System

Abstract

An object of the system according to the embodiment is to effectively convey morally contents that a parent wants to teach a child.SOLUTION: A system includes a reception unit, a generation unit, a feedback unit, and a language conversion unit. The reception unit receives an input of a theme, a character, and a role. The generation unit analyzes the information received by the reception unit and generates a storyboard and a scenario according to the theme. The feedback unit extracts and feeds back a moral message obtained by the child based on the storyboard and the scenario generated by the generation unit. The language conversion unit converts the generated comic into a plurality of languages.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 limited means for parents to effectively convey the moral lessons they want to teach their children, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively convey the moral content that parents want to teach their children. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a feedback unit, and a language conversion unit. The reception unit receives input of the theme, characters, and roles. The generation unit analyzes the information received by the reception unit and generates a storyboard and scenario according to the theme. The feedback unit extracts and feeds back moral messages that children should understand based on the storyboard and scenario generated by the generation unit. The language conversion unit converts the generated manga into multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment can effectively convey the moral content that parents want to teach their children. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention allows parents to easily create manga manga about moral lessons they want to teach their children. In this system, the user selects the theme, characters, and roles they want to teach their child. The AI ​​then generates storyboards and scenarios with multiple endings based on each theme. The moral message children receive varies depending on the story's choices and outcomes, allowing them to enjoy the story while learning moral judgment. The system can also extract what the child learned from the generated results and provide feedback. Furthermore, the generated manga is available in multiple languages, making it accessible to both Japanese and foreign users. This allows the system to easily create manga about moral lessons parents want to teach their children. For example, a user can select a theme such as "cooperation with friends" or "honesty" and assign characters such as "Child A," "Child B," and "Teacher." This information is input into the AI. The AI ​​then analyzes the input information and generates storyboards and scenarios with multiple endings based on each theme. For example, if the theme is "cooperation with friends," a story with multiple endings, such as a successful ending resulting from cooperation or a failed ending resulting from non-cooperation, is generated. The moral message children receive varies depending on the choices and outcomes of the generated story. For example, if a child chooses an ending where they succeed by cooperating, they can learn the importance of cooperation. On the other hand, if they choose an ending where they fail by not cooperating, they can learn the risks of not cooperating. Furthermore, it is possible to extract what a child has learned from the generated results and provide feedback. For example, by analyzing the ending chosen by the child and the progress of the story, it is possible to provide feedback on what moral message the child received. In addition, the generated manga is compatible with multiple languages ​​and can be used by foreigners as well as Japanese people. For example, manga can be generated in multiple languages, such as English and Chinese, making it possible to provide moral education to children who speak different languages.This allows the system to easily turn the moral content that parents want to teach their children into manga, allowing children to learn moral judgment while having fun.

[0029] A moral education system according to an embodiment includes a reception unit, a generation unit, a feedback unit, and a language conversion unit. The reception unit collects themes, characters, and roles input by a user. For example, the reception unit allows a user to select themes such as "cooperation with friends" or "honesty" and set characters such as "Child A," "Child B," and "Teacher." The reception unit can also provide an interface for accurately collecting the information input by the user. The generation unit uses a generation AI to generate storyboards and scenarios with multiple endings based on the theme. For example, the generation unit causes the generation AI to generate storyboards and scenarios with the theme of "cooperation with friends." The generation AI can generate storyboards and scenarios with multiple endings using techniques such as deep learning and reinforcement learning. For example, the generation AI generates stories with multiple endings, such as an ending where cooperation leads to success and an ending where failure leads to non-cooperation. The feedback unit extracts what the child has learned from the generated results and provides feedback. For example, the feedback unit analyzes the ending selected by the child and the progress of the story, and provides feedback on the moral message the child received. The feedback unit can provide feedback in various forms, such as text feedback or visual feedback. The language conversion unit converts the generated manga into multiple languages. For example, the language conversion unit uses a generation AI to convert the generated manga into multiple languages, such as English and Chinese. The language conversion unit can convert the generated manga into multiple languages ​​using technologies such as neural machine translation and rule-based translation. As a result, the moral education system according to the embodiment allows parents to easily turn moral content they want to teach their children into manga, allowing children to learn moral judgment while having fun.

[0030] The reception unit can collect themes, characters, and roles entered by the user. For example, the reception unit allows the user to select themes such as "cooperation with friends" or "honesty" and set characters such as "Child A," "Child B," and "Teacher." The reception unit can also provide an interface for accurately collecting the information entered by the user. For example, the reception unit can provide a form or check boxes for collecting the themes, characters, and roles entered by the user. The reception unit can also save the information entered by the user so that it can be referenced later. This allows the accurate collection of information entered by the user to improve the accuracy of the generated storyboard and scenario. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the themes, characters, and roles entered by the user into AI, which can then analyze and collect the information.

[0031] The generation unit can use the generation AI to generate storyboards and scenarios with multiple endings according to a theme. The generation unit uses the generation AI to generate storyboards and scenarios with multiple endings according to a theme. For example, the generation unit can have the generation AI generate storyboards and scenarios with a theme of "cooperation with friends." The generation AI can generate storyboards and scenarios with multiple endings using technologies such as deep learning and reinforcement learning. For example, the generation AI generates stories with multiple endings, such as an ending where cooperation leads to success and an ending where failure occurs due to not cooperating. For example, when generating storyboards and scenarios with a theme of "cooperation with friends," the generation AI can generate stories with multiple endings, such as an ending where cooperation leads to success and an ending where failure occurs due to not cooperating. The generation AI can also extract moral messages that children gain from the generated storyboards and scenarios. This allows children to learn different moral messages by generating storyboards and scenarios with multiple endings. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can have the generation AI generate storyboards and scenarios according to a theme.

[0032] The feedback unit can extract what the child has learned from the generated results and provide feedback. The feedback unit can extract what the child has learned from the generated results and provide feedback. For example, the feedback unit can analyze the ending chosen by the child and the progress of the story, and provide feedback on the moral message the child has received. The feedback unit can provide feedback in various forms, such as text feedback or visual feedback. For example, the feedback unit can provide moral messages such as "the importance of cooperation" or "the importance of being honest" based on the ending chosen by the child. The feedback unit can also provide appropriate feedback according to the child's progress in the story. For example, the feedback unit can evaluate and provide feedback on moral judgments based on choices chosen by the child during the story. This can improve learning effectiveness by providing feedback on the moral messages the child has learned. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the generated results into AI, which can extract what the child has learned and provide feedback.

