System

Abstract

An object of a system according to an embodiment is to easily create a story or a picture book including content desired to be learned by a child.SOLUTION: A system includes a keyword input part, a story generation part, a picture generation part, and a picture book generation part. The keyword input unit receives a keyword input by a user. The story generation unit generates a story on the basis of the keyword received by the keyword input unit. The picture generation section generates a picture based on the story generated by the story generation section. The picture book generation unit generates a picture book by combining a story and a picture.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 had the problem of making it difficult to easily create stories and picture books that contain the content that children want to learn.

[0005] The system according to the embodiment aims to easily create stories and picture books that include content that you want children to learn. [Means for solving the problem]

[0006] The system according to the embodiment includes a keyword input unit, a story generation unit, an illustration generation unit, and a picture book generation unit. The keyword input unit accepts keywords entered by a user. The story generation unit generates a story based on the keywords accepted by the keyword input unit. The illustration generation unit generates illustrations based on the story generated by the story generation unit. The picture book generation unit generates a picture book by combining the story and illustrations. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to easily create stories and picture books that include the content that you want children to learn. [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 that automatically creates character strings and pictures containing the content of a story based on keywords entered by a user, and then creates a picture book based on the character strings and pictures. This allows the picture book creation system to automatically create a story and pictures based on keywords entered by a user, and provide the story and pictures as a picture book.

[0029] A picture book generation system according to an embodiment includes a keyword input unit, a story generation unit, an image generation unit, and a picture book generation unit. The keyword input unit accepts keywords input by a user. For example, the user can enter keywords such as "friendship" or "courage." The story generation unit generates a story based on the keywords accepted by the keyword input unit. For example, the generation AI generates a story with a friendship theme based on the input keywords. The image generation unit generates images based on the story generated by the story generation unit. For example, the generation AI generates appropriate images to match the characters and scenes that appear in the story. The picture book generation unit combines the story and images to generate a picture book. For example, the generation AI combines the generated story and images to generate a digital picture book. This allows the picture book generation system to automatically generate a story and images based on the keywords entered by the user and provide them as a picture book.

[0030] The keyword input section allows the AI ​​to automatically generate follow-up questions to understand the user's intentions and goals, creating more specific prompts. For example, when a user inputs the keyword "friendship," the AI ​​automatically generates follow-up questions such as "What kind of friendship story do you want to depict?" and "What points do you particularly want to emphasize about friendship?" to more specifically grasp the user's intentions. Similarly, when the keyword "nature conservation" is input, the AI ​​poses questions such as "What kind of natural environment do you want to protect?" and "What specific nature conservation activities do you want to depict?" to clarify the user's goals. Furthermore, when the keyword "courage" is input, the AI ​​automatically generates questions such as "In what situation do you want to depict a story showing courage?" and "What are the characteristics of a character who shows courage?" to further specify the story prompt. This allows the AI ​​to more specifically grasp the user's intentions and goals, improving the accuracy of story generation.

[0031] The keyword input unit automatically suggests related sub-keywords in response to input keywords, enriching the content of the story. For example, when a user inputs the keyword "friendship," the generation AI automatically suggests related sub-keywords such as "trust," "cooperation," and "sincerity," enriching the content of the story. In addition, when the keyword "nature conservation" is input, the generation AI suggests sub-keywords such as "recycling," "tree planting," and "ocean protection," enriching the specific episodes of the story. Furthermore, when the keyword "courage" is input, the generation AI suggests sub-keywords such as "challenge," "overcoming difficulties," and "self-sacrifice," deepening the theme of the story. This enriches the content of the story and generates a story that matches the user's intentions.

[0032] The keyword input unit supports voice input and handwriting input, improving user convenience. For example, the keyword input unit allows the user to input the keyword "friendship" by voice input, and the generation AI analyzes the voice and uses it as a story prompt. The keyword input unit also allows the keyword "nature conservation" to be input by handwriting input, and the generation AI recognizes the handwritten characters and generates the content of the story. Furthermore, the keyword input unit allows the keyword "courage" to be input by voice input or handwriting input, allowing the user to input keywords more intuitively. This allows the user to input keywords more intuitively and improves convenience.

[0033] The keyword input unit supports keyword input in different languages, enabling multilingual story generation. For example, if a user inputs the keyword "friendship" in English, the generation AI analyzes the keyword and generates a story in English. The keyword input unit also allows the keyword "nature conservation" to be input in French, causing the generation AI to generate a story in French. The keyword input unit also allows the keyword "courage" to be input in Chinese, causing the generation AI to generate a story in Chinese. This enables multilingual story generation and improves user convenience.

