system

Abstract

The system according to the embodiment aims to enable children to easily create picture books based on stories they have thought up themselves. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit inputs a story that a child has thought up themselves. The analysis unit analyzes the story input by the reception unit and adds to the story. The creation unit creates images based on the story added to by the analysis unit. The creation unit combines the story and images generated by the creation unit to create a picture book.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of creating a picture book based on a story that a child came up with themselves was complicated and time-consuming.

[0005] The system according to the embodiment aims to enable children to easily create picture books based on stories they have thought up themselves. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit inputs a story that a child has thought up themselves. The analysis unit analyzes the story input by the reception unit and adds to the story. The creation unit creates images based on the story added by the analysis unit. The creation unit combines the story and images generated by the creation unit to create a picture book. [Effects of the Invention]

[0007] The system according to the embodiment allows children to easily create picture books based on stories they have thought up themselves. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A picture book creation system according to an embodiment of the present invention is a system for creating a picture book by supplementing a story and creating images based on a story that a child has created themselves. The picture book creation system inputs a story that a child has created themselves, and a generation AI analyzes the story, supplements the story, creates images based on the story, and combines the generated story and images to create a picture book. For example, the picture book creation system inputs a story that a child has created themselves. For example, the picture book creation system analyzes the story input in text format, and the generation AI analyzes the flow of the story using natural language processing technology and supplements it based on an existing story database. Next, the picture book creation system uses the generation AI to generate images corresponding to each scene in the story. The generation AI uses deep learning technology to generate images corresponding to each scene in the story. For example, the generation AI receives a prompt such as, "Please generate an image that matches this scene," and generates an image that matches the scene. Next, the picture book creation system combines the generated story and images to create a picture book. A user can preview the generated picture book and use an interface to change images and edit text using drag and drop. This allows an original picture book based on a story that a child has created themselves to be completed. As a result, the picture book creation system can foster children's creativity, be used as an educational tool, and support the expansion of picture book creation. For example, by having children turn their own stories into picture books in school classes, it can cultivate their creativity and expressiveness. Also, by creating picture books together at home, parents and children can deepen their communication.

[0029] A picture book creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit inputs a story created by a child. The story created by a child may include, but is not limited to, text, pictures, and audio. The reception unit may provide, for example, a text input interface, allowing the child to input the story using a keyboard. The reception unit may also provide a voice input interface, allowing the child to dictate the story using a microphone. The reception unit may also provide an image input interface, allowing the child to scan a drawing drawn by the child and input it as the story. For example, the reception unit may provide a text input interface, allowing the child to input the story using a keyboard. The reception unit may also provide a voice input interface, allowing the child to dictate the story using a microphone. The reception unit may also provide an image input interface, allowing the child to scan a drawing drawn by the child and input it as the story. The analysis unit uses a generation AI to analyze the story input by the reception unit and supplement the narrative. The analysis may be performed using, for example, natural language processing technology, but is not limited to, the example. For example, the generation AI analyzes the flow of a story using a text generation AI (e.g., LLM) and supplements it based on an existing story database. The analysis unit can also perform grammatical analysis and semantic analysis to supplement the flow of the story. The analysis unit can also use the generation AI to extract important parts of the story and supplement it. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The generation AI uses keyword extraction technology to pick out particularly important information in the story and supplement it based on that. The generation unit uses the generation AI to create images based on the story supplemented by the analysis unit. Image creation is performed using, for example, deep learning technology, but is not limited to such an example. For example, the generation AI uses an image generation AI (e.g., GAN) to generate images corresponding to each scene in the story. The generation unit can also use multimodal generation AI to generate images based on the content of the story.The generation unit can also use the generation AI to extract important parts of a text and generate images based on them. For example, the image generation AI has learned from a large amount of image data and has advanced image generation capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information in a story and generate images based on that information. The creation unit combines the story generated by the generation unit with images to create a picture book. Creation can include, but is not limited to, layout of images and text. For example, the creation unit provides an interface that allows users to preview the generated picture book and change images and edit text using drag and drop. The creation unit can also convert the generated picture book into a format for printing. The creation unit can also save the generated picture book as an e-book. For example, the creation unit provides an interface that allows users to preview the generated picture book and change images and edit text using drag and drop. The creation unit can also convert the generated picture book into a format for printing. The creation unit can also save the generated picture book as an e-book. As a result, the picture book creation system according to the embodiment can supplement the story and create images based on a story that a child has thought up himself, and create a picture book.

[0030] The analysis unit can analyze the flow of the story using natural language processing technology and supplement the story based on an existing story database. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can analyze each word of the story using morphological analysis and analyze the structure of sentences using grammatical analysis. The analysis unit can also understand the meaning of the story using semantic analysis and supplement the story based on an existing story database. For example, the analysis unit can analyze each word of the story using morphological analysis and analyze the structure of sentences using grammatical analysis. The analysis unit can also understand the meaning of the story using semantic analysis and supplement the story based on an existing story database. In this way, the natural language processing technology can be used to analyze the flow of the story and provide appropriate supplementation. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input a story to a generation AI to analyze the flow of the story, and the generation AI can analyze the flow of the story and provide supplementation.

[0031] The generation unit can generate images corresponding to each scene of the story using deep learning technology. Deep learning technology includes, but is not limited to, for example, a convolutional neural network (CNN) and a generative model. The generation unit can also generate images corresponding to each scene of the story using a generative model. For example, the generation unit can generate images corresponding to each scene of the story using a convolutional neural network (CNN). The generation unit can also generate images corresponding to each scene of the story using a generative model. In this way, images corresponding to each scene of the story can be generated using deep learning technology. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the generation unit can input a story to a generative AI to generate images corresponding to each scene of the story, and the generative AI can generate the images.

[0032] The creation unit may provide an interface that allows a user to preview the generated picture book and change images and edit text by dragging and dropping. Examples of the interface include, but are not limited to, a drag-and-drop function and a text editing function. For example, the creation unit may provide a drag-and-drop function that allows a user to change images by dragging and dropping. The creation unit may also provide a text editing function that allows a user to edit text. For example, the creation unit may provide a drag-and-drop function that allows a user to change images by dragging and dropping. The creation unit may also provide a text editing function that allows a user to edit text. This allows a user to preview the generated picture book and edit images and text. Some or all of the above-described processing in the creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the creation unit may preview the generated picture book and cause the generation AI to edit images and text.

[0033] The reception unit can analyze the child's past story input history and select the optimal input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the child has previously preferred voice input, the reception unit can preferentially suggest voice input. Furthermore, if the child has previously frequently used text input, the reception unit can enhance the text input interface. Furthermore, if the child has previously frequently used images, the reception unit can provide an interface that supports image input. For example, if the child has previously preferred voice input, the reception unit can preferentially suggest voice input. Furthermore, if the child has previously frequently used text input, the reception unit can enhance the text input interface. Furthermore, if the child has previously frequently used images, the reception unit can provide an interface that supports image input. In this way, the optimal input method can be selected by analyzing the child's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the child's past input history into the generation AI, which can then select the optimal input method.

[0034] When a story is input, the reception unit can filter the input based on the child's current interests and concerns. Interests and concerns can be identified based on, for example, survey results, past input history, etc., but are not limited to these examples. For example, if a child has recently shown an interest in dinosaurs, the reception unit can preferentially accept input of stories related to dinosaurs. The reception unit can also prompt the child to enter related stories based on the themes of books the child has recently read. The reception unit can also suggest related story input based on characters in movies the child has recently watched. For example, if a child has recently shown an interest in dinosaurs, the reception unit can preferentially accept input of stories related to dinosaurs. The reception unit can also prompt the child to enter related stories based on the themes of books the child has recently read. The reception unit can also suggest related story input based on characters in movies the child has recently watched. In this way, by filtering the story input based on the child's interests and concerns, more interesting stories can be input. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the child's interests and concerns into the generation AI, which can then perform the filtering.

[0035] When inputting a story, the reception unit can select an appropriate input means depending on the child's input method. Examples of input means include, but are not limited to, voice input, text input, and image input. For example, if the child selects voice input, the reception unit inputs the story using voice recognition technology. Furthermore, if the child selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the child selects image input, the reception unit can input the story using image recognition technology. For example, if the child selects voice input, the reception unit inputs the story using voice recognition technology. Furthermore, if the child selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the child selects image input, the reception unit can input the story using image recognition technology. This allows for smooth story input by selecting the optimal input means depending on the child's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's input method to the generation AI, which can then select an appropriate input means.

