system

Abstract

The system according to this embodiment aims to support the process of creating stories together with children. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a questioning unit. The reception unit receives information to determine the outline of the story. The generation unit generates a story based on the information received by the reception unit. The generation unit generates pictures that match the story generated by the generation unit. The questioning unit asks questions that are relevant to the progression of the story.

Description

Technical Field

[0004] ,

[0006] , , , ,

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

Background Art

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[0007] The system according to this embodiment can support the process of creating stories together with children. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The story generation agent according to an embodiment of the present invention is a system aimed at creating stories together with children. This story generation agent allows children to create stories with free-thinking ideas and foster their imagination by having the user input information to determine the outline of the story, having the generation AI generate story ideas and a storyline based on the input information, and having the image generation AI generate pictures that match the story. For example, the user inputs the name of the main character, age, interests, the theme of the story, what they want to convey, the proportion of the story to be supplemented, and the style of the illustrations. The generation AI generates a story based on this information, and the image generation AI generates pictures that match the story. During the story generation process, questions are asked at key points in the story, and the story progresses based on the user's answers. For example, questions such as "Where did Yuki decide to go?" and "What kind of adventure tools did Yuki and Takuma take with them?" are asked. This allows the user to participate in the progression of the story and create a story with free-thinking ideas. In addition, the generated story is displayed together with pictures generated by the image generation AI based on the style of illustrations selected by the user. This allows users to enjoy the visual elements of the story as well. Furthermore, the present invention provides users of the story generation agent with services such as the use of illustrations by picture book creators, regular delivery of created books, browsing of picture books by other users, and opportunities for publication through contests. This allows users to share the joy of story creation and form a community. In turn, the story generation agent can create stories together with children and nurture their imagination.

[0029] The story generation agent according to this embodiment comprises a reception unit, a generation unit, another generation unit, and a questioning unit. The reception unit receives information from the user to determine the outline of the story. This information includes, but is not limited to, character settings, plot outlines, and themes. The reception unit, for example, stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to generate a story based on the information entered by the reception unit. The generation unit generates the story using, for example, a storytelling algorithm or procedural generation technology. When the generation AI generates story ideas and a story, the generation unit determines the development of the story and the actions of the characters based on the information entered by the user. The generation unit, for example, receives a prompt from the generation AI that "the protagonist goes on an adventure" and generates the development of the story. The generation unit uses an image generation AI to generate images that match the generated story. The generation unit generates images using, for example, an image generation algorithm or deep learning technology. The generation unit determines the details of an image based on the style of the image selected by the user when the image generation AI generates an image that matches the scene in the story. For example, the generation unit receives a prompt from the image generation AI saying, "Draw a scene of the protagonist on an adventure in a watercolor style," and generates the image. The questioning unit is involved in the progression of the story. For example, the questioning unit asks the user questions at key points in the story and advances the story based on the user's answers. For example, the questioning unit asks, "Where did the protagonist decide to go next?" and determines the development of the story based on the user's answer. The questioning unit saves the user's answers in a database and provides them to the generation unit. As a result, the story generation agent according to this embodiment allows the user to input information to determine the outline of the story, display the generated story and images, and participate in the progression of the story.

[0030] The reception section receives information from the user to determine the core structure of the story. This information includes, but is not limited to, character settings, plot outlines, and themes. The reception section stores the user-entered information in a database and provides it to the generation section. Specifically, the reception section provides an interface for efficiently collecting user-entered information. The interface includes text input fields, dropdown menus, and checkboxes, and is designed to allow users to easily enter information. For example, the character settings section has fields for entering detailed information such as the character's name, age, gender, personality, and background. The plot outline section has fields for entering the general flow of the story's beginning, middle, and end, and the theme section has a field for entering the central theme or message of the story. The reception section also has a function to verify the information entered by the user in real time, checking for missing or contradictory information. For example, if a character's age and background are inconsistent, it displays a warning to the user and prompts them to correct it. The reception section also has a function to save and reuse information that the user has entered in the past. This saves the user the trouble of re-entering information they have already entered. Furthermore, the reception desk also has a function to encrypt and store the information entered by users, protecting their privacy. This allows users to enter information with peace of mind.

[0031] The generation unit uses a generation AI to generate a story based on the information entered by the reception unit. The generation unit generates stories using, for example, storytelling algorithms and procedural generation techniques. When the generation AI generates story ideas and plots, the generation unit determines the story's development and character actions based on the information entered by the user. For example, the generation unit receives a prompt such as "the protagonist goes on an adventure" and generates the story's development. Specifically, the generation AI utilizes natural language processing techniques to analyze the character settings and plot outline entered by the user and construct the flow of the story. For example, it determines what actions the protagonist will take and what difficulties they will face based on their personality and background. The generation AI also generates stories that include messages and lessons aligned with the story's theme. Furthermore, the generation unit presents the generated story to the user, allowing the user to modify and add to it. This allows the user to customize the generated story to their liking. The generation unit can also regenerate the story, reflecting the user's modifications and additions. This allows the user to create a story they are satisfied with.

[0032] The generation unit uses image generation AI to create images that match the generated story. The generation unit generates images using, for example, image generation algorithms and deep learning technology. When the image generation AI generates images that match the story scenes, the generation unit determines the details of the images based on the art style selected by the user. For example, the generation unit receives a prompt from the image generation AI to "draw a scene of the protagonist on an adventure in a watercolor style" and generates the image. Specifically, the image generation AI analyzes the characters and backgrounds that appear in the story scenes and generates the image based on that. For example, in a scene where the protagonist is adventuring in a forest, it will depict the trees and animals of the forest and the protagonist in detail. It also adjusts the color scheme and touch according to the art style selected by the user. For example, in a watercolor style, it generates images using soft colors and blurring effects. Furthermore, the generation unit also has a function to present the generated image to the user and allow the user to make modifications and additions. This allows the user to customize the generated image to their liking. The generation unit can also regenerate the image to reflect the modifications and additions made by the user. This allows users to create images they are satisfied with.

[0033] The questioning unit is involved in the progression of the story. For example, the questioning unit asks the user questions at key points in the story and advances the story based on the user's answers. For example, the questioning unit might ask, "Where did the protagonist decide to go next?" and determine the story's development based on the user's answer. Specifically, the questioning unit has an algorithm to ask questions at the appropriate timing according to the story's progression. For example, it asks questions at the story's climax or when important choices arise, reflecting the user's opinion. The questioning unit also has the function of saving the user's answers in a database and providing them to the generation unit. This allows the generation unit to regenerate the story based on the user's answers, ensuring that the story's progression aligns with the user's intentions. Furthermore, the questioning unit also has the function of determining story branching and endings based on the user's answers. For example, the story's ending is designed to change depending on the answers the user chooses. This allows the user to control the story's development through their choices, enabling a more interactive experience. The questioning unit encourages active user engagement with the story, making the story's progression more engaging.

[0034] The display unit can display the generated story and images. The display unit displays the generated story and images based on, for example, the screen layout, display timing, and display format. The display unit can, for example, display the story text and images simultaneously. The display unit can also display images sequentially in accordance with the progress of the story. For example, the display unit displays the corresponding image as the story scene progresses. In this way, by displaying the generated story and images, the visual elements can also be enjoyed. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can optimize the display method using an AI model for displaying the generated story and images.

[0035] The narrative progression unit can advance the story based on user responses. For example, the progression unit advances the story based on branching paths based on user choices or progression based on the passage of time. For example, the progression unit determines the story's development when the user selects "where the protagonist goes next." The progression unit can also ask the user questions in line with the story's progression and advance the story based on the user's answers. For example, the progression unit might ask the user "what kind of adventure will the protagonist have next?" and determine the story's development based on the user's answer. In this way, the user can participate in the story's progression by advancing the story based on their answers. Some or all of the above processes in the progression unit may be performed using AI, for example, or not. For example, the progression unit can optimize the story's progression using an AI model that takes user responses as input and outputs the story's progression.

[0036] The service provider can offer the use of picture book creators' illustrations, regular delivery of created books, access to other users' picture books, and opportunities for publication through contests. The service provider can offer the use of picture book creators' illustrations based on, for example, license agreements and usage fees. The service provider can offer regular delivery of created books based on, for example, delivery frequency, delivery method, and delivery destination. The service provider can offer access to other users' picture books based on, for example, viewing permissions, viewing methods, and viewing restrictions. The service provider can offer opportunities for publication through contests based on, for example, application methods, judging criteria, and publishing contracts. By providing services such as the use of picture book creators' illustrations, regular delivery of created books, access to other users' picture books, and opportunities for publication through contests, the joy of story creation can be shared and a community can be formed. Some or all of the above processes in the service provider can be performed using, for example, AI, or not using AI. For example, the service provider can optimize the provision of services using an AI model that takes the user's usage history as input and outputs the optimal service.