[0034] The story generation unit can learn the user's past input history and preferences and generate a personalized story. For example, the story generation unit learns keywords the user has previously input and the history of stories generated, and the generation AI generates a personalized story tailored to the user's preferences. The story generation unit also sets story themes and characters that match the user's preferences and interests based on the user's past input history. Furthermore, the story generation unit allows the generation AI, having learned the user's preferences, to generate a more individualized story based on the keywords entered by the user. This improves user satisfaction by generating personalized stories based on the user's past input history and preferences.

[0035] The story generation unit can provide an interactive story that allows the user to choose from multiple different endings and branching points during the story generation process. For example, the story generation unit provides an interactive story in which the generation AI prepares multiple endings and branching points during the story generation process, allowing the user to change the story's development by making selections. The story generation unit also generates a story that leads to different endings by the user making choices during the story. For example, in a friendship story, the ending of the story changes by selecting the character's actions. Furthermore, the story generation unit provides an interactive story in which the generation AI sets multiple branching points within the story, allowing the user to freely change the story's development by making selections. This provides an interactive story in which the user can choose the story's development, enhancing the user's sense of participation.

[0036] The story generation unit can enhance the educational value by incorporating historical facts and scientific knowledge when generating the content of a story. For example, the story generation unit enhances the educational value by incorporating historical facts when the generation AI generates the content of a story. For example, famous historical episodes of friendship could be incorporated into a story about friendship. The story generation unit also enhances the educational value by incorporating scientific knowledge into the story. For example, scientific knowledge about environmental protection could be incorporated into a story about nature conservation. Furthermore, the story generation unit incorporates historical facts and scientific knowledge when the generation AI generates the content of a story, thereby providing a story that children can enjoy while learning. For example, scientific experiments and discoveries could be incorporated into a story about courage. In this way, the educational value of incorporating historical facts and scientific knowledge is enhanced.

[0037] The story generation unit can also provide the generated story as an audiobook using voice synthesis technology. For example, the story generation unit provides a story generated by the generation AI as an audiobook using voice synthesis technology. For example, it makes it possible to listen to a story about friendship audibly. The story generation unit also provides an audiobook that can be enjoyed not only visually but also audibly by reading the content of the story using voice synthesis technology. For example, it makes it possible to listen to a story about nature conservation audibly. Furthermore, the story generation unit provides the generated story as an audiobook using voice synthesis technology so that children can listen to it and enjoy it audibly. For example, it makes it possible to listen to a story about courage audibly. In this way, by providing the generated story as an audiobook using voice synthesis technology, it can be enjoyed not only visually but also audibly.

[0038] The picture generation unit can provide options to allow a user to select different art styles and techniques when creating pictures based on the content of a story. For example, the picture generation unit provides options to allow a user to select different art styles (e.g., anime-style, realistic, picture book-style) when the generation AI creates pictures based on the content of a story. The picture generation unit also provides options to allow the generation AI to select different techniques (e.g., watercolor painting, oil painting, digital art) to match the scenes of the story. Furthermore, the picture generation unit provides options to allow the generation AI to select different art styles and techniques when the user creates pictures based on the content of a story, allowing the user to customize the visuals of the picture book. In this way, by providing options to select different art styles and techniques, the user can customize the visuals of the picture book.

[0039] The picture generation unit can add a customization function that allows the user to specify a specific character design or background during the picture generation process. For example, the picture generation unit provides a customization function that allows the user to specify a specific character design (e.g., hair color, clothing, facial expression) when the generation AI creates a picture. The picture generation unit also adds a customization function that allows the user to specify a specific background (e.g., forest, ocean, city) to match a story scene. Furthermore, the picture generation unit provides a customization function that allows the generation AI to specify a specific character design or background when the user creates a picture based on the content of the story, thereby enabling the visuals of the picture book to be personalized. Thus, by adding a customization function that allows the user to specify a specific character design or background, the visuals of the picture book can be personalized.

[0040] The picture generation unit can use 3D modeling technology to generate pictures, creating three-dimensional images that can be viewed in VR or AR. For example, the picture generation unit uses 3D modeling technology to generate pictures that three-dimensionally represent story scenes, enabling viewing in VR or AR. The picture generation unit also uses 3D modeling technology to create story characters and backgrounds in three dimensions, allowing users to experience the story through VR or AR devices. Furthermore, the picture generation unit uses 3D modeling technology to create three-dimensional images based on the content of the story, enabling users to enjoy the story in VR or AR. As a result, by creating three-dimensional images using 3D modeling technology and enabling viewing in VR or AR, users can experience the story in a more three-dimensional way.