[0036] When inputting a story, the reception unit can prioritize inputting highly relevant stories by taking into account the child's geographical location information. The geographical location information is obtained based on, for example, GPS data, local cultural information, etc., but is not limited to these examples. For example, if the child is at the beach, the reception unit can prioritize inputting stories related to the sea. Furthermore, if the child is in the mountains, the reception unit can prioritize inputting stories related to mountains. Furthermore, if the child is in an urban area, the reception unit can prioritize inputting stories related to cities. For example, if the child is at the beach, the reception unit can prioritize inputting stories related to the sea. Furthermore, if the child is in the mountains, the reception unit can prioritize inputting stories related to mountains. Furthermore, if the child is in an urban area, the reception unit can prioritize inputting stories related to cities. In this way, highly relevant stories can be prioritized by taking the child's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's geographical location information to the generation AI, which can then prioritize inputting highly relevant stories.

[0037] When inputting a story, the reception unit can analyze the child's social media activity and input related stories. Social media activity is analyzed based on, for example, the content of posts, the number of likes, etc., but is not limited to these examples. The reception unit can input related stories based on, for example, content shared by the child on social media. The reception unit can also input related stories with reference to the activity of the child's friends on social media. The reception unit can also analyze the content posted by the child on social media and input related stories. For example, the reception unit can input related stories based on the content shared by the child on social media. The reception unit can also input related stories with reference to the activity of the child's friends on social media. The reception unit can also analyze the content posted by the child on social media and input related stories. In this way, related stories can be input by analyzing the child's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's social media activity to the generation AI, which can then input related stories.

[0038] When inputting a story, the reception unit can customize the input method by reflecting the child's past feedback. The feedback can be reflected based on, for example, past ratings, comments, etc., but is not limited to such examples. For example, if the child has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the child has frequently used text input in the past, the reception unit can enhance the text input interface. Furthermore, if the child has frequently used images in the past, the reception unit can provide an interface that supports image input. For example, if the child has preferred voice input in the past, the reception unit preferentially suggests voice input. Furthermore, if the child has frequently used text input in the past, the reception unit can enhance the text input interface. Furthermore, if the child has frequently used images in the past, the reception unit can provide an interface that supports image input. In this way, the input method can be customized by reflecting the child's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's past feedback into the generation AI, which can customize the input method.

[0039] When supplementing a story, the analysis unit can adjust the level of detail of the supplement based on the importance of the story. The importance of the story is evaluated based on, for example, the length of the story, the number of main characters, etc., but is not limited to such examples. For example, the analysis unit provides detailed supplementary information in important scenes to add depth to the story. The analysis unit can also provide brief supplementary information in unimportant scenes to smooth the flow of the story. The analysis unit can also provide particularly detailed supplementary information in climax scenes to emphasize the excitement of the story. For example, the analysis unit provides detailed supplementary information in important scenes to add depth to the story. The analysis unit can also provide brief supplementary information in unimportant scenes to smooth the flow of the story. The analysis unit can also provide particularly detailed supplementary information in climax scenes to emphasize the excitement of the story. In this way, by adjusting the level of detail of the supplementary information based on the importance of the story, the depth of the story can be increased. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of a story into the generation AI, which can then adjust the level of detail in the supplementary information.

[0040] When supplementing a story, the analysis unit can apply different supplementation algorithms depending on the story category. Story categories are classified into, for example, adventure, fantasy, and education, but are not limited to these examples. For example, the analysis unit can apply an algorithm that supplements magical and adventurous elements to stories in the fantasy category. The analysis unit can also apply an algorithm that supplements futuristic technology and space elements to stories in the science fiction category. The analysis unit can also apply an algorithm that supplements humorous and comedic elements to stories in the comedy category. For example, the analysis unit can apply an algorithm that supplements magical and adventurous elements to stories in the fantasy category. The analysis unit can also apply an algorithm that supplements futuristic technology and space elements to stories in the science fiction category. The analysis unit can also apply an algorithm that supplements humorous and comedic elements to stories in the comedy category. In this way, by applying different supplementation algorithms depending on the story category, more appropriate supplementation can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the story into the generation AI, which can then apply different supplemental algorithms.

[0041] When supplementing a story, the analysis unit can improve the accuracy of the supplement by referring to the child's past supplement results. Past supplement results may be referenced, for example, based on past successes and failures, but are not limited to such examples. For example, the analysis unit can analyze the patterns of supplements that the child has previously preferred and provide similar supplements. The analysis unit can also avoid supplement patterns that the child has previously dissatisfied with. The analysis unit can also apply an algorithm to improve the accuracy of the supplement based on the child's past supplement results. For example, the analysis unit can analyze the patterns of supplements that the child has previously preferred and provide similar supplements. The analysis unit can also avoid supplement patterns that the child has previously dissatisfied with. The analysis unit can also apply an algorithm to improve the accuracy of the supplement based on the child's past supplement results. In this way, the accuracy of the supplement can be improved by referring to the child's past supplement results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the child's past supplement results into the generation AI, which can then improve the accuracy of the supplement.

[0042] When supplementing a story, the analysis unit can determine the priority of the supplement based on the time of submission of the story. The time of submission is evaluated based on, for example, the submission date, the submission time, etc., but is not limited to such examples. For example, the analysis unit prioritizes supplementing a story that has just been submitted. The analysis unit can also postpone stories that have been submitted a long time ago. The analysis unit can also dynamically adjust the priority of the supplement based on the time of submission. For example, the analysis unit prioritizes supplementing a story that has just been submitted. The analysis unit can also postpone stories that have been submitted a long time ago. The analysis unit can also dynamically adjust the priority of the supplement based on the time of submission. In this way, by determining the priority of the supplement based on the time of submission of the story, efficient supplementation can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the time of submission of the story into the generation AI, and the generation AI can determine the priority of the supplement.

[0043] When supplementing a story, the analysis unit can adjust the order of the supplement based on the relevance of the story. The relevance of the story is evaluated based on, for example, thematic similarity, character commonality, etc., but is not limited to these examples. For example, the analysis unit prioritizes supplements related to the main theme of the story. The analysis unit can also postpone supplements related to sub-themes of the story. The analysis unit can also dynamically adjust the order of supplements based on the relevance of the story. For example, the analysis unit prioritizes supplements related to the main theme of the story. The analysis unit can also postpone supplements related to sub-themes of the story. The analysis unit can also dynamically adjust the order of supplements based on the relevance of the story. By adjusting the order of supplements based on the relevance of the story, efficient supplements can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the story into the generation AI, which can then adjust the order of supplements.

[0044] When supplementing a story, the analysis unit can adjust the use of supplementary terminology according to the child's level of expertise. The level of expertise is evaluated based on, for example, age, learning history, etc., but is not limited to such examples. For example, if the child has specialized knowledge, the analysis unit can provide supplementary terminology that uses a lot of specialized terminology. Furthermore, if the child does not have specialized knowledge, the analysis unit can provide supplementary terminology that explains things in simple terms. Furthermore, the analysis unit can dynamically adjust the use of supplementary terminology according to the child's level of expertise. For example, if the child has specialized knowledge, the analysis unit can provide supplementary terminology that explains things in simple terms. Furthermore, the analysis unit can dynamically adjust the use of supplementary terminology according to the child's level of expertise. In this way, adjusting the use of supplementary terminology according to the child's level of expertise allows for more appropriate supplementation. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the child's level of expertise into the generation AI, which can then adjust the use of supplementary terminology.

[0045] When generating images, the generation unit can adjust the level of detail of the images based on the importance of the story. The importance of the story is evaluated based on, for example, the length of the story, the number of main characters, etc., but is not limited to these examples. For example, the generation unit generates images including detailed depictions for important scenes. The generation unit can also generate images including concise depictions for unimportant scenes. The generation unit can also generate images including particularly detailed depictions for climax scenes. For example, the generation unit generates images including detailed depictions for important scenes. The generation unit can also generate images including concise depictions for unimportant scenes. The generation unit can also generate images including particularly detailed depictions for climax scenes. In this way, by adjusting the level of detail of the images based on the importance of the story, more appropriate images are generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the story into the generation AI, and the generation AI can adjust the level of detail of the images.

