unit records the learning progress of students and reports it to teachers and parents. For example, the progress management unit visualizes the level of understanding of each topic by the student and displays it in graphs and charts. The progress management unit can also identify areas in which the student is struggling with a particular topic and provide additional learning resources. Furthermore, the progress management unit can suggest the next topic to study based on the student's learning progress. This makes it possible to efficiently manage students' learning progress and respond to individual learning needs.

[0104] The educational support system may further include a collaborative learning unit. The collaborative learning unit provides a function for students to cooperate with each other in their learning. For example, the collaborative learning unit may provide a forum for group discussions, encouraging students to work together to solve cybersecurity problems. The collaborative learning unit may also provide a platform for students to provide feedback to each other. Furthermore, the collaborative learning unit may provide tools for students to work together on projects, fostering teamwork. This allows students to learn collaboratively and gain a deeper understanding.

[0105] The education support system can further include a motivation unit. The motivation unit provides a function to increase a child's motivation to learn. For example, the motivation unit may award badges or points when a child achieves a specific goal. The motivation unit may also visualize the results achieved by a child as he or she progresses with his or her studies, allowing the child to feel a sense of progress. Furthermore, the motivation unit may display encouraging messages or animations to help the child have positive feelings about learning. This makes it easier for the child to maintain his or her motivation to learn.

[0106] The educational support system may further include a customization unit. The customization unit customizes the learning content based on the individual learning style and interests of each child. For example, if a child prefers visual learning, the customization unit may provide learning materials that make extensive use of visual aids. If a child prefers auditory learning, the customization unit may also provide audio learning materials. Furthermore, the customization unit may provide learning materials that incorporate relevant topics and examples based on the child's interests. This allows each child to progress in their learning in a way that suits them best.

[0107] The education support system may further include an emotion analysis unit. The emotion analysis unit analyzes a child's facial expressions and tone of voice to estimate the child's current emotional state. For example, if the emotion analysis unit detects that the child is anxious, the system may provide the child with advice to relax. If the child is excited, the emotion analysis unit may also provide the child with advice to improve concentration. If the child is tired, the emotion analysis unit may also suggest that the child take a break. This makes it possible to provide support according to the child's emotional state.

[0108] The educational support system may further include a resource management unit. The resource management unit efficiently manages and provides resources necessary for learning. For example, the resource management unit automatically collects and organizes teaching materials and reference materials needed by students. The resource management unit may also provide appropriate resources when a student requests additional learning resources on a specific topic. Furthermore, the resource management unit may manage devices and software used by students for learning and optimize the learning environment. This allows students to efficiently use the resources they need.

[0109] The educational support system can further include an evaluation unit. The evaluation unit evaluates the learning outcomes of students and provides feedback. For example, the evaluation unit analyzes the results of tests and assignments taken by students and assigns grades. The evaluation unit can also provide individual feedback based on the students' learning progress. Furthermore, the evaluation unit can identify the students' strengths and weaknesses and propose future learning plans. This allows students to objectively understand their learning outcomes and obtain guidelines for moving on to the next step.

[0110] The educational support system may further include an interactive section. The interactive section helps students learn through interactive experiences during their studies. For example, the interactive section may enable students to learn through cybersecurity simulation games. The interactive section may also enable students to experience real cyberattack scenarios using virtual reality (VR). Furthermore, the interactive section may provide an interactive platform for students to collaborate with other students to solve problems. This allows students to learn through practical experiences.

[0111] The education support system can further include an emotion sharing unit. The emotion sharing unit provides a function that enables students to share their emotions with other students and teachers. For example, the emotion sharing unit may provide a bulletin board where students can anonymously post what they felt during class. The emotion sharing unit may also provide a chat function where students can express their emotions using emojis and stamps. Furthermore, the emotion sharing unit may provide tools that enable teachers to understand students' emotions and provide appropriate support. This makes it easier for students to express their emotions and facilitates communication with teachers and other students.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The checking unit asks the generating AI questions in advance and checks the answers. For example, the checking unit asks the generating AI questions related to cybersecurity and threat detection, and verifies that the generating AI generates appropriate answers. Specifically, the checking unit asks the question, "What is a phishing attack?" and verifies that the generating AI answers, "A phishing attack is a method of stealing personal information using fake websites or emails." Step 2: The demonstration team will demonstrate cybersecurity and threat detection using generative AI. For example, they will demonstrate how generative AI can detect "this email may be a phishing attack" and suggest countermeasures such as "do not click on the link." Step 3: The suggestion unit provides the child with the countermeasures proposed by the generation AI. For example, the suggestion unit provides the child with the countermeasure "Do not click on the link." Step 4: The composition department inputs composition instructions into the generation AI, which then composes the music. For example, instructions might be given such as "Compose a string quartet piece in C major in under three minutes" or "Compose a four-minute lullaby in the Okinawan scale," and the generation AI then composes the music. Step 5: The providing unit provides the child with the musical score composed by the generation AI. For example, the score for a "string quartet piece in C major" is provided to the child.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A check unit that asks questions to the generated AI in advance and checks the answers; A demonstration unit that uses generative AI during classes to demonstrate cybersecurity and threat detection based on the questions and answers checked by the checking unit; a suggestion section that suggests countermeasures to children based on the content demonstrated by the demonstration section; a composition unit that inputs composition instructions to the generation AI and assists in composition; a providing unit that provides the musical score composed by the composing unit; A system characterized by:

2. The checking unit Ask the Generative AI questions related to cybersecurity and threat detection, and the Generative AI generates answers.

2. The system of claim 1.

3. The demonstration section: Use generative AI to detect threats and propose countermeasures 2. The system of claim 1.

4. The proposal unit Providing children with measures suggested by the generative AI 2. The system of claim 1.

5. The providing unit Providing children with sheet music composed by generative AI 2. The system of claim 1.

6. The checking unit Estimate the teacher's emotions and adjust the content and difficulty of questions based on the estimated teacher's emotions 2. The system of claim 1.

7. The checking unit When asking questions, refer to previous lessons to make the questions more relevant 2. The system of claim 1.

8. The checking unit When conducting questions, adjust the level of detail of the questions based on the student's level of understanding.

2. The system of claim 1.

9. The checking unit Estimate teacher sentiment and determine question order based on estimated teacher sentiment 2. The system of claim 1.

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