[0041] The picture generation unit can provide an interface that allows the user to manually modify and edit the generated pictures. The picture generation unit, for example, provides an interface that allows the user to manually modify and edit pictures created by the generation AI, allowing the user to customize the visuals of the picture book. The picture generation unit also provides tools (e.g., paint tools, filter functions) that the user can use to manually edit the generated pictures, allowing the user to individualize the visuals of the picture book. The picture generation unit also provides an interface that allows the user to manually modify and edit pictures created by the generation AI, allowing the user to freely customize the visuals of the picture book. Thus, by providing an interface that allows the user to manually modify and edit the generated pictures, the visuals of the picture book can be freely customized.

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

[0043] The picture book generation system can further include a voice input unit. The voice input unit allows the user to input keywords by voice, and the generation AI analyzes the voice and uses it as a prompt for the story. For example, if the user inputs the keyword "friendship" by voice, the generation AI analyzes the voice and generates a story with a friendship theme. Also, if the user inputs the keyword "nature conservation" by voice, the voice input unit allows the generation AI to analyze the voice and generate a story with a nature conservation theme. Furthermore, if the user inputs the keyword "courage" by voice, the voice input unit allows the generation AI to analyze the voice and generate a story with a courage theme. This allows the user to input keywords by voice, improving convenience.

[0044] The picture book generation system can also support keyword input in different languages, enabling multilingual story generation. For example, if a user inputs the keyword "friendship" in English, the generation AI analyzes the keyword and generates a story in English. If a user inputs the keyword "nature conservation" in French, the generation AI analyzes the keyword and generates a story in French. If a user inputs the keyword "courage" in Chinese, the generation AI analyzes the keyword and generates a story in Chinese. This enables multilingual story generation and improves user convenience.

[0045] The picture book generation system can further learn the user's past input history and preferences in the story generation unit to generate a personalized story. For example, the generation AI learns keywords the user has previously input and the history of stories generated, and generates a personalized story tailored to the user's preferences. The story generation unit also sets story themes and characters that match the user's preferences and interests based on the user's past input history. Furthermore, the story generation unit allows the generation AI, having learned the user's preferences, to generate a more individualized story based on the keywords entered by the user. This improves user satisfaction by generating a personalized story based on the user's past input history and preferences.

[0046] The picture book generation system can further provide an interactive story in which the story generation unit prepares multiple different endings and branching points during the story generation process, allowing the user to choose from them. For example, the generation AI prepares multiple endings and branching points during the story generation process, providing an interactive story in which the user can change the story's development by making selections. The story generation unit also generates a story that leads to different endings by the user making choices during the story. For example, in a story about friendship, the ending of the story changes by selecting the character's actions. Furthermore, the story generation unit provides an interactive story in which the generation AI sets multiple branching points within the story, allowing the user to freely change the story's development by making selections. This provides an interactive story in which the user can choose the story's development, increasing the user's sense of participation.

[0047] The picture book generation system can further enhance the educational value by incorporating historical facts and scientific knowledge when generating the content of the story in the story generation section. For example, the generation AI can enhance the educational value by incorporating historical facts when generating the content of the story. For example, a famous historical episode of friendship can be incorporated into a story about friendship. The story generation section can also enhance the educational value by incorporating scientific knowledge into the story. For example, scientific knowledge about environmental protection can be incorporated into a story about nature conservation. Furthermore, the story generation section can incorporate historical facts and scientific knowledge when generating the content of the story, thereby providing a story that children can enjoy while learning. For example, scientific experiments and discoveries can be incorporated into a story about courage. In this way, incorporating historical facts and scientific knowledge enhances the educational value.

[0048] The picture book generation system can also provide the generated story as an audiobook using speech synthesis technology in the story generation unit. For example, a story generated by the generation AI can be provided as an audiobook using speech synthesis technology. For example, a story about friendship can be listened to aloud. The story generation unit also provides an audiobook that can be enjoyed not only visually but also aurally by reading the content of the story using speech synthesis technology. For example, a story about nature conservation can be listened to aloud. The story generation unit also provides the generated story as an audiobook using speech synthesis technology so that children can enjoy listening to it aurally. For example, a story about courage can be listened to aloud. In this way, by providing the generated story as an audiobook using speech synthesis technology, it can be enjoyed not only visually but also aurally.

