System

Abstract

An object of the system according to the embodiment is to create an original story or picture book according to an individual personality.SOLUTION: A system includes an interaction unit, a generation unit, and a picture book creation unit. The interaction unit gives an instruction related to the development of the story in an interactive manner using the generated AI. The generation section generates a story based on the content instructed by the interaction section. The picture book creation section creates an original picture book based on the story generated by the generation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to create original stories and picture books that suited individual personalities.

[0005] The system according to the embodiment aims to create original stories and picture books that suit individual personalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a generation unit, and a picture book creation unit. The dialogue unit uses a generation AI to interactively give instructions regarding the development of the story. The generation unit generates a story based on the instructions given by the dialogue unit. The picture book creation unit creates an original picture book based on the story generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can create original stories and picture books that suit individual personalities. [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 story generation system according to an embodiment of the present invention is a system that uses a generation AI to interactively give instructions regarding the development of a story, generate a story, and create an original picture book. As a result, the story generation system generates a story based on the user's instructions and creates an original picture book, thereby expanding children's imagination.

[0029] A story generation system according to an embodiment includes a dialogue unit, a generation unit, and a picture book creation unit. The dialogue unit interactively gives instructions regarding the development of a story using a generation AI. For example, the dialogue unit determines the development of a story when a user asks, "What kind of personality does the main character have?" and the user answers, "A brave knight." The dialogue unit can also determine the development of a story when the user asks, "What adventure will you have next?" and the user answers, "I will fight a dragon." The dialogue unit can also determine the development of a story when the user asks, "What kind of animal is the main character?" and the user answers, "A rabbit." The generation unit generates a story based on the content of instructions provided by the dialogue unit. For example, if the user instructs the generation unit that "The main character is a brave knight who fights a dragon," the generation AI analyzes the instruction and generates a story in which a brave knight fights a dragon. If the user instructs the generation unit that "A rabbit searches for treasure in the forest," the generation AI can analyze the instruction and generate a story in which a rabbit searches for treasure in the forest. Furthermore, the generation unit can ask a user, "What kind of personality does the main character have?" and the user answers, "A brave knight," and the generation AI can analyze this instruction and generate a story in which a brave knight fights a dragon. The picture book creation unit creates an original picture book based on the story generated by the generation unit. For example, the picture book creation unit adds illustrations to match the generated story and formats it into a picture book. The picture book creation unit can also add illustrations to match the generated story and format it into a picture book. The picture book creation unit can also add illustrations to match the generated story and format it into a picture book. In this way, the story generation system according to the embodiment generates a story based on a user's instruction and creates an original picture book, thereby developing a child's imagination. For example, the story generation system can create an original picture book based on the generated story and develop a child's imagination. The story generation system can also create an original picture book based on the generated story and develop a child's imagination. The story generation system can also create an original picture book based on the generated story and develop a child's imagination.

[0030] The dialogue unit can generate questions optimized for individual users based on the user's past dialogue history. For example, the dialogue unit uses a generation AI to analyze the user's past dialogue history and generate questions based on the user's favorite themes and characters. The dialogue unit also learns the story development patterns selected by the user in the past and generates new questions based on that. For example, if the user likes adventure stories, the dialogue unit can preferentially generate questions related to adventure. The dialogue unit also uses a generation AI to refer to the user's past dialogue history and generate questions that include elements in which the user previously showed interest. For example, if the user is interested in magic, it can generate questions related to magic. This allows the dialogue unit to provide a more personalized story development by generating questions optimized based on the user's past dialogue history.

[0031] The dialogue unit can have a function that allows multiple users to converse simultaneously and collaboratively create a story. For example, the dialogue unit uses a generation AI to simultaneously analyze input from multiple users and generate a story that reflects each of their opinions. For example, a story can be created together with family and friends. In addition, multiple users take turns instructing the development of the story, and the generation AI generates the story based on that. For example, user A can decide the personality of the protagonist, and user B can decide the content of the adventure. In addition, the dialogue unit uses a generation AI to integrate the dialogues of multiple users and generate a collaboratively created story. For example, a story can be created together with classmates. This allows multiple users to collaboratively create a story, providing a wider variety of story developments.

[0032] When analyzing a user's instructions, the generation unit can automatically detect ambiguity in the instructions and return specific questions. For example, the generation unit uses a generation AI to analyze the user's instructions, automatically detect ambiguous parts, and return specific questions. For example, the generation unit may ask, "What kind of personality does the main character have?" If the user's instructions are ambiguous, the generation AI may present specific options to allow the user to make a selection. For example, the generation unit may ask, "Is the main character brave? Or kind?" The generation unit may also use a generation AI to analyze the user's instructions and generate specific questions to fill in the ambiguous parts. For example, the generation unit may ask, "What adventure will you go on next?" This allows the generation AI to automatically detect ambiguity in the user's instructions and return specific questions, thereby providing a clearer story development.

