System

Abstract

An object of the system according to the embodiment is to enable elementary school students to enjoy and effectively learn basic concepts of economy.SOLUTION: A system includes an explanation unit, a game unit, and an optimization unit. The explanation section explains economic concepts such as the value of money, income and expenditure, savings and investment, and entrepreneur spirit to elementary school children. The game unit provides an environment in which elementary school students can actively participate in learning through a quiz or a simulation game. The optimization unit optimizes the learning content and the degree of difficulty in accordance with the degree of understanding and interest of each elementary school student.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 made it difficult for elementary school students to learn basic economic concepts in an enjoyable and effective way.

[0005] The system according to the embodiment aims to enable elementary school students to learn basic economic concepts in a fun and effective way. [Means for solving the problem]

[0006] The system according to the embodiment includes an explanation unit, a game unit, and an optimization unit. The explanation unit explains economic concepts such as the value of money, income and expenditure, savings and investment, and entrepreneurship to elementary school students. The game unit provides an environment in which elementary school students can actively participate in learning through quizzes or simulation games. The optimization unit optimizes the learning content and difficulty level according to the level of understanding and interests of each elementary school student. [Effects of the Invention]

[0007] The system according to the embodiment allows elementary school students to learn basic economic concepts in a fun and effective way. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online learning service according to an embodiment of the present invention is an AI-based learning service that allows elementary school students to learn about money and economics in a fun way. This learning service uses AI characters to provide economic education tailored to each student's level of understanding. This allows elementary school students to acquire economic literacy, contributing to their future financial independence and the development of an entrepreneurial mindset.

[0029] An online learning service according to an embodiment includes an explanation unit, a game unit, and an optimization unit. The explanation unit uses an AI character to explain economic concepts such as the value of money, income and expenditure, savings and investment, and entrepreneurship to elementary school students. For example, the AI ​​character poses questions such as, "Money is used to buy things, but do you know how to get it?" to pique the child's interest. The generation AI receives prompts based on the child's level of understanding and interest and generates appropriate explanations based on those prompts. The game unit provides an environment in which elementary school students can actively participate in learning through quizzes and simulation games. For example, in a virtual store management game, children can naturally understand how the economy works while experiencing things like purchasing products, setting prices, and advertising. The generation AI analyzes the game progress and the child's responses and provides appropriate feedback and advice. The optimization unit optimizes the learning content and difficulty level to suit each elementary school student's level of understanding and interest. For example, if a child shows interest in savings, the generation AI explains the importance and methods of saving to that child in detail. The difficulty level is also adjusted to allow children to progress smoothly. As a result, the online learning service according to this embodiment allows elementary school students to learn basic economic concepts in a fun way. For example, the AI ​​character carefully answers the students' questions and helps them solidify their knowledge by linking it to real-life economic experiences. Parents are provided with an analysis report of their children's learning progress and level of understanding, supporting communication and economic education at home. Parents themselves can also review basic economic concepts through dialogue with the AI ​​character and use this knowledge in their conversations with their children.

[0030] The explanation unit can generate scenarios incorporating specific examples from a child's daily life to explain economic concepts. For example, an AI character can generate scenarios incorporating specific examples from a child's daily life to explain economic concepts. For example, the unit can use the example of a child's pocket money to explain the value of money and how to use it. Specifically, it can use a scenario such as, "What can you do to save your pocket money and buy a new toy?" This makes it easier for children to understand economic concepts by relating them to their daily lives.

[0031] The explanation unit can provide customized explanations of economic concepts according to each child's individual level of understanding, based on the child's past learning history. For example, the explanation unit can provide customized explanations of economic concepts according to each child's individual level of understanding, based on the child's past learning history. For example, it can introduce new concepts while reviewing what was previously learned. Specifically, it can explain in the form of, "Do you remember how to save money that we learned last time? This time, let's learn how to increase that savings." This makes it possible to provide explanations of economic concepts tailored to each child.

[0032] The game section can provide a realistic economic experience by simulating how in-game choices or actions affect the real-world economic situation. The game section can provide a realistic economic experience by simulating how in-game choices or actions affect the real-world economic situation. For example, it can simulate the impact that product pricing has on sales. Specifically, learning can take the form of "Let's simulate how sales change when the price of a product is raised." This allows children to learn through realistic economic experiences.

