System

Abstract

An object of a system according to an embodiment is to easily create a new picture book and to allow a family member to read and hear the picture book.SOLUTION: A system includes a keyword input unit, a sentence generation unit, an image generation unit, a picture book generation unit, a voice model generation unit, and a reading unit. The keyword input unit inputs a keyword. The sentence generation unit generates a sentence based on the keyword input by the keyword input unit. The image generation unit generates an image based on the sentence generated by the sentence generation unit. The picture book generation unit generates a picture book by combining the text and the image generated by the text generation unit and the image generation unit. The voice model generation unit learns the voice of the family and generates a voice model. The reading unit reads the picture book using the speech model generated by the speech model generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to easily create new picture books and read them aloud to family members.

[0005] The system according to the embodiment aims to easily create a new picture book and have it read aloud by family members. [Means for solving the problem]

[0006] The system according to the embodiment includes a keyword input unit, a sentence generation unit, an image generation unit, a picture book generation unit, an audio model generation unit, and a reading-aloud unit. The keyword input unit inputs keywords. The sentence generation unit generates sentences based on the keywords input by the keyword input unit. The image generation unit generates images based on sentences generated by the sentence generation unit. The picture book generation unit generates a picture book by combining sentences and images generated by the sentence generation unit and the image generation unit. The audio model generation unit learns the voices of family members and generates an audio model. The reading-aloud unit reads a picture book using the audio model generated by the audio model generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily create a new picture book and have it read aloud by family members. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The picture book generation system according to the embodiment of the present invention is a system in which a generation AI creates a picture book and reads it aloud in the voices of family members. As a result, the picture book generation system can create a picture book using the generation AI and read it aloud in the voices of family members.

[0029] A picture book generation system according to an embodiment includes a keyword input unit, a sentence generation unit, an image generation unit, a picture book generation unit, an audio model generation unit, and a reading-aloud unit. The keyword input unit allows a user to input keywords related to the theme or content of a picture book. For example, the user inputs keywords such as "adventure," "animals," and "friendship." The sentence generation unit generates sentences based on the keywords input by the keyword input unit. For example, the generation AI generates story sentences using a text generation AI (e.g., LLM). The image generation unit generates images based on the sentences generated by the sentence generation unit. For example, the generation AI analyzes the content of the sentences and generates appropriate visuals. The picture book generation unit generates a picture book by combining the sentences and images generated by the sentence generation unit and the image generation unit. For example, the generation AI optimizes the arrangement of the sentences and images to create an easy-to-read layout. The audio model generation unit learns the voices of family members and generates an audio model. For example, the voices of the mother and father are recorded and the audio data is input into the generation AI. The reading-aloud unit reads the picture book to a child using the audio model generated by the audio model generation unit. For example, the generation AI reads a sentence such as "Once upon a time, there was..." in a mother's voice. This allows the picture book generation system to create a picture book using the generation AI and read it aloud in the voices of family members.

[0030] The keyword input unit can refer to the user's past input history and automatically suggest highly relevant keywords. For example, when a user inputs a keyword, the keyword input unit refers to the history of previously input keywords and automatically suggests highly relevant keywords. For example, if a user previously input "adventure," keywords such as "exploration" and "adventure story" are suggested. Furthermore, when a keyword is input, the keyword input unit displays related keywords in real time based on the user's past input history. For example, if "animals" is input, keywords such as "zoo" and "wild animals" are suggested. Furthermore, when a user inputs a keyword, the keyword input unit analyzes the user's past input history and automatically completes highly relevant keywords. For example, if "friendship" is input, keywords such as "friendship story" and "friends" are suggested. In this way, by referring to the user's past input history and automatically suggesting highly relevant keywords, the efficiency of keyword input is improved.

[0031] The keyword input unit can customize the generation AI's responses according to the user's age and interests. The keyword input unit customizes the generation AI's responses based on the user's age and interests, for example. For example, it generates stories in simple language for children and provides complex stories for adults. The keyword input unit also generates an appropriate story based on the user's age and interests when a keyword is entered, allowing the generation AI to generate an appropriate story. For example, for a child who loves animals, it generates a story with an animal as the main character. The keyword input unit also customizes the generation AI's responses to keywords based on the user's profile information. For example, it generates stories with related themes for users of a specific age group or with specific interests. This allows the generation AI to provide more appropriate stories by customizing the generation AI's responses according to the user's age and interests.

[0032] The keyword input unit supports voice input, allowing a story to be generated simply by the user speaking. The keyword input unit, for example, supports keyword input as voice input, allowing a story to be generated simply by the user speaking. For example, if a user speaks "adventure," the generation AI generates an adventure story. The keyword input unit also uses voice input to build a system that allows a story to be generated simply by the user speaking. For example, if a user speaks "animals," the generation AI generates a story featuring animals. The keyword input unit also uses voice recognition technology to input keywords simply by the user speaking, and the generation AI generates a story based on those keywords. For example, if a user speaks "friendship," the generation AI generates a friendship story. This allows for voice input, making it possible to generate a story simply by the user speaking.

[0033] The keyword input unit displays popular keywords used by other users, allowing the user to use them as a reference for ideas. For example, when a keyword is input, the keyword input unit displays popular keywords used by other users, allowing the user to use them as a reference for ideas. For example, when "adventure" is input, popular keywords such as "exploration" and "adventure story" are displayed. The keyword input unit also displays popular keywords used by other users in real time, building a system that allows the user to use them as a reference for ideas. For example, when "animals" is input, popular keywords such as "zoo" and "wild animals" are displayed. The keyword input unit also suggests popular keywords used by other users when a keyword is input, making it easier for the user to get ideas. For example, when "friendship" is input, popular keywords such as "friendship story" and "friends" are displayed. This makes it easier for the user to get ideas by displaying popular keywords used by other users.