[0033] The generation unit can generate multiple story variations based on user instructions and provide the user with options. For example, the generation unit generates stories with different endings by using a generation AI to generate multiple story variations based on user instructions and provide the user with options. For example, the generation unit generates stories with different endings. The generation unit can also generate different scenarios based on user instructions and allow the user to select one. For example, it can generate scenarios with different adventure content or character personalities. The generation unit can also generate multiple story variations based on user instructions and allow the user to select the one they like best. For example, it can generate stories with different settings or themes. This allows the generation of multiple story variations and provides the user with options, thereby providing a wider variety of story developments.

[0034] The generation unit can apply a different style or genre to each story scene based on the user's instructions. For example, the generation AI can apply a different style or genre to each story scene based on the user's instructions. For example, an action style for adventure scenes and a drama style for emotional scenes. The generation unit can also apply a different genre to each story scene based on the user's instructions, allowing the user to select. For example, a horror scene or a comedy scene can be selected. The generation unit can also apply a different style or genre to each story scene based on the user's instructions, increasing the diversity of the story. For example, a fantasy style or a science fiction style can be applied. This allows the generation AI to apply a different style or genre to each story scene, thereby providing a more diverse story development.

[0035] The generation unit can support various input methods, such as voice input or handwriting input, when a user inputs instructions. For example, the generation unit may support voice input so that the generation AI can input story instructions by speaking. For example, instructions can be input using a microphone. The generation unit may also enable a user to input story instructions using handwriting input. For example, instructions can be input by hand using a tablet or stylus pen. The generation unit may also support various input methods so that the generation AI can input story instructions in a way that is most convenient for the user. For example, keyboard input or touch input can be supported. This allows the generation AI to support various input methods so that the user can input story instructions in a way that is most convenient for the user.

[0036] The picture book creation unit can automatically generate detailed illustrations for each scene based on the story generated by the generation AI. For example, the picture book creation unit automatically generates detailed illustrations based on each scene of the story generated by the generation AI. For example, it generates illustrations that match the adventure scenes of the story. Furthermore, the picture book creation unit automatically generates illustrations in different styles for each scene of the story based on instructions from the user. For example, it can generate illustrations in a fantasy style or a realistic style. Furthermore, the picture book creation unit automatically generates detailed background and character illustrations for each scene according to the content of the story. For example, it can draw detailed scenes in the forest or scenes fighting a dragon. This makes it possible to provide a more visually appealing picture book by automatically generating detailed illustrations for each scene.

[0037] The picture book creation unit allows a user to select a specific scene in a story and generate detailed illustrations or animations for that scene. For example, the picture book creation unit allows a user to select a specific scene in a story and the generation AI generates detailed illustrations for that scene. For example, an illustration is generated that matches an adventure scene selected by the user. The picture book creation unit also allows the generation AI to generate animations for specific scenes based on the user's selection. For example, it is possible to animate scenes in which characters move or scenes in which the background changes. The picture book creation unit also allows a user to select a specific scene in a story and the generation AI generates detailed background and character illustrations for that scene. For example, it is possible to draw detailed backgrounds that match the scene selected by the user. This allows a more interactive picture book to be provided by allowing a user to select a specific scene and generate detailed illustrations and animations for that scene.

[0038] The picture book creation unit can create an interactive digital picture book based on the story generated by the generation AI, allowing the user to directly participate in the story. For example, the picture book creation unit creates an interactive digital picture book based on the story generated by the generation AI, allowing the user to choose how the story unfolds. For example, the story changes depending on the options selected by the user. The picture book creation unit also creates an interactive digital picture book that allows the user to control the characters in the story. For example, the user can move the characters to explore the story scenes. The picture book creation unit also creates an interactive digital picture book based on the story generated by the generation AI, allowing the user to customize the story scenes. For example, the user can change the background of a scene or the costumes of the characters. This makes it possible to provide a more immersive story experience by creating an interactive digital picture book that allows the user to directly participate in the story.