[0033] The game club can analyze data on children's behavior in the game and provide feedback tailored to each child's individual learning style. For example, the game club can analyze data on children's behavior in the game and provide feedback tailored to each child's individual learning style. For example, it can provide appropriate advice based on the choices and actions made in the game. Specifically, it can provide feedback in the form of, "The pricing of the product is going well. Let's think about advertising next." This makes it possible to provide feedback tailored to each child.

[0034] The optimization unit can monitor a child's learning progress in real time and adjust the difficulty level at the appropriate time. The optimization unit, for example, monitors a child's learning progress in real time and adjusts the difficulty level at the appropriate time. For example, if the child's learning progress is fast, the difficulty level is increased, and if the child's learning progress is slow, the difficulty level is decreased. Specifically, the optimization unit adjusts the difficulty level in the following way: "The child's learning is progressing smoothly, so let's make the next assignment a little more difficult." This makes it possible to adjust the difficulty level according to the child's learning progress.

[0035] The optimization unit divides learning content into small steps and gradually increases the difficulty level, allowing children to progress through their studies without straining themselves. For example, the optimization unit divides learning content into small steps and gradually increases the difficulty level, allowing children to progress through their studies without straining themselves. For example, starting with basic concepts and gradually moving on to more complex content. Specifically, the optimization unit could proceed in the form of "first learn about the value of money, then learn about income and expenses." This allows children to progress through their studies without straining themselves.

[0036] The optimization unit can provide content that corresponds to different learning styles, allowing children to select the most effective learning method. For example, the optimization unit provides content that corresponds to different learning styles, allowing children to select the most effective learning method. For example, visual content is provided. Specifically, learning is done in the form of "Let's draw a diagram of the flow of money." This allows children to select the most effective learning method.

[0037] The optimization unit can link learning content with other subjects to provide a comprehensive learning experience. For example, the optimization unit links learning content with other subjects to provide a comprehensive learning experience. For example, learning basic economic concepts by linking them with mathematics. Specifically, learning can be done in the form of "learning the balance between income and expenditure through financial calculations." This allows children to learn economic concepts through a comprehensive learning experience.

[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 explanation section can help children learn economic concepts related to their future dreams and goals. For example, explain economic concepts related to the field a child is interested in as a future career. Specifically, explain in the form of, "If you want to be a doctor in the future, try learning about medical expenses and hospital management." This makes it easier for children to understand economic concepts in relation to their future dreams.

[0040] The gaming club can provide cooperative games in which children work together with other players to solve economic challenges. For example, multiple children can manage a virtual town together and aim to develop it. Specifically, the game progresses in a format where children "build a town with their friends and work together to enrich the lives of its residents." This allows children to understand economic concepts while learning the importance of cooperation.

[0041] The optimization department can introduce a reward system according to the child's learning progress to increase motivation to learn. For example, a child can earn badges and points each time they complete a specific task. Specifically, rewards are provided in the form of "Once you complete the savings task, you can earn a savings master badge." This allows children to continue learning while having fun.

[0042] The gaming club can provide economic games that teach children about the impact their economic choices have on the environment and society. For example, through a game in which children build sustainable business models, they can learn about the importance of environmental protection and social contribution. Specifically, the game could be structured as "Let's create a business that incorporates recycling." This helps children understand the relationship between economic activity and social responsibility.

[0043] The optimization unit can suggest future learning plans based on a child's learning history. For example, it analyzes past learning content and progress and suggests what to learn next. Specifically, it provides a learning plan in the form of "Next, let's learn about investments." This allows children to learn economic concepts in a systematic way.

[0044] The optimization unit can suggest specific support methods to parents based on their child's learning data. For example, it analyzes a child's learning progress and level of understanding and suggests support methods at home. Specifically, it might suggest, "Since your child is learning about saving, let's make a piggy bank together." This makes it easier for parents to support their child's learning at home.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The explanation section uses an AI character to explain economic concepts such as the value of money, income and expenditure, savings and investment, and entrepreneurship to elementary school students. For example, the AI ​​character asks questions such as, "Money is used to buy things, but do you know how to get it?" to pique the child's interest. The generation AI receives prompts based on the child's level of understanding and interest, and generates appropriate explanations based on those prompts. Step 2: The game club provides an environment where elementary school students can actively participate in learning through quizzes and simulation games. For example, in a virtual store management game, students can naturally understand how the economy works by experiencing things like purchasing products, setting prices, and advertising. The generative AI analyzes the progress of the game and the children's reactions, and provides appropriate feedback and advice. Step 3: The optimization unit optimizes the learning content and difficulty level to suit each elementary school student's level of understanding and interests. For example, if a child shows interest in saving, the generation AI will explain in detail to that child the importance of saving and how to do it. The difficulty level can also be adjusted to allow the child to progress through the learning process without difficulty.

