system

Abstract

The system according to this embodiment aims to enable children to learn history in an enjoyable way. [Solution] The system according to the embodiment comprises a generation unit, a selection unit, a customization unit, and an immersive unit. The generation unit generates a story that allows children to experience important moments in history. The selection unit allows selection of era, region, and theme. The customization unit provides customization of characters and interactive options. The immersive unit enhances the sense of realism with sound and animation.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that children are less likely to be interested in learning history and the learning effect is low.

[0005] The system according to the embodiment aims to enable children to enjoy learning history.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a selection unit, a customization unit, and an immersion unit. The generation unit generates a story that allows children to experience important moments in history. The selection unit enables selection of an era, a region, and a theme. The customization unit provides customization of characters appearing in the story and interactive options. The immersion unit enhances the immersion with voice and animation. [Effects of the Invention]

[0007] The system according to this embodiment can enable children to learn history in an enjoyable way. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The story creation system according to an embodiment of the present invention generates stories that allow children to experience important moments in history, with selectable time periods, regions, and themes, customizable characters, interactive options, and enhanced realism through audio and animation. This story creation system generates stories that allow children to experience important moments in history. In this process, the generating AI creates stories that will interest children, based on historical events and figures. For example, stories themed around battles in the Sengoku period or the construction of the pyramids in ancient Egypt are conceivable. This allows children to learn while experiencing important moments in history. Next, it allows selection of time periods, regions, and themes, and provides customizable characters and interactive options. For example, children can choose their favorite era or region and enjoy stories related to that era or region. Furthermore, they can customize characters, and the story includes interactive elements where the development changes based on their choices. This allows children to learn according to their interests and preferences. Finally, it enhances realism through audio and animation. For example, it includes voices of characters speaking within the story and animations recreating historical scenes. This allows children to learn history using both sight and sound, promoting a deeper understanding. This system is designed to solve problems in children's history studies. For example, it addresses the problem that children easily get bored when simply memorizing historical events and dates by engaging them with fun stories. It also facilitates deeper understanding through visual and interactive elements, addressing the difficulty of grasping complex historical backgrounds and cause-and-effect relationships. Furthermore, it visualizes progress with an automatic report generation function, making it easier for parents and teachers to grasp children's learning progress and understanding. Finally, it addresses the problem of the inability to individually optimize learning by providing a personalized learning experience. It can customize themes and content to match children's interests and grade levels, and automatically select appropriate difficulty levels and themes using learning history.This ensures that learning is tailored to each child. Finally, addressing the burden on parents and teachers, AI can take over learning support. The AI ​​can lead the learning process and share progress, seamlessly supporting both home learning and school education. This allows the story creation system to make learning fun and engaging, allowing children to experience important moments in history.

[0029] The story creation system according to this embodiment comprises a generation unit, a selection unit, a customization unit, and an immersive unit. The generation unit generates stories that allow children to experience important moments in history. The generation unit, for example, uses a generation AI to create stories that will interest children based on historical events and figures. For example, the generation unit can generate stories themed on battles in the Sengoku period or stories themed on the construction of the pyramids in ancient Egypt. The generation unit can also adjust the content of the stories so that children can learn while experiencing important moments in history. For example, the generation unit can include voices of characters speaking in the story and animations that recreate historical scenes. The selection unit allows selection of era, region, and theme. For example, the selection unit allows children to choose their favorite era or region and enjoy stories related to that era or region. The selection unit can also provide options so that children can learn according to their interests and preferences. For example, the selection unit allows children to choose their favorite era or region and enjoy stories related to that era or region. The customization unit provides character customization and interactive options. The customization section allows, for example, children to customize the characters themselves. The customization section can also provide interactive elements where the story unfolds differently depending on the user's choices. For example, the customization section allows children to customize the characters themselves and provides interactive elements where the story unfolds differently depending on their choices. The immersion section enhances the sense of realism with sound and animation. For example, the immersion section can include voices of characters speaking in the story and animations recreating historical scenes. This allows the story creation system according to the embodiment to allow children to learn while having fun experiencing important moments in history.

[0030] The generation unit generates stories that allow children to experience important moments in history. For example, using generative AI, the generation unit creates stories that will interest children based on historical events and figures. Specifically, the generative AI extracts relevant information from a vast historical database and generates stories using natural language processing technology. For example, in a story themed on the battles of the Sengoku period, the generative AI collects information on major events, figures, and tactics of the Sengoku period and uses this to create a story that will interest children. Similarly, in a story themed on the construction of the pyramids of ancient Egypt, the generative AI collects information on the pyramid construction process, lifestyles of the time, and important figures and uses this to generate a story. The generation unit can also adjust the content of the stories so that children can learn while experiencing important moments in history. For example, the generation unit can include voices of characters speaking in the story and animations that recreate historical scenes. This allows children to learn history in a visually and aurally immersive way. Furthermore, the generation unit can adjust the difficulty level and content of the stories according to the child's age and level of understanding. For example, younger children can be provided with simple language and short stories, while older children can be given more detailed and complex stories. This allows the generator to provide an optimal learning experience tailored to each individual child.

[0031] The selection section allows users to choose eras, regions, and themes. For example, it allows children to select their favorite era or region and enjoy stories related to that era or region. Specifically, the selection section is designed to allow children to easily select eras, regions, and themes through the user interface. For example, it offers eras such as ancient, medieval, and modern, and regions such as Asia, Europe, and America as options. Themes include categories that are likely to interest children, such as war, culture, and science and technology. The selection section can also provide options that allow children to learn according to their interests and preferences. For example, it allows children to choose their favorite era or region and enjoy stories related to that era or region. This allows children to learn based on their interests, increasing their motivation to learn. Furthermore, the selection section automatically filters relevant stories and information based on the child's selection, providing optimal content. For example, if a child selects medieval Europe, stories and information related to medieval Europe will be displayed preferentially. In this way, the selection section supports children in learning efficiently.

[0032] The customization section provides character customization and interactive choices. For example, it allows children to customize their own characters. Specifically, it provides an interface that allows them to freely change the appearance, personality, and equipment of their characters. For example, children can select hairstyles, clothing, weapons, etc., to create their own original characters. The customization section can also provide interactive elements in the story where the plot changes based on the child's choices. For example, the customization section allows children to customize their own characters and provides interactive elements in the story where the plot changes based on their choices. This allows children to experience how the story changes based on their choices and become more deeply immersed in learning. Furthermore, the customization section can adjust the difficulty and content of the story based on the child's choices. For example, the story's progression and the difficulty of the tasks can change according to the characteristics of the character the child chooses. This allows the customization section to provide an optimal learning experience tailored to each child.

[0033] The immersive element enhances the sense of realism through sound and animation. For example, it can include voice acting by characters in the story and animation recreating historical scenes. Specifically, it uses professional voice recordings and high-quality animation to enhance the story's realism. For instance, the voice acting of characters in the story is emotionally rich and realistic, allowing children to experience the events as if they were actually there. Furthermore, the animation recreating historical scenes is meticulously detailed, allowing children to learn history visually as well. This makes it easier for children to become engrossed in the story, increasing its learning effectiveness. In addition to sound and animation, the immersive element also utilizes sound effects and background music to further enhance the story's atmosphere. For example, battle scenes use powerful sound effects and tense background music to capture children's attention. The immersive element also provides a function that allows children to select their own voices and animations. This allows children to customize the story to their preferences and enjoy it even more. This allows the immersive experience to enable children to learn while having fun and experiencing important moments in history.