[0034] The image generation unit can provide an interface that allows the user to manually make corrections to the generated image. The image generation unit provides, for example, an interface that allows the user to manually make corrections to the generated image. For example, it provides tools for changing the color or shape of the image. The image generation unit also builds an interface that allows the user to manually make corrections to the generated image. For example, it provides functions for deleting part of the image or adding new elements. The image generation unit also develops an interface that allows the user to manually make corrections to the generated image. For example, it provides tools for changing the background of the image or adjusting the facial expression of a character. This allows the user to manually make corrections to the generated image, making it possible to provide a more customized picture book.

[0035] The image generation unit can generate customized images based on the colors and style specified by the user. For example, when generating an image, the image generation unit generates a customized image based on the colors and style specified by the user. For example, if a user specifies "blue sky" or "cartoon style," the image will be generated exactly as specified. The image generation unit also builds a system in which a generation AI generates customized images based on the colors and style specified by the user. For example, if a user specifies "warm colors" or "realistic style," the image will be generated exactly as specified. The image generation unit also provides a customization function for reflecting the colors and style specified by the user when generating an image. For example, if a user specifies "monochrome" or "fantasy style," the image will be generated exactly as specified. This allows for the provision of more personalized picture books by generating customized images based on the colors and style specified by the user.

[0036] The image generation unit can correspond image generation to 3D models to create a three-dimensional picture book. The image generation unit, for example, corresponds image generation to 3D models to build a system for creating a three-dimensional picture book. For example, characters and backgrounds are generated as 3D models to provide a three-dimensional picture book. The image generation unit also converts the generated images into 3D models to create a three-dimensional picture book. For example, story scenes are reproduced in 3D space, allowing users to freely change the viewpoint. The image generation unit also corresponds image generation to 3D models to develop a system for providing a three-dimensional picture book. For example, characters and objects are generated as 3D models to create a picture book that users can interactively operate. In this way, by corresponding image generation to 3D models, a three-dimensional picture book can be provided.

[0037] The image generation unit can animate the generated images to provide a picture book with movement. The image generation unit, for example, builds a system that animates the generated images to provide a picture book with movement. For example, it adds animations such as characters moving or backgrounds changing. The image generation unit also adds animations after image generation to create a picture book with movement. For example, it generates animations in which characters move in accordance with scenes in the story. The image generation unit also animates the generated images to provide a picture book that users can enjoy interactively. For example, it adds animations in which characters move when a user clicks. In this way, by animating the generated images, a picture book with movement can be provided.

[0038] The picture book generation unit can provide a function that allows the user to freely customize the layout of the generated picture book. The picture book generation unit provides, for example, a function that allows the user to freely customize the layout of the generated picture book. For example, the order of pages can be changed or the position of text can be adjusted. The picture book generation unit also builds an interface that allows the user to customize the layout of the generated picture book. For example, it provides a function that allows the position of images and text to be changed by drag and drop. The picture book generation unit also develops a system that allows the user to freely customize the layout of the generated picture book. For example, it provides a function that allows the background color and font of pages to be changed. This allows the user to freely customize the layout of the generated picture book, making it possible to provide a more personalized picture book.

[0039] The picture book generation unit can provide a function that allows the number of pages and font size of a picture book to be adjusted according to the user's preferences. The picture book generation unit provides a function that allows the user to adjust, for example, the number of pages and font size of a generated picture book according to the user's preferences. For example, the number of pages can be increased or decreased, or the font size can be increased. The picture book generation unit also builds an interface that allows the user to freely adjust the number of pages and font size of a picture book. For example, a function that allows the user to change the number of pages and font size using a slider can be provided. The picture book generation unit also develops a system that allows the user to customize the number of pages and font size of a generated picture book according to the user's preferences. For example, the number of pages can be reduced to create a shorter picture book, or the font size can be reduced to include more information. This allows the user to adjust the number of pages and font size of a picture book according to their preferences, making it possible to provide a more personalized picture book.

[0040] The picture book generation unit can generate picture books in multiple languages, making them available to international users. The picture book generation unit, for example, builds a system that makes generated picture books available to multiple languages, making them available to international users. For example, it translates the book into languages ​​such as English, French, and Chinese. The picture book generation unit also provides a function that allows the generation AI to automatically translate the picture book based on the language selected by the user. For example, when a user selects Japanese, a picture book in Japanese is generated. The picture book generation unit also develops a picture book generation system that supports multiple languages, making it available to international users. For example, when a user selects a language, a picture book translated into that language is generated. In this way, picture books can be generated in multiple languages, making them available to international users.

[0041] The picture book generation unit provides the contents of the picture book as a digital book with audio, making it suitable for visually impaired people. The picture book generation unit, for example, provides the contents of the generated picture book as a digital book with audio, building a system that also accommodates visually impaired people. For example, it adds audio data in which the generation AI reads the text aloud. The picture book generation unit also provides digital books with audio, allowing visually impaired people to enjoy picture books. For example, it adds a function that explains the contents of each page aloud. The picture book generation unit also develops a system that generates digital books with audio for visually impaired people. For example, it provides a function that reads the text of the generated picture book aloud. In this way, the content of the picture book can be provided as a digital book with audio, making it suitable for visually impaired people.

[0042] The voice model generation unit can provide a function that allows the user to fine-tune the trained voice model. The voice model generation unit, for example, builds a system that provides a function that allows the user to fine-tune the trained voice model. For example, it provides an interface that allows the user to adjust the tone and speed of the voice. The voice model generation unit also develops a function that allows the user to fine-tune the trained voice model. For example, it provides a tool that allows the user to adjust the pitch and volume of the voice. The voice model generation unit also provides a system that allows the user to freely fine-tune the trained voice model. For example, it provides a function that allows the user to adjust the emotional expression and intonation of the voice. This allows the user to fine-tune the trained voice model, thereby providing more personalized reading.

[0043] The voice model generation unit can learn not only the voices of family members, but also the cries of pets and environmental sounds, allowing for more realistic reading aloud. The voice model generation unit can, for example, learn not only the voices of family members, but also the cries of pets and environmental sounds, building a system that provides more realistic reading aloud. For example, it can learn dog barks and bird chirps. The voice model generation unit can also learn pet cries and environmental sounds, creating a model that enables the generation AI to provide more realistic reading aloud. For example, it can learn cat meows and the sound of wind. The voice model generation unit can also learn pet cries and environmental sounds in addition to the voices of family members, developing a system that provides more realistic reading aloud. For example, it can learn pet cries and the sound of rain. By learning pet cries and environmental sounds, it is possible to provide more realistic reading aloud.