[0037] The reception desk can analyze the user's past story creation history and select the optimal information input method. The reception desk analyzes the past story creation history using, for example, data mining techniques and types of historical data. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has previously preferred. The reception desk can also suggest relevant input items by referring to the themes and styles of stories the user has created in the past. Furthermore, the reception desk can extract specific patterns from the user's past story creation history and suggest efficient information input methods. In this way, the optimal information input method can be selected by analyzing the user's past story creation history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can optimize the information input method using an AI model that takes the user's past story creation history data as input and outputs the optimal information input method.

[0038] The reception desk can customize input fields based on the user's current interests and preferences when information is entered. The reception desk identifies current interests and preferences, for example, using surveys or analysis of behavioral history. The reception desk can reflect themes or characters that the user has recently been interested in in the input fields. The reception desk can also add questions related to topics that the user is currently interested in. Furthermore, the reception desk can suggest story settings and characters based on the user's recent activities and hobbies. This can increase the user's motivation to create a story by customizing input fields based on their current interests and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can optimize input fields using an AI model that takes the user's current interest and preference data as input and customizes the input fields.

[0039] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during information input. The reception desk can identify geographical location information using, for example, GPS data or IP address. For example, if the user is in a specific region, the reception desk can suggest story settings and characters related to that region. If the user is traveling, the reception desk can also suggest story themes and scenes related to the travel destination. Furthermore, if the user is at home, the reception desk can suggest story ideas related to events in the home or daily life. In this way, by prioritizing input of highly relevant information considering the user's geographical location, it is possible to suggest story settings and characters. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can optimize the priority of information using an AI model that takes the user's geographical location information as input and outputs highly relevant information.

[0040] The reception desk can analyze the user's social media activity and prompt them to input relevant information when they enter data. The reception desk analyzes social media activity, for example, by analyzing the content of posts and the number of followers. For example, the reception desk can suggest story themes and characters based on what the user has recently shared on social media. The reception desk can also suggest story ideas related to topics of accounts and groups that the user follows. Furthermore, the reception desk can suggest story settings and scenes based on posts and articles that the user has shown interest in on social media. In this way, story themes and characters can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can optimize the priority of information using an AI model that takes the user's social media activity data as input and outputs relevant information.

[0041] The generation unit can adjust the level of detail generated based on the importance of the story during story generation. The generation unit evaluates the importance of a story based on factors such as user ratings and the importance of the story's theme. For example, the generation unit can provide detailed descriptions in the climax of a story to heighten the tension. It can also provide concise descriptions in the introduction to ensure a smooth progression of the story. Furthermore, it can provide emotionally moving descriptions in the conclusion of a story to leave a lasting impression on the reader. In this way, adjusting the level of detail generated based on the importance of the story can ensure a smooth progression of the story. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the level of detail generated by using an AI model that takes story importance data as input and outputs the level of detail of the generated story.

[0042] The generation unit can apply different generation algorithms depending on the category of the story when generating a story. For example, the generation unit classifies the story category based on genre, target age, theme, etc. For example, in the case of an adventure story, the generation unit applies an algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the generation unit can also apply an algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the generation unit can apply an algorithm that includes learning elements and knowledge. By applying different generation algorithms depending on the category of the story, the story can progress more smoothly. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the generation algorithm using an AI model that takes story category data as input and outputs a generation algorithm.

[0043] The generation unit can determine the generation priority based on the story's submission date when generating a story. For example, the generation unit identifies the story's submission date based on factors such as the submission deadline and submission frequency. For example, the generation unit prioritizes generating a story when the deadline is approaching. It can also prioritize generating other stories when the submission date is far off. Furthermore, the generation unit can adjust the level of detail in the generation according to the submission date. This allows for a smoother progression of the story by determining the generation priority based on the story's submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the generation priority using an AI model that takes story submission date data as input and outputs the generation priority.

[0044] The generation unit can adjust the generation order based on the relevance of the stories during story generation. The generation unit evaluates the relevance of stories based on factors such as thematic consistency and character relationships. The generation unit generates related stories sequentially, for example, by considering the context of the stories. The generation unit can also adjust the generation order by considering the themes and character relationships of the stories. Furthermore, the generation unit can optimize the generation order according to the progress of the story. This allows for smoother story progression by adjusting the generation order based on the relevance of the stories. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the generation order using an AI model that takes story relevance data as input and outputs the generation order.

[0045] The generation unit can adjust the level of detail in the images based on important scenes in the story during image generation. For example, the generation unit identifies important scenes in the story based on climax scenes or key moments for characters. For example, in the climax scenes of the story, the generation unit can provide detailed descriptions to heighten the tension. The generation unit can also provide concise descriptions in the introductory parts of the story to ensure a smooth progression of the narrative. Furthermore, the generation unit can provide emotionally moving descriptions in the ending of the story to leave a lasting impression on the reader. In this way, by adjusting the level of detail in the images based on important scenes in the story, the visual elements of the story can be enjoyed. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the level of detail in the images using an AI model that takes important scene data from the story as input and outputs the level of detail in the images.

[0046] The generation unit can apply different generation algorithms depending on the story category when generating images. For example, the generation unit classifies story categories based on genre, target age, theme, etc. For example, in the case of an adventure story, the generation unit applies an algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the generation unit can also apply an algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the generation unit can apply an algorithm that includes learning elements and knowledge. In this way, by applying different generation algorithms depending on the story category, the visual elements of the story can be enjoyed. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can optimize the generation algorithm using an AI model that takes story category data as input and outputs a generation algorithm.

[0047] The generation unit can determine the priority of images based on the story's submission timing when generating images. For example, the generation unit identifies the story's submission timing based on factors such as the submission deadline and submission frequency. For example, if the deadline is approaching, the generation unit will prioritize generating images. Conversely, if the submission timing is far off, the generation unit can prioritize generating other images. Furthermore, the generation unit can adjust the level of detail of the generation according to the submission timing. This allows users to enjoy the visual elements of the story by determining the priority of images based on the story's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the priority of images using an AI model that takes story submission timing data as input and outputs the priority of images.

[0048] The generation unit can adjust the order of images based on their relevance to the story during image generation. The generation unit evaluates the relevance of the story based on factors such as thematic consistency and character relationships. The generation unit generates related images in sequence, for example, taking into account the context of the story. The generation unit can also adjust the generation order by considering the themes and character relationships of the story. Furthermore, the generation unit can optimize the generation order according to the progress of the story. This allows users to enjoy the visual elements of the story by adjusting the order of images based on their relevance to the story. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the order of images using an AI model that takes story relevance data as input and outputs the order of images.

[0049] The questioning unit can adjust the level of detail of questions based on the progress of the story. For example, the questioning unit can adjust the level of detail of questions based on changes in the questions or the level of detail of questions based on the progress of the story. For example, in the introductory part of the story, the questioning unit can ask concise questions to ensure a smooth progression of the story. Also, in the climactic scene of the story, the questioning unit can ask detailed questions to encourage deeper exploration of the story. Furthermore, in the concluding part of the story, the questioning unit can ask emotionally charged questions to emphasize the emotional aspects of the story. In this way, by adjusting the level of detail of questions based on the progress of the story, the progression of the story can be made smoother. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can optimize the level of detail of questions using an AI model that takes story progress data as input and outputs the level of detail of questions.

[0050] The questioning unit can apply different questioning algorithms depending on the category of the story when asking questions. The questioning unit applies a questioning algorithm based, for example, on the selection and application method of the algorithm according to the category. For example, in the case of an adventure story, the questioning unit asks questions related to action and adventure. In the case of a fantasy story, the questioning unit can also ask questions related to magic and fantastical elements. Furthermore, in the case of an educational story, the questioning unit can also ask questions related to learning elements and knowledge. By applying different questioning algorithms according to the category of the story, the progression of the story can be made smoother. Some or all of the above processing in the questioning unit may be performed using, for example, AI, or not using AI. For example, the questioning unit can optimize the questioning algorithm using an AI model that takes story category data as input and outputs a questioning algorithm.

[0051] The questioning unit can prioritize questions based on the story's submission timing. For example, the questioning unit can identify the story's submission timing based on factors such as the submission deadline and submission frequency. If the deadline is approaching, the questioning unit will prioritize important questions. If the submission date is far off, the questioning unit can also ask detailed questions sequentially. Furthermore, the questioning unit can adjust the level of detail of the questions according to the submission timing. This allows for smoother story progression by prioritizing questions based on the story's submission timing. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can optimize question prioritization using an AI model that takes story submission timing data as input and outputs question priorities.

[0052] The questioning unit can adjust the content of questions based on their relevance to the story. For example, the questioning unit can adjust the content of questions based on changes in the relevance of the story or the level of detail of the questions. For example, the questioning unit can ask related questions in sequence, taking into account the context of the story. The questioning unit can also adjust the content of questions by considering the themes and character relationships of the story. Furthermore, the questioning unit can optimize the content of questions according to the progress of the story. By adjusting the content of questions based on the relevance of the story, the story can progress more smoothly. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can optimize the content of questions using an AI model that takes story relevance data as input and outputs question content.