[0039] The picture book creation unit can add audio narration or sound effects based on the story generated by the generation AI to create a more realistic picture book. For example, the picture book creation unit adds audio narration to the story generated by the generation AI to create a picture book that users can enjoy while listening to the story. For example, narration by a professional voice actor can be added. The picture book creation unit can also add sound effects to the story generated by the generation AI to provide sound effects that match the scenes in the story. For example, the sound of wind or footsteps can be added to adventure scenes. The picture book creation unit can also add audio narration and sound effects to the story generated by the generation AI to create a picture book that users can feel more realistic about. For example, character lines and background sounds can be added. In this way, by adding audio narration and sound effects, a more realistic picture book can be provided.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The dialogue unit can generate questions optimized for individual users based on the user's past dialogue history. For example, the generation AI analyzes the user's past dialogue history and generates questions based on the user's favorite themes and characters. The dialogue unit also learns the story development patterns selected by the user in the past and generates new questions based on that. For example, if the user likes adventure stories, it can preferentially generate questions related to adventure. The dialogue unit also references the user's past dialogue history and generates questions that include elements in which the user previously expressed interest. For example, if the user is interested in magic, it can generate questions related to magic. This allows for the generation of questions optimized based on the user's past dialogue history, providing a more personalized story development.

[0042] The dialogue unit can have the function of allowing multiple users to converse simultaneously and collaboratively create a story. For example, the generation AI can analyze the input of multiple users simultaneously and generate a story that reflects each of their opinions. For example, a story can be created together with family and friends. The dialogue unit also allows multiple users to take turns instructing the development of the story, and the generation AI generates the story based on that. For example, user A can decide the personality of the protagonist, and user B can decide the content of the adventure. The dialogue unit also allows the generation AI to integrate the dialogues of multiple users and generate a collaboratively created story. For example, a story can be created together with classmates. This allows multiple users to collaboratively create a story, providing a wider variety of story developments.

[0043] When analyzing a user's instructions, the generation unit can automatically detect ambiguity in the instructions and return specific questions. For example, the generation AI analyzes the user's instructions, automatically detects ambiguity, and returns specific questions. For example, it could ask, "What kind of personality does the main character have?" Furthermore, if the user's instructions are ambiguous, the generation AI presents specific options to allow the user to make a selection. For example, it could ask, "Is the main character brave? Or kind?" Furthermore, the generation unit can analyze the user's instructions and generate specific questions to fill in the ambiguity. For example, it could ask, "What adventure will you go on next?" This allows the generation AI to automatically detect ambiguity in the user's instructions and return specific questions, thereby providing a clearer story development.

[0044] The generation unit can generate multiple story variations based on user instructions and provide the user with choices. For example, the generation AI can generate multiple story variations based on user instructions and provide the user with choices. For example, it can generate stories with different endings. The generation unit can also generate different scenarios based on user instructions and allow the user to select one. For example, it can generate scenarios with different adventure content or character personalities. The generation unit can also generate multiple story variations based on user instructions and allow the user to select the one they like best. For example, it can generate stories with different settings or themes. This allows the generation AI to generate multiple story variations and provide the user with choices, thereby providing a wider variety of story developments.

[0045] The generation unit can apply a different style or genre to each story scene based on the user's instructions. For example, the generation AI can apply a different style or genre to each story scene based on the user's instructions. For example, an action style for adventure scenes and a drama style for emotional scenes. The generation unit can also apply a different genre to each story scene based on the user's instructions, allowing the user to select. For example, a horror scene or a comedy scene can be selected. The generation unit can also apply a different style or genre to each story scene based on the user's instructions, increasing the diversity of the story. For example, a fantasy style or a science fiction style can be applied. This allows the generation AI to apply a different style or genre to each story scene, thereby providing a more diverse story development.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The dialogue unit uses the generation AI to interactively give instructions regarding the development of the story. For example, the user may ask, "What kind of personality does the main character have?" and the user's answer, "A brave knight," will determine the development of the story. The user may also ask, "What adventure will you have next?" and the user's answer, "Fight a dragon," will determine the development of the story. Furthermore, the user may ask, "What kind of animal is the main character?" and the user's answer, "A rabbit," will determine the development of the story. Step 2: The generation unit generates a story based on the instructions given by the dialogue unit. For example, if the user instructs, "The main character is a brave knight who fights a dragon," the generation AI analyzes this instruction and generates a story about a brave knight fighting a dragon. Alternatively, if the user instructs, "A rabbit searches for treasure in the forest," the generation AI can analyze this instruction and generate a story about a rabbit searching for treasure in the forest. Step 3: The picture book creation unit creates an original picture book based on the story generated by the generation unit. For example, it adds illustrations to match the generated story and formats it into a picture book. This allows the user to obtain an original picture book.