[0034] The analysis unit can automatically analyze learning progress and comprehension and report the results to parents and teachers. For example, the analysis unit clarifies how learning progress is measured. For instance, it measures learning progress based on test results, study time, and achievement levels. The analysis unit also clarifies how comprehension is evaluated. For example, it evaluates comprehension based on quiz accuracy, comprehension tests, and feedback. This makes it easier for parents and teachers to understand a child's learning progress and comprehension. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the analysis of learning progress and comprehension into an AI, which can then output the analysis results.

[0035] The suggestion unit can identify points to review and propose the next learning plan. For example, the suggestion unit clarifies how to identify points to review. For example, it can identify points to review based on important concepts, frequently mistaken problems, past test results, etc. The suggestion unit also clarifies the specific content and methods of the learning plan. For example, it can propose a learning plan based on a learning schedule, goal setting, learning methods, etc. This allows children to create learning plans efficiently. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input points to review into AI, and the AI ​​can propose the next learning plan.

[0036] The customization section can customize themes and content to suit children's interests and grade levels. For example, the customization section clarifies how to identify children's interests. For example, it can identify children's interests based on questionnaires, observations, past selection history, etc. The customization section also clarifies which grade levels are targeted. For example, it may target first graders, second graders in middle school, third graders in high school, etc. This ensures that learning is tailored to each individual child. Some or all of the above processing in the customization section may be performed using AI, or not. For example, the customization section can input themes and content tailored to children's interests and grade levels into an AI, which can then perform the customization.

[0037] The selection unit can automatically select appropriate difficulty levels and themes by utilizing the learning history. The selection unit clarifies, for example, the specific content and methods of utilizing the learning history. For example, it utilizes the learning history based on past learning content, test results, and learning time. The selection unit also clarifies the specific criteria and adjustment methods for difficulty levels. For example, it adjusts the difficulty level based on the difficulty of the questions, the depth of the learning content, and the assessment of understanding. This ensures that appropriate difficulty levels and themes are selected based on the learning history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the learning history into AI, which can then automatically select appropriate difficulty levels and themes.

[0038] The lead unit allows AI to lead learning and share progress on behalf of parents or teachers. The lead unit clarifies, for example, how the AI ​​will lead learning. For example, the AI ​​will lead learning based on progress management, providing feedback, and suggesting learning content. The lead unit also clarifies how progress will be shared. For example, progress will be shared based on the format of reports, frequency of sharing, and recipients. This reduces the burden on parents and teachers and ensures that learning progress is shared. Some or all of the above processes in the lead unit may be performed using AI or not. For example, the lead unit can input learning progress into the AI, and the AI ​​can share the progress.

[0039] The generation unit can adjust the level of detail of historical events to generate stories tailored to a child's understanding. For example, if a child is knowledgeable about history, the generation unit can generate a story that includes detailed background information and specialized terminology. If a child is a beginner, the generation unit can generate a simple story that focuses on basic information. Furthermore, if a child is interested in a particular theme, the generation unit can add detailed episodes related to that theme. This ensures that stories are generated that are tailored to the child's understanding. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's understanding data into a generating AI, which can then adjust the level of detail of the story.

[0040] The generation unit can include highly relevant content by referencing the child's past learning history when generating stories. For example, the generation unit can include relevant historical events in the story to review what the child has learned in the past. The generation unit can also generate new relevant stories based on themes the child has shown interest in. Furthermore, the generation unit can focus on including content from areas where the child struggles to reinforce those areas. This results in the generation of highly relevant stories based on the child's past learning history. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's past learning history data into a generation AI, which can then generate a story containing highly relevant content.

[0041] The generation unit can provide different historical perspectives based on the child's interests and concerns when generating stories. For example, if the child is interested in the Sengoku period (Warring States period), the generation unit can generate stories from different perspectives on the Sengoku period. Similarly, if the child is interested in ancient Egypt, the generation unit can generate stories from different perspectives on the construction of the pyramids. Furthermore, if the child is interested in medieval Europe, the generation unit can generate stories from different perspectives on knights and castles. This provides historical perspectives tailored to the child's interests and concerns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For instance, the generation unit can input data on the child's interests into a generation AI, which can then generate stories offering different historical perspectives.

[0042] The generation unit can select appropriate content when generating a story, taking into account the child's cultural background. For example, if the child is familiar with Japanese culture, the generation unit can generate a story related to Japanese history. If the child is familiar with Western culture, the generation unit can generate a story related to Western history. Furthermore, if the child is interested in multiculturalism, the generation unit can generate a story that incorporates the histories of different cultures. This ensures that appropriate content is selected according to the child's cultural background. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's cultural background data into a generation AI, and the generation AI can generate a story in which appropriate content is selected.

[0043] The selection unit can present the most suitable option by referring to the child's past selection history when presenting choices. For example, the selection unit can present related options based on the options the child has previously chosen. It can also present different options based on options the child has previously avoided. Furthermore, the selection unit can analyze the child's selection history and present the option that is most interesting to them. This ensures that the optimal option is presented based on the child's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's past selection history data into a generating AI, which can then present the optimal option.

[0044] The selection unit can adjust the number and content of the options presented according to the child's learning progress. For example, if the child is in the early stages of learning, the selection unit will present fewer options. If the child is more advanced in their learning, the selection unit can present more options. Furthermore, the selection unit can adjust the content of the options according to the child's level of understanding. This ensures that the number and content of the options are adjusted according to the child's learning progress. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's learning progress data into a generating AI, which can then adjust the number and content of the options.

[0045] The selection unit can provide different options based on the child's interests and preferences when presenting choices. For example, if the child is interested in the Sengoku period (Warring States period), the selection unit can provide options related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the selection unit can provide options related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the selection unit can provide options related to medieval Europe. This ensures that choices are tailored to the child's interests. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For instance, the selection unit can input data on the child's interests into a generating AI, which can then provide different options.

[0046] The selection unit can customize how choices are presented according to the child's learning style. For example, if the child is a visual learner, the selection unit can present choices that include images or videos. If the child is an auditory learner, the selection unit can present choices that are explained aloud. Furthermore, if the child is an experiential learner, the selection unit can present interactive choices. This customizes how choices are presented according to the learning style. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the child's learning style data into a generating AI, which can then customize how choices are presented.

[0047] The customization unit can suggest the optimal customization when customizing characters by referring to the child's past selection history. For example, the customization unit can suggest relevant customizations based on the characteristics of characters the child has previously chosen. It can also suggest different customizations based on the characteristics of characters the child has previously avoided. Furthermore, the customization unit can analyze the child's selection history and suggest the most interesting customization. This ensures that the optimal customization is suggested based on past selection history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the child's past selection history data into a generating AI, which can then suggest the optimal customization.

[0048] The customization unit can adjust the scope of customization when customizing characters, according to the child's learning progress. For example, if the child is in the early stages of learning, the customization unit provides basic customization options. If the child is more advanced in their learning, the customization unit can provide more customization options. Furthermore, the customization unit can adjust the scope of customization according to the child's level of understanding. This ensures that the scope of customization is adjusted according to the learning progress. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the child's learning progress data into a generating AI, which can then adjust the scope of customization.

[0049] The customization section can provide different customization options based on the child's interests when customizing characters. For example, if the child is interested in the Sengoku period (Warring States period), the customization section can provide a customization option in the style of a Sengoku period warlord. If the child is interested in ancient Egypt, the customization section can provide a customization option in the style of an ancient Egyptian pharaoh. Furthermore, if the child is interested in medieval Europe, the customization section can provide a customization option in the style of a medieval European knight. This ensures that customization options are provided according to the child's interests. Some or all of the above processing in the customization section may be performed using AI, for example, or without AI. For example, the customization section can input data on the child's interests into a generating AI, which can then provide different customization options.

