System

Abstract

An object of the system according to the embodiment is to analyze a picture drawn by a child and propose optimal educational content.SOLUTION: A system includes a picture analysis unit, a story generation unit, and an educational content suggestion unit. The picture analysis unit analyzes a picture drawn by a child. The story generation unit generates a story on the basis of a result analyzed by the picture analysis unit. The educational content suggestion unit suggests educational content on the basis of the story generated by the story generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not yet fully established a method for utilizing children's drawings in education, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze pictures drawn by children and propose optimal educational content. [Means for solving the problem]

[0006] The system according to the embodiment includes a picture analysis unit, a story generation unit, and an educational content proposal unit. The picture analysis unit analyzes a picture drawn by a child. The story generation unit generates a story based on the analysis result by the picture analysis unit. The educational content proposal unit proposes educational content based on the story generated by the story generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze pictures drawn by children and suggest optimal educational content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The picture book creation system according to the embodiment of the present invention is a system that automatically analyzes pictures drawn by children, generates a story using a generation AI, and suggests educational content. As a result, the picture book creation system can provide learning opportunities for children by generating a story based on pictures drawn by children and suggesting educational content.

[0029] A picture book creation system according to an embodiment includes a picture analysis unit, a story generation unit, and an educational content suggestion unit. The picture analysis unit analyzes pictures drawn by children. For example, the picture analysis unit digitizes and analyzes hand-drawn pictures using scanning technology. It can also directly analyze pictures submitted in digital format. Furthermore, the picture analysis unit analyzes the content of the pictures using image recognition technology. For example, the picture analysis unit scans hand-drawn pictures with a high-resolution scanner and analyzes the content using image recognition technology. Digital pictures submitted in a specific file format can be directly analyzed. Image recognition technology recognizes and analyzes the content of pictures with high accuracy. The story generation unit uses a generation AI to generate a story based on the analysis results of the picture analysis unit. For example, the generation AI generates a story using a text generation AI (e.g., LLM). It can also generate a story based on the content of the pictures using a multimodal generation AI. It can also generate a story based on the themes and characters of the pictures. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI generates an appropriate story based on the theme and characters of the picture. The educational content suggestion unit suggests educational content based on the story generated by the story generation unit. For example, the educational content suggestion unit suggests related educational content based on the content of the generated story. The educational content suggestion unit can also suggest optimal educational content based on the age and interests of a child. The educational content suggestion unit can also suggest related educational programs based on the theme of the generated story. For example, if the generated story contains many scientific elements, the educational content suggestion unit suggests educational content related to science. The educational content suggestion unit suggests related educational programs, for example, based on the theme of the generated story. As a result, the picture book generation system according to the embodiment can provide learning opportunities for children by generating stories based on pictures drawn by children and suggesting educational content.For example, the output unit can provide the generated stories and educational content through a web or mobile application, print the results using a printer if paper feedback is desired, or send the results via email, providing quick feedback by sending the results directly to parents or teachers.

[0030] The picture analysis unit can analyze color and shape patterns contained in a child's drawing and adjust the tone and atmosphere of the story based on the analysis. For example, the picture analysis unit analyzes the colors of a child's drawing and generates a story with a fun atmosphere if there are many bright colors, and generates a story with a mysterious atmosphere if there are many dark colors. The picture analysis unit also analyzes the shape patterns of the drawing and generates a story with a soft tone if there are many round shapes, and generates a story with a tense tone if there are many sharp shapes. Furthermore, the picture analysis unit analyzes combinations of color and shape and generates, for example, a fun adventure story if there are many bright colors and round shapes, and generates a suspense story if there are many dark colors and sharp shapes. This allows the tone and atmosphere of the story to be adjusted based on the color and shape patterns.

[0031] The picture analysis unit can analyze the facial expressions and movements of characters in a picture and generate a story that reflects the emotions and actions of the characters. For example, the picture analysis unit analyzes the facial expressions of characters in a picture and generates a fun story if there are many smiling characters, and a touching story if there are many sad characters. The picture analysis unit also analyzes the movements of characters and generates an action-packed story if there are many running characters, and a quiet story if there are many sitting characters. Furthermore, the picture analysis unit analyzes the combination of facial expressions and movements and generates, for example, a fun adventure story if there are many smiling running characters, and a touching drama if there are many sad sitting characters. This makes it possible to generate a story that reflects the emotions and actions of characters based on their facial expressions and movements.

[0032] The story generation unit can generate stories of multiple different genres based on a picture drawn by a child and provide options. For example, the story generation unit analyzes a picture drawn by a child and generates stories of multiple genres, such as adventure, fantasy, and mystery, for the child to choose from. The story generation unit also generates stories of different genres based on the content of the picture; for example, if the picture contains many animals, it provides an animal adventure story or an animal fantasy story. Furthermore, the story generation unit generates stories of different genres based on a picture drawn by a child, allowing parents and children to enjoy choosing between options. For example, it can generate an adventure story and a mystery story from the same picture. In this way, stories of multiple genres can be generated and options can be provided.

