System

Abstract

An object of the system according to the embodiment is to generate a story and an illustration individualized in accordance with a child's hobbies and thoughts.SOLUTION: A system includes a hobby thinking collection part, a story generation part, and an illustration generation part. The hobby and thought collection unit collects information on hobbies and thoughts of the child using the generated AI. The story generation unit generates a story based on the information collected by the thought-of-interest collection unit. The illustration generation unit generates an illustration according to the story generated by the story generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to create personalized stories and illustrations that match a child's tastes and thoughts.

[0005] The system according to the embodiment aims to generate personalized stories and illustrations that match a child's tastes and thoughts. [Means for solving the problem]

[0006] The system according to the embodiment includes a hobby and thought collection unit, a story generation unit, and an illustration generation unit. The hobby and thought collection unit uses a generation AI to collect information about a child's hobbies and thoughts. The story generation unit generates a story based on the information collected by the hobby and thought collection unit. The illustration generation unit generates illustrations that match the story generated by the story generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate personalized stories and illustrations that are tailored to a child's tastes and thoughts. [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 original book creation system according to an embodiment of the present invention is a system in which a generation AI generates stories and illustrations based on a child's hobbies and thoughts to create an original book. This allows the original book creation system to create an original book that matches a child's hobbies and thoughts.

[0029] The original book creation system according to the embodiment includes a hobby and thought collection unit, a story generation unit, and an illustration generation unit. The hobby and thought collection unit collects information about a child's hobbies and thoughts. For example, the information is collected from questionnaires provided by parents and teachers, or from data such as the child's favorite books, movies, and games. The hobby and thought collection unit can also collect behavioral data about the child's daily life using a sensor. For example, the sensor records the time and location of the child's play and inputs the data into the generation AI. The hobby and thought collection unit can also analyze the child's friendships and social activities and generate a story taking into account the hobbies and interests of the child's friends. For example, the hobbies and interests of the child's friends can be investigated and the data can be provided to the generation AI. The story generation unit generates a story based on the information collected by the hobby and thought collection unit. For example, the generation AI generates a story that matches the child's hobbies and thoughts based on the collected information. The generation AI generates a story using a text generation AI (e.g., LLM). The generation AI can also generate story content using a multimodal generation AI. For example, the generation AI generates an "adventure story featuring dinosaurs." The illustration generation unit generates illustrations that match the story generated by the story generation unit. For example, the generation AI generates illustrations that match the story. The generation AI generates illustrations using the image generation AI. The generation AI also generates appropriate illustrations based on the content of the story and integrates them with the story. For example, the generation AI generates "illustrations depicting dinosaurs and adventure scenes." This allows the original book generation system according to the embodiment to generate original books based on children's hobbies and thoughts.

[0030] The hobby and thought collection unit can collect behavioral data of children's daily lives using sensors and input it into the generation AI. For example, the hobby and thought collection unit records the time and place where children play using sensors and inputs that data into the generation AI. For example, the sensor can collect the time children spend playing in the park and the types of play equipment they use, and provide this to the generation AI. This makes it possible to collect hobbies and thoughts based on behavioral data of children's daily lives.

[0031] The hobby and thought collection unit can analyze a child's friendships and social activities and generate stories that take into account the hobbies and interests of their friends. The hobby and thought collection unit, for example, analyzes a child's friendships and collects information about the hobbies and interests of their friends. For example, it investigates the hobbies and interests of the friends the child plays with and provides that data to the generation AI. This makes it possible to generate stories that take into account the child's friendships and social activities.

[0032] The hobby and thought collection unit can understand hobbies and thoughts by inputting children's drawings and artwork into the generative AI in addition to questionnaires provided by parents and teachers. For example, the hobby and thought collection unit collects children's drawings and artwork into the generative AI in addition to questionnaires provided by parents and teachers. For example, it can analyze a child's drawing of a dinosaur to identify an interest in dinosaurs. This makes it possible to understand a child's hobbies and thoughts based on the children's drawings and artwork.

[0033] The hobby and thought collection unit can analyze the viewing history of videos and anime watched by children and identify themes of interest. For example, the hobby and thought collection unit collects the viewing history of videos and anime watched by children and inputs it into the generation AI. For example, it analyzes dinosaur anime that children frequently watch and identifies an interest in dinosaurs. This makes it possible to identify themes of interest based on the viewing history of videos and anime watched by children.