[0050] The customization unit can suggest appropriate customizations when customizing characters, taking into account the child's cultural background. For example, if the child is familiar with Japanese culture, the customization unit can offer customization options related to Japanese history. If the child is familiar with Western culture, the customization unit can offer customization options related to Western history. Furthermore, if the child is interested in multiculturalism, the customization unit can offer customization options that incorporate the histories of different cultures. This ensures that appropriate customizations are suggested according to the cultural background. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the child's cultural background data into a generating AI, which can then suggest appropriate customizations.

[0051] The immersion unit can provide optimal presentations by referring to the child's past learning history when creating an immersive experience. For example, the immersion unit can use audio and animation related to themes the child has shown interest in in the past. It can also provide similar presentation styles based on the presentation styles the child has enjoyed in the past. Furthermore, the immersion unit can analyze the child's learning history and provide the most effective presentation method. This ensures that optimal presentations are provided based on past learning history. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's past learning history data into a generating AI, which can then provide optimal presentations.

[0052] The immersion unit can adjust the level of detail in the immersive experience according to the child's learning progress. For example, if the child is in the initial stages of learning, the immersion unit provides a simple experience. If the child is progressing in their learning, the immersion unit can provide a more detailed experience. Furthermore, the immersion unit can adjust the level of detail in the experience according to the child's level of understanding. This ensures that the level of detail in the experience is adjusted according to the learning progress. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's learning progress data into a generating AI, which can then adjust the level of detail in the experience.

[0053] The immersion unit can provide different presentation options based on the child's interests when creating an immersive experience. For example, if the child is interested in the Sengoku period (Warring States period), the immersion unit will use audio and animation from that period. Similarly, if the child is interested in ancient Egypt, the immersion unit can use audio and animation from ancient Egypt. Furthermore, if the child is interested in medieval Europe, the immersion unit can use audio and animation from medieval Europe. This provides presentation options tailored to the child's interests. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input data on the child's interests into a generating AI, which can then provide different presentation options.

[0054] The immersion unit can select appropriate effects when creating an immersive experience, taking into account the child's cultural background. For example, if the child is familiar with Japanese culture, the immersion unit will use audio and animation related to Japanese history. If the child is familiar with Western culture, the immersion unit can use audio and animation related to Western history. Furthermore, if the child is interested in multiculturalism, the immersion unit can use audio and animation that incorporate the histories of different cultures. This ensures that appropriate effects are selected according to the cultural background. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's cultural background data into a generating AI, which can then select appropriate effects.

[0055] The analysis unit can improve accuracy by referring to a child's past learning history when analyzing learning progress and comprehension. For example, the analysis unit can accurately analyze a child's current level of comprehension based on their past learning history. Furthermore, the analysis unit can analyze a child's past learning history to analyze their comprehension in specific areas in detail. In addition, the analysis unit can analyze a child's learning history and suggest the most effective learning methods. This enables highly accurate analysis based on past learning history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's past learning history data into a generating AI, which can then perform a highly accurate analysis.

[0056] The analysis unit can provide different analytical perspectives based on the child's interests when analyzing learning progress and comprehension. For example, if the child is interested in the Sengoku period (Warring States period), the analysis unit can provide analytical perspectives related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the analysis unit can provide analytical perspectives related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the analysis unit can provide analytical perspectives related to medieval Europe. This ensures that different analytical perspectives are provided according to the child's interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the child's interests into a generating AI, which can then provide different analytical perspectives.

[0057] The suggestion unit can make optimal suggestions by referring to the child's past learning history when proposing a learning plan. For example, the suggestion unit can propose an optimal learning plan based on the child's past learning history. Furthermore, the suggestion unit can propose a learning plan for a specific subject based on the child's past learning history. In addition, the suggestion unit can analyze the child's learning history and propose the most effective learning plan. This results in the proposal of an optimal learning plan based on past learning history. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the child's past learning history data into a generating AI, which can then propose an optimal learning plan.

[0058] The suggestion unit can offer different suggestion options based on the child's interests when suggesting a learning plan. For example, if the child is interested in the Sengoku period (Warring States period), the suggestion unit can suggest a learning plan related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the suggestion unit can suggest a learning plan related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the suggestion unit can suggest a learning plan related to medieval Europe. This provides different suggestion options tailored to the child's interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the child's interests into a generating AI, which can then provide different suggestion options.

[0059] The lead unit can provide the optimal lead method by referring to the child's past learning history when leading the learning process. For example, the lead unit can provide the optimal lead method based on the child's past learning history. Furthermore, the lead unit can provide a lead method for a specific subject based on the child's past learning history. In addition, the lead unit can analyze the child's learning history and provide the most effective lead method. This ensures that the optimal lead method is provided based on past learning history. Some or all of the above processing in the lead unit may be performed using AI, for example, or without AI. For example, the lead unit can input the child's past learning history data into a generating AI, which can then provide the optimal lead method.

[0060] The lead function can provide different lead options based on the child's interests and preferences when leading the learning process. For example, if the child is interested in the Sengoku period (Warring States period), the lead function can provide lead options related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the lead function can provide lead options related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the lead function can provide lead options related to medieval Europe. This ensures that different lead options are provided according to the child's interests. Some or all of the above processing in the lead function may be performed using AI, for example, or without AI. For example, the lead function can input data on the child's interests into a generating AI, which can then provide different lead options.

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

[0062] The generation unit can adjust the level of detail of historical events to generate stories tailored to the child's understanding. For example, if the child is knowledgeable about history, it can generate a story that includes detailed background information and specialized terminology. If the child is a beginner, it can generate a simple story that focuses on basic information. Furthermore, if the child is interested in a particular theme, it can add detailed episodes related to that theme. This ensures that stories are generated that are appropriate for the child's understanding. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's understanding data into a generating AI, which can then adjust the level of detail of the story.

[0063] The generation unit can include highly relevant content by referencing the child's past learning history when generating stories. For example, it can include relevant historical events in the story to review what the child has learned in the past. It can also generate new stories based on themes the child has shown interest in. Furthermore, it can focus on including content from areas where the child struggles to reinforce those areas. This results in the generation of highly relevant stories based on the child's past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's past learning history data into a generation AI, which can then generate stories containing highly relevant content.

[0064] The selection unit can present the most suitable option by referring to the child's past selection history when presenting choices. For example, it can present related options based on the options the child has previously chosen. It can also present different options based on the options the child has previously avoided. Furthermore, it can analyze the child's selection history and present the option that is most interesting to them. This ensures that the optimal option is presented based on the child's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's past selection history data into a generating AI, which can then present the optimal option.

[0065] The customization unit can suggest the optimal customization when customizing characters by referring to the child's past selection history. For example, it can suggest relevant customizations based on the characteristics of characters the child has previously chosen. It can also suggest different customizations based on the characteristics of characters the child has previously avoided. Furthermore, it can analyze the child's selection history and suggest the most interesting customization. This ensures that the optimal customization is suggested based on past selection history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the child's past selection history data into a generating AI, which can then suggest the optimal customization.

[0066] The immersion unit can provide optimal experiences by referring to the child's past learning history when creating an immersive experience. For example, it can use audio and animation related to themes the child has shown interest in in the past. It can also provide similar experiences based on the experience styles the child has enjoyed in the past. Furthermore, it can analyze the child's learning history and provide the most effective experience method. This ensures that the optimal experience is provided based on the child's past learning history. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's past learning history data into a generating AI, which can then provide the optimal experience.