[0046] When generating images, the generation unit can apply different generation algorithms depending on the story category. Story categories are classified into, for example, adventure, fantasy, education, etc., but are not limited to these examples. For example, the generation unit applies an algorithm to generate images including magical and adventurous elements to stories in the fantasy category. Furthermore, the generation unit can also apply an algorithm to generate images including futuristic technology and space elements to stories in the science fiction category. Furthermore, the generation unit can also apply an algorithm to generate images including humorous and comedic elements to stories in the comedy category. For example, the generation unit applies an algorithm to generate images including magical and adventurous elements to stories in the fantasy category. Furthermore, the generation unit can also apply an algorithm to generate images including futuristic technology and space elements to stories in the science fiction category. Furthermore, the generation unit can also apply an algorithm to generate images including humorous and comedic elements to stories in the comedy category. Thus, by applying different generation algorithms depending on the story category, more appropriate images are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a story category to the generation AI, which can then apply different generation algorithms.

[0047] When generating an image, the generation unit can improve the accuracy of generation by referring to the child's past generation results. Past generation results can be referenced, for example, based on past successes and failures, but are not limited to such examples. For example, the generation unit can analyze the style of images that the child previously preferred and generate an image in a similar style. The generation unit can also avoid image styles that the child previously disliked. The generation unit can also apply an algorithm that improves the accuracy of generation based on the child's past generation results. For example, the generation unit can analyze the style of images that the child previously preferred and generate an image in a similar style. The generation unit can also avoid image styles that the child previously disliked. The generation unit can also apply an algorithm that improves the accuracy of generation based on the child's past generation results. In this way, the accuracy of generation can be improved by referring to the child's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the child's past generation results into the generation AI, which can improve the accuracy of generation.

[0048] When generating images, the generation unit can determine the generation priority based on the submission time of the story. The submission time is evaluated based on, for example, the submission date, the submission time, etc., but is not limited to these examples. For example, the generation unit prioritizes image generation for recently submitted stories. The generation unit can also postpone stories that have been submitted for a long time. The generation unit can also dynamically adjust the priority of image generation based on the submission time. For example, the generation unit prioritizes image generation for recently submitted stories. The generation unit can also postpone stories that have been submitted for a long time. The generation unit can also dynamically adjust the priority of image generation based on the submission time. In this way, determining the generation priority based on the submission time of the story enables efficient image generation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of the story into the generation AI, and the generation AI can determine the generation priority.

[0049] When generating images, the generation unit can adjust the order of generation based on the relevance of the story. The relevance of the story is evaluated based on, for example, a matching theme, commonality of characters, etc., but is not limited to these examples. For example, the generation unit prioritizes generating images related to the main theme of the story. The generation unit can also postpone images related to sub-themes of the story. The generation unit can also dynamically adjust the order of image generation based on the relevance of the story. For example, the generation unit prioritizes generating images related to the main theme of the story. The generation unit can also postpone images related to sub-themes of the story. The generation unit can also dynamically adjust the order of image generation based on the relevance of the story. In this way, adjusting the order of generation based on the relevance of the story enables efficient image generation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the story to the generation AI, and the generation AI can adjust the order of generation.

[0050] When generating an image, the generation unit can adjust the use of technical terminology in the generated image according to the child's level of expertise. The level of expertise is evaluated based on, for example, age, learning history, etc., but is not limited to such examples. For example, if the child has technical expertise, the generation unit generates an image that uses a lot of technical terminology. Furthermore, if the child does not have technical expertise, the generation unit can generate an image that explains things in simple terms. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generated image according to the child's level of expertise. For example, if the child has technical expertise, the generation unit generates an image that uses a lot of technical terminology. Furthermore, if the child does not have technical expertise, the generation unit can generate an image that explains things in simple terms. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generated image according to the child's level of expertise. In this way, by adjusting the use of technical terminology in the generated image according to the child's level of expertise, more appropriate images are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the child's level of expertise into the generation AI, and the generation AI can adjust the use of technical terminology in the generated image.

[0051] When creating a picture book, the creation unit can adjust the level of detail in the creation based on the importance of the story. The importance of the story is evaluated based on, for example, the length of the story, the number of main characters, etc., but is not limited to these examples. For example, the creation unit can create pages including detailed descriptions for important scenes. The creation unit can also create pages including concise descriptions for unimportant scenes. The creation unit can also create pages including particularly detailed descriptions for climax scenes. For example, the creation unit can create pages including detailed descriptions for important scenes. The creation unit can also create pages including concise descriptions for unimportant scenes. The creation unit can also create pages including particularly detailed descriptions for climax scenes. In this way, by adjusting the level of detail in the creation based on the importance of the story, a more appropriate picture book is created. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the importance of the story into the generation AI, and the generation AI can adjust the level of detail in the creation.

[0052] When creating a picture book, the creation unit can apply different creation algorithms depending on the story category. Story categories are classified into, for example, adventure, fantasy, and education, but are not limited to these examples. For example, the creation unit can apply an algorithm to create pages including magic and adventure elements to stories in the fantasy category. The creation unit can also apply an algorithm to create pages including futuristic technology and space elements to stories in the science fiction category. The creation unit can also apply an algorithm to create pages including humor and comedy elements to stories in the comedy category. For example, the creation unit can apply an algorithm to create pages including magic and adventure elements to stories in the fantasy category. The creation unit can also apply an algorithm to create pages including futuristic technology and space elements to stories in the science fiction category. The creation unit can also apply an algorithm to create pages including humor and comedy elements to stories in the comedy category. In this way, by applying different creation algorithms depending on the story category, a more appropriate picture book can be created. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input a story category to the generation AI, which can then apply different creation algorithms.

[0053] When creating a picture book, the creation unit can improve the accuracy of the creation by referring to the child's past creation results. Past creation results can be referenced, for example, based on past successes and failures, but are not limited to such examples. For example, the creation unit can analyze the style of picture books that the child previously liked and create a picture book in a similar style. The creation unit can also avoid picture book styles that the child previously disliked. The creation unit can also apply an algorithm to improve the accuracy of the creation based on the child's past creation results. For example, the creation unit can analyze the style of picture books that the child previously liked and create a picture book in a similar style. The creation unit can also avoid picture book styles that the child previously disliked. The creation unit can also apply an algorithm to improve the accuracy of the creation based on the child's past creation results. In this way, the accuracy of the creation can be improved by referring to the child's past creation results. Some or all of the above-mentioned processing in the creation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the creation unit can input the child's past creation results into the generation AI, which can improve the accuracy of the creation.

[0054] When creating a picture book, the creation unit can determine the creation priority based on the submission time of the story. The submission time is evaluated based on, for example, the submission date, the submission time, etc., but is not limited to such examples. For example, the creation unit prioritizes creating a picture book for a story that has just been submitted. The creation unit can also postpone stories that have been submitted a long time ago. The creation unit can also dynamically adjust the priority of creating a picture book based on the submission time. For example, the creation unit prioritizes creating a picture book for a story that has just been submitted. The creation unit can also postpone stories that have been submitted a long time ago. The creation unit can also dynamically adjust the priority of creating a picture book based on the submission time. In this way, by determining the creation priority based on the submission time of the story, picture books can be created efficiently. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the submission time of the story into the generation AI, and the generation AI can determine the creation priority.

[0055] When creating a picture book, the creation unit can adjust the creation order based on the relevance of the stories. The relevance of the stories is evaluated based on, for example, thematic consistency, commonality of characters, etc., but is not limited to these examples. For example, the creation unit prioritizes creating pages related to the main theme of the story. The creation unit can also postpone pages related to sub-themes of the story. The creation unit can also dynamically adjust the order of picture book creation based on the relevance of the stories. For example, the creation unit prioritizes creating pages related to the main theme of the story. The creation unit can also postpone pages related to sub-themes of the story. The creation unit can also dynamically adjust the order of picture book creation based on the relevance of the stories. In this way, adjusting the order of creation based on the relevance of the stories enables efficient picture book creation. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the relevance of the stories into the generation AI, and the generation AI can adjust the order of creation.

[0056] When creating a picture book, the creation unit can adjust the use of technical terms in the creation depending on the child's level of expertise. The level of expertise is evaluated based on, for example, age, learning history, etc., but is not limited to such examples. For example, if the child has technical expertise, the creation unit can create a picture book that uses a lot of technical terms. Also, if the child does not have technical expertise, the creation unit can create a picture book that explains things in simple terms. The creation unit can also dynamically adjust the use of technical terms in the creation depending on the child's level of expertise. For example, if the child has technical expertise, the creation unit can create a picture book that uses a lot of technical terms. Also, if the child does not have technical expertise, the creation unit can create a picture book that explains things in simple terms. Also, the creation unit can dynamically adjust the use of technical terms in the creation depending on the child's level of expertise. In this way, by adjusting the use of technical terms in the creation depending on the child's level of expertise, a more appropriate picture book is created. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the child's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the creation.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When analyzing a story, the analysis unit analyzes the themes and character trends of the child's past stories and can provide appropriate supplements to the current story. For example, if a child has written many animal-themed stories in the past, supplements related to animals can be prioritized. Also, if a child likes adventure-themed stories, supplements can be provided that emphasize adventure elements. Furthermore, if a child frequently uses a particular character, supplements related to that character can be provided. This makes it possible to provide a more consistent story by providing supplements that reflect the trends of the child's past stories.

