System

Abstract

An object of a system according to an embodiment is to provide a tool with which moral education can be easily performed even at home.SOLUTION: A system includes a teaching material generation part, a scenario provision part, and a quiz generation part. The teaching material generation unit generates a teaching material related to the moral by using the generation AI. The scenario providing unit provides a scenario that promotes a conversation between a parent and a child. The quiz generation unit generates a quiz or a game related to a moral.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 does not provide sufficient tools for effective moral education at home, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a tool that allows moral education to be easily provided at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a teaching material generation unit, a scenario providing unit, and a quiz generation unit. The teaching material generation unit generates teaching materials related to morality using a generation AI. The scenario providing unit provides a scenario that promotes dialogue between parents and children. The quiz generation unit generates a quiz or game related to morality. [Effects of the Invention]

[0007] The system according to the embodiment can provide a tool that allows moral education to be easily conducted at home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A moral education tool according to an embodiment of the present invention is a system that allows users to easily conduct moral education at home. This system uses generative AI to generate moral teaching materials and scenarios, promoting dialogue and learning between parents and children. This allows users to easily conduct moral education at home. For example, the moral education tool provides opportunities for parents and children to think deeply about morality through dialogue, and allows users to learn in a fun way through quizzes and games. Furthermore, users can deepen their understanding of morality visually by watching videos and animations.

[0029] A moral education tool according to an embodiment includes a generation AI, a teaching material generation unit, a scenario providing unit, and a quiz generation unit. The generation AI generates moral education materials based on a theme or topic input by a user. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to generate stories and questions related to the theme input by the user. The teaching material generation unit generates moral education materials using the generation AI. For example, the teaching material generation unit provides the stories and questions generated by the generation AI as teaching materials. The scenario providing unit provides a scenario that promotes parent-child dialogue using the generation AI. For example, the scenario providing unit provides a scenario that promotes parent-child discussion based on a question list generated by the generation AI. The quiz generation unit generates moral quizzes and games using the generation AI. For example, the quiz generation unit allows parents and children to deepen their understanding of morality by answering quizzes and games generated by the generation AI together. This allows the moral education tool to easily provide moral education at home. For example, it provides opportunities for parents and children to think deeply about morality through dialogue, and allows them to learn in a fun way through quizzes and games. Furthermore, watching videos and animations can visually deepen understanding of morality.

[0030] The teaching material generation unit can provide individually optimized teaching materials by reflecting the user's past learning history. For example, the generation AI in the teaching material generation unit analyzes the user's past learning history and generates teaching materials according to the user's learning progress and level of understanding. For example, it suggests the next content to learn based on the themes learned in the past and the level of understanding. The teaching material generation unit also evaluates the user's level of understanding of a specific theme based on the user's learning history and generates teaching materials according to the evaluation results. For example, it provides supplementary teaching materials for themes with a shallow understanding. The teaching material generation unit also generates teaching materials tailored to the user's interests and concerns by reflecting the learning history. For example, it provides new teaching materials related to themes that the user has shown interest in in the past. This makes it possible to provide teaching materials optimized for the user.

[0031] The teaching material generation unit can reflect the user's family structure or living environment to make the content more realistic and easy to empathize with. For example, the teaching material generation unit uses a generation AI to analyze the user's family structure and living environment and generate a story based on that. For example, it provides stories for families with siblings, or stories that suit urban and rural living environments. The teaching material generation unit also generates realistic scenarios based on information about the user's living environment. For example, it provides stories that reflect the characteristics of the school or region. The teaching material generation unit also takes into account the family structure and living environment and generates stories using characters and settings that the user can easily empathize with. For example, it provides stories featuring children of the same age. This makes it possible to provide teaching materials that the user can easily empathize with.

[0032] The teaching material generation unit can incorporate different cultural or historical backgrounds to provide opportunities to learn morality from a global perspective. For example, the generation AI of the teaching material generation unit generates teaching materials that incorporate different cultural or historical backgrounds. For example, it provides teaching materials based on moral stories and legends from around the world. The teaching material generation unit also generates teaching materials that reflect the values ​​and ethics of different cultures. For example, it provides morality teaching materials based on Asian or European culture. The teaching material generation unit also generates teaching materials based on historical events and people, providing opportunities to learn morality from a global perspective. For example, it provides teaching materials based on episodes of great historical figures. This allows morality to be learned from a global perspective.

