system

Abstract

The system according to this embodiment aims to generate stories using children's drawings and stimulate their creative drive. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, an assembly unit, and a provision unit. The reception unit inputs a picture drawn by a child. The generation unit analyzes the picture input by the reception unit and generates text. The assembly unit assembles a story based on the text generated by the generation unit. The provision unit provides the story assembled by the assembly unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to utilize a picture drawn by a child to generate a story and it is not sufficient as a means to stimulate creativity.

[0005] The system according to the embodiment aims to utilize a picture drawn by a child to generate a story and stimulate creativity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an assembly unit, and a provision unit. The reception unit receives a drawing made by a child as input. The generation unit analyzes the drawing input by the reception unit and generates text. The assembly unit assembles a story based on the text generated by the generation unit. The provision unit provides the story assembled by the assembly unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate stories using children's drawings and stimulate their creative drive. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The picture book generation system according to an embodiment of the present invention is a system that generates and provides original picture books from drawings made by children. The picture book generation system takes a child's drawing as input, a generating AI generates text from the drawing, and constructs a story based on that text. In this way, an original picture book is generated. Furthermore, the picture book generation system gains insight into educational needs from the child's drawing and theme, and generates individually optimized learning materials. This can stimulate children's creativity and motivate them to learn. For example, the picture book generation system takes a child's drawing as input. In this case, the drawing is input as digital data by methods such as scanning or taking a photograph. For example, a child's drawing of an animal is photographed with a smartphone, and the image is input into the generating AI. Next, the generating AI analyzes the input drawing and generates text. The generating AI understands the content of the drawing and generates story text based on that. For example, if a drawing of an animal is input, it generates a story in which that animal is the main character. Based on the generated text, the generating AI constructs the story. Based on the generated text, the generating AI considers the structure and development of the story and completes the story of the picture book. For example, picture books with content that children can enjoy, such as stories about animals going on adventures or stories about playing with friends, are generated. Furthermore, the picture book generation system gains insight into educational needs from children's drawings and themes, and generates individually optimized learning materials. The picture book generation system analyzes children's drawings and themes and suggests learning content and materials that are suitable for that child. For example, if a child draws a picture of an animal, it will generate learning materials related to animals. In this way, learning materials tailored to the child's interests and concerns are provided. This mechanism stimulates children's creativity and motivates them to learn. When children see their drawings take shape as picture books or learning materials, their interest in creative activities increases, and their motivation to learn improves. For example, when a child's drawing is completed as a picture book, they can feel pride and a sense of accomplishment for their work. In addition, the provision of individually optimized learning materials enhances the effectiveness of the child's learning. As a result, the picture book generation system can generate and provide original picture books from children's drawings.

[0029] The picture book generation system according to this embodiment comprises a reception unit, a generation unit, an assembly unit, and a provision unit. The reception unit inputs drawings made by children. Children's drawings include, but are not limited to, hand-drawn pictures, digital drawings, and pictures based on specific themes. The reception unit can, for example, digitize and read hand-drawn pictures using scanning technology. The reception unit can also directly read pictures submitted in digital format. Furthermore, the reception unit can read printed pictures using OCR technology. For example, the reception unit scans hand-drawn pictures with a high-resolution scanner and converts them into text information using OCR technology. Digital pictures can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The generation unit uses generation AI to analyze the pictures input by the reception unit and generate text. The generation unit understands the content of the pictures using, for example, image recognition technology or pattern recognition algorithms and generates story text based on that understanding. For example, if a picture of an animal is input, the generation unit generates a story in which that animal is the main character. The generation unit uses a generation AI to understand the content of the pictures and generate story text based on that understanding. The assembly unit assembles the story based on the text generated by the generation unit. The assembly unit uses the generation AI to consider the structure and development of the story based on the generated text and complete the picture book's story. For example, the assembly unit generates picture books with content that children can enjoy, such as stories about animals going on adventures or stories about playing with friends. The assembly unit uses the generation AI to consider the structure and development of the story based on the generated text and complete the picture book's story. The provision unit provides the story assembled by the assembly unit. The provision unit can provide the story in various ways, such as in digital format or as a printed material. For example, the provision unit can provide the generated picture book in digital format so that children and guardians can view it on their smartphones or tablets. Alternatively, the provision unit can provide the generated picture book as a printed material so that children and guardians can hold and read it. In this way, the picture book generation system according to this embodiment can generate and provide original picture books from pictures drawn by children.

[0030] The reception desk inputs drawings made by children. These drawings include, but are not limited to, hand-drawn pictures, digital drawings, and drawings based on specific themes. For example, the reception desk digitizes and reads hand-drawn pictures using scanning technology. Specifically, a high-resolution scanner is used to accurately digitize hand-drawn pictures down to the smallest detail. This scanner has high color reproduction capabilities and can faithfully reproduce fine lines and color gradations. The reception desk can also directly read drawings submitted in digital format. Digital drawings are often submitted in common image file formats such as JPEG, PNG, and TIFF. These files are read using specialized software and pre-processed for analysis. Furthermore, the reception desk can read printed drawings using OCR technology. OCR technology recognizes printed characters and shapes with high accuracy and converts them into digital data. For example, the reception desk scans a hand-drawn picture with a high-resolution scanner and converts it into text information using OCR technology. This allows the text and explanatory notes contained in the hand-drawn picture to be captured as digital data. Digital drawings can also be directly read if they are submitted in a specific file format. OCR technology accurately recognizes printed characters and converts them into digital text. This allows the reception area to efficiently digitize various types of images and send them to the next processing stage.

[0031] The generation unit uses a generation AI to analyze the image input by the reception unit and generate text. For example, the generation unit uses image recognition technology and pattern recognition algorithms to understand the content of the image and generate story text based on that understanding. Specifically, the generation AI analyzes the input image and identifies the objects and characters depicted. For example, if an image of an animal is input, it will generate a story in which that animal is the main character. The generation AI uses image recognition technology to analyze the type of animal, its expression, and its actions, and constructs the story's plot based on that. Furthermore, the generation AI also considers information such as the background, colors, and arrangement of the image to determine the atmosphere and setting of the story. For example, it can generate a variety of stories depending on the content of the image, such as a story about animals adventuring in a forest or a story about friends playing on the beach. The generation unit can use the generation AI to understand the content of the image and generate story text based on that understanding. The generated text uses concise and easy-to-understand language so that children can enjoy it. The generation AI can also automatically generate the story's progression and character dialogue according to the content of the image. This allows the generation unit to quickly and accurately generate an original story based on the input image.

[0032] The assembly unit constructs the story based on the text generated by the generation unit. Using a generation AI, the assembly unit considers the structure and development of the story based on the generated text, and completes the picture book's story. Specifically, the assembly unit organizes the generated text paragraph by paragraph and constructs the flow of the story. For example, it appropriately places each part of the story, such as the introduction, the middle section, the climax, and the ending, to create an easy-to-read story. Furthermore, the assembly unit designs the page layout of the picture book based on the generated text. Each page is designed to be visually enjoyable, with a good balance of illustrations and text. For example, in a scene where animals are on an adventure, a large illustration of the animals is drawn, and text is placed around it. The assembly unit can also use the generation AI to consider the structure and development of the story and complete the picture book's story. The generation AI suggests the optimal story development according to the story's theme, the characters' personalities, and the progression of the story. This allows the assembly unit to generate picture books that children can enjoy. For example, it can create picture books that incorporate themes that children are interested in, such as stories about animals on adventure or stories about playing with friends. The assembly unit can use generation AI to develop the story structure and development based on the generated text, completing the picture book's narrative. This allows the assembly unit to quickly and efficiently produce original picture books.

[0033] The provider department provides the stories assembled by the assembly department. The provider department can provide the stories in various ways, such as digitally or in print. Specifically, the provider department can provide the generated picture books in digital format, allowing children and guardians to view them on smartphones and tablets. Digital picture books can incorporate interactive elements, allowing children to enjoy reading them. For example, page-turning animations and character movement effects can be added. The provider department can also provide the generated picture books in print, allowing children and guardians to hold and read them. Printed picture books are created using high-quality paper and printing technology, allowing for long-term preservation. Furthermore, the provider department can offer customization options for the picture books. For example, adding a child's name or a specific message to the picture book can create a more personalized book. This allows the provider department to provide children and guardians with original picture books in diverse formats for them to enjoy. In addition, the provider department can provide picture books to a wide range of users through picture book distribution platforms. For example, generated picture books can be sold using online stores or e-book platforms. This will allow the publishing department to promote the spread and use of picture books and provide children with plenty of enjoyment.