[0053] The display unit can adjust the level of detail based on important scenes in the story during display. For example, the display unit adjusts the level of detail based on changes in the display or the level of detail based on important scenes in the story. For example, the display unit can provide detailed descriptions in the climax of the story to heighten the tension. The display unit can also provide concise descriptions in the introduction of the story to ensure a smooth progression of the narrative. Furthermore, the display unit can provide moving descriptions in the conclusion of the story to leave a lasting impression on the reader. In this way, by adjusting the level of detail based on important scenes in the story, the visual elements of the story can be enjoyed. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can optimize the level of detail using an AI model that takes important scene data from the story as input and outputs the level of detail.

[0054] The display unit can apply different display algorithms depending on the category of the story during display. The display unit applies a display algorithm based, for example, on the selection and application method of the algorithm according to the category. For example, in the case of an adventure story, the display unit applies a display algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the display unit can also apply a display algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the display unit can apply a display algorithm that includes learning elements and knowledge. In this way, by applying different display algorithms according to the category of the story, the visual elements of the story can be enjoyed. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can optimize the display algorithm using an AI model that takes story category data as input and outputs a display algorithm.

[0055] The display unit can determine the display priority based on the story's submission date when displaying it. The display unit identifies the story's submission date based on factors such as the submission deadline and submission frequency. For example, if the deadline is approaching, the display unit will prioritize displaying important information. If the submission date is far away, the display unit can also sequentially display detailed information. Furthermore, the display unit can adjust the level of detail displayed according to the submission date. This allows users to enjoy the visual elements of the story by determining the display priority based on the story's submission date. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can optimize the display priority using an AI model that takes story submission date data as input and outputs the display priority.

[0056] The display unit can adjust the displayed content based on the relevance of the story during display. For example, the display unit adjusts the displayed content based on changes in the display based on the relevance of the story or the level of detail in the display. For example, the display unit displays related information sequentially, taking into account the context of the story. The display unit can also adjust the displayed content considering the themes and character relationships of the story. Furthermore, the display unit can optimize the displayed content according to the progress of the story. This allows users to enjoy the visual elements of the story by adjusting the displayed content based on the relevance of the story. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can optimize the displayed content using an AI model that takes story relevance data as input and outputs the displayed content.

[0057] The plot progression unit can adjust the level of detail in the plot progression based on important scenes in the story. For example, the plot progression unit adjusts the level of detail in the plot progression based on changes in the plot progression based on important scenes in the story. For example, in the climax scene of the story, the plot progression unit can provide detailed descriptions to heighten the tension. Also, in the introductory part of the story, the plot progression unit can provide concise descriptions to make the plot progression smoother. Furthermore, in the final part of the story, the plot progression unit can provide moving descriptions to make it memorable for the reader. In this way, the plot progression unit can make the plot progression smoother by adjusting the level of detail in the plot progression based on important scenes in the story. Some or all of the above processing in the plot progression unit may be performed using AI, for example, or not using AI. For example, the plot progression unit can optimize the level of detail in the plot progression using an AI model that takes important scene data of the story as input and outputs the level of detail in the plot progression.

[0058] The narrative progression unit can apply different progression algorithms depending on the category of the story during progression. The progression unit applies a progression algorithm based, for example, on the selection and application method of the algorithm according to the category. For example, in the case of an adventure story, the progression unit applies a progression algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the progression unit can also apply a progression algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the progression unit can apply a progression algorithm that includes learning elements and knowledge. In this way, by applying different progression algorithms according to the category of the story, the progression of the story can be made smoother. Some or all of the above processing in the progression unit may be performed using, for example, AI, or not using AI. For example, the progression unit can optimize the progression algorithm using an AI model that takes story category data as input and outputs a progression algorithm.

[0059] The progress unit can determine the priority of the story's progress based on its submission timing. For example, the progress unit can identify the story's submission timing based on factors such as submission deadlines and submission frequency. For example, if the deadline is approaching, the progress unit can prioritize the processing of important information. If the submission date is far off, the progress unit can also process detailed information sequentially. Furthermore, the progress unit can adjust the level of detail in the process according to the submission timing. This allows for smoother story progression by determining the priority of the process based on the story's submission timing. Some or all of the above processing in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can optimize the priority of the process using an AI model that takes story submission timing data as input and outputs the priority of the process.

[0060] The progression unit can adjust the content of the story based on the relevance of the narrative as it progresses. For example, the progression unit adjusts the content of the story based on changes in the progression based on the relevance of the narrative or the level of detail in the progression. For example, the progression unit advances relevant information sequentially, taking into account the context of the story. The progression unit can also adjust the content of the story by taking into account the themes of the story and the relevance of the characters. Furthermore, the progression unit can optimize the content of the story according to the progress of the story. In this way, the story can progress smoothly by adjusting the content of the story based on the relevance of the narrative. Some or all of the above processes in the progression unit may be performed using AI, for example, or not using AI. For example, the progression unit can optimize the content of the story using an AI model that takes narrative relevance data as input and outputs the content of the story.

[0061] The service provider can provide the most suitable service by referring to the user's past usage history when providing the service. For example, the service provider can customize and provide the most suitable service based on the user's past usage history. For example, the service provider can suggest related services based on the services the user has used in the past. The service provider can also customize and provide the most suitable service based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and prioritize providing the most frequently used services. In this way, by providing the most suitable service by referring to the user's past usage history, the service provider can provide the most suitable service to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize the service provision method using an AI model that takes the user's past usage history data as input and outputs the most suitable service.

[0062] The service provider can customize the service content based on the user's current interests and preferences when providing the service. For example, the service provider can customize and provide the service content based on the user's current interests and preferences. For example, the service provider can provide services related to themes or topics that the user has recently become interested in. The service provider can also customize and provide services related to fields that the user is currently interested in. Furthermore, the service provider can suggest the most suitable service based on the user's recent activities and hobbies. In this way, by customizing the service content based on the user's current interests and preferences, the service provider can provide the user with the most suitable service. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can optimize the service content using an AI model that takes the user's current interest data as input and outputs the service content.

[0063] The service provider can prioritize providing highly relevant services by considering the user's geographical location information when providing services. The service provider can identify geographical location information using, for example, GPS data or IP addresses. For example, if the user is in a specific region, the service provider can provide services related to that region. Furthermore, if the user is traveling, the service provider can also provide services related to the travel destination. In addition, if the user is at home, the service provider can also provide services that can be used within the home. By prioritizing the provision of highly relevant services while considering the user's geographical location information, the service provider can provide the user with the most optimal service. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can optimize the service provision method using an AI model that takes the user's geographical location information as input and outputs highly relevant services.

[0064] The service provider can analyze a user's social media activity and provide relevant services when providing services. The service provider can analyze social media activity using methods such as analyzing posted content and analyzing followers. The service provider can provide relevant services based on content recently shared by the user on social media. The service provider can also provide services related to topics of accounts and groups that the user follows. Furthermore, the service provider can provide relevant services based on posts and articles that the user has shown interest in on social media. In this way, by analyzing the user's social media activity and providing relevant services, the service provider can provide the most suitable service to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can optimize the method of service provision using an AI model that takes user social media activity data as input and outputs relevant services.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception desk can generate music and sound effects related to the story's progression based on user input. For example, it can generate adventurous music or fantasy-style sound effects to match the story's theme selected by the user. The reception desk can also provide different music and sound effects for each scene as the story progresses. Furthermore, it can generate music and sound effects that match the emotional scenes of the story based on user input. This allows users to enjoy not only the visual elements of the story, but also the auditory elements.

[0067] The reception desk can analyze the user's past story creation history and select the most suitable information input method. For example, it can prioritize suggesting input methods the user has previously preferred (such as voice or text). The reception desk can also suggest relevant input fields based on the themes and styles of stories the user has created in the past. Furthermore, the reception desk can extract specific patterns from the user's past story creation history and suggest efficient information input methods. In this way, by analyzing the user's past story creation history, the optimal information input method can be selected.

[0068] The input field can be customized based on the user's current interests and preferences during information entry. For example, it can reflect themes or characters the user has recently been interested in in the input fields. The input field can also add questions related to topics the user is currently interested in. Furthermore, the input field can suggest story settings and characters based on the user's recent activities and hobbies. By customizing the input field based on the user's current interests and preferences, this can increase their motivation to create stories.

[0069] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, if a user is in a specific region, it can suggest story settings and characters related to that region. If a user is traveling, it can also suggest story themes and scenes related to their travel destination. Furthermore, if a user is at home, it can suggest story ideas related to events in the home or daily life. In this way, by prioritizing input of highly relevant information based on the user's geographical location, it can suggest story settings and characters.

[0070] The reception desk can analyze the user's social media activity during information entry and prompt them to input relevant information. For example, it can suggest story themes and characters based on what the user has recently shared on social media. The reception desk can also suggest story ideas related to topics of accounts and groups the user follows. Furthermore, it can suggest story settings and scenes based on posts and articles the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it is possible to suggest story themes and characters.