[0059] When previewing a generated picture book, the creation unit can customize the interface by reflecting the child's past feedback. For example, if a child has previously preferred a particular layout, that layout can be displayed preferentially. Also, if a child has previously preferred a particular font or color, those can be provided as default settings. Furthermore, by avoiding elements that the child has previously disliked, a more comfortable preview environment can be provided. In this way, by providing an interface that reflects the child's past feedback, a more user-friendly picture book creation environment can be realized.

[0060] When analyzing a story, the analysis unit can adjust the supplementary content based on the child's current interests. For example, if a child has recently become interested in dinosaurs, it will prioritize supplementary content related to dinosaurs. It can also provide related supplementary content based on the theme of a book the child has recently read. It can also provide related supplementary content based on characters in a movie the child has recently seen. This makes it possible to provide a more interesting story by providing supplementary content based on the child's interests.

[0061] When generating an image, the generation unit can improve the accuracy of generation by referring to the child's past generation results. For example, the generation unit analyzes the image styles that the child has liked in the past and generates an image in a similar style. It can also avoid image styles that the child has disliked in the past. Furthermore, an algorithm that improves the accuracy of generation can be applied based on the child's past generation results. In this way, the accuracy of generation can be improved by referring to the child's past generation results.

[0062] The reception unit can analyze the child's past story input history and select the optimal input method. For example, if the child has previously preferred voice input, it can preferentially suggest voice input. Also, if the child has previously used text input frequently, it can enhance the text input interface. Furthermore, if the child has previously used images frequently, it can provide an interface that supports image input. In this way, the optimal input method can be selected by analyzing the child's past input history.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit inputs a story that the child has thought up himself / herself. The story that the child has thought up himself / herself may include text, pictures, voice, etc. The reception unit provides a text input interface, a voice input interface, and an image input interface, and the child can input the story using a keyboard, microphone, or scanner. Step 2: The analysis unit uses the generation AI to analyze the story entered by the reception unit and supplement the story. The analysis is performed using natural language processing technology, and a text generation AI (e.g., LLM) is used to analyze the flow of the story and supplement it based on an existing story database. It can also perform grammatical analysis and semantic analysis to supplement the flow of the story. Furthermore, the generation AI uses keyword extraction technology to extract important parts of the story and supplement it. Step 3: The generation unit uses a generation AI to create images based on the story supplemented by the analysis unit. Image creation is carried out using deep learning technology, and image generation AI (e.g., GAN) is used to generate images corresponding to each scene in the story. Images can also be generated based on the content of the story using multimodal generation AI. The generation AI uses keyword extraction technology to pick out particularly important information in the story and generate images based on that information. Step 4: The creation unit combines the story and images generated by the generation unit to create a picture book. The creation unit provides an interface that allows users to layout images and text, preview the generated picture book, change images by drag and drop, and edit text. The created picture book can also be converted into a format for printing or saved as an e-book.

[0065] (Example 2) A picture book creation system according to an embodiment of the present invention is a system for creating a picture book by supplementing a story and creating images based on a story that a child has created themselves. The picture book creation system inputs a story that a child has created themselves, and a generation AI analyzes the story, supplements the story, creates images based on the story, and combines the generated story and images to create a picture book. For example, the picture book creation system inputs a story that a child has created themselves. For example, the picture book creation system analyzes the story input in text format, and the generation AI analyzes the flow of the story using natural language processing technology and supplements it based on an existing story database. Next, the picture book creation system uses the generation AI to generate images corresponding to each scene in the story. The generation AI uses deep learning technology to generate images corresponding to each scene in the story. For example, the generation AI receives a prompt such as, "Please generate an image that matches this scene," and generates an image that matches the scene. Next, the picture book creation system combines the generated story and images to create a picture book. A user can preview the generated picture book and use an interface to change images and edit text using drag and drop. This allows an original picture book based on a story that a child has created themselves to be completed. As a result, the picture book creation system can foster children's creativity, be used as an educational tool, and support the expansion of picture book creation. For example, by having children turn their own stories into picture books in school classes, it can cultivate their creativity and expressiveness. Also, by creating picture books together at home, parents and children can deepen their communication.

[0066] A picture book creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit inputs a story created by a child. The story created by a child may include, but is not limited to, text, pictures, and audio. The reception unit may provide, for example, a text input interface, allowing the child to input the story using a keyboard. The reception unit may also provide a voice input interface, allowing the child to dictate the story using a microphone. The reception unit may also provide an image input interface, allowing the child to scan a drawing drawn by the child and input it as the story. For example, the reception unit may provide a text input interface, allowing the child to input the story using a keyboard. The reception unit may also provide a voice input interface, allowing the child to dictate the story using a microphone. The reception unit may also provide an image input interface, allowing the child to scan a drawing drawn by the child and input it as the story. The analysis unit uses a generation AI to analyze the story input by the reception unit and supplement the narrative. The analysis may be performed using, for example, natural language processing technology, but is not limited to, the example. For example, the generation AI analyzes the flow of a story using a text generation AI (e.g., LLM) and supplements it based on an existing story database. The analysis unit can also perform grammatical analysis and semantic analysis to supplement the flow of the story. The analysis unit can also use the generation AI to extract important parts of the story and supplement it. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The generation AI uses keyword extraction technology to pick out particularly important information in the story and supplement it based on that. The generation unit uses the generation AI to create images based on the story supplemented by the analysis unit. Image creation is performed using, for example, deep learning technology, but is not limited to such an example. For example, the generation AI uses an image generation AI (e.g., GAN) to generate images corresponding to each scene in the story. The generation unit can also use multimodal generation AI to generate images based on the content of the story.The generation unit can also use the generation AI to extract important parts of a text and generate images based on them. For example, the image generation AI has learned from a large amount of image data and has advanced image generation capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information in a story and generate images based on that information. The creation unit combines the story generated by the generation unit with images to create a picture book. Creation can include, but is not limited to, layout of images and text. For example, the creation unit provides an interface that allows users to preview the generated picture book and change images and edit text using drag and drop. The creation unit can also convert the generated picture book into a format for printing. The creation unit can also save the generated picture book as an e-book. For example, the creation unit provides an interface that allows users to preview the generated picture book and change images and edit text using drag and drop. The creation unit can also convert the generated picture book into a format for printing. The creation unit can also save the generated picture book as an e-book. As a result, the picture book creation system according to the embodiment can supplement the story and create images based on a story that a child has thought up himself, and create a picture book.

[0067] The analysis unit can analyze the flow of the story using natural language processing technology and supplement the story based on an existing story database. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can analyze each word of the story using morphological analysis and analyze the structure of sentences using grammatical analysis. The analysis unit can also understand the meaning of the story using semantic analysis and supplement the story based on an existing story database. For example, the analysis unit can analyze each word of the story using morphological analysis and analyze the structure of sentences using grammatical analysis. The analysis unit can also understand the meaning of the story using semantic analysis and supplement the story based on an existing story database. In this way, the natural language processing technology can be used to analyze the flow of the story and provide appropriate supplementation. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input a story to a generation AI to analyze the flow of the story, and the generation AI can analyze the flow of the story and provide supplementation.

[0068] The generation unit can generate images corresponding to each scene of the story using deep learning technology. Deep learning technology includes, but is not limited to, for example, a convolutional neural network (CNN) and a generative model. The generation unit can also generate images corresponding to each scene of the story using a generative model. For example, the generation unit can generate images corresponding to each scene of the story using a convolutional neural network (CNN). The generation unit can also generate images corresponding to each scene of the story using a generative model. In this way, images corresponding to each scene of the story can be generated using deep learning technology. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the generation unit can input a story to a generative AI to generate images corresponding to each scene of the story, and the generative AI can generate the images.