[0033] The language conversion unit can convert the generated manga into multiple languages. The language conversion unit converts the generated manga into multiple languages. For example, the language conversion unit uses a generation AI to convert the generated manga into multiple languages, such as English and Chinese. The language conversion unit can convert the generated manga into multiple languages ​​using technologies such as neural machine translation and rule-based translation. For example, the language conversion unit can translate the generated manga into English to provide moral education to English-speaking children. The language conversion unit can also translate the generated manga into Chinese to provide moral education to Chinese-speaking children. This makes it possible to support multiple languages ​​and provide moral education to children who speak different languages. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the generated manga into an AI, which then converts it into multiple languages.

[0034] The reception unit can analyze past input history and suggest the optimal input method. The reception unit analyzes past input history and suggests the optimal input method. For example, the reception unit automatically displays themes, characters, and roles that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes, characters, and roles to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history into AI, which then suggests the optimal input method.

[0035] The reception unit can present topic candidates based on the user's current areas of interest upon input. The reception unit presents topic candidates based on the user's current areas of interest upon input. For example, the reception unit can suggest related topics based on keywords recently searched by the user or content recently viewed by the user. The reception unit can also present related topics based on topics in which the user has shown interest on social media. Furthermore, the reception unit can analyze trends in themes previously selected by the user and suggest new related themes. By presenting themes based on the user's areas of interest, a more interesting theme can be selected. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current areas of interest into AI, which then presents topic candidates.

[0036] The reception unit can select the optimal input means depending on the user's input method at the time of input. The reception unit can select the optimal input means depending on the user's input method at the time of input. For example, if the user selects voice input, the reception unit can input the theme, characters, and roles using voice recognition technology. Also, if the user selects text input, the reception unit can input using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can set the characters and roles using image recognition technology. This can improve input convenience by selecting the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's input method to AI, which can select the optimal input means.

[0037] The reception unit can prioritize the presentation of highly relevant themes based on the user's geographical location information at the time of input. The reception unit prioritizes the presentation of highly relevant themes based on the user's geographical location information at the time of input. For example, if the user is in a specific area, the reception unit can prioritize the presentation of themes related to that area. Furthermore, if the user is traveling, the reception unit can prioritize the presentation of themes related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize the presentation of themes related to ethics in the home. In this way, by taking the user's geographical location information into consideration, more relevant themes can be presented. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can then present highly relevant themes.

[0038] The reception unit can analyze the user's social media activity at the time of input and present related themes. The reception unit can analyze the user's social media activity at the time of input and present related themes. For example, if the user posts about "cooperation" on social media, the reception unit can present themes related to cooperation. Furthermore, if the user mentions "honesty" on social media, the reception unit can present themes related to honesty. Furthermore, if the user talks about "friendship" on social media, the reception unit can present themes related to friendship. In this way, by presenting themes based on the user's social media activity, it is possible to select a more interesting theme. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can present related themes.

[0039] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit customizes the input method by reflecting the user's past feedback at the time of input. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has preferred image input in the past, the reception unit can preferentially suggest image input. In this way, by reflecting past feedback, it is possible to provide the optimal input method for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI, which can customize the input method.

[0040] The generation unit can adjust the level of detail of the storyboard and scenario based on the importance of the theme during generation. The generation unit can adjust the level of detail of the storyboard and scenario based on the importance of the theme during generation. For example, the generation unit generates detailed storyboards and scenarios for an important theme. The generation unit can also generate concise storyboards and scenarios for a general theme. Furthermore, the generation unit can generate particularly detailed storyboards and scenarios for a theme in which the user is particularly interested. In this way, by adjusting the level of detail based on the importance of the theme, a more appropriate story can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the theme to the generation AI, which can then adjust the level of detail of the storyboard and scenario.

[0041] The generation unit can apply different generation algorithms depending on the theme category during generation. The generation unit can apply different generation algorithms depending on the theme category during generation. For example, in the case of an educational theme, the generation unit can apply a generation algorithm specialized for education. In addition, in the case of a highly entertaining theme, the generation unit can also apply a generation algorithm specialized for entertainment. Furthermore, in the case of a social theme, the generation unit can apply a generation algorithm specialized for social issues. In this way, by applying a generation algorithm depending on the theme category, a more appropriate story can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the theme category to the generation AI, which can then apply an appropriate generation algorithm.

[0042] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to storyboards and scenarios that the user liked in the past. The generation unit can also improve the accuracy of generation by referring to storyboards and scenarios that the user avoided in the past. Furthermore, the generation unit can improve the accuracy of generation based on the user's past feedback. In this way, the accuracy of generation can be improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past generation results into the generation AI, which can improve the accuracy of generation.

[0043] The generation unit can determine the generation priority based on the submission time of the theme at the time of generation. The generation unit determines the generation priority based on the submission time of the theme at the time of generation. For example, the generation unit can prioritize generating storyboards and scenarios for themes with high urgency. The generation unit can also prioritize generating storyboards and scenarios for themes with an approaching submission deadline. Furthermore, the generation unit can also prioritize generating storyboards and scenarios for themes in which the user is particularly interested. In this way, by determining the priority based on the submission time of the theme, themes with high urgency can be generated preferentially. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the submission time of the theme to the generation AI, and the generation AI can determine the generation priority.

[0044] The generation unit can adjust the order of generation based on the relevance of the themes during generation. The generation unit can adjust the order of generation based on the relevance of the themes during generation. For example, the generation unit prioritizes generating highly relevant themes. The generation unit can also postpone generating less relevant themes. Furthermore, the generation unit can prioritize generating themes in which the user is particularly interested. In this way, by adjusting the order of generation based on the relevance of the themes, it is possible to prioritize generating more relevant themes. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of the themes to the generation AI, and the generation AI can adjust the order of generation.

[0045] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit can generate storyboards and scenarios that use a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can generate storyboards and scenarios that avoid technical terminology. Furthermore, the generation unit can generate storyboards and scenarios that use appropriate technical terminology according to the user's level of expertise. This makes it possible to provide a story that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's level of expertise to the generation AI, which can adjust the use of technical terminology.

[0046] The feedback unit can provide appropriate feedback by referring to the child's past learning history when providing feedback. The feedback unit can provide appropriate feedback by referring to the child's past learning history when providing feedback. For example, the feedback unit provides relevant feedback based on content the child has learned in the past. The feedback unit can also provide feedback according to the child's level of understanding from the child's past learning history. Furthermore, the feedback unit can analyze the child's past learning history and provide the most effective feedback. This can improve learning effectiveness by providing optimal feedback based on the child's past learning history. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the child's past learning history into AI, which can provide appropriate feedback.

[0047] The feedback unit can customize the content of the feedback based on the child's current level of understanding when providing feedback. The feedback unit customizes the content of the feedback based on the child's current level of understanding when providing feedback. For example, the feedback unit provides feedback suggesting a next step based on what the child understands. The feedback unit can also provide feedback including supplementary explanations based on what the child does not understand. Furthermore, the feedback unit can evaluate the child's current level of understanding and provide appropriate feedback. This allows for customizing the content of the feedback according to the child's current level of understanding, thereby supporting more effective learning. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's current level of understanding into AI, which can then customize the content of the feedback.

