System

Abstract

An object of the system according to the embodiment is to provide an opportunity for equal education to children in the Slam street.SOLUTION: A system according to an embodiment includes an educational AI, terminals, and schools on a Metaverse. The educational AI analyzes the learning progress and understanding of the children and generates a customized educational program based on the analysis result. The terminals provide educational programs generated by the educational AI. The school on the Metaverse conducts education based on the education program provided to the terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has led to unequal educational opportunities, with a particular lack of education for children in slums.

[0005] The system according to the embodiment aims to provide equal educational opportunities to children in slums. [Means for solving the problem]

[0006] The system according to the embodiment includes an educational AI, a terminal, and a school on the metaverse. The educational AI analyzes children's learning progress and level of understanding and generates a customized educational program based on the analysis results. The terminal provides the educational program generated by the educational AI. The school on the metaverse provides education based on the educational program provided to the terminal. [Effects of the Invention]

[0007] The system according to the embodiment can provide equal educational opportunities to children in slums. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The education system according to the embodiment of the present invention is a system that provides high-quality education to children in slums and improves their future, thereby providing educational opportunities to children in slums, changing the consciousness of the entire society, and realizing sustainable growth.

[0029] An educational system according to an embodiment includes an educational AI, a terminal, and a school on the metaverse. The educational AI analyzes children's learning progress and level of understanding and generates a customized educational program based on the analysis results. For example, if a child does not understand basic mathematical concepts, the educational AI generates a program to carefully teach the child from the basics. The educational AI also uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate an optimal educational program based on the children's learning progress and level of understanding. The terminal is a donated tablet or PC, and provides the educational program generated by the educational AI. For example, the terminal displays the program generated by the educational AI, allowing children to follow it to advance their studies. The school on the metaverse includes virtual classrooms and virtual campuses, allowing children to receive an education without physical constraints. For example, in the school on the metaverse, children can participate in virtual classrooms and take classes provided by the educational AI. This allows the educational system according to an embodiment to provide high-quality education to children in slums and improve their future.

[0030] Educational AI can analyze not only children's learning progress but also their individual interests and hobbies, and generate customized educational programs based on that. For example, educational AI can analyze children's learning history and interests to generate individually customized educational programs. For example, if a child is interested in science, it can provide programs including science-related materials and experiments. Educational AI can also periodically conduct surveys and quizzes to analyze children's interests and generate customized educational programs based on the results. For example, if a child shows an interest in music, it can provide music-related learning content. Educational AI can also analyze children's online activities and social media data to identify their interests. For example, if a child frequently watches a particular YouTube channel, it can provide educational content related to that channel. This allows educational programs to be provided based on children's interests and hobbies.

[0031] Educational AI can generate programs that optimize learning progress by taking into account children's home environment and living situation. For example, educational AI analyzes children's home environment and living situation and customizes learning programs based on that. For example, it sets an appropriate learning pace taking into account the home's internet connection status and the parents' educational level. Educational AI also flexibly adjusts learning schedules by taking into account children's daily routines and family circumstances. For example, if a child can only study at night due to family circumstances, it provides a learning program suitable for that time. Educational AI also collects data about children's home environment and provides learning support based on that data. For example, if a family needs learning support, it provides online individualized instruction and support. This makes it possible to provide learning programs that are tailored to children's home environment and living situation.

[0032] Educational AI can incorporate creative elements such as music and art into its programs to bring out children's creativity. For example, educational AI can incorporate elements of music and art into the programs it generates to bring out children's creativity. For example, it can provide programs for math lessons using music or learning history through art. Educational AI can also suggest creative projects based on children's interests. For example, it can provide painting assignments based on children's favorite themes or assignments for children to compose their own music. Educational AI can also provide interactive art and music tools to stimulate children's creativity. For example, it can provide learning programs using digital painting tools or music creation software. This makes it possible to provide educational programs that bring out children's creativity.