[0047] (Example 2) An online learning service according to an embodiment of the present invention is an AI-based learning service that allows elementary school students to learn about money and economics in a fun way. This learning service uses AI characters to provide economic education tailored to each student's level of understanding. This allows elementary school students to acquire economic literacy, contributing to their future financial independence and the development of an entrepreneurial mindset.

[0048] An online learning service according to an embodiment includes an explanation unit, a game unit, and an optimization unit. The explanation unit uses an AI character to explain economic concepts such as the value of money, income and expenditure, savings and investment, and entrepreneurship to elementary school students. For example, the AI ​​character poses questions such as, "Money is used to buy things, but do you know how to get it?" to pique the child's interest. The generation AI receives prompts based on the child's level of understanding and interest and generates appropriate explanations based on those prompts. The game unit provides an environment in which elementary school students can actively participate in learning through quizzes and simulation games. For example, in a virtual store management game, children can naturally understand how the economy works while experiencing things like purchasing products, setting prices, and advertising. The generation AI analyzes the game progress and the child's responses and provides appropriate feedback and advice. The optimization unit optimizes the learning content and difficulty level to suit each elementary school student's level of understanding and interest. For example, if a child shows interest in savings, the generation AI explains the importance and methods of saving to that child in detail. The difficulty level is also adjusted to allow children to progress smoothly. As a result, the online learning service according to this embodiment allows elementary school students to learn basic economic concepts in a fun way. For example, the AI ​​character carefully answers the students' questions and helps them solidify their knowledge by linking it to real-life economic experiences. Parents are provided with an analysis report of their children's learning progress and level of understanding, supporting communication and economic education at home. Parents themselves can also review basic economic concepts through dialogue with the AI ​​character and use this knowledge in their conversations with their children.

[0049] The explanation unit can generate scenarios incorporating specific examples from a child's daily life to explain economic concepts. For example, an AI character can generate scenarios incorporating specific examples from a child's daily life to explain economic concepts. For example, the unit can use the example of a child's pocket money to explain the value of money and how to use it. Specifically, it can use a scenario such as, "What can you do to save your pocket money and buy a new toy?" This makes it easier for children to understand economic concepts by relating them to their daily lives.

[0050] The explanation unit can provide customized explanations of economic concepts according to each child's individual level of understanding, based on the child's past learning history. For example, the explanation unit can provide customized explanations of economic concepts according to each child's individual level of understanding, based on the child's past learning history. For example, it can introduce new concepts while reviewing what was previously learned. Specifically, it can explain in the form of, "Do you remember how to save money that we learned last time? This time, let's learn how to increase that savings." This makes it possible to provide explanations of economic concepts tailored to each child.

[0051] The explanation unit can use the emotion estimation function to grasp a child's interests and concerns in real time and explain economic concepts accordingly. The explanation unit can, for example, use the emotion estimation function to grasp a child's interests and concerns in real time and explain economic concepts accordingly. For example, it can provide a detailed explanation for a topic that the child is interested in. Specifically, it can explain in the form of, "It seems you're interested in how to use money, so let's learn more about how to use money today." This makes it possible to explain economic concepts according to a child's interests and concerns.

[0052] The game section can provide a realistic economic experience by simulating how in-game choices or actions affect the real-world economic situation. The game section can provide a realistic economic experience by simulating how in-game choices or actions affect the real-world economic situation. For example, it can simulate the impact that product pricing has on sales. Specifically, learning can take the form of "Let's simulate how sales change when the price of a product is raised." This allows children to learn through realistic economic experiences.

[0053] The game club can analyze data on children's behavior in the game and provide feedback tailored to each child's individual learning style. For example, the game club can analyze data on children's behavior in the game and provide feedback tailored to each child's individual learning style. For example, it can provide appropriate advice based on the choices and actions made in the game. Specifically, it can provide feedback in the form of, "The pricing of the product is going well. Let's think about advertising next." This makes it possible to provide feedback tailored to each child.

