System

Abstract

An object of the system according to the embodiment is to easily create and provide an original picture book with a child as a main character.SOLUTION: A system includes a photograph acquisition unit, a generation unit, and a provision unit. The photograph acquisition unit acquires a photograph of the child from the requester. The generation section generates an original picture book based on the photograph of the child acquired by the photograph acquisition section. The provision section provides the original picture book generated by the generation section to the client.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to easily create original picture books with children as the main characters.

[0005] The system according to the embodiment aims to easily create and provide original picture books with children as the main characters. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo acquisition unit, a generation unit, and a provision unit. The photo acquisition unit acquires photos of a child from a client. The generation unit generates an original picture book based on the photos of the child acquired by the photo acquisition unit. The provision unit provides the original picture book generated by the generation unit to the client. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to easily create and provide original picture books with children as the main characters. [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 creation system according to the embodiment of the present invention is a system in which an AI generator creates an original picture book using a child's photograph and provides it to a client. This allows the picture book creation system to create an original picture book based on a child's photograph and provide it to a client.

[0029] A picture book creation system according to an embodiment includes a photo acquisition unit, a generation unit, and a providing unit. The photo acquisition unit acquires a photo of a child from a requester. For example, the photo acquisition unit acquires digital photos uploaded online by the requester. The photo acquisition unit can also scan paper photos mailed by the requester and convert them into digital data. The photo acquisition unit can also directly acquire photos taken by the requester with a smartphone. The generation unit generates an original picture book based on the photo of the child acquired by the photo acquisition unit. For example, the generation unit uses a generation AI to create an adventure story featuring the photo of the child as the main character. The generation unit can also use the generation AI to generate illustrations based on the photo of the child to compose the pages of the picture book. The generation unit can also use the generation AI to generate a story based on the photo of the child and create the content of the picture book. The providing unit provides the original picture book generated by the generation unit to the requester. For example, the providing unit provides digital data of the generated picture book to the requester online. The providing unit can also print the generated picture book and mail it to the requester. The providing unit can also provide the generated picture book in a format that can be viewed on a digital device. This allows the picture book creation system to obtain a photo of a child from a client, create an original picture book based on the photo, and provide it to the client.

[0030] The generation unit can generate a 3D model from a photo of a child and use the 3D model to create an interactive picture book. For example, the generation unit inputs a photo of a child into the generation AI and generates a 3D model. The generation unit uses the 3D model to create an interactive picture book that can be moved as a character in the picture book. For example, the character can move when the child touches the screen. The generation unit can also use the generation AI to animate the 3D model and create scenes for the interactive picture book. The generation unit can also use the generation AI to generate an interactive picture book story based on the 3D model. This makes it possible to generate a 3D model from a photo of a child and use the model to create an interactive picture book.

[0031] The generation unit can simulate a child's voice based on a photo of the child and automatically generate narration. For example, the generation unit inputs a photo of the child into the generation AI and simulates a child's voice from the photo. The generated voice is used to automatically generate narration for a picture book. For example, the lines spoken by a character are played in a child's voice. The generation unit can also use the generation AI to synthesize a child's voice and generate narration that matches the story of the picture book. The generation unit can also use the generation AI to generate narration that includes emotional expressions based on the child's voice. In this way, a child's voice can be simulated based on a photo of the child and narration for a picture book can be automatically generated.

[0032] The generation unit can take in not only photos of children but also photos of family and pets to generate a picture book featuring multiple characters. For example, the generation unit inputs not only photos of children but also photos of family and pets into the generation AI and generates a picture book featuring multiple characters based on those photos. For example, it can create a story in which the whole family goes on an adventure. The generation unit can also use the generation AI to generate characters based on photos of family and pets to compose scenes for the picture book. The generation unit can also use the generation AI to generate a story based on photos of family and pets to create the contents of the picture book. This makes it possible to take in not only photos of children but also photos of family and pets to generate a picture book featuring multiple characters.