[0067] The suggestion unit can make optimal suggestions by referring to the child's past learning history when proposing a learning plan. For example, it can propose an optimal learning plan based on the child's past learning history. It can also propose a learning plan for a specific subject based on the child's past learning history. Furthermore, it can analyze the child's learning history and propose the most effective learning plan. This results in the proposal of an optimal learning plan based on past learning history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the child's past learning history data into a generating AI, which can then propose an optimal learning plan.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The generation unit generates stories that allow children to experience important moments in history. Using generative AI, the generation unit creates stories based on historical events and figures that will interest children. For example, it can generate stories about battles in the Sengoku period or the construction of the pyramids in ancient Egypt. The generation unit can also adjust the content of the stories so that children can learn while experiencing important moments in history. Step 2: The selection section allows children to choose the era, region, and theme. The selection section allows children to choose their favorite era and region and enjoy stories related to that era and region. The selection section also provides options so that children can learn at their own pace according to their interests and preferences. Step 3: The customization section provides character customization and interactive choices. The customization section allows children to customize the characters themselves. It also provides interactive elements in the story where the plot changes depending on their choices. Step 4: The immersion section enhances the sense of realism with sound and animation. The immersion section can include voices of characters speaking within the story and animations that recreate historical scenes. This allows children to learn while having fun experiencing important moments in history.

[0070] (Example of form 2) The story creation system according to an embodiment of the present invention generates stories that allow children to experience important moments in history, with selectable time periods, regions, and themes, customizable characters, interactive options, and enhanced realism through audio and animation. This story creation system generates stories that allow children to experience important moments in history. In this process, the generating AI creates stories that will interest children, based on historical events and figures. For example, stories themed around battles in the Sengoku period or the construction of the pyramids in ancient Egypt are conceivable. This allows children to learn while experiencing important moments in history. Next, it allows selection of time periods, regions, and themes, and provides customizable characters and interactive options. For example, children can choose their favorite era or region and enjoy stories related to that era or region. Furthermore, they can customize characters, and the story includes interactive elements where the development changes based on their choices. This allows children to learn according to their interests and preferences. Finally, it enhances realism through audio and animation. For example, it includes voices of characters speaking within the story and animations recreating historical scenes. This allows children to learn history using both sight and sound, promoting a deeper understanding. This system is designed to solve problems in children's history studies. For example, it addresses the problem that children easily get bored when simply memorizing historical events and dates by engaging them with fun stories. It also facilitates deeper understanding through visual and interactive elements, addressing the difficulty of grasping complex historical backgrounds and cause-and-effect relationships. Furthermore, it visualizes progress with an automatic report generation function, making it easier for parents and teachers to grasp children's learning progress and understanding. Finally, it addresses the problem of the inability to individually optimize learning by providing a personalized learning experience. It can customize themes and content to match children's interests and grade levels, and automatically select appropriate difficulty levels and themes using learning history.This ensures that learning is tailored to each child. Finally, addressing the burden on parents and teachers, AI can take over learning support. The AI ​​can lead the learning process and share progress, seamlessly supporting both home learning and school education. This allows the story creation system to make learning fun and engaging, allowing children to experience important moments in history.

[0071] The story creation system according to this embodiment comprises a generation unit, a selection unit, a customization unit, and an immersive unit. The generation unit generates stories that allow children to experience important moments in history. The generation unit, for example, uses a generation AI to create stories that will interest children based on historical events and figures. For example, the generation unit can generate stories themed on battles in the Sengoku period or stories themed on the construction of the pyramids in ancient Egypt. The generation unit can also adjust the content of the stories so that children can learn while experiencing important moments in history. For example, the generation unit can include voices of characters speaking in the story and animations that recreate historical scenes. The selection unit allows selection of era, region, and theme. For example, the selection unit allows children to choose their favorite era or region and enjoy stories related to that era or region. The selection unit can also provide options so that children can learn according to their interests and preferences. For example, the selection unit allows children to choose their favorite era or region and enjoy stories related to that era or region. The customization unit provides character customization and interactive options. The customization section allows, for example, children to customize the characters themselves. The customization section can also provide interactive elements where the story unfolds differently depending on the user's choices. For example, the customization section allows children to customize the characters themselves and provides interactive elements where the story unfolds differently depending on their choices. The immersion section enhances the sense of realism with sound and animation. For example, the immersion section can include voices of characters speaking in the story and animations recreating historical scenes. This allows the story creation system according to the embodiment to allow children to learn while having fun experiencing important moments in history.

[0072] The generation unit generates stories that allow children to experience important moments in history. For example, using generative AI, the generation unit creates stories that will interest children based on historical events and figures. Specifically, the generative AI extracts relevant information from a vast historical database and generates stories using natural language processing technology. For example, in a story themed on the battles of the Sengoku period, the generative AI collects information on major events, figures, and tactics of the Sengoku period and uses this to create a story that will interest children. Similarly, in a story themed on the construction of the pyramids of ancient Egypt, the generative AI collects information on the pyramid construction process, lifestyles of the time, and important figures and uses this to generate a story. The generation unit can also adjust the content of the stories so that children can learn while experiencing important moments in history. For example, the generation unit can include voices of characters speaking in the story and animations that recreate historical scenes. This allows children to learn history in a visually and aurally immersive way. Furthermore, the generation unit can adjust the difficulty level and content of the stories according to the child's age and level of understanding. For example, younger children can be provided with simple language and short stories, while older children can be given more detailed and complex stories. This allows the generator to provide an optimal learning experience tailored to each individual child.

[0073] The selection section allows users to choose eras, regions, and themes. For example, it allows children to select their favorite era or region and enjoy stories related to that era or region. Specifically, the selection section is designed to allow children to easily select eras, regions, and themes through the user interface. For example, it offers eras such as ancient, medieval, and modern, and regions such as Asia, Europe, and America as options. Themes include categories that are likely to interest children, such as war, culture, and science and technology. The selection section can also provide options that allow children to learn according to their interests and preferences. For example, it allows children to choose their favorite era or region and enjoy stories related to that era or region. This allows children to learn based on their interests, increasing their motivation to learn. Furthermore, the selection section automatically filters relevant stories and information based on the child's selection, providing optimal content. For example, if a child selects medieval Europe, stories and information related to medieval Europe will be displayed preferentially. In this way, the selection section supports children in learning efficiently.

[0074] The customization section provides character customization and interactive choices. For example, it allows children to customize their own characters. Specifically, it provides an interface that allows them to freely change the appearance, personality, and equipment of their characters. For example, children can select hairstyles, clothing, weapons, etc., to create their own original characters. The customization section can also provide interactive elements in the story where the plot changes based on the child's choices. For example, the customization section allows children to customize their own characters and provides interactive elements in the story where the plot changes based on their choices. This allows children to experience how the story changes based on their choices and become more deeply immersed in learning. Furthermore, the customization section can adjust the difficulty and content of the story based on the child's choices. For example, the story's progression and the difficulty of the tasks can change according to the characteristics of the character the child chooses. This allows the customization section to provide an optimal learning experience tailored to each child.

[0075] The immersive element enhances the sense of realism through sound and animation. For example, it can include voice acting by characters in the story and animation recreating historical scenes. Specifically, it uses professional voice recordings and high-quality animation to enhance the story's realism. For instance, the voice acting of characters in the story is emotionally rich and realistic, allowing children to experience the events as if they were actually there. Furthermore, the animation recreating historical scenes is meticulously detailed, allowing children to learn history visually as well. This makes it easier for children to become engrossed in the story, increasing its learning effectiveness. In addition to sound and animation, the immersive element also utilizes sound effects and background music to further enhance the story's atmosphere. For example, battle scenes use powerful sound effects and tense background music to capture children's attention. The immersive element also provides a function that allows children to select their own voices and animations. This allows children to customize the story to their preferences and enjoy it even more. This allows the immersive experience to enable children to learn while having fun and experiencing important moments in history.