[0033] The teaching material generation unit can provide the generated teaching materials in the form of an audiobook using voice or music, allowing for auditory learning. For example, the teaching material generation unit converts the teaching materials generated by the generation AI into audio data and provides it in audiobook format. For example, it provides a story with narration. The teaching material generation unit can also generate an audiobook that incorporates music and sound effects, allowing for auditory learning. For example, it can add music and sound effects that match the scenes in the story. The teaching material generation unit can also provide an audiobook with adjusted audio speed and tone to make it easier for the user to learn auditorily. For example, it can provide slower narration for children. This allows for auditory learning.

[0034] The scenario providing unit can provide a scenario that reflects the user's past dialogue history and promotes continuous learning. For example, the scenario providing unit uses a generation AI to analyze the user's past dialogue history and generate a new dialogue scenario based on that history. For example, it provides new questions based on themes discussed in the past. The scenario providing unit also generates a scenario that promotes continuous learning by reflecting the dialogue history. For example, it provides follow-up questions based on the content of the previous dialogue. The scenario providing unit also generates a scenario that prevents a break in the dialogue between parent and child based on the user's dialogue history. For example, it revisits an unresolved topic from a previous dialogue. In this way, it is possible to provide a scenario that promotes continuous learning.

[0035] The scenario providing unit can provide a scenario that reflects the user's interests and concerns and improves the quality of the dialogue. For example, the scenario providing unit uses a generation AI to analyze the user's interests and concerns and generate a dialogue scenario based on the results. For example, it provides questions related to themes that interest the user. The scenario providing unit also generates a scenario according to the user's interests and improves the quality of the dialogue. For example, it provides a discussion based on topics that the user has shown interest in. The scenario providing unit also generates a scenario that deepens the dialogue between parents and children by reflecting the interests and concerns. For example, it provides dialogue based on common hobbies and interests. This makes it possible to provide a scenario that improves the quality of the dialogue.

[0036] The scenario providing unit can provide the generated scenario in visual note or mind map format, making it easier to understand visually. For example, the scenario providing unit converts the dialogue scenario generated by the generation AI into a visual note and displays it visually. For example, it indicates important points with diagrams or icons. The scenario providing unit can also convert the dialogue scenario into a mind map format and visually organize related keywords and concepts. This allows the overall picture of the dialogue to be understood at a glance. The scenario providing unit can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display dialogue scenarios. For example, it can provide a function for visualizing scenarios with drag and drop. This makes it possible to provide scenarios that are visually easy to understand.

[0037] The quiz generation unit can provide individually optimized questions by reflecting the user's past answer history. For example, the quiz generation unit uses a generation AI to analyze the user's past answer history and generate new quizzes or games based on that history. For example, it provides review questions based on questions that the user got wrong in the past. The quiz generation unit also generates quizzes or games according to the user's level of understanding by reflecting the answer history. For example, it provides supplementary questions for topics that the user has a shallow understanding of. The quiz generation unit also generates quizzes or games tailored to the user's interests and concerns based on the user's answer history. For example, it provides new questions related to topics that the user has shown interest in in the past. This makes it possible to provide individually optimized questions.

[0038] The quiz generation unit can provide questions that reflect the user's interests and concerns, allowing the user to learn while having fun. For example, the quiz generation unit uses a generation AI to analyze the user's interests and concerns and generate quizzes and games based on the results. For example, it provides questions related to themes that interest the user. The quiz generation unit also generates quizzes and games that correspond to the user's interests, allowing the user to learn while having fun. For example, it provides games based on topics in which the user has shown interest. The quiz generation unit also generates quizzes and games that the user can enjoy more by reflecting the user's interests and concerns. For example, it provides questions based on common hobbies and concerns. This makes it possible to provide questions that allow the user to learn while having fun.

[0039] The quiz generation unit can generate quizzes or games that incorporate different cultural or historical backgrounds and provide an opportunity to learn morality from a global perspective. For example, the generation AI of the quiz generation unit generates quizzes or games that incorporate different cultural or historical backgrounds. For example, it provides questions based on moral stories and legends from around the world. The quiz generation unit also generates quizzes or games that reflect the values ​​and ethics of different cultures. For example, it provides questions based on Asian or European culture. The quiz generation unit also generates quizzes or games based on historical events or people, providing an opportunity to learn morality from a global perspective. For example, it provides questions based on episodes of great historical figures. This makes it possible to provide quizzes or games that allow users to learn morality from a global perspective.