[0048] (Example 2) The story generation system according to an embodiment of the present invention is a system that uses a generation AI to interactively give instructions regarding the development of a story, generate a story, and create an original picture book. As a result, the story generation system generates a story based on the user's instructions and creates an original picture book, thereby expanding children's imagination.

[0049] A story generation system according to an embodiment includes a dialogue unit, a generation unit, and a picture book creation unit. The dialogue unit interactively gives instructions regarding the development of a story using a generation AI. For example, the dialogue unit determines the development of a story when a user asks, "What kind of personality does the main character have?" and the user answers, "A brave knight." The dialogue unit can also determine the development of a story when the user asks, "What adventure will you have next?" and the user answers, "I will fight a dragon." The dialogue unit can also determine the development of a story when the user asks, "What kind of animal is the main character?" and the user answers, "A rabbit." The generation unit generates a story based on the content of instructions provided by the dialogue unit. For example, if the user instructs the generation unit that "The main character is a brave knight who fights a dragon," the generation AI analyzes the instruction and generates a story in which a brave knight fights a dragon. If the user instructs the generation unit that "A rabbit searches for treasure in the forest," the generation AI can analyze the instruction and generate a story in which a rabbit searches for treasure in the forest. Furthermore, the generation unit can ask a user, "What kind of personality does the main character have?" and the user answers, "A brave knight," and the generation AI can analyze this instruction and generate a story in which a brave knight fights a dragon. The picture book creation unit creates an original picture book based on the story generated by the generation unit. For example, the picture book creation unit adds illustrations to match the generated story and formats it into a picture book. The picture book creation unit can also add illustrations to match the generated story and format it into a picture book. The picture book creation unit can also add illustrations to match the generated story and format it into a picture book. In this way, the story generation system according to the embodiment generates a story based on a user's instruction and creates an original picture book, thereby developing a child's imagination. For example, the story generation system can create an original picture book based on the generated story and develop a child's imagination. The story generation system can also create an original picture book based on the generated story and develop a child's imagination. The story generation system can also create an original picture book based on the generated story and develop a child's imagination.

[0050] The dialogue unit can analyze the tone or speed of the user's voice and dynamically generate questions that correspond to the user's emotional state. For example, the dialogue unit's generation AI analyzes the user's tone and speed in real time, generating adventurous questions if the user is excited and calm questions if the user is calm. The dialogue unit can also suggest tense scenes if the user's voice tone is high, and relaxing scenes if the user's voice tone is low. The dialogue unit can also analyze the user's voice speed and suggest a fast-paced story development if the user is speaking quickly, and a slow-paced story development if the user is speaking slowly. This allows the dialogue unit to generate questions that correspond to the user's emotional state, thereby providing a more appropriate story development.

[0051] The dialogue unit can generate questions optimized for individual users based on the user's past dialogue history. For example, the dialogue unit uses a generation AI to analyze the user's past dialogue history and generate questions based on the user's favorite themes and characters. The dialogue unit also learns the story development patterns selected by the user in the past and generates new questions based on that. For example, if the user likes adventure stories, the dialogue unit can preferentially generate questions related to adventure. The dialogue unit also uses a generation AI to refer to the user's past dialogue history and generate questions that include elements in which the user previously showed interest. For example, if the user is interested in magic, it can generate questions related to magic. This allows the dialogue unit to provide a more personalized story development by generating questions optimized based on the user's past dialogue history.

[0052] The dialogue unit can use the emotion estimation function to suggest story developments based on the user's emotions. For example, the generation AI in the dialogue unit analyzes the user's emotions in real time and suggests positive developments if the user is happy, and moving developments if the user is sad. The dialogue unit also adjusts the tone and theme of the story according to the user's emotional state. For example, it can suggest action scenes if the user is excited, and calm scenes if the user is relaxed. The dialogue unit also uses the emotion estimation function to suggest story developments that the user can easily empathize with. For example, it can generate stories that include scenes that are likely to move the user. In this way, by suggesting story developments based on the user's emotions, it is possible to provide a story that the user can easily empathize with emotionally.

[0053] The dialogue unit can analyze the user's gestures or facial expressions and adjust the story development based on them. For example, the dialogue unit's generation AI can analyze the user's facial expressions and suggest a humorous development if the user is smiling, and a serious development if the user is serious. The dialogue unit can also analyze the user's gestures and suggest active scenes if the user frequently waves their hands, and calm scenes if the user is quiet. The dialogue unit's generation AI can also analyze the user's facial expressions and gestures in real time and dynamically adjust the story development based on the user's reactions. For example, it can add a surprise element if the user expresses surprise. This allows the dialogue unit to provide a more interactive story experience by adjusting the story development based on the user's gestures and facial expressions.