[0076] The analysis unit can automatically analyze learning progress and comprehension and report the results to parents and teachers. For example, the analysis unit clarifies how learning progress is measured. For instance, it measures learning progress based on test results, study time, and achievement levels. The analysis unit also clarifies how comprehension is evaluated. For example, it evaluates comprehension based on quiz accuracy, comprehension tests, and feedback. This makes it easier for parents and teachers to understand a child's learning progress and comprehension. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the analysis of learning progress and comprehension into an AI, which can then output the analysis results.

[0077] The suggestion unit can identify points to review and propose the next learning plan. For example, the suggestion unit clarifies how to identify points to review. For example, it can identify points to review based on important concepts, frequently mistaken problems, past test results, etc. The suggestion unit also clarifies the specific content and methods of the learning plan. For example, it can propose a learning plan based on a learning schedule, goal setting, learning methods, etc. This allows children to create learning plans efficiently. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input points to review into AI, and the AI ​​can propose the next learning plan.

[0078] The customization section can customize themes and content to suit children's interests and grade levels. For example, the customization section clarifies how to identify children's interests. For example, it can identify children's interests based on questionnaires, observations, past selection history, etc. The customization section also clarifies which grade levels are targeted. For example, it may target first graders, second graders in middle school, third graders in high school, etc. This ensures that learning is tailored to each individual child. Some or all of the above processing in the customization section may be performed using AI, or not. For example, the customization section can input themes and content tailored to children's interests and grade levels into an AI, which can then perform the customization.

[0079] The selection unit can automatically select appropriate difficulty levels and themes by utilizing the learning history. The selection unit clarifies, for example, the specific content and methods of utilizing the learning history. For example, it utilizes the learning history based on past learning content, test results, and learning time. The selection unit also clarifies the specific criteria and adjustment methods for difficulty levels. For example, it adjusts the difficulty level based on the difficulty of the questions, the depth of the learning content, and the assessment of understanding. This ensures that appropriate difficulty levels and themes are selected based on the learning history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the learning history into AI, which can then automatically select appropriate difficulty levels and themes.

[0080] The lead unit allows AI to lead learning and share progress on behalf of parents or teachers. The lead unit clarifies, for example, how the AI ​​will lead learning. For example, the AI ​​will lead learning based on progress management, providing feedback, and suggesting learning content. The lead unit also clarifies how progress will be shared. For example, progress will be shared based on the format of reports, frequency of sharing, and recipients. This reduces the burden on parents and teachers and ensures that learning progress is shared. Some or all of the above processes in the lead unit may be performed using AI or not. For example, the lead unit can input learning progress into the AI, and the AI ​​can share the progress.

[0081] The generation unit can estimate a child's emotions and adjust the story's progression based on those emotions. For example, if the child is excited, the generation unit can speed up the story's progression and increase the number of action scenes. If the child is bored, the generation unit can add new characters or surprise elements to the story. Furthermore, if the child is feeling anxious, the generation unit can create a gentler, more reassuring narrative. This allows for story development that responds to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input child emotion data into a generation AI, which can then adjust the story's progression.

[0082] The generation unit can adjust the level of detail of historical events to generate stories tailored to a child's understanding. For example, if a child is knowledgeable about history, the generation unit can generate a story that includes detailed background information and specialized terminology. If a child is a beginner, the generation unit can generate a simple story that focuses on basic information. Furthermore, if a child is interested in a particular theme, the generation unit can add detailed episodes related to that theme. This ensures that stories are generated that are tailored to the child's understanding. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's understanding data into a generating AI, which can then adjust the level of detail of the story.

[0083] The generation unit can include highly relevant content by referencing the child's past learning history when generating stories. For example, the generation unit can include relevant historical events in the story to review what the child has learned in the past. The generation unit can also generate new relevant stories based on themes the child has shown interest in. Furthermore, the generation unit can focus on including content from areas where the child struggles to reinforce those areas. This results in the generation of highly relevant stories based on the child's past learning history. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's past learning history data into a generation AI, which can then generate a story containing highly relevant content.

[0084] The generation unit can estimate a child's emotions and adjust the difficulty level of the story based on the estimated emotions. For example, if the child is confident, the generation unit can generate a story that includes difficult problems and challenges. If the child is feeling anxious, the generation unit can generate a story that includes easy problems and lots of support. Furthermore, if the child is excited, the generation unit can generate a story that includes problems of moderate difficulty. This makes it possible to adjust the difficulty level of the story according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input child emotion data into the generation AI, and the generation AI can adjust the difficulty level of the story.

[0085] The generation unit can provide different historical perspectives based on the child's interests and concerns when generating stories. For example, if the child is interested in the Sengoku period (Warring States period), the generation unit can generate stories from different perspectives on the Sengoku period. Similarly, if the child is interested in ancient Egypt, the generation unit can generate stories from different perspectives on the construction of the pyramids. Furthermore, if the child is interested in medieval Europe, the generation unit can generate stories from different perspectives on knights and castles. This provides historical perspectives tailored to the child's interests and concerns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For instance, the generation unit can input data on the child's interests into a generation AI, which can then generate stories offering different historical perspectives.

[0086] The generation unit can select appropriate content when generating a story, taking into account the child's cultural background. For example, if the child is familiar with Japanese culture, the generation unit can generate a story related to Japanese history. If the child is familiar with Western culture, the generation unit can generate a story related to Western history. Furthermore, if the child is interested in multiculturalism, the generation unit can generate a story that incorporates the histories of different cultures. This ensures that appropriate content is selected according to the child's cultural background. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's cultural background data into a generation AI, and the generation AI can generate a story in which appropriate content is selected.

[0087] The selection unit can estimate a child's emotions and adjust the way choices are presented based on the estimated emotions. For example, if a child is excited, the selection unit may present visually appealing choices. If a child is anxious, the selection unit may present simple and easy-to-understand choices. Furthermore, if a child is bored, the selection unit may present interesting choices. This adjusts the way choices are presented according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input child emotion data into a generative AI, which can then adjust the way choices are presented.

[0088] The selection unit can present the most suitable option by referring to the child's past selection history when presenting choices. For example, the selection unit can present related options based on the options the child has previously chosen. It can also present different options based on options the child has previously avoided. Furthermore, the selection unit can analyze the child's selection history and present the option that is most interesting to them. This ensures that the optimal option is presented based on the child's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's past selection history data into a generating AI, which can then present the optimal option.

[0089] The selection unit can adjust the number and content of the options presented according to the child's learning progress. For example, if the child is in the early stages of learning, the selection unit will present fewer options. If the child is more advanced in their learning, the selection unit can present more options. Furthermore, the selection unit can adjust the content of the options according to the child's level of understanding. This ensures that the number and content of the options are adjusted according to the child's learning progress. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's learning progress data into a generating AI, which can then adjust the number and content of the options.

[0090] The selection unit can estimate the child's emotions and adjust the order of the options based on the estimated emotions. For example, if the child is excited, the selection unit may present the most interesting option first. If the child is anxious, the selection unit may present the simplest option first. Furthermore, if the child is bored, the selection unit may present an option containing a surprise element first. This adjusts the order of the options according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the child's emotion data into the generative AI, which can then adjust the order of the options.

[0091] The selection unit can provide different options based on the child's interests and preferences when presenting choices. For example, if the child is interested in the Sengoku period (Warring States period), the selection unit can provide options related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the selection unit can provide options related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the selection unit can provide options related to medieval Europe. This ensures that choices are tailored to the child's interests. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For instance, the selection unit can input data on the child's interests into a generating AI, which can then provide different options.

[0092] The selection unit can customize how choices are presented according to the child's learning style. For example, if the child is a visual learner, the selection unit can present choices that include images or videos. If the child is an auditory learner, the selection unit can present choices that are explained aloud. Furthermore, if the child is an experiential learner, the selection unit can present interactive choices. This customizes how choices are presented according to the learning style. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit can input the child's learning style data into a generating AI, which can then customize how choices are presented.