[0033] The story generation unit can collaborate with pictures drawn by other children based on the content of the picture to jointly generate a story. The story generation unit, for example, analyzes a picture drawn by a child and combines it with pictures drawn by other children to jointly generate a story. For example, it creates a story in which characters drawn by multiple children go on an adventure together. The story generation unit also collaborates with pictures drawn by other children based on the content of the picture to generate a story that incorporates different perspectives and ideas. For example, it combines backgrounds and characters drawn by different children. Furthermore, the story generation unit jointly generates a story based on the pictures drawn by children, allowing parents and children to enjoy it together. For example, it combines pictures drawn by multiple children into a single story. This allows collaboration with pictures drawn by other children to jointly generate a story.

[0034] The educational content suggestion unit can analyze themes and motifs contained in pictures drawn by children and suggest related educational content based on the themes and motifs. For example, the educational content suggestion unit analyzes the themes contained in pictures drawn by children, and if there are many animals, it suggests educational content related to animals. The educational content suggestion unit also analyzes the motif of the picture, and if the theme is space, it suggests educational content related to space. Furthermore, the educational content suggestion unit suggests related educational content based on the theme or motif. For example, if the theme is nature, it suggests educational content related to natural science. In this way, it is possible to suggest related educational content based on the theme or motif.

[0035] The educational content suggestion unit can analyze the arrangement of characters and objects in a picture and suggest educational content for improving spatial awareness. For example, the educational content suggestion unit analyzes the arrangement of characters and objects in a picture and suggests puzzles or games for improving spatial awareness. The educational content suggestion unit also suggests educational content for improving spatial awareness based on the arrangement of characters and objects. For example, it provides a 3D model for understanding three-dimensional arrangements. Furthermore, the educational content suggestion unit analyzes the arrangement of a picture and suggests activities for improving spatial awareness. For example, it provides exercises for understanding the positional relationships of characters. This makes it possible to suggest educational content for improving spatial awareness based on the arrangement of characters and objects.

[0036] The educational content suggestion unit can suggest educational content in different academic fields based on pictures drawn by the child. For example, the educational content suggestion unit analyzes a picture drawn by the child and suggests educational content in different academic fields such as science, history, and art. For example, science educational content is provided for a picture with many scientific elements. The educational content suggestion unit also suggests educational content in different academic fields based on the content of the picture. For example, history educational content is provided for a picture containing a historical theme. Furthermore, the educational content suggestion unit suggests educational content in different academic fields based on the picture drawn by the child to broaden the child's interests. For example, art educational content is provided for a picture with many artistic elements. In this way, educational content in different academic fields can be suggested.

[0037] The educational content suggestion unit can suggest educational content that parents and children can learn together based on the content of the picture, thereby promoting collaborative learning. For example, the educational content suggestion unit analyzes a picture drawn by a child and suggests educational content that parents and children can learn together. For example, it provides science experiments and crafts that parents and children can enjoy. The educational content suggestion unit also suggests educational content that parents and children can learn together based on the content of the picture, thereby promoting collaborative learning. For example, it provides picture books and learning materials that parents and children can read together. Furthermore, the educational content suggestion unit suggests educational content that parents and children can learn together based on the picture drawn by the child, thereby promoting parent-child communication. For example, it provides projects and activities that parents and children can work on together. In this way, it is possible to suggest educational content that parents and children can learn together and promote collaborative learning.

[0038] The story generation unit can analyze the fine details contained in a drawing drawn by a child and generate a high-quality illustration that reflects those details. The story generation unit, for example, analyzes the fine details of a drawing drawn by a child and generates a high-quality illustration that reflects those details. For example, it faithfully reproduces the details of a character's facial expression and clothing. The story generation unit also analyzes the details in the drawing and generates a high-quality illustration of details such as the background and accessories. For example, it depicts the texture of the background scenery and accessories in detail. Furthermore, the story generation unit generates a high-quality illustration that looks like it was drawn by a professional illustrator based on the details of the drawing drawn by a child. For example, it depicts the character's hair and the sparkle in their eyes in detail. This makes it possible to generate a high-quality illustration that reflects the fine details.

[0039] The story generation unit can analyze the personality and characteristics of the characters in the picture and set the lines and actions of the characters in detail based on that. The story generation unit, for example, analyzes the personality and characteristics of the characters in the picture and sets the lines and actions of the characters based on that personality. For example, lively lines and actions are set for a lively character. The story generation unit also sets the role and actions in the story in detail based on the character's characteristics. For example, adventurous actions are set for a brave character. Furthermore, the story generation unit sets lines and actions that reflect the character's personality, adding depth to the story. For example, clever lines and actions are set for an intelligent character. In this way, lines and actions can be set in detail based on the character's personality and characteristics.