[0034] The story generation unit analyzes the child's past statements and conversations and can reflect topics of particular interest in the story. For example, the story generation unit collects the child's past statements and conversations and inputs them into the generation AI. For example, it can analyze what the child said about dinosaurs and generate a story featuring dinosaurs. This makes it possible to reflect topics of interest in the story based on the child's past statements and conversations.

[0035] The story generation unit can generate stories of an appropriate level of difficulty by taking into account the language level and theme according to the child's age and developmental stage. For example, the story generation unit sets a language level according to the child's age and developmental stage, and the generation AI generates stories of an appropriate level of difficulty. For example, it creates stories using simple language for young children. This makes it possible to generate stories of an appropriate level of difficulty according to the child's age and developmental stage.

[0036] The story generation unit can create stories from a global perspective, incorporating elements from different cultures and regions. The story generation unit generates stories that incorporate elements from different cultures and regions, for example. For example, it creates stories based on legends and folk tales from around the world. This makes it possible to create stories from a global perspective, incorporating elements from different cultures and regions.

[0037] The story generation unit can generate stories that not only feature characters and animals that children like, but also incorporate future technology and settings in other worlds. For example, the story generation unit can generate stories that not only feature characters and animals that children like, but also incorporate future technology. For example, it can create stories that feature robots and spaceships. This makes it possible to generate stories that incorporate future technology and settings in other worlds.

[0038] The illustration generation unit can analyze a child's drawings or a favorite art style and generate illustrations based on that. For example, the illustration generation unit collects drawings by children, analyzes their art style, and inputs it into the generation AI. For example, based on a child's drawing of a dinosaur, it generates an illustration in the same style. This allows illustrations to be generated based on a child's drawing or a favorite art style.

[0039] The illustration generation unit can analyze a child's visual preferences and generate illustrations that match them. For example, the illustration generation unit investigates a child's visual preferences and generates illustrations based on them. For example, it creates colorful and attractive illustrations based on the child's favorite colors and designs. This makes it possible to generate illustrations that match a child's visual preferences.

[0040] The illustration generation unit incorporates 3D modeling and animation when generating illustrations, allowing it to provide more three-dimensional, dynamic visuals. For example, the illustration generation unit incorporates 3D modeling when generating illustrations to provide three-dimensional visuals. For example, it creates a 3D model of a dinosaur and generates scenes that match the story. The illustration generation unit also incorporates animation to provide dynamic visuals. For example, it uses animation to represent scenes in which characters move. This allows it to provide three-dimensional, dynamic visuals that incorporate 3D modeling and animation.

[0041] The illustration generation unit can refer to an artist or character design that the child likes and generate an illustration in a similar style. For example, the illustration generation unit collects works by artists that the child likes and generates an illustration by referring to that style. For example, a character is created based on the style of an animator that the child likes. This makes it possible to generate an illustration in a style that refers to an artist or character design that the child likes.

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

[0043] The original book generation system further includes an audio collection unit. The audio collection unit collects what a child talks about on a daily basis and can analyze the audio data to identify hobbies and thoughts. For example, it can collect what a child talks about with family and friends and analyze their interests in specific topics. The audio collection unit can also identify the child's favorite songs and music genres and provide that data to the generation AI. This makes it possible to identify hobbies and thoughts based on the child's audio data and generate a more personalized story.

[0044] The original book creation system further includes a feedback collection unit. The feedback collection unit collects children's impressions and evaluations of completed books after they have read them, and this data can be reflected in the next book creation. For example, after reading a book, children input their favorite parts and areas for improvement as feedback. The feedback collection unit also collects evaluations from parents and teachers, which can be used as data to improve overall satisfaction. This makes it possible to create original books that are more satisfying based on children's feedback.

[0045] The original book creation system further includes a learning progress tracking unit. The learning progress tracking unit tracks a child's learning progress and grades, and can adjust the content of the story based on that data. For example, it can generate an educational story that complements weak areas in a particular subject. The learning progress tracking unit can also provide a story that matches the child's learning style and pace. This makes it possible to create an original book that takes the child's learning progress into consideration.