[0033] Educational AI can add content to programs to promote intercultural understanding and international exchange, cultivating a global perspective in children. For example, educational AI can provide content related to the cultures and history of countries around the world to promote intercultural understanding. For example, it can generate programs to learn about the traditions and customs of different countries. Educational AI can also provide platforms for online interaction with children from other countries to promote international exchange. For example, it can provide programs for intercultural exchange through video calls and chats. Educational AI can also promote discussions on international issues and challenges to cultivate a global perspective. For example, it can provide programs to discuss environmental and human rights issues. This makes it possible to provide educational programs that cultivate a global perspective in children.

[0034] When a device is donated, the educational AI can be automatically installed and the initial settings completed. For example, a system can be built in which the educational AI is automatically installed when the device is donated. For example, when the device is connected to the internet, the educational AI is automatically downloaded and the initial settings completed. In addition, a system can be introduced in which the initial settings of devices with the educational AI installed can be remotely performed before they are distributed to children. For example, the educational AI is remotely configured at the same time as the device is donated. In addition, the educational AI can be pre-installed on donated devices so that they can be used immediately when children receive them. For example, the educational AI can be installed and the initial settings completed before the device is donated. This allows the donated devices to be ready for use immediately.

[0035] Schools in the metaverse can implement curricula and evaluation systems similar to those in real schools to manage learning progress. For example, schools in the metaverse can implement curricula similar to those in real schools to ensure that children receive a high-quality education. For example, they can provide a curriculum that includes basic subjects such as Japanese, mathematics, and science. Schools in the metaverse can also implement evaluation systems to manage children's learning progress. For example, they can create a system to evaluate learning outcomes through regular tests and assignments and record grades. Schools in the metaverse can also implement learning management systems similar to those in real schools to monitor children's learning progress in real time. For example, teachers can check children's learning status and provide support as needed. This allows schools in the metaverse to provide a high-quality education.

[0036] Schools on the metaverse can introduce virtual exchange programs that allow children from different regions and countries to interact. For example, schools on the metaverse could introduce virtual exchange programs that allow children from different regions and countries to interact. For example, they could provide programs that encourage intercultural exchange through video calls and chats. Virtual exchange programs could also provide opportunities for children to work on projects with friends from different regions and countries. For example, they could implement projects to solve problems in international teams. Furthermore, to promote intercultural understanding, schools on the metaverse could hold online events in which children from different regions and countries participate. For example, cultural festivals and sports tournaments could be held in virtual space. This could provide an environment in which children from different regions and countries can interact.

[0037] Schools in the metaverse could incorporate extracurricular activities such as sports, music, and art to provide a comprehensive education for children. For example, sports competitions and music concerts could be held in virtual space. Extracurricular activities could also provide opportunities for children to develop skills in areas of interest. For example, art classes and music lessons could be held in virtual space. Special areas for extracurricular activities could also be set up in metaverse schools, allowing children to participate freely. For example, sports gyms and art studios could be set up in virtual space. This could provide a comprehensive education for children.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The education system can also be equipped with health management functions. For example, it can monitor children's health and provide appropriate health advice. Specifically, it can collect data on children's diet and exercise and recommend a balanced diet and moderate exercise. It can also conduct regular health checks and, if necessary, coordinate with medical institutions for health management. It can also provide health-related educational content to help children develop healthy lifestyles. This can help maintain children's health and improve their learning outcomes.

[0040] Educational systems can also be equipped with career support functions. For example, they can suggest future career paths based on children's interests and aptitudes. Specifically, they can analyze children's learning histories and interests to introduce suitable occupations and further education. They can also offer work experience programs to give children the opportunity to gain experience in the workplace. They can also provide career counseling to help children think about their future. This helps children develop a clear vision for their future and work hard towards their goals.

[0041] The education system can also incorporate environmental education functions. For example, it can provide children with opportunities to learn about environmental issues. Specifically, it can provide educational materials and activities related to environmental protection to help children develop environmental awareness. It can also provide children with opportunities to participate in actual environmental protection activities, allowing them to gain practical experience. Furthermore, environmental projects can help children develop a proactive attitude toward environmental issues in their local and global communities. This can help children develop a sense of environmental responsibility and contribute to the realization of a sustainable society.