[0093] The customization unit can estimate a child's emotions and adjust the personalities and roles of characters based on those estimated emotions. For example, if the child is excited, the customization unit can introduce an active and adventurous character. If the child is feeling anxious, the customization unit can introduce a kind and reassuring character. Furthermore, if the child is bored, the customization unit can introduce a humorous and interesting character. This adjusts the personalities and roles of characters according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input child emotion data into a generative AI, which can then adjust the personalities and roles of characters.

[0094] The customization unit can suggest the optimal customization when customizing characters by referring to the child's past selection history. For example, the customization unit can suggest relevant customizations based on the characteristics of characters the child has previously chosen. It can also suggest different customizations based on the characteristics of characters the child has previously avoided. Furthermore, the customization unit can analyze the child's selection history and suggest the most interesting customization. This ensures that the optimal customization is suggested based on past selection history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the child's past selection history data into a generating AI, which can then suggest the optimal customization.

[0095] The customization unit can adjust the scope of customization when customizing characters, according to the child's learning progress. For example, if the child is in the early stages of learning, the customization unit provides basic customization options. If the child is more advanced in their learning, the customization unit can provide more customization options. Furthermore, the customization unit can adjust the scope of customization according to the child's level of understanding. This ensures that the scope of customization is adjusted according to the learning progress. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the child's learning progress data into a generating AI, which can then adjust the scope of customization.

[0096] The customization unit can estimate a child's emotions and adjust the appearance of the characters based on those emotions. For example, if the child is excited, the customization unit can display a character with a colorful and flashy appearance. If the child is feeling anxious, the customization unit can display a character with calmer colors. Furthermore, if the child is bored, the customization unit can display a character with a unique and funny appearance. In this way, the appearance of the characters is adjusted according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, or not using AI. For example, the customization unit can input the child's emotion data into a generative AI, which can then adjust the appearance of the characters.

[0097] The customization section can provide different customization options based on the child's interests when customizing characters. For example, if the child is interested in the Sengoku period (Warring States period), the customization section can provide a customization option in the style of a Sengoku period warlord. If the child is interested in ancient Egypt, the customization section can provide a customization option in the style of an ancient Egyptian pharaoh. Furthermore, if the child is interested in medieval Europe, the customization section can provide a customization option in the style of a medieval European knight. This ensures that customization options are provided according to the child's interests. Some or all of the above processing in the customization section may be performed using AI, for example, or without AI. For example, the customization section can input data on the child's interests into a generating AI, which can then provide different customization options.

[0098] The customization unit can suggest appropriate customizations when customizing characters, taking into account the child's cultural background. For example, if the child is familiar with Japanese culture, the customization unit can offer customization options related to Japanese history. If the child is familiar with Western culture, the customization unit can offer customization options related to Western history. Furthermore, if the child is interested in multiculturalism, the customization unit can offer customization options that incorporate the histories of different cultures. This ensures that appropriate customizations are suggested according to the cultural background. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the child's cultural background data into a generating AI, which can then suggest appropriate customizations.

[0099] The presence unit can estimate a child's emotions and adjust the way it expresses the child's emotions through voice and animation. For example, if the child is excited, the presence unit will use energetic voice and dynamic animation. If the child is feeling anxious, the presence unit will use calm voice and gentle animation. Furthermore, if the child is bored, the presence unit can use humorous voice and funny animation. This adjusts the way it expresses the child's emotions through voice and animation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presence unit may be performed using AI, or not using AI. For example, the presence unit can input child emotion data into the generative AI, which can then adjust the way it expresses the child's emotions through voice and animation.

[0100] The immersion unit can provide optimal presentations by referring to the child's past learning history when creating an immersive experience. For example, the immersion unit can use audio and animation related to themes the child has shown interest in in the past. It can also provide similar presentation styles based on the presentation styles the child has enjoyed in the past. Furthermore, the immersion unit can analyze the child's learning history and provide the most effective presentation method. This ensures that optimal presentations are provided based on past learning history. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's past learning history data into a generating AI, which can then provide optimal presentations.

[0101] The immersion unit can adjust the level of detail in the immersive experience according to the child's learning progress. For example, if the child is in the initial stages of learning, the immersion unit provides a simple experience. If the child is progressing in their learning, the immersion unit can provide a more detailed experience. Furthermore, the immersion unit can adjust the level of detail in the experience according to the child's level of understanding. This ensures that the level of detail in the experience is adjusted according to the learning progress. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's learning progress data into a generating AI, which can then adjust the level of detail in the experience.

[0102] The presence unit can estimate a child's emotions and adjust the timing of audio and animation based on the estimated emotions. For example, if the child is excited, the presence unit can use fast-paced audio and animation. If the child is anxious, the presence unit can use slow-paced audio and animation. Furthermore, if the child is bored, the presence unit can use audio and animation with varying tempos. This adjusts the timing of audio and animation according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presence unit may be performed using AI, or not using AI. For example, the presence unit can input child emotion data into the generative AI, which can then adjust the timing of audio and animation.

[0103] The immersion unit can provide different presentation options based on the child's interests when creating an immersive experience. For example, if the child is interested in the Sengoku period (Warring States period), the immersion unit will use audio and animation from that period. Similarly, if the child is interested in ancient Egypt, the immersion unit can use audio and animation from ancient Egypt. Furthermore, if the child is interested in medieval Europe, the immersion unit can use audio and animation from medieval Europe. This provides presentation options tailored to the child's interests. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input data on the child's interests into a generating AI, which can then provide different presentation options.

[0104] The immersion unit can select appropriate effects when creating an immersive experience, taking into account the child's cultural background. For example, if the child is familiar with Japanese culture, the immersion unit will use audio and animation related to Japanese history. If the child is familiar with Western culture, the immersion unit can use audio and animation related to Western history. Furthermore, if the child is interested in multiculturalism, the immersion unit can use audio and animation that incorporate the histories of different cultures. This ensures that appropriate effects are selected according to the cultural background. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's cultural background data into a generating AI, which can then select appropriate effects.

[0105] The analysis unit can estimate a child's emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. For example, if a child is excited, the analysis unit will use an analysis method that includes a lot of positive feedback. If a child is feeling anxious, the analysis unit will use an analysis method that provides reassurance. Furthermore, if a child is bored, the analysis unit may use an analysis method that engages their interest. This adjusts the analysis method for learning progress and comprehension according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input child emotion data into a generative AI, which can then adjust the analysis method for learning progress and comprehension.

[0106] The analysis unit can improve accuracy by referring to a child's past learning history when analyzing learning progress and comprehension. For example, the analysis unit can accurately analyze a child's current level of comprehension based on their past learning history. Furthermore, the analysis unit can analyze a child's past learning history to analyze their comprehension in specific areas in detail. In addition, the analysis unit can analyze a child's learning history and suggest the most effective learning methods. This enables highly accurate analysis based on past learning history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's past learning history data into a generating AI, which can then perform a highly accurate analysis.

[0107] The analysis unit can estimate a child's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if a child is excited, the analysis unit can present visually appealing analysis results. If a child is anxious, the analysis unit can present simple and easy-to-understand analysis results. Furthermore, if a child is bored, the analysis unit can present engaging analysis results. In this way, the presentation method of the analysis results is adjusted according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input child emotion data into a generative AI, and the generative AI can adjust the presentation method of the analysis results.