[0040] The story generation unit can generate illustrations in different styles based on pictures drawn by the child and provide options. The story generation unit, for example, analyzes a picture drawn by the child and generates illustrations in different styles, such as anime-style or realistic-style, allowing the child to choose. For example, the same character can be drawn in both anime-style and realistic-style. The story generation unit can also generate illustrations in different styles based on the content of the picture, allowing parents and children to enjoy the options together. For example, the background can be drawn in both anime-style and realistic-style. The story generation unit can also generate illustrations in different styles based on the picture drawn by the child and allowing parents and children to enjoy the options together. For example, the same scene can be drawn in both anime-style and realistic-style. This allows illustrations in different styles to be generated and options to be provided.

[0041] The story generation unit can collaborate with pictures drawn by other children based on the content of the picture to jointly create a picture book. The story generation unit, for example, analyzes a picture drawn by a child and combines it with pictures drawn by other children to jointly create a picture book. For example, a picture book is created in which characters drawn by multiple children go on an adventure together. The story generation unit also collaborates with pictures drawn by other children based on the content of the picture to create a picture book that incorporates different perspectives and ideas. For example, backgrounds and characters drawn by different children are combined. Furthermore, the story generation unit can collaboratively create a picture book based on the pictures drawn by children, allowing parents and children to enjoy it together. For example, pictures drawn by multiple children can be compiled into a single picture book. This allows collaboration with pictures drawn by other children to jointly create a picture book.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The picture analysis unit can analyze musical elements contained in a child's drawing and incorporate music into the story based on that analysis. For example, if a picture contains an instrument, the sound of that instrument can be used as background music for the story. Also, if the picture contains a pattern that suggests rhythm or melody, music can be generated based on that and incorporated into the story. Furthermore, the picture analysis unit can analyze a scene in which a character in the picture is singing and recreate that song in the story. This allows the picture book generation system to provide a story that incorporates musical elements.

[0044] The story generation unit can analyze natural elements contained in a picture drawn by a child and generate a story that includes environmental education based on that analysis. For example, if the picture contains many plants and animals, a story that conveys the importance of nature conservation and ecosystems centered on those elements can be generated. Also, if the picture contains natural elements such as water and air, an educational story about environmental issues can be generated based on that analysis. Furthermore, the story generation unit can analyze the natural elements in the picture and, based on that, suggest activities that provide children with opportunities to interact with nature. This makes it possible to provide a story that includes environmental education.

[0045] The educational content suggestion unit can analyze cultural elements contained in a picture drawn by a child and suggest educational content that promotes intercultural understanding based on the analysis. For example, if the picture depicts traditional clothing or buildings from a different country, content for learning about the culture or history of that country can be suggested based on the analysis. Also, if the picture contains different languages, language learning content can be suggested based on the analysis. Furthermore, the educational content suggestion unit can analyze cultural elements in the picture and suggest activities that promote intercultural exchange based on the analysis. This makes it possible to provide educational content that promotes intercultural understanding.

[0046] The picture analysis unit can analyze the scientific elements contained in a child's drawing and generate a story that includes science education based on that analysis. For example, if the drawing depicts space or a scientific experiment, it can generate a story that explains space exploration or the principles of science based on that analysis. Also, if the drawing depicts a robot or technology, it can generate a story that explores technological advances and future possibilities based on that analysis. Furthermore, the picture analysis unit can analyze the scientific elements in the drawing and suggest activities that will pique children's interest in science based on that analysis. This makes it possible to provide stories that include science education.

[0047] The educational content suggestion unit can analyze mathematical elements contained in a child's drawing and suggest content including mathematics education based on that analysis. For example, if a drawing contains geometric shapes or patterns, content for learning geometric principles can be suggested based on that analysis. Also, if a drawing contains numbers or calculations, content for solving arithmetic problems can be suggested based on that analysis. Furthermore, the educational content suggestion unit can analyze mathematical elements in a drawing and suggest activities that will stimulate children's interest in mathematics based on that analysis. This makes it possible to provide content including mathematics education.

[0048] The picture analysis unit can analyze historical elements contained in pictures drawn by children and generate stories that include history education based on the analysis. For example, if a picture contains historical buildings or people, a story that explains the events and culture of that era can be generated based on the historical elements. Also, if a picture contains symbols of war or peace, a story that teaches the historical background can be generated based on the symbols. Furthermore, the picture analysis unit can analyze historical elements in pictures and suggest activities that will arouse children's interest in history based on the analysis. This makes it possible to provide stories that include history education.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The picture analysis unit analyzes the picture drawn by the child. For example, the picture analysis unit digitizes and analyzes a hand-drawn picture using scanning technology. It is also possible to directly analyze a picture submitted in digital format. Furthermore, the picture analysis unit analyzes the content of the picture using image recognition technology. For example, the picture analysis unit scans a hand-drawn picture with a high-resolution scanner and analyzes the content using image recognition technology. Digital pictures submitted in a specific file format can be directly analyzed. Image recognition technology recognizes and analyzes the content of the picture with high accuracy. Step 2: In the story generation unit, the generation AI generates a story based on the results of the analysis by the picture analysis unit. For example, the generation AI generates a story using a text generation AI (e.g., LLM). The generation AI can also use a multimodal generation AI to generate a story based on the content of the picture. The generation AI can also generate a story based on the theme or characters of the picture. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI generates an appropriate story based on the theme and characters of the picture. Step 3: The educational content suggestion unit suggests educational content based on the story generated by the story generation unit. For example, the educational content suggestion unit suggests related educational content based on the content of the generated story. The educational content suggestion unit can also suggest optimal educational content based on the age and interests of the child. The educational content suggestion unit can also suggest related educational programs based on the theme of the generated story. For example, if the generated story contains many scientific elements, the educational content suggestion unit suggests educational content related to science. The educational content suggestion unit can suggest related educational programs based on the theme of the generated story, for example.