[0040] The quiz generation unit can provide the generated quiz or game in a multiplayer mode so that it can be enjoyed with family or friends. The quiz generation unit, for example, provides a quiz or game generated by the generation AI in a multiplayer mode. For example, it provides a quiz in a format where family and friends can compete together. The quiz generation unit also generates quizzes or games that can be enjoyed together with family and friends by introducing a multiplayer mode. For example, it provides a game in a team competition format. The quiz generation unit also adjusts the content of the quiz or game so that it can be enjoyed together with family and friends. For example, it provides a game in a format where players solve problems cooperatively. This makes it possible to provide quizzes or games that can be enjoyed together with family and friends.

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

[0042] The moral education tool can also provide learning materials that are tailored to the user's learning style. For example, a user who prefers visual learning can be provided with learning materials that make extensive use of diagrams and illustrations. A user who prefers auditory learning can be provided with learning materials that include audio guides. Furthermore, a user who prefers tactile learning can be provided with learning materials that include interactive elements. This allows the tool to provide learning materials that are optimized for the user's learning style.

[0043] The moral education tool can also be equipped with a function to visualize the user's learning progress. For example, the learning progress can be displayed in graphs or charts. The degree of achievement toward learning goals can also be displayed in real time. Furthermore, past learning history can be displayed in a timeline format, allowing the user to see at a glance how their learning has progressed. This makes it easier for the user to understand their own learning progress.

[0044] The moral education tool can also suggest a study schedule based on the user's lifestyle. For example, it can suggest optimal study times that fit the user's daily rhythm. It can also send reminders to encourage study outside of the user's busy hours. Furthermore, it can provide mini-lessons that can be learned effectively in a short amount of time, based on the user's lifestyle. This allows the user to continue studying without straining themselves.

[0045] The moral education tool can also provide teaching materials in different languages. For example, it can provide teaching materials in multiple languages, such as English and Spanish. It can also provide teaching materials that reflect the culture and values ​​of different languages. Furthermore, it can automatically generate appropriate teaching materials according to the language selected by the user. This makes it possible to provide moral education that is tailored to users who speak different languages.

[0046] The moral education tool can also set learning goals based on the user's interests. For example, it can set learning goals related to topics that interest the user. It can also suggest learning goals according to the user's interests. Furthermore, it can display the user's progress toward the learning goals in real time. This allows the user to set learning goals based on their own interests.

[0047] The moral education tool may further include a function for sharing the user's learning results. For example, the user may share the learning goals and progress he or she has achieved with family and friends. The tool may also provide a function for sharing learning results on social networking sites. Furthermore, the tool may provide a function for sharing learning results and competing with other users. This allows users to share their learning results and increase their motivation.

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

[0049] Step 1: The teaching material generation unit uses generative AI to generate moral learning materials. Based on themes and topics input by the user, generative AI (e.g., text generation AI such as GPT-3 or BERT) is used to generate stories and questions, which are then provided as teaching materials. Step 2: The scenario provider uses the generation AI to provide a scenario that promotes dialogue between parents and children. Based on the question list generated by the generation AI, the scenario provider provides a scenario that promotes discussion between parents and children. Step 3: The quiz generation unit uses the AI ​​to generate quizzes and games related to morality. Parents and children can deepen their understanding of morality by answering the quizzes and games generated by the AI ​​together.

[0050] (Example 2) A moral education tool according to an embodiment of the present invention is a system that allows users to easily conduct moral education at home. This system uses generative AI to generate moral teaching materials and scenarios, promoting dialogue and learning between parents and children. This allows users to easily conduct moral education at home. For example, the moral education tool provides opportunities for parents and children to think deeply about morality through dialogue, and allows users to learn in a fun way through quizzes and games. Furthermore, users can deepen their understanding of morality visually by watching videos and animations.