[0108] The analysis unit can provide different analytical perspectives based on the child's interests when analyzing learning progress and comprehension. For example, if the child is interested in the Sengoku period (Warring States period), the analysis unit can provide analytical perspectives related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the analysis unit can provide analytical perspectives related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the analysis unit can provide analytical perspectives related to medieval Europe. This ensures that different analytical perspectives are provided according to the child's interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the child's interests into a generating AI, which can then provide different analytical perspectives.

[0109] The suggestion unit can estimate a child's emotions and adjust the method of suggesting the next learning plan based on the estimated emotions. For example, if the child is excited, the suggestion unit can suggest a challenging learning plan. If the child is feeling anxious, the suggestion unit can suggest a reassuring learning plan. Furthermore, if the child is bored, the suggestion unit can suggest an engaging learning plan. In this way, the method of suggesting a learning plan is adjusted according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the child's emotion data into a generative AI, and the generative AI can adjust the method of suggesting the next learning plan.

[0110] The suggestion unit can make optimal suggestions by referring to the child's past learning history when proposing a learning plan. For example, the suggestion unit can propose an optimal learning plan based on the child's past learning history. Furthermore, the suggestion unit can propose a learning plan for a specific subject based on the child's past learning history. In addition, the suggestion unit can analyze the child's learning history and propose the most effective learning plan. This results in the proposal of an optimal learning plan based on past learning history. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the child's past learning history data into a generating AI, which can then propose an optimal learning plan.

[0111] The suggestion unit can estimate a child's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the child is excited, the suggestion unit will prioritize challenging suggestions. If the child is feeling anxious, the suggestion unit can prioritize reassuring suggestions. Furthermore, if the child is bored, the suggestion unit can prioritize interesting suggestions. This determines the priority of suggestions according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input child emotion data into a generative AI, which can then determine the priority of suggestions.

[0112] The suggestion unit can offer different suggestion options based on the child's interests when suggesting a learning plan. For example, if the child is interested in the Sengoku period (Warring States period), the suggestion unit can suggest a learning plan related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the suggestion unit can suggest a learning plan related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the suggestion unit can suggest a learning plan related to medieval Europe. This provides different suggestion options tailored to the child's interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the child's interests into a generating AI, which can then provide different suggestion options.

[0113] The lead unit can estimate a child's emotions and adjust the learning lead method based on the estimated emotions. For example, if the child is excited, the lead unit will use an energetic lead method. If the child is feeling anxious, the lead unit will use a reassuring lead method. Furthermore, if the child is bored, the lead unit can use an engaging lead method. This adjusts the learning lead method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lead unit may be performed using AI, or not using AI. For example, the lead unit can input child emotion data into the generative AI, which can then adjust the learning lead method.

[0114] The lead unit can provide the optimal lead method by referring to the child's past learning history when leading the learning process. For example, the lead unit can provide the optimal lead method based on the child's past learning history. Furthermore, the lead unit can provide a lead method for a specific subject based on the child's past learning history. In addition, the lead unit can analyze the child's learning history and provide the most effective lead method. This ensures that the optimal lead method is provided based on past learning history. Some or all of the above processing in the lead unit may be performed using AI, for example, or without AI. For example, the lead unit can input the child's past learning history data into a generating AI, which can then provide the optimal lead method.

[0115] The lead unit can estimate the child's emotions and adjust how learning progress is shared based on the estimated emotions. For example, if the child is excited, the lead unit will use a progress sharing method that includes a lot of positive feedback. If the child is anxious, the lead unit will use a progress sharing method that provides reassurance. Furthermore, if the child is bored, the lead unit may use a progress sharing method that is engaging. This adjusts how learning progress is shared according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lead unit may be performed using AI, or not using AI. For example, the lead unit can input the child's emotion data into the generative AI, which can then adjust how learning progress is shared.

[0116] The lead function can provide different lead options based on the child's interests and preferences when leading the learning process. For example, if the child is interested in the Sengoku period (Warring States period), the lead function can provide lead options related to the Sengoku period. Similarly, if the child is interested in ancient Egypt, the lead function can provide lead options related to ancient Egypt. Furthermore, if the child is interested in medieval Europe, the lead function can provide lead options related to medieval Europe. This ensures that different lead options are provided according to the child's interests. Some or all of the above processing in the lead function may be performed using AI, for example, or without AI. For example, the lead function can input data on the child's interests into a generating AI, which can then provide different lead options.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The generation unit can estimate a child's emotions and adjust the story's progression based on those emotions. For example, if the child is excited, the story's progression can be sped up and action scenes increased. If the child is bored, new characters or surprise elements can be added to the story. Furthermore, if the child is feeling anxious, the story can be made gentler to provide a sense of security. This allows for story development that responds to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input child emotion data into the generation AI, which can then adjust the story's progression.

[0119] The generation unit can adjust the level of detail of historical events to generate stories tailored to the child's understanding. For example, if the child is knowledgeable about history, it can generate a story that includes detailed background information and specialized terminology. If the child is a beginner, it can generate a simple story that focuses on basic information. Furthermore, if the child is interested in a particular theme, it can add detailed episodes related to that theme. This ensures that stories are generated that are appropriate for the child's understanding. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's understanding data into a generating AI, which can then adjust the level of detail of the story.

[0120] The selection unit can estimate the child's emotions and adjust the way choices are presented based on the estimated emotions. For example, if the child is excited, it can present visually appealing choices. If the child is anxious, it can present simple and easy-to-understand choices. Furthermore, if the child is bored, it can present choices that pique their interest. This adjusts the way choices are presented according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the child's emotion data into a generative AI, which can then adjust the way choices are presented.

[0121] The customization unit can estimate a child's emotions and adjust the personalities and roles of characters based on those estimated emotions. For example, if a child is excited, an active and adventurous character may appear. If a child is feeling anxious, a kind and reassuring character may appear. Furthermore, if a child is bored, a humorous and interesting character may appear. This adjusts the personalities and roles of characters according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input child emotion data into a generative AI, which can then adjust the personalities and roles of characters.

[0122] The presence unit can estimate a child's emotions and adjust the way it expresses the child's emotions through voice and animation. For example, if the child is excited, it can use energetic voice and dynamic animation. If the child is anxious, it can use calm voice and gentle animation. Furthermore, if the child is bored, it can use humorous voice and funny animation. This adjusts the way it expresses the child's emotions through voice and animation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presence unit may be performed using AI, or not using AI. For example, the presence unit can input child emotion data into the generative AI, which can then adjust the way it expresses the child's emotions through voice and animation.

[0123] The generation unit can include highly relevant content by referencing the child's past learning history when generating stories. For example, it can include relevant historical events in the story to review what the child has learned in the past. It can also generate new stories based on themes the child has shown interest in. Furthermore, it can focus on including content from areas where the child struggles to reinforce those areas. This results in the generation of highly relevant stories based on the child's past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the child's past learning history data into a generation AI, which can then generate stories containing highly relevant content.

[0124] The selection unit can present the most suitable option by referring to the child's past selection history when presenting choices. For example, it can present related options based on the options the child has previously chosen. It can also present different options based on the options the child has previously avoided. Furthermore, it can analyze the child's selection history and present the option that is most interesting to them. This ensures that the optimal option is presented based on the child's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's past selection history data into a generating AI, which can then present the optimal option.

[0125] The customization unit can suggest the optimal customization when customizing characters by referring to the child's past selection history. For example, it can suggest relevant customizations based on the characteristics of characters the child has previously chosen. It can also suggest different customizations based on the characteristics of characters the child has previously avoided. Furthermore, it can analyze the child's selection history and suggest the most interesting customization. This ensures that the optimal customization is suggested based on past selection history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the child's past selection history data into a generating AI, which can then suggest the optimal customization.