[0051] (Example 2) The picture book creation system according to the embodiment of the present invention is a system that automatically analyzes pictures drawn by children, generates a story using a generation AI, and suggests educational content. As a result, the picture book creation system can provide learning opportunities for children by generating a story based on pictures drawn by children and suggesting educational content.

[0052] A picture book creation system according to an embodiment includes a picture analysis unit, a story generation unit, and an educational content suggestion unit. The picture analysis unit analyzes pictures drawn by children. For example, the picture analysis unit digitizes and analyzes hand-drawn pictures using scanning technology. It can also directly analyze pictures submitted in digital format. Furthermore, the picture analysis unit analyzes the content of the pictures using image recognition technology. For example, the picture analysis unit scans hand-drawn pictures with a high-resolution scanner and analyzes the content using image recognition technology. Digital pictures submitted in a specific file format can be directly analyzed. Image recognition technology recognizes and analyzes the content of pictures with high accuracy. The story generation unit uses a generation AI to generate a story based on the analysis results of the picture analysis unit. For example, the generation AI generates a story using a text generation AI (e.g., LLM). It can also generate a story based on the content of the pictures using a multimodal generation AI. It can also generate a story based on the themes and characters of the pictures. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI generates an appropriate story based on the theme and characters of the picture. The educational content suggestion unit suggests educational content based on the story generated by the story generation unit. For example, the educational content suggestion unit suggests related educational content based on the content of the generated story. The educational content suggestion unit can also suggest optimal educational content based on the age and interests of a child. The educational content suggestion unit can also suggest related educational programs based on the theme of the generated story. For example, if the generated story contains many scientific elements, the educational content suggestion unit suggests educational content related to science. The educational content suggestion unit suggests related educational programs, for example, based on the theme of the generated story. As a result, the picture book generation system according to the embodiment can provide learning opportunities for children by generating stories based on pictures drawn by children and suggesting educational content.For example, the output unit can provide the generated stories and educational content through a web or mobile application, print the results using a printer if paper feedback is desired, or send the results via email, providing quick feedback by sending the results directly to parents or teachers.

[0053] The picture analysis unit can analyze color and shape patterns contained in a child's drawing and adjust the tone and atmosphere of the story based on the analysis. For example, the picture analysis unit analyzes the colors of a child's drawing and generates a story with a fun atmosphere if there are many bright colors, and generates a story with a mysterious atmosphere if there are many dark colors. The picture analysis unit also analyzes the shape patterns of the drawing and generates a story with a soft tone if there are many round shapes, and generates a story with a tense tone if there are many sharp shapes. Furthermore, the picture analysis unit analyzes combinations of color and shape and generates, for example, a fun adventure story if there are many bright colors and round shapes, and generates a suspense story if there are many dark colors and sharp shapes. This allows the tone and atmosphere of the story to be adjusted based on the color and shape patterns.

[0054] The picture analysis unit can analyze the facial expressions and movements of characters in a picture and generate a story that reflects the emotions and actions of the characters. For example, the picture analysis unit analyzes the facial expressions of characters in a picture and generates a fun story if there are many smiling characters, and a touching story if there are many sad characters. The picture analysis unit also analyzes the movements of characters and generates an action-packed story if there are many running characters, and a quiet story if there are many sitting characters. Furthermore, the picture analysis unit analyzes the combination of facial expressions and movements and generates, for example, a fun adventure story if there are many smiling running characters, and a touching drama if there are many sad sitting characters. This makes it possible to generate a story that reflects the emotions and actions of characters based on their facial expressions and movements.

[0055] The picture analysis unit can use the emotion estimation function to estimate the emotion a child was feeling when drawing a picture and generate a story based on that emotion. For example, the picture analysis unit estimates the emotion a child was feeling when drawing a picture, and generates a fun story if the emotion of joy was strong, and a moving story if the emotion of sadness was strong. The picture analysis unit also sets character actions and lines that reflect the emotion a child was feeling when drawing a picture based on the emotion estimation data. For example, if the emotion of joy was strong, positive actions and lines are set. Furthermore, the picture analysis unit uses the emotion estimation function to reflect the emotion a child was feeling when drawing a picture in the tone and theme of the story. For example, if the emotion of surprise was strong, a story with many surprise elements is generated. In this way, a story can be generated based on the emotion a child was feeling when drawing a picture.