[0034] The analysis unit can analyze educational needs. For example, the analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. For example, the analysis unit will suggest learning materials related to animals to a child who has drawn a picture of an animal. The analysis unit needs to clarify the methods and criteria for analyzing educational needs. For example, the analysis unit can analyze educational needs using methods such as analyzing learning history or analyzing survey results. This allows the analysis unit to analyze educational needs from a child's drawings and themes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a child's drawings and themes into an AI and have the AI ​​perform the analysis of educational needs.

[0035] The material generation unit can generate individually optimized learning materials. For example, the material generation unit can generate individually optimized learning materials based on a child's drawings or themes. For example, for a child who has drawn a picture of an animal, the material generation unit will generate learning materials related to animals. The material generation unit needs to clarify the specific content and generation method of the individually optimized learning materials. For example, the material generation unit can generate materials categorized by grade level or materials based on interests. This allows the material generation unit to generate individually optimized learning materials based on a child's drawings or themes. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input a child's drawings or themes into the generation AI and have the generation AI execute the generation of individually optimized learning materials.

[0036] The generation unit can understand the content of an image and generate story text based on that understanding. For example, the generation unit can use image recognition technology or contextual analysis to understand the content of an image and generate story text based on that understanding. For example, if an image of an animal is input, the generation unit will generate a story in which that animal is the main character. The generation unit can use a generation AI to understand the content of an image and generate story text based on that understanding. This allows the generation unit to understand the content of an image and generate story text. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input the content of an image into the generation AI and have the generation AI generate story text.

[0037] The assembly unit can complete the story of a picture book by considering the structure and development of the story based on the generated text. The assembly unit can complete the story of a picture book by considering the structure and development of the story using, for example, plot structure and character settings. For example, the assembly unit can generate picture books with content that children can enjoy, such as stories about animals going on adventures or stories about playing with friends. The assembly unit can complete the story of a picture book by considering the structure and development of the story based on the generated text using a generation AI. In this way, the assembly unit can complete the story of a picture book by considering the structure and development of the story based on the generated text. Some or all of the above processes in the assembly unit are performed using a generation AI. For example, the assembly unit can input the generated text into the generation AI and have the generation AI devise the structure and development of the story.

[0038] The analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. For example, the analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. For example, if a child draws an animal, the analysis unit will suggest learning materials related to animals. The analysis unit needs to clarify the specific content and method of suggesting learning content and materials suitable for the child. For example, the analysis unit can suggest age-appropriate materials or materials based on interests. In this way, the analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the child's drawings and themes into AI and have the AI ​​suggest learning content and materials.

[0039] The material generation unit can generate learning materials about animals for children who have drawn pictures of animals. For example, the material generation unit can generate learning materials about animal ecology and animal classification. For example, the material generation unit can generate materials that include detailed explanations of animal ecology. The material generation unit can also generate materials about animal classification. For example, the material generation unit can generate materials that explain the types and characteristics of animals. The material generation unit can also generate materials about animal ecosystems. For example, the material generation unit can generate materials about animal habitats and food chains. In this way, the material generation unit can generate learning materials about animals for children who have drawn pictures of animals. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input a picture of an animal into the generation AI and have the generation AI execute the generation of learning materials about animals.

[0040] The reception desk can analyze the child's past drawing submission history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that have been frequently used in the past (such as scanning or taking photos). For example, the reception desk can suggest the optimal input method based on the input methods that have been frequently used in the past. The reception desk can also analyze the child's tendency to submit drawings at specific times based on past submission history and prompt them to submit drawings at those times. For example, the reception desk can display a message prompting the child to submit a drawing at a specific time based on past submission history. The reception desk can also suggest input methods that the child prefers based on past submission history. For example, the reception desk can suggest input methods that the child prefers based on past submission history. In this way, the reception desk can analyze the child's past drawing submission history and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the past drawing submission history into AI and have the AI ​​select the optimal input method.

[0041] The reception unit can filter the input of pictures based on the child's current interests and concerns. For example, the reception unit can prioritize inputting pictures related to themes the child has recently been interested in. The reception unit can also input pictures related to a specific character if the child is interested in that character. The reception unit can also input pictures related to a new theme if the child shows interest in that theme. This allows the reception unit to filter the input pictures based on the child's current interests and concerns. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the child's current interests and concerns into the AI ​​and have the AI ​​perform the filtering of the input pictures.

[0042] The reception system can prioritize inputting images that are highly relevant to the child's geographical location when inputting images. For example, the reception system can prioritize inputting images related to the area where the child lives. Furthermore, if the child is traveling, the reception system can prioritize inputting images related to their travel destination. Similarly, if the child is at school, the reception system can prioritize inputting images related to school. This allows the reception system to prioritize inputting images that are highly relevant to the child's geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the child's geographical location into the AI ​​and have the AI ​​input highly relevant images.

[0043] The reception system can analyze a child's social media activity when inputting drawings and input relevant drawings. For example, the reception system can prioritize inputting drawings that the child has shared on social media. The reception system can also prioritize inputting drawings that the child has "liked" on social media. The reception system can also prioritize inputting drawings of artists that the child follows on social media. This allows the reception system to analyze the child's social media activity and input relevant drawings. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the child's social media activity into AI and have AI input relevant drawings.

[0044] The generation unit can adjust the level of detail in the text based on the importance of the images when generating text. For example, the generation unit can generate text with detailed descriptions for important images. The generation unit can also generate concise text for less important images. The generation unit can also adjust the length of the text according to the importance of the images. In this way, the generation unit can adjust the level of detail in the text based on the importance of the images. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the images into the generation AI and have the generation AI perform the adjustment of the level of detail in the text.

[0045] The generation unit can apply different generation algorithms depending on the category of the image when generating text. For example, for an image of an animal, the generation unit can apply an algorithm that generates text containing knowledge about the animal. For example, for an image of an animal, the generation unit can apply an algorithm that generates text containing knowledge about the animal. For example, for an image of a landscape, the generation unit can apply an algorithm that specializes in depicting landscapes. For example, for an image of a character, the generation unit can apply an algorithm that generates text containing the character's personality and background. For example, for an image of a character, the generation unit can apply an algorithm that generates text containing the character's personality and background. In this way, the generation unit can apply different generation algorithms depending on the category of the image. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the category of the image into the generation AI and have the generation AI execute the application of the generation algorithm.

[0046] The generation unit can determine the priority of text based on the submission date of the images when generating text. For example, the generation unit will prioritize generating text for recently submitted images. The generation unit can also postpone generating text for older images. The generation unit can also adjust the order of text generation according to the submission date. This allows the generation unit to determine the priority of text based on the submission date of the images. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the submission date of the images into the generation AI and have the generation AI determine the priority of the text.

[0047] The generation unit can adjust the order of text based on the relevance of the images when generating text. For example, the generation unit can prioritize generating text for images that are highly relevant. The generation unit can also postpone generating text for images that are less relevant. The generation unit can also adjust the order of text generation according to the relevance of the images. In this way, the generation unit can adjust the order of text based on the relevance of the images. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of the images into the generation AI and have the generation AI perform the adjustment of the text order.

[0048] The assembly unit can improve the accuracy of the story by considering the interrelationships between images when assembling the story. For example, the assembly unit can construct the story by considering the relationships between characters in the images. The assembly unit can also construct the story by considering the continuity of scenes in the images. The assembly unit can also construct the story by considering the consistency of the themes in the images. In this way, the assembly unit can improve the accuracy of the story by considering the interrelationships between images. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input the interrelationships between images into the generation AI and have the generation AI perform the story accuracy improvement.