[0044] The voice model generation unit can provide a function that allows a trained voice model to be shared with other users. The voice model generation unit, for example, builds a system that provides a function that allows a trained voice model to be shared with other users. For example, the voices of family members can be shared with other users to read aloud in different voices. The voice model generation unit also develops a function that allows a user to share a trained voice model. For example, a voice model can be shared with friends or relatives to enjoy stories in different voices. The voice model generation unit also provides a system that allows a trained voice model to be easily shared with other users. For example, the voice model can be stored in the cloud so that other users can access it. In this way, the trained voice model can be shared with other users to enjoy reading aloud in different voices.

[0045] The reading aloud unit can automatically generate background music and sound effects when reading aloud, thereby enhancing the sense of realism. The reading aloud unit, for example, builds a system that automatically generates background music and sound effects when reading aloud, thereby enhancing the sense of realism. For example, music and sound effects are added to match the scenes in the story. The reading aloud unit also automatically generates background music and sound effects to enhance the sense of realism of the reading aloud. For example, exciting music is added for adventure scenes, and calm music is added for quiet scenes. The reading aloud unit also automatically generates background music and sound effects when reading aloud, thereby allowing the user to feel a sense of realism. For example, animal cries and wind sounds are added. In this way, the sense of realism of the reading aloud can be enhanced by automatically generating background music and sound effects.

[0046] The reading unit can provide a function that allows the speed and tone of reading to be adjusted according to the user's preferences. The reading unit, for example, builds a system that provides a function that allows the speed and tone of reading to be adjusted according to the user's preferences. For example, the reading speed can be increased or the tone can be raised. The reading unit also develops an interface that allows the user to freely adjust the speed and tone of reading to the user. For example, it provides a function that allows the speed and tone to be changed using a slider. The reading unit also provides a system that allows the speed and tone of reading to be customized according to the user's preferences. For example, it provides a function that reads at a slow speed or in a calm tone. This allows the speed and tone of reading to be adjusted according to the user's preferences, making it possible to provide a more personalized reading experience.

[0047] The reading unit can provide a function that allows readings to be provided by live streaming and that can be shared with family members in remote locations. The reading unit, for example, builds a system that provides readings by live streaming and that can be shared with family members in remote locations. For example, grandparents read to their grandchildren from a remote location. The reading unit also develops a system that uses the live streaming function to allow family members in remote locations to enjoy picture books together. For example, parents read to their children even when they are on a business trip. The reading unit also provides a function that allows readings to be provided by live streaming and that allows all family members to enjoy picture books at the same time. For example, family members can read picture books together even if they are in different locations. This allows readings to be provided by live streaming and that can be shared with family members in remote locations.

[0048] The reading unit can provide an archive function that records the content of what is read aloud and allows it to be played back later. The reading unit, for example, builds a system that provides an archive function that records the content of what is read aloud and allows it to be played back later. For example, it allows children to listen to their favorite stories as many times as they like. The reading unit can also develop a system that uses the recording function to save the content of what is read aloud and allow it to be played back later. For example, it allows children to listen to picture books by themselves even when their parents are busy. The reading unit also provides a function that records the content of what is read aloud and saves it as an archive. For example, it allows picture books read aloud in the voices of family members to be kept as memories. This allows the content of what is read aloud to be recorded and played back later, so that users can enjoy it again whenever they like.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The picture book generation system can also be equipped with a function that allows the user to select the genre of the story according to their preferences. For example, if the user selects a genre such as "fantasy," "mystery," or "comedy," a story that fits that genre will be generated. In addition, the picture book generation system can use the generation AI to adjust the tone and style of the story based on the genre selected by the user. For example, if fantasy is selected, a story that includes magic and adventure will be generated, and if mystery is selected, a story that includes solving mysteries and suspense will be generated. This makes it possible to provide a variety of stories that suit the user's preferences.

[0051] The picture book generation system can also be equipped with a function that allows users to customize the characters that appear in the story. For example, if a user specifies the name, appearance, and personality of a character, the generation AI will generate a story based on that information. In addition, a more detailed story can be generated by the user entering the character relationships and background settings. For example, characters such as siblings, friends, and pets can be added, and their respective roles and relationships can be set. This allows users to enjoy stories featuring their own original characters.

[0052] The picture book generation system can also provide interactive choices as the story progresses. For example, a mechanism can be introduced in which the development of the story changes depending on the user's choices during the story. The generation AI can also generate different endings and scenes depending on the choices the user makes. For example, during an adventure, the system can present options such as "take the left path" or "take the right path," and the development of the story will change depending on the path chosen. It can also provide an interactive experience in which the user's choices change the character's actions and the ending of the story. This allows the user to actively participate in the story and enjoy a more immersive experience.

[0053] The picture book generation system can also be equipped with a function to automatically generate background music and sound effects in accordance with the progress of the story. For example, adding appropriate music and sound effects according to the scene in the story can enhance the sense of realism. It can also provide an interface that allows users to select the music and sound effects to be used in specific scenes. For example, exciting music can be added to adventure scenes, and calm music to moving scenes. In addition, adding sound effects such as animal cries, wind sounds, and rain sounds can create a more realistic atmosphere in the story. This makes it possible to provide users with a more emotional experience through music and sound effects.