[0051] A moral education tool according to an embodiment includes a generation AI, a teaching material generation unit, a scenario providing unit, and a quiz generation unit. The generation AI generates moral education materials based on a theme or topic input by a user. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to generate stories and questions related to the theme input by the user. The teaching material generation unit generates moral education materials using the generation AI. For example, the teaching material generation unit provides the stories and questions generated by the generation AI as teaching materials. The scenario providing unit provides a scenario that promotes parent-child dialogue using the generation AI. For example, the scenario providing unit provides a scenario that promotes parent-child discussion based on a question list generated by the generation AI. The quiz generation unit generates moral quizzes and games using the generation AI. For example, the quiz generation unit allows parents and children to deepen their understanding of morality by answering quizzes and games generated by the generation AI together. This allows the moral education tool to easily provide moral education at home. For example, it provides opportunities for parents and children to think deeply about morality through dialogue, and allows them to learn in a fun way through quizzes and games. Furthermore, watching videos and animations can visually deepen understanding of morality.

[0052] The teaching material generation unit can provide individually optimized teaching materials by reflecting the user's past learning history. For example, the generation AI in the teaching material generation unit analyzes the user's past learning history and generates teaching materials according to the user's learning progress and level of understanding. For example, it suggests the next content to learn based on the themes learned in the past and the level of understanding. The teaching material generation unit also evaluates the user's level of understanding of a specific theme based on the user's learning history and generates teaching materials according to the evaluation results. For example, it provides supplementary teaching materials for themes with a shallow understanding. The teaching material generation unit also generates teaching materials tailored to the user's interests and concerns by reflecting the learning history. For example, it provides new teaching materials related to themes that the user has shown interest in in the past. This makes it possible to provide teaching materials optimized for the user.

[0053] The teaching material generation unit can reflect the user's family structure or living environment to make the content more realistic and easy to empathize with. For example, the teaching material generation unit uses a generation AI to analyze the user's family structure and living environment and generate a story based on that. For example, it provides stories for families with siblings, or stories that suit urban and rural living environments. The teaching material generation unit also generates realistic scenarios based on information about the user's living environment. For example, it provides stories that reflect the characteristics of the school or region. The teaching material generation unit also takes into account the family structure and living environment and generates stories using characters and settings that the user can easily empathize with. For example, it provides stories featuring children of the same age. This makes it possible to provide teaching materials that the user can easily empathize with.

[0054] The teaching material generation unit uses the emotion estimation function to generate a story development that corresponds to the user's emotional state, thereby eliciting emotional empathy. The teaching material generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and adjust the story development based on the results. For example, if the user is feeling sad, the teaching material generation unit provides an encouraging story. The teaching material generation unit also changes the ending and development of the story according to the user's emotional state. For example, if the user is excited, the teaching material generation unit provides an adventurous story. The teaching material generation unit also generates a story that the user is most likely to empathize with based on the emotion estimation data. For example, the teaching material generation unit selects a theme that is likely to move the user and provides a story based on that theme. This makes it possible to provide a story that corresponds to the user's emotions.

[0055] The teaching material generation unit can incorporate different cultural or historical backgrounds to provide opportunities to learn morality from a global perspective. For example, the generation AI of the teaching material generation unit generates teaching materials that incorporate different cultural or historical backgrounds. For example, it provides teaching materials based on moral stories and legends from around the world. The teaching material generation unit also generates teaching materials that reflect the values ​​and ethics of different cultures. For example, it provides morality teaching materials based on Asian or European culture. The teaching material generation unit also generates teaching materials based on historical events and people, providing opportunities to learn morality from a global perspective. For example, it provides teaching materials based on episodes of great historical figures. This allows morality to be learned from a global perspective.

[0056] The teaching material generation unit can provide the generated teaching materials in the form of an audiobook using voice or music, allowing for auditory learning. For example, the teaching material generation unit converts the teaching materials generated by the generation AI into audio data and provides it in audiobook format. For example, it provides a story with narration. The teaching material generation unit can also generate an audiobook that incorporates music and sound effects, allowing for auditory learning. For example, it can add music and sound effects that match the scenes in the story. The teaching material generation unit can also provide an audiobook with adjusted audio speed and tone to make it easier for the user to learn auditorily. For example, it can provide slower narration for children. This allows for auditory learning.