[0054] The dialogue unit can have a function that allows multiple users to converse simultaneously and collaboratively create a story. For example, the dialogue unit uses a generation AI to simultaneously analyze input from multiple users and generate a story that reflects each of their opinions. For example, a story can be created together with family and friends. In addition, multiple users take turns instructing the development of the story, and the generation AI generates the story based on that. For example, user A can decide the personality of the protagonist, and user B can decide the content of the adventure. In addition, the dialogue unit uses a generation AI to integrate the dialogues of multiple users and generate a collaboratively created story. For example, a story can be created together with classmates. This allows multiple users to collaboratively create a story, providing a wider variety of story developments.

[0055] The dialogue unit can use the emotion estimation function to analyze the user's emotions in real time during the dialogue and generate questions that elicit positive emotions. For example, the dialogue unit uses a generation AI to analyze the user's emotions in real time and generate questions that elicit positive emotions. For example, it generates humorous questions that will make the user smile. The dialogue unit also suggests scenarios that the generation AI will use to elicit positive emotions depending on the user's emotional state. For example, it can suggest calm scenes that will help the user relax. The dialogue unit also uses the emotion estimation function to suggest story developments that will evoke positive emotions in the user. For example, it can generate stories that include scenes that will move the user. This makes it possible to provide a better story experience by analyzing the user's emotions in real time and generating questions that elicit positive emotions.

[0056] When analyzing a user's instructions, the generation unit can automatically detect ambiguity in the instructions and return specific questions. For example, the generation unit uses a generation AI to analyze the user's instructions, automatically detect ambiguous parts, and return specific questions. For example, the generation unit may ask, "What kind of personality does the main character have?" If the user's instructions are ambiguous, the generation AI may present specific options to allow the user to make a selection. For example, the generation unit may ask, "Is the main character brave? Or kind?" The generation unit may also use a generation AI to analyze the user's instructions and generate specific questions to fill in the ambiguous parts. For example, the generation unit may ask, "What adventure will you go on next?" This allows the generation AI to automatically detect ambiguity in the user's instructions and return specific questions, thereby providing a clearer story development.

[0057] The generation unit can generate multiple story variations based on user instructions and provide the user with options. For example, the generation unit generates stories with different endings by using a generation AI to generate multiple story variations based on user instructions and provide the user with options. For example, the generation unit generates stories with different endings. The generation unit can also generate different scenarios based on user instructions and allow the user to select one. For example, it can generate scenarios with different adventure content or character personalities. The generation unit can also generate multiple story variations based on user instructions and allow the user to select the one they like best. For example, it can generate stories with different settings or themes. This allows the generation of multiple story variations and provides the user with options, thereby providing a wider variety of story developments.

[0058] The generation unit can use the emotion estimation function to adjust the tone or theme of the story according to the user's emotions. For example, the generation AI analyzes the user's emotions in real time and generates a story with a positive tone if the user is happy, or an emotional one if the user is sad. The generation unit also adjusts the theme of the story according to the user's emotional state. For example, it can select an action theme if the user is excited, or a calm theme if the user is relaxed. The generation unit also uses the emotion estimation function to adjust the tone and theme of the story to one that the user can easily empathize with. For example, it can generate a story that includes scenes that the user is likely to be moved by. In this way, by adjusting the tone and theme of the story according to the user's emotions, it is possible to provide a story that the user can easily empathize with emotionally.

[0059] The generation unit can apply a different style or genre to each story scene based on the user's instructions. For example, the generation AI can apply a different style or genre to each story scene based on the user's instructions. For example, an action style for adventure scenes and a drama style for emotional scenes. The generation unit can also apply a different genre to each story scene based on the user's instructions, allowing the user to select. For example, a horror scene or a comedy scene can be selected. The generation unit can also apply a different style or genre to each story scene based on the user's instructions, increasing the diversity of the story. For example, a fantasy style or a science fiction style can be applied. This allows the generation AI to apply a different style or genre to each story scene, thereby providing a more diverse story development.

[0060] The generation unit can support various input methods, such as voice input or handwriting input, when a user inputs instructions. For example, the generation unit may support voice input so that the generation AI can input story instructions by speaking. For example, instructions can be input using a microphone. The generation unit may also enable a user to input story instructions using handwriting input. For example, instructions can be input by hand using a tablet or stylus pen. The generation unit may also support various input methods so that the generation AI can input story instructions in a way that is most convenient for the user. For example, keyboard input or touch input can be supported. This allows the generation AI to support various input methods so that the user can input story instructions in a way that is most convenient for the user.