[0033] The generation unit can have a function that allows the selection of a theme according to a season or event. For example, the generation unit adds a function to the generation AI that allows the selection of a theme according to a season or event, and generates a picture book based on a specific theme such as Christmas or a birthday. For example, creating a Christmas adventure story. The generation unit can also use the generation AI to generate a story based on a theme according to a season or event. The generation unit can also use the generation AI to generate characters based on a theme according to a season or event, and compose a scene for the picture book. This allows the addition of a function that allows the selection of a theme according to a season or event.

[0034] The generation unit can have a function to analyze the illustration style of an existing picture book and generate new scenes and characters in the same style. For example, the generation unit has the generation AI analyze the illustration style of an existing picture book and generate new scenes and characters based on that style. For example, creating a sequel to an existing picture book in the same style. The generation unit can also use the generation AI to generate new characters based on the illustration style. The generation unit can also use the generation AI to generate new scenes based on the illustration style. This makes it possible to analyze the illustration style of an existing picture book and generate new scenes and characters in the same style.

[0035] The generation unit can have a function to analyze the story of an existing picture book and automatically generate a sequel or spin-off. For example, the generation unit has the generation AI analyze the story of an existing picture book and automatically generate a sequel based on that story. For example, a new adventure is created as a continuation of the story of an existing picture book. The generation unit can also use the generation AI to generate a spin-off based on the story. The generation unit can also use the generation AI to generate new characters based on the story and create the content of the sequel or spin-off. In this way, the story of an existing picture book can be analyzed and sequels and spin-offs can be automatically generated.

[0036] The generation unit can be equipped with a function to make existing picture books multilingual and enable the generation of picture books in different languages. For example, the generation unit adds a function to the generation AI to translate the text of existing picture books into multiple languages, enabling the generation of picture books in different languages. For example, it supports languages ​​such as English, French, and Chinese. The generation unit can also use the generation AI to translate the text of the picture book based on the multilingual support. The generation unit can also use the generation AI to generate the content of the picture book based on the multilingual support. This makes it possible to make existing picture books multilingual and enable the generation of picture books in different languages.

[0037] The generation unit can have a function to generate animations and video content based on characters from existing picture books. For example, the generation unit adds a function to the generation AI to generate animations based on characters from existing picture books, and creates video content in which the characters from the picture books move. For example, it creates an animation in which the characters go on an adventure. The generation unit can also generate video content based on characters using the generation AI. The generation unit can also generate animations based on characters using the generation AI, and animate scenes from picture books. This makes it possible to generate animations and video content based on characters from existing picture books.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The generator can incorporate educational elements into the content of a picture book based on a child's photograph. For example, the generator can use the generation AI to create a scene in which a character based on a child's photograph learns the alphabet and numbers. The generator can also use the generation AI to create an adventure story in which a character based on a child's photograph learns about nature and animals. The generator can also use the generation AI to create a story in which a character based on a child's photograph learns about historical events and people. In this way, the picture book creation system can incorporate educational elements into original picture books based on children's photographs.

[0040] The generation unit can incorporate interactive quizzes and puzzles into the content of a picture book based on a child's photograph. For example, the generation unit can use the generation AI to create a scene in which a character based on a child's photograph answers a quiz in a story. The generation unit can also use the generation AI to create a scene in which a character based on a child's photograph solves a puzzle. Furthermore, the generation unit can use the generation AI to create a scene in which a character based on a child's photograph navigates a maze. In this way, the picture book creation system can incorporate interactive quizzes and puzzles into an original picture book based on a child's photograph.

[0041] The generation unit can incorporate a child's growth record into the content of a picture book based on the child's photograph. For example, the generation unit can use the generation AI to create a scene depicting the growth process of a character based on the child's photograph. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph experiences a special event such as a birthday or an entrance ceremony. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph makes a new friend. In this way, the picture book creation system can incorporate a child's growth record into an original picture book based on the child's photograph.

[0042] The generation unit can reflect a child's likes and interests in the content of a picture book based on the child's photograph. For example, the generation unit can use generation AI to create a scene in which a character based on the child's photograph goes on an adventure with a favorite animal or character. The generation unit can also use generation AI to create a scene in which a character based on the child's photograph enjoys a favorite sport or hobby. Furthermore, the generation unit can use generation AI to create a scene in which a character based on the child's photograph visits a favorite place. In this way, the picture book creation system can reflect a child's likes and interests in an original picture book based on the child's photograph.