[0054] The picture book generation system can also be equipped with a function that allows users to control the progress of the story with voice commands. For example, the user can control the progress of the story by issuing voice commands such as "next page," "previous page," or "read again." Voice commands can also be used to select character actions and story development. For example, by using commands such as "move the character left" or "advance to the next scene," the progress of the story can be controlled by voice. This allows the user to control the story without using their hands, providing a more intuitive and convenient experience.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: In the keyword input section, the user inputs keywords related to the theme or content of the picture book. For example, the user inputs keywords such as "adventure," "animals," and "friendship." Step 2: The sentence generation unit generates sentences based on the keywords entered by the keyword input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate narrative sentences. Step 3: The image generation unit generates images based on the text generated by the text generation unit. For example, the generation AI analyzes the content of the text and generates appropriate visuals. Step 4: The picture book generation unit combines the text and images generated by the text generation unit and the image generation unit to generate a picture book. For example, the generation AI optimizes the placement of text and images to create an easy-to-read layout. Step 5: The voice model generation unit learns the voices of family members and generates a voice model. For example, the voices of the mother and father are recorded and the voice data is input into the generation AI. Step 6: The reading unit reads the picture book aloud using the voice model generated by the voice model generation unit. For example, the generation AI reads a sentence such as "Once upon a time..." in a mother's voice.

[0057] (Example 2) The picture book generation system according to the embodiment of the present invention is a system in which a generation AI creates a picture book and reads it aloud in the voices of family members. As a result, the picture book generation system can create a picture book using the generation AI and read it aloud in the voices of family members.

[0058] A picture book generation system according to an embodiment includes a keyword input unit, a sentence generation unit, an image generation unit, a picture book generation unit, an audio model generation unit, and a reading-aloud unit. The keyword input unit allows a user to input keywords related to the theme or content of a picture book. For example, the user inputs keywords such as "adventure," "animals," and "friendship." The sentence generation unit generates sentences based on the keywords input by the keyword input unit. For example, the generation AI generates story sentences using a text generation AI (e.g., LLM). The image generation unit generates images based on the sentences generated by the sentence generation unit. For example, the generation AI analyzes the content of the sentences and generates appropriate visuals. The picture book generation unit generates a picture book by combining the sentences and images generated by the sentence generation unit and the image generation unit. For example, the generation AI optimizes the arrangement of the sentences and images to create an easy-to-read layout. The audio model generation unit learns the voices of family members and generates an audio model. For example, the voices of the mother and father are recorded and the audio data is input into the generation AI. The reading-aloud unit reads the picture book to a child using the audio model generated by the audio model generation unit. For example, the generation AI reads a sentence such as "Once upon a time, there was..." in a mother's voice. This allows the picture book generation system to create a picture book using the generation AI and read it aloud in the voices of family members.

[0059] The keyword input unit can refer to the user's past input history and automatically suggest highly relevant keywords. For example, when a user inputs a keyword, the keyword input unit refers to the history of previously input keywords and automatically suggests highly relevant keywords. For example, if a user previously input "adventure," keywords such as "exploration" and "adventure story" are suggested. Furthermore, when a keyword is input, the keyword input unit displays related keywords in real time based on the user's past input history. For example, if "animals" is input, keywords such as "zoo" and "wild animals" are suggested. Furthermore, when a user inputs a keyword, the keyword input unit analyzes the user's past input history and automatically completes highly relevant keywords. For example, if "friendship" is input, keywords such as "friendship story" and "friends" are suggested. In this way, by referring to the user's past input history and automatically suggesting highly relevant keywords, the efficiency of keyword input is improved.

[0060] The keyword input unit can customize the generation AI's responses according to the user's age and interests. The keyword input unit customizes the generation AI's responses based on the user's age and interests, for example. For example, it generates stories in simple language for children and provides complex stories for adults. The keyword input unit also generates an appropriate story based on the user's age and interests when a keyword is entered, allowing the generation AI to generate an appropriate story. For example, for a child who loves animals, it generates a story with an animal as the main character. The keyword input unit also customizes the generation AI's responses to keywords based on the user's profile information. For example, it generates stories with related themes for users of a specific age group or with specific interests. This allows the generation AI to provide more appropriate stories by customizing the generation AI's responses according to the user's age and interests.

[0061] The keyword input unit can use the emotion estimation function to analyze the emotional nuances of keywords entered by the user and generate a story that matches the emotion. For example, the keyword input unit uses the emotion estimation function to analyze the emotional nuances of keywords entered by the user and generate a story that matches that emotion. For example, if "sad" is entered, an emotional story is generated. The keyword input unit also analyzes the emotion of keywords entered by the user and generates a story that matches that emotion. For example, if "fun" is entered, a cheerful and fun story is generated. The keyword input unit also uses the emotion estimation function to analyze the emotion of the user when entering keywords in real time and generate a story that matches that emotion. For example, if "scary" is entered, a thrilling story is generated. In this way, a more emotional experience can be provided by generating a story that matches the user's emotion.

[0062] The keyword input unit supports voice input, allowing a story to be generated simply by the user speaking. The keyword input unit, for example, supports keyword input as voice input, allowing a story to be generated simply by the user speaking. For example, if a user speaks "adventure," the generation AI generates an adventure story. The keyword input unit also uses voice input to build a system that allows a story to be generated simply by the user speaking. For example, if a user speaks "animals," the generation AI generates a story featuring animals. The keyword input unit also uses voice recognition technology to input keywords simply by the user speaking, and the generation AI generates a story based on those keywords. For example, if a user speaks "friendship," the generation AI generates a friendship story. This allows for voice input, making it possible to generate a story simply by the user speaking.

[0063] The keyword input unit displays popular keywords used by other users, allowing the user to use them as a reference for ideas. For example, when a keyword is input, the keyword input unit displays popular keywords used by other users, allowing the user to use them as a reference for ideas. For example, when "adventure" is input, popular keywords such as "exploration" and "adventure story" are displayed. The keyword input unit also displays popular keywords used by other users in real time, building a system that allows the user to use them as a reference for ideas. For example, when "animals" is input, popular keywords such as "zoo" and "wild animals" are displayed. The keyword input unit also suggests popular keywords used by other users when a keyword is input, making it easier for the user to get ideas. For example, when "friendship" is input, popular keywords such as "friendship story" and "friends" are displayed. This makes it easier for the user to get ideas by displaying popular keywords used by other users.