[0046] The original book creation system further includes a health data collection unit. The health data collection unit collects information about a child's health condition and exercise habits, and can adjust the content of the story based on that data. For example, if a child is not getting enough exercise, a story encouraging exercise is created. The health data collection unit can also analyze a child's eating habits and sleep patterns to provide a story supporting a healthy lifestyle. This makes it possible to create an original book that takes a child's health condition into consideration.

[0047] The original book creation system further includes an environmental data collection unit. The environmental data collection unit collects environmental data about the area where the child lives and can adjust the content of the story based on that data. For example, a story is created that reflects the climate and natural environment of the area where the child lives. The environmental data collection unit can also provide a story based on the culture and history of the area. This makes it possible to create an original book that takes into account the environment in which the child lives.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The hobby and thought collection unit collects information about the child's hobbies and thoughts. For example, it collects information from questionnaires provided by parents and teachers, or data such as the child's favorite books, movies, and games. The hobby and thought collection unit can also collect behavioral data from the child's daily life using sensors. For example, it can use sensors to record the time and place the child is playing and input that data into the generation AI. Furthermore, the hobby and thought collection unit can analyze the child's friendships and social activities and generate a story that takes into account the hobbies and interests of their friends. For example, it can investigate the hobbies and interests of the friends the child plays with and provide that data to the generation AI. Step 2: The story generation unit generates a story based on the information collected by the hobby and thought collection unit. For example, the generation AI generates a story that matches the child's hobbies and thoughts based on the collected information. The generation AI generates the story using a text generation AI (e.g., LLM). The generation AI can also generate the content of the story using a multimodal generation AI. For example, the generation AI generates an "adventure story featuring dinosaurs." Step 3: The illustration generation unit generates illustrations that match the story generated by the story generation unit. For example, the generation AI generates illustrations that match the story. The generation AI generates illustrations using the image generation AI. The generation AI also generates appropriate illustrations based on the content of the story and integrates them with the story. For example, the generation AI generates "illustrations depicting dinosaurs and adventure scenes."

[0050] (Example 2) The original book creation system according to an embodiment of the present invention is a system in which a generation AI generates stories and illustrations based on a child's hobbies and thoughts to create an original book. This allows the original book creation system to create an original book that matches a child's hobbies and thoughts.

[0051] The original book creation system according to the embodiment includes a hobby and thought collection unit, a story generation unit, and an illustration generation unit. The hobby and thought collection unit collects information about a child's hobbies and thoughts. For example, the information is collected from questionnaires provided by parents and teachers, or from data such as the child's favorite books, movies, and games. The hobby and thought collection unit can also collect behavioral data about the child's daily life using a sensor. For example, the sensor records the time and location of the child's play and inputs the data into the generation AI. The hobby and thought collection unit can also analyze the child's friendships and social activities and generate a story taking into account the hobbies and interests of the child's friends. For example, the hobbies and interests of the child's friends can be investigated and the data can be provided to the generation AI. The story generation unit generates a story based on the information collected by the hobby and thought collection unit. For example, the generation AI generates a story that matches the child's hobbies and thoughts based on the collected information. The generation AI generates a story using a text generation AI (e.g., LLM). The generation AI can also generate story content using a multimodal generation AI. For example, the generation AI generates an "adventure story featuring dinosaurs." The illustration generation unit generates illustrations that match the story generated by the story generation unit. For example, the generation AI generates illustrations that match the story. The generation AI generates illustrations using the image generation AI. The generation AI also generates appropriate illustrations based on the content of the story and integrates them with the story. For example, the generation AI generates "illustrations depicting dinosaurs and adventure scenes." This allows the original book generation system according to the embodiment to generate original books based on children's hobbies and thoughts.

[0052] The hobby and thought collection unit can collect behavioral data of children's daily lives using sensors and input it into the generation AI. For example, the hobby and thought collection unit records the time and place where children play using sensors and inputs that data into the generation AI. For example, the sensor can collect the time children spend playing in the park and the types of play equipment they use, and provide this to the generation AI. This makes it possible to collect hobbies and thoughts based on behavioral data of children's daily lives.

