System

Abstract

An object of a system according to an embodiment is to provide a place where a child can create a picture book by himself / herself.SOLUTION: A system includes a story input part, an analysis part, and a generation part. The story input section allows the child to input a story. The analysis section analyzes the story input by the story input section. The generation section generates a story of the picture book based on the story analyzed by the analysis section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for children to create their own picture books, limiting the opportunities for them to express their creativity.

[0005] The system according to the embodiment aims to provide a place where children can create their own picture books. [Means for solving the problem]

[0006] The system according to the embodiment includes a story input unit, an analysis unit, and a generation unit. The story input unit allows a child to input a story. The analysis unit analyzes the story input by the story input unit. The generation unit generates a story for a picture book based on the story analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a place where children can create their own picture books. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A picture book creation system according to an embodiment of the present invention is a system for creating a picture book based on a story that a child has thought up themselves. In this system, a generation AI analyzes the story and compiles it into a picture book. This allows the picture book creation system to create a picture book based on a story that a child has thought up themselves. For example, by allowing a child to use their imagination to create a story and then compile it into a picture book, it is possible to develop creativity and expressiveness. Furthermore, by utilizing the generation AI, children can easily create picture books, allowing them to learn while having fun.

[0029] A picture book creation system according to an embodiment includes a story input unit, an analysis unit, and a generation unit. A child inputs a story through the story input unit. For example, the child can input the story using a keyboard or tablet. The story input unit can also allow the child to speak the story using voice input. The analysis unit analyzes the story input by the story input unit. For example, the generation AI analyzes the content of the story using natural language processing technology. The analysis unit can also analyze the emotional elements of the story using emotion analysis technology. The generation unit generates a picture book story based on the story analyzed by the analysis unit. For example, the generation AI generates a picture book story based on the content of the story. The generation unit can also create a deeper story by setting detailed characters and background for the story. This allows the picture book creation system according to an embodiment to create a picture book based on a story that a child has thought up themselves. For example, a child can use their imagination to create a story and then compile it into a picture book, thereby fostering their creativity and expressiveness. Furthermore, the generation AI allows children to easily create picture books, allowing them to learn while having fun.

[0030] The story input unit provides an interface that allows children to input a story by voice, and the generation AI can recognize the voice and convert it into text. For example, when a child speaks a story using a microphone, the generation AI converts the voice into text in real time. For example, if a child says, "One day, a little rabbit went on an adventure in the forest," the content is displayed as text. The story input unit also uses voice recognition technology to automatically convert the story told by the child into text, and the generation AI analyzes the story based on that text. For example, if a child speaks, "The rabbit met a friend," the content is saved as text. The story input unit also converts the story told by the child into text using voice recognition technology, and the generation AI generates a picture book story based on that text. For example, if a child speaks, "The rabbit found treasure in the forest," the content is displayed as text, and the generation AI generates a story. This allows children to input stories by voice.

[0031] The story input unit scans a picture drawn by a child, and the generation AI can generate a story based on that picture. For example, the story input unit scans a picture drawn by a child, and the generation AI analyzes the picture to generate a story. For example, if a child draws a picture of a rabbit, the generation AI generates a rabbit adventure story based on that picture. The story input unit also analyzes the scanned picture using image recognition technology, and the generation AI generates a story based on its content. For example, based on a picture of a forest drawn by a child, the generation AI generates a story set in a forest. The story input unit also scans a picture drawn by a child, and the generation AI analyzes the content of the picture to generate a story. For example, based on a picture of a castle drawn by a child, the generation AI generates a story set in a castle. In this way, stories can be generated based on pictures drawn by children.

[0032] The story input unit can provide a collaboration function that allows a child to create a story collaboratively with other children when inputting a story. The story input unit, for example, provides a function that allows a child to create a story collaboratively with other children online when inputting a story. For example, multiple children input stories at the same time, and the generation AI integrates the content to generate a single story. The story input unit also uses the collaboration function so that children take turns inputting stories, and the generation AI generates a story based on the content. For example, one child inputs "The rabbit went to the forest," and the next child continues, "There he met a friend." The story input unit also provides an interface for children to create a story collaboratively, and the generation AI generates a story based on the content. For example, children input a story in a chat format, and the generation AI generates a picture book story based on the content. This allows children to create a story collaboratively.

[0033] The story input unit can add a function in which the generation AI provides advice and hints in real time when a child is inputting a story. The story input unit adds a function in which the generation AI provides advice and hints in real time when a child is inputting a story. For example, if a child inputs, "The rabbit went to the forest," the generation AI suggests, "Let's think about what happens next." The story input unit also adds a function in which the generation AI provides advice in real time as the story progresses. For example, if a child inputs, "The rabbit met a friend," the generation AI asks, "What kind of animal is that friend?" The story input unit also adds a function in which the generation AI provides hints in real time when a child is inputting a story. For example, if a child inputs, "The rabbit went on an adventure," the generation AI suggests, "What is the purpose of the adventure?" This allows a child to receive advice and hints in real time when inputting a story.