[0061] The generation unit can use the emotion estimation function to generate multiple story endings based on the user's emotions and provide the user with options. For example, the generation unit uses a generation AI to analyze the user's emotions in real time and generate a happy ending if the user is happy, or a touching ending if the user is sad. The generation unit also generates multiple endings based on the user's emotional state, allowing the user to choose from them. For example, it can provide positive endings and negative endings. The generation unit also uses the emotion estimation function to generate multiple endings that the user can easily empathize with, allowing the user to select the one they like best. For example, it can provide a touching ending or a surprise ending. In this way, by generating multiple story endings based on the user's emotions and providing options, it is possible to provide a story that the user can easily empathize with emotionally.

[0062] The picture book creation unit can automatically generate detailed illustrations for each scene based on the story generated by the generation AI. For example, the picture book creation unit automatically generates detailed illustrations based on each scene of the story generated by the generation AI. For example, it generates illustrations that match the adventure scenes of the story. Furthermore, the picture book creation unit automatically generates illustrations in different styles for each scene of the story based on instructions from the user. For example, it can generate illustrations in a fantasy style or a realistic style. Furthermore, the picture book creation unit automatically generates detailed background and character illustrations for each scene according to the content of the story. For example, it can draw detailed scenes in the forest or scenes fighting a dragon. This makes it possible to provide a more visually appealing picture book by automatically generating detailed illustrations for each scene.

[0063] The picture book creation unit allows a user to select a specific scene in a story and generate detailed illustrations or animations for that scene. For example, the picture book creation unit allows a user to select a specific scene in a story and the generation AI generates detailed illustrations for that scene. For example, an illustration is generated that matches an adventure scene selected by the user. The picture book creation unit also allows the generation AI to generate animations for specific scenes based on the user's selection. For example, it is possible to animate scenes in which characters move or scenes in which the background changes. The picture book creation unit also allows a user to select a specific scene in a story and the generation AI generates detailed background and character illustrations for that scene. For example, it is possible to draw detailed backgrounds that match the scene selected by the user. This allows a more interactive picture book to be provided by allowing a user to select a specific scene and generate detailed illustrations and animations for that scene.

[0064] The picture book creation unit can use the emotion estimation function to adjust the color or design according to the user's emotion for each story scene. For example, the generation AI in the picture book creation unit analyzes the user's emotion in real time and uses bright colors when the user is happy and calm colors when the user is sad. The generation AI in the picture book creation unit also adjusts the color and design for each story scene according to the user's emotional state. For example, it can use bright colors when the user is excited and calm colors when the user is relaxed. The picture book creation unit also uses the emotion estimation function to adjust the color and design for each story scene to one that the user can easily empathize with emotionally. For example, it can use warm colors for scenes that move the user. In this way, by adjusting the color and design according to the user's emotion, it is possible to provide a picture book that is easier for the user to empathize with emotionally.

[0065] The picture book creation unit can create an interactive digital picture book based on the story generated by the generation AI, allowing the user to directly participate in the story. For example, the picture book creation unit creates an interactive digital picture book based on the story generated by the generation AI, allowing the user to choose how the story unfolds. For example, the story changes depending on the options selected by the user. The picture book creation unit also creates an interactive digital picture book that allows the user to control the characters in the story. For example, the user can move the characters to explore the story scenes. The picture book creation unit also creates an interactive digital picture book based on the story generated by the generation AI, allowing the user to customize the story scenes. For example, the user can change the background of a scene or the costumes of the characters. This makes it possible to provide a more immersive story experience by creating an interactive digital picture book that allows the user to directly participate in the story.

[0066] The picture book creation unit can add audio narration or sound effects based on the story generated by the generation AI to create a more realistic picture book. For example, the picture book creation unit adds audio narration to the story generated by the generation AI to create a picture book that users can enjoy while listening to the story. For example, narration by a professional voice actor can be added. The picture book creation unit can also add sound effects to the story generated by the generation AI to provide sound effects that match the scenes in the story. For example, the sound of wind or footsteps can be added to adventure scenes. The picture book creation unit can also add audio narration and sound effects to the story generated by the generation AI to create a picture book that users can feel more realistic about. For example, character lines and background sounds can be added. In this way, by adding audio narration and sound effects, a more realistic picture book can be provided.