[0053] The hobby and thought collection unit can analyze a child's friendships and social activities and generate stories that take into account the hobbies and interests of their friends. The hobby and thought collection unit, for example, analyzes a child's friendships and collects information about the hobbies and interests of their friends. For example, it investigates the hobbies and interests of the friends the child plays with and provides that data to the generation AI. This makes it possible to generate stories that take into account the child's friendships and social activities.

[0054] The hobby and thought collection unit can use the emotion estimation function to analyze what emotions a child has about a specific topic and collect hobbies and thoughts based on those emotions. The hobby and thought collection unit can, for example, use the emotion estimation function to analyze in real time what emotions a child has about a specific topic. For example, it analyzes whether a child is excited or happy about the topic of dinosaurs. This makes it possible to collect hobbies and thoughts based on the child's emotions.

[0055] The hobby and thought collection unit can understand hobbies and thoughts by inputting children's drawings and artwork into the generative AI in addition to questionnaires provided by parents and teachers. For example, the hobby and thought collection unit collects children's drawings and artwork into the generative AI in addition to questionnaires provided by parents and teachers. For example, it can analyze a child's drawing of a dinosaur to identify an interest in dinosaurs. This makes it possible to understand a child's hobbies and thoughts based on the children's drawings and artwork.

[0056] The hobby and thought collection unit can analyze the viewing history of videos and anime watched by children and identify themes of interest. For example, the hobby and thought collection unit collects the viewing history of videos and anime watched by children and inputs it into the generation AI. For example, it analyzes dinosaur anime that children frequently watch and identifies an interest in dinosaurs. This makes it possible to identify themes of interest based on the viewing history of videos and anime watched by children.

[0057] The hobby and thought collection unit uses the emotion estimation function to collect emotional responses to books read by the child and movies watched by the child, and can identify the child's hobbies and thoughts based on the data. The hobby and thought collection unit, for example, uses the emotion estimation function to collect emotional responses to books read by the child. For example, the hobby and thought collection unit analyzes the emotions felt when the child reads a book about dinosaurs and identifies the child's interest in dinosaurs. This makes it possible to identify the child's hobbies and thoughts based on the child's emotional responses to books read and movies watched.

[0058] The story generation unit analyzes the child's past statements and conversations and can reflect topics of particular interest in the story. For example, the story generation unit collects the child's past statements and conversations and inputs them into the generation AI. For example, it can analyze what the child said about dinosaurs and generate a story featuring dinosaurs. This makes it possible to reflect topics of interest in the story based on the child's past statements and conversations.

[0059] The story generation unit can generate stories of an appropriate level of difficulty by taking into account the language level and theme according to the child's age and developmental stage. For example, the story generation unit sets a language level according to the child's age and developmental stage, and the generation AI generates stories of an appropriate level of difficulty. For example, it creates stories using simple language for young children. This makes it possible to generate stories of an appropriate level of difficulty according to the child's age and developmental stage.

[0060] The story generation unit can use the emotion estimation function to generate a story development that will make the child feel the most positive emotions. The story generation unit, for example, uses the emotion estimation function to generate a story development that will make the child feel the most positive emotions. For example, the story generation unit creates a story that includes many scenes that make the child feel joy and excitement. This makes it possible to generate a story development that will make the child feel the most positive emotions.

[0061] The story generation unit can create stories from a global perspective, incorporating elements from different cultures and regions. The story generation unit generates stories that incorporate elements from different cultures and regions, for example. For example, it creates stories based on legends and folk tales from around the world. This makes it possible to create stories from a global perspective, incorporating elements from different cultures and regions.

[0062] The story generation unit can generate stories that not only feature characters and animals that children like, but also incorporate future technology and settings in other worlds. For example, the story generation unit can generate stories that not only feature characters and animals that children like, but also incorporate future technology. For example, it can create stories that feature robots and spaceships. This makes it possible to generate stories that incorporate future technology and settings in other worlds.

[0063] The story generation unit can use the emotion estimation function to monitor the child's emotional reaction to each scene in the story in real time and adjust the optimal story development. The story generation unit, for example, uses the emotion estimation function to monitor the child's emotional reaction to each scene in the story in real time. For example, it analyzes what emotions the child is feeling in a particular scene. This allows the child's emotional reaction to each scene in the story to be monitored in real time and the optimal story development to be adjusted.