[0034] The analysis unit can provide a function that understands the context of a story and generates multiple endings. For example, the analysis unit provides a function that allows the generation AI to analyze the context of a story and generate multiple endings. For example, for a rabbit adventure story, the analysis unit generates "an ending where the rabbit finds treasure" and "an ending where the rabbit is reunited with his friends." The analysis unit also allows the generation AI to suggest multiple endings based on the content of the story entered by a child. For example, the analysis unit generates "an ending where the rabbit leaves the forest and goes on a new adventure" and "an ending where the rabbit returns home and spends time with his family." The analysis unit also provides a function that allows the generation AI to understand the context of a story and generate different endings. For example, the analysis unit generates "an ending where the rabbit overcomes difficulties and grows" and "an ending where the rabbit makes new friends." This allows the generation AI to understand the context of a story and generate multiple endings.

[0035] The analysis unit allows the generation AI to set the personalities and backgrounds of story characters in detail, generating a deeper story. For example, the analysis unit allows the generation AI to set the personalities and backgrounds of story characters in detail, generating a deeper story. For example, the rabbit's personality is set to "brave and curious" and the background adds "the reason he goes on an adventure to protect his family." The analysis unit also allows the generation AI to set detailed personalities and backgrounds for story characters input by a child. For example, the rabbit's friend's personality is set to "kind and helpful" and the background adds "past experiences of being helped." The analysis unit also allows the generation AI to set the personalities and backgrounds of story characters in detail, digging deeper into the story. For example, the rabbit's enemy character's personality is set to "cunning and planned" and the background adds "past trauma." This allows the generation AI to set the characters' personalities and backgrounds in detail, generating a deeper story.

[0036] The analysis unit can automatically determine the genre of the story and generate a story that matches the genre. For example, the analysis unit allows the generation AI to automatically determine the genre of the story and generate a story that matches that genre. For example, if the story input by a child is in the adventure genre, the generation AI will generate a story that suits the adventure. The analysis unit also allows the generation AI to determine the genre based on the content of the story input by the child and propose a story that matches that genre. For example, if the story is in the fantasy genre, the generation AI will generate a story that includes magic and other worlds. The analysis unit also allows the generation AI to automatically determine the genre of the story and generate a story that matches that genre. For example, if the story is in the mystery genre, the generation AI will generate a story that includes puzzles and suspense elements. This makes it possible to automatically determine the genre of the story and generate a story that matches the genre.

[0037] The analysis unit can automatically add story settings and generate richer stories. For example, the analysis unit allows the generation AI to automatically add story settings and generate richer stories. For example, the time and place setting of "medieval forest" is added to a rabbit adventure story. The analysis unit also allows the generation AI to automatically add settings based on the content of the story entered by a child. For example, the analysis unit adds the setting of "future city" and generates a story in which the rabbit has an adventure in the futuristic city. The analysis unit also allows the generation AI to automatically add story settings and enrich the cultural background. For example, the analysis unit adds the setting of "traditional Japanese village" and generates a story in which the rabbit has an adventure in that village. In this way, story settings can be automatically added and richer stories can be generated.

[0038] The generation unit can apply different art styles to each story scene to generate a visually diverse picture book. For example, the generation AI applies different art styles to each story scene to generate a visually diverse picture book. For example, a realistic art style is applied to adventure scenes, and a fantasy art style is applied to dream scenes. The generation unit also selects different art styles for each scene depending on the content of the story input by the child. For example, a natural landscape art style is applied to a scene where the rabbit is adventuring in the forest, and a warm art style is applied to a scene where the rabbit is returning home. The generation unit also increases the visual diversity of the picture book by applying different art styles to each story scene. For example, a cartoon-style art style is applied to a scene where the rabbit is playing with his friends, and a dark art style is applied to a scene where the rabbit is facing difficulties. This allows different art styles to be applied to each scene to generate a visually diverse picture book.

[0039] The generation unit can generate more lifelike pictures by depicting the movements and facial expressions of story characters in detail. For example, in a scene where a rabbit jumps, the generation AI depicts the movements and facial expressions of the story characters in detail, generating more lifelike pictures. For example, in a scene where a rabbit jumps, the movement is depicted realistically and the facial expression is also depicted in detail. The generation unit also depicts the movements and facial expressions of the characters in detail based on the content of the story input by the child. For example, in a scene where the rabbit is surprised, the surprised expression is depicted in detail and the movement is also expressed realistically. The generation unit also depicts the movements and facial expressions of the story characters in detail, making the picture book pictures more lifelike. For example, in a scene where the rabbit smiles, the smile is depicted in detail and the movement is expressed naturally. This allows the movement and facial expressions of the characters to be depicted in detail, generating more lifelike pictures.

[0040] The generation unit generates 3D models based on story scenes and can create a three-dimensional picture book. For example, the generation unit uses a generation AI to generate 3D models based on story scenes and create a three-dimensional picture book. For example, a scene of a rabbit adventuring in the forest is represented in a 3D model to create a three-dimensional picture book. The generation unit also generates 3D models based on the content of a story input by a child and creates a three-dimensional picture book. For example, a scene of a rabbit playing with friends is represented in a 3D model to create a three-dimensional picture book. The generation unit also generates 3D models based on story scenes and can enhance the visual appeal of the picture book. For example, a scene of a rabbit returning home is represented in a 3D model to create a three-dimensional picture book. In this way, a three-dimensional picture book can be created by generating 3D models based on story scenes.