[0071] The generation unit can adjust the level of detail generated based on the importance of each part of the story. For example, it can provide detailed descriptions in the climax of a story to heighten the tension. Conversely, it can use concise descriptions in the introduction to ensure a smooth narrative progression. Furthermore, it can provide emotionally moving descriptions in the conclusion to leave a lasting impression on the reader. In this way, by adjusting the level of detail generated based on the importance of each part of the story, the narrative can be made to progress more smoothly.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception unit receives information from the user to determine the core of the story. This information includes, for example, character settings, a plot outline, and themes. The reception unit saves the information entered by the user to a database and provides it to the generation unit. Step 2: The generation unit uses a generation AI to generate a story based on the information input by the reception unit. The generation unit uses storytelling algorithms and procedural generation techniques to generate the story and determine the plot development and character actions. For example, if the generation AI receives the prompt "the protagonist goes on an adventure," it generates the plot development. Step 3: The generation unit uses image generation AI to generate images that match the generated story. The generation unit generates images using image generation algorithms and deep learning technology, and determines the details of the images based on the style of images selected by the user. For example, if the image generation AI receives a prompt such as "Draw a scene of the protagonist on an adventure in a watercolor style," it will generate an image. Step 4: The questioning unit is involved in the progression of the story. The questioning unit asks the user questions at key points in the story and advances the story based on the user's answers. For example, it might ask, "Where did the protagonist decide to go next?" and determine the story's development based on the user's answer. The questioning unit saves the user's answers in a database and provides them to the generation unit.

[0074] (Example of form 2) The story generation agent according to an embodiment of the present invention is a system aimed at creating stories together with children. This story generation agent allows children to create stories with free-thinking ideas and foster their imagination by having the user input information to determine the outline of the story, having the generation AI generate story ideas and a storyline based on the input information, and having the image generation AI generate pictures that match the story. For example, the user inputs the name of the main character, age, interests, the theme of the story, what they want to convey, the proportion of the story to be supplemented, and the style of the illustrations. The generation AI generates a story based on this information, and the image generation AI generates pictures that match the story. During the story generation process, questions are asked at key points in the story, and the story progresses based on the user's answers. For example, questions such as "Where did Yuki decide to go?" and "What kind of adventure tools did Yuki and Takuma take with them?" are asked. This allows the user to participate in the progression of the story and create a story with free-thinking ideas. In addition, the generated story is displayed together with pictures generated by the image generation AI based on the style of illustrations selected by the user. This allows users to enjoy the visual elements of the story as well. Furthermore, the present invention provides users of the story generation agent with services such as the use of illustrations by picture book creators, regular delivery of created books, browsing of picture books by other users, and opportunities for publication through contests. This allows users to share the joy of story creation and form a community. In turn, the story generation agent can create stories together with children and nurture their imagination.

[0075] The story generation agent according to this embodiment comprises a reception unit, a generation unit, another generation unit, and a questioning unit. The reception unit receives information from the user to determine the outline of the story. This information includes, but is not limited to, character settings, plot outlines, and themes. The reception unit, for example, stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to generate a story based on the information entered by the reception unit. The generation unit generates the story using, for example, a storytelling algorithm or procedural generation technology. When the generation AI generates story ideas and a story, the generation unit determines the development of the story and the actions of the characters based on the information entered by the user. The generation unit, for example, receives a prompt from the generation AI that "the protagonist goes on an adventure" and generates the development of the story. The generation unit uses an image generation AI to generate images that match the generated story. The generation unit generates images using, for example, an image generation algorithm or deep learning technology. The generation unit determines the details of an image based on the style of the image selected by the user when the image generation AI generates an image that matches the scene in the story. For example, the generation unit receives a prompt from the image generation AI saying, "Draw a scene of the protagonist on an adventure in a watercolor style," and generates the image. The questioning unit is involved in the progression of the story. For example, the questioning unit asks the user questions at key points in the story and advances the story based on the user's answers. For example, the questioning unit asks, "Where did the protagonist decide to go next?" and determines the development of the story based on the user's answer. The questioning unit saves the user's answers in a database and provides them to the generation unit. As a result, the story generation agent according to this embodiment allows the user to input information to determine the outline of the story, display the generated story and images, and participate in the progression of the story.

[0076] The reception section receives information from the user to determine the core structure of the story. This information includes, but is not limited to, character settings, plot outlines, and themes. The reception section stores the user-entered information in a database and provides it to the generation section. Specifically, the reception section provides an interface for efficiently collecting user-entered information. The interface includes text input fields, dropdown menus, and checkboxes, and is designed to allow users to easily enter information. For example, the character settings section has fields for entering detailed information such as the character's name, age, gender, personality, and background. The plot outline section has fields for entering the general flow of the story's beginning, middle, and end, and the theme section has a field for entering the central theme or message of the story. The reception section also has a function to verify the information entered by the user in real time, checking for missing or contradictory information. For example, if a character's age and background are inconsistent, it displays a warning to the user and prompts them to correct it. The reception section also has a function to save and reuse information that the user has entered in the past. This saves the user the trouble of re-entering information they have already entered. Furthermore, the reception desk also has a function to encrypt and store the information entered by users, protecting their privacy. This allows users to enter information with peace of mind.

[0077] The generation unit uses a generation AI to generate a story based on the information entered by the reception unit. The generation unit generates stories using, for example, storytelling algorithms and procedural generation techniques. When the generation AI generates story ideas and plots, the generation unit determines the story's development and character actions based on the information entered by the user. For example, the generation unit receives a prompt such as "the protagonist goes on an adventure" and generates the story's development. Specifically, the generation AI utilizes natural language processing techniques to analyze the character settings and plot outline entered by the user and construct the flow of the story. For example, it determines what actions the protagonist will take and what difficulties they will face based on their personality and background. The generation AI also generates stories that include messages and lessons aligned with the story's theme. Furthermore, the generation unit presents the generated story to the user, allowing the user to modify and add to it. This allows the user to customize the generated story to their liking. The generation unit can also regenerate the story, reflecting the user's modifications and additions. This allows the user to create a story they are satisfied with.

[0078] The generation unit uses image generation AI to create images that match the generated story. The generation unit generates images using, for example, image generation algorithms and deep learning technology. When the image generation AI generates images that match the story scenes, the generation unit determines the details of the images based on the art style selected by the user. For example, the generation unit receives a prompt from the image generation AI to "draw a scene of the protagonist on an adventure in a watercolor style" and generates the image. Specifically, the image generation AI analyzes the characters and backgrounds that appear in the story scenes and generates the image based on that. For example, in a scene where the protagonist is adventuring in a forest, it will depict the trees and animals of the forest and the protagonist in detail. It also adjusts the color scheme and touch according to the art style selected by the user. For example, in a watercolor style, it generates images using soft colors and blurring effects. Furthermore, the generation unit also has a function to present the generated image to the user and allow the user to make modifications and additions. This allows the user to customize the generated image to their liking. The generation unit can also regenerate the image to reflect the modifications and additions made by the user. This allows users to create images they are satisfied with.

[0079] The questioning unit is involved in the progression of the story. For example, the questioning unit asks the user questions at key points in the story and advances the story based on the user's answers. For example, the questioning unit might ask, "Where did the protagonist decide to go next?" and determine the story's development based on the user's answer. Specifically, the questioning unit has an algorithm to ask questions at the appropriate timing according to the story's progression. For example, it asks questions at the story's climax or when important choices arise, reflecting the user's opinion. The questioning unit also has the function of saving the user's answers in a database and providing them to the generation unit. This allows the generation unit to regenerate the story based on the user's answers, ensuring that the story's progression aligns with the user's intentions. Furthermore, the questioning unit also has the function of determining story branching and endings based on the user's answers. For example, the story's ending is designed to change depending on the answers the user chooses. This allows the user to control the story's development through their choices, enabling a more interactive experience. The questioning unit encourages active user engagement with the story, making the story's progression more engaging.

[0080] The display unit can display the generated story and images. The display unit displays the generated story and images based on, for example, the screen layout, display timing, and display format. The display unit can, for example, display the story text and images simultaneously. The display unit can also display images sequentially in accordance with the progress of the story. For example, the display unit displays the corresponding image as the story scene progresses. In this way, by displaying the generated story and images, the visual elements can also be enjoyed. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can optimize the display method using an AI model for displaying the generated story and images.

[0081] The narrative progression unit can advance the story based on user responses. For example, the progression unit advances the story based on branching paths based on user choices or progression based on the passage of time. For example, the progression unit determines the story's development when the user selects "where the protagonist goes next." The progression unit can also ask the user questions in line with the story's progression and advance the story based on the user's answers. For example, the progression unit might ask the user "what kind of adventure will the protagonist have next?" and determine the story's development based on the user's answer. In this way, the user can participate in the story's progression by advancing the story based on their answers. Some or all of the above processes in the progression unit may be performed using AI, for example, or not. For example, the progression unit can optimize the story's progression using an AI model that takes user responses as input and outputs the story's progression.