[0056] The story generation unit can generate stories of multiple different genres based on a picture drawn by a child and provide options. For example, the story generation unit analyzes a picture drawn by a child and generates stories of multiple genres, such as adventure, fantasy, and mystery, for the child to choose from. The story generation unit also generates stories of different genres based on the content of the picture; for example, if the picture contains many animals, it provides an animal adventure story or an animal fantasy story. Furthermore, the story generation unit generates stories of different genres based on a picture drawn by a child, allowing parents and children to enjoy choosing between options. For example, it can generate an adventure story and a mystery story from the same picture. In this way, stories of multiple genres can be generated and options can be provided.

[0057] The story generation unit can collaborate with pictures drawn by other children based on the content of the picture to jointly generate a story. The story generation unit, for example, analyzes a picture drawn by a child and combines it with pictures drawn by other children to jointly generate a story. For example, it creates a story in which characters drawn by multiple children go on an adventure together. The story generation unit also collaborates with pictures drawn by other children based on the content of the picture to generate a story that incorporates different perspectives and ideas. For example, it combines backgrounds and characters drawn by different children. Furthermore, the story generation unit jointly generates a story based on the pictures drawn by children, allowing parents and children to enjoy it together. For example, it combines pictures drawn by multiple children into a single story. This allows collaboration with pictures drawn by other children to jointly generate a story.

[0058] The story generation unit uses the emotion estimation function to generate a story that reflects the emotions a parent feels toward their child's drawing, thereby creating content that can be enjoyed by both parents and children. The story generation unit, for example, estimates the emotions a parent feels toward their child's drawing and generates a story that reflects those emotions. For example, it generates a fun story for a drawing that makes a parent feel happy, and an emotional story for a drawing that makes a parent feel moved. The story generation unit also uses the emotion estimation function to reflect the emotions a parent feels toward their child's drawing in the tone and theme of the story. For example, it generates a story that includes many surprise elements for a drawing that makes a parent feel surprised. Furthermore, the story generation unit generates a story that can be enjoyed by both parents and children based on the emotions a parent feels toward their child's drawing. For example, it generates a heartwarming story for a drawing that makes a parent feel safe, and an adventure story for a drawing that makes a parent feel excited. In this way, a story that reflects the emotions a parent feels toward their child's drawing can be generated, creating content that can be enjoyed by both parents and children.

[0059] The educational content suggestion unit can analyze themes and motifs contained in pictures drawn by children and suggest related educational content based on the themes and motifs. For example, the educational content suggestion unit analyzes the themes contained in pictures drawn by children, and if there are many animals, it suggests educational content related to animals. The educational content suggestion unit also analyzes the motif of the picture, and if the theme is space, it suggests educational content related to space. Furthermore, the educational content suggestion unit suggests related educational content based on the theme or motif. For example, if the theme is nature, it suggests educational content related to natural science. In this way, it is possible to suggest related educational content based on the theme or motif.

[0060] The educational content suggestion unit can analyze the arrangement of characters and objects in a picture and suggest educational content for improving spatial awareness. For example, the educational content suggestion unit analyzes the arrangement of characters and objects in a picture and suggests puzzles or games for improving spatial awareness. The educational content suggestion unit also suggests educational content for improving spatial awareness based on the arrangement of characters and objects. For example, it provides a 3D model for understanding three-dimensional arrangements. Furthermore, the educational content suggestion unit analyzes the arrangement of a picture and suggests activities for improving spatial awareness. For example, it provides exercises for understanding the positional relationships of characters. This makes it possible to suggest educational content for improving spatial awareness based on the arrangement of characters and objects.

[0061] The educational content suggestion unit can use the emotion estimation function to analyze the emotion a child felt when drawing a picture and suggest educational content based on that emotion. For example, the educational content suggestion unit estimates the emotion a child felt when drawing a picture and suggests fun educational content if the emotion was strong positive, and relaxing educational content if the emotion was strong negative. The educational content suggestion unit also suggests educational content that reflects the emotion a child felt when drawing a picture based on the emotion estimation data. For example, if the emotion of joy was strong, it suggests a fun activity. Furthermore, the educational content suggestion unit uses the emotion estimation function to suggest educational content based on the emotion a child felt when drawing a picture. For example, if the emotion of surprise was strong, it suggests educational content that encourages new discoveries. In this way, educational content can be suggested based on the emotion a child felt when drawing a picture.