[0049] The assembly unit can construct a story while considering the attribute information of the person who submitted the drawing. For example, the assembly unit can construct a story with appropriate content according to the age of the person who submitted the drawing. The assembly unit can also construct a story with interesting content according to the gender of the person who submitted the drawing. The assembly unit can also select a story theme according to the interests and concerns of the person who submitted the drawing. In this way, the assembly unit can construct a story while considering the attribute information of the person who submitted the drawing. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input the attribute information of the person who submitted the drawing into the generation AI and have the generation AI perform the story construction.

[0050] The assembly unit can construct a story while considering the geographical distribution of the images. For example, if the locations in the images are different, the assembly unit can construct a story that connects each location. The assembly unit can also construct a story centered on the same location if the locations in the images are the same. The assembly unit can also construct a story that utilizes the relationships between related locations in the images. In this way, the assembly unit can construct a story while considering the geographical distribution of the images. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input the geographical distribution of the images into the generation AI and have the generation AI perform the story construction.

[0051] The assembly unit can improve the accuracy of the story by referring to relevant literature for the illustrations when assembling the story. For example, the assembly unit can construct the story by referring to literature related to the characters in the illustrations. The assembly unit can also construct the story by referring to literature related to the scenes in the illustrations. For example, the assembly unit can construct the story by referring to literature related to the scenes in the illustrations. The assembly unit can also construct the story by referring to literature related to the themes in the illustrations. For example, the assembly unit can construct the story by referring to literature related to the themes in the illustrations. In this way, the assembly unit can improve the accuracy of the story by referring to relevant literature for the illustrations. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input relevant literature for the illustrations into the generation AI and have the generation AI perform the story accuracy improvement.

[0052] The delivery unit can select the optimal delivery method by referring to the child's past story reception history when delivering a story. For example, the delivery unit can prioritize delivery methods that have been well-received in the past. The delivery unit can also deliver stories at specific time periods based on past history. For example, the delivery unit can deliver stories at specific time periods based on past history. The delivery unit can also select delivery methods that children prefer based on past history. For example, the delivery unit can select delivery methods that children prefer based on past history. This allows the delivery unit to select the optimal delivery method by referring to the child's past story reception history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input past story reception history into AI and have the AI ​​select the optimal delivery method.

[0053] The delivery unit can select the optimal delivery method when delivering a story, taking into account the child's device information. For example, if the child is using a smartphone, the delivery unit can select a delivery method that matches the screen size. For example, if the child is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. For example, if the child is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. For example, if the child is using a smartwatch, the delivery unit can select a concise and highly visible delivery method. In this way, the delivery unit can select the optimal delivery method by taking into account the child's device information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the child's device information into the AI ​​and have the AI ​​select the optimal delivery method.

[0054] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing educational needs. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also extract specific patterns from past analysis data and optimize the analysis algorithm. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. This allows the analysis unit to optimize its analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into AI and have AI perform the optimization of the analysis algorithm.

[0055] The analysis unit can weight the analysis data based on the submission date of the drawings when analyzing educational needs. For example, the analysis unit can give higher weight to recently submitted drawings. The analysis unit can also give lower weight to older drawings. The analysis unit can also adjust the weighting of the analysis data according to the submission date. In this way, the analysis unit can weight the analysis data based on the submission date of the drawings. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the drawings into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0056] The material generation unit can generate optimal materials by referring to the child's past learning history. For example, the material generation unit can generate materials that are easy for the child to understand based on their past learning history. The material generation unit can also generate materials that reinforce areas where the child struggles, based on their past learning history. The material generation unit can also analyze the child's past learning history and generate materials that will interest the child. In this way, the material generation unit can generate optimal materials by referring to the child's past learning history. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's past learning history into the generation AI and have the generation AI generate optimal materials.

[0057] The material generation unit can customize the content of the materials based on the child's current learning situation when generating them. For example, the material generation unit can generate materials that are easy for the child to understand based on the child's current learning situation. The material generation unit can also generate materials that reinforce areas where the child struggles, based on the child's current learning situation. The material generation unit can also analyze the child's current learning situation and generate materials that will capture the child's interest. In this way, the material generation unit can customize the content of the materials based on the child's current learning situation. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's current learning situation into the generation AI and have the generation AI perform the customization of the material content.

[0058] The material generation unit can generate optimal materials by considering the child's geographical location information during material generation. For example, the material generation unit can generate materials related to the area where the child lives. Furthermore, if the child is traveling, the material generation unit can generate materials related to their travel destination. Also, if the child is at school, the material generation unit can generate materials related to school. This allows the material generation unit to generate optimal materials by considering the child's geographical location information. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's geographical location information into the generation AI and have the generation AI generate the optimal materials.

[0059] The material generation unit can analyze a child's social media activity and suggest content for the materials when generating them. For example, the material generation unit can generate materials related to content that the child has shared on social media. For example, the material generation unit can generate materials related to content that the child has shared on social media. For example, the material generation unit can generate materials related to content that the child has "liked" on social media. For example, the material generation unit can generate materials related to accounts that the child follows on social media. For example, the material generation unit can generate materials related to accounts that the child follows on social media. In this way, the material generation unit can analyze a child's social media activity and suggest content for the materials. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's social media activity into the generation AI and have the generation AI suggest content for the materials.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The input system analyzes the color information of a child's drawing and adjusts the story's atmosphere based on that color. For example, if a brightly colored drawing is input, the system generates a cheerful story. If a darkly colored drawing is input, it can generate a mysterious story. Furthermore, if a drawing with high color contrast is input, the system can generate a story with a dynamic development. In this way, the system can adjust the story's atmosphere based on the color information of the drawing.

[0062] The analysis unit can analyze the style of a child's drawing and determine the genre of the story based on that style. For example, if a realistic style drawing is input, the analysis unit will generate a realistic story. It can also generate a comical story if a cartoon style drawing is input. Furthermore, if an abstract style drawing is input, it can generate a philosophical story. In this way, the analysis unit can determine the genre of the story based on the style of the drawing.

[0063] The educational material generation unit can generate educational materials related to science experiments and crafts based on the themes of children's drawings. For example, if a drawing of an animal is input, it can generate educational materials for science experiments related to animal ecology. If a drawing of space is input, it can generate educational materials for crafts related to space. Furthermore, if a drawing of a plant is input, it can generate educational materials for science experiments related to plant growth. In this way, the educational material generation unit can generate educational materials related to science experiments and crafts based on the themes of children's drawings.

[0064] The assembly unit can analyze the background information of a child's drawing and determine the setting of the story based on that background. For example, if a forest is drawn, the story will be set in a forest. Similarly, if the sea is drawn, the story can be set in a sea. Furthermore, if a city is drawn, the story can be set in a city. In this way, the assembly unit can determine the setting of the story based on the background information of the drawing.

[0065] The service provider can add relevant music and sound effects based on the theme of the child's drawing to create a story. For example, if the drawing is of an animal, animal sounds can be added. If the drawing is of space, space sound effects can be added. Furthermore, if the drawing is of nature, nature sounds can be added. In this way, the service provider can create a story based on the theme of the drawing by adding relevant music and sound effects.

[0066] The analysis unit can analyze the details of a child's drawing and adjust the complexity of the story based on those details. For example, if a detailed drawing is created, a complex story will be generated. Conversely, if a simple drawing is created, a simple story can be generated. Furthermore, if a drawing is partially detailed, a story focusing on that part can be generated. In this way, the analysis unit can adjust the complexity of the story based on the details of the drawing.

[0067] The generation unit can analyze the movements of characters in a child's drawing and generate action scenes for the story based on those movements. For example, if a character is drawn running, it can generate a chase scene. If a character is drawn flying, it can generate an aerial battle scene. Furthermore, if characters are drawn fighting, it can generate a battle scene. In this way, the generation unit can generate action scenes for the story based on the movements of the characters.