[0042] The education system can also incorporate leadership development functions. For example, it can provide programs to help children acquire leadership skills. Specifically, it can provide opportunities for children to demonstrate leadership through group work and projects. It can also provide teaching materials for learning leadership theory and practical skills. It can also provide feedback and coaching for leadership development, helping children to establish their own leadership style. This will enable children to grow as future leaders and contribute to society.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The educational AI analyzes the children's learning progress and level of understanding, and generates a customized educational program based on the analysis results. For example, if a child does not understand basic mathematical concepts, the educational AI generates a program to carefully teach the child from the basics. The educational AI also uses generative AI (for example, text generation AI or multimodal generation AI) to generate an optimal educational program based on the children's learning progress and level of understanding. Step 2: The devices are donated tablets, computers, etc., and the educational programs generated by the educational AI are provided. For example, the devices display the programs generated by the educational AI, allowing children to follow along with their studies. Step 3: Schools in the metaverse include virtual classrooms and virtual campuses where children can receive education without physical constraints. For example, a school in the metaverse could allow children to attend virtual classrooms and take lessons provided by educational AI.

[0045] (Example 2) The education system according to the embodiment of the present invention is a system that provides high-quality education to children in slums and improves their future, thereby providing educational opportunities to children in slums, changing the consciousness of the entire society, and realizing sustainable growth.

[0046] An educational system according to an embodiment includes an educational AI, a terminal, and a school on the metaverse. The educational AI analyzes children's learning progress and level of understanding and generates a customized educational program based on the analysis results. For example, if a child does not understand basic mathematical concepts, the educational AI generates a program to carefully teach the child from the basics. The educational AI also uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate an optimal educational program based on the children's learning progress and level of understanding. The terminal is a donated tablet or PC, and provides the educational program generated by the educational AI. For example, the terminal displays the program generated by the educational AI, allowing children to follow it to advance their studies. The school on the metaverse includes virtual classrooms and virtual campuses, allowing children to receive an education without physical constraints. For example, in the school on the metaverse, children can participate in virtual classrooms and take classes provided by the educational AI. This allows the educational system according to an embodiment to provide high-quality education to children in slums and improve their future.

[0047] Educational AI can analyze not only children's learning progress but also their individual interests and hobbies, and generate customized educational programs based on that. For example, educational AI can analyze children's learning history and interests to generate individually customized educational programs. For example, if a child is interested in science, it can provide programs including science-related materials and experiments. Educational AI can also periodically conduct surveys and quizzes to analyze children's interests and generate customized educational programs based on the results. For example, if a child shows an interest in music, it can provide music-related learning content. Educational AI can also analyze children's online activities and social media data to identify their interests. For example, if a child frequently watches a particular YouTube channel, it can provide educational content related to that channel. This allows educational programs to be provided based on children's interests and hobbies.

[0048] Educational AI can generate programs that optimize learning progress by taking into account children's home environment and living situation. For example, educational AI analyzes children's home environment and living situation and customizes learning programs based on that. For example, it sets an appropriate learning pace taking into account the home's internet connection status and the parents' educational level. Educational AI also flexibly adjusts learning schedules by taking into account children's daily routines and family circumstances. For example, if a child can only study at night due to family circumstances, it provides a learning program suitable for that time. Educational AI also collects data about children's home environment and provides learning support based on that data. For example, if a family needs learning support, it provides online individualized instruction and support. This makes it possible to provide learning programs that are tailored to children's home environment and living situation.

[0049] Educational AI can use its emotion estimation function to analyze children's emotions in real time while they are learning and generate educational programs to reduce stress and anxiety. For example, educational AI analyzes children's facial expressions and voices to estimate their emotions in real time while they are learning. For example, if a child is feeling stressed, it will suggest relaxing content or a break. Educational AI can also use its emotion estimation function to collect emotional data while children are learning and generate programs to reduce stress and anxiety. For example, if a child is feeling anxious, it will provide encouraging messages and positive feedback. Educational AI can also monitor children's emotional state and adjust their learning programs. For example, if a child is tired, it will suggest a short break or a refreshing activity. This makes it possible to provide educational programs that correspond to children's emotional state.