[0041] The generation unit can generate animations based on story scenes and create a moving picture book. For example, the generation unit uses a generation AI to generate animations based on story scenes and create a moving picture book. For example, a scene of a rabbit adventuring in the forest is animated to create a moving picture book. The generation unit also uses a generation AI to generate animations based on the content of a story input by a child and create a moving picture book. For example, a scene of a rabbit playing with friends is animated to create a moving picture book. The generation unit also uses a generation AI to generate animations based on story scenes and enhance the visual appeal of the picture book. For example, a scene of a rabbit returning home is animated to create a moving picture book. In this way, animations can be generated based on story scenes and a moving picture book can be created.

[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 story input unit can provide a collaboration function that allows a child to create a story collaboratively with other children when inputting a story. For example, a function is provided that allows a child to create a story collaboratively with other children online when inputting a story. For example, multiple children input stories at the same time, and the generation AI integrates the content to generate a single story. The story input unit also uses the collaboration function so that children take turns inputting stories, and the generation AI generates a story based on the content. For example, one child inputs "The rabbit went to the forest," and the next child continues, "There he met a friend." The story input unit also provides an interface for children to create a story collaboratively, and the generation AI generates a story based on the content. For example, children input a story in a chat format, and the generation AI generates a picture book story based on the content. This allows children to create a story collaboratively.

[0044] The story input unit can add a function in which the generation AI provides advice and hints in real time when a child is inputting a story. For example, when a child inputs a story, the generation AI can provide advice and hints in real time. For example, if a child inputs "The rabbit went to the forest," the generation AI can suggest, "Let's think about what happens next." The story input unit also adds a function in which the generation AI provides advice in real time as the story progresses. For example, if a child inputs "The rabbit met a friend," the generation AI can ask, "What kind of animal is that friend?" The story input unit also adds a function in which the generation AI provides hints in real time when a child is inputting a story. For example, if a child inputs "The rabbit went on an adventure," the generation AI can suggest, "What is the purpose of the adventure?" This allows children to receive advice and hints in real time when inputting a story.

[0045] The analysis unit can provide a function that understands the context of a story and generates multiple endings. For example, the generation AI analyzes the context of a story and provides a function that generates multiple endings. For example, for a rabbit adventure story, it generates "an ending where the rabbit finds treasure" and "an ending where the rabbit is reunited with his friends." The analysis unit also provides a function that allows the generation AI to suggest multiple endings based on the content of the story entered by a child. For example, it generates "an ending where the rabbit leaves the forest and goes on a new adventure" and "an ending where the rabbit returns home and spends time with his family." The analysis unit also provides a function that allows the generation AI to understand the context of a story and generate different endings. For example, it generates "an ending where the rabbit overcomes difficulties and grows" and "an ending where the rabbit makes new friends." This makes it possible to understand the context of a story and generate multiple endings.

[0046] The analysis unit can set the personalities and backgrounds of story characters in detail, generating a deeper story. For example, the generation AI can set the personalities and backgrounds of story characters in detail to generate a deeper story. For example, the rabbit's personality can be set as "brave and curious" and its background can be added with "the reason he goes on an adventure to protect his family." The analysis unit also sets detailed personalities and backgrounds for story characters input by a child. For example, the rabbit's friend's personality can be set as "kind and helpful" and its background can be added with "past experiences of being helped." The analysis unit also sets the personalities and backgrounds of story characters in detail to dig deeper into the story. For example, the rabbit's enemy character's personality can be set as "cunning and planned" and its background can be added with "past trauma." This allows the generation AI to set the characters' personalities and backgrounds in detail and generate a deeper story.

[0047] The analysis unit can automatically determine the genre of the story and generate a story that matches the genre. For example, the generation AI can automatically determine the genre of the story and generate a story that matches that genre. For example, if the story entered by a child is in the adventure genre, the generation AI will generate a story that suits the adventure. The analysis unit also allows the generation AI to determine the genre based on the content of the story entered by the child and propose a story that matches that genre. For example, if the story is in the fantasy genre, the generation AI will generate a story that includes magic and other worlds. The analysis unit also allows the generation AI to automatically determine the genre of the story and generate a story that matches that genre. For example, if the story is in the mystery genre, the generation AI will generate a story that includes puzzles and suspense elements. This makes it possible to automatically determine the genre of the story and generate a story that matches the genre.

[0048] The analysis unit can automatically add story settings and generate richer stories. For example, the generation AI can automatically add story settings and generate richer stories. For example, to a rabbit adventure story, the time and place setting of "medieval forest" is added. The analysis unit also allows the generation AI to automatically add settings based on the content of the story entered by the child. For example, the analysis unit can add the setting of "future city" and generate a story in which the rabbit goes on an adventure in the futuristic city. The analysis unit also allows the generation AI to automatically add story settings and enrich the cultural background. For example, the analysis unit can add the setting of "traditional Japanese village" and generate a story in which the rabbit goes on an adventure in that village. In this way, story settings can be automatically added and richer stories can be generated.