[0062] The educational content suggestion unit can suggest educational content in different academic fields based on pictures drawn by the child. For example, the educational content suggestion unit analyzes a picture drawn by the child and suggests educational content in different academic fields such as science, history, and art. For example, science educational content is provided for a picture with many scientific elements. The educational content suggestion unit also suggests educational content in different academic fields based on the content of the picture. For example, history educational content is provided for a picture containing a historical theme. Furthermore, the educational content suggestion unit suggests educational content in different academic fields based on the picture drawn by the child to broaden the child's interests. For example, art educational content is provided for a picture with many artistic elements. In this way, educational content in different academic fields can be suggested.

[0063] The educational content suggestion unit can suggest educational content that parents and children can learn together based on the content of the picture, thereby promoting collaborative learning. For example, the educational content suggestion unit analyzes a picture drawn by a child and suggests educational content that parents and children can learn together. For example, it provides science experiments and crafts that parents and children can enjoy. The educational content suggestion unit also suggests educational content that parents and children can learn together based on the content of the picture, thereby promoting collaborative learning. For example, it provides picture books and learning materials that parents and children can read together. Furthermore, the educational content suggestion unit suggests educational content that parents and children can learn together based on the picture drawn by the child, thereby promoting parent-child communication. For example, it provides projects and activities that parents and children can work on together. In this way, it is possible to suggest educational content that parents and children can learn together and promote collaborative learning.

[0064] The educational content suggestion unit uses the emotion estimation function to suggest educational content that reflects the emotions that parents feel toward their child's drawing, thereby deepening learning between parents and children. The educational content suggestion unit, for example, estimates the emotions that parents feel toward their child's drawing and suggests educational content that reflects those emotions. For example, it suggests fun educational content for a drawing that makes the parent feel happy, and inspirational educational content for a drawing that makes the parent feel moved. The educational content suggestion unit also uses the emotion estimation function to suggest educational content based on the emotions that parents feel toward their child's drawing. For example, it suggests relaxing educational content for a drawing that makes the parent feel at ease. Furthermore, the educational content suggestion unit suggests educational content that deepens learning between parents and children based on the emotions that parents feel toward their child's drawing. For example, it suggests adventurous educational content for a drawing that makes the parent feel excited. In this way, educational content that reflects the emotions that parents feel toward their child's drawing can be suggested, thereby deepening learning between parents and children.

[0065] The story generation unit can analyze the fine details contained in a drawing drawn by a child and generate a high-quality illustration that reflects those details. The story generation unit, for example, analyzes the fine details of a drawing drawn by a child and generates a high-quality illustration that reflects those details. For example, it faithfully reproduces the details of a character's facial expression and clothing. The story generation unit also analyzes the details in the drawing and generates a high-quality illustration of details such as the background and accessories. For example, it depicts the texture of the background scenery and accessories in detail. Furthermore, the story generation unit generates a high-quality illustration that looks like it was drawn by a professional illustrator based on the details of the drawing drawn by a child. For example, it depicts the character's hair and the sparkle in their eyes in detail. This makes it possible to generate a high-quality illustration that reflects the fine details.

[0066] The story generation unit can analyze the personality and characteristics of the characters in the picture and set the lines and actions of the characters in detail based on that. The story generation unit, for example, analyzes the personality and characteristics of the characters in the picture and sets the lines and actions of the characters based on that personality. For example, lively lines and actions are set for a lively character. The story generation unit also sets the role and actions in the story in detail based on the character's characteristics. For example, adventurous actions are set for a brave character. Furthermore, the story generation unit sets lines and actions that reflect the character's personality, adding depth to the story. For example, clever lines and actions are set for an intelligent character. In this way, lines and actions can be set in detail based on the character's personality and characteristics.

[0067] The story generation unit uses the emotion estimation function to create a picture book that reflects the emotion a child felt when drawing the picture, thereby eliciting emotional empathy. The story generation unit, for example, estimates the emotion a child felt when drawing the picture and creates a picture book that reflects that emotion. For example, if the emotion of joy was strong, it creates a fun story, and if the emotion of sadness was strong, it creates a moving story. The story generation unit also sets character expressions and dialogue that reflect the emotion a child felt when drawing the picture, based on the emotion estimation data. For example, if the emotion of joy was strong, it sets a smiling character and positive dialogue. Furthermore, the story generation unit uses the emotion estimation function to reflect the emotion a child felt when drawing the picture in the tone and theme of the story. For example, if the emotion of surprise was strong, it creates a story that includes many surprise elements. In this way, a picture book that reflects the emotion a child felt when drawing the picture can be created and elicit emotional empathy.

[0068] The story generation unit can generate illustrations in different styles based on pictures drawn by the child and provide options. The story generation unit, for example, analyzes a picture drawn by the child and generates illustrations in different styles, such as anime-style or realistic-style, allowing the child to choose. For example, the same character can be drawn in both anime-style and realistic-style. The story generation unit can also generate illustrations in different styles based on the content of the picture, allowing parents and children to enjoy the options together. For example, the background can be drawn in both anime-style and realistic-style. The story generation unit can also generate illustrations in different styles based on the picture drawn by the child and allowing parents and children to enjoy the options together. For example, the same scene can be drawn in both anime-style and realistic-style. This allows illustrations in different styles to be generated and options to be provided.