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

[0050] Step 1: The keyword input unit accepts keywords entered by the user. For example, the user can enter keywords such as "friendship" or "courage." Step 2: The story generation unit generates a story based on the keywords received by the keyword input unit. For example, the generation AI generates a story with a friendship theme based on the input keywords. Step 3: The picture generation unit generates pictures based on the story generated by the story generation unit. For example, the generation AI generates appropriate pictures to match the characters and scenes that appear in the story. Step 4: The picture book generator combines the story and the pictures to generate a picture book. For example, the generation AI combines the generated story and the pictures to generate a digital picture book.

[0051] (Example 2) A picture book creation system according to an embodiment of the present invention is a system that automatically creates character strings and pictures containing the content of a story based on keywords entered by a user, and then creates a picture book based on the character strings and pictures. This allows the picture book creation system to automatically create a story and pictures based on keywords entered by a user, and provide the story and pictures as a picture book.

[0052] A picture book generation system according to an embodiment includes a keyword input unit, a story generation unit, an image generation unit, and a picture book generation unit. The keyword input unit accepts keywords input by a user. For example, the user can enter keywords such as "friendship" or "courage." The story generation unit generates a story based on the keywords accepted by the keyword input unit. For example, the generation AI generates a story with a friendship theme based on the input keywords. The image generation unit generates images based on the story generated by the story generation unit. For example, the generation AI generates appropriate images to match the characters and scenes that appear in the story. The picture book generation unit combines the story and images to generate a picture book. For example, the generation AI combines the generated story and images to generate a digital picture book. This allows the picture book generation system to automatically generate a story and images based on the keywords entered by the user and provide them as a picture book.

[0053] The keyword input section allows the AI ​​to automatically generate follow-up questions to understand the user's intentions and goals, creating more specific prompts. For example, when a user inputs the keyword "friendship," the AI ​​automatically generates follow-up questions such as "What kind of friendship story do you want to depict?" and "What points do you particularly want to emphasize about friendship?" to more specifically grasp the user's intentions. Similarly, when the keyword "nature conservation" is input, the AI ​​poses questions such as "What kind of natural environment do you want to protect?" and "What specific nature conservation activities do you want to depict?" to clarify the user's goals. Furthermore, when the keyword "courage" is input, the AI ​​automatically generates questions such as "In what situation do you want to depict a story showing courage?" and "What are the characteristics of a character who shows courage?" to further specify the story prompt. This allows the AI ​​to more specifically grasp the user's intentions and goals, improving the accuracy of story generation.

[0054] The keyword input unit automatically suggests related sub-keywords in response to input keywords, enriching the content of the story. For example, when a user inputs the keyword "friendship," the generation AI automatically suggests related sub-keywords such as "trust," "cooperation," and "sincerity," enriching the content of the story. In addition, when the keyword "nature conservation" is input, the generation AI suggests sub-keywords such as "recycling," "tree planting," and "ocean protection," enriching the specific episodes of the story. Furthermore, when the keyword "courage" is input, the generation AI suggests sub-keywords such as "challenge," "overcoming difficulties," and "self-sacrifice," deepening the theme of the story. This enriches the content of the story and generates a story that matches the user's intentions.

[0055] The keyword input unit uses the emotion estimation function to analyze the emotions associated with keywords entered by the user and can set a story theme based on the emotions. For example, when a user enters the keyword "friendship," the generation AI uses the emotion estimation function to analyze the user's emotions, and if the emotion is strong, sets the theme to "a fun adventure of friendship." Similarly, when the keyword input unit enters the keyword "nature conservation," the generation AI uses the emotion estimation function to analyze the user's emotions, and if the emotion is strong and passionate, sets the theme to "a courageous effort to protect nature." Furthermore, when the keyword input unit enters the keyword "courage," the generation AI uses the emotion estimation function to analyze the user's emotions, and if the emotion is strong and moving, sets the theme to "a story of courage overcoming difficulties." This allows the generation of a story theme based on the user's emotions, generating a more emotionally rich story.

[0056] The keyword input unit supports voice input and handwriting input, improving user convenience. For example, the keyword input unit allows the user to input the keyword "friendship" by voice input, and the generation AI analyzes the voice and uses it as a story prompt. The keyword input unit also allows the keyword "nature conservation" to be input by handwriting input, and the generation AI recognizes the handwritten characters and generates the content of the story. Furthermore, the keyword input unit allows the keyword "courage" to be input by voice input or handwriting input, allowing the user to input keywords more intuitively. This allows the user to input keywords more intuitively and improves convenience.