[0049] The generation unit can apply different art styles to each story scene to generate visually diverse picture books. For example, the generation AI can apply different art styles to each story scene to generate visually diverse picture books. For example, a realistic art style is applied to adventure scenes, and a fantasy art style is applied to dream scenes. The generation unit also selects different art styles for each scene depending on the content of the story input by the child. For example, a natural landscape art style is applied to a scene where the rabbit is adventuring in the forest, and a warm art style is applied to a scene where the rabbit is returning home. The generation unit also applies different art styles to each story scene to increase the visual diversity of the picture book. For example, a cartoon-style art style is applied to a scene where the rabbit is playing with his friends, and a dark art style is applied to a scene where the rabbit is facing difficulties. This allows the generation of visually diverse picture books by applying different art styles to each scene.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The story input unit allows the child to input a story. For example, the child can input a story using a keyboard or tablet. The story input unit also allows the child to speak the story using voice input. Step 2: The analysis unit analyzes the story input by the story input unit. For example, the generation AI analyzes the content of the story using natural language processing technology. The analysis unit can also analyze the emotional elements of the story using emotion analysis technology. Step 3: The generator generates a picture book story based on the story analyzed by the analyzer. For example, the generator AI generates a picture book story based on the content of the story. The generator can also create a deeper story by setting the characters and background of the story in detail.

[0052] (Example 2) A picture book creation system according to an embodiment of the present invention is a system for creating a picture book based on a story that a child has thought up themselves. In this system, a generation AI analyzes the story and compiles it into a picture book. This allows the picture book creation system to create a picture book based on a story that a child has thought up themselves. For example, by allowing a child to use their imagination to create a story and then compile it into a picture book, it is possible to develop creativity and expressiveness. Furthermore, by utilizing the generation AI, children can easily create picture books, allowing them to learn while having fun.

[0053] A picture book creation system according to an embodiment includes a story input unit, an analysis unit, and a generation unit. A child inputs a story through the story input unit. For example, the child can input the story using a keyboard or tablet. The story input unit can also allow the child to speak the story using voice input. The analysis unit analyzes the story input by the story input unit. For example, the generation AI analyzes the content of the story using natural language processing technology. The analysis unit can also analyze the emotional elements of the story using emotion analysis technology. The generation unit generates a picture book story based on the story analyzed by the analysis unit. For example, the generation AI generates a picture book story based on the content of the story. The generation unit can also create a deeper story by setting detailed characters and background for the story. This allows the picture book creation system according to an embodiment to create a picture book based on a story that a child has thought up themselves. For example, a child can use their imagination to create a story and then compile it into a picture book, thereby fostering their creativity and expressiveness. Furthermore, the generation AI allows children to easily create picture books, allowing them to learn while having fun.

[0054] The story input unit provides an interface that allows children to input a story by voice, and the generation AI can recognize the voice and convert it into text. For example, when a child speaks a story using a microphone, the generation AI converts the voice into text in real time. For example, if a child says, "One day, a little rabbit went on an adventure in the forest," the content is displayed as text. The story input unit also uses voice recognition technology to automatically convert the story told by the child into text, and the generation AI analyzes the story based on that text. For example, if a child speaks, "The rabbit met a friend," the content is saved as text. The story input unit also converts the story told by the child into text using voice recognition technology, and the generation AI generates a picture book story based on that text. For example, if a child speaks, "The rabbit found treasure in the forest," the content is displayed as text, and the generation AI generates a story. This allows children to input stories by voice.

[0055] The story input unit scans a picture drawn by a child, and the generation AI can generate a story based on that picture. For example, the story input unit scans a picture drawn by a child, and the generation AI analyzes the picture to generate a story. For example, if a child draws a picture of a rabbit, the generation AI generates a rabbit adventure story based on that picture. The story input unit also analyzes the scanned picture using image recognition technology, and the generation AI generates a story based on its content. For example, based on a picture of a forest drawn by a child, the generation AI generates a story set in a forest. The story input unit also scans a picture drawn by a child, and the generation AI analyzes the content of the picture to generate a story. For example, based on a picture of a castle drawn by a child, the generation AI generates a story set in a castle. In this way, stories can be generated based on pictures drawn by children.

[0056] The story input unit can use the emotion estimation function to analyze the emotions a child is expressing when inputting a story and provide an interface for eliciting positive emotions. For example, when a child is inputting a story, the story input unit analyzes facial expressions and vocal tone and provides an interface for eliciting positive emotions using the emotion estimation function. For example, when a child speaks with a smile, the generation AI analyzes that emotion and displays positive feedback. The story input unit also uses the emotion estimation function to analyze the emotions a child is expressing when inputting a story in real time and provides advice for eliciting positive emotions. For example, when a child is inputting a story, the generation AI displays an encouraging message such as, "That's a great idea!" The story input unit also uses the emotion estimation function to analyze the emotions a child is expressing when inputting a story and provides an interface for eliciting positive emotions. For example, when a child is inputting a story, the generation AI suggests, "Let's think of a more fun adventure!" This makes it possible to elicit positive emotions in the child.