[0064] The keyword input unit can use the emotion estimation function to analyze the emotion a user has when entering keywords in real time and suggest keywords that elicit positive emotions. For example, the keyword input unit uses the emotion estimation function to analyze the emotion a user has when entering keywords in real time and suggest keywords that elicit positive emotions. For example, if the user enters "sad," keywords such as "fun" and "happy" are suggested. The keyword input unit also builds a system that analyzes the emotion a user has when entering keywords and suggests keywords that elicit positive emotions. For example, if the user enters "fear," keywords such as "relief" and "fun" are suggested. The keyword input unit also uses the emotion estimation function to analyze the user's emotion in real time and suggest keywords that elicit positive emotions. For example, if the user enters "anger," keywords such as "peace" and "joy" are suggested. In this way, a better story can be generated by analyzing the user's emotion in real time and suggesting keywords that elicit positive emotions.

[0065] The image generation unit can provide an interface that allows the user to manually make corrections to the generated image. The image generation unit provides, for example, an interface that allows the user to manually make corrections to the generated image. For example, it provides tools for changing the color or shape of the image. The image generation unit also builds an interface that allows the user to manually make corrections to the generated image. For example, it provides functions for deleting part of the image or adding new elements. The image generation unit also develops an interface that allows the user to manually make corrections to the generated image. For example, it provides tools for changing the background of the image or adjusting the facial expression of a character. This allows the user to manually make corrections to the generated image, making it possible to provide a more customized picture book.

[0066] The image generation unit can generate customized images based on the colors and style specified by the user. For example, when generating an image, the image generation unit generates a customized image based on the colors and style specified by the user. For example, if a user specifies "blue sky" or "cartoon style," the image will be generated exactly as specified. The image generation unit also builds a system in which a generation AI generates customized images based on the colors and style specified by the user. For example, if a user specifies "warm colors" or "realistic style," the image will be generated exactly as specified. The image generation unit also provides a customization function for reflecting the colors and style specified by the user when generating an image. For example, if a user specifies "monochrome" or "fantasy style," the image will be generated exactly as specified. This allows for the provision of more personalized picture books by generating customized images based on the colors and style specified by the user.

[0067] The image generation unit can use the emotion estimation function to generate images that match the emotional tone of the story. The image generation unit, for example, uses the emotion estimation function to generate images that match the emotional tone of the story. For example, it generates images with dark tones for sad scenes and images with bright tones for happy scenes. The image generation unit also analyzes the emotional tone of the story and builds a system that generates images that match that tone. For example, it generates warm images for moving scenes. The image generation unit also uses the emotion estimation function to generate images that match the emotional tone of the story. For example, it generates sharp images for tense scenes and soft images for relaxing scenes. In this way, by generating images that match the emotional tone of the story, it is possible to provide a more emotional experience.

[0068] The image generation unit can correspond image generation to 3D models to create a three-dimensional picture book. The image generation unit, for example, corresponds image generation to 3D models to build a system for creating a three-dimensional picture book. For example, characters and backgrounds are generated as 3D models to provide a three-dimensional picture book. The image generation unit also converts the generated images into 3D models to create a three-dimensional picture book. For example, story scenes are reproduced in 3D space, allowing users to freely change the viewpoint. The image generation unit also corresponds image generation to 3D models to develop a system for providing a three-dimensional picture book. For example, characters and objects are generated as 3D models to create a picture book that users can interactively operate. In this way, by corresponding image generation to 3D models, a three-dimensional picture book can be provided.

[0069] The image generation unit can animate the generated images to provide a picture book with movement. The image generation unit, for example, builds a system that animates the generated images to provide a picture book with movement. For example, it adds animations such as characters moving or backgrounds changing. The image generation unit also adds animations after image generation to create a picture book with movement. For example, it generates animations in which characters move in accordance with scenes in the story. The image generation unit also animates the generated images to provide a picture book that users can enjoy interactively. For example, it adds animations in which characters move when a user clicks. In this way, by animating the generated images, a picture book with movement can be provided.

[0070] The image generation unit can use the emotion estimation function to analyze the emotional response of a user when viewing an image and select the most appropriate image. The image generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional response of a user when viewing an image and selects the most appropriate image. For example, images that make the user smile are preferentially displayed. The image generation unit also analyzes the user's emotional response in real time and selects the most appropriate image based on the results. For example, an image that surprises the user is selected and displayed. The image generation unit also uses the emotion estimation function to develop a system that analyzes the user's emotional response and automatically selects the most appropriate image. For example, images that move the user are preferentially displayed. In this way, a more emotional experience can be provided by analyzing the user's emotional response and selecting the most appropriate image.

[0071] The picture book generation unit can provide a function that allows the user to freely customize the layout of the generated picture book. The picture book generation unit provides, for example, a function that allows the user to freely customize the layout of the generated picture book. For example, the order of pages can be changed or the position of text can be adjusted. The picture book generation unit also builds an interface that allows the user to customize the layout of the generated picture book. For example, it provides a function that allows the position of images and text to be changed by drag and drop. The picture book generation unit also develops a system that allows the user to freely customize the layout of the generated picture book. For example, it provides a function that allows the background color and font of pages to be changed. This allows the user to freely customize the layout of the generated picture book, making it possible to provide a more personalized picture book.

[0072] The picture book generation unit can provide a function that allows the number of pages and font size of a picture book to be adjusted according to the user's preferences. The picture book generation unit provides a function that allows the user to adjust, for example, the number of pages and font size of a generated picture book according to the user's preferences. For example, the number of pages can be increased or decreased, or the font size can be increased. The picture book generation unit also builds an interface that allows the user to freely adjust the number of pages and font size of a picture book. For example, a function that allows the user to change the number of pages and font size using a slider can be provided. The picture book generation unit also develops a system that allows the user to customize the number of pages and font size of a generated picture book according to the user's preferences. For example, the number of pages can be reduced to create a shorter picture book, or the font size can be reduced to include more information. This allows the user to adjust the number of pages and font size of a picture book according to their preferences, making it possible to provide a more personalized picture book.