[0067] The picture book creation unit can use the emotion estimation function to automatically generate music or sound effects corresponding to the user's emotions and add them to the picture book. For example, the generation AI in the picture book creation unit analyzes the user's emotions in real time and automatically generates cheerful music when the user is happy and calm music when the user is sad. The picture book creation unit also automatically generates sound effects appropriate for each story scene according to the user's emotional state and adds them to the picture book. For example, intense sound effects can be used when the user is excited and calm sound effects can be used when the user is relaxed. The picture book creation unit also uses the emotion estimation function to automatically generate music and sound effects that the user can easily empathize with emotionally for each story scene and add them to the picture book. For example, moving music can be used for scenes that move the user. In this way, by automatically generating music and sound effects corresponding to the user's emotions and adding them to the picture book, a picture book that is easier for the user to empathize with emotionally can be provided.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The dialogue unit can analyze the tone or speed of the user's voice and dynamically generate questions that correspond to the user's emotional state. For example, the generation AI can analyze the user's tone and speed in real time, generating adventurous questions if the user is excited and calm questions if the user is calm. The dialogue unit can also suggest tense scenes if the user's voice tone is high, and relaxing scenes if the user's voice tone is low. The dialogue unit can also analyze the user's voice speed, suggesting a fast-paced story development if the user is speaking quickly, and a slow-paced story development if the user is speaking slowly. This allows the system to generate questions that correspond to the user's emotional state, providing a more appropriate story development.

[0070] The dialogue unit can generate questions optimized for individual users based on the user's past dialogue history. For example, the generation AI analyzes the user's past dialogue history and generates questions based on the user's favorite themes and characters. The dialogue unit also learns the story development patterns selected by the user in the past and generates new questions based on that. For example, if the user likes adventure stories, it can preferentially generate questions related to adventure. The dialogue unit also references the user's past dialogue history and generates questions that include elements in which the user previously expressed interest. For example, if the user is interested in magic, it can generate questions related to magic. This allows for the generation of questions optimized based on the user's past dialogue history, providing a more personalized story development.

[0071] The dialogue unit can use the emotion estimation function to suggest story developments based on the user's emotions. For example, the generation AI can analyze the user's emotions in real time and suggest a positive development if the user is happy, or a moving development if the user is sad. The dialogue unit can also adjust the tone and theme of the story according to the user's emotional state. For example, it can suggest action scenes if the user is excited, or calm scenes if the user is relaxed. The dialogue unit can also use the emotion estimation function to suggest story developments that the user can easily empathize with. For example, it can generate stories that include scenes that are likely to move the user. This makes it possible to provide stories that the user can easily empathize with emotionally by suggesting story developments based on the user's emotions.

[0072] The dialogue unit can analyze the user's gestures or facial expressions and adjust the story development based on them. For example, the generation AI can analyze the user's facial expression and suggest a humorous development if the user is smiling, and a serious development if the user is serious. The dialogue unit can also analyze the user's gestures and suggest active scenes if the user frequently waves their hands, and calm scenes if the user is quiet. The dialogue unit can also analyze the user's facial expressions and gestures in real time and dynamically adjust the story development based on the user's reactions. For example, it can add a surprise element if the user expresses surprise. This allows the story development to be adjusted based on the user's gestures and facial expressions, providing a more interactive story experience.

[0073] The dialogue unit can have the function of allowing multiple users to converse simultaneously and collaboratively create a story. For example, the generation AI can analyze the input of multiple users simultaneously and generate a story that reflects each of their opinions. For example, a story can be created together with family and friends. The dialogue unit also allows multiple users to take turns instructing the development of the story, and the generation AI generates the story based on that. For example, user A can decide the personality of the protagonist, and user B can decide the content of the adventure. The dialogue unit also allows the generation AI to integrate the dialogues of multiple users and generate a collaboratively created story. For example, a story can be created together with classmates. This allows multiple users to collaboratively create a story, providing a wider variety of story developments.

[0074] The dialogue unit can use the emotion estimation function to analyze the user's emotions in real time during the dialogue and generate questions that elicit positive emotions. For example, the generation AI can analyze the user's emotions in real time and generate questions that elicit positive emotions. For example, it can generate humorous questions that will make the user smile. The dialogue unit also suggests scenarios that the generation AI can use to elicit positive emotions depending on the user's emotional state. For example, it can suggest calm scenes that will help the user relax. The dialogue unit also uses the emotion estimation function to suggest story developments that will evoke positive emotions in the user. For example, it can generate stories that include scenes that will move the user. This makes it possible to provide a better story experience by analyzing the user's emotions in real time and generating questions that elicit positive emotions.