[0057] The story input unit can provide a collaboration function that allows a child to create a story collaboratively with other children when inputting a story. The story input unit, for example, provides a function that allows a child to create a story collaboratively with other children online when inputting a story. For example, multiple children input stories at the same time, and the generation AI integrates the content to generate a single story. The story input unit also uses the collaboration function so that children take turns inputting stories, and the generation AI generates a story based on the content. For example, one child inputs "The rabbit went to the forest," and the next child continues, "There he met a friend." The story input unit also provides an interface for children to create a story collaboratively, and the generation AI generates a story based on the content. For example, children input a story in a chat format, and the generation AI generates a picture book story based on the content. This allows children to create a story collaboratively.

[0058] The story input unit can add a function in which the generation AI provides advice and hints in real time when a child is inputting a story. The story input unit adds a function in which the generation AI provides advice and hints in real time when a child is inputting a story. For example, if a child inputs, "The rabbit went to the forest," the generation AI suggests, "Let's think about what happens next." The story input unit also adds a function in which the generation AI provides advice in real time as the story progresses. For example, if a child inputs, "The rabbit met a friend," the generation AI asks, "What kind of animal is that friend?" The story input unit also adds a function in which the generation AI provides hints in real time when a child is inputting a story. For example, if a child inputs, "The rabbit went on an adventure," the generation AI suggests, "What is the purpose of the adventure?" This allows a child to receive advice and hints in real time when inputting a story.

[0059] The story input unit can use the emotion estimation function to provide a function that suggests a story theme and characters based on the emotion a child is feeling when inputting a story. For example, the story input unit uses the emotion estimation function to analyze the emotion a child is feeling when inputting a story and suggest a story theme and characters based on that emotion. For example, if a child is talking happily, the generation AI may suggest, "How about an adventure theme?" The story input unit also provides a function that analyzes the emotion a child is feeling when inputting a story in real time and suggests characters based on that emotion. For example, if a child is talking excitedly, the generation AI may suggest, "Let's have a brave hero appear." The story input unit also uses the emotion estimation function to provide a function that suggests a story theme and characters based on the emotion a child is feeling when inputting a story. For example, if a child is talking sadly, the generation AI may suggest, "Let's create an inspiring story." In this way, story themes and characters can be suggested based on the child's emotion.

[0060] The analysis unit can provide a function that understands the context of a story and generates multiple endings. For example, the analysis unit provides a function that allows the generation AI to analyze the context of a story and generate multiple endings. For example, for a rabbit adventure story, the analysis unit generates "an ending where the rabbit finds treasure" and "an ending where the rabbit is reunited with his friends." The analysis unit also allows the generation AI to suggest multiple endings based on the content of the story entered by a child. For example, the analysis unit generates "an ending where the rabbit leaves the forest and goes on a new adventure" and "an ending where the rabbit returns home and spends time with his family." The analysis unit also provides a function that allows the generation AI to understand the context of a story and generate different endings. For example, the analysis unit generates "an ending where the rabbit overcomes difficulties and grows" and "an ending where the rabbit makes new friends." This allows the generation AI to understand the context of a story and generate multiple endings.

[0061] The analysis unit allows the generation AI to set the personalities and backgrounds of story characters in detail, generating a deeper story. For example, the analysis unit allows the generation AI to set the personalities and backgrounds of story characters in detail, generating a deeper story. For example, the rabbit's personality is set to "brave and curious" and the background adds "the reason he goes on an adventure to protect his family." The analysis unit also allows the generation AI to set detailed personalities and backgrounds for story characters input by a child. For example, the rabbit's friend's personality is set to "kind and helpful" and the background adds "past experiences of being helped." The analysis unit also allows the generation AI to set the personalities and backgrounds of story characters in detail, digging deeper into the story. For example, the rabbit's enemy character's personality is set to "cunning and planned" and the background adds "past trauma." This allows the generation AI to set the characters' personalities and backgrounds in detail, generating a deeper story.

[0062] The analysis unit can use the emotion estimation function to analyze the emotions of the characters in each scene of the story and generate a story development based on the emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of the characters in each scene of the story and generate a story development based on those emotions. For example, in a scene where the rabbit feels fear, the generation AI generates a development that reflects that emotion. The analysis unit also uses the generation AI to analyze the emotions of the characters in each scene of the story and propose a story development based on those emotions. For example, in a scene where the rabbit feels joy, the generation AI generates a positive development that reflects that emotion. The analysis unit also uses the emotion estimation function to analyze the emotions of the characters in each scene of the story and generate a story development based on those emotions. For example, in a scene where the rabbit feels sad, the generation AI generates a moving development that reflects that emotion. This makes it possible to generate a story development based on the emotions of the characters.

[0063] The analysis unit can automatically determine the genre of the story and generate a story that matches the genre. For example, the analysis unit allows the generation AI to automatically determine the genre of the story and generate a story that matches that genre. For example, if the story input by a child is in the adventure genre, the generation AI will generate a story that suits the adventure. The analysis unit also allows the generation AI to determine the genre based on the content of the story input by the child and propose a story that matches that genre. For example, if the story is in the fantasy genre, the generation AI will generate a story that includes magic and other worlds. The analysis unit also allows the generation AI to automatically determine the genre of the story and generate a story that matches that genre. For example, if the story is in the mystery genre, the generation AI will generate a story that includes puzzles and suspense elements. This makes it possible to automatically determine the genre of the story and generate a story that matches the genre.