[0057] The teaching material generation unit can use the emotion estimation function to identify the theme in which the user is most interested and generate teaching materials based on that theme. The teaching material generation unit, for example, uses the emotion estimation function to identify the theme in which the user is most interested. For example, it analyzes the user's facial expressions and voice and extracts themes that the user is interested in. The teaching material generation unit also generates teaching materials based on the themes of interest based on the user's emotional response data. For example, it selects a theme with a high number of positive emotional responses and provides teaching materials based on that theme. The teaching material generation unit also collects emotion estimation data in real time and dynamically generates teaching materials according to the user's interests. For example, it adjusts the content of the teaching materials every time the user's emotions change. This makes it possible to provide teaching materials based on the user's interests.

[0058] The scenario providing unit can provide a scenario that reflects the user's past dialogue history and promotes continuous learning. For example, the scenario providing unit uses a generation AI to analyze the user's past dialogue history and generate a new dialogue scenario based on that history. For example, it provides new questions based on themes discussed in the past. The scenario providing unit also generates a scenario that promotes continuous learning by reflecting the dialogue history. For example, it provides follow-up questions based on the content of the previous dialogue. The scenario providing unit also generates a scenario that prevents a break in the dialogue between parent and child based on the user's dialogue history. For example, it revisits an unresolved topic from a previous dialogue. In this way, it is possible to provide a scenario that promotes continuous learning.

[0059] The scenario providing unit can provide a scenario that reflects the user's interests and concerns and improves the quality of the dialogue. For example, the scenario providing unit uses a generation AI to analyze the user's interests and concerns and generate a dialogue scenario based on the results. For example, it provides questions related to themes that interest the user. The scenario providing unit also generates a scenario according to the user's interests and improves the quality of the dialogue. For example, it provides a discussion based on topics that the user has shown interest in. The scenario providing unit also generates a scenario that deepens the dialogue between parents and children by reflecting the interests and concerns. For example, it provides dialogue based on common hobbies and interests. This makes it possible to provide a scenario that improves the quality of the dialogue.

[0060] The scenario providing unit uses the emotion estimation function to generate a dialogue scenario according to the user's emotional state, thereby eliciting emotional empathy. The scenario providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and adjust the dialogue scenario based on the results. For example, if the user is feeling down, it provides encouraging questions. The scenario providing unit also generates a scenario that changes the content and tone of the dialogue according to the user's emotional state. For example, if the user is excited, it provides questions that pique the user's interest. The scenario providing unit also generates a dialogue scenario that the user is most likely to empathize with, based on the emotion estimation data. For example, it selects a theme that is likely to move the user and provides a dialogue based on that theme. This makes it possible to provide a dialogue scenario that corresponds to the user's emotions.

[0061] The scenario providing unit can provide the generated scenario in visual note or mind map format, making it easier to understand visually. For example, the scenario providing unit converts the dialogue scenario generated by the generation AI into a visual note and displays it visually. For example, it indicates important points with diagrams or icons. The scenario providing unit can also convert the dialogue scenario into a mind map format and visually organize related keywords and concepts. This allows the overall picture of the dialogue to be understood at a glance. The scenario providing unit can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display dialogue scenarios. For example, it can provide a function for visualizing scenarios with drag and drop. This makes it possible to provide scenarios that are visually easy to understand.

[0062] The scenario providing unit can use the emotion estimation function to identify the dialogue theme in which the user is most interested and generate a scenario based on that theme. The scenario providing unit, for example, uses the emotion estimation function to identify the dialogue theme in which the user is most interested. For example, it analyzes the user's facial expressions and voice and extracts themes that the user is interested in. The scenario providing unit also generates a dialogue scenario based on the theme of interest based on the user's emotional response data. For example, it selects a theme with a high number of positive emotional responses and provides a dialogue based on that theme. The scenario providing unit also collects emotion estimation data in real time and dynamically generates a dialogue scenario based on the user's interests. For example, it adjusts the content of the scenario every time the user's emotions change. This makes it possible to provide a dialogue scenario based on the user's interests.

[0063] The quiz generation unit can provide individually optimized questions by reflecting the user's past answer history. For example, the quiz generation unit uses a generation AI to analyze the user's past answer history and generate new quizzes or games based on that history. For example, it provides review questions based on questions that the user got wrong in the past. The quiz generation unit also generates quizzes or games according to the user's level of understanding by reflecting the answer history. For example, it provides supplementary questions for topics that the user has a shallow understanding of. The quiz generation unit also generates quizzes or games tailored to the user's interests and concerns based on the user's answer history. For example, it provides new questions related to topics that the user has shown interest in in the past. This makes it possible to provide individually optimized questions.