[0082] The service provider can offer the use of picture book creators' illustrations, regular delivery of created books, access to other users' picture books, and opportunities for publication through contests. The service provider can offer the use of picture book creators' illustrations based on, for example, license agreements and usage fees. The service provider can offer regular delivery of created books based on, for example, delivery frequency, delivery method, and delivery destination. The service provider can offer access to other users' picture books based on, for example, viewing permissions, viewing methods, and viewing restrictions. The service provider can offer opportunities for publication through contests based on, for example, application methods, judging criteria, and publishing contracts. By providing services such as the use of picture book creators' illustrations, regular delivery of created books, access to other users' picture books, and opportunities for publication through contests, the joy of story creation can be shared and a community can be formed. Some or all of the above processes in the service provider can be performed using, for example, AI, or not using AI. For example, the service provider can optimize the provision of services using an AI model that takes the user's usage history as input and outputs the optimal service.

[0083] The reception unit can estimate the user's emotions and adjust the timing of information input to determine the story's framework based on the estimated emotions. The reception unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is excited, the reception unit can immediately prompt information input to increase the user's motivation to create a story. Also, if the user is tired, the reception unit can suggest a break and allow the user to input information in a relaxed state. Furthermore, if the user is focused, the reception unit can allow continuous information input to avoid interrupting the flow of story creation. In this way, by adjusting the timing of information input based on the user's emotions, the user's motivation to create a story can be increased. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception desk can optimize the timing of information input by using an AI model that takes user emotion data as input and outputs the timing of information input.

[0084] The reception desk can analyze the user's past story creation history and select the optimal information input method. The reception desk analyzes the past story creation history using, for example, data mining techniques and types of historical data. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has previously preferred. The reception desk can also suggest relevant input items by referring to the themes and styles of stories the user has created in the past. Furthermore, the reception desk can extract specific patterns from the user's past story creation history and suggest efficient information input methods. In this way, the optimal information input method can be selected by analyzing the user's past story creation history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can optimize the information input method using an AI model that takes the user's past story creation history data as input and outputs the optimal information input method.

[0085] The reception desk can customize input fields based on the user's current interests and preferences when information is entered. The reception desk identifies current interests and preferences, for example, using surveys or analysis of behavioral history. The reception desk can reflect themes or characters that the user has recently been interested in in the input fields. The reception desk can also add questions related to topics that the user is currently interested in. Furthermore, the reception desk can suggest story settings and characters based on the user's recent activities and hobbies. This can increase the user's motivation to create a story by customizing input fields based on their current interests and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can optimize input fields using an AI model that takes the user's current interest and preference data as input and customizes the input fields.

[0086] The reception unit can estimate the user's emotions and determine the priority of the information to be input based on the estimated emotions. The reception unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is excited, the reception unit will prioritize inputting important information. If the user is relaxed, the reception unit can also prompt the user to input detailed information sequentially. Furthermore, if the user is in a hurry, the reception unit can prioritize inputting only the minimum necessary information. This ensures that the flow of story creation is not interrupted by determining the priority of the information to be input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can optimize the priority of information using an AI model that takes user emotion data as input and outputs the priority of information.

[0087] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during information input. The reception desk can identify geographical location information using, for example, GPS data or IP address. For example, if the user is in a specific region, the reception desk can suggest story settings and characters related to that region. If the user is traveling, the reception desk can also suggest story themes and scenes related to the travel destination. Furthermore, if the user is at home, the reception desk can suggest story ideas related to events in the home or daily life. In this way, by prioritizing input of highly relevant information considering the user's geographical location, it is possible to suggest story settings and characters. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can optimize the priority of information using an AI model that takes the user's geographical location information as input and outputs highly relevant information.

[0088] The reception desk can analyze the user's social media activity and prompt them to input relevant information when they enter data. The reception desk analyzes social media activity, for example, by analyzing the content of posts and the number of followers. For example, the reception desk can suggest story themes and characters based on what the user has recently shared on social media. The reception desk can also suggest story ideas related to topics of accounts and groups that the user follows. Furthermore, the reception desk can suggest story settings and scenes based on posts and articles that the user has shown interest in on social media. In this way, story themes and characters can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can optimize the priority of information using an AI model that takes the user's social media activity data as input and outputs relevant information.

[0089] The generation unit can estimate the user's emotions and adjust the story generation method based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the generation unit generates a story that progresses at a leisurely pace. Also, if the user is excited, the generation unit can generate a story that emphasizes action and adventure elements. Furthermore, if the user is sad, the generation unit can generate a touching and heartwarming story. In this way, by adjusting the story generation method based on the user's emotions, the story can progress smoothly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can optimize the story generation method using an AI model that takes user emotion data as input and outputs a story generation method.

[0090] The generation unit can adjust the level of detail generated based on the importance of the story during story generation. The generation unit evaluates the importance of a story based on factors such as user ratings and the importance of the story's theme. For example, the generation unit can provide detailed descriptions in the climax of a story to heighten the tension. It can also provide concise descriptions in the introduction to ensure a smooth progression of the story. Furthermore, it can provide emotionally moving descriptions in the conclusion of a story to leave a lasting impression on the reader. In this way, adjusting the level of detail generated based on the importance of the story can ensure a smooth progression of the story. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the level of detail generated by using an AI model that takes story importance data as input and outputs the level of detail of the generated story.

[0091] The generation unit can apply different generation algorithms depending on the category of the story when generating a story. For example, the generation unit classifies the story category based on genre, target age, theme, etc. For example, in the case of an adventure story, the generation unit applies an algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the generation unit can also apply an algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the generation unit can apply an algorithm that includes learning elements and knowledge. By applying different generation algorithms depending on the category of the story, the story can progress more smoothly. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the generation algorithm using an AI model that takes story category data as input and outputs a generation algorithm.

[0092] The generation unit can estimate the user's emotions and adjust the length of the story based on those emotions. The generation unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is in a hurry, the generation unit generates a short, concise story. If the user is relaxed, the generation unit can generate a longer story with more detailed descriptions. Furthermore, if the user is excited, the generation unit can generate a faster-paced story. This allows for a smoother narrative progression by adjusting the story length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can optimize the story length using an AI model that takes user emotion data as input and outputs the story length.

[0093] The generation unit can determine the generation priority based on the story's submission date when generating a story. For example, the generation unit identifies the story's submission date based on factors such as the submission deadline and submission frequency. For example, the generation unit prioritizes generating a story when the deadline is approaching. It can also prioritize generating other stories when the submission date is far off. Furthermore, the generation unit can adjust the level of detail in the generation according to the submission date. This allows for a smoother progression of the story by determining the generation priority based on the story's submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the generation priority using an AI model that takes story submission date data as input and outputs the generation priority.

[0094] The generation unit can adjust the generation order based on the relevance of the stories during story generation. The generation unit evaluates the relevance of stories based on factors such as thematic consistency and character relationships. The generation unit generates related stories sequentially, for example, by considering the context of the stories. The generation unit can also adjust the generation order by considering the themes and character relationships of the stories. Furthermore, the generation unit can optimize the generation order according to the progress of the story. This allows for smoother story progression by adjusting the generation order based on the relevance of the stories. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the generation order using an AI model that takes story relevance data as input and outputs the generation order.

[0095] The generation unit can estimate the user's emotions and adjust the image generation method based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the generation unit can generate images with soft colors. The generation unit can also generate images with vivid colors if the user is excited. Furthermore, if the user is sad, the generation unit can generate images with calm colors. In this way, by adjusting the image generation method based on the user's emotions, the visual elements of the story can be enjoyed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can optimize the image generation method using an AI model that takes user emotion data as input and outputs an image generation method.

[0096] The generation unit can adjust the level of detail in the images based on important scenes in the story during image generation. For example, the generation unit identifies important scenes in the story based on climax scenes or key moments for characters. For example, in the climax scenes of the story, the generation unit can provide detailed descriptions to heighten the tension. The generation unit can also provide concise descriptions in the introductory parts of the story to ensure a smooth progression of the narrative. Furthermore, the generation unit can provide emotionally moving descriptions in the ending of the story to leave a lasting impression on the reader. In this way, by adjusting the level of detail in the images based on important scenes in the story, the visual elements of the story can be enjoyed. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the level of detail in the images using an AI model that takes important scene data from the story as input and outputs the level of detail in the images.

[0097] The generation unit can apply different generation algorithms depending on the story category when generating images. For example, the generation unit classifies story categories based on genre, target age, theme, etc. For example, in the case of an adventure story, the generation unit applies an algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the generation unit can also apply an algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the generation unit can apply an algorithm that includes learning elements and knowledge. In this way, by applying different generation algorithms depending on the story category, the visual elements of the story can be enjoyed. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can optimize the generation algorithm using an AI model that takes story category data as input and outputs a generation algorithm.