[0064] The analysis unit can automatically add story settings and generate richer stories. For example, the analysis unit allows the generation AI to automatically add story settings and generate richer stories. For example, the time and place setting of "medieval forest" is added to a rabbit adventure story. The analysis unit also allows the generation AI to automatically add settings based on the content of the story entered by a child. For example, the analysis unit adds the setting of "future city" and generates a story in which the rabbit has an adventure in the futuristic city. The analysis unit also allows the generation AI to automatically add story settings and enrich the cultural background. For example, the analysis unit adds the setting of "traditional Japanese village" and generates a story in which the rabbit has an adventure in that village. In this way, story settings can be automatically added and richer stories can be generated.

[0065] The analysis unit can use the emotion estimation function to predict the reader's emotional response in each scene of the story and generate a story development based on the emotion. The analysis unit, for example, uses the emotion estimation function to predict the reader's emotional response in each scene of the story and generate a story development based on the emotion. For example, in a scene in which the reader feels surprised, the generation AI generates a development that reflects that emotion. The analysis unit also has the generation AI predict the reader's emotional response in each scene of the story and propose a story development based on that emotion. For example, in a scene in which the reader feels moved, the generation AI generates an emotional development that reflects that emotion. The analysis unit also uses the emotion estimation function to predict the reader's emotional response in each scene of the story and generate a story development based on that emotion. For example, in a scene in which the reader feels laughing, the generation AI generates a humorous development that reflects that emotion. In this way, the reader's emotional response can be predicted and a story development based on emotion can be generated.

[0066] The generation unit can apply different art styles to each story scene to generate a visually diverse picture book. For example, the generation AI applies different art styles to each story scene to generate a visually diverse picture book. For example, a realistic art style is applied to adventure scenes, and a fantasy art style is applied to dream scenes. The generation unit also selects different art styles for each scene depending on the content of the story input by the child. For example, a natural landscape art style is applied to a scene where the rabbit is adventuring in the forest, and a warm art style is applied to a scene where the rabbit is returning home. The generation unit also increases the visual diversity of the picture book by applying different art styles to each story scene. For example, a cartoon-style art style is applied to a scene where the rabbit is playing with his friends, and a dark art style is applied to a scene where the rabbit is facing difficulties. This allows different art styles to be applied to each scene to generate a visually diverse picture book.

[0067] The generation unit can generate more lifelike pictures by depicting the movements and facial expressions of story characters in detail. For example, in a scene where a rabbit jumps, the generation AI depicts the movements and facial expressions of the story characters in detail, generating more lifelike pictures. For example, in a scene where a rabbit jumps, the movement is depicted realistically and the facial expression is also depicted in detail. The generation unit also depicts the movements and facial expressions of the characters in detail based on the content of the story input by the child. For example, in a scene where the rabbit is surprised, the surprised expression is depicted in detail and the movement is also expressed realistically. The generation unit also depicts the movements and facial expressions of the story characters in detail, making the picture book pictures more lifelike. For example, in a scene where the rabbit smiles, the smile is depicted in detail and the movement is expressed naturally. This allows the movement and facial expressions of the characters to be depicted in detail, generating more lifelike pictures.

[0068] The generation unit can use the emotion estimation function to generate pictures that reflect the emotions of characters in story scenes. For example, the generation unit uses the emotion estimation function to analyze the emotions of characters in story scenes and generate pictures that reflect those emotions. For example, in a scene where a rabbit is sad, it generates a picture that expresses that sadness. The generation unit also uses the generation AI to analyze the emotions of characters in story scenes and generate pictures that reflect those emotions. For example, in a scene where the rabbit is happy, it generates a picture that expresses that joy. The generation unit also uses the emotion estimation function to generate pictures that reflect the emotions of characters in story scenes. For example, in a scene where the rabbit is surprised, it generates a picture that expresses that surprise. In this way, it is possible to generate pictures that reflect the emotions of characters.

[0069] The generation unit generates 3D models based on story scenes and can create a three-dimensional picture book. For example, the generation unit uses a generation AI to generate 3D models based on story scenes and create a three-dimensional picture book. For example, a scene of a rabbit adventuring in the forest is represented in a 3D model to create a three-dimensional picture book. The generation unit also generates 3D models based on the content of a story input by a child and creates a three-dimensional picture book. For example, a scene of a rabbit playing with friends is represented in a 3D model to create a three-dimensional picture book. The generation unit also generates 3D models based on story scenes and can enhance the visual appeal of the picture book. For example, a scene of a rabbit returning home is represented in a 3D model to create a three-dimensional picture book. In this way, a three-dimensional picture book can be created by generating 3D models based on story scenes.