[0050] Educational AI can incorporate creative elements such as music and art into its programs to bring out children's creativity. For example, educational AI can incorporate elements of music and art into the programs it generates to bring out children's creativity. For example, it can provide programs for math lessons using music or learning history through art. Educational AI can also suggest creative projects based on children's interests. For example, it can provide painting assignments based on children's favorite themes or assignments for children to compose their own music. Educational AI can also provide interactive art and music tools to stimulate children's creativity. For example, it can provide learning programs using digital painting tools or music creation software. This makes it possible to provide educational programs that bring out children's creativity.

[0051] Educational AI can add content to programs to promote intercultural understanding and international exchange, cultivating a global perspective in children. For example, educational AI can provide content related to the cultures and history of countries around the world to promote intercultural understanding. For example, it can generate programs to learn about the traditions and customs of different countries. Educational AI can also provide platforms for online interaction with children from other countries to promote international exchange. For example, it can provide programs for intercultural exchange through video calls and chats. Educational AI can also promote discussions on international issues and challenges to cultivate a global perspective. For example, it can provide programs to discuss environmental and human rights issues. This makes it possible to provide educational programs that cultivate a global perspective in children.

[0052] Educational AI can use its emotion estimation function to generate programs incorporating gamification elements to increase children's motivation to learn. For example, educational AI can use its emotion estimation function to generate programs incorporating gamification elements to increase children's motivation to learn. For example, it can provide a system that allows children to earn points and badges according to their learning progress. Educational AI can also monitor children's emotional state and add game elements if their motivation to learn is declining. For example, it can provide programs that incorporate quiz-style questions and mini-games. Educational AI can also design reward systems to increase children's motivation to learn based on children's emotional data. For example, it can generate programs that provide virtual items or rewards when learning goals are achieved. This makes it possible to provide educational programs that increase children's motivation to learn.

[0053] When a device is donated, the educational AI can be automatically installed and the initial settings completed. For example, a system can be built in which the educational AI is automatically installed when the device is donated. For example, when the device is connected to the internet, the educational AI is automatically downloaded and the initial settings completed. In addition, a system can be introduced in which the initial settings of devices with the educational AI installed can be remotely performed before they are distributed to children. For example, the educational AI is remotely configured at the same time as the device is donated. In addition, the educational AI can be pre-installed on donated devices so that they can be used immediately when children receive them. For example, the educational AI can be installed and the initial settings completed before the device is donated. This allows the donated devices to be ready for use immediately.

[0054] Schools in the metaverse can implement curricula and evaluation systems similar to those in real schools to manage learning progress. For example, schools in the metaverse can implement curricula similar to those in real schools to ensure that children receive a high-quality education. For example, they can provide a curriculum that includes basic subjects such as Japanese, mathematics, and science. Schools in the metaverse can also implement evaluation systems to manage children's learning progress. For example, they can create a system to evaluate learning outcomes through regular tests and assignments and record grades. Schools in the metaverse can also implement learning management systems similar to those in real schools to monitor children's learning progress in real time. For example, teachers can check children's learning status and provide support as needed. This allows schools in the metaverse to provide a high-quality education.

[0055] A virtual classroom on the metaverse can monitor children's emotional states and provide appropriate support. For example, a virtual classroom on the metaverse can incorporate an emotion estimation function to monitor children's emotional states. For example, it can analyze children's facial expressions and voices to understand their emotional state in real time. Furthermore, a system can be built using the emotion estimation function to provide support according to children's emotional states. For example, if a child is feeling stressed, it can suggest relaxing content or a break. Furthermore, a virtual classroom on the metaverse can monitor children's emotional states and enable teachers to provide appropriate support. For example, it can provide individual instruction or counseling according to their emotional states. This makes it possible to provide support according to children's emotional states.