[0098] The generation unit can estimate the user's emotions and adjust the style of the illustration based on those emotions. The generation unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the generation unit can generate an illustration with a soft touch. If the user is excited, the generation unit can also generate an illustration with a strong touch. Furthermore, if the user is sad, the generation unit can generate an illustration with a calm touch. This allows users to enjoy the visual elements of a story by adjusting the style of the illustration based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can optimize the style of the illustration using an AI model that takes user emotion data as input and outputs an illustration style.

[0099] The generation unit can determine the priority of images based on the story's submission timing when generating images. For example, the generation unit identifies the story's submission timing based on factors such as the submission deadline and submission frequency. For example, if the deadline is approaching, the generation unit will prioritize generating images. Conversely, if the submission timing is far off, the generation unit can prioritize generating other images. Furthermore, the generation unit can adjust the level of detail of the generation according to the submission timing. This allows users to enjoy the visual elements of the story by determining the priority of images based on the story's submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the priority of images using an AI model that takes story submission timing data as input and outputs the priority of images.

[0100] The generation unit can adjust the order of images based on their relevance to the story during image generation. The generation unit evaluates the relevance of the story based on factors such as thematic consistency and character relationships. The generation unit generates related images in sequence, for example, taking into account the context of the story. The generation unit can also adjust the generation order by considering the themes and character relationships of the story. Furthermore, the generation unit can optimize the generation order according to the progress of the story. This allows users to enjoy the visual elements of the story by adjusting the order of images based on their relevance to the story. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can optimize the order of images using an AI model that takes story relevance data as input and outputs the order of images.

[0101] The questioning unit can estimate the user's emotions and adjust the content of the questions based on the estimated emotions. The questioning unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the questioning unit can ask detailed questions to encourage deeper exploration of the story. If the user is excited, the questioning unit can ask concise questions to smooth the progression of the story. Furthermore, if the user is sad, the questioning unit can ask emotionally charged questions to emphasize the emotional aspects of the story. In this way, by adjusting the content of the questions based on the user's emotions, the progression of the story can be made smoother. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the questioning unit may be performed using, for example, AI, or not using AI. For example, the questioning unit can optimize the content of the questions using an AI model that takes user emotion data as input and outputs the content of the questions.

[0102] The questioning unit can adjust the level of detail of questions based on the progress of the story. For example, the questioning unit can adjust the level of detail of questions based on changes in the questions or the level of detail of questions based on the progress of the story. For example, in the introductory part of the story, the questioning unit can ask concise questions to ensure a smooth progression of the story. Also, in the climactic scene of the story, the questioning unit can ask detailed questions to encourage deeper exploration of the story. Furthermore, in the concluding part of the story, the questioning unit can ask emotionally charged questions to emphasize the emotional aspects of the story. In this way, by adjusting the level of detail of questions based on the progress of the story, the progression of the story can be made smoother. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can optimize the level of detail of questions using an AI model that takes story progress data as input and outputs the level of detail of questions.

[0103] The questioning unit can apply different questioning algorithms depending on the category of the story when asking questions. The questioning unit applies a questioning algorithm based, for example, on the selection and application method of the algorithm according to the category. For example, in the case of an adventure story, the questioning unit asks questions related to action and adventure. In the case of a fantasy story, the questioning unit can also ask questions related to magic and fantastical elements. Furthermore, in the case of an educational story, the questioning unit can also ask questions related to learning elements and knowledge. By applying different questioning algorithms according to the category of the story, the progression of the story can be made smoother. Some or all of the above processing in the questioning unit may be performed using, for example, AI, or not using AI. For example, the questioning unit can optimize the questioning algorithm using an AI model that takes story category data as input and outputs a questioning algorithm.

[0104] The questioning unit can estimate the user's emotions and adjust the order of questions based on the estimated emotions. The questioning unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the questioning unit may ask detailed questions sequentially to encourage deeper exploration of the story. Conversely, if the user is excited, the questioning unit may prioritize concise questions to smooth the narrative progression. Furthermore, if the user is sad, the questioning unit may prioritize emotionally charged questions to emphasize the emotional aspects of the story. This allows for smoother narrative progression by adjusting the order of questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or not. For example, the questioning unit can optimize the order of questions using an AI model that takes user emotion data as input and outputs the order of questions.

[0105] The questioning unit can prioritize questions based on the story's submission timing. For example, the questioning unit can identify the story's submission timing based on factors such as the submission deadline and submission frequency. If the deadline is approaching, the questioning unit will prioritize important questions. If the submission date is far off, the questioning unit can also ask detailed questions sequentially. Furthermore, the questioning unit can adjust the level of detail of the questions according to the submission timing. This allows for smoother story progression by prioritizing questions based on the story's submission timing. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can optimize question prioritization using an AI model that takes story submission timing data as input and outputs question priorities.

[0106] The questioning unit can adjust the content of questions based on their relevance to the story. For example, the questioning unit can adjust the content of questions based on changes in the relevance of the story or the level of detail of the questions. For example, the questioning unit can ask related questions in sequence, taking into account the context of the story. The questioning unit can also adjust the content of questions by considering the themes and character relationships of the story. Furthermore, the questioning unit can optimize the content of questions according to the progress of the story. By adjusting the content of questions based on the relevance of the story, the story can progress more smoothly. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not. For example, the questioning unit can optimize the content of questions using an AI model that takes story relevance data as input and outputs question content.

[0107] The display unit can estimate the user's emotions and adjust the display method of the story and images based on the estimated user emotions. The display unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the display unit can provide a display method with soft colors. The display unit can also provide a display method with vivid colors if the user is excited. Furthermore, the display unit can provide a display method with calm colors if the user is sad. In this way, by adjusting the display method of the story and images based on the user's emotions, the visual elements of the story can be enjoyed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can optimize the display method using an AI model that takes user emotion data as input and outputs the display method of the story and images.

[0108] The display unit can adjust the level of detail based on important scenes in the story during display. For example, the display unit adjusts the level of detail based on changes in the display or the level of detail based on important scenes in the story. For example, the display unit can provide detailed descriptions in the climax of the story to heighten the tension. The display unit can also provide concise descriptions in the introduction of the story to ensure a smooth progression of the narrative. Furthermore, the display unit can provide moving descriptions in the conclusion of the story to leave a lasting impression on the reader. In this way, by adjusting the level of detail based on important scenes in the story, the visual elements of the story can be enjoyed. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can optimize the level of detail using an AI model that takes important scene data from the story as input and outputs the level of detail.

[0109] The display unit can apply different display algorithms depending on the category of the story during display. The display unit applies a display algorithm based, for example, on the selection and application method of the algorithm according to the category. For example, in the case of an adventure story, the display unit applies a display algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the display unit can also apply a display algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the display unit can apply a display algorithm that includes learning elements and knowledge. In this way, by applying different display algorithms according to the category of the story, the visual elements of the story can be enjoyed. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can optimize the display algorithm using an AI model that takes story category data as input and outputs a display algorithm.

[0110] The display unit can estimate the user's emotions and adjust the display order based on the estimated emotions. The display unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the display unit will sequentially display detailed information. The display unit can also prioritize displaying important information if the user is excited. Furthermore, if the user is sad, the display unit can prioritize displaying emotional information. In this way, by adjusting the display order based on the user's emotions, the user can enjoy the visual elements of the story. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can optimize the display order using an AI model that takes user emotion data as input and outputs the display order.

[0111] The display unit can determine the display priority based on the story's submission date when displaying it. The display unit identifies the story's submission date based on factors such as the submission deadline and submission frequency. For example, if the deadline is approaching, the display unit will prioritize displaying important information. If the submission date is far away, the display unit can also sequentially display detailed information. Furthermore, the display unit can adjust the level of detail displayed according to the submission date. This allows users to enjoy the visual elements of the story by determining the display priority based on the story's submission date. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can optimize the display priority using an AI model that takes story submission date data as input and outputs the display priority.

[0112] The display unit can adjust the displayed content based on the relevance of the story during display. For example, the display unit adjusts the displayed content based on changes in the display based on the relevance of the story or the level of detail in the display. For example, the display unit displays related information sequentially, taking into account the context of the story. The display unit can also adjust the displayed content considering the themes and character relationships of the story. Furthermore, the display unit can optimize the displayed content according to the progress of the story. This allows users to enjoy the visual elements of the story by adjusting the displayed content based on the relevance of the story. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can optimize the displayed content using an AI model that takes story relevance data as input and outputs the displayed content.

[0113] The narrative unit can estimate the user's emotions and adjust the narrative progression based on those emotions. The narrative unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For instance, if the user is relaxed, the narrative unit will progress at a leisurely pace. Conversely, if the user is excited, the narrative unit can also progress at a faster pace. Furthermore, if the user is sad, the narrative unit can emphasize emotional scenes. This allows for a smoother narrative progression by adjusting the narrative progression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the narrative unit may be performed using, for example, AI, or not. For example, the narrative unit can optimize the narrative progression using an AI model that takes user emotion data as input and outputs a narrative progression method.