[0068] The service provider can deliver a story by adding relevant visual effects based on the theme of the child's drawing. For example, if the drawing is about magic, magical effects can be added. Similarly, if the drawing is about adventure, adventure effects can be added. Furthermore, if the drawing is about the future, futuristic effects can be added. This allows the service provider to deliver a story by adding relevant visual effects based on the theme of the drawing.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk inputs the child's drawing. For example, hand-drawn pictures are digitized and read using scanning technology. It can also directly read drawings submitted in digital format. Furthermore, printed drawings can be read using OCR technology. Step 2: The generation unit analyzes the image input by the reception unit and generates text. For example, it uses image recognition technology or pattern recognition algorithms to understand the content of the image and generates story text based on that understanding. If an image of an animal is input, it generates a story in which that animal is the main character. Step 3: The assembly unit assembles the story based on the text generated by the generation unit. Using the generation AI, it considers the structure and development of the story based on the generated text and completes the picture book's story. For example, it generates picture books with content that children can enjoy, such as a story about animals going on an adventure or a story about friends playing together. Step 4: The delivery unit provides the story assembled by the assembly unit. For example, the story can be provided in various ways, such as digitally or in print. The generated picture book can be provided digitally so that children and their guardians can view it on their smartphones or tablets. Alternatively, the generated picture book can be provided in print so that children and their guardians can hold and read it.

[0071] (Example of form 2) The picture book generation system according to an embodiment of the present invention is a system that generates and provides original picture books from drawings made by children. The picture book generation system takes a child's drawing as input, a generating AI generates text from the drawing, and constructs a story based on that text. In this way, an original picture book is generated. Furthermore, the picture book generation system gains insight into educational needs from the child's drawing and theme, and generates individually optimized learning materials. This can stimulate children's creativity and motivate them to learn. For example, the picture book generation system takes a child's drawing as input. In this case, the drawing is input as digital data by methods such as scanning or taking a photograph. For example, a child's drawing of an animal is photographed with a smartphone, and the image is input into the generating AI. Next, the generating AI analyzes the input drawing and generates text. The generating AI understands the content of the drawing and generates story text based on that. For example, if a drawing of an animal is input, it generates a story in which that animal is the main character. Based on the generated text, the generating AI constructs the story. Based on the generated text, the generating AI considers the structure and development of the story and completes the story of the picture book. For example, picture books with content that children can enjoy, such as stories about animals going on adventures or stories about playing with friends, are generated. Furthermore, the picture book generation system gains insight into educational needs from children's drawings and themes, and generates individually optimized learning materials. The picture book generation system analyzes children's drawings and themes and suggests learning content and materials that are suitable for that child. For example, if a child draws a picture of an animal, it will generate learning materials related to animals. In this way, learning materials tailored to the child's interests and concerns are provided. This mechanism stimulates children's creativity and motivates them to learn. When children see their drawings take shape as picture books or learning materials, their interest in creative activities increases, and their motivation to learn improves. For example, when a child's drawing is completed as a picture book, they can feel pride and a sense of accomplishment for their work. In addition, the provision of individually optimized learning materials enhances the effectiveness of the child's learning. As a result, the picture book generation system can generate and provide original picture books from children's drawings.

[0072] The picture book generation system according to this embodiment comprises a reception unit, a generation unit, an assembly unit, and a provision unit. The reception unit inputs drawings made by children. Children's drawings include, but are not limited to, hand-drawn pictures, digital drawings, and pictures based on specific themes. The reception unit can, for example, digitize and read hand-drawn pictures using scanning technology. The reception unit can also directly read pictures submitted in digital format. Furthermore, the reception unit can read printed pictures using OCR technology. For example, the reception unit scans hand-drawn pictures with a high-resolution scanner and converts them into text information using OCR technology. Digital pictures can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The generation unit uses generation AI to analyze the pictures input by the reception unit and generate text. The generation unit understands the content of the pictures using, for example, image recognition technology or pattern recognition algorithms and generates story text based on that understanding. For example, if a picture of an animal is input, the generation unit generates a story in which that animal is the main character. The generation unit uses a generation AI to understand the content of the pictures and generate story text based on that understanding. The assembly unit assembles the story based on the text generated by the generation unit. The assembly unit uses the generation AI to consider the structure and development of the story based on the generated text and complete the picture book's story. For example, the assembly unit generates picture books with content that children can enjoy, such as stories about animals going on adventures or stories about playing with friends. The assembly unit uses the generation AI to consider the structure and development of the story based on the generated text and complete the picture book's story. The provision unit provides the story assembled by the assembly unit. The provision unit can provide the story in various ways, such as in digital format or as a printed material. For example, the provision unit can provide the generated picture book in digital format so that children and guardians can view it on their smartphones or tablets. Alternatively, the provision unit can provide the generated picture book as a printed material so that children and guardians can hold and read it. In this way, the picture book generation system according to this embodiment can generate and provide original picture books from pictures drawn by children.

[0073] The reception desk inputs drawings made by children. These drawings include, but are not limited to, hand-drawn pictures, digital drawings, and drawings based on specific themes. For example, the reception desk digitizes and reads hand-drawn pictures using scanning technology. Specifically, a high-resolution scanner is used to accurately digitize hand-drawn pictures down to the smallest detail. This scanner has high color reproduction capabilities and can faithfully reproduce fine lines and color gradations. The reception desk can also directly read drawings submitted in digital format. Digital drawings are often submitted in common image file formats such as JPEG, PNG, and TIFF. These files are read using specialized software and pre-processed for analysis. Furthermore, the reception desk can read printed drawings using OCR technology. OCR technology recognizes printed characters and shapes with high accuracy and converts them into digital data. For example, the reception desk scans a hand-drawn picture with a high-resolution scanner and converts it into text information using OCR technology. This allows the text and explanatory notes contained in the hand-drawn picture to be captured as digital data. Digital drawings can also be directly read if they are submitted in a specific file format. OCR technology accurately recognizes printed characters and converts them into digital text. This allows the reception area to efficiently digitize various types of images and send them to the next processing stage.

[0074] The generation unit uses a generation AI to analyze the image input by the reception unit and generate text. For example, the generation unit uses image recognition technology and pattern recognition algorithms to understand the content of the image and generate story text based on that understanding. Specifically, the generation AI analyzes the input image and identifies the objects and characters depicted. For example, if an image of an animal is input, it will generate a story in which that animal is the main character. The generation AI uses image recognition technology to analyze the type of animal, its expression, and its actions, and constructs the story's plot based on that. Furthermore, the generation AI also considers information such as the background, colors, and arrangement of the image to determine the atmosphere and setting of the story. For example, it can generate a variety of stories depending on the content of the image, such as a story about animals adventuring in a forest or a story about friends playing on the beach. The generation unit can use the generation AI to understand the content of the image and generate story text based on that understanding. The generated text uses concise and easy-to-understand language so that children can enjoy it. The generation AI can also automatically generate the story's progression and character dialogue according to the content of the image. This allows the generation unit to quickly and accurately generate an original story based on the input image.

[0075] The assembly unit constructs the story based on the text generated by the generation unit. Using a generation AI, the assembly unit considers the structure and development of the story based on the generated text, and completes the picture book's story. Specifically, the assembly unit organizes the generated text paragraph by paragraph and constructs the flow of the story. For example, it appropriately places each part of the story, such as the introduction, the middle section, the climax, and the ending, to create an easy-to-read story. Furthermore, the assembly unit designs the page layout of the picture book based on the generated text. Each page is designed to be visually enjoyable, with a good balance of illustrations and text. For example, in a scene where animals are on an adventure, a large illustration of the animals is drawn, and text is placed around it. The assembly unit can also use the generation AI to consider the structure and development of the story and complete the picture book's story. The generation AI suggests the optimal story development according to the story's theme, the characters' personalities, and the progression of the story. This allows the assembly unit to generate picture books that children can enjoy. For example, it can create picture books that incorporate themes that children are interested in, such as stories about animals on adventure or stories about playing with friends. The assembly unit can use generation AI to develop the story structure and development based on the generated text, completing the picture book's narrative. This allows the assembly unit to quickly and efficiently produce original picture books.