[0043] The generation unit can reflect the child's future dreams and goals in the content of a picture book based on the child's photograph. For example, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph experiences a future career. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph works hard to achieve his or her dreams. Furthermore, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph achieves a goal. In this way, the picture book creation system can reflect the child's future dreams and goals in an original picture book based on the child's photograph.

[0044] The generation unit can incorporate the child's friends and school life into the content of a picture book based on the child's photograph. For example, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph plays with friends. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph participates in a class or event at school. Furthermore, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph works together with friends to solve a problem. In this way, the picture book creation system can incorporate the child's friends and school life into an original picture book based on the child's photograph.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The photo acquisition unit acquires photos of the child from the requester. For example, it acquires digital photos uploaded by the requester online. It can also scan paper photos sent by mail and convert them into digital data. It can also directly acquire photos taken by the requester with their smartphone. Step 2: The generation unit generates an original picture book based on the child's photo acquired by the photo acquisition unit. For example, the generation AI can be used to create an adventure story with the child's photo as the main character. The generation AI can also be used to generate illustrations based on the child's photo to compose the pages of the picture book. Furthermore, the generation AI can be used to generate a story based on the child's photo to create the contents of the picture book. Step 3: The providing unit provides the original picture book generated by the generating unit to the requester. For example, digital data of the generated picture book may be provided to the requester online. The generated picture book may also be printed and mailed to the requester. Furthermore, the generated picture book may also be provided in a format that can be viewed on a digital device.

[0047] (Example 2) The picture book creation system according to the embodiment of the present invention is a system in which an AI generator creates an original picture book using a child's photograph and provides it to a client. This allows the picture book creation system to create an original picture book based on a child's photograph and provide it to a client.

[0048] A picture book creation system according to an embodiment includes a photo acquisition unit, a generation unit, and a providing unit. The photo acquisition unit acquires a photo of a child from a requester. For example, the photo acquisition unit acquires digital photos uploaded online by the requester. The photo acquisition unit can also scan paper photos mailed by the requester and convert them into digital data. The photo acquisition unit can also directly acquire photos taken by the requester with a smartphone. The generation unit generates an original picture book based on the photo of the child acquired by the photo acquisition unit. For example, the generation unit uses a generation AI to create an adventure story featuring the photo of the child as the main character. The generation unit can also use the generation AI to generate illustrations based on the photo of the child to compose the pages of the picture book. The generation unit can also use the generation AI to generate a story based on the photo of the child and create the content of the picture book. The providing unit provides the original picture book generated by the generation unit to the requester. For example, the providing unit provides digital data of the generated picture book to the requester online. The providing unit can also print the generated picture book and mail it to the requester. The providing unit can also provide the generated picture book in a format that can be viewed on a digital device. This allows the picture book creation system to obtain a photo of a child from a client, create an original picture book based on the photo, and provide it to the client.

[0049] The generation unit can generate a 3D model from a photo of a child and use the 3D model to create an interactive picture book. For example, the generation unit inputs a photo of a child into the generation AI and generates a 3D model. The generation unit uses the 3D model to create an interactive picture book that can be moved as a character in the picture book. For example, the character can move when the child touches the screen. The generation unit can also use the generation AI to animate the 3D model and create scenes for the interactive picture book. The generation unit can also use the generation AI to generate an interactive picture book story based on the 3D model. This makes it possible to generate a 3D model from a photo of a child and use the model to create an interactive picture book.

[0050] The generation unit can simulate a child's voice based on a photo of the child and automatically generate narration. For example, the generation unit inputs a photo of the child into the generation AI and simulates a child's voice from the photo. The generated voice is used to automatically generate narration for a picture book. For example, the lines spoken by a character are played in a child's voice. The generation unit can also use the generation AI to synthesize a child's voice and generate narration that matches the story of the picture book. The generation unit can also use the generation AI to generate narration that includes emotional expressions based on the child's voice. In this way, a child's voice can be simulated based on a photo of the child and narration for a picture book can be automatically generated.