[0056] Schools on the metaverse can introduce virtual exchange programs that allow children from different regions and countries to interact. For example, schools on the metaverse could introduce virtual exchange programs that allow children from different regions and countries to interact. For example, they could provide programs that encourage intercultural exchange through video calls and chats. Virtual exchange programs could also provide opportunities for children to work on projects with friends from different regions and countries. For example, they could implement projects to solve problems in international teams. Furthermore, to promote intercultural understanding, schools on the metaverse could hold online events in which children from different regions and countries participate. For example, cultural festivals and sports tournaments could be held in virtual space. This could provide an environment in which children from different regions and countries can interact.

[0057] Schools in the metaverse could incorporate extracurricular activities such as sports, music, and art to provide a comprehensive education for children. For example, sports competitions and music concerts could be held in virtual space. Extracurricular activities could also provide opportunities for children to develop skills in areas of interest. For example, art classes and music lessons could be held in virtual space. Special areas for extracurricular activities could also be set up in metaverse schools, allowing children to participate freely. For example, sports gyms and art studios could be set up in virtual space. This could provide a comprehensive education for children.

[0058] Schools on the metaverse can analyze children's emotional states and provide feedback to maintain a positive learning environment. For example, schools on the metaverse can introduce an emotion estimation function to analyze children's emotional states. For example, they can analyze children's facial expressions and voices to understand their emotional state in real time. They can also use the emotion estimation function to build a system that provides feedback based on children's emotional states. For example, if a child is feeling stressed, they can be suggested to take a break or find content that will help them relax. Schools on the metaverse can also monitor children's emotional states and enable teachers to provide appropriate support. For example, they can provide individualized instruction or counseling based on their emotional states. This helps maintain a positive learning environment.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The education system can also be equipped with health management functions. For example, it can monitor children's health and provide appropriate health advice. Specifically, it can collect data on children's diet and exercise and recommend a balanced diet and moderate exercise. It can also conduct regular health checks and, if necessary, coordinate with medical institutions for health management. It can also provide health-related educational content to help children develop healthy lifestyles. This can help maintain children's health and improve their learning outcomes.

[0061] Educational systems can also be equipped with career support functions. For example, they can suggest future career paths based on children's interests and aptitudes. Specifically, they can analyze children's learning histories and interests to introduce suitable occupations and further education. They can also offer work experience programs to give children the opportunity to gain experience in the workplace. They can also provide career counseling to help children think about their future. This helps children develop a clear vision for their future and work hard towards their goals.

[0062] The education system can also incorporate environmental education functions. For example, it can provide children with opportunities to learn about environmental issues. Specifically, it can provide educational materials and activities related to environmental protection to help children develop environmental awareness. It can also provide children with opportunities to participate in actual environmental protection activities, allowing them to gain practical experience. Furthermore, environmental projects can help children develop a proactive attitude toward environmental issues in their local and global communities. This can help children develop a sense of environmental responsibility and contribute to the realization of a sustainable society.

[0063] The education system can also incorporate leadership development functions. For example, it can provide programs to help children acquire leadership skills. Specifically, it can provide opportunities for children to demonstrate leadership through group work and projects. It can also provide teaching materials for learning leadership theory and practical skills. It can also provide feedback and coaching for leadership development, helping children to establish their own leadership style. This will enable children to grow as future leaders and contribute to society.

[0064] Educational systems can also be equipped with mental health support functions. For example, they can monitor children's mental health status and provide appropriate support. Specifically, they can provide relaxation programs and counseling to reduce children's stress and anxiety. They can also provide educational content on mental health so children can learn how to manage their emotions and stress. Furthermore, if children experience mental health problems, they can be supported by connecting with professional counselors and medical institutions. This can help maintain children's mental health and improve their learning outcomes.