[0076] The provider department provides the stories assembled by the assembly department. The provider department can provide the stories in various ways, such as digitally or in print. Specifically, the provider department can provide the generated picture books in digital format, allowing children and guardians to view them on smartphones and tablets. Digital picture books can incorporate interactive elements, allowing children to enjoy reading them. For example, page-turning animations and character movement effects can be added. The provider department can also provide the generated picture books in print, allowing children and guardians to hold and read them. Printed picture books are created using high-quality paper and printing technology, allowing for long-term preservation. Furthermore, the provider department can offer customization options for the picture books. For example, adding a child's name or a specific message to the picture book can create a more personalized book. This allows the provider department to provide children and guardians with original picture books in diverse formats for them to enjoy. In addition, the provider department can provide picture books to a wide range of users through picture book distribution platforms. For example, generated picture books can be sold using online stores or e-book platforms. This will allow the publishing department to promote the spread and use of picture books and provide children with plenty of enjoyment.

[0077] The analysis unit can analyze educational needs. For example, the analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. For example, the analysis unit will suggest learning materials related to animals to a child who has drawn a picture of an animal. The analysis unit needs to clarify the methods and criteria for analyzing educational needs. For example, the analysis unit can analyze educational needs using methods such as analyzing learning history or analyzing survey results. This allows the analysis unit to analyze educational needs from a child's drawings and themes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a child's drawings and themes into an AI and have the AI ​​perform the analysis of educational needs.

[0078] The material generation unit can generate individually optimized learning materials. For example, the material generation unit can generate individually optimized learning materials based on a child's drawings or themes. For example, for a child who has drawn a picture of an animal, the material generation unit will generate learning materials related to animals. The material generation unit needs to clarify the specific content and generation method of the individually optimized learning materials. For example, the material generation unit can generate materials categorized by grade level or materials based on interests. This allows the material generation unit to generate individually optimized learning materials based on a child's drawings or themes. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input a child's drawings or themes into the generation AI and have the generation AI execute the generation of individually optimized learning materials.

[0079] The generation unit can understand the content of an image and generate story text based on that understanding. For example, the generation unit can use image recognition technology or contextual analysis to understand the content of an image and generate story text based on that understanding. For example, if an image of an animal is input, the generation unit will generate a story in which that animal is the main character. The generation unit can use a generation AI to understand the content of an image and generate story text based on that understanding. This allows the generation unit to understand the content of an image and generate story text. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input the content of an image into the generation AI and have the generation AI generate story text.

[0080] The assembly unit can complete the story of a picture book by considering the structure and development of the story based on the generated text. The assembly unit can complete the story of a picture book by considering the structure and development of the story using, for example, plot structure and character settings. For example, the assembly unit can generate picture books with content that children can enjoy, such as stories about animals going on adventures or stories about playing with friends. The assembly unit can complete the story of a picture book by considering the structure and development of the story based on the generated text using a generation AI. In this way, the assembly unit can complete the story of a picture book by considering the structure and development of the story based on the generated text. Some or all of the above processes in the assembly unit are performed using a generation AI. For example, the assembly unit can input the generated text into the generation AI and have the generation AI devise the structure and development of the story.

[0081] The analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. For example, the analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. For example, if a child draws an animal, the analysis unit will suggest learning materials related to animals. The analysis unit needs to clarify the specific content and method of suggesting learning content and materials suitable for the child. For example, the analysis unit can suggest age-appropriate materials or materials based on interests. In this way, the analysis unit can analyze a child's drawings and themes and suggest learning content and materials suitable for that child. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the child's drawings and themes into AI and have the AI ​​suggest learning content and materials.

[0082] The material generation unit can generate learning materials about animals for children who have drawn pictures of animals. For example, the material generation unit can generate learning materials about animal ecology and animal classification. For example, the material generation unit can generate materials that include detailed explanations of animal ecology. The material generation unit can also generate materials about animal classification. For example, the material generation unit can generate materials that explain the types and characteristics of animals. The material generation unit can also generate materials about animal ecosystems. For example, the material generation unit can generate materials about animal habitats and food chains. In this way, the material generation unit can generate learning materials about animals for children who have drawn pictures of animals. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input a picture of an animal into the generation AI and have the generation AI execute the generation of learning materials about animals.

[0083] The reception unit can estimate the child's emotions and adjust the timing of drawing input based on the estimated emotions. For example, if the child is excited, the reception unit can provide an interface that prompts the child to immediately input a drawing. For example, the reception unit can display a message prompting the child to input a drawing when the child is excited. The reception unit can also suggest a break if the child is tired and encourage them to input a drawing later. For example, the reception unit can display a message suggesting a break when the child is tired. The reception unit can also adjust the interface to allow the child to continue inputting drawings if they are focused. For example, the reception unit can display a message prompting the child to continue inputting drawings when they are focused. This allows the reception unit to adjust the timing of drawing input based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception desk can input children's emotional data into a generating AI and have the AI ​​perform an emotional estimation.

[0084] The reception desk can analyze the child's past drawing submission history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that have been frequently used in the past (such as scanning or taking photos). For example, the reception desk can suggest the optimal input method based on the input methods that have been frequently used in the past. The reception desk can also analyze the child's tendency to submit drawings at specific times based on past submission history and prompt them to submit drawings at those times. For example, the reception desk can display a message prompting the child to submit a drawing at a specific time based on past submission history. The reception desk can also suggest input methods that the child prefers based on past submission history. For example, the reception desk can suggest input methods that the child prefers based on past submission history. In this way, the reception desk can analyze the child's past drawing submission history and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the past drawing submission history into AI and have the AI ​​select the optimal input method.

[0085] The reception unit can filter the input of pictures based on the child's current interests and concerns. For example, the reception unit can prioritize inputting pictures related to themes the child has recently been interested in. The reception unit can also input pictures related to a specific character if the child is interested in that character. The reception unit can also input pictures related to a new theme if the child shows interest in that theme. This allows the reception unit to filter the input pictures based on the child's current interests and concerns. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the child's current interests and concerns into the AI ​​and have the AI ​​perform the filtering of the input pictures.

[0086] The reception unit can estimate the child's emotions and determine the priority of the input images based on the estimated emotions. For example, if the child is excited, the reception unit will prioritize inputting images with fun themes. For example, if the child is excited, the reception unit will prioritize inputting images with fun themes. The reception unit can also prioritize inputting images related to learning if the child is calm. For example, if the child is calm, the reception unit will prioritize inputting images related to learning. The reception unit can also prioritize inputting images with simple themes if the child is tired. For example, if the child is tired, the reception unit will prioritize inputting images with simple themes. In this way, the reception unit can determine the priority of the input images based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception desk can input children's emotional data into a generating AI and have the AI ​​perform an emotional estimation.

[0087] The reception system can prioritize inputting images that are highly relevant to the child's geographical location when inputting images. For example, the reception system can prioritize inputting images related to the area where the child lives. Furthermore, if the child is traveling, the reception system can prioritize inputting images related to their travel destination. Similarly, if the child is at school, the reception system can prioritize inputting images related to school. This allows the reception system to prioritize inputting images that are highly relevant to the child's geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the child's geographical location into the AI ​​and have the AI ​​input highly relevant images.

[0088] The reception system can analyze a child's social media activity when inputting drawings and input relevant drawings. For example, the reception system can prioritize inputting drawings that the child has shared on social media. The reception system can also prioritize inputting drawings that the child has "liked" on social media. The reception system can also prioritize inputting drawings of artists that the child follows on social media. This allows the reception system to analyze the child's social media activity and input relevant drawings. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the child's social media activity into AI and have AI input relevant drawings.

[0089] The generation unit can estimate a child's emotions and adjust the expression of the generated text based on the estimated emotions. For example, if the child is excited, the generation unit can generate text using lively expressions. For example, if the child is excited, the generation unit can generate text using lively expressions. The generation unit can also generate text using calm expressions if the child is calm. For example, if the child is sad, the generation unit can generate text using comforting expressions. For example, if the child is sad, the generation unit can generate text using comforting expressions. In this way, the generation unit can adjust the expression of the generated text based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input child emotional data into the generation AI and have the generation AI adjust the way the text is expressed.

[0090] The generation unit can adjust the level of detail in the text based on the importance of the images when generating text. For example, the generation unit can generate text with detailed descriptions for important images. The generation unit can also generate concise text for less important images. The generation unit can also adjust the length of the text according to the importance of the images. In this way, the generation unit can adjust the level of detail in the text based on the importance of the images. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the images into the generation AI and have the generation AI perform the adjustment of the level of detail in the text.