[0073] The picture book generation unit can use the emotion estimation function to automatically generate a page layout that matches the emotional flow of the story. For example, the picture book generation unit uses the emotion estimation function to build a system that automatically generates a page layout that matches the emotional flow of the story. For example, large images are used in moving scenes and text is emphasized in tense scenes. The picture book generation unit also analyzes the emotional flow of the story and automatically generates a page layout that matches that flow. For example, a layout with bright colors is used for happy scenes and a layout with dark colors for sad scenes. The picture book generation unit also uses the emotion estimation function to develop a system that automatically generates a page layout that matches the emotional flow of the story. For example, the page design is changed as emotions increase. In this way, a more emotional experience can be provided by automatically generating a page layout that matches the emotional flow of the story.

[0074] The picture book generation unit can generate picture books in multiple languages, making them available to international users. The picture book generation unit, for example, builds a system that makes generated picture books available to multiple languages, making them available to international users. For example, it translates the book into languages ​​such as English, French, and Chinese. The picture book generation unit also provides a function that allows the generation AI to automatically translate the picture book based on the language selected by the user. For example, when a user selects Japanese, a picture book in Japanese is generated. The picture book generation unit also develops a picture book generation system that supports multiple languages, making it available to international users. For example, when a user selects a language, a picture book translated into that language is generated. In this way, picture books can be generated in multiple languages, making them available to international users.

[0075] The picture book generation unit provides the contents of the picture book as a digital book with audio, making it suitable for visually impaired people. The picture book generation unit, for example, provides the contents of the generated picture book as a digital book with audio, building a system that also accommodates visually impaired people. For example, it adds audio data in which the generation AI reads the text aloud. The picture book generation unit also provides digital books with audio, allowing visually impaired people to enjoy picture books. For example, it adds a function that explains the contents of each page aloud. The picture book generation unit also develops a system that generates digital books with audio for visually impaired people. For example, it provides a function that reads the text of the generated picture book aloud. In this way, the content of the picture book can be provided as a digital book with audio, making it suitable for visually impaired people.

[0076] The picture book generation unit can use the emotion estimation function to analyze the emotional response of a user when reading a picture book and propose an optimal layout. The picture book generation unit, for example, uses the emotion estimation function to analyze the emotional response of a user when reading a picture book and builds a system that proposes an optimal layout. For example, it uses the layout of a page that moved the user as a reference. The picture book generation unit also analyzes the user's emotional response in real time and proposes an optimal layout based on the results. For example, it applies the layout of a page that made the user smile to other pages as well. The picture book generation unit also uses the emotion estimation function to develop a system that analyzes the user's emotional response and automatically proposes an optimal layout. For example, it reflects the layout of a page that excited the user on other pages as well. In this way, a more emotional experience can be provided by analyzing the user's emotional response and proposing an optimal layout.

[0077] The voice model generation unit collects voice data containing different emotional expressions when learning the voices of family members, thereby enabling emotionally rich reading. For example, when learning the voices of family members, the voice model generation unit collects voice data containing different emotional expressions to enable emotionally rich reading. For example, voices of joy, sadness, surprise, etc. are recorded. The voice model generation unit also collects voice data containing different emotional expressions to create a model that enables the generation AI to perform emotional reading. For example, it learns the voice of a mother's joy and the voice of a father's sadness. The voice model generation unit also collects voice data containing emotional expressions when learning the voices of family members, thereby building a system that enables the generation AI to perform emotional reading. For example, it learns the voices of family members' surprise and relief. In this way, by collecting voice data containing different emotional expressions, emotionally rich reading can be achieved.

[0078] The voice model generation unit can provide a function that allows the user to fine-tune the trained voice model. The voice model generation unit, for example, builds a system that provides a function that allows the user to fine-tune the trained voice model. For example, it provides an interface that allows the user to adjust the tone and speed of the voice. The voice model generation unit also develops a function that allows the user to fine-tune the trained voice model. For example, it provides a tool that allows the user to adjust the pitch and volume of the voice. The voice model generation unit also provides a system that allows the user to freely fine-tune the trained voice model. For example, it provides a function that allows the user to adjust the emotional expression and intonation of the voice. This allows the user to fine-tune the trained voice model, thereby providing more personalized reading.

[0079] The voice model generation unit uses the emotion estimation function to analyze the emotional nuances of the family members' voices and realize optimal reading aloud. The voice model generation unit, for example, uses the emotion estimation function to analyze the emotional nuances of the family members' voices and builds a system that realizes optimal reading aloud. For example, it analyzes a mother's gentle voice or a father's powerful voice. The voice model generation unit also analyzes the emotional nuances of the family members' voices and performs optimal reading aloud based on the results. For example, it reads a story in an emotionally rich voice. The voice model generation unit also uses the emotion estimation function to analyze the emotional nuances of the family members' voices and develops a system that automatically realizes optimal reading aloud. For example, it changes the tone of the voice according to the intensity of the emotion. In this way, optimal reading aloud can be realized by analyzing the emotional nuances of the family members' voices.

[0080] The voice model generation unit can learn not only the voices of family members, but also the cries of pets and environmental sounds, allowing for more realistic reading aloud. The voice model generation unit can, for example, learn not only the voices of family members, but also the cries of pets and environmental sounds, building a system that provides more realistic reading aloud. For example, it can learn dog barks and bird chirps. The voice model generation unit can also learn pet cries and environmental sounds, creating a model that enables the generation AI to provide more realistic reading aloud. For example, it can learn cat meows and the sound of wind. The voice model generation unit can also learn pet cries and environmental sounds in addition to the voices of family members, developing a system that provides more realistic reading aloud. For example, it can learn pet cries and the sound of rain. By learning pet cries and environmental sounds, it is possible to provide more realistic reading aloud.

[0081] The voice model generation unit can provide a function that allows a trained voice model to be shared with other users. The voice model generation unit, for example, builds a system that provides a function that allows a trained voice model to be shared with other users. For example, the voices of family members can be shared with other users to read aloud in different voices. The voice model generation unit also develops a function that allows a user to share a trained voice model. For example, a voice model can be shared with friends or relatives to enjoy stories in different voices. The voice model generation unit also provides a system that allows a trained voice model to be easily shared with other users. For example, the voice model can be stored in the cloud so that other users can access it. In this way, the trained voice model can be shared with other users to enjoy reading aloud in different voices.