[0051] The generation unit can use the emotion estimation function to adjust the tone and content of the story based on the emotion estimated from a photo of the child. For example, the generation unit inputs a photo of the child into the generation AI and uses the emotion estimation function to analyze the emotion estimated from the photo. Based on the emotion, the generation unit adjusts the tone and content of the story in the picture book. For example, if the photo shows a smiling child, the story will have a brighter tone. The generation unit can also use the generation AI to change the story scenario based on the emotion estimation function. The generation unit can also use the generation AI to change the lines of the characters based on the emotion estimation function. In this way, the emotion estimation function can be used to adjust the tone and content of the story based on the emotion estimated from the photo of the child.

[0052] The generation unit can take in not only photos of children but also photos of family and pets to generate a picture book featuring multiple characters. For example, the generation unit inputs not only photos of children but also photos of family and pets into the generation AI and generates a picture book featuring multiple characters based on those photos. For example, it can create a story in which the whole family goes on an adventure. The generation unit can also use the generation AI to generate characters based on photos of family and pets to compose scenes for the picture book. The generation unit can also use the generation AI to generate a story based on photos of family and pets to create the contents of the picture book. This makes it possible to take in not only photos of children but also photos of family and pets to generate a picture book featuring multiple characters.

[0053] The generation unit can have a function that allows the selection of a theme according to a season or event. For example, the generation unit adds a function to the generation AI that allows the selection of a theme according to a season or event, and generates a picture book based on a specific theme such as Christmas or a birthday. For example, creating a Christmas adventure story. The generation unit can also use the generation AI to generate a story based on a theme according to a season or event. The generation unit can also use the generation AI to generate characters based on a theme according to a season or event, and compose a scene for the picture book. This allows the addition of a function that allows the selection of a theme according to a season or event.

[0054] The generation unit can use the emotion estimation function to automatically generate music and sound effects that match the child's emotions for each scene in the picture book. For example, the generation unit adds the emotion estimation function to the generation AI and automatically generates music and sound effects that match the child's emotions for each scene in the picture book. For example, cheerful music is played in scenes where a child is smiling. The generation unit can also use the generation AI to generate music and sound effects for each scene based on the emotion estimation function. The generation unit can also use the generation AI to generate sound effects for each scene based on the emotion estimation function. In this way, the emotion estimation function can be used to automatically generate music and sound effects that match the child's emotions for each scene in the picture book.

[0055] The generation unit can have a function to analyze the illustration style of an existing picture book and generate new scenes and characters in the same style. For example, the generation unit has the generation AI analyze the illustration style of an existing picture book and generate new scenes and characters based on that style. For example, creating a sequel to an existing picture book in the same style. The generation unit can also use the generation AI to generate new characters based on the illustration style. The generation unit can also use the generation AI to generate new scenes based on the illustration style. This makes it possible to analyze the illustration style of an existing picture book and generate new scenes and characters in the same style.

[0056] The generation unit can have a function to analyze the story of an existing picture book and automatically generate a sequel or spin-off. For example, the generation unit has the generation AI analyze the story of an existing picture book and automatically generate a sequel based on that story. For example, a new adventure is created as a continuation of the story of an existing picture book. The generation unit can also use the generation AI to generate a spin-off based on the story. The generation unit can also use the generation AI to generate new characters based on the story and create the content of the sequel or spin-off. In this way, the story of an existing picture book can be analyzed and sequels and spin-offs can be automatically generated.

[0057] The generation unit can use the emotion estimation function to customize each scene in an existing picture book to reflect the child's emotions. For example, the generation unit adds the emotion estimation function to the generation AI and customizes each scene in an existing picture book to reflect the child's emotions. For example, customizing to a brighter tone in a scene where a child is smiling. The generation unit can also use the generation AI to customize each scene based on the emotion estimation function. The generation unit can also use the generation AI to change the character's facial expression based on the emotion estimation function. This makes it possible to customize each scene in an existing picture book to reflect the child's emotions.

[0058] The generation unit can be equipped with a function to make existing picture books multilingual and enable the generation of picture books in different languages. For example, the generation unit adds a function to the generation AI to translate the text of existing picture books into multiple languages, enabling the generation of picture books in different languages. For example, it supports languages ​​such as English, French, and Chinese. The generation unit can also use the generation AI to translate the text of the picture book based on the multilingual support. The generation unit can also use the generation AI to generate the content of the picture book based on the multilingual support. This makes it possible to make existing picture books multilingual and enable the generation of picture books in different languages.