[0048] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. For example, the feedback unit improves the feedback method based on feedback provided by the user in the past. Also, if the user prefers a specific feedback method, the feedback unit can preferentially provide that method. Furthermore, the feedback unit can analyze the user's feedback and suggest the most effective feedback method. In this way, a more effective feedback method can be provided by reflecting the user's feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback into AI, which can improve the feedback method.

[0049] The feedback unit can select an appropriate feedback method by taking into account the child's geographical location information when providing feedback. The feedback unit can select an appropriate feedback method by taking into account the child's geographical location information when providing feedback. For example, if the child is in a specific area, the feedback unit can provide feedback related to the area. Also, if the child is traveling, the feedback unit can provide feedback related to the travel destination. Furthermore, if the child is at home, the feedback unit can provide feedback related to morals within the home. In this way, by taking the child's geographical location information into account, more relevant feedback can be provided. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the child's geographical location information into AI, which can then select an appropriate feedback method.

[0050] The feedback unit may analyze the child's social media activity and suggest a means of feedback when providing feedback. The feedback unit may analyze the child's social media activity and suggest a means of feedback when providing feedback. For example, if a child posts about "cooperation" on social media, the feedback unit may provide feedback related to cooperation. Furthermore, if a child mentions "honesty" on social media, the feedback unit may provide feedback related to honesty. Furthermore, if a child talks about "friendship" on social media, the feedback unit may provide feedback related to friendship. In this way, by suggesting a means of feedback based on the child's social media activity, more effective feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the child's social media activity into AI, which may suggest a means of feedback.

[0051] The feedback unit can customize the feedback method by reflecting the child's past feedback when providing feedback. The feedback unit can customize the feedback method by reflecting the child's past feedback when providing feedback. For example, the feedback unit provides feedback based on feedback methods that the child has preferred in the past. The feedback unit can also provide feedback based on feedback methods that the child has avoided in the past. Furthermore, the feedback unit can analyze the child's past feedback and suggest the most effective feedback method. In this way, a more effective feedback method can be provided by reflecting the child's past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's past feedback into AI, which can then customize the feedback method.

[0052] The language conversion unit can select an appropriate conversion algorithm by referring to past conversion history during language conversion. The language conversion unit selects an appropriate conversion algorithm by referring to past conversion history during language conversion. For example, the language conversion unit selects an optimal conversion algorithm based on language conversion methods that the user has previously preferred. The language conversion unit can also select an optimal conversion algorithm based on language conversion methods that the user has previously avoided. Furthermore, the language conversion unit can analyze the user's past conversion history and select the most effective conversion algorithm. In this way, by selecting an optimal conversion algorithm based on the past conversion history, more effective language conversion can be provided. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input past conversion history into AI, which selects an appropriate conversion algorithm.

[0053] The language conversion unit can customize the content of the conversion based on the user's language settings during language conversion. The language conversion unit customizes the content of the conversion based on the user's language settings during language conversion. For example, the language conversion unit automatically sets the content of the language conversion based on the language settings of the user's device. The language conversion unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the language conversion unit can provide language conversion in that language. This makes it possible to provide more appropriate language conversion by customizing the content of the conversion based on the user's language settings. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's language settings into AI, which then customizes the content of the conversion.

[0054] The language conversion unit can improve the conversion method by reflecting user feedback during language conversion. The language conversion unit improves the conversion method by reflecting user feedback during language conversion. For example, the language conversion unit improves the language conversion method based on feedback previously provided by the user. Also, if the user prefers a specific language conversion method, the language conversion unit can preferentially provide that method. Furthermore, the language conversion unit can analyze the user's feedback and suggest the most effective language conversion method. In this way, by reflecting the user's feedback, a more effective language conversion method can be provided. Some or all of the above-mentioned processing in the language conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the language conversion unit can input user feedback into AI, which can improve the conversion method.

[0055] The language conversion unit can select the optimal language conversion method by taking into account the user's geographical location information during language conversion. The language conversion unit selects the optimal language conversion method by taking into account the user's geographical location information during language conversion. For example, if the user is in a specific area, the language conversion unit can provide language conversion related to that area. Furthermore, if the user is traveling, the language conversion unit can also provide language conversion related to the travel destination. Furthermore, if the user is at home, the language conversion unit can provide language conversion related to ethics within the home. In this way, by taking into account the user's geographical location information, more relevant language conversion can be provided. Some or all of the above-described processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's geographical location information into AI, which can then select the optimal language conversion method.

[0056] During language conversion, the language conversion unit can analyze the user's social media activity to suggest a language conversion method. During language conversion, the language conversion unit analyzes the user's social media activity to suggest a language conversion method. For example, if a user posts about "cooperation" on social media, the language conversion unit can provide a language conversion related to cooperation. Furthermore, if a user mentions "honesty" on social media, the language conversion unit can provide a language conversion related to honesty. Furthermore, if a user talks about "friendship" on social media, the language conversion unit can provide a language conversion related to friendship. In this way, by suggesting a language conversion method based on the user's social media activity, more effective language conversion can be provided. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's social media activity into AI, which can then suggest a language conversion method.

[0057] The language conversion unit can customize the language conversion method by reflecting the user's past feedback during language conversion. The language conversion unit customizes the language conversion method by reflecting the user's past feedback during language conversion. For example, the language conversion unit provides language conversion based on language conversion methods that the user has previously preferred. The language conversion unit can also provide language conversion based on language conversion methods that the user has previously avoided. Furthermore, the language conversion unit can analyze the user's past feedback and suggest the most effective language conversion method. In this way, a more effective language conversion method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's past feedback into AI, which can then customize the language conversion method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display themes, characters, and roles that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes, characters, and roles to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the past input history into AI, which can then suggest the optimal input method.

[0060] The generation unit can adjust the level of detail of the storyboard and scenario based on the importance of the theme during generation. For example, the generation unit can generate a detailed storyboard and scenario for an important theme. The generation unit can also generate a concise storyboard and scenario for a general theme. Furthermore, the generation unit can generate a particularly detailed storyboard and scenario for a theme in which the user is particularly interested. This makes it possible to provide a more appropriate story by adjusting the level of detail based on the importance of the theme. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the theme to the generation AI, which can then adjust the level of detail of the storyboard and scenario.

[0061] The reception unit can present topic candidates based on the user's current areas of interest. For example, the reception unit can suggest related topics based on keywords recently searched by the user or content recently viewed by the user. The reception unit can also present related topics based on topics in which the user has shown interest on social media. Furthermore, the reception unit can analyze trends in themes previously selected by the user and suggest new related topics. In this way, by presenting themes based on the user's areas of interest, it is possible to select a more interesting topic. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current areas of interest into AI, which then presents topic candidates.