[0069] The creation unit may provide an interface that allows a user to preview the generated picture book and change images and edit text by dragging and dropping. Examples of the interface include, but are not limited to, a drag-and-drop function and a text editing function. For example, the creation unit may provide a drag-and-drop function that allows a user to change images by dragging and dropping. The creation unit may also provide a text editing function that allows a user to edit text. For example, the creation unit may provide a drag-and-drop function that allows a user to change images by dragging and dropping. The creation unit may also provide a text editing function that allows a user to edit text. This allows a user to preview the generated picture book and edit images and text. Some or all of the above-described processing in the creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the creation unit may preview the generated picture book and cause the generation AI to edit images and text.

[0070] The reception unit can estimate the child's emotions and adjust the timing of story input based on the estimated child's emotions. The child's emotions are estimated using technologies such as, but not limited to, facial expression recognition and voice analysis. The reception unit can, for example, analyze the child's facial expression using facial expression recognition technology to estimate the emotion. The reception unit can also analyze the child's tone and speed of voice using voice analysis technology to estimate the emotion. For example, the reception unit can analyze the child's facial expression using facial expression recognition technology to estimate the emotion. The reception unit can also analyze the child's tone and speed of voice using voice analysis technology to estimate the emotion. This makes it possible to adjust the timing of story input based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the child's facial expressions and voice data into the generation AI to estimate the child's emotions, and the generation AI can estimate the emotions and adjust the input timing.

[0071] The reception unit can analyze the child's past story input history and select the optimal input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the child has previously preferred voice input, the reception unit can preferentially suggest voice input. Furthermore, if the child has previously frequently used text input, the reception unit can enhance the text input interface. Furthermore, if the child has previously frequently used images, the reception unit can provide an interface that supports image input. For example, if the child has previously preferred voice input, the reception unit can preferentially suggest voice input. Furthermore, if the child has previously frequently used text input, the reception unit can enhance the text input interface. Furthermore, if the child has previously frequently used images, the reception unit can provide an interface that supports image input. In this way, the optimal input method can be selected by analyzing the child's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the child's past input history into the generation AI, which can then select the optimal input method.

[0072] When a story is input, the reception unit can filter the input based on the child's current interests and concerns. Interests and concerns can be identified based on, for example, survey results, past input history, etc., but are not limited to these examples. For example, if a child has recently shown an interest in dinosaurs, the reception unit can preferentially accept input of stories related to dinosaurs. The reception unit can also prompt the child to enter related stories based on the themes of books the child has recently read. The reception unit can also suggest related story input based on characters in movies the child has recently watched. For example, if a child has recently shown an interest in dinosaurs, the reception unit can preferentially accept input of stories related to dinosaurs. The reception unit can also prompt the child to enter related stories based on the themes of books the child has recently read. The reception unit can also suggest related story input based on characters in movies the child has recently watched. In this way, by filtering the story input based on the child's interests and concerns, more interesting stories can be input. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the child's interests and concerns into the generation AI, which can then perform the filtering.

[0073] When inputting a story, the reception unit can select an appropriate input means depending on the child's input method. Examples of input means include, but are not limited to, voice input, text input, and image input. For example, if the child selects voice input, the reception unit inputs the story using voice recognition technology. Furthermore, if the child selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the child selects image input, the reception unit can input the story using image recognition technology. For example, if the child selects voice input, the reception unit inputs the story using voice recognition technology. Furthermore, if the child selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the child selects image input, the reception unit can input the story using image recognition technology. This allows for smooth story input by selecting the optimal input means depending on the child's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's input method to the generation AI, which can then select an appropriate input means.

[0074] The reception unit can estimate the child's emotions and determine the priority of stories to be input based on the estimated child's emotions. The priority of stories can be determined based on, for example, the intensity of emotions, the importance of stories, etc., but is not limited to these examples. For example, if the child is excited, the reception unit can prioritize inputting action-packed stories to maintain the excitement. Also, if the child is relaxed, the reception unit can prioritize inputting stories with a relaxing atmosphere. Also, if the child is sad, the reception unit can prioritize inputting moving stories to lighten the mood. For example, if the child is excited, the reception unit can prioritize inputting action-packed stories to maintain the excitement. Also, if the child is relaxed, the reception unit can prioritize inputting stories with a relaxing atmosphere. Also, if the child is sad, the reception unit can prioritize inputting moving stories to lighten the mood. In this way, by determining the priority of stories based on the child's emotions, more appropriate stories can be input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation 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-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit may input a child's facial expression and voice data to the generation AI to estimate the child's emotions, and the generation AI may estimate the emotions and determine the priority of the stories.

[0075] When inputting a story, the reception unit can prioritize inputting highly relevant stories by taking into account the child's geographical location information. The geographical location information is obtained based on, for example, GPS data, local cultural information, etc., but is not limited to these examples. For example, if the child is at the beach, the reception unit can prioritize inputting stories related to the sea. Furthermore, if the child is in the mountains, the reception unit can prioritize inputting stories related to mountains. Furthermore, if the child is in an urban area, the reception unit can prioritize inputting stories related to cities. For example, if the child is at the beach, the reception unit can prioritize inputting stories related to the sea. Furthermore, if the child is in the mountains, the reception unit can prioritize inputting stories related to mountains. Furthermore, if the child is in an urban area, the reception unit can prioritize inputting stories related to cities. In this way, highly relevant stories can be prioritized by taking the child's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's geographical location information to the generation AI, which can then prioritize inputting highly relevant stories.

[0076] When inputting a story, the reception unit can analyze the child's social media activity and input related stories. Social media activity is analyzed based on, for example, the content of posts, the number of likes, etc., but is not limited to these examples. The reception unit can input related stories based on, for example, content shared by the child on social media. The reception unit can also input related stories with reference to the activity of the child's friends on social media. The reception unit can also analyze the content posted by the child on social media and input related stories. For example, the reception unit can input related stories based on the content shared by the child on social media. The reception unit can also input related stories with reference to the activity of the child's friends on social media. The reception unit can also analyze the content posted by the child on social media and input related stories. In this way, related stories can be input by analyzing the child's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's social media activity to the generation AI, which can then input related stories.

[0077] When inputting a story, the reception unit can customize the input method by reflecting the child's past feedback. The feedback can be reflected based on, for example, past ratings, comments, etc., but is not limited to such examples. For example, if the child has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the child has frequently used text input in the past, the reception unit can enhance the text input interface. Furthermore, if the child has frequently used images in the past, the reception unit can provide an interface that supports image input. For example, if the child has preferred voice input in the past, the reception unit preferentially suggests voice input. Furthermore, if the child has frequently used text input in the past, the reception unit can enhance the text input interface. Furthermore, if the child has frequently used images in the past, the reception unit can provide an interface that supports image input. In this way, the input method can be customized by reflecting the child's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the child's past feedback into the generation AI, which can customize the input method.

[0078] The analysis unit can estimate the child's emotions and adjust the story supplement method based on the estimated child's emotions. Emotions are estimated using technologies such as, but not limited to, facial expression recognition and voice analysis. For example, the analysis unit can analyze the child's facial expression using facial expression recognition technology to estimate the emotion. The analysis unit can also analyze the tone and speed of the child's voice using voice analysis technology to estimate the emotion. For example, the analysis unit can analyze the child's facial expression using facial expression recognition technology to estimate the emotion. The analysis unit can also analyze the tone and speed of the child's voice using voice analysis technology to estimate the emotion. This allows for more appropriate story supplementation by adjusting the story supplement method based on 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the child's facial expressions and voice data into the generation AI to estimate the child's emotions, and the generation AI can then estimate the emotions and adjust the capture method.

[0079] When supplementing a story, the analysis unit can adjust the level of detail of the supplement based on the importance of the story. The importance of the story is evaluated based on, for example, the length of the story, the number of main characters, etc., but is not limited to such examples. For example, the analysis unit provides detailed supplementary information in important scenes to add depth to the story. The analysis unit can also provide brief supplementary information in unimportant scenes to smooth the flow of the story. The analysis unit can also provide particularly detailed supplementary information in climax scenes to emphasize the excitement of the story. For example, the analysis unit provides detailed supplementary information in important scenes to add depth to the story. The analysis unit can also provide brief supplementary information in unimportant scenes to smooth the flow of the story. The analysis unit can also provide particularly detailed supplementary information in climax scenes to emphasize the excitement of the story. In this way, by adjusting the level of detail of the supplementary information based on the importance of the story, the depth of the story can be increased. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of a story into the generation AI, which can then adjust the level of detail in the supplementary information.