[0091] The generation unit can apply different generation algorithms depending on the category of the image when generating text. For example, for an image of an animal, the generation unit can apply an algorithm that generates text containing knowledge about the animal. For example, for an image of an animal, the generation unit can apply an algorithm that generates text containing knowledge about the animal. For example, for an image of a landscape, the generation unit can apply an algorithm that specializes in depicting landscapes. For example, for an image of a character, the generation unit can apply an algorithm that generates text containing the character's personality and background. For example, for an image of a character, the generation unit can apply an algorithm that generates text containing the character's personality and background. In this way, the generation unit can apply different generation algorithms depending on the category of the image. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the category of the image into the generation AI and have the generation AI execute the application of the generation algorithm.

[0092] The generation unit can estimate a child's emotions and adjust the length of the generated text based on the estimated emotions. For example, if the child is excited, the generation unit can generate short, concise text. For example, if the child is excited, the generation unit can generate short, concise text. The generation unit can also generate longer text with detailed explanations if the child is calm. For example, if the child is calm, the generation unit can generate longer text with detailed explanations. The generation unit can also generate concise, easy-to-read text if the child is tired. For example, if the child is tired, the generation unit can generate concise, easy-to-read text. In this way, the generation unit can adjust the length of the generated text based on the child's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit can input child emotional data into the generation AI and have the generation AI adjust the length of the text.

[0093] The generation unit can determine the priority of text based on the submission date of the images when generating text. For example, the generation unit will prioritize generating text for recently submitted images. The generation unit can also postpone generating text for older images. The generation unit can also adjust the order of text generation according to the submission date. This allows the generation unit to determine the priority of text based on the submission date of the images. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the submission date of the images into the generation AI and have the generation AI determine the priority of the text.

[0094] The generation unit can adjust the order of text based on the relevance of the images when generating text. For example, the generation unit can prioritize generating text for images that are highly relevant. The generation unit can also postpone generating text for images that are less relevant. The generation unit can also adjust the order of text generation according to the relevance of the images. In this way, the generation unit can adjust the order of text based on the relevance of the images. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of the images into the generation AI and have the generation AI perform the adjustment of the text order.

[0095] The assembly unit can estimate a child's emotions and adjust the story structure based on the estimated emotions. For example, if the child is excited, the assembly unit can construct a story with many action scenes. For example, if the child is excited, the assembly unit can construct a story with many action scenes. For example, if the child is calm, the assembly unit can construct a story with many calm scenes. For example, if the child is sad, the assembly unit can construct a story with many comforting scenes. For example, if the child is sad, the assembly unit can construct a story with many comforting scenes. In this way, the assembly unit can adjust the story structure based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assembly unit is performed using generative AI. For example, the assembly unit can input children's emotional data into a generating AI and have the AI ​​adjust the story's structure.

[0096] The assembly unit can improve the accuracy of the story by considering the interrelationships between images when assembling the story. For example, the assembly unit can construct the story by considering the relationships between characters in the images. The assembly unit can also construct the story by considering the continuity of scenes in the images. The assembly unit can also construct the story by considering the consistency of the themes in the images. In this way, the assembly unit can improve the accuracy of the story by considering the interrelationships between images. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input the interrelationships between images into the generation AI and have the generation AI perform the story accuracy improvement.

[0097] The assembly unit can construct a story while considering the attribute information of the person who submitted the drawing. For example, the assembly unit can construct a story with appropriate content according to the age of the person who submitted the drawing. The assembly unit can also construct a story with interesting content according to the gender of the person who submitted the drawing. The assembly unit can also select a story theme according to the interests and concerns of the person who submitted the drawing. In this way, the assembly unit can construct a story while considering the attribute information of the person who submitted the drawing. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input the attribute information of the person who submitted the drawing into the generation AI and have the generation AI perform the story construction.

[0098] The assembly unit can estimate a child's emotions and adjust the story's development based on those emotions. For example, if the child is excited, the assembly unit can construct a story with many fast-paced scenes. For example, if the child is excited, the assembly unit can construct a story with many fast-paced scenes. For example, if the child is calm, the assembly unit can construct a story with a slow pace. For example, if the child is calm, the assembly unit can construct a story with a slow pace. For example, if the child is sad, the assembly unit can construct a story with an emotional development. For example, if the child is sad, the assembly unit can construct an emotional development. In this way, the assembly unit can adjust the story's development based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the assembly unit are performed using the generative AI. For example, the assembly unit can input child emotion data into the generative AI and have the generative AI adjust the story's development.

[0099] The assembly unit can construct a story while considering the geographical distribution of the images. For example, if the locations in the images are different, the assembly unit can construct a story that connects each location. The assembly unit can also construct a story centered on the same location if the locations in the images are the same. The assembly unit can also construct a story that utilizes the relationships between related locations in the images. In this way, the assembly unit can construct a story while considering the geographical distribution of the images. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input the geographical distribution of the images into the generation AI and have the generation AI perform the story construction.

[0100] The assembly unit can improve the accuracy of the story by referring to relevant literature for the illustrations when assembling the story. For example, the assembly unit can construct the story by referring to literature related to the characters in the illustrations. The assembly unit can also construct the story by referring to literature related to the scenes in the illustrations. For example, the assembly unit can construct the story by referring to literature related to the scenes in the illustrations. The assembly unit can also construct the story by referring to literature related to the themes in the illustrations. For example, the assembly unit can construct the story by referring to literature related to the themes in the illustrations. In this way, the assembly unit can improve the accuracy of the story by referring to relevant literature for the illustrations. Some or all of the above processing in the assembly unit is performed using a generation AI. For example, the assembly unit can input relevant literature for the illustrations into the generation AI and have the generation AI perform the story accuracy improvement.

[0101] The delivery unit can estimate a child's emotions and adjust the way the story is delivered based on the estimated emotions. For example, if the child is excited, the delivery unit can deliver the story in a visually stimulating way. For example, if the child is excited, the delivery unit can deliver the story in a visually stimulating way. The delivery unit can also deliver the story in a calm way if the child is calm. For example, if the child is calm, the delivery unit can deliver the story in a calm way. The delivery unit can also deliver the story in a comforting way if the child is sad. For example, if the child is sad, the delivery unit can deliver the story in a comforting way. In this way, the delivery unit can adjust the way the story is delivered based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input child emotion data into a generative AI and have the generative AI adjust the way the story is delivered.

[0102] The delivery unit can select the optimal delivery method by referring to the child's past story reception history when delivering a story. For example, the delivery unit can prioritize delivery methods that have been well-received in the past. The delivery unit can also deliver stories at specific time periods based on past history. For example, the delivery unit can deliver stories at specific time periods based on past history. The delivery unit can also select delivery methods that children prefer based on past history. For example, the delivery unit can select delivery methods that children prefer based on past history. This allows the delivery unit to select the optimal delivery method by referring to the child's past story reception history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input past story reception history into AI and have the AI ​​select the optimal delivery method.

[0103] The story provider can estimate a child's emotions and adjust the order in which stories are presented based on the estimated emotions. For example, if a child is excited, the provider will prioritize offering fun stories. For example, if a child is excited, the provider will prioritize offering fun stories. The provider can also prioritize offering calming stories if a child is calm. For example, if a child is calm, the provider will prioritize offering calming stories. The provider can also prioritize offering comforting stories if a child is sad. For example, if a child is sad, the provider will prioritize offering comforting stories. In this way, the provider can adjust the order in which stories are presented based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 processing in the provider may be performed using AI or not using AI. For example, the delivery unit can input children's emotional data into a generating AI and have the AI ​​adjust the order in which the stories are presented.

[0104] The delivery unit can select the optimal delivery method when delivering a story, taking into account the child's device information. For example, if the child is using a smartphone, the delivery unit can select a delivery method that matches the screen size. For example, if the child is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. For example, if the child is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. For example, if the child is using a smartwatch, the delivery unit can select a concise and highly visible delivery method. In this way, the delivery unit can select the optimal delivery method by taking into account the child's device information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the child's device information into the AI ​​and have the AI ​​select the optimal delivery method.