[0062] When providing feedback, the feedback unit can provide appropriate feedback by referring to the child's past learning history. For example, the feedback unit can provide relevant feedback based on the content the child has learned in the past. The feedback unit can also provide feedback according to the child's level of understanding from the child's past learning history. Furthermore, the feedback unit can analyze the child's past learning history and provide the most effective feedback. This can improve learning effectiveness by providing optimal feedback based on the child's past learning history. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the child's past learning history into AI, which can provide appropriate feedback.

[0063] During generation, the generation unit can apply different generation algorithms depending on the theme category. For example, in the case of an educational theme, the generation unit can apply a generation algorithm specialized for education. In addition, in the case of a highly entertaining theme, the generation unit can also apply a generation algorithm specialized for entertainment. Furthermore, in the case of a social theme, the generation unit can also apply a generation algorithm specialized for social issues. In this way, by applying a generation algorithm depending on the theme category, a more appropriate story can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the theme category to the generation AI, which can then apply an appropriate generation algorithm.

[0064] When providing feedback, the feedback unit can customize the content of the feedback based on the child's current level of understanding. For example, the feedback unit can provide feedback suggesting the next step based on what the child understands. The feedback unit can also provide feedback including supplementary explanations based on what the child does not understand. Furthermore, the feedback unit can evaluate the child's current level of understanding and provide appropriate feedback. This allows for customizing the content of the feedback according to the child's current level of understanding, thereby supporting more effective learning. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's current level of understanding into AI, which can then customize the content of the feedback.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit collects the themes, characters, and roles entered by the user. For example, the reception unit allows the user to select themes such as "cooperation with friends" or "being honest" and set characters such as "Child A," "Child B," and "Teacher." The reception unit also provides an interface for accurately collecting the information entered by the user. Step 2: The generator uses the generation AI to generate storyboards and scenarios with multiple endings based on a theme. For example, the generator has the generation AI generate storyboards and scenarios with the theme of "cooperation with friends." The generator AI uses techniques such as deep learning and reinforcement learning to generate stories with multiple endings, such as endings that are successful through cooperation and endings that are unsuccessful through non-cooperation. Step 3: The feedback section extracts what the child has learned from the generated results and provides feedback. For example, the feedback section analyzes the ending chosen by the child and the progress of the story, and provides feedback on what moral message the child has received. The feedback section provides feedback in various forms, such as text feedback and visual feedback. Step 4: The language conversion unit converts the generated manga into multiple languages. For example, the language conversion unit uses a generation AI to convert the generated manga into multiple languages, such as English and Chinese. The language conversion unit can convert the generated manga into multiple languages ​​using technologies such as neural machine translation and rule-based translation.

[0067] (Example 2) A system according to an embodiment of the present invention allows parents to easily create manga manga about moral lessons they want to teach their children. In this system, the user selects the theme, characters, and roles they want to teach their child. The AI ​​then generates storyboards and scenarios with multiple endings based on each theme. The moral message children receive varies depending on the story's choices and outcomes, allowing them to enjoy the story while learning moral judgment. The system can also extract what the child learned from the generated results and provide feedback. Furthermore, the generated manga is available in multiple languages, making it accessible to both Japanese and foreign users. This allows the system to easily create manga about moral lessons parents want to teach their children. For example, a user can select a theme such as "cooperation with friends" or "honesty" and assign characters such as "Child A," "Child B," and "Teacher." This information is input into the AI. The AI ​​then analyzes the input information and generates storyboards and scenarios with multiple endings based on each theme. For example, if the theme is "cooperation with friends," a story with multiple endings, such as a successful ending resulting from cooperation or a failed ending resulting from non-cooperation, is generated. The moral message children receive varies depending on the choices and outcomes of the generated story. For example, if a child chooses an ending where they succeed by cooperating, they can learn the importance of cooperation. On the other hand, if they choose an ending where they fail by not cooperating, they can learn the risks of not cooperating. Furthermore, it is possible to extract what a child has learned from the generated results and provide feedback. For example, by analyzing the ending chosen by the child and the progress of the story, it is possible to provide feedback on what moral message the child received. In addition, the generated manga is compatible with multiple languages ​​and can be used by foreigners as well as Japanese people. For example, manga can be generated in multiple languages, such as English and Chinese, making it possible to provide moral education to children who speak different languages.This allows the system to easily turn the moral content that parents want to teach their children into manga, allowing children to learn moral judgment while having fun.

[0068] A moral education system according to an embodiment includes a reception unit, a generation unit, a feedback unit, and a language conversion unit. The reception unit collects themes, characters, and roles input by a user. For example, the reception unit allows a user to select themes such as "cooperation with friends" or "honesty" and set characters such as "Child A," "Child B," and "Teacher." The reception unit can also provide an interface for accurately collecting the information input by the user. The generation unit uses a generation AI to generate storyboards and scenarios with multiple endings based on the theme. For example, the generation unit causes the generation AI to generate storyboards and scenarios with the theme of "cooperation with friends." The generation AI can generate storyboards and scenarios with multiple endings using techniques such as deep learning and reinforcement learning. For example, the generation AI generates stories with multiple endings, such as an ending where cooperation leads to success and an ending where failure leads to non-cooperation. The feedback unit extracts what the child has learned from the generated results and provides feedback. For example, the feedback unit analyzes the ending selected by the child and the progress of the story, and provides feedback on the moral message the child received. The feedback unit can provide feedback in various forms, such as text feedback or visual feedback. The language conversion unit converts the generated manga into multiple languages. For example, the language conversion unit uses a generation AI to convert the generated manga into multiple languages, such as English and Chinese. The language conversion unit can convert the generated manga into multiple languages ​​using technologies such as neural machine translation and rule-based translation. As a result, the moral education system according to the embodiment allows parents to easily turn moral content they want to teach their children into manga, allowing children to learn moral judgment while having fun.

[0069] The reception unit can collect themes, characters, and roles entered by the user. For example, the reception unit allows the user to select themes such as "cooperation with friends" or "honesty" and set characters such as "Child A," "Child B," and "Teacher." The reception unit can also provide an interface for accurately collecting the information entered by the user. For example, the reception unit can provide a form or check boxes for collecting the themes, characters, and roles entered by the user. The reception unit can also save the information entered by the user so that it can be referenced later. This allows the accurate collection of information entered by the user to improve the accuracy of the generated storyboard and scenario. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the themes, characters, and roles entered by the user into AI, which can then analyze and collect the information.