[0082] The voice model generation unit can use the emotion estimation function to analyze the emotion a user feels when recording and suggest the optimal recording method. The voice model generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotion a user feels when recording and suggests the optimal recording method. For example, the system provides advice to help the user record in a relaxed state. The voice model generation unit also analyzes the user's emotion and suggests the optimal recording method based on the results. For example, if the user is nervous, the system suggests a way to relax. The voice model generation unit also uses the emotion estimation function to develop a system that analyzes the user's emotion in real time and suggests the optimal recording method. For example, the system provides advice to help the user record in a happy mood. In this way, a better voice model can be created by analyzing the emotion a user feels when recording and suggesting the optimal recording method.

[0083] The reading aloud unit can automatically generate background music and sound effects when reading aloud, thereby enhancing the sense of realism. The reading aloud unit, for example, builds a system that automatically generates background music and sound effects when reading aloud, thereby enhancing the sense of realism. For example, music and sound effects are added to match the scenes in the story. The reading aloud unit also automatically generates background music and sound effects to enhance the sense of realism of the reading aloud. For example, exciting music is added for adventure scenes, and calm music is added for quiet scenes. The reading aloud unit also automatically generates background music and sound effects when reading aloud, thereby allowing the user to feel a sense of realism. For example, animal cries and wind sounds are added. In this way, the sense of realism of the reading aloud can be enhanced by automatically generating background music and sound effects.

[0084] The reading unit can provide a function that allows the speed and tone of reading to be adjusted according to the user's preferences. The reading unit, for example, builds a system that provides a function that allows the speed and tone of reading to be adjusted according to the user's preferences. For example, the reading speed can be increased or the tone can be raised. The reading unit also develops an interface that allows the user to freely adjust the speed and tone of reading to the user. For example, it provides a function that allows the speed and tone to be changed using a slider. The reading unit also provides a system that allows the speed and tone of reading to be customized according to the user's preferences. For example, it provides a function that reads at a slow speed or in a calm tone. This allows the speed and tone of reading to be adjusted according to the user's preferences, making it possible to provide a more personalized reading experience.

[0085] The reading unit can use the emotion estimation function to analyze a child's emotional response while being read to and suggest the optimal reading method. For example, the reading unit uses the emotion estimation function to build a system that analyzes a child's emotional response while being read to and suggests the optimal reading method. For example, if the child is excited, it reads in a calm tone. The reading unit also analyzes a child's emotional response in real time and suggests the optimal reading method based on the results. For example, if the child is smiling, it reads in a cheerful tone. The reading unit also uses the emotion estimation function to develop a system that analyzes a child's emotional response while being read to and automatically suggests the optimal reading method. For example, if the child is concentrating, it reads at a slower speed. In this way, a better reading experience can be provided by analyzing a child's emotional response while being read to and suggesting the optimal reading method.

[0086] The reading unit can provide a function that allows readings to be provided by live streaming and that can be shared with family members in remote locations. The reading unit, for example, builds a system that provides readings by live streaming and that can be shared with family members in remote locations. For example, grandparents read to their grandchildren from a remote location. The reading unit also develops a system that uses the live streaming function to allow family members in remote locations to enjoy picture books together. For example, parents read to their children even when they are on a business trip. The reading unit also provides a function that allows readings to be provided by live streaming and that allows all family members to enjoy picture books at the same time. For example, family members can read picture books together even if they are in different locations. This allows readings to be provided by live streaming and that can be shared with family members in remote locations.

[0087] The reading unit can provide an archive function that records the content of what is read aloud and allows it to be played back later. The reading unit, for example, builds a system that provides an archive function that records the content of what is read aloud and allows it to be played back later. For example, it allows children to listen to their favorite stories as many times as they like. The reading unit can also develop a system that uses the recording function to save the content of what is read aloud and allow it to be played back later. For example, it allows children to listen to picture books by themselves even when their parents are busy. The reading unit also provides a function that records the content of what is read aloud and saves it as an archive. For example, it allows picture books read aloud in the voices of family members to be kept as memories. This allows the content of what is read aloud to be recorded and played back later, so that users can enjoy it again whenever they like.

[0088] The reading unit can use the emotion estimation function to analyze the child's emotional response while reading in real time and continuously adjust the optimal reading. For example, the reading unit uses the emotion estimation function to build a system that analyzes the child's emotional response while reading in real time and continuously adjusts the optimal reading. For example, if the child is excited, the reading unit reads in a calm tone. The reading unit also analyzes the child's emotional response in real time and continuously adjusts the optimal reading based on the results. For example, if the child smiles, the reading unit reads in a cheerful tone. The reading unit also uses the emotion estimation function to develop a system that analyzes the child's emotional response while reading in real time and automatically adjusts the optimal reading. For example, if the child is concentrating, the reading unit reads at a slower speed. In this way, the reading unit can analyze the child's emotional response while reading in real time and continuously adjust the optimal reading, providing a better reading experience.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The picture book generation system can also be equipped with a function that allows the user to select the genre of the story according to their preferences. For example, if the user selects a genre such as "fantasy," "mystery," or "comedy," a story that fits that genre will be generated. In addition, the picture book generation system can use the generation AI to adjust the tone and style of the story based on the genre selected by the user. For example, if fantasy is selected, a story that includes magic and adventure will be generated, and if mystery is selected, a story that includes solving mysteries and suspense will be generated. This makes it possible to provide a variety of stories that suit the user's preferences.

[0091] The picture book generation system can also be equipped with a function that allows users to customize the characters that appear in the story. For example, if a user specifies the name, appearance, and personality of a character, the generation AI will generate a story based on that information. In addition, a more detailed story can be generated by the user entering the character relationships and background settings. For example, characters such as siblings, friends, and pets can be added, and their respective roles and relationships can be set. This allows users to enjoy stories featuring their own original characters.

[0092] The picture book generation system can also provide interactive choices as the story progresses. For example, a mechanism can be introduced in which the development of the story changes depending on the user's choices during the story. The generation AI can also generate different endings and scenes depending on the choices the user makes. For example, during an adventure, the system can present options such as "take the left path" or "take the right path," and the development of the story will change depending on the path chosen. It can also provide an interactive experience in which the user's choices change the character's actions and the ending of the story. This allows the user to actively participate in the story and enjoy a more immersive experience.