[0105] The analysis unit can estimate a child's emotions and adjust the method of analyzing educational needs based on the estimated emotions. For example, if the child is excited, the analysis unit can analyze educational needs based on themes that will capture the child's interest. For example, if the child is excited, the analysis unit can analyze educational needs based on themes that will capture the child's interest. For example, if the child is calm, the analysis unit can analyze educational needs based on themes that will allow the child to concentrate on learning. For example, if the child is calm, the analysis unit can analyze educational needs based on themes that will allow the child to concentrate on learning. For example, if the child is sad, the analysis unit can analyze educational needs based on comforting themes. For example, if the child is sad, the analysis unit can analyze educational needs based on comforting themes. In this way, the analysis unit can adjust the method of analyzing educational needs based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input children's emotional data into a generating AI and have the generating AI adjust the method for analyzing educational needs.

[0106] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing educational needs. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also extract specific patterns from past analysis data and optimize the analysis algorithm. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. This allows the analysis unit to optimize its analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into AI and have AI perform the optimization of the analysis algorithm.

[0107] The analysis unit can estimate a child's emotions and prioritize educational needs based on the estimated emotions. For example, if the child is excited, the analysis unit will prioritize educational needs related to topics that will interest them. For example, if the child is excited, the analysis unit will prioritize educational needs related to topics that will interest them. For example, if the child is calm, the analysis unit will prioritize educational needs related to topics that will help them concentrate on learning. For example, if the child is calm, the analysis unit will prioritize educational needs related to topics that will help them concentrate on learning. For example, if the child is sad, the analysis unit will prioritize educational needs related to topics that will comfort them. For example, if the child is sad, the analysis unit will prioritize educational needs related to topics that will comfort them. In this way, the analysis unit can prioritize educational needs based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input children's emotional data into a generating AI and have the generating AI determine the priorities of their educational needs.

[0108] The analysis unit can weight the analysis data based on the submission date of the drawings when analyzing educational needs. For example, the analysis unit can give higher weight to recently submitted drawings. The analysis unit can also give lower weight to older drawings. The analysis unit can also adjust the weighting of the analysis data according to the submission date. In this way, the analysis unit can weight the analysis data based on the submission date of the drawings. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the drawings into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0109] The material generation unit can estimate a child's emotions and adjust the content of the generated materials based on the estimated emotions. For example, if the child is excited, the material generation unit can generate fun materials. For example, if the child is excited, the material generation unit can generate fun materials. The material generation unit can also generate materials that will help the child concentrate if the child is calm. For example, if the child is calm, the material generation unit can generate materials that will help the child concentrate. The material generation unit can also generate comforting materials if the child is sad. For example, if the child is sad, the material generation unit can generate comforting materials. In this way, the material generation unit can adjust the content of the generated materials based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input children's emotional data into a generating AI, which can then adjust the content of the materials.

[0110] The material generation unit can generate optimal materials by referring to the child's past learning history. For example, the material generation unit can generate materials that are easy for the child to understand based on their past learning history. The material generation unit can also generate materials that reinforce areas where the child struggles, based on their past learning history. The material generation unit can also analyze the child's past learning history and generate materials that will interest the child. In this way, the material generation unit can generate optimal materials by referring to the child's past learning history. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's past learning history into the generation AI and have the generation AI generate optimal materials.

[0111] The material generation unit can customize the content of the materials based on the child's current learning situation when generating them. For example, the material generation unit can generate materials that are easy for the child to understand based on the child's current learning situation. The material generation unit can also generate materials that reinforce areas where the child struggles, based on the child's current learning situation. The material generation unit can also analyze the child's current learning situation and generate materials that will capture the child's interest. In this way, the material generation unit can customize the content of the materials based on the child's current learning situation. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's current learning situation into the generation AI and have the generation AI perform the customization of the material content.

[0112] The material generation unit can estimate a child's emotions and determine the priority of the materials to generate based on the estimated emotions. For example, if the child is excited, the material generation unit will prioritize generating materials with fun content. For example, if the child is excited, the material generation unit will prioritize generating materials with fun content. Also, if the child is calm, the material generation unit can prioritize generating materials with content that will help the child concentrate. For example, if the child is calm, the material generation unit will prioritize generating materials with content that will help the child concentrate. Also, if the child is sad, the material generation unit can prioritize generating materials with comforting content. For example, if the child is sad, the material generation unit will prioritize generating materials with comforting content. In this way, the material generation unit can determine the priority of the materials to generate based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input children's emotional data into a generating AI and have the AI ​​determine the priority of the materials.

[0113] The material generation unit can generate optimal materials by considering the child's geographical location information during material generation. For example, the material generation unit can generate materials related to the area where the child lives. Furthermore, if the child is traveling, the material generation unit can generate materials related to their travel destination. Also, if the child is at school, the material generation unit can generate materials related to school. This allows the material generation unit to generate optimal materials by considering the child's geographical location information. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's geographical location information into the generation AI and have the generation AI generate the optimal materials.

[0114] The material generation unit can analyze a child's social media activity and suggest content for the materials when generating them. For example, the material generation unit can generate materials related to content that the child has shared on social media. For example, the material generation unit can generate materials related to content that the child has shared on social media. For example, the material generation unit can generate materials related to content that the child has "liked" on social media. For example, the material generation unit can generate materials related to accounts that the child follows on social media. For example, the material generation unit can generate materials related to accounts that the child follows on social media. In this way, the material generation unit can analyze a child's social media activity and suggest content for the materials. Some or all of the above processing in the material generation unit is performed using a generation AI. For example, the material generation unit can input the child's social media activity into the generation AI and have the generation AI suggest content for the materials.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The input system analyzes the color information of a child's drawing and adjusts the story's atmosphere based on that color. For example, if a brightly colored drawing is input, the system generates a cheerful story. If a darkly colored drawing is input, it can generate a mysterious story. Furthermore, if a drawing with high color contrast is input, the system can generate a story with a dynamic development. In this way, the system can adjust the story's atmosphere based on the color information of the drawing.

[0117] The analysis unit can analyze the style of a child's drawing and determine the genre of the story based on that style. For example, if a realistic style drawing is input, the analysis unit will generate a realistic story. It can also generate a comical story if a cartoon style drawing is input. Furthermore, if an abstract style drawing is input, it can generate a philosophical story. In this way, the analysis unit can determine the genre of the story based on the style of the drawing.

[0118] The educational material generation unit can generate educational materials related to science experiments and crafts based on the themes of children's drawings. For example, if a drawing of an animal is input, it can generate educational materials for science experiments related to animal ecology. If a drawing of space is input, it can generate educational materials for crafts related to space. Furthermore, if a drawing of a plant is input, it can generate educational materials for science experiments related to plant growth. In this way, the educational material generation unit can generate educational materials related to science experiments and crafts based on the themes of children's drawings.

[0119] The generation unit can analyze the facial expressions of characters in a child's drawing and adjust the emotional tone of the story based on those expressions. For example, if a smiling character is drawn, it can generate a story with a cheerful tone. If a character with a sad expression is drawn, it can generate a story with an emotional tone. Furthermore, if a character with a surprised expression is drawn, it can generate a story with a suspenseful tone. In this way, the generation unit can adjust the emotional tone of the story based on the characters' facial expressions.

[0120] The assembly unit can analyze the background information of a child's drawing and determine the setting of the story based on that background. For example, if a forest is drawn, the story will be set in a forest. Similarly, if the sea is drawn, the story can be set in a sea. Furthermore, if a city is drawn, the story can be set in a city. In this way, the assembly unit can determine the setting of the story based on the background information of the drawing.

[0121] The service provider can add relevant music and sound effects based on the theme of the child's drawing to create a story. For example, if the drawing is of an animal, animal sounds can be added. If the drawing is of space, space sound effects can be added. Furthermore, if the drawing is of nature, nature sounds can be added. In this way, the service provider can create a story based on the theme of the drawing by adding relevant music and sound effects.

[0122] The analysis unit can analyze the details of a child's drawing and adjust the complexity of the story based on those details. For example, if a detailed drawing is created, a complex story will be generated. Conversely, if a simple drawing is created, a simple story can be generated. Furthermore, if a drawing is partially detailed, a story focusing on that part can be generated. In this way, the analysis unit can adjust the complexity of the story based on the details of the drawing.