[0057] The keyword input unit supports keyword input in different languages, enabling multilingual story generation. For example, if a user inputs the keyword "friendship" in English, the generation AI analyzes the keyword and generates a story in English. The keyword input unit also allows the keyword "nature conservation" to be input in French, causing the generation AI to generate a story in French. The keyword input unit also allows the keyword "courage" to be input in Chinese, causing the generation AI to generate a story in Chinese. This enables multilingual story generation and improves user convenience.

[0058] The keyword input unit uses the emotion estimation function to analyze the user's emotions in real time when they enter keywords and can make suggestions to elicit positive emotions. For example, when a user enters the keyword "friendship," the generation AI uses the emotion estimation function to analyze the user's emotions in real time and display an encouraging message to elicit positive emotions. In addition, when the keyword input unit enters the keyword "nature conservation," the generation AI uses the emotion estimation function to analyze the user's emotions and presents success stories for eliciting positive emotions. Furthermore, when the keyword input unit enters the keyword "courage," the generation AI uses the emotion estimation function to analyze the user's emotions in real time and provides advice to elicit positive emotions. This improves the accuracy of story generation by analyzing the user's emotions in real time and making suggestions to elicit positive emotions.

[0059] The story generation unit can learn the user's past input history and preferences and generate a personalized story. For example, the story generation unit learns keywords the user has previously input and the history of stories generated, and the generation AI generates a personalized story tailored to the user's preferences. The story generation unit also sets story themes and characters that match the user's preferences and interests based on the user's past input history. Furthermore, the story generation unit allows the generation AI, having learned the user's preferences, to generate a more individualized story based on the keywords entered by the user. This improves user satisfaction by generating personalized stories based on the user's past input history and preferences.

[0060] The story generation unit can provide an interactive story that allows the user to choose from multiple different endings and branching points during the story generation process. For example, the story generation unit provides an interactive story in which the generation AI prepares multiple endings and branching points during the story generation process, allowing the user to change the story's development by making selections. The story generation unit also generates a story that leads to different endings by the user making choices during the story. For example, in a friendship story, the ending of the story changes by selecting the character's actions. Furthermore, the story generation unit provides an interactive story in which the generation AI sets multiple branching points within the story, allowing the user to freely change the story's development by making selections. This provides an interactive story in which the user can choose the story's development, enhancing the user's sense of participation.

[0061] The story generation unit can use the emotion estimation function to depict in detail the emotions of characters in each scene of the story, thereby generating an emotionally rich story. For example, the generation AI in the story generation unit uses the emotion estimation function to depict in detail the emotions of characters in each scene of the story, thereby generating an emotionally rich story. For example, in a story of friendship, the character's joy and sadness are depicted in detail. The story generation unit also analyzes the emotions of characters in the story in real time and depicts scenes based on those emotions. For example, in a story of courage, the character's fear and courage are depicted in detail. Furthermore, the story generation unit uses the emotion estimation function to analyze the emotions of characters in each scene of the story and depicts in detail the development of the story based on those emotions. For example, in a story about nature conservation, the character's passion and determination are depicted in detail. In this way, an emotionally rich story is generated by depicting the characters' emotions in detail.

[0062] The story generation unit can enhance the educational value by incorporating historical facts and scientific knowledge when generating the content of a story. For example, the story generation unit enhances the educational value by incorporating historical facts when the generation AI generates the content of a story. For example, famous historical episodes of friendship could be incorporated into a story about friendship. The story generation unit also enhances the educational value by incorporating scientific knowledge into the story. For example, scientific knowledge about environmental protection could be incorporated into a story about nature conservation. Furthermore, the story generation unit incorporates historical facts and scientific knowledge when the generation AI generates the content of a story, thereby providing a story that children can enjoy while learning. For example, scientific experiments and discoveries could be incorporated into a story about courage. In this way, the educational value of incorporating historical facts and scientific knowledge is enhanced.

[0063] The story generation unit can also provide the generated story as an audiobook using voice synthesis technology. For example, the story generation unit provides a story generated by the generation AI as an audiobook using voice synthesis technology. For example, it makes it possible to listen to a story about friendship audibly. The story generation unit also provides an audiobook that can be enjoyed not only visually but also audibly by reading the content of the story using voice synthesis technology. For example, it makes it possible to listen to a story about nature conservation audibly. Furthermore, the story generation unit provides the generated story as an audiobook using voice synthesis technology so that children can listen to it and enjoy it audibly. For example, it makes it possible to listen to a story about courage audibly. In this way, by providing the generated story as an audiobook using voice synthesis technology, it can be enjoyed not only visually but also audibly.