[0069] The story generation unit can collaborate with pictures drawn by other children based on the content of the picture to jointly create a picture book. The story generation unit, for example, analyzes a picture drawn by a child and combines it with pictures drawn by other children to jointly create a picture book. For example, a picture book is created in which characters drawn by multiple children go on an adventure together. The story generation unit also collaborates with pictures drawn by other children based on the content of the picture to create a picture book that incorporates different perspectives and ideas. For example, backgrounds and characters drawn by different children are combined. Furthermore, the story generation unit can collaboratively create a picture book based on the pictures drawn by children, allowing parents and children to enjoy it together. For example, pictures drawn by multiple children can be compiled into a single picture book. This allows collaboration with pictures drawn by other children to jointly create a picture book.

[0070] The story generation unit uses the emotion estimation function to create a picture book that reflects the emotions that parents feel toward their child's drawing, making it possible to create content that both parents and children can enjoy. The story generation unit, for example, estimates the emotions that parents feel toward their child's drawing and creates a picture book that reflects those emotions. For example, it creates a fun story for a picture that makes a parent feel happy, and an emotional story for a picture that makes a parent feel moved. The story generation unit also uses the emotion estimation function to reflect the emotions that parents feel toward their child's drawing in the tone and theme of the story. For example, it creates a story that includes many surprise elements for a picture that makes a parent feel surprised. Furthermore, the story generation unit creates a picture book that both parents and children can enjoy based on the emotions that parents feel toward their child's drawing. For example, it creates a heartwarming story for a picture that makes a parent feel reassured, and an adventure story for a picture that makes a parent feel excited. In this way, it is possible to create a picture book that reflects the emotions that parents feel toward their child's drawing, making it possible to create content that both parents and children can enjoy.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The picture analysis unit can analyze musical elements contained in a child's drawing and incorporate music into the story based on that analysis. For example, if a picture contains an instrument, the sound of that instrument can be used as background music for the story. Also, if the picture contains a pattern that suggests rhythm or melody, music can be generated based on that and incorporated into the story. Furthermore, the picture analysis unit can analyze a scene in which a character in the picture is singing and recreate that song in the story. This allows the picture book generation system to provide a story that incorporates musical elements.

[0073] The story generation unit can analyze natural elements contained in a picture drawn by a child and generate a story that includes environmental education based on that analysis. For example, if the picture contains many plants and animals, a story that conveys the importance of nature conservation and ecosystems centered on those elements can be generated. Also, if the picture contains natural elements such as water and air, an educational story about environmental issues can be generated based on that analysis. Furthermore, the story generation unit can analyze the natural elements in the picture and, based on that, suggest activities that provide children with opportunities to interact with nature. This makes it possible to provide a story that includes environmental education.

[0074] The educational content suggestion unit can analyze cultural elements contained in a picture drawn by a child and suggest educational content that promotes intercultural understanding based on the analysis. For example, if the picture depicts traditional clothing or buildings from a different country, content for learning about the culture or history of that country can be suggested based on the analysis. Also, if the picture contains different languages, language learning content can be suggested based on the analysis. Furthermore, the educational content suggestion unit can analyze cultural elements in the picture and suggest activities that promote intercultural exchange based on the analysis. This makes it possible to provide educational content that promotes intercultural understanding.

[0075] The story generation unit can use the emotion estimation function to generate a character growth story that reflects the emotions felt by the child when drawing the picture. For example, a story is generated in which the character overcomes difficulties and grows, reflecting the joy and sense of accomplishment felt by the child when drawing the picture. The emotion estimation function can also be used to generate a story in which the character deepens friendships and family bonds based on the emotions felt by the child when drawing the picture. Furthermore, the emotion estimation function can also be used to generate a story in which the character increases self-esteem, reflecting the emotions felt by the child when drawing the picture. This makes it possible to provide a character growth story that reflects emotions.

[0076] The picture analysis unit can analyze the scientific elements contained in a child's drawing and generate a story that includes science education based on that analysis. For example, if the drawing depicts space or a scientific experiment, it can generate a story that explains space exploration or the principles of science based on that analysis. Also, if the drawing depicts a robot or technology, it can generate a story that explores technological advances and future possibilities based on that analysis. Furthermore, the picture analysis unit can analyze the scientific elements in the drawing and suggest activities that will pique children's interest in science based on that analysis. This makes it possible to provide stories that include science education.

[0077] The story generation unit can use the emotion estimation function to generate an interactive story that reflects the emotions a child felt when drawing a picture. For example, the story generation unit can reflect the excitement and curiosity a child felt when drawing a picture, provide options in the story, and allow the child to decide how the story will unfold. The emotion estimation function can also be used to incorporate interactive elements in the story in which characters react according to the emotions felt by the child when drawing a picture. Furthermore, the emotion estimation function can be used to reflect the emotions felt by the child when drawing a picture, and provide audio and animation in the story that correspond to the emotions. In this way, an interactive story that reflects emotions can be provided.