[0114] The plot progression unit can adjust the level of detail in the plot progression based on important scenes in the story. For example, the plot progression unit adjusts the level of detail in the plot progression based on changes in the plot progression based on important scenes in the story. For example, in the climax scene of the story, the plot progression unit can provide detailed descriptions to heighten the tension. Also, in the introductory part of the story, the plot progression unit can provide concise descriptions to make the plot progression smoother. Furthermore, in the final part of the story, the plot progression unit can provide moving descriptions to make it memorable for the reader. In this way, the plot progression unit can make the plot progression smoother by adjusting the level of detail in the plot progression based on important scenes in the story. Some or all of the above processing in the plot progression unit may be performed using AI, for example, or not using AI. For example, the plot progression unit can optimize the level of detail in the plot progression using an AI model that takes important scene data of the story as input and outputs the level of detail in the plot progression.

[0115] The narrative progression unit can apply different progression algorithms depending on the category of the story during progression. The progression unit applies a progression algorithm based, for example, on the selection and application method of the algorithm according to the category. For example, in the case of an adventure story, the progression unit applies a progression algorithm that emphasizes action scenes and tense scenes. In the case of a fantasy story, the progression unit can also apply a progression algorithm that emphasizes magic and fantastical elements. Furthermore, in the case of an educational story, the progression unit can apply a progression algorithm that includes learning elements and knowledge. In this way, by applying different progression algorithms according to the category of the story, the progression of the story can be made smoother. Some or all of the above processing in the progression unit may be performed using, for example, AI, or not using AI. For example, the progression unit can optimize the progression algorithm using an AI model that takes story category data as input and outputs a progression algorithm.

[0116] The narrative unit can estimate the user's emotions and adjust the order of presentation based on those emotions. The narrative unit estimates the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the narrative unit will sequentially present detailed information. If the user is excited, the narrative unit can prioritize presenting important information. Furthermore, if the user is sad, the narrative unit can prioritize presenting emotionally moving information. This allows for a smoother narrative progression by adjusting the order of presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the narrative unit may be performed using, for example, AI, or not. For example, the narrative unit can optimize the order of presentation using an AI model that takes user emotion data as input and outputs the order of presentation.

[0117] The progress unit can determine the priority of the story's progress based on its submission timing. For example, the progress unit can identify the story's submission timing based on factors such as submission deadlines and submission frequency. For example, if the deadline is approaching, the progress unit can prioritize the processing of important information. If the submission date is far off, the progress unit can also process detailed information sequentially. Furthermore, the progress unit can adjust the level of detail in the process according to the submission timing. This allows for smoother story progression by determining the priority of the process based on the story's submission timing. Some or all of the above processing in the progress unit may be performed using AI, for example, or without AI. For example, the progress unit can optimize the priority of the process using an AI model that takes story submission timing data as input and outputs the priority of the process.

[0118] The progression unit can adjust the content of the story based on the relevance of the narrative as it progresses. For example, the progression unit adjusts the content of the story based on changes in the progression based on the relevance of the narrative or the level of detail in the progression. For example, the progression unit advances relevant information sequentially, taking into account the context of the story. The progression unit can also adjust the content of the story by taking into account the themes of the story and the relevance of the characters. Furthermore, the progression unit can optimize the content of the story according to the progress of the story. In this way, the story can progress smoothly by adjusting the content of the story based on the relevance of the narrative. Some or all of the above processes in the progression unit may be performed using AI, for example, or not using AI. For example, the progression unit can optimize the content of the story using an AI model that takes narrative relevance data as input and outputs the content of the story.

[0119] The service provider can estimate the user's emotions and adjust the service delivery method based on the estimated emotions. The service provider can estimate the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the service provider can provide the service with an interface in soft colors. If the user is excited, the service provider can also provide the service with an interface in bright colors. Furthermore, if the user is sad, the service provider can also provide the service with an interface in calm colors. In this way, by adjusting the service delivery method based on the user's emotions, the service provider can provide the optimal service to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can optimize the service delivery method using an AI model that takes user emotion data as input and outputs a service delivery method.

[0120] The service provider can provide the most suitable service by referring to the user's past usage history when providing the service. For example, the service provider can customize and provide the most suitable service based on the user's past usage history. For example, the service provider can suggest related services based on the services the user has used in the past. The service provider can also customize and provide the most suitable service based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and prioritize providing the most frequently used services. In this way, by providing the most suitable service by referring to the user's past usage history, the service provider can provide the most suitable service to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize the service provision method using an AI model that takes the user's past usage history data as input and outputs the most suitable service.

[0121] The service provider can customize the service content based on the user's current interests and preferences when providing the service. For example, the service provider can customize and provide the service content based on the user's current interests and preferences. For example, the service provider can provide services related to themes or topics that the user has recently become interested in. The service provider can also customize and provide services related to fields that the user is currently interested in. Furthermore, the service provider can suggest the most suitable service based on the user's recent activities and hobbies. In this way, by customizing the service content based on the user's current interests and preferences, the service provider can provide the user with the most suitable service. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can optimize the service content using an AI model that takes the user's current interest data as input and outputs the service content.

[0122] The service provider can estimate the user's emotions and determine service priorities based on those emotions. The service provider can estimate the user's emotions using, for example, facial recognition technology or voice analysis technology. For example, if the user is relaxed, the service provider can sequentially provide detailed services. Furthermore, if the user is excited, the service provider can prioritize providing important services. Additionally, if the user is sad, the service provider can prioritize providing emotionally moving services. This allows the service provider to provide the most optimal service to the user by determining service priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can optimize service priorities using an AI model that takes user emotion data as input and outputs service priorities.

[0123] The service provider can prioritize providing highly relevant services by considering the user's geographical location information when providing services. The service provider can identify geographical location information using, for example, GPS data or IP addresses. For example, if the user is in a specific region, the service provider can provide services related to that region. Furthermore, if the user is traveling, the service provider can also provide services related to the travel destination. In addition, if the user is at home, the service provider can also provide services that can be used within the home. By prioritizing the provision of highly relevant services while considering the user's geographical location information, the service provider can provide the user with the most optimal service. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can optimize the service provision method using an AI model that takes the user's geographical location information as input and outputs highly relevant services.

[0124] The service provider can analyze a user's social media activity and provide relevant services when providing services. The service provider can analyze social media activity using methods such as analyzing posted content and analyzing followers. The service provider can provide relevant services based on content recently shared by the user on social media. The service provider can also provide services related to topics of accounts and groups that the user follows. Furthermore, the service provider can provide relevant services based on posts and articles that the user has shown interest in on social media. In this way, by analyzing the user's social media activity and providing relevant services, the service provider can provide the most suitable service to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can optimize the method of service provision using an AI model that takes user social media activity data as input and outputs relevant services.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The reception desk can generate music and sound effects related to the story's progression based on user input. For example, it can generate adventurous music or fantasy-style sound effects to match the story's theme selected by the user. The reception desk can also provide different music and sound effects for each scene as the story progresses. Furthermore, it can generate music and sound effects that match the emotional scenes of the story based on user input. This allows users to enjoy not only the visual elements of the story, but also the auditory elements.

[0127] The display unit can adjust the color tone and font based on the user's emotions when displaying the generated story and images. For example, if the user is relaxed, soft colors and an easy-to-read font will be used. If the user is excited, vibrant colors and a strong font can be used. Furthermore, if the user is sad, calm colors and a gentle font can be used. This provides a display method that matches the user's emotions, allowing them to enjoy the visual elements of the story more.

[0128] The narrative unit can estimate the user's emotions and adjust the pace of the story based on those emotions. For example, if the user is relaxed, the story will progress at a leisurely pace. If the user is excited, the story can progress at a faster pace. Furthermore, if the user is sad, the story can progress by emphasizing emotional scenes. In this way, the story can progress smoothly by adjusting the pace of the story based on the user's emotions.

[0129] The service provider can estimate the user's emotions and adjust the service delivery method based on those estimates. For example, if the user is relaxed, the service can be delivered using a soft-colored interface. If the user is excited, the service can be delivered using a bright-colored interface. Furthermore, if the user is sad, the service can be delivered using a calm-colored interface. By adjusting the service delivery method based on the user's emotions, the service provider can deliver the most optimal service to the user.

[0130] The reception desk can estimate the user's emotions and adjust the timing of information input to determine the story's framework based on those estimated emotions. For example, if the user is excited, it can immediately prompt for information input to increase their motivation to create a story. If the user is tired, it can suggest a break and allow them to input information in a relaxed state. Furthermore, if the user is focused, it can allow for continuous information input to avoid interrupting the story creation process. In this way, by adjusting the timing of information input based on the user's emotions, it is possible to increase their motivation to create a story.