[0064] The illustration generation unit can analyze a child's drawings or a favorite art style and generate illustrations based on that. For example, the illustration generation unit collects drawings by children, analyzes their art style, and inputs it into the generation AI. For example, based on a child's drawing of a dinosaur, it generates an illustration in the same style. This allows illustrations to be generated based on a child's drawing or a favorite art style.

[0065] The illustration generation unit can analyze a child's visual preferences and generate illustrations that match them. For example, the illustration generation unit investigates a child's visual preferences and generates illustrations based on them. For example, it creates colorful and attractive illustrations based on the child's favorite colors and designs. This makes it possible to generate illustrations that match a child's visual preferences.

[0066] The illustration generation unit incorporates 3D modeling and animation when generating illustrations, allowing it to provide more three-dimensional, dynamic visuals. For example, the illustration generation unit incorporates 3D modeling when generating illustrations to provide three-dimensional visuals. For example, it creates a 3D model of a dinosaur and generates scenes that match the story. The illustration generation unit also incorporates animation to provide dynamic visuals. For example, it uses animation to represent scenes in which characters move. This allows it to provide three-dimensional, dynamic visuals that incorporate 3D modeling and animation.

[0067] The illustration generation unit can refer to an artist or character design that the child likes and generate an illustration in a similar style. For example, the illustration generation unit collects works by artists that the child likes and generates an illustration by referring to that style. For example, a character is created based on the style of an animator that the child likes. This makes it possible to generate an illustration in a style that refers to an artist or character design that the child likes.

[0068] The illustration generation unit can use the emotion estimation function to monitor the child's emotional response to the illustration in real time and adjust the optimal design. The illustration generation unit, for example, uses the emotion estimation function to monitor the child's emotional response to the illustration in real time. For example, it analyzes what emotion the child has toward a specific illustration. The illustration generation unit also uses the emotion estimation function to adjust the optimal design. For example, it changes the design to one that evokes the child's most positive emotion. This allows the child's emotional response to the illustration to be monitored in real time and adjust the optimal design.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The original book generation system further includes an audio collection unit. The audio collection unit collects what a child talks about on a daily basis and can analyze the audio data to identify hobbies and thoughts. For example, it can collect what a child talks about with family and friends and analyze their interests in specific topics. The audio collection unit can also identify the child's favorite songs and music genres and provide that data to the generation AI. This makes it possible to identify hobbies and thoughts based on the child's audio data and generate a more personalized story.

[0071] The original book creation system further includes a feedback collection unit. The feedback collection unit collects children's impressions and evaluations of completed books after they have read them, and this data can be reflected in the next book creation. For example, after reading a book, children input their favorite parts and areas for improvement as feedback. The feedback collection unit also collects evaluations from parents and teachers, which can be used as data to improve overall satisfaction. This makes it possible to create original books that are more satisfying based on children's feedback.

[0072] The original book creation system further includes a learning progress tracking unit. The learning progress tracking unit tracks a child's learning progress and grades, and can adjust the content of the story based on that data. For example, it can generate an educational story that complements weak areas in a particular subject. The learning progress tracking unit can also provide a story that matches the child's learning style and pace. This makes it possible to create an original book that takes the child's learning progress into consideration.

[0073] The original book creation system further includes a health data collection unit. The health data collection unit collects information about a child's health condition and exercise habits, and can adjust the content of the story based on that data. For example, if a child is not getting enough exercise, a story encouraging exercise is created. The health data collection unit can also analyze a child's eating habits and sleep patterns to provide a story supporting a healthy lifestyle. This makes it possible to create an original book that takes a child's health condition into consideration.

[0074] The original book creation system further includes an environmental data collection unit. The environmental data collection unit collects environmental data about the area where the child lives and can adjust the content of the story based on that data. For example, a story is created that reflects the climate and natural environment of the area where the child lives. The environmental data collection unit can also provide a story based on the culture and history of the area. This makes it possible to create an original book that takes into account the environment in which the child lives.