[0075] When analyzing a user's instructions, the generation unit can automatically detect ambiguity in the instructions and return specific questions. For example, the generation AI analyzes the user's instructions, automatically detects ambiguity, and returns specific questions. For example, it could ask, "What kind of personality does the main character have?" Furthermore, if the user's instructions are ambiguous, the generation AI presents specific options to allow the user to make a selection. For example, it could ask, "Is the main character brave? Or kind?" Furthermore, the generation unit can analyze the user's instructions and generate specific questions to fill in the ambiguity. For example, it could ask, "What adventure will you go on next?" This allows the generation AI to automatically detect ambiguity in the user's instructions and return specific questions, thereby providing a clearer story development.

[0076] The generation unit can generate multiple story variations based on user instructions and provide the user with choices. For example, the generation AI can generate multiple story variations based on user instructions and provide the user with choices. For example, it can generate stories with different endings. The generation unit can also generate different scenarios based on user instructions and allow the user to select one. For example, it can generate scenarios with different adventure content or character personalities. The generation unit can also generate multiple story variations based on user instructions and allow the user to select the one they like best. For example, it can generate stories with different settings or themes. This allows the generation AI to generate multiple story variations and provide the user with choices, thereby providing a wider variety of story developments.

[0077] The generation unit can use the emotion estimation function to adjust the tone or theme of the story according to the user's emotions. For example, the generation AI analyzes the user's emotions in real time and generates a story with a positive tone if the user is happy, or an emotional one if the user is sad. The generation unit also adjusts the theme of the story according to the user's emotional state. For example, it can select an action theme if the user is excited, or a calm theme if the user is relaxed. The generation unit also uses the emotion estimation function to adjust the tone and theme of the story to one that the user can easily empathize with. For example, it can generate a story that includes scenes that the user is likely to be moved by. In this way, by adjusting the tone and theme of the story according to the user's emotions, it is possible to provide a story that the user can easily empathize with emotionally.

[0078] The generation unit can apply a different style or genre to each story scene based on the user's instructions. For example, the generation AI can apply a different style or genre to each story scene based on the user's instructions. For example, an action style for adventure scenes and a drama style for emotional scenes. The generation unit can also apply a different genre to each story scene based on the user's instructions, allowing the user to select. For example, a horror scene or a comedy scene can be selected. The generation unit can also apply a different style or genre to each story scene based on the user's instructions, increasing the diversity of the story. For example, a fantasy style or a science fiction style can be applied. This allows the generation AI to apply a different style or genre to each story scene, thereby providing a more diverse story development.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The dialogue unit uses the generation AI to interactively give instructions regarding the development of the story. For example, the user may ask, "What kind of personality does the main character have?" and the user's answer, "A brave knight," will determine the development of the story. The user may also ask, "What adventure will you have next?" and the user's answer, "Fight a dragon," will determine the development of the story. Furthermore, the user may ask, "What kind of animal is the main character?" and the user's answer, "A rabbit," will determine the development of the story. Step 2: The generation unit generates a story based on the instructions given by the dialogue unit. For example, if the user instructs, "The main character is a brave knight who fights a dragon," the generation AI analyzes this instruction and generates a story about a brave knight fighting a dragon. Alternatively, if the user instructs, "A rabbit searches for treasure in the forest," the generation AI can analyze this instruction and generate a story about a rabbit searching for treasure in the forest. Step 3: The picture book creation unit creates an original picture book based on the story generated by the generation unit. For example, it adds illustrations to match the generated story and formats it into a picture book. This allows the user to obtain an original picture book.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 dialogue section that uses generative AI to give interactive instructions on the development of the story, and a generation unit that generates a story based on the content instructed by the dialogue unit; a picture book creation unit that creates an original picture book based on the story created by the creation unit. A system characterized by:

2. The dialogue unit Analyzing the tone or rate of a user's voice and dynamically generating questions according to the user's emotional state 2. The system of claim 1.

3. The dialogue unit Analyzing the user's gestures or facial expressions and adjusting the storyline accordingly 2. The system of claim 1.

4. The generation unit When analyzing user instructions, it automatically detects ambiguity and returns specific questions.

2. The system of claim 1.

5. The generation unit Adjusting the tone or theme of a story depending on the user's emotions 2. The system of claim 1.

6. The picture book creation unit Based on the story generated by the AI, detailed illustrations for each scene are automatically generated.

2. The system of claim 1.

7. The picture book creation unit Adjust the color or design according to the user's emotions for each scene in the story 2. The system of claim 1.

8. The picture book creation unit Automatically generate music or sound effects according to the user's emotions and add them to a picture book 2. The system of claim 1.

Images