[0064] The story generation unit uses the emotion estimation function to monitor the user's emotional reactions in real time during the story generation process and generate an optimal story. For example, the story generation unit uses a generation AI to monitor the user's emotional reactions in real time during the story generation process and generate an optimal story based on that data. For example, if the user is moved, it sets an emotional ending. The story generation unit also uses the emotion estimation function to analyze the user's emotional reactions during the story generation process and, if the user has strong positive emotions, it generates a story that emphasizes those emotions. Furthermore, the story generation unit uses the generation AI to monitor the user's emotional reactions in real time during the story generation process and adjust the development of the story based on that data. For example, if the user is enjoying themselves, it adds fun scenes. In this way, the story generation unit monitors the user's emotional reactions in real time and generates an optimal story, thereby improving user satisfaction.

[0065] The picture generation unit can provide options to allow a user to select different art styles and techniques when creating pictures based on the content of a story. For example, the picture generation unit provides options to allow a user to select different art styles (e.g., anime-style, realistic, picture book-style) when the generation AI creates pictures based on the content of a story. The picture generation unit also provides options to allow the generation AI to select different techniques (e.g., watercolor painting, oil painting, digital art) to match the scenes of the story. Furthermore, the picture generation unit provides options to allow the generation AI to select different art styles and techniques when the user creates pictures based on the content of a story, allowing the user to customize the visuals of the picture book. In this way, by providing options to select different art styles and techniques, the user can customize the visuals of the picture book.

[0066] The picture generation unit can add a customization function that allows the user to specify a specific character design or background during the picture generation process. For example, the picture generation unit provides a customization function that allows the user to specify a specific character design (e.g., hair color, clothing, facial expression) when the generation AI creates a picture. The picture generation unit also adds a customization function that allows the user to specify a specific background (e.g., forest, ocean, city) to match a story scene. Furthermore, the picture generation unit provides a customization function that allows the generation AI to specify a specific character design or background when the user creates a picture based on the content of the story, thereby enabling the visuals of the picture book to be personalized. Thus, by adding a customization function that allows the user to specify a specific character design or background, the visuals of the picture book can be personalized.

[0067] The picture generation unit can use the emotion estimation function to generate pictures that visually express the emotions of characters in story scenes. For example, the picture generation unit uses the emotion estimation function to generate pictures that visually express the emotions (e.g., joy, sadness, surprise) of characters in story scenes. The picture generation unit also analyzes the emotions of characters in a story in real time and generates pictures based on those emotions. For example, in a story of friendship, the joy and sadness of characters are visually expressed. Furthermore, the picture generation unit uses the emotion estimation function to analyze the emotions of characters in each scene of the story and generates pictures based on those emotions. For example, in a story of courage, the fear and courage of characters are visually expressed. In this way, by generating pictures that visually express the emotions of characters, the emotional expression of the story is enriched.

[0068] The picture generation unit can use 3D modeling technology to generate pictures, creating three-dimensional images that can be viewed in VR or AR. For example, the picture generation unit uses 3D modeling technology to generate pictures that three-dimensionally represent story scenes, enabling viewing in VR or AR. The picture generation unit also uses 3D modeling technology to create story characters and backgrounds in three dimensions, allowing users to experience the story through VR or AR devices. Furthermore, the picture generation unit uses 3D modeling technology to create three-dimensional images based on the content of the story, enabling users to enjoy the story in VR or AR. As a result, by creating three-dimensional images using 3D modeling technology and enabling viewing in VR or AR, users can experience the story in a more three-dimensional way.

[0069] The picture generation unit can provide an interface that allows the user to manually modify and edit the generated pictures. The picture generation unit, for example, provides an interface that allows the user to manually modify and edit pictures created by the generation AI, allowing the user to customize the visuals of the picture book. The picture generation unit also provides tools (e.g., paint tools, filter functions) that the user can use to manually edit the generated pictures, allowing the user to individualize the visuals of the picture book. The picture generation unit also provides an interface that allows the user to manually modify and edit pictures created by the generation AI, allowing the user to freely customize the visuals of the picture book. Thus, by providing an interface that allows the user to manually modify and edit the generated pictures, the visuals of the picture book can be freely customized.