[0065] The education system can also use emotion estimation to provide customized feedback tailored to children's learning styles. For example, it can analyze children's emotional state and provide feedback that will motivate them to learn. Specifically, it can provide positive feedback that makes children feel a sense of accomplishment or encouraging messages that will motivate them to take on new challenges. It can also adjust the difficulty and content of learning programs according to children's emotional state. Furthermore, if children are feeling stressed, the emotion estimation function can suggest relaxing content or breaks. This can increase children's motivation to learn and support effective learning.

[0066] The education system can further use the emotion estimation function to provide programs to improve children's social skills. For example, it can analyze children's emotional states and provide feedback to improve their communication and cooperation skills. Specifically, it can provide programs that teach children how to appropriately express and understand emotions through dialogue and cooperation with others. It can also use the emotion estimation function to suggest relaxation and stress management methods when children are feeling stressed. It can also provide projects to help children develop a proactive attitude toward social issues, thereby improving their social skills and building healthy relationships.

[0067] The education system can also use the emotion estimation function to make suggestions to optimize children's learning environments. For example, it can analyze children's emotional state and suggest environments suitable for learning. Specifically, it can suggest environments where children can easily concentrate or relax. The emotion estimation function can also be used to provide advice on adjusting the learning environment if children are feeling stressed. Furthermore, it can provide tools and resources to customize the learning environment according to the children's emotional state. This allows children to learn in the optimal learning environment, improving their learning effectiveness.

[0068] The education system can further use the emotion estimation function to provide new methods for evaluating children's learning outcomes. For example, it can analyze children's emotional states and provide indicators for evaluating their learning progress and outcomes. Specifically, it can reflect the sense of accomplishment and satisfaction that children feel while learning in the evaluation. It can also use the emotion estimation function to evaluate children's learning enthusiasm and motivation. Furthermore, it can provide feedback for evaluating learning outcomes based on the children's emotional states. This allows for more accurate evaluation of children's learning outcomes and more effective feedback.

[0069] The education system can further use emotion estimation to analyze children's emotions in real time while they are learning and generate educational programs to reduce stress and anxiety. For example, it can analyze children's facial expressions and voices to estimate their emotions in real time while they are learning. For example, if a child is feeling stressed, it can suggest relaxing content or a break. The education AI can also use emotion estimation to collect emotional data while children are learning and generate programs to reduce stress and anxiety. For example, if a child is feeling anxious, it can provide encouraging messages or positive feedback. The education AI can also monitor children's emotional state and adjust the learning program. For example, if a child is tired, it can suggest a short break or a refreshing activity. This makes it possible to provide educational programs that correspond to children's emotional state.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The educational AI analyzes the children's learning progress and level of understanding, and generates a customized educational program based on the analysis results. For example, if a child does not understand basic mathematical concepts, the educational AI generates a program to carefully teach the child from the basics. The educational AI also uses generative AI (for example, text generation AI or multimodal generation AI) to generate an optimal educational program based on the children's learning progress and level of understanding. Step 2: The devices are donated tablets, computers, etc., and the educational programs generated by the educational AI are provided. For example, the devices display the programs generated by the educational AI, allowing children to follow along with their studies. Step 3: Schools in the metaverse include virtual classrooms and virtual campuses where children can receive education without physical constraints. For example, a school in the metaverse could allow children to attend virtual classrooms and take lessons provided by educational AI.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 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. Educational AI and A terminal and With a school on the metaverse, The educational AI is Analyze children's learning progress and level of understanding, and create customized educational programs based on the results of the analysis. providing it to the terminal; Education will be provided at a school on the metaverse. A system characterized by:

2. The educational AI is Analyzing the children's learning progress as well as their individual interests and concerns, and creating the customized educational program based on that.

2. The system of claim 1.

3. The educational AI is Create a program to optimize learning progress, taking into account the children's home environment and living situation.

2. The system of claim 1.

4. The educational AI is Analyzing the emotions of the children during learning in real time and generating the educational program to reduce stress and anxiety 2. The system of claim 1.

5. The educational AI is Incorporating creative elements such as music and art into the program to bring out the creativity of the children 2. The system of claim 1.

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