[0054] The game club can use the emotion estimation function to adjust the difficulty and content of the game in real time according to the child's emotional state. For example, the game club uses the emotion estimation function to adjust the difficulty and content of the game in real time according to the child's emotional state. For example, if the child is having fun, the difficulty is increased, and if the child is feeling stressed, the difficulty is decreased. Specifically, the difficulty is adjusted in the following way: "Since the child seems to be having fun, let's make the next stage a little more difficult." This makes it possible to adjust the difficulty and content of the game according to the child's emotional state.

[0055] The optimization unit can monitor a child's learning progress in real time and adjust the difficulty level at the appropriate time. The optimization unit, for example, monitors a child's learning progress in real time and adjusts the difficulty level at the appropriate time. For example, if the child's learning progress is fast, the difficulty level is increased, and if the child's learning progress is slow, the difficulty level is decreased. Specifically, the optimization unit adjusts the difficulty level in the following way: "The child's learning is progressing smoothly, so let's make the next assignment a little more difficult." This makes it possible to adjust the difficulty level according to the child's learning progress.

[0056] The optimization unit divides learning content into small steps and gradually increases the difficulty level, allowing children to progress through their studies without straining themselves. For example, the optimization unit divides learning content into small steps and gradually increases the difficulty level, allowing children to progress through their studies without straining themselves. For example, starting with basic concepts and gradually moving on to more complex content. Specifically, the optimization unit could proceed in the form of "first learn about the value of money, then learn about income and expenses." This allows children to progress through their studies without straining themselves.

[0057] The optimization unit can use the emotion estimation function to understand a child's motivation to learn and their level of concentration, and provide learning content that matches that. The optimization unit can, for example, use the emotion estimation function to understand a child's motivation to learn and their level of concentration, and provide learning content that matches that. For example, if the child's motivation to learn is high, the difficulty level can be increased, and if the child's motivation is low, the difficulty level can be lowered. Specifically, the adjustment can be made in the form of, "Since your motivation to learn is high, let's make the next assignment a little more difficult." This makes it possible to provide learning content that matches a child's motivation to learn and their level of concentration.

[0058] The optimization unit can provide content that corresponds to different learning styles, allowing children to select the most effective learning method. For example, the optimization unit provides content that corresponds to different learning styles, allowing children to select the most effective learning method. For example, visual content is provided. Specifically, learning is done in the form of "Let's draw a diagram of the flow of money." This allows children to select the most effective learning method.

[0059] The optimization unit can link learning content with other subjects to provide a comprehensive learning experience. For example, the optimization unit links learning content with other subjects to provide a comprehensive learning experience. For example, learning basic economic concepts by linking them with mathematics. Specifically, learning can be done in the form of "learning the balance between income and expenditure through financial calculations." This allows children to learn economic concepts through a comprehensive learning experience.

[0060] The optimization unit can use the emotion estimation function to identify topics that children are most interested in and provide learning content related to those topics. For example, the optimization unit can use the emotion estimation function to identify topics that children are most interested in and provide learning content related to those topics. For example, it can provide quizzes and assignments related to topics that children have shown interest in. Specifically, it can learn in the form of, "Since you seem to be interested in how to spend money, let's give you a quiz related to that topic this time." This makes it possible to provide learning content related to topics that children are most interested in.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The explanation section can help children learn economic concepts related to their future dreams and goals. For example, explain economic concepts related to the field a child is interested in as a future career. Specifically, explain in the form of, "If you want to be a doctor in the future, try learning about medical expenses and hospital management." This makes it easier for children to understand economic concepts in relation to their future dreams.

[0063] The gaming club can provide cooperative games in which children work together with other players to solve economic challenges. For example, multiple children can manage a virtual town together and aim to develop it. Specifically, the game progresses in a format where children "build a town with their friends and work together to enrich the lives of its residents." This allows children to understand economic concepts while learning the importance of cooperation.

[0064] The optimization department can introduce a reward system according to the child's learning progress to increase motivation to learn. For example, a child can earn badges and points each time they complete a specific task. Specifically, rewards are provided in the form of "Once you complete the savings task, you can earn a savings master badge." This allows children to continue learning while having fun.