[0075] The original book generation system can further use its emotion estimation function to analyze the child's feelings toward a particular character and adjust the character's role and behavior based on those feelings. For example, if a child has positive feelings toward a particular character, the system can make that character the center of the story. Alternatively, if a child feels fear or anxiety toward a particular character, the system can reduce the number of scenes in which that character appears. This makes it possible to generate an original book in which the character's role and behavior are adjusted based on the child's feelings.

[0076] The original book generation system also uses an emotion estimation function to monitor in real time how a child feels about specific scenes in the story and adjust the development of the scenes based on those emotions. For example, if a child feels excited or happy about a particular scene, the system can extend that scene or add a similar scene. Also, if a child feels anxious or scared about a particular scene, the system can shorten that scene or change its content. This makes it possible to generate an original book in which the development of scenes is adjusted based on the child's emotions.

[0077] The original book generation system can also use an emotion estimation function to analyze how a child feels about the ending of the story and adjust the ending based on that emotion. For example, if a child prefers happy endings, the system can make the story have a positive ending. Or, if a child likes suspense or surprises, it can prepare an unexpected ending. This makes it possible to generate an original book with an ending adjusted based on the child's emotions.

[0078] The original book generation system can further use an emotion estimation function to analyze how a child feels about the story theme and adjust the theme based on that emotion. For example, if a child likes adventure or fantasy, it can generate a story centered around that theme. Alternatively, if a child prefers realistic themes, it can provide stories depicting everyday life. This makes it possible to generate an original book with a theme adjusted based on the child's emotions.

[0079] The original book generation system can further use the emotion estimation function to identify scenes in the story to which a child has a particularly emotional reaction and emphasize those scenes. For example, if a child feels strong joy or excitement in a particular scene, the system can describe that scene in detail. Also, if a child is moved or sheds tears in a particular scene, the system can make that scene more moving. This makes it possible to generate an original book that emphasizes scenes that show a particularly emotional reaction based on the child's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The hobby and thought collection unit collects information about the child's hobbies and thoughts. For example, it collects information from questionnaires provided by parents and teachers, or data such as the child's favorite books, movies, and games. The hobby and thought collection unit can also collect behavioral data from the child's daily life using sensors. For example, it can use sensors to record the time and place the child is playing and input that data into the generation AI. Furthermore, the hobby and thought collection unit can analyze the child's friendships and social activities and generate a story that takes into account the hobbies and interests of their friends. For example, it can investigate the hobbies and interests of the friends the child plays with and provide that data to the generation AI. Step 2: The story generation unit generates a story based on the information collected by the hobby and thought collection unit. For example, the generation AI generates a story that matches the child's hobbies and thoughts based on the collected information. The generation AI generates the story using a text generation AI (e.g., LLM). The generation AI can also generate the content of the story using a multimodal generation AI. For example, the generation AI generates an "adventure story featuring dinosaurs." Step 3: The illustration generation unit generates illustrations that match the story generated by the story generation unit. For example, the generation AI generates illustrations that match the story. The generation AI generates illustrations using the image generation AI. The generation AI also generates appropriate illustrations based on the content of the story and integrates them with the story. For example, the generation AI generates "illustrations depicting dinosaurs and adventure scenes."

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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. Using generative AI, The Hobby and Thought Collection Department collects information on children's hobbies and thoughts, a story generation unit that generates a story based on the information collected by the hobby and thought collection unit; an illustration generation unit that generates illustrations that match the story generated by the story generation unit; A system characterized by:

2. The hobby thought collection unit Analyze how your child feels about a particular topic and gather their hobbies and thoughts based on those feelings 2. The system of claim 1.

3. The hobby thought collection unit Analyze your child's viewing history of videos and anime to identify topics of interest 2. The system of claim 1.

4. The story generation unit Analyzes the child's past statements and conversations, and incorporates topics of particular interest into the story 2. The system of claim 1.

5. The story generation unit Generate storylines that evoke the most positive emotions in children 2. The system of claim 1.

6. The illustration generation unit Analyze your child's drawings or your favorite art style and generate illustrations based on them 2. The system of claim 1.

7. The illustration generation unit Identify the style or theme of illustrations that evokes the most positive emotions in children and generate illustrations based on that.

2. The system of claim 1.

8. The illustration generation unit Monitor children's emotional responses to illustrations in real time and adjust the design to suit them.

2. The system of claim 1.

Images