[0070] The generation unit can generate animations based on story scenes and create a moving picture book. For example, the generation unit uses a generation AI to generate animations based on story scenes and create a moving picture book. For example, a scene of a rabbit adventuring in the forest is animated to create a moving picture book. The generation unit also uses a generation AI to generate animations based on the content of a story input by a child and create a moving picture book. For example, a scene of a rabbit playing with friends is animated to create a moving picture book. The generation unit also uses a generation AI to generate animations based on story scenes and enhance the visual appeal of the picture book. For example, a scene of a rabbit returning home is animated to create a moving picture book. In this way, animations can be generated based on story scenes and a moving picture book can be created.

[0071] The generation unit can use the emotion estimation function to predict the reader's emotional response in a story scene and generate a picture based on that emotion. For example, the generation unit uses the emotion estimation function to predict the reader's emotional response in a story scene and generate a picture based on that emotion. For example, in a scene where the reader feels surprised, it generates a picture that expresses that surprise. Furthermore, the generation unit uses the generation AI to predict the reader's emotional response in a story scene and generate a picture based on that emotion. For example, in a scene where the reader feels moved, it generates a picture that expresses that emotion. Furthermore, the generation unit uses the emotion estimation function to predict the reader's emotional response in a story scene and generate a picture based on that emotion. For example, in a scene where the reader feels laughing, it generates a picture that expresses that laughter. In this way, the reader's emotional response can be predicted and a picture based on that emotion can be generated.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The story input unit can provide a collaboration function that allows a child to create a story collaboratively with other children when inputting a story. For example, a function is provided that allows a child to create a story collaboratively with other children online when inputting a story. For example, multiple children input stories at the same time, and the generation AI integrates the content to generate a single story. The story input unit also uses the collaboration function so that children take turns inputting stories, and the generation AI generates a story based on the content. For example, one child inputs "The rabbit went to the forest," and the next child continues, "There he met a friend." The story input unit also provides an interface for children to create a story collaboratively, and the generation AI generates a story based on the content. For example, children input a story in a chat format, and the generation AI generates a picture book story based on the content. This allows children to create a story collaboratively.

[0074] The story input unit can add a function in which the generation AI provides advice and hints in real time when a child is inputting a story. For example, when a child inputs a story, the generation AI can provide advice and hints in real time. For example, if a child inputs "The rabbit went to the forest," the generation AI can suggest, "Let's think about what happens next." The story input unit also adds a function in which the generation AI provides advice in real time as the story progresses. For example, if a child inputs "The rabbit met a friend," the generation AI can ask, "What kind of animal is that friend?" The story input unit also adds a function in which the generation AI provides hints in real time when a child is inputting a story. For example, if a child inputs "The rabbit went on an adventure," the generation AI can suggest, "What is the purpose of the adventure?" This allows children to receive advice and hints in real time when inputting a story.

[0075] The story input unit can use the emotion estimation function to provide a function that suggests story themes and characters based on the emotions a child feels when entering a story. For example, the emotion estimation function can be used to analyze the emotions a child feels when entering a story, and suggest story themes and characters based on those emotions. For example, if a child is talking happily, the generation AI can suggest, "How about an adventure theme?" The story input unit can also analyze the emotions a child feels when entering a story in real time, and provide a function that suggests characters based on those emotions. For example, if a child is talking excitedly, the generation AI can suggest, "Let's have a brave hero appear." The story input unit can also use the emotion estimation function to provide a function that suggests story themes and characters based on the emotions a child feels when entering a story. For example, if a child is talking sadly, the generation AI can suggest, "Let's create an inspiring story." This makes it possible to suggest story themes and characters based on a child's emotions.

[0076] The analysis unit can provide a function that understands the context of a story and generates multiple endings. For example, the generation AI analyzes the context of a story and provides a function that generates multiple endings. For example, for a rabbit adventure story, it generates "an ending where the rabbit finds treasure" and "an ending where the rabbit is reunited with his friends." The analysis unit also provides a function that allows the generation AI to suggest multiple endings based on the content of the story entered by a child. For example, it generates "an ending where the rabbit leaves the forest and goes on a new adventure" and "an ending where the rabbit returns home and spends time with his family." The analysis unit also provides a function that allows the generation AI to understand the context of a story and generate different endings. For example, it generates "an ending where the rabbit overcomes difficulties and grows" and "an ending where the rabbit makes new friends." This makes it possible to understand the context of a story and generate multiple endings.

[0077] The analysis unit can set the personalities and backgrounds of story characters in detail, generating a deeper story. For example, the generation AI can set the personalities and backgrounds of story characters in detail to generate a deeper story. For example, the rabbit's personality can be set as "brave and curious" and its background can be added with "the reason he goes on an adventure to protect his family." The analysis unit also sets detailed personalities and backgrounds for story characters input by a child. For example, the rabbit's friend's personality can be set as "kind and helpful" and its background can be added with "past experiences of being helped." The analysis unit also sets the personalities and backgrounds of story characters in detail to dig deeper into the story. For example, the rabbit's enemy character's personality can be set as "cunning and planned" and its background can be added with "past trauma." This allows the generation AI to set the characters' personalities and backgrounds in detail and generate a deeper story.