[0070] The generation unit can use the generation AI to generate storyboards and scenarios with multiple endings according to a theme. The generation unit uses the generation AI to generate storyboards and scenarios with multiple endings according to a theme. For example, the generation unit can have the generation AI generate storyboards and scenarios with a theme of "cooperation with friends." The generation AI can generate storyboards and scenarios with multiple endings using technologies such as deep learning and reinforcement learning. For example, the generation AI generates stories with multiple endings, such as an ending where cooperation leads to success and an ending where failure occurs due to not cooperating. For example, when generating storyboards and scenarios with a theme of "cooperation with friends," the generation AI can generate stories with multiple endings, such as an ending where cooperation leads to success and an ending where failure occurs due to not cooperating. The generation AI can also extract moral messages that children gain from the generated storyboards and scenarios. This allows children to learn different moral messages by generating storyboards and scenarios with multiple endings. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can have the generation AI generate storyboards and scenarios according to a theme.

[0071] The feedback unit can extract what the child has learned from the generated results and provide feedback. The feedback unit can extract what the child has learned from the generated results and provide feedback. For example, the feedback unit can analyze the ending chosen by the child and the progress of the story, and provide feedback on the moral message the child has received. The feedback unit can provide feedback in various forms, such as text feedback or visual feedback. For example, the feedback unit can provide moral messages such as "the importance of cooperation" or "the importance of being honest" based on the ending chosen by the child. The feedback unit can also provide appropriate feedback according to the child's progress in the story. For example, the feedback unit can evaluate and provide feedback on moral judgments based on choices chosen by the child during the story. This can improve learning effectiveness by providing feedback on the moral messages the child has learned. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the generated results into AI, which can extract what the child has learned and provide feedback.

[0072] The language conversion unit can convert the generated manga into multiple languages. The language conversion unit converts the generated manga into multiple languages. For example, the language conversion unit uses a generation AI to convert the generated manga into multiple languages, such as English and Chinese. The language conversion unit can convert the generated manga into multiple languages ​​using technologies such as neural machine translation and rule-based translation. For example, the language conversion unit can translate the generated manga into English to provide moral education to English-speaking children. The language conversion unit can also translate the generated manga into Chinese to provide moral education to Chinese-speaking children. This makes it possible to support multiple languages ​​and provide moral education to children who speak different languages. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the generated manga into an AI, which then converts it into multiple languages.

[0073] The reception unit can estimate the user's emotions and adjust the input method for the theme, character, and role based on the estimated user's emotions. The reception unit can estimate the user's emotions and adjust the input method for the theme, character, and role based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of the theme, character, and role. This allows for a more appropriate input experience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotions into the AI, which can then infer the emotions and adjust the input method based on the inferred emotions.

[0074] The reception unit can analyze past input history and suggest the optimal input method. The reception unit analyzes past input history and suggests the optimal input method. For example, the reception unit automatically displays themes, characters, and roles that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes, characters, and roles to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history into AI, which then suggests the optimal input method.

[0075] The reception unit can present topic candidates based on the user's current areas of interest upon input. The reception unit presents topic candidates based on the user's current areas of interest upon input. For example, the reception unit can suggest related topics based on keywords recently searched by the user or content recently viewed by the user. The reception unit can also present related topics based on topics in which the user has shown interest on social media. Furthermore, the reception unit can analyze trends in themes previously selected by the user and suggest new related themes. By presenting themes based on the user's areas of interest, a more interesting theme can be selected. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current areas of interest into AI, which then presents topic candidates.

[0076] The reception unit can select the optimal input means depending on the user's input method at the time of input. The reception unit can select the optimal input means depending on the user's input method at the time of input. For example, if the user selects voice input, the reception unit can input the theme, characters, and roles using voice recognition technology. Also, if the user selects text input, the reception unit can input using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can set the characters and roles using image recognition technology. This can improve input convenience by selecting the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's input method to AI, which can select the optimal input means.

[0077] The reception unit can estimate the user's emotions and determine the priority of themes to be input based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of themes to be input based on the estimated user emotions. For example, if the user is excited, the reception unit can preferentially present energetic themes. Also, if the user is calm, the reception unit can preferentially present calm themes. Furthermore, if the user is tired, the reception unit can preferentially present relaxing themes. This allows the selection of more appropriate themes by determining the priority of themes according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotions to an AI, which can estimate the emotions and determine the priority of themes based on the estimated emotions.

[0078] The reception unit can prioritize the presentation of highly relevant themes based on the user's geographical location information at the time of input. The reception unit prioritizes the presentation of highly relevant themes based on the user's geographical location information at the time of input. For example, if the user is in a specific area, the reception unit can prioritize the presentation of themes related to that area. Furthermore, if the user is traveling, the reception unit can prioritize the presentation of themes related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize the presentation of themes related to ethics in the home. In this way, by taking the user's geographical location information into consideration, more relevant themes can be presented. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can then present highly relevant themes.

[0079] The reception unit can analyze the user's social media activity at the time of input and present related themes. The reception unit can analyze the user's social media activity at the time of input and present related themes. For example, if the user posts about "cooperation" on social media, the reception unit can present themes related to cooperation. Furthermore, if the user mentions "honesty" on social media, the reception unit can present themes related to honesty. Furthermore, if the user talks about "friendship" on social media, the reception unit can present themes related to friendship. In this way, by presenting themes based on the user's social media activity, it is possible to select a more interesting theme. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, and the AI ​​can present related themes.

[0080] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit customizes the input method by reflecting the user's past feedback at the time of input. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has preferred image input in the past, the reception unit can preferentially suggest image input. In this way, by reflecting past feedback, it is possible to provide the optimal input method for the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI, which can customize the input method.

[0081] The generation unit can estimate the user's emotions and adjust the way the storyboard and scenario are presented based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the way the storyboard and scenario are presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a storyboard and scenario that progresses in a calm tone. Also, if the user is excited, the generation unit can generate a storyboard and scenario that progresses in an energetic tone. Furthermore, if the user is sad, the generation unit can generate a storyboard and scenario that soothes the emotions. This allows for a more appropriate story to be provided by adjusting the way the storyboard and scenario are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotions into the generation AI, which can then infer the emotions and adjust the way the storyboard and scenario are expressed based on the inferred emotions.

[0082] The generation unit can adjust the level of detail of the storyboard and scenario based on the importance of the theme during generation. The generation unit can adjust the level of detail of the storyboard and scenario based on the importance of the theme during generation. For example, the generation unit generates detailed storyboards and scenarios for an important theme. The generation unit can also generate concise storyboards and scenarios for a general theme. Furthermore, the generation unit can generate particularly detailed storyboards and scenarios for a theme in which the user is particularly interested. In this way, by adjusting the level of detail based on the importance of the theme, a more appropriate story can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the theme to the generation AI, which can then adjust the level of detail of the storyboard and scenario.