[0070] The picture generation unit uses the emotion estimation function to analyze the emotional response of the user when viewing a picture and can suggest the most suitable picture. For example, the picture generation unit uses the emotion estimation function to analyze the emotional response of the user when viewing a picture in real time and suggests the most suitable picture based on that data. The picture generation unit also analyzes the user's emotional response and, if the user has a strong positive emotion, suggests a picture that emphasizes that emotion. For example, if the user is happy, it suggests a picture with bright colors. Furthermore, the picture generation unit uses the emotion estimation function to analyze the user's emotional response when viewing a picture and adjusts the content and style of the picture based on that data. For example, if the user is moved, it suggests a picture that emphasizes an emotional scene. In this way, by analyzing the user's emotional response and suggesting the most suitable picture, user satisfaction is improved.

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

[0072] The picture book generation system can further include a voice input unit. The voice input unit allows the user to input keywords by voice, and the generation AI analyzes the voice and uses it as a prompt for the story. For example, if the user inputs the keyword "friendship" by voice, the generation AI analyzes the voice and generates a story with a friendship theme. Also, if the user inputs the keyword "nature conservation" by voice, the voice input unit allows the generation AI to analyze the voice and generate a story with a nature conservation theme. Furthermore, if the user inputs the keyword "courage" by voice, the voice input unit allows the generation AI to analyze the voice and generate a story with a courage theme. This allows the user to input keywords by voice, improving convenience.

[0073] The picture book generation system can further use its emotion estimation function to analyze the emotions associated with keywords entered by the user and set a story theme based on those emotions. For example, when a user enters the keyword "friendship," the generation AI uses the emotion estimation function to analyze the user's emotions, and if the emotion is strong and positive, it sets the theme to "A fun adventure of friendship." When a user enters the keyword "nature conservation," the generation AI uses the emotion estimation function to analyze the user's emotions, and if the emotion is strong and passionate, it sets the theme to "Brave activities to protect nature." When a user enters the keyword "courage," the generation AI uses the emotion estimation function to analyze the user's emotions, and if the emotion is strong and moving, it sets the theme to "A story of courage that overcomes difficulties." This allows the system to set a story theme based on the user's emotions and generate a more emotionally rich story.

[0074] The picture book generation system can also support keyword input in different languages, enabling multilingual story generation. For example, if a user inputs the keyword "friendship" in English, the generation AI analyzes the keyword and generates a story in English. If a user inputs the keyword "nature conservation" in French, the generation AI analyzes the keyword and generates a story in French. If a user inputs the keyword "courage" in Chinese, the generation AI analyzes the keyword and generates a story in Chinese. This enables multilingual story generation and improves user convenience.

[0075] The picture book generation system also uses an emotion estimation function to analyze the user's emotions in real time when they enter keywords and make suggestions to elicit positive emotions. For example, when a user enters the keyword "friendship," the generation AI uses the emotion estimation function to analyze the user's emotions in real time and display an encouraging message to elicit positive emotions. When a user enters the keyword "nature conservation," the generation AI uses the emotion estimation function to analyze the user's emotions and presents success stories for eliciting positive emotions. Furthermore, when a user enters the keyword "courage," the generation AI uses the emotion estimation function to analyze the user's emotions in real time and provide advice to elicit positive emotions. This improves the accuracy of story generation by analyzing the user's emotions in real time and making suggestions to elicit positive emotions.

[0076] The picture book generation system can further learn the user's past input history and preferences in the story generation unit to generate a personalized story. For example, the generation AI learns keywords the user has previously input and the history of stories generated, and generates a personalized story tailored to the user's preferences. The story generation unit also sets story themes and characters that match the user's preferences and interests based on the user's past input history. Furthermore, the story generation unit allows the generation AI, having learned the user's preferences, to generate a more individualized story based on the keywords entered by the user. This improves user satisfaction by generating a personalized story based on the user's past input history and preferences.

[0077] The picture book generation system can further provide an interactive story in which the story generation unit prepares multiple different endings and branching points during the story generation process, allowing the user to choose from them. For example, the generation AI prepares multiple endings and branching points during the story generation process, providing an interactive story in which the user can change the story's development by making selections. The story generation unit also generates a story that leads to different endings by the user making choices during the story. For example, in a story about friendship, the ending of the story changes by selecting the character's actions. Furthermore, the story generation unit provides an interactive story in which the generation AI sets multiple branching points within the story, allowing the user to freely change the story's development by making selections. This provides an interactive story in which the user can choose the story's development, increasing the user's sense of participation.