[0078] The analysis unit can use the emotion estimation function to analyze the emotions of characters in each scene of the story and generate a story development based on those emotions. For example, the emotion estimation function can be used to analyze the emotions of characters in each scene of the story and generate a story development based on those emotions. For example, in a scene where the rabbit feels fear, the generation AI generates a development that reflects that emotion. The analysis unit also uses the generation AI to analyze the emotions of characters in each scene of the story and propose a story development based on those emotions. For example, in a scene where the rabbit feels joy, the generation AI generates a positive development that reflects that emotion. The analysis unit also uses the emotion estimation function to analyze the emotions of characters in each scene of the story and generate a story development based on those emotions. For example, in a scene where the rabbit feels sad, the generation AI generates a moving development that reflects that emotion. This makes it possible to generate a story development based on the emotions of characters.

[0079] The analysis unit can automatically determine the genre of the story and generate a story that matches the genre. For example, the generation AI can automatically determine the genre of the story and generate a story that matches that genre. For example, if the story entered by a child is in the adventure genre, the generation AI will generate a story that suits the adventure. The analysis unit also allows the generation AI to determine the genre based on the content of the story entered by the child and propose a story that matches that genre. For example, if the story is in the fantasy genre, the generation AI will generate a story that includes magic and other worlds. The analysis unit also allows the generation AI to automatically determine the genre of the story and generate a story that matches that genre. For example, if the story is in the mystery genre, the generation AI will generate a story that includes puzzles and suspense elements. This makes it possible to automatically determine the genre of the story and generate a story that matches the genre.

[0080] The analysis unit can automatically add story settings and generate richer stories. For example, the generation AI can automatically add story settings and generate richer stories. For example, to a rabbit adventure story, the time and place setting of "medieval forest" is added. The analysis unit also allows the generation AI to automatically add settings based on the content of the story entered by the child. For example, the analysis unit can add the setting of "future city" and generate a story in which the rabbit goes on an adventure in the futuristic city. The analysis unit also allows the generation AI to automatically add story settings and enrich the cultural background. For example, the analysis unit can add the setting of "traditional Japanese village" and generate a story in which the rabbit goes on an adventure in that village. In this way, story settings can be automatically added and richer stories can be generated.

[0081] The generation unit can apply different art styles to each story scene to generate visually diverse picture books. For example, the generation AI can apply different art styles to each story scene to generate visually diverse picture books. For example, a realistic art style is applied to adventure scenes, and a fantasy art style is applied to dream scenes. The generation unit also selects different art styles for each scene depending on the content of the story input by the child. For example, a natural landscape art style is applied to a scene where the rabbit is adventuring in the forest, and a warm art style is applied to a scene where the rabbit is returning home. The generation unit also applies different art styles to each story scene to increase the visual diversity of the picture book. For example, a cartoon-style art style is applied to a scene where the rabbit is playing with his friends, and a dark art style is applied to a scene where the rabbit is facing difficulties. This allows the generation of visually diverse picture books by applying different art styles to each scene.

[0082] The generation unit can use the emotion estimation function to generate pictures that reflect the emotions of characters in story scenes. For example, the emotion estimation function is used to analyze the emotions of characters in story scenes and generate pictures that reflect those emotions. For example, in a scene where a rabbit is sad, a picture that expresses that sadness is generated. The generation unit also uses the generation AI to analyze the emotions of characters in story scenes and generate pictures that reflect those emotions. For example, in a scene where the rabbit is happy, a picture that expresses that joy is generated. The generation unit also uses the emotion estimation function to generate pictures that reflect the emotions of characters in story scenes. For example, in a scene where the rabbit is surprised, a picture that expresses that surprise is generated. In this way, pictures that reflect the emotions of characters can be generated.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The story input unit allows the child to input a story. For example, the child can input a story using a keyboard or tablet. The story input unit also allows the child to speak the story using voice input. Step 2: The analysis unit analyzes the story input by the story input unit. For example, the generation AI analyzes the content of the story using natural language processing technology. The analysis unit can also analyze the emotional elements of the story using emotion analysis technology. Step 3: The generator generates a picture book story based on the story analyzed by the analyzer. For example, the generator AI generates a picture book story based on the content of the story. The generator can also create a deeper story by setting the characters and background of the story in detail.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

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

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

[0129] In the 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 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.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0133] The data processing system 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 story input section where a child inputs a story; an analysis unit that analyzes the story input by the story input unit; a generation unit that generates a story for a picture book based on the story analyzed by the analysis unit. A system characterized by:

2. The story input unit It provides an interface where children can input stories by voice, and the generative AI recognizes the voice and converts it into text.

2. The system of claim 1.

3. The story input unit A child's drawing is scanned and a generative AI generates a story based on the drawing.

2. The system of claim 1.

4. The story input unit Analyzing emotions when children input stories and providing an interface to elicit positive emotions 2. The system of claim 1.

5. The story input unit Provide collaboration features that allow children to collaborate with other children as they enter their stories 2. The system of claim 1.

6. The story input unit Add a feature where generative AI provides real-time advice and hints as children type their stories.

2. The system of claim 1.

7. The story input unit Provides a feature that suggests story themes and characters based on the emotions a child feels when entering a story.

2. The system of claim 1.

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