[0083] The generation unit can apply different generation algorithms depending on the theme category during generation. The generation unit can apply different generation algorithms depending on the theme category during generation. For example, in the case of an educational theme, the generation unit can apply a generation algorithm specialized for education. In addition, in the case of a highly entertaining theme, the generation unit can also apply a generation algorithm specialized for entertainment. Furthermore, in the case of a social theme, the generation unit can apply a generation algorithm specialized for social issues. In this way, by applying a generation algorithm depending on the theme category, a more appropriate story can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the theme category to the generation AI, which can then apply an appropriate generation algorithm.

[0084] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to storyboards and scenarios that the user liked in the past. The generation unit can also improve the accuracy of generation by referring to storyboards and scenarios that the user avoided in the past. Furthermore, the generation unit can improve the accuracy of generation based on the user's past feedback. In this way, the accuracy of generation can be improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past generation results into the generation AI, which can improve the accuracy of generation.

[0085] The generation unit can estimate the user's emotions and adjust the length of the storyboard and scenario based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the length of the storyboard and scenario based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short and concise storyboard and scenario. If the user is relaxed, the generation unit can generate a longer storyboard and scenario with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a storyboard and scenario with visually stimulating effects. This allows for a more appropriate story by adjusting the length of the storyboard and scenario according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotions into the generation AI, which can infer the emotions and adjust the length of the storyboard and scenario based on the estimated emotions.

[0086] The generation unit can determine the generation priority based on the submission time of the theme at the time of generation. The generation unit determines the generation priority based on the submission time of the theme at the time of generation. For example, the generation unit can prioritize generating storyboards and scenarios for themes with high urgency. The generation unit can also prioritize generating storyboards and scenarios for themes with an approaching submission deadline. Furthermore, the generation unit can also prioritize generating storyboards and scenarios for themes in which the user is particularly interested. In this way, by determining the priority based on the submission time of the theme, themes with high urgency can be generated preferentially. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the submission time of the theme to the generation AI, and the generation AI can determine the generation priority.

[0087] The generation unit can adjust the order of generation based on the relevance of the themes during generation. The generation unit can adjust the order of generation based on the relevance of the themes during generation. For example, the generation unit prioritizes generating highly relevant themes. The generation unit can also postpone generating less relevant themes. Furthermore, the generation unit can prioritize generating themes in which the user is particularly interested. In this way, by adjusting the order of generation based on the relevance of the themes, it is possible to prioritize generating more relevant themes. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of the themes to the generation AI, and the generation AI can adjust the order of generation.

[0088] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit can generate storyboards and scenarios that use a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can generate storyboards and scenarios that avoid technical terminology. Furthermore, the generation unit can generate storyboards and scenarios that use appropriate technical terminology according to the user's level of expertise. This makes it possible to provide a story that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's level of expertise to the generation AI, which can adjust the use of technical terminology.

[0089] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide feedback in a calm tone. Furthermore, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide feedback that focuses on the main points. This allows for adjusting the feedback method according to the user's emotions to provide more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotions into an AI, which can estimate the emotions and adjust the feedback method based on the estimated emotions.

[0090] The feedback unit can provide appropriate feedback by referring to the child's past learning history when providing feedback. The feedback unit can provide appropriate feedback by referring to the child's past learning history when providing feedback. For example, the feedback unit provides relevant feedback based on content the child has learned in the past. The feedback unit can also provide feedback according to the child's level of understanding from the child's past learning history. Furthermore, the feedback unit can analyze the child's past learning history and provide the most effective feedback. This can improve learning effectiveness by providing optimal feedback based on the child's past learning history. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the child's past learning history into AI, which can provide appropriate feedback.

[0091] The feedback unit can customize the content of the feedback based on the child's current level of understanding when providing feedback. The feedback unit customizes the content of the feedback based on the child's current level of understanding when providing feedback. For example, the feedback unit provides feedback suggesting a next step based on what the child understands. The feedback unit can also provide feedback including supplementary explanations based on what the child does not understand. Furthermore, the feedback unit can evaluate the child's current level of understanding and provide appropriate feedback. This allows for customizing the content of the feedback according to the child's current level of understanding, thereby supporting more effective learning. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's current level of understanding into AI, which can then customize the content of the feedback.

[0092] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. For example, the feedback unit improves the feedback method based on feedback provided by the user in the past. Also, if the user prefers a specific feedback method, the feedback unit can preferentially provide that method. Furthermore, the feedback unit can analyze the user's feedback and suggest the most effective feedback method. In this way, a more effective feedback method can be provided by reflecting the user's feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback into AI, which can improve the feedback method.

[0093] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. For example, if the user is nervous, the feedback unit can prioritize providing important feedback. Also, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can prioritize providing feedback that focuses on the main points. In this way, by determining the priority of feedback according to the user's emotions, it is possible to prioritize providing more important feedback. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotions into an AI, which can estimate the emotions and determine the priority of feedback based on the estimated emotions.

[0094] The feedback unit can select an appropriate feedback method by taking into account the child's geographical location information when providing feedback. The feedback unit can select an appropriate feedback method by taking into account the child's geographical location information when providing feedback. For example, if the child is in a specific area, the feedback unit can provide feedback related to the area. Also, if the child is traveling, the feedback unit can provide feedback related to the travel destination. Furthermore, if the child is at home, the feedback unit can provide feedback related to morals within the home. In this way, by taking the child's geographical location information into account, more relevant feedback can be provided. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the child's geographical location information into AI, which can then select an appropriate feedback method.

[0095] The feedback unit may analyze the child's social media activity and suggest a means of feedback when providing feedback. The feedback unit may analyze the child's social media activity and suggest a means of feedback when providing feedback. For example, if a child posts about "cooperation" on social media, the feedback unit may provide feedback related to cooperation. Furthermore, if a child mentions "honesty" on social media, the feedback unit may provide feedback related to honesty. Furthermore, if a child talks about "friendship" on social media, the feedback unit may provide feedback related to friendship. In this way, by suggesting a means of feedback based on the child's social media activity, more effective feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the child's social media activity into AI, which may suggest a means of feedback.

[0096] The feedback unit can customize the feedback method by reflecting the child's past feedback when providing feedback. The feedback unit can customize the feedback method by reflecting the child's past feedback when providing feedback. For example, the feedback unit provides feedback based on feedback methods that the child has preferred in the past. The feedback unit can also provide feedback based on feedback methods that the child has avoided in the past. Furthermore, the feedback unit can analyze the child's past feedback and suggest the most effective feedback method. In this way, a more effective feedback method can be provided by reflecting the child's past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's past feedback into AI, which can then customize the feedback method.