[0059] The generation unit can have a function to generate animations and video content based on characters from existing picture books. For example, the generation unit adds a function to the generation AI to generate animations based on characters from existing picture books, and creates video content in which the characters from the picture books move. For example, it creates an animation in which the characters go on an adventure. The generation unit can also generate video content based on characters using the generation AI. The generation unit can also generate animations based on characters using the generation AI, and animate scenes from picture books. This makes it possible to generate animations and video content based on characters from existing picture books.

[0060] The generation unit can use the emotion estimation function to collect children's emotional reactions to scenes in existing picture books and display popular scenes in a ranking format. The generation unit, for example, adds the emotion estimation function to the generation AI and collects children's emotional reactions to scenes in existing picture books. For example, it identifies scenes that make children smile. The generation unit can also use the generation AI to analyze the emotional reactions for each scene based on the emotion estimation function. The generation unit can also use the generation AI to display popular scenes in a ranking format based on the emotion estimation function. In this way, children's emotional reactions to scenes in existing picture books can be collected and popular scenes can be displayed in a ranking format.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The generator can incorporate educational elements into the content of a picture book based on a child's photograph. For example, the generator can use the generation AI to create a scene in which a character based on a child's photograph learns the alphabet and numbers. The generator can also use the generation AI to create an adventure story in which a character based on a child's photograph learns about nature and animals. The generator can also use the generation AI to create a story in which a character based on a child's photograph learns about historical events and people. In this way, the picture book creation system can incorporate educational elements into original picture books based on children's photographs.

[0063] The generation unit can incorporate interactive quizzes and puzzles into the content of a picture book based on a child's photograph. For example, the generation unit can use the generation AI to create a scene in which a character based on a child's photograph answers a quiz in a story. The generation unit can also use the generation AI to create a scene in which a character based on a child's photograph solves a puzzle. Furthermore, the generation unit can use the generation AI to create a scene in which a character based on a child's photograph navigates a maze. In this way, the picture book creation system can incorporate interactive quizzes and puzzles into an original picture book based on a child's photograph.

[0064] The generation unit can use the emotion estimation function to dynamically change the facial expression of a picture book character based on the emotion estimated from a child's photo. For example, the generation unit can create a scene in which the character smiles based on the emotion estimated from a child's photo using the generation AI. The generation unit can also create a scene in which the character is surprised based on the emotion estimated from a child's photo using the generation AI. Furthermore, the generation unit can also create a scene in which the character is sad based on the emotion estimated from a child's photo using the generation AI. In this way, the emotion estimation function can be used to dynamically change the facial expression of a picture book character based on the emotion estimated from a child's photo.

[0065] The generation unit can use the emotion estimation function to add background sounds and environmental sounds appropriate for a scene in a picture book based on the emotion estimated from a child's photo. For example, the generation unit can add birdsong and wind sounds to a happy scene based on the emotion estimated from a child's photo using the generation AI. The generation unit can also add thunder and rain sounds to a tense scene based on the emotion estimated from a child's photo using the generation AI. Furthermore, the generation unit can add quiet piano sounds to a sad scene based on the emotion estimated from a child's photo using the generation AI. In this way, the emotion estimation function can be used to add background sounds and environmental sounds appropriate for a scene in a picture book based on the emotion estimated from a child's photo.

[0066] The generation unit can use the emotion estimation function to adjust color and lighting effects appropriate for a scene in a picture book based on the emotion estimated from the child's photo. For example, the generation unit can add bright colors and lighting to happy scenes based on the emotion estimated from the child's photo using the generation AI. The generation unit can also add dark colors and lighting to tense scenes based on the emotion estimated from the child's photo using the generation AI. Furthermore, the generation unit can add dim colors and lighting to sad scenes based on the emotion estimated from the child's photo using the generation AI. In this way, the emotion estimation function can adjust color and lighting effects appropriate for a scene in a picture book based on the emotion estimated from the child's photo.