[0064] The quiz generation unit can provide questions that reflect the user's interests and concerns, allowing the user to learn while having fun. For example, the quiz generation unit uses a generation AI to analyze the user's interests and concerns and generate quizzes and games based on the results. For example, it provides questions related to themes that interest the user. The quiz generation unit also generates quizzes and games that correspond to the user's interests, allowing the user to learn while having fun. For example, it provides games based on topics in which the user has shown interest. The quiz generation unit also generates quizzes and games that the user can enjoy more by reflecting the user's interests and concerns. For example, it provides questions based on common hobbies and concerns. This makes it possible to provide questions that allow the user to learn while having fun.

[0065] The quiz generation unit uses the emotion estimation function to generate a quiz or game that corresponds to the user's emotional state, thereby eliciting emotional empathy. The quiz generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and adjusts the quiz or game based on the results. For example, if the user is feeling down, it provides encouraging questions. The quiz generation unit also changes the content and tone of the quiz or game according to the user's emotional state. For example, if the user is excited, it provides interesting questions. The quiz generation unit also generates a quiz or game that the user can most easily empathize with based on the emotion estimation data. For example, it selects a theme that is likely to move the user and provides questions based on that theme. This makes it possible to provide a quiz or game that corresponds to the user's emotions.

[0066] The quiz generation unit can generate quizzes or games that incorporate different cultural or historical backgrounds and provide an opportunity to learn morality from a global perspective. For example, the generation AI of the quiz generation unit generates quizzes or games that incorporate different cultural or historical backgrounds. For example, it provides questions based on moral stories and legends from around the world. The quiz generation unit also generates quizzes or games that reflect the values ​​and ethics of different cultures. For example, it provides questions based on Asian or European culture. The quiz generation unit also generates quizzes or games based on historical events or people, providing an opportunity to learn morality from a global perspective. For example, it provides questions based on episodes of great historical figures. This makes it possible to provide quizzes or games that allow users to learn morality from a global perspective.

[0067] The quiz generation unit can provide the generated quiz or game in a multiplayer mode so that it can be enjoyed with family or friends. The quiz generation unit, for example, provides a quiz or game generated by the generation AI in a multiplayer mode. For example, it provides a quiz in a format where family and friends can compete together. The quiz generation unit also generates quizzes or games that can be enjoyed together with family and friends by introducing a multiplayer mode. For example, it provides a game in a team competition format. The quiz generation unit also adjusts the content of the quiz or game so that it can be enjoyed together with family and friends. For example, it provides a game in a format where players solve problems cooperatively. This makes it possible to provide quizzes or games that can be enjoyed together with family and friends.

[0068] The quiz generation unit can use the emotion estimation function to identify the theme in which the user is most interested and generate a quiz or game based on that theme. The quiz generation unit, for example, uses the emotion estimation function to identify the theme in which the user is most interested. For example, it analyzes the user's facial expressions and voice to extract themes that the user is interested in. The quiz generation unit also generates quizzes or games based on the themes of interest based on the user's emotional response data. For example, it selects a theme that has a high number of positive emotional responses and provides questions based on that theme. The quiz generation unit also collects emotion estimation data in real time and dynamically generates quizzes or games based on the user's interests. For example, it adjusts the content of the questions each time the user's emotions change. This makes it possible to provide quizzes or games based on the user's interests.

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

[0070] The moral education tool can also provide learning materials that are tailored to the user's learning style. For example, a user who prefers visual learning can be provided with learning materials that make extensive use of diagrams and illustrations. A user who prefers auditory learning can be provided with learning materials that include audio guides. Furthermore, a user who prefers tactile learning can be provided with learning materials that include interactive elements. This allows the tool to provide learning materials that are optimized for the user's learning style.

[0071] The moral education tool can also be equipped with a function to visualize the user's learning progress. For example, the learning progress can be displayed in graphs or charts. The degree of achievement toward learning goals can also be displayed in real time. Furthermore, past learning history can be displayed in a timeline format, allowing the user to see at a glance how their learning has progressed. This makes it easier for the user to understand their own learning progress.