[0097] The language conversion unit can estimate the user's emotions and adjust the language conversion method based on the estimated user emotions. The language conversion unit can estimate the user's emotions and adjust the language conversion method based on the estimated user emotions. For example, if the user is nervous, the language conversion unit can provide concise and easy-to-understand language conversion. Furthermore, if the user is relaxed, the language conversion unit can provide language conversion with detailed explanations. Furthermore, if the user is in a hurry, the language conversion unit can provide quick and concise language conversion. This allows for adjusting the language conversion method according to the user's emotions to provide more appropriate language conversion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the language conversion unit can be performed using, for example, an AI, or without an AI. For example, the language conversion unit can input the user's emotions into an AI, which can estimate the emotions and adjust the language conversion method based on the estimated emotions.

[0098] The language conversion unit can select an appropriate conversion algorithm by referring to past conversion history during language conversion. The language conversion unit selects an appropriate conversion algorithm by referring to past conversion history during language conversion. For example, the language conversion unit selects an optimal conversion algorithm based on language conversion methods that the user has previously preferred. The language conversion unit can also select an optimal conversion algorithm based on language conversion methods that the user has previously avoided. Furthermore, the language conversion unit can analyze the user's past conversion history and select the most effective conversion algorithm. In this way, by selecting an optimal conversion algorithm based on the past conversion history, more effective language conversion can be provided. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input past conversion history into AI, which selects an appropriate conversion algorithm.

[0099] The language conversion unit can customize the content of the conversion based on the user's language settings during language conversion. The language conversion unit customizes the content of the conversion based on the user's language settings during language conversion. For example, the language conversion unit automatically sets the content of the language conversion based on the language settings of the user's device. The language conversion unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the language conversion unit can provide language conversion in that language. This makes it possible to provide more appropriate language conversion by customizing the content of the conversion based on the user's language settings. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's language settings into AI, which then customizes the content of the conversion.

[0100] The language conversion unit can improve the conversion method by reflecting user feedback during language conversion. The language conversion unit improves the conversion method by reflecting user feedback during language conversion. For example, the language conversion unit improves the language conversion method based on feedback previously provided by the user. Also, if the user prefers a specific language conversion method, the language conversion unit can preferentially provide that method. Furthermore, the language conversion unit can analyze the user's feedback and suggest the most effective language conversion method. In this way, by reflecting the user's feedback, a more effective language conversion method can be provided. Some or all of the above-mentioned processing in the language conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the language conversion unit can input user feedback into AI, which can improve the conversion method.

[0101] The language conversion unit can estimate the user's emotions and determine the priority of language conversions based on the estimated user emotions. The language conversion unit can estimate the user's emotions and determine the priority of language conversions based on the estimated user emotions. For example, if the user is nervous, the language conversion unit can prioritize providing important language conversions. The language conversion unit can also provide detailed language conversions if the user is relaxed. Furthermore, if the user is in a hurry, the language conversion unit can prioritize providing language conversions that focus on the main points. This allows the priority of language conversions to be determined according to the user's emotions, thereby prioritizing more important language conversions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the language conversion unit can be performed using, for example, an AI, or without an AI. For example, the language conversion unit can input the user's emotions into an AI, which can estimate the emotions and determine the priority of language conversions based on the estimated emotions.

[0102] The language conversion unit can select the optimal language conversion method by taking into account the user's geographical location information during language conversion. The language conversion unit selects the optimal language conversion method by taking into account the user's geographical location information during language conversion. For example, if the user is in a specific area, the language conversion unit can provide language conversion related to that area. Furthermore, if the user is traveling, the language conversion unit can also provide language conversion related to the travel destination. Furthermore, if the user is at home, the language conversion unit can provide language conversion related to ethics within the home. In this way, by taking into account the user's geographical location information, more relevant language conversion can be provided. Some or all of the above-described processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's geographical location information into AI, which can then select the optimal language conversion method.

[0103] During language conversion, the language conversion unit can analyze the user's social media activity to suggest a language conversion method. During language conversion, the language conversion unit analyzes the user's social media activity to suggest a language conversion method. For example, if a user posts about "cooperation" on social media, the language conversion unit can provide a language conversion related to cooperation. Furthermore, if a user mentions "honesty" on social media, the language conversion unit can provide a language conversion related to honesty. Furthermore, if a user talks about "friendship" on social media, the language conversion unit can provide a language conversion related to friendship. In this way, by suggesting a language conversion method based on the user's social media activity, more effective language conversion can be provided. Some or all of the above-mentioned processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's social media activity into AI, which can then suggest a language conversion method.

[0104] The language conversion unit can customize the language conversion method by reflecting the user's past feedback during language conversion. The language conversion unit customizes the language conversion method by reflecting the user's past feedback during language conversion. For example, the language conversion unit provides language conversion based on language conversion methods that the user has previously preferred. The language conversion unit can also provide language conversion based on language conversion methods that the user has previously avoided. Furthermore, the language conversion unit can analyze the user's past feedback and suggest the most effective language conversion method. In this way, a more effective language conversion method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the language conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the language conversion unit can input the user's past feedback into AI, which can then customize the language conversion method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, feedback unit, and language conversion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can collect themes, characters, and roles input by the user using the reception device 38 of the smart device 14. The generation unit uses a generation AI by the specific processing unit 290 of the data processing device 12 to generate a storyboard and scenario with multiple endings according to the theme. The feedback unit extracts what the child has learned from the results generated by the specific processing unit 290 of the data processing device 12 and provides feedback. The language conversion unit converts the manga generated by the specific processing unit 290 of the data processing device 12 into multiple languages. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, feedback unit, and language conversion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can collect themes, characters, and roles input by the user using the microphone 238 of the smart glasses 214. The generation unit generates a storyboard and scenario with multiple endings according to the theme using a generation AI by the specific processing unit 290 of the data processing device 12. The feedback unit extracts what the child has learned from the results generated by the specific processing unit 290 of the data processing device 12 and provides feedback. The language conversion unit converts the manga generated by the specific processing unit 290 of the data processing device 12 into multiple languages. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, feedback unit, and language conversion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can collect themes, characters, and roles input by the user using the microphone 238 of the headset-type terminal 314. The generation unit generates a storyboard and scenario with multiple endings according to the theme using a generation AI by the specific processing unit 290 of the data processing device 12. The feedback unit extracts what the child has learned from the results generated by the specific processing unit 290 of the data processing device 12 and provides feedback. The language conversion unit converts the manga generated by the specific processing unit 290 of the data processing device 12 into multiple languages. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, feedback unit, and language conversion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can collect themes, characters, and roles input by the user using the microphone 238 of the robot 414. The generation unit generates a storyboard and scenario with multiple endings according to the theme using a generation AI by the specific processing unit 290 of the data processing device 12. The feedback unit extracts what the child has learned from the results generated by the specific processing unit 290 of the data processing device 12 and provides feedback. The language conversion unit converts the manga generated by the specific processing unit 290 of the data processing device 12 into multiple languages.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display themes, characters, and roles that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes, characters, and roles to be used in a specific time period based on the user's past input history. This can improve the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the past input history into AI, which can then suggest the optimal input method.