[0080] When supplementing a story, the analysis unit can apply different supplementation algorithms depending on the story category. Story categories are classified into, for example, adventure, fantasy, and education, but are not limited to these examples. For example, the analysis unit can apply an algorithm that supplements magical and adventurous elements to stories in the fantasy category. The analysis unit can also apply an algorithm that supplements futuristic technology and space elements to stories in the science fiction category. The analysis unit can also apply an algorithm that supplements humorous and comedic elements to stories in the comedy category. For example, the analysis unit can apply an algorithm that supplements magical and adventurous elements to stories in the fantasy category. The analysis unit can also apply an algorithm that supplements futuristic technology and space elements to stories in the science fiction category. The analysis unit can also apply an algorithm that supplements humorous and comedic elements to stories in the comedy category. In this way, by applying different supplementation algorithms depending on the story category, more appropriate supplementation can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the story into the generation AI, which can then apply different supplemental algorithms.

[0081] When supplementing a story, the analysis unit can improve the accuracy of the supplement by referring to the child's past supplement results. Past supplement results may be referenced, for example, based on past successes and failures, but are not limited to such examples. For example, the analysis unit can analyze the patterns of supplements that the child has previously preferred and provide similar supplements. The analysis unit can also avoid supplement patterns that the child has previously dissatisfied with. The analysis unit can also apply an algorithm to improve the accuracy of the supplement based on the child's past supplement results. For example, the analysis unit can analyze the patterns of supplements that the child has previously preferred and provide similar supplements. The analysis unit can also avoid supplement patterns that the child has previously dissatisfied with. The analysis unit can also apply an algorithm to improve the accuracy of the supplement based on the child's past supplement results. In this way, the accuracy of the supplement can be improved by referring to the child's past supplement results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the child's past supplement results into the generation AI, which can then improve the accuracy of the supplement.

[0082] The analysis unit can estimate the child's emotions and adjust the length of the commentary based on the estimated child's emotions. The length of the commentary can be adjusted based on, for example, the intensity of the emotion, the importance of the story, etc., but is not limited to these examples. For example, if the child is excited, the analysis unit can provide a short, to-the-point commentary. Furthermore, if the child is relaxed, the analysis unit can provide a longer commentary with detailed explanations. Furthermore, if the child is sad, the analysis unit can provide a commentary with a touching episode. For example, if the child is excited, the analysis unit can provide a short, to-the-point commentary. Furthermore, if the child is relaxed, the analysis unit can provide a longer commentary with detailed explanations. Furthermore, if the child is sad, the analysis unit can provide a commentary with a touching episode. In this way, by adjusting the length of the commentary based on the child's emotions, more appropriate commentary can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the child's facial expression and voice data to the generation AI to estimate the child's emotions, and the generation AI may estimate the emotions and adjust the length of the supplement.

[0083] When supplementing a story, the analysis unit can determine the priority of the supplement based on the time of submission of the story. The time of submission is evaluated based on, for example, the submission date, the submission time, etc., but is not limited to such examples. For example, the analysis unit prioritizes supplementing a story that has just been submitted. The analysis unit can also postpone stories that have been submitted a long time ago. The analysis unit can also dynamically adjust the priority of the supplement based on the time of submission. For example, the analysis unit prioritizes supplementing a story that has just been submitted. The analysis unit can also postpone stories that have been submitted a long time ago. The analysis unit can also dynamically adjust the priority of the supplement based on the time of submission. In this way, by determining the priority of the supplement based on the time of submission of the story, efficient supplementation can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the time of submission of the story into the generation AI, and the generation AI can determine the priority of the supplement.

[0084] When supplementing a story, the analysis unit can adjust the order of the supplement based on the relevance of the story. The relevance of the story is evaluated based on, for example, thematic similarity, character commonality, etc., but is not limited to these examples. For example, the analysis unit prioritizes supplements related to the main theme of the story. The analysis unit can also postpone supplements related to sub-themes of the story. The analysis unit can also dynamically adjust the order of supplements based on the relevance of the story. For example, the analysis unit prioritizes supplements related to the main theme of the story. The analysis unit can also postpone supplements related to sub-themes of the story. The analysis unit can also dynamically adjust the order of supplements based on the relevance of the story. By adjusting the order of supplements based on the relevance of the story, efficient supplements can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the story into the generation AI, which can then adjust the order of supplements.

[0085] When supplementing a story, the analysis unit can adjust the use of supplementary terminology according to the child's level of expertise. The level of expertise is evaluated based on, for example, age, learning history, etc., but is not limited to such examples. For example, if the child has specialized knowledge, the analysis unit can provide supplementary terminology that uses a lot of specialized terminology. Furthermore, if the child does not have specialized knowledge, the analysis unit can provide supplementary terminology that explains things in simple terms. Furthermore, the analysis unit can dynamically adjust the use of supplementary terminology according to the child's level of expertise. For example, if the child has specialized knowledge, the analysis unit can provide supplementary terminology that explains things in simple terms. Furthermore, the analysis unit can dynamically adjust the use of supplementary terminology according to the child's level of expertise. In this way, adjusting the use of supplementary terminology according to the child's level of expertise allows for more appropriate supplementation. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the child's level of expertise into the generation AI, which can then adjust the use of supplementary terminology.

[0086] The generation unit can estimate the child's emotion and adjust the image generation method based on the estimated emotion. Emotions are estimated using technologies such as, but not limited to, facial expression recognition and voice analysis. For example, the generation unit can analyze the child's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also analyze the tone and speed of the child's voice using voice analysis technology to estimate the emotion. For example, the generation unit can analyze the child's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also analyze the tone and speed of the child's voice using voice analysis technology to estimate the emotion. In this way, by adjusting the image generation method based on the child's emotion, a more appropriate image is generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the child's facial expressions and voice data into the generation AI to estimate the child's emotions, and the generation AI can estimate the emotions and adjust the way the image is generated.

[0087] When generating images, the generation unit can adjust the level of detail of the images based on the importance of the story. The importance of the story is evaluated based on, for example, the length of the story, the number of main characters, etc., but is not limited to these examples. For example, the generation unit generates images including detailed depictions for important scenes. The generation unit can also generate images including concise depictions for unimportant scenes. The generation unit can also generate images including particularly detailed depictions for climax scenes. For example, the generation unit generates images including detailed depictions for important scenes. The generation unit can also generate images including concise depictions for unimportant scenes. The generation unit can also generate images including particularly detailed depictions for climax scenes. In this way, by adjusting the level of detail of the images based on the importance of the story, more appropriate images are generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the story into the generation AI, and the generation AI can adjust the level of detail of the images.

[0088] When generating images, the generation unit can apply different generation algorithms depending on the story category. Story categories are classified into, for example, adventure, fantasy, education, etc., but are not limited to these examples. For example, the generation unit applies an algorithm to generate images including magical and adventurous elements to stories in the fantasy category. Furthermore, the generation unit can also apply an algorithm to generate images including futuristic technology and space elements to stories in the science fiction category. Furthermore, the generation unit can also apply an algorithm to generate images including humorous and comedic elements to stories in the comedy category. For example, the generation unit applies an algorithm to generate images including magical and adventurous elements to stories in the fantasy category. Furthermore, the generation unit can also apply an algorithm to generate images including futuristic technology and space elements to stories in the science fiction category. Furthermore, the generation unit can also apply an algorithm to generate images including humorous and comedic elements to stories in the comedy category. Thus, by applying different generation algorithms depending on the story category, more appropriate images are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a story category to the generation AI, which can then apply different generation algorithms.

[0089] When generating an image, the generation unit can improve the accuracy of generation by referring to the child's past generation results. Past generation results can be referenced, for example, based on past successes and failures, but are not limited to such examples. For example, the generation unit can analyze the style of images that the child previously preferred and generate an image in a similar style. The generation unit can also avoid image styles that the child previously disliked. The generation unit can also apply an algorithm that improves the accuracy of generation based on the child's past generation results. For example, the generation unit can analyze the style of images that the child previously preferred and generate an image in a similar style. The generation unit can also avoid image styles that the child previously disliked. The generation unit can also apply an algorithm that improves the accuracy of generation based on the child's past generation results. In this way, the accuracy of generation can be improved by referring to the child's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the child's past generation results into the generation AI, which can improve the accuracy of generation.