[0123] The generation unit can analyze the movements of characters in a child's drawing and generate action scenes for the story based on those movements. For example, if a character is drawn running, it can generate a chase scene. If a character is drawn flying, it can generate an aerial battle scene. Furthermore, if characters are drawn fighting, it can generate a battle scene. In this way, the generation unit can generate action scenes for the story based on the movements of the characters.

[0124] The assembly unit can estimate the emotions expressed in a child's drawing and adjust the story's climax based on those emotions. For example, if joy is estimated, it can generate a happy ending climax. If sadness is estimated, it can generate an emotional climax. Furthermore, if surprise is estimated, it can generate a surprise ending climax. In this way, the assembly unit can adjust the story's climax based on the child's emotions.

[0125] The service provider can deliver a story by adding relevant visual effects based on the theme of the child's drawing. For example, if the drawing is about magic, magical effects can be added. Similarly, if the drawing is about adventure, adventure effects can be added. Furthermore, if the drawing is about the future, futuristic effects can be added. This allows the service provider to deliver a story by adding relevant visual effects based on the theme of the drawing.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The reception desk inputs the child's drawing. For example, hand-drawn pictures are digitized and read using scanning technology. It can also directly read drawings submitted in digital format. Furthermore, printed drawings can be read using OCR technology. Step 2: The generation unit analyzes the image input by the reception unit and generates text. For example, it uses image recognition technology or pattern recognition algorithms to understand the content of the image and generates story text based on that understanding. If an image of an animal is input, it generates a story in which that animal is the main character. Step 3: The assembly unit assembles the story based on the text generated by the generation unit. Using the generation AI, it considers the structure and development of the story based on the generated text and completes the picture book's story. For example, it generates picture books with content that children can enjoy, such as a story about animals going on an adventure or a story about friends playing together. Step 4: The delivery unit provides the story assembled by the assembly unit. For example, the story can be provided in various ways, such as digitally or in print. The generated picture book can be provided digitally so that children and their guardians can view it on their smartphones or tablets. Alternatively, the generated picture book can be provided in print so that children and their guardians can hold and read it.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, assembly unit, provision unit, analysis unit, and educational material generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit scans the child's drawing using the camera 42 of the smart device 14 and converts it into digital data by the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the drawing using generation AI by the specific processing unit 290 of the data processing unit 12 and generates text. The assembly unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that assembles a story based on the generated text. The provision unit is implemented by the control unit 46A of the smart device 14 as a processing unit that provides the generated picture book in digital format. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the child's drawing and theme and gains insight into educational needs. The educational material generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that generates individually optimized learning materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, assembly unit, provision unit, analysis unit, and educational material generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit scans the child's drawing using the camera 42 of the smart glasses 214 and converts it into digital data by the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the drawing using generation AI by the specific processing unit 290 of the data processing unit 12 and generates text. The assembly unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that assembles a story based on the generated text. The provision unit is implemented by the control unit 46A of the smart glasses 214 as a processing unit that provides the generated picture book in digital format. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the child's drawing and theme and gains insight into educational needs. The educational material generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that generates individually optimized learning materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, generation unit, assembly unit, provision unit, analysis unit, and educational material generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit scans the child's drawing using the camera 42 of the headset terminal 314 and converts it into digital data by the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the drawing using generation AI by the specific processing unit 290 of the data processing unit 12 and generates text. The assembly unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that assembles a story based on the generated text. The provision unit is implemented by the control unit 46A of the headset terminal 314 as a processing unit that provides the generated picture book in digital format. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the child's drawing and theme and gains insight into educational needs. The educational material generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that generates individually optimized learning materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the reception unit, generation unit, assembly unit, provision unit, analysis unit, and educational material generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit scans a child's drawing using the camera 42 of the robot 414 and converts it into digital data using the specific processing unit 290 of the data processing unit 12. The generation unit analyzes the drawing using generation AI and generates text using the specific processing unit 290 of the data processing unit 12. The assembly unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that assembles a story based on the generated text. The provision unit is implemented by the control unit 46A of the robot 414 as a processing unit that provides the generated picture book in digital format. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the child's drawing and theme and gains insight into educational needs. The educational material generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that generates individually optimized learning materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A reception desk where children's drawings are entered, A generation unit analyzes the image input by the reception unit and generates text, An assembly unit that constructs a story based on the text generated by the generation unit, The assembly unit provides a story assembled by the assembly unit. A system characterized by the following features. (Note 2) It includes an analysis unit for analyzing educational needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a learning material generation unit that generates individually optimized learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Understand the content of the image and generate narrative text based on it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned assembly section is Based on the generated text, consider the structure and development of the story to complete the picture book's narrative. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze children's drawings and themes to suggest learning content and materials that are suitable for each child. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned teaching material generation unit, For children who draw pictures of animals, we generate learning materials about animals. The system described in Appendix 3, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the child's emotions and adjusts the timing of drawing input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the child's past drawing submission history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting drawings, filtering is performed based on the child's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the child's emotions and determines the priority of the input images based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting drawings, the system prioritizes inputting drawings that are highly relevant to the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When inputting drawings, the system analyzes the child's social media activity and inputs relevant drawings. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates a child's emotions and adjusts the way text is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating text, adjust the level of detail in the text based on the importance of the image. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating text, different generation algorithms are applied depending on the category of the image. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the child's emotions and adjusts the length of the generated text based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating text, the text priority is determined based on when the images were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating text, the order of the text is adjusted based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned assembly section is The system estimates the child's emotions and adjusts the story's structure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned assembly section is When constructing a story, consider the interrelationships between the images to improve the accuracy of the narrative. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned assembly section is When constructing the story, take into account the attribute information of the person who submitted the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned assembly section is The system estimates the child's emotions and adjusts the story's progression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned assembly section is When constructing the story, consider the geographical distribution of the images. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned assembly section is When constructing the story, refer to related literature for illustrations to improve the accuracy of the narrative. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the way the story is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a story, the system will refer to the child's past story-receiving history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the order in which stories are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing stories, the optimal delivery method is selected considering the child's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, We estimate children's emotions and adjust the method of analyzing their educational needs based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned analysis unit, When analyzing educational needs, we optimize the analysis algorithm by referring to past analysis data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit, Estimate a child's emotions and prioritize their educational needs based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned analysis unit, When analyzing educational needs, the analysis data is weighted based on when the drawings were submitted. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned teaching material generation unit, The system estimates the child's emotions and adjusts the content of the materials generated based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned teaching material generation unit, When generating learning materials, the system references the child's past learning history to generate the most suitable materials. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned teaching material generation unit, When generating learning materials, customize the content based on the child's current learning situation. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned teaching material generation unit, The system estimates children's emotions and prioritizes the materials generated based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned teaching material generation unit, When generating educational materials, the system takes into account the child's geographical location to generate the most suitable materials. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned teaching material generation unit, When creating educational materials, we analyze children's social media activity to suggest content for the materials. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where children's drawings are entered, A generation unit analyzes the image input by the reception unit and generates text, An assembly unit that constructs a story based on the text generated by the generation unit, The assembly unit provides a story assembled by the assembly unit. A system characterized by the following features.

2. It includes an analysis unit for analyzing educational needs. The system according to feature 1.

3. It is equipped with a learning material generation unit that generates individually optimized learning materials. The system according to feature 1.

4. The generating unit is Understand the content of the image and generate narrative text based on it. The system according to feature 1.

5. The aforementioned assembly section is Based on the generated text, consider the structure and development of the story to complete the picture book's narrative. The system according to feature 1.

6. The aforementioned analysis unit, We analyze children's drawings and themes to suggest learning content and materials that are suitable for each child. The system according to feature 2.

7. The aforementioned teaching material generation unit, For children who draw pictures of animals, we generate learning materials about animals. The system according to claim 3.

8. The aforementioned reception unit is It estimates the child's emotions and adjusts the timing of drawing input based on the estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the child's past drawing submission history to select the optimal input method. The system according to feature 1.

Images