[0072] The moral education tool can also suggest a study schedule based on the user's lifestyle. For example, it can suggest optimal study times that fit the user's daily rhythm. It can also send reminders to encourage study outside of the user's busy hours. Furthermore, it can provide mini-lessons that can be learned effectively in a short amount of time, based on the user's lifestyle. This allows the user to continue studying without straining themselves.

[0073] The moral education tool can also provide feedback according to the user's emotional state. For example, if the user is feeling stressed, it can provide feedback that helps the user relax. If the user is losing motivation, it can provide an encouraging message. If the user feels a sense of accomplishment, it can provide praising feedback. In this way, it is possible to provide appropriate feedback according to the user's emotional state.

[0074] The moral education tool can also provide teaching materials in different languages. For example, it can provide teaching materials in multiple languages, such as English and Spanish. It can also provide teaching materials that reflect the culture and values ​​of different languages. Furthermore, it can automatically generate appropriate teaching materials according to the language selected by the user. This makes it possible to provide moral education that is tailored to users who speak different languages.

[0075] The moral education tool can also adjust the learning pace according to the user's emotional state. For example, if the user is tired, the learning pace can be slowed down. If the user is concentrating, the learning pace can be sped up. Furthermore, the difficulty of the learning content can be adjusted according to the user's emotional state. This makes it possible to provide a learning pace optimized for the user's emotional state.

[0076] The moral education tool can also set learning goals based on the user's interests. For example, it can set learning goals related to topics that interest the user. It can also suggest learning goals according to the user's interests. Furthermore, it can display the user's progress toward the learning goals in real time. This allows the user to set learning goals based on their own interests.

[0077] The moral education tool can further provide learning content according to the user's emotional state. For example, if the user is relaxed, learning content that helps the user to relax can be provided. Alternatively, if the user is excited, learning content that calms the user can be provided. Furthermore, the tone and style of the learning content can be adjusted according to the user's emotional state. This allows the tool to provide learning content that is optimized for the user's emotional state.

[0078] The moral education tool may further include a function for sharing the user's learning results. For example, the user may share the learning goals and progress he or she has achieved with family and friends. The tool may also provide a function for sharing learning results on social networking sites. Furthermore, the tool may provide a function for sharing learning results and competing with other users. This allows users to share their learning results and increase their motivation.

[0079] The moral education tool can further provide a learning environment that corresponds to the user's emotional state. For example, it can provide a learning environment that allows the user to relax. It can also provide a learning environment that allows the user to concentrate. Furthermore, it can adjust the music and lighting in the learning environment according to the user's emotional state. This allows it to provide a learning environment that is optimized for the user's emotional state.

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

[0081] Step 1: The teaching material generation unit uses generative AI to generate moral learning materials. Based on themes and topics input by the user, generative AI (e.g., text generation AI such as GPT-3 or BERT) is used to generate stories and questions, which are then provided as teaching materials. Step 2: The scenario provider uses the generation AI to provide a scenario that promotes dialogue between parents and children. Based on the question list generated by the generation AI, the scenario provider provides a scenario that promotes discussion between parents and children. Step 3: The quiz generation unit uses the AI ​​to generate quizzes and games related to morality. Parents and children can deepen their understanding of morality by answering the quizzes and games generated by the AI ​​together.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a teaching material generation unit that generates teaching materials related to morality using a generative AI; a scenario providing unit that provides a scenario that promotes dialogue between parents and children; a quiz generator for generating a quiz or game about morality; Equipped with A system characterized by:

2. The teaching material generation unit Provide individually optimized learning materials that reflect the user's past learning history 2. The system of claim 1.

3. The teaching material generation unit Provide opportunities to learn morality from a global perspective, incorporating different cultural or historical backgrounds.

2. The system of claim 1.

4. The scenario providing unit Providing scenarios that reflect the user's past interaction history and encourage continuous learning 2. The system of claim 1.

5. The quiz generation unit Provide individually optimized questions based on the user's past answer history 2. The system of claim 1.

6. The teaching material generation unit Generate a storyline that responds to the user's emotional state and elicits emotional empathy 2. The system of claim 1.

7. The scenario providing unit Generate dialogue scenarios based on the user's emotional state to elicit emotional empathy 2. The system of claim 1.

8. The quiz generation unit Generate quizzes or games based on the user's emotional state to elicit emotional empathy 2. The system of claim 1.

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