[0126] The immersion unit can provide optimal experiences by referring to the child's past learning history when creating an immersive experience. For example, it can use audio and animation related to themes the child has shown interest in in the past. It can also provide similar experiences based on the experience styles the child has enjoyed in the past. Furthermore, it can analyze the child's learning history and provide the most effective experience method. This ensures that the optimal experience is provided based on the child's past learning history. Some or all of the above processing in the immersion unit may be performed using AI, for example, or without AI. For example, the immersion unit can input the child's past learning history data into a generating AI, which can then provide the optimal experience.

[0127] The suggestion unit can make optimal suggestions by referring to the child's past learning history when proposing a learning plan. For example, it can propose an optimal learning plan based on the child's past learning history. It can also propose a learning plan for a specific subject based on the child's past learning history. Furthermore, it can analyze the child's learning history and propose the most effective learning plan. This results in the proposal of an optimal learning plan based on past learning history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the child's past learning history data into a generating AI, which can then propose an optimal learning plan.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The generation unit generates stories that allow children to experience important moments in history. Using generative AI, the generation unit creates stories based on historical events and figures that will interest children. For example, it can generate stories about battles in the Sengoku period or the construction of the pyramids in ancient Egypt. The generation unit can also adjust the content of the stories so that children can learn while experiencing important moments in history. Step 2: The selection section allows children to choose the era, region, and theme. The selection section allows children to choose their favorite era and region and enjoy stories related to that era and region. The selection section also provides options so that children can learn at their own pace according to their interests and preferences. Step 3: The customization section provides character customization and interactive choices. The customization section allows children to customize the characters themselves. It also provides interactive elements in the story where the plot changes depending on their choices. Step 4: The immersion section enhances the sense of realism with sound and animation. The immersion section can include voices of characters speaking within the story and animations that recreate historical scenes. This allows children to learn while having fun experiencing important moments in history.

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0133] Each of the multiple elements described above, including the generation unit, selection unit, customization unit, and realism unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and uses generation AI to create a story based on historical events and people. The selection unit is implemented by the specific processing unit 290 of the data processing device 12 and allows users to select the era, region, and theme. The customization unit is implemented by the control unit 46A of the smart device 14 and provides customization of characters and interactive choices. The realism unit is implemented by the specific processing unit 290 of the data processing device 12 and enhances the realism with sound and animation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 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.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the generation unit, selection unit, customization unit, and immersion unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and uses generation AI to create a story based on historical events and people. The selection unit is implemented by the specific processing unit 290 of the data processing device 12 and allows selection of era, region, and theme. The customization unit is implemented by the control unit 46A of the smart glasses 214 and provides customization of characters and interactive choices. The immersion unit is implemented by the specific processing unit 290 of the data processing device 12 and enhances the sense of presence with sound and animation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the generation unit, selection unit, customization unit, and immersion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and uses generation AI to create a story based on historical events and people. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows selection of era, region, and theme. The customization unit is implemented by the control unit 46A of the headset terminal 314 and provides customization of characters and interactive choices. The immersion unit is implemented by the specific processing unit 290 of the data processing unit 12 and enhances the sense of presence with sound and animation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the generation unit, selection unit, customization unit, and realism unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and uses generation AI to create a story based on historical events and people. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows selection of era, region, and theme. The customization unit is implemented by the control unit 46A of the robot 414 and provides customization of characters and interactive choices. The realism unit is implemented by the specific processing unit 290 of the data processing unit 12 and enhances realism with sound and animation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0183] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) A generation unit that generates stories in which children can experience important moments in history, A selection section where you can choose the era, region, and theme, The customization section offers character customization and interactive options, It features an immersive section that enhances the sense of presence with sound and animation, and A system characterized by the following features. (Note 2) It includes an analytics unit that automatically analyzes learning progress and comprehension levels and reports them to parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a suggestion section that identifies points to review and proposes the next learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a customization section that allows you to customize themes and content to suit children's interests and grade levels. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a selection section that automatically chooses appropriate difficulty levels and themes based on learning history. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features a lead-based learning system where AI takes over from parents and teachers and shares progress updates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The system estimates the child's emotions and adjusts the story's progression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Adjust the level of detail in historical events to generate stories that match the child's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating stories, refer to the child's past learning history to include highly relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates the child's emotions and adjusts the difficulty level of the story based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating stories, provide different historical perspectives based on children's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating stories, select appropriate content that takes into account the children's cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is The system estimates the child's emotions and adjusts the way choices are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is When presenting options, the system refers to the child's past choice history to suggest the most suitable option. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is When presenting options, adjust the number and content of options according to the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is The system estimates the child's emotions and adjusts the order of the options based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is When presenting options, offer different options based on the child's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is When presenting options, customize the way the options are presented according to the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned customization unit is The system estimates the children's emotions and adjusts the characters' personalities and roles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned customization unit is When customizing characters, the system refers to the child's past selection history to suggest the optimal customization. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned customization unit is When customizing characters, adjust the scope of customization according to the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned customization unit is The system estimates the child's emotions and adjusts the character's appearance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned customization unit is When customizing characters, offer different customization options based on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned customization unit is When customizing characters, we suggest appropriate customizations that take into account the child's cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned immersive section is, It estimates a child's emotions and adjusts the way voices and animations are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned immersive section is, When creating an immersive experience, the system provides optimal presentation by referencing the child's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned immersive section is, When creating an immersive experience, the level of detail in the presentation is adjusted according to the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned immersive section is, It estimates the child's emotions and adjusts the timing of audio and animations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned immersive section is, When creating an immersive experience, offer different presentation options based on children's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned immersive section is, When creating an immersive experience, select appropriate techniques that take into account the cultural background of children. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is We estimate the child's emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit is When analyzing learning progress and comprehension, referencing the child's past learning history improves accuracy. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned analysis unit is We estimate the child's emotions and adjust the presentation method of the analysis results based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned analysis unit is When analyzing learning progress and comprehension, provide different analytical perspectives based on the child's interests and concerns. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned proposal section is, We estimate the child's emotions and adjust the method of suggesting the next learning plan based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When proposing a learning plan, we refer to the child's past learning history to make the most appropriate suggestions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned proposal section is, Estimate the child's emotions and determine the priority of proposals based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When proposing a learning plan, offer different suggestion options based on the child's interests and preferences. The system described in Appendix 3, characterized by the features described herein. (Note 39) The lead portion is, It estimates the child's emotions and adjusts the learning approach based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 40) The lead portion is, When leading a child through a learning process, the system provides the optimal leading method by referring to the child's past learning history. The system described in Appendix 6, characterized by the features described herein. (Note 41) The lead portion is, The system estimates the child's emotions and adjusts how learning progress is shared based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 42) The lead portion is, When leading a learning session, provide different leading options based on the child's interests and concerns. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that generates stories in which children can experience important moments in history, A selection section where you can choose the era, region, and theme, The customization section offers character customization and interactive options, It features an immersive section that enhances the sense of presence with sound and animation, and A system characterized by the following features.

2. It includes an analytics unit that automatically analyzes learning progress and comprehension levels and reports them to parents and teachers. The system according to feature 1.

3. It includes a suggestion section that identifies points to review and proposes the next learning plan. The system according to feature 1.

4. It features a customization section that allows you to customize themes and content to suit children's interests and grade levels. The system according to feature 1.

5. It features a selection section that automatically chooses appropriate difficulty levels and themes based on learning history. The system according to feature 1.

6. It features a lead-based system where AI takes over the learning process from parents and teachers and shares progress updates. The system according to feature 1.

7. The generating unit is The system estimates the child's emotions and adjusts the story's progression based on those estimated emotions. The system according to feature 1.

8. The generating unit is Adjust the level of detail in historical events to generate stories that match the child's level of understanding. The system according to feature 1.

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