[0078] The educational content suggestion unit can analyze mathematical elements contained in a child's drawing and suggest content including mathematics education based on that analysis. For example, if a drawing contains geometric shapes or patterns, content for learning geometric principles can be suggested based on that analysis. Also, if a drawing contains numbers or calculations, content for solving arithmetic problems can be suggested based on that analysis. Furthermore, the educational content suggestion unit can analyze mathematical elements in a drawing and suggest activities that will stimulate children's interest in mathematics based on that analysis. This makes it possible to provide content including mathematics education.

[0079] The story generation unit can use the emotion estimation function to generate an adventure story that reflects the emotions felt by the child when drawing the picture. For example, a story in which a character explores an unknown world is generated, reflecting the excitement and adventurous spirit felt by the child when drawing the picture. The emotion estimation function can also be used to generate an adventure story in which a character overcomes difficulties and grows, based on the emotions felt by the child when drawing the picture. Furthermore, the emotion estimation function can also be used to generate an adventure story in which a character values ​​friendship and teamwork, reflecting the emotions felt by the child when drawing the picture. In this way, an adventure story that reflects emotions can be provided.

[0080] The picture analysis unit can analyze historical elements contained in pictures drawn by children and generate stories that include history education based on the analysis. For example, if a picture contains historical buildings or people, a story that explains the events and culture of that era can be generated based on the historical elements. Also, if a picture contains symbols of war or peace, a story that teaches the historical background can be generated based on the symbols. Furthermore, the picture analysis unit can analyze historical elements in pictures and suggest activities that will arouse children's interest in history based on the analysis. This makes it possible to provide stories that include history education.

[0081] The story generation unit can use the emotion estimation function to generate a fantasy story that reflects the emotions a child felt when drawing a picture. For example, it can generate a story in which a character goes on an adventure in a magical world, reflecting the dreams and hopes the child felt when drawing a picture. It can also use the emotion estimation function to generate a fantasy story in which a character overcomes difficulties and grows, based on the emotions the child felt when drawing a picture. It can also use the emotion estimation function to generate a fantasy story in which a character cherishes friendship and love, reflecting the emotions the child felt when drawing a picture. In this way, it is possible to provide a fantasy story that reflects emotions.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The picture analysis unit analyzes the picture drawn by the child. For example, the picture analysis unit digitizes and analyzes a hand-drawn picture using scanning technology. It is also possible to directly analyze a picture submitted in digital format. Furthermore, the picture analysis unit analyzes the content of the picture using image recognition technology. For example, the picture analysis unit scans a hand-drawn picture with a high-resolution scanner and analyzes the content using image recognition technology. Digital pictures submitted in a specific file format can be directly analyzed. Image recognition technology recognizes and analyzes the content of the picture with high accuracy. Step 2: In the story generation unit, the generation AI generates a story based on the results of the analysis by the picture analysis unit. For example, the generation AI generates a story using a text generation AI (e.g., LLM). The generation AI can also use a multimodal generation AI to generate a story based on the content of the picture. The generation AI can also generate a story based on the theme or characters of the picture. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI generates an appropriate story based on the theme and characters of the picture. Step 3: The educational content suggestion unit suggests educational content based on the story generated by the story generation unit. For example, the educational content suggestion unit suggests related educational content based on the content of the generated story. The educational content suggestion unit can also suggest optimal educational content based on the age and interests of the child. The educational content suggestion unit can also suggest related educational programs based on the theme of the generated story. For example, if the generated story contains many scientific elements, the educational content suggestion unit suggests educational content related to science. The educational content suggestion unit can suggest related educational programs based on the theme of the generated story, for example.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A picture analysis section that analyzes pictures drawn by children, a story generation unit that generates a story based on the result of the analysis by the picture analysis unit; an educational content suggestion unit that suggests educational content based on the story generated by the story generation unit; A system characterized by:

2. The picture analysis unit Analyzing color and shape patterns in a child's drawing and adjusting the tone and mood of the story based on that 2. The system of claim 1.

3. The picture analysis unit Analyze the facial expressions and movements of the characters in the picture and generate the story that reflects the characters' emotions and actions.

2. The system of claim 1.

4. The picture analysis unit Estimate the emotion a child felt when drawing a picture and generate a story based on that emotion.

2. The system of claim 1.

5. The story generation unit Based on the child's drawings, the system generates stories of multiple different genres and provides options 2. The system of claim 1.

6. The story generation unit Based on the content of the drawing, the child collaborates with other children's drawings to jointly generate the story.

2. The system of claim 1.

7. The story generation unit The story is generated to reflect the feelings that parents have about their children's drawings, making it enjoyable for both parents and children.

2. The system of claim 1.

8. The educational content proposal unit Analyzes themes and motifs contained in children's drawings and suggests relevant educational content based on them 2. The system of claim 1.

Images