[0093] The picture book generation system can also be equipped with a function to automatically generate background music and sound effects in accordance with the progress of the story. For example, adding appropriate music and sound effects according to the scene in the story can enhance the sense of realism. It can also provide an interface that allows users to select the music and sound effects to be used in specific scenes. For example, exciting music can be added to adventure scenes, and calm music to moving scenes. In addition, adding sound effects such as animal cries, wind sounds, and rain sounds can create a more realistic atmosphere in the story. This makes it possible to provide users with a more emotional experience through music and sound effects.

[0094] The picture book generation system can also be equipped with a function that allows users to control the progress of the story with voice commands. For example, the user can control the progress of the story by issuing voice commands such as "next page," "previous page," or "read again." Voice commands can also be used to select character actions and story development. For example, by using commands such as "move the character left" or "advance to the next scene," the progress of the story can be controlled by voice. This allows the user to control the story without using their hands, providing a more intuitive and convenient experience.

[0095] The picture book generation system can also use an emotion estimation function to generate a story ending that matches the user's emotions. For example, the emotion estimation function analyzes the user's emotions as they read the story and generates an ending that matches those emotions. It can also introduce a mechanism that changes the ending if the user's emotions change during the story. For example, if the user feels sad during the story, a touching ending is generated, and if the user feels happy, a happy ending is generated. The emotion estimation function can also be used to adjust the story development in real time to match the user's emotions. This makes it possible to provide a story that is in tune with the user's emotions.

[0096] The picture book generation system can further use the emotion estimation function to analyze the emotions a user feels after finishing a story and suggest the next story to read based on those emotions. For example, after a user finishes reading a moving story, it can suggest a moving story to read next. Also, after a user finishes reading an enjoyable story, it can suggest an enjoyable story to read next. It can also use the emotion estimation function to analyze the user's emotions and suggest a story genre or theme based on those emotions. For example, if the user wants to relax, it can suggest a relaxing story, and if the user wants to get excited, it can suggest an exciting story. This makes it possible to provide a more personalized experience by suggesting the next story that matches the user's emotions.

[0097] The picture book generation system can further use an emotion estimation function to analyze the user's emotions in real time as the story progresses and adjust the story development according to those emotions. For example, if the user's emotions become heightened during the story, the story development can be sped up to match those emotions. Conversely, if the user's emotions calm down during the story, the story development can be slowed down. The emotion estimation function can also be used to analyze the user's emotions in real time and adjust the story scenes and character behavior according to those emotions. For example, if the user is surprised, a surprising scene can be added, and if the user laughs, a happy scene can be added. This makes it possible to provide a story development that matches the user's emotions, thereby providing a more emotional experience.

[0098] The picture book generation system can further use an emotion estimation function to analyze the emotions of the user as they read the story and adjust the difficulty of the story based on those emotions. For example, the emotion estimation function analyzes the user's emotions as they read the story and increases or decreases the difficulty of the story according to those emotions. Also, if the user's emotions become heightened during the story, the difficulty can be increased to provide a challenging development. Also, if the user's emotions calm down during the story, the difficulty can be decreased to provide a relaxing development. In this way, a more personalized experience can be provided by adjusting the difficulty of the story to match the user's emotions.

[0099] The picture book generation system can further use an emotion estimation function to analyze the emotions of the user after they finish reading a story, and provide an evaluation of the story and feedback based on those emotions. For example, the emotion estimation function analyzes the user's emotions after they finish reading a story, and evaluates the story based on those emotions. The emotion estimation function can also analyze the user's emotions after they finish reading a story, and provide feedback based on those emotions. For example, if the user is moved, feedback such as "That was a moving story" is provided, and if the user feels happy, feedback such as "That was a fun story" is provided. In this way, by providing evaluations and feedback based on the user's emotions, a more personalized experience can be provided.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: In the keyword input section, the user inputs keywords related to the theme or content of the picture book. For example, the user inputs keywords such as "adventure," "animals," and "friendship." Step 2: The sentence generation unit generates sentences based on the keywords entered by the keyword input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate narrative sentences. Step 3: The image generation unit generates images based on the text generated by the text generation unit. For example, the generation AI analyzes the content of the text and generates appropriate visuals. Step 4: The picture book generation unit combines the text and images generated by the text generation unit and the image generation unit to generate a picture book. For example, the generation AI optimizes the placement of text and images to create an easy-to-read layout. Step 5: The voice model generation unit learns the voices of family members and generates a voice model. For example, the voices of the mother and father are recorded and the voice data is input into the generation AI. Step 6: The reading unit reads the picture book aloud using the voice model generated by the voice model generation unit. For example, the generation AI reads a sentence such as "Once upon a time..." in a mother's voice.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0138] The 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.

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

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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 section for inputting a keyword; a sentence generation unit that generates a sentence based on the keywords input by the keyword input unit; an image generation unit that generates an image based on the sentence generated by the sentence generation unit; a picture book generation unit that generates a picture book by combining the texts and images generated by the text generation unit and the image generation unit; a voice model generation unit that learns the voices of family members and generates a voice model; a reading unit that reads a picture book aloud using the voice model generated by the voice model generation unit. A system characterized by:

2. The keyword input unit Refer to the user's past input history and automatically suggest highly relevant keywords 2. The system of claim 1.

3. The image generation unit Provide an interface that allows users to manually modify the generated image 2. The system of claim 1.

4. The picture book generation unit Provides a function that allows users to freely customize the layout of the generated picture book 2. The system of claim 1.

5. The voice model generation unit When learning the voices of family members, audio data containing different emotional expressions is collected to enable emotionally rich reading.

2. The system of claim 1.

6. The reading section Automatically generate background music and sound effects to enhance the sense of realism when reading aloud 2. The system of claim 1.

7. The keyword input unit Analyzes the emotional nuances of keywords entered by the user and generates a story that matches that emotion.

2. The system of claim 1.

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