[0067] The generation unit can incorporate a child's growth record into the content of a picture book based on the child's photograph. For example, the generation unit can use the generation AI to create a scene depicting the growth process of a character based on the child's photograph. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph experiences a special event such as a birthday or an entrance ceremony. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph makes a new friend. In this way, the picture book creation system can incorporate a child's growth record into an original picture book based on the child's photograph.

[0068] The generation unit can reflect a child's likes and interests in the content of a picture book based on the child's photograph. For example, the generation unit can use generation AI to create a scene in which a character based on the child's photograph goes on an adventure with a favorite animal or character. The generation unit can also use generation AI to create a scene in which a character based on the child's photograph enjoys a favorite sport or hobby. Furthermore, the generation unit can use generation AI to create a scene in which a character based on the child's photograph visits a favorite place. In this way, the picture book creation system can reflect a child's likes and interests in an original picture book based on the child's photograph.

[0069] The generation unit can use the emotion estimation function to add character movements appropriate for picture book scenes based on emotions estimated from a child's photo. For example, the generation unit can create a scene in which a character jumps for joy based on emotions estimated from a child's photo using the generation AI. The generation unit can also create a scene in which a character steps back in surprise based on emotions estimated from a child's photo using the generation AI. Furthermore, the generation unit can create a scene in which a character sheds tears in sadness based on emotions estimated from a child's photo using the generation AI. In this way, the emotion estimation function can be used to add character movements appropriate for picture book scenes based on emotions estimated from a child's photo.

[0070] The generation unit can reflect the child's future dreams and goals in the content of a picture book based on the child's photograph. For example, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph experiences a future career. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph works hard to achieve his or her dreams. Furthermore, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph achieves a goal. In this way, the picture book creation system can reflect the child's future dreams and goals in an original picture book based on the child's photograph.

[0071] The generation unit can incorporate the child's friends and school life into the content of a picture book based on the child's photograph. For example, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph plays with friends. The generation unit can also use the generation AI to create a scene in which a character based on the child's photograph participates in a class or event at school. Furthermore, the generation unit can use the generation AI to create a scene in which a character based on the child's photograph works together with friends to solve a problem. In this way, the picture book creation system can incorporate the child's friends and school life into an original picture book based on the child's photograph.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The photo acquisition unit acquires photos of the child from the requester. For example, it acquires digital photos uploaded by the requester online. It can also scan paper photos sent by mail and convert them into digital data. It can also directly acquire photos taken by the requester with their smartphone. Step 2: The generation unit generates an original picture book based on the child's photo acquired by the photo acquisition unit. For example, the generation AI can be used to create an adventure story with the child's photo as the main character. The generation AI can also be used to generate illustrations based on the child's photo to compose the pages of the picture book. Furthermore, the generation AI can be used to generate a story based on the child's photo to create the contents of the picture book. Step 3: The providing unit provides the original picture book generated by the generating unit to the requester. For example, digital data of the generated picture book may be provided to the requester online. The generated picture book may also be printed and mailed to the requester. Furthermore, the generated picture book may also be provided in a format that can be viewed on a digital device.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 photo acquisition unit that acquires a photo of the child from the client; a generation unit that generates an original picture book based on the photograph of the child acquired by the photograph acquisition unit; a providing unit that provides the original picture book generated by the generating unit to a client. A system characterized by:

2. The generation unit A 3D model is generated from the photograph of the child, and the 3D model is used to create an interactive storybook. The system of claim 1 .

3. The generation unit A picture book featuring multiple characters is generated by incorporating not only the child's photo but also photos of family and pets. The system of claim 1 .

4. The generation unit It has the ability to analyze the illustration style of existing picture books and generate new scenes and characters in the same style. The system of claim 1 .

5. The generation unit Using emotion estimation, the tone and content of the story are adjusted based on the emotions inferred from the child's photo. The system of claim 1 .

6. The generation unit Equipped with a function that allows you to select themes according to the season or event The system of claim 1 .

7. The generation unit Using emotion estimation functionality, we customize existing picture book scenes to reflect children's emotions. The system of claim 1 .

8. The generation unit It has the ability to generate animations and video content based on characters from existing picture books. The system of claim 1 .

Images