[0090] The generation unit can estimate the child's emotion and adjust the length of the generated image based on the estimated emotion. The length of the image is adjusted based on, for example, the intensity of the emotion, the importance of the story, etc., but is not limited to these examples. For example, if the child is excited, the generation unit generates a short, to-the-point image. Also, if the child is relaxed, the generation unit can generate a longer image with detailed descriptions. Also, if the child is sad, the generation unit can generate an image depicting a moving scene. For example, if the child is excited, the generation unit generates a short, to-the-point image. Also, if the child is relaxed, the generation unit can generate a longer image with detailed descriptions. Also, if the child is sad, the generation unit can generate an image depicting a moving scene. In this way, by adjusting the length of the generated image based on the child's emotion, more appropriate images are generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input facial expression and voice data of a child to the generation AI to estimate the child's emotion, and the generation AI may estimate the emotion and adjust the length of the image.

[0091] When generating images, the generation unit can determine the generation priority based on the submission time of the story. The submission time is evaluated based on, for example, the submission date, the submission time, etc., but is not limited to these examples. For example, the generation unit prioritizes image generation for recently submitted stories. The generation unit can also postpone stories that have been submitted for a long time. The generation unit can also dynamically adjust the priority of image generation based on the submission time. For example, the generation unit prioritizes image generation for recently submitted stories. The generation unit can also postpone stories that have been submitted for a long time. The generation unit can also dynamically adjust the priority of image generation based on the submission time. In this way, determining the generation priority based on the submission time of the story enables efficient image generation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of the story into the generation AI, and the generation AI can determine the generation priority.

[0092] When generating images, the generation unit can adjust the order of generation based on the relevance of the story. The relevance of the story is evaluated based on, for example, a matching theme, commonality of characters, etc., but is not limited to these examples. For example, the generation unit prioritizes generating images related to the main theme of the story. The generation unit can also postpone images related to sub-themes of the story. The generation unit can also dynamically adjust the order of image generation based on the relevance of the story. For example, the generation unit prioritizes generating images related to the main theme of the story. The generation unit can also postpone images related to sub-themes of the story. The generation unit can also dynamically adjust the order of image generation based on the relevance of the story. In this way, adjusting the order of generation based on the relevance of the story enables efficient image generation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the story to the generation AI, and the generation AI can adjust the order of generation.

[0093] When generating an image, the generation unit can adjust the use of technical terminology in the generated image according to the child's level of expertise. The level of expertise is evaluated based on, for example, age, learning history, etc., but is not limited to such examples. For example, if the child has technical expertise, the generation unit generates an image that uses a lot of technical terminology. Furthermore, if the child does not have technical expertise, the generation unit can generate an image that explains things in simple terms. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generated image according to the child's level of expertise. For example, if the child has technical expertise, the generation unit generates an image that uses a lot of technical terminology. Furthermore, if the child does not have technical expertise, the generation unit can generate an image that explains things in simple terms. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generated image according to the child's level of expertise. In this way, by adjusting the use of technical terminology in the generated image according to the child's level of expertise, more appropriate images are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the child's level of expertise into the generation AI, and the generation AI can adjust the use of technical terminology in the generated image.

[0094] The creation unit can estimate the child's emotions and adjust the picture book creation method based on the estimated child's emotions. Emotions are estimated using technologies such as, but not limited to, facial expression recognition and voice analysis. For example, the creation unit can analyze the child's facial expressions using facial expression recognition technology to estimate the emotions. The creation unit can also analyze the tone and speed of the child's voice using voice analysis technology to estimate the emotions. For example, the creation unit can analyze the child's facial expressions using facial expression recognition technology to estimate the emotions. The creation unit can also analyze the tone and speed of the child's voice using voice analysis technology to estimate the emotions. This allows the creation of a more appropriate picture book by adjusting the picture book creation method based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. Some or all of the above-described processing in the creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the creation department can input the child's facial expressions and voice data into the generation AI to estimate the child's emotions, and the generation AI can then infer the emotions and adjust the way the picture book is created.

[0095] When creating a picture book, the creation unit can adjust the level of detail in the creation based on the importance of the story. The importance of the story is evaluated based on, for example, the length of the story, the number of main characters, etc., but is not limited to these examples. For example, the creation unit can create pages including detailed descriptions for important scenes. The creation unit can also create pages including concise descriptions for unimportant scenes. The creation unit can also create pages including particularly detailed descriptions for climax scenes. For example, the creation unit can create pages including detailed descriptions for important scenes. The creation unit can also create pages including concise descriptions for unimportant scenes. The creation unit can also create pages including particularly detailed descriptions for climax scenes. In this way, by adjusting the level of detail in the creation based on the importance of the story, a more appropriate picture book is created. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the importance of the story into the generation AI, and the generation AI can adjust the level of detail in the creation.

[0096] When creating a picture book, the creation unit can apply different creation algorithms depending on the story category. Story categories are classified into, for example, adventure, fantasy, and education, but are not limited to these examples. For example, the creation unit can apply an algorithm to create pages including magic and adventure elements to stories in the fantasy category. The creation unit can also apply an algorithm to create pages including futuristic technology and space elements to stories in the science fiction category. The creation unit can also apply an algorithm to create pages including humor and comedy elements to stories in the comedy category. For example, the creation unit can apply an algorithm to create pages including magic and adventure elements to stories in the fantasy category. The creation unit can also apply an algorithm to create pages including futuristic technology and space elements to stories in the science fiction category. The creation unit can also apply an algorithm to create pages including humor and comedy elements to stories in the comedy category. In this way, by applying different creation algorithms depending on the story category, a more appropriate picture book can be created. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input a story category to the generation AI, which can then apply different creation algorithms.

[0097] When creating a picture book, the creation unit can improve the accuracy of the creation by referring to the child's past creation results. Past creation results can be referenced, for example, based on past successes and failures, but are not limited to such examples. For example, the creation unit can analyze the style of picture books that the child previously liked and create a picture book in a similar style. The creation unit can also avoid picture book styles that the child previously disliked. The creation unit can also apply an algorithm to improve the accuracy of the creation based on the child's past creation results. For example, the creation unit can analyze the style of picture books that the child previously liked and create a picture book in a similar style. The creation unit can also avoid picture book styles that the child previously disliked. The creation unit can also apply an algorithm to improve the accuracy of the creation based on the child's past creation results. In this way, the accuracy of the creation can be improved by referring to the child's past creation results. Some or all of the above-mentioned processing in the creation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the creation unit can input the child's past creation results into the generation AI, which can improve the accuracy of the creation.

[0098] The creation unit can estimate the child's emotions and adjust the length of the picture book to be created based on the estimated child's emotions. The length of the picture book is adjusted based on, for example, the intensity of the emotion, the importance of the story, etc., but is not limited to these examples. For example, if the child is excited, the creation unit can create a short, concise picture book. Furthermore, if the child is relaxed, the creation unit can create a longer picture book with detailed descriptions. Furthermore, if the child is sad, the creation unit can create a picture book with many moving scenes. For example, if the child is excited, the creation unit can create a short, concise picture book. Furthermore, if the child is relaxed, the creation unit can create a longer picture book with detailed descriptions. Furthermore, if the child is sad, the creation unit can create a picture book with many moving scenes. By adjusting the length of the picture book to be created based on the child's emotions, a more appropriate picture book can be created. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the creation unit may be performed using, or without, the generation AI. For example, the creation unit may input a child's facial expression and voice data into the generation AI to estimate the child's emotions, and the generation AI may estimate the emotions and adjust the length of the picture book.

[0099] When creating a picture book, the creation unit can determine the creation priority based on the submission time of the story. The submission time is evaluated based on, for example, the submission date, the submission time, etc., but is not limited to such examples. For example, the creation unit prioritizes creating a picture book for a story that has just been submitted. The creation unit can also postpone stories that have been submitted a long time ago. The creation unit can also dynamically adjust the priority of creating a picture book based on the submission time. For example, the creation unit prioritizes creating a picture book for a story that has just been submitted. The creation unit can also postpone stories that have been submitted a long time ago. The creation unit can also dynamically adjust the priority of creating a picture book based on the submission time. In this way, by determining the creation priority based on the submission time of the story, picture books can be created efficiently. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the submission time of the story into the generation AI, and the generation AI can determine the creation priority.

[0100] When creating a picture book, the creation unit can adjust the creation order based on the relevance of the stories. The relevance of the stories is evaluated based on, for example, thematic consistency, commonality of characters, etc., but is not limited to these examples. For example, the creation unit prioritizes creating pages related to the main theme of the story. The creation unit can also postpone pages related to sub-themes of the story. The creation unit can also dynamically adjust the order of picture book creation based on the relevance of the stories. For example, the creation unit prioritizes creating pages related to the main theme of the story. The creation unit can also postpone pages related to sub-themes of the story. The creation unit can also dynamically adjust the order of picture book creation based on the relevance of the stories. In this way, adjusting the order of creation based on the relevance of the stories enables efficient picture book creation. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the relevance of the stories into the generation AI, and the generation AI can adjust the order of creation.