[0131] The reception desk can analyze the user's past story creation history and select the most suitable information input method. For example, it can prioritize suggesting input methods the user has previously preferred (such as voice or text). The reception desk can also suggest relevant input fields based on the themes and styles of stories the user has created in the past. Furthermore, the reception desk can extract specific patterns from the user's past story creation history and suggest efficient information input methods. In this way, by analyzing the user's past story creation history, the optimal information input method can be selected.

[0132] The input field can be customized based on the user's current interests and preferences during information entry. For example, it can reflect themes or characters the user has recently been interested in in the input fields. The input field can also add questions related to topics the user is currently interested in. Furthermore, the input field can suggest story settings and characters based on the user's recent activities and hobbies. By customizing the input field based on the user's current interests and preferences, this can increase their motivation to create stories.

[0133] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, if a user is in a specific region, it can suggest story settings and characters related to that region. If a user is traveling, it can also suggest story themes and scenes related to their travel destination. Furthermore, if a user is at home, it can suggest story ideas related to events in the home or daily life. In this way, by prioritizing input of highly relevant information based on the user's geographical location, it can suggest story settings and characters.

[0134] The reception desk can analyze the user's social media activity during information entry and prompt them to input relevant information. For example, it can suggest story themes and characters based on what the user has recently shared on social media. The reception desk can also suggest story ideas related to topics of accounts and groups the user follows. Furthermore, it can suggest story settings and scenes based on posts and articles the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it is possible to suggest story themes and characters.

[0135] The generation unit can adjust the level of detail generated based on the importance of each part of the story. For example, it can provide detailed descriptions in the climax of a story to heighten the tension. Conversely, it can use concise descriptions in the introduction to ensure a smooth narrative progression. Furthermore, it can provide emotionally moving descriptions in the conclusion to leave a lasting impression on the reader. In this way, by adjusting the level of detail generated based on the importance of each part of the story, the narrative can be made to progress more smoothly.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The reception unit receives information from the user to determine the core of the story. This information includes, for example, character settings, a plot outline, and themes. The reception unit saves the information entered by the user to a database and provides it to the generation unit. Step 2: The generation unit uses a generation AI to generate a story based on the information input by the reception unit. The generation unit uses storytelling algorithms and procedural generation techniques to generate the story and determine the plot development and character actions. For example, if the generation AI receives the prompt "the protagonist goes on an adventure," it generates the plot development. Step 3: The generation unit uses image generation AI to generate images that match the generated story. The generation unit generates images using image generation algorithms and deep learning technology, and determines the details of the images based on the style of images selected by the user. For example, if the image generation AI receives a prompt such as "Draw a scene of the protagonist on an adventure in a watercolor style," it will generate an image. Step 4: The questioning unit is involved in the progression of the story. The questioning unit asks the user questions at key points in the story and advances the story based on the user's answers. For example, it might ask, "Where did the protagonist decide to go next?" and determine the story's development based on the user's answer. The questioning unit saves the user's answers in a database and provides them to the generation unit.

[0138] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0141] Each of the multiple elements described above, including the reception unit, generation unit, questioning unit, display unit, progress unit, and service provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs information to determine the outline of the story. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, where a story is generated using a generation AI. The questioning unit is implemented by the control unit 46A of the smart device 14, where questions related to the progress of the story are asked. The display unit is implemented by the display 40A of the smart device 14, where the generated story and illustrations are displayed. The progress unit is implemented by the specific processing unit 290 of the data processing device 12, where the story progresses based on the user's answers. The service provision unit is implemented by the specific processing unit 290 of the data processing device 12, where services such as the use of illustrations by picture book creators and the regular delivery of the created books are provided. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, questioning unit, display unit, progression unit, and service provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs information to determine the outline of the story. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, where a story is generated using a generation AI. The questioning unit is implemented by the control unit 46A of the smart glasses 214, where questions related to the progression of the story are asked. The display unit is implemented by the display 40A of the smart glasses 214, where the generated story and illustrations are displayed. The progression unit is implemented by the specific processing unit 290 of the data processing device 12, where the story progresses based on the user's answers. The service provision unit is implemented by the specific processing unit 290 of the data processing device 12, where services such as the use of illustrations by picture book creators and the regular delivery of the created books are provided. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, generation unit, questioning unit, display unit, progress unit, and service provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs information to determine the outline of the story. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a story is generated using a generation AI. The questioning unit is implemented by the control unit 46A of the headset terminal 314, where questions related to the progress of the story are asked. The display unit is implemented by the display 40A of the headset terminal 314, where the generated story and illustrations are displayed. The progress unit is implemented by the specific processing unit 290 of the data processing unit 12, where the story progresses based on the user's answers. The service provision unit is implemented by the specific processing unit 290 of the data processing unit 12, where services such as the use of illustrations by picture book creators and the regular delivery of the created books are provided. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the reception unit, generation unit, questioning unit, display unit, progress unit, and service provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs information to determine the outline of the story. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a story is generated using a generation AI. The questioning unit is implemented by the control unit 46A of the robot 414, where questions related to the progress of the story are asked. The display unit is implemented by the display 40A of the robot 414, where the generated story and pictures are displayed. The progress unit is implemented by the specific processing unit 290 of the data processing unit 12, where the story progresses based on the user's answers. The service provision unit is implemented by the specific processing unit 290 of the data processing unit 12, where services such as the use of picture book creators' illustrations and the regular delivery of the created books are provided. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0191] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] 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.

[0201] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) A reception area where information is entered to determine the core of the story, A generation unit that generates a story based on the information input by the reception unit, A generation unit that generates pictures that match the story generated by the aforementioned generation unit, It includes a section for questions that are relevant to the progression of the story. A system characterized by the following features. (Note 2) It features a display unit that shows the generated story and images. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a progression section that advances the story based on user responses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The platform includes a service department that provides opportunities for using picture book creators' illustrations, regular delivery of created books, browsing other users' picture books, and publishing through contests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of information input to determine the core of the story based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past story creation history and select the appropriate information input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering information, input fields are customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter information, the system prioritizes inputting information that is highly relevant based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter information, the system analyzes their social media activity and prompts them to enter relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the story generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a story, adjust the level of detail based on the importance of the story. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a story, different generation algorithms are applied depending on the story category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the length of the story based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating stories, the generation priority is determined based on when the stories were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating stories, adjust the generation order based on the relevance of the stories. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the image generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating images, adjust the level of detail based on important scenes in the story. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating images, different generation algorithms are applied depending on the story category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the art style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating images, the priority of the images is determined based on when the story was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating images, the order of the images is adjusted based on their relevance to the story. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question section is, The system estimates the user's emotions and adjusts the content of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned question section is, When asking questions, adjust the level of detail based on the progress of the story. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned question section is, When asking questions, different question algorithms are applied depending on the category of the story. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned question section is, The system estimates the user's emotions and adjusts the order of questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned question section is, When asking questions, prioritize them based on when the stories were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned question section is, When asking questions, adjust the content of the questions based on their relevance to the story. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is It estimates the user's emotions and adjusts how the story and images are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying, adjust the level of detail based on important scenes in the story. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying, different display algorithms are applied depending on the story category. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned display unit is It estimates the user's emotions and adjusts the display order based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned display unit is When displaying stories, the display priority is determined based on when the stories were submitted. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned display unit is When displaying, adjust the displayed content based on the relevance of the story. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned progress section is, It estimates the user's emotions and adjusts the story's progression based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned progress section is, During progression, adjust the level of detail based on important scenes in the story. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned progress section is, During gameplay, different progression algorithms are applied depending on the story category. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned progress section is, It estimates the user's emotions and adjusts the order of the process based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned progress section is, During the process, prioritize the progress based on when the stories are submitted. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned progress section is, During the process, adjust the content of the story based on its relevance to the narrative. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned service provision unit, We estimate the user's emotions and adjust the way we deliver the service based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned service provision unit, When providing services, we refer to the user's past usage history to provide the most suitable service. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned service provision unit, When providing the service, the service content is customized based on the user's current interests and preferences. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned service provision unit, It estimates user sentiment and prioritizes services based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned service provision unit, When providing services, we prioritize providing highly relevant services by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned service provision unit, When providing the service, we analyze the user's social media activity and provide relevant services. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where information is entered to determine the core of the story, A generation unit that generates a story based on the information input by the reception unit, A generation unit that generates pictures that match the story generated by the aforementioned generation unit, It includes a section for questions that are relevant to the progression of the story. A system characterized by the following features.

2. It features a display unit that shows the generated story and images. The system according to feature 1.

3. It features a progression section that advances the story based on user responses. The system according to feature 1.

4. The platform includes a service department that provides opportunities for using picture book creators' illustrations, regular delivery of created books, browsing other users' picture books, and publishing through contests. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of information input to determine the core of the story based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past story creation history and select the appropriate information input method. The system according to feature 1.

7. The aforementioned reception unit is When entering information, input fields are customized based on the user's current interests and preferences. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When users enter information, the system prioritizes inputting information that is highly relevant based on their geographical location. The system according to feature 1.

Images