[0065] The explanation section uses emotion estimation to select words and expressions that are easy for children to understand. For example, if a child is confused by a difficult word, it will explain it in simpler terms. Specifically, it will explain in a way that says, "If the word savings is difficult for you, try using the word save." This allows children to learn economic concepts in a way that is easier to understand.

[0066] The gaming club can provide economic games that teach children about the impact their economic choices have on the environment and society. For example, through a game in which children build sustainable business models, they can learn about the importance of environmental protection and social contribution. Specifically, the game could be structured as "Let's create a business that incorporates recycling." This helps children understand the relationship between economic activity and social responsibility.

[0067] The optimization unit can suggest future learning plans based on a child's learning history. For example, it analyzes past learning content and progress and suggests what to learn next. Specifically, it provides a learning plan in the form of "Next, let's learn about investments." This allows children to learn economic concepts in a systematic way.

[0068] Using emotion estimation, the game club can help children build upon their successes in the game. For example, it can provide positive feedback when a child succeeds and send encouraging messages when they fail. Specifically, it can provide feedback in the form of "Good job! Try a more difficult challenge next time." This helps children to continue learning with confidence.

[0069] The explanation module uses emotion estimation to provide explanations using storytelling that children find interesting. For example, it can explain economic concepts using characters and stories that children find interesting. Specifically, it can explain in the form of, "Let's talk about a story about an adventurer saving money to find treasure." This allows children to learn economic concepts while engaging in the process.

[0070] The optimization unit can suggest specific support methods to parents based on their child's learning data. For example, it analyzes a child's learning progress and level of understanding and suggests support methods at home. Specifically, it might suggest, "Since your child is learning about saving, let's make a piggy bank together." This makes it easier for parents to support their child's learning at home.

[0071] The optimization unit can use the emotion estimation function to provide measures to reduce the stress and anxiety that children feel about their studies. For example, if a child is feeling stressed, it can provide relaxing content. Specifically, it provides measures such as "Take a short break and listen to some relaxing music." This allows children to continue studying without feeling stressed.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The explanation section uses an AI character to explain economic concepts such as the value of money, income and expenditure, savings and investment, and entrepreneurship to elementary school students. For example, the AI ​​character asks questions such as, "Money is used to buy things, but do you know how to get it?" to pique the child's interest. The generation AI receives prompts based on the child's level of understanding and interest, and generates appropriate explanations based on those prompts. Step 2: The game club provides an environment where elementary school students can actively participate in learning through quizzes and simulation games. For example, in a virtual store management game, students can naturally understand how the economy works by experiencing things like purchasing products, setting prices, and advertising. The generative AI analyzes the progress of the game and the children's reactions, and provides appropriate feedback and advice. Step 3: The optimization unit optimizes the learning content and difficulty level to suit each elementary school student's level of understanding and interests. For example, if a child shows interest in saving, the generation AI will explain in detail to that child the importance of saving and how to do it. The difficulty level can also be adjusted to allow the child to progress through the learning process without difficulty.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The 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.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 system that uses AI characters to provide economic education according to the level of understanding of elementary school students. The AI ​​character is: an explanation section that explains economic concepts such as the value of money, income and expenditure, savings and investment, and entrepreneurship to the elementary school students; a game club that provides an environment in which the elementary school students can actively participate in learning through quizzes or simulation games; It also has an optimization unit that optimizes the learning content and difficulty level according to the level of understanding and interests of each elementary school student. A system characterized by:

2. The explanation section Using emotion estimation function, the child's interests and concerns are grasped in real time, and the economic concepts are explained accordingly.

2. The system of claim 1.

3. The game section Provide a realistic economic experience by simulating how in-game choices or actions affect real-world economic situations 2. The system of claim 1.

4. The game section Analyze the child's in-game behavior data and provide feedback tailored to their individual learning style.

2. The system of claim 1.

5. The optimization unit Monitor the child's learning progress in real time and adjust the difficulty level at the appropriate time.

2. The system of claim 1.

6. The optimization unit Using emotion estimation function, the learning motivation and concentration of the child are grasped and the learning content is provided according to the child's motivation and concentration.

2. The system of claim 1.

7. The optimization unit Providing content that caters to different learning styles, allowing the child to choose how they learn most effectively 2. The system of claim 1.

8. The optimization unit Using emotion estimation to identify topics that interest the child most and provide the learning content related to those topics 2. The system of claim 1.

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