[0107] The generation unit can adjust the level of detail of the storyboard and scenario based on the importance of the theme during generation. For example, the generation unit can generate a detailed storyboard and scenario for an important theme. The generation unit can also generate a concise storyboard and scenario for a general theme. Furthermore, the generation unit can generate a particularly detailed storyboard and scenario for a theme in which the user is particularly interested. This makes it possible to provide a more appropriate story by adjusting the level of detail based on the importance of the theme. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the theme to the generation AI, which can then adjust the level of detail of the storyboard and scenario.

[0108] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide feedback in a calm tone. Furthermore, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide feedback that focuses on the main points. By adjusting the feedback method according to the user's emotions, more appropriate feedback can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or without an AI. For example, the feedback unit can input the user's emotions into an AI, which can estimate the emotions and adjust the feedback method based on the estimated emotions.

[0109] The reception unit can present topic candidates based on the user's current areas of interest. For example, the reception unit can suggest related topics based on keywords recently searched by the user or content recently viewed by the user. The reception unit can also present related topics based on topics in which the user has shown interest on social media. Furthermore, the reception unit can analyze trends in themes previously selected by the user and suggest new related topics. In this way, by presenting themes based on the user's areas of interest, it is possible to select a more interesting topic. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current areas of interest into AI, which then presents topic candidates.

[0110] The generation unit can estimate the user's emotions and adjust the way the storyboard and scenario are presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a storyboard and scenario that progresses in a calm tone. If the user is excited, the generation unit can also generate a storyboard and scenario that progresses in an energetic tone. Furthermore, if the user is sad, the generation unit can generate a storyboard and scenario that soothes the user's emotions. This allows for a more appropriate story to be provided by adjusting the way the storyboard and scenario are presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotions into the generation AI, which can then estimate the emotions and adjust the way the storyboard and scenario are presented based on the estimated emotions.

[0111] When providing feedback, the feedback unit can provide appropriate feedback by referring to the child's past learning history. For example, the feedback unit can provide relevant feedback based on the content the child has learned in the past. The feedback unit can also provide feedback according to the child's level of understanding from the child's past learning history. Furthermore, the feedback unit can analyze the child's past learning history and provide the most effective feedback. This can improve learning effectiveness by providing optimal feedback based on the child's past learning history. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the child's past learning history into AI, which can provide appropriate feedback.

[0112] The reception unit can estimate the user's emotions and determine the priority of themes to be input based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize energetic themes. Furthermore, if the user is calm, the reception unit can prioritize calm themes. Furthermore, if the user is tired, the reception unit can prioritize relaxing themes. By determining the priority of themes according to the user's emotions, more appropriate themes can be selected. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotions into an AI, which can estimate the emotions and prioritize themes based on the estimated emotions.

[0113] During generation, the generation unit can apply different generation algorithms depending on the theme category. For example, in the case of an educational theme, the generation unit can apply a generation algorithm specialized for education. In addition, in the case of a highly entertaining theme, the generation unit can also apply a generation algorithm specialized for entertainment. Furthermore, in the case of a social theme, the generation unit can also apply a generation algorithm specialized for social issues. In this way, by applying a generation algorithm depending on the theme category, a more appropriate story can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the theme category to the generation AI, which can then apply an appropriate generation algorithm.

[0114] When providing feedback, the feedback unit can customize the content of the feedback based on the child's current level of understanding. For example, the feedback unit can provide feedback suggesting the next step based on what the child understands. The feedback unit can also provide feedback including supplementary explanations based on what the child does not understand. Furthermore, the feedback unit can evaluate the child's current level of understanding and provide appropriate feedback. This allows for customizing the content of the feedback according to the child's current level of understanding, thereby supporting more effective learning. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's current level of understanding into AI, which can then customize the content of the feedback.

[0115] The generation unit can estimate the user's emotions and adjust the length of the storyboard and scenario based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short and concise storyboard and scenario. If the user is relaxed, the generation unit can generate a longer storyboard and scenario with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a storyboard and scenario with visually stimulating effects. This allows for a more appropriate narrative by adjusting the length of the storyboard and scenario according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotions into the generation AI, which can then estimate the emotions and adjust the length of the storyboard and scenario based on the estimated emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit collects the themes, characters, and roles entered by the user. For example, the reception unit allows the user to select themes such as "cooperation with friends" or "being honest" and set characters such as "Child A," "Child B," and "Teacher." The reception unit also provides an interface for accurately collecting the information entered by the user. Step 2: The generator uses the generation AI to generate storyboards and scenarios with multiple endings based on a theme. For example, the generator has the generation AI generate storyboards and scenarios with the theme of "cooperation with friends." The generator AI uses techniques such as deep learning and reinforcement learning to generate stories with multiple endings, such as endings that are successful through cooperation and endings that are unsuccessful through non-cooperation. Step 3: The feedback section extracts what the child has learned from the generated results and provides feedback. For example, the feedback section analyzes the ending chosen by the child and the progress of the story, and provides feedback on what moral message the child has received. The feedback section provides feedback in various forms, such as text feedback and visual feedback. Step 4: The language conversion unit converts the generated manga into multiple languages. For example, the language conversion unit uses a generation AI to convert the generated manga into multiple languages, such as English and Chinese. The language conversion unit can convert the generated manga into multiple languages ​​using technologies such as neural machine translation and rule-based translation.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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 AI 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 AI 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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 AI 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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, in order to avoid confusion and to 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 reception unit for receiving input of the theme, characters, and roles; a generation unit that analyzes the information received by the reception unit and generates a storyboard and a scenario according to a theme; a feedback unit that extracts and feeds back moral messages that children should receive based on the storyboard and scenario generated by the generation unit; A language conversion unit that converts the generated manga into multiple languages. A system characterized by:

2. The reception unit Collect user-entered themes, characters, and roles 2. The system of claim 1.

3. The generation unit Generative AI generates storyboards and scenarios with multiple endings based on themes.

2. The system of claim 1.

4. The feedback unit Extract what the child has learned from the generated results and provide feedback 2. The system of claim 1.

5. The language conversion unit Convert the generated manga into multiple languages 2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting the input method for themes, characters, and roles based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyzes past input history and suggests appropriate input methods 2. The system of claim 1.

8. The reception unit As you type, suggest topics based on your current interests 2. The system of claim 1.

Images