[0078] The picture book generation system further uses the emotion estimation function in the story generation unit to depict in detail the emotions of the characters in each scene of the story, thereby generating an emotionally rich story. For example, the generation AI uses the emotion estimation function to depict in detail the emotions of the characters in each scene of the story, generating an emotionally rich story. For example, in a story of friendship, the characters' joy and sadness are depicted in detail. The story generation unit also analyzes the emotions of the characters in the story in real time and depicts scenes based on those emotions. For example, in a story of courage, the characters' fear and courage are depicted in detail. The story generation unit also uses the emotion estimation function to analyze the emotions of the characters in each scene of the story and depicts in detail the development of the story based on those emotions. For example, in a story about nature conservation, the characters' passion and determination are depicted in detail. In this way, an emotionally rich story is generated by depicting the characters' emotions in detail.

[0079] The picture book generation system can further enhance the educational value by incorporating historical facts and scientific knowledge when generating the content of the story in the story generation section. For example, the generation AI can enhance the educational value by incorporating historical facts when generating the content of the story. For example, a famous historical episode of friendship can be incorporated into a story about friendship. The story generation section can also enhance the educational value by incorporating scientific knowledge into the story. For example, scientific knowledge about environmental protection can be incorporated into a story about nature conservation. Furthermore, the story generation section can incorporate historical facts and scientific knowledge when generating the content of the story, thereby providing a story that children can enjoy while learning. For example, scientific experiments and discoveries can be incorporated into a story about courage. In this way, incorporating historical facts and scientific knowledge enhances the educational value.

[0080] The picture book generation system can also provide the generated story as an audiobook using speech synthesis technology in the story generation unit. For example, a story generated by the generation AI can be provided as an audiobook using speech synthesis technology. For example, a story about friendship can be listened to aloud. The story generation unit also provides an audiobook that can be enjoyed not only visually but also aurally by reading the content of the story using speech synthesis technology. For example, a story about nature conservation can be listened to aloud. The story generation unit also provides the generated story as an audiobook using speech synthesis technology so that children can enjoy listening to it aurally. For example, a story about courage can be listened to aloud. In this way, by providing the generated story as an audiobook using speech synthesis technology, it can be enjoyed not only visually but also aurally.

[0081] The picture book generation system further uses an emotion estimation function in the story generation unit to monitor the user's emotional responses in real time during the story generation process and generate an optimal story. For example, the generation AI monitors the user's emotional responses in real time during the story generation process and generates an optimal story based on that data. For example, if the user is moved, it sets a moving ending. The story generation unit also uses the emotion estimation function to analyze the user's emotional responses during the story generation process and generates a story that emphasizes strong positive emotions if they are present. Furthermore, the story generation unit uses the generation AI to monitor the user's emotional responses in real time during the story generation process and adjust the story development based on that data. For example, if the user is enjoying themselves, it adds fun scenes. In this way, the system monitors the user's emotional responses in real time and generates an optimal story, thereby improving user satisfaction.

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

[0083] Step 1: The keyword input unit accepts keywords entered by the user. For example, the user can enter keywords such as "friendship" or "courage." Step 2: The story generation unit generates a story based on the keywords received by the keyword input unit. For example, the generation AI generates a story with a friendship theme based on the input keywords. Step 3: The picture generation unit generates pictures based on the story generated by the story generation unit. For example, the generation AI generates appropriate pictures to match the characters and scenes that appear in the story. Step 4: The picture book generator combines the story and the pictures to generate a picture book. For example, the generation AI combines the generated story and the pictures to generate a digital picture book.

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

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

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

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

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. a keyword input unit that accepts keywords input by a user; a story generation unit that generates a story based on the keywords received by the keyword input unit; a picture generation unit that generates pictures based on the story generated by the story generation unit; a picture book creation unit that creates a picture book by combining the story and the pictures. A system characterized by:

2. The keyword input unit The AI ​​automatically generates follow-up questions to understand the user's intent and purpose, creating more specific prompts.

2. The system of claim 1.

3. The keyword input unit Automatically suggest related sub-keywords when entering keywords to enrich the content of the story 2. The system of claim 1.

4. The keyword input unit Analyzes the sentiment of the keywords entered by the user and sets the theme of the story based on the sentiment 2. The system of claim 1.

5. The keyword input unit Supports voice input and handwriting input to improve user convenience 2. The system of claim 1.

Images