[0101] When creating a picture book, the creation unit can adjust the use of technical terms in the creation depending on the child's level of expertise. The level of expertise is evaluated based on, for example, age, learning history, etc., but is not limited to such examples. For example, if the child has technical expertise, the creation unit can create a picture book that uses a lot of technical terms. Also, if the child does not have technical expertise, the creation unit can create a picture book that explains things in simple terms. The creation unit can also dynamically adjust the use of technical terms in the creation depending on the child's level of expertise. For example, if the child has technical expertise, the creation unit can create a picture book that uses a lot of technical terms. Also, if the child does not have technical expertise, the creation unit can create a picture book that explains things in simple terms. Also, the creation unit can dynamically adjust the use of technical terms in the creation depending on the child's level of expertise. In this way, by adjusting the use of technical terms in the creation depending on the child's level of expertise, a more appropriate picture book is created. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the child's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the creation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and creation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit allows a child to input a story using a text input interface or a voice input interface of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the story using a generation AI to supplement the narrative. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates images using deep learning technology. The creation unit is realized, for example, by the control unit 46A of the smart device 14 and combines the generated story with images to create a picture book. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and creation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit allows a child to input a story using a text input interface or a voice input interface of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the story using a generation AI to supplement the narrative. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates images using deep learning technology. The creation unit is realized, for example, by the control unit 46A of the smart glasses 214, and creates a picture book by combining the generated story and images. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and creation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit allows a child to input a story using a text input interface or a voice input interface of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the story using a generation AI to supplement the narrative. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates images using deep learning technology. The creation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and creates a picture book by combining the generated story and images. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and creation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows a child to input a story using a text input interface or a voice input interface of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the story using a generation AI to supplement the narrative. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates images using deep learning technology. The creation unit is realized, for example, by the control unit 46A of the robot 414, and creates a picture book by combining the generated story and images.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When a child is inputting a story, the reception unit can dynamically change the color and design of the interface based on the child's current mood. For example, if a child is excited, a bright and colorful design can be provided. If a child is relaxed, a calm color design can be provided. Furthermore, if a child is sad, a comforting warm color design can be provided. This allows for a more comfortable story input environment by providing an interface that responds to the child's emotions.

[0104] When analyzing a story, the analysis unit analyzes the themes and character trends of the child's past stories and can provide appropriate supplements to the current story. For example, if a child has written many animal-themed stories in the past, supplements related to animals can be prioritized. Also, if a child likes adventure-themed stories, supplements can be provided that emphasize adventure elements. Furthermore, if a child frequently uses a particular character, supplements related to that character can be provided. This makes it possible to provide a more consistent story by providing supplements that reflect the trends of the child's past stories.

[0105] When generating images, the generation unit can adjust the style and color of the images based on the child's current mood. For example, if the child is excited, a vivid and dynamic image can be generated. If the child is relaxed, a calm image with soft colors can be generated. Furthermore, if the child is sad, a comforting image with warm colors can be generated. This makes it possible to create a picture book that is more in tune with the child's emotions by providing images that correspond to the child's emotions.

[0106] When previewing a generated picture book, the creation unit can customize the interface by reflecting the child's past feedback. For example, if a child has previously preferred a particular layout, that layout can be displayed preferentially. Also, if a child has previously preferred a particular font or color, those can be provided as default settings. Furthermore, by avoiding elements that the child has previously disliked, a more comfortable preview environment can be provided. In this way, by providing an interface that reflects the child's past feedback, a more user-friendly picture book creation environment can be realized.

[0107] The reception unit can estimate the child's emotions and suggest a story input method based on the estimated emotions. For example, if the child is excited, suggesting voice input can help the child input the story more smoothly. If the child is relaxed, suggesting text input can provide time for the child to carefully think about the story. Furthermore, if the child is sad, suggesting image input can help the child express their emotions more easily. In this way, by suggesting an input method according to the child's emotions, a more appropriate story input environment can be provided.

[0108] When analyzing a story, the analysis unit can adjust the supplementary content based on the child's current interests. For example, if a child has recently become interested in dinosaurs, it will prioritize supplementary content related to dinosaurs. It can also provide related supplementary content based on the theme of a book the child has recently read. It can also provide related supplementary content based on characters in a movie the child has recently seen. This makes it possible to provide a more interesting story by providing supplementary content based on the child's interests.

[0109] When generating an image, the generation unit can improve the accuracy of generation by referring to the child's past generation results. For example, the generation unit analyzes the image styles that the child has liked in the past and generates an image in a similar style. It can also avoid image styles that the child has disliked in the past. Furthermore, an algorithm that improves the accuracy of generation can be applied based on the child's past generation results. In this way, the accuracy of generation can be improved by referring to the child's past generation results.

[0110] The creation unit can estimate the child's emotions and adjust the number of pages in the picture book based on the estimated emotions. For example, if the child is excited, a short, to-the-point picture book can be created. If the child is relaxed, a longer picture book with detailed descriptions can be created. Furthermore, if the child is sad, a picture book with many moving scenes can be created. In this way, a more appropriate picture book can be created by adjusting the number of pages in the picture book based on the child's emotions.

[0111] The reception unit can analyze the child's past story input history and select the optimal input method. For example, if the child has previously preferred voice input, it can preferentially suggest voice input. Also, if the child has previously used text input frequently, it can enhance the text input interface. Furthermore, if the child has previously used images frequently, it can provide an interface that supports image input. In this way, the optimal input method can be selected by analyzing the child's past input history.

[0112] The analysis unit can estimate the child's emotions and adjust the way the story is supplemented based on the estimated emotions. For example, if the child is excited, a short, to-the-point supplement can be provided. If the child is relaxed, a longer supplement including detailed explanations can be provided. Furthermore, if the child is sad, a supplement including a touching episode can be provided. This allows for more appropriate supplementation by adjusting the length of the supplement based on the child's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit inputs a story that the child has thought up himself / herself. The story that the child has thought up himself / herself may include text, pictures, voice, etc. The reception unit provides a text input interface, a voice input interface, and an image input interface, and the child can input the story using a keyboard, microphone, or scanner. Step 2: The analysis unit uses the generation AI to analyze the story entered by the reception unit and supplement the story. The analysis is performed using natural language processing technology, and a text generation AI (e.g., LLM) is used to analyze the flow of the story and supplement it based on an existing story database. It can also perform grammatical analysis and semantic analysis to supplement the flow of the story. Furthermore, the generation AI uses keyword extraction technology to extract important parts of the story and supplement it. Step 3: The generation unit uses a generation AI to create images based on the story supplemented by the analysis unit. Image creation is carried out using deep learning technology, and image generation AI (e.g., GAN) is used to generate images corresponding to each scene in the story. Images can also be generated based on the content of the story using multimodal generation AI. The generation AI uses keyword extraction technology to pick out particularly important information in the story and generate images based on that information. Step 4: The creation unit combines the story and images generated by the generation unit to create a picture book. The creation unit provides an interface that allows users to layout images and text, preview the generated picture book, change images by drag and drop, and edit text. The created picture book can also be converted into a format for printing or saved as an e-book.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception area where children can input their own stories, an analysis unit that analyzes the story input by the reception unit and supplements the story; a generator that creates images based on the story captured by the analyzer; a creation unit that creates a picture book by combining the story and images generated by the creation unit. A system characterized by:

2. The analysis unit Analyze the flow of the story using natural language processing technology and supplement it with existing narrative databases 2. The system of claim 1.

3. The generation unit Using deep learning technology, images corresponding to each scene in the story are generated.

2. The system of claim 1.

4. The creation unit It provides an interface that allows you to preview the generated picture book, change images by dragging and dropping, and edit text.

2. The system of claim 1.

5. The reception unit Estimate the child's emotions and adjust the timing of story input based on the estimated emotions.

2. The system of claim 1.

6. The reception unit Analyze your child's past story entry history and select the appropriate entry method 2. The system of claim 1.

7. The reception unit As stories are entered, they are filtered based on the child's current interests and concerns.

2. The system of claim 1.

8. The reception unit When inputting a story, choose the appropriate input method depending on the child's input method.

2. The system of claim 1.

Images