System

Abstract

An object of the system according to the embodiment is to automatically generate a unique comic book even for a user having no technical ability.SOLUTION: A system includes a story setting input part, a character setting input part, an expression style selection part, and a comic generation part. The story setting input unit receives story settings input by a user. The character setting input unit analyzes the story setting received by the story setting input unit. The expression style selection unit generates the character setting analyzed by the character setting input unit. The cartoon generation unit automatically generates a cartoon based on the expression style selected by the expression style selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for users without technical skills to create their own manga, and it has been time-consuming and costly.

[0005] The system according to the embodiment aims to enable even users without technical skills to automatically create their own original manga. [Means for solving the problem]

[0006] The system according to the embodiment includes a story setting input unit, a character setting input unit, an expression style selection unit, and a manga generation unit. The story setting input unit accepts a story setting input by a user. The character setting input unit analyzes the story setting accepted by the story setting input unit. The expression style selection unit generates a character setting analyzed by the character setting input unit. The manga generation unit automatically generates a manga based on the expression style selected by the expression style selection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even non-technical users to automatically create their own comics. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A manga creation system according to an embodiment of the present invention is a system that automatically creates manga based on a story setting, character setting, and expression style input by a user. This allows even users without technical skills to create and express their own manga.

[0029] A manga generation system according to an embodiment includes a story setting input unit, a character setting input unit, an expression style selection unit, and a manga generation unit. The story setting input unit accepts a story setting input by a user. For example, the user may input a prompt such as "A story about a protagonist going on an adventure." The story setting input unit accepts input for the generation AI to generate a story framework. The character setting input unit analyzes the story setting accepted by the story setting input unit. For example, the generation AI may analyze a prompt such as "The protagonist is a brave boy, and his friend is a magical girl" and generate the character's appearance and personality. The expression style selection unit generates the character setting analyzed by the character setting input unit. For example, the user can select a style such as "serious tone" or "comical touch." The generation AI applies a manga visual style based on this selection. The manga generation unit automatically generates a manga based on the expression style selected by the expression style selection unit. For example, the generation AI creates a storyboard and arranges the layout and dialogue for each scene. This allows manga to be automatically generated based on the story setting, character settings, and expression style entered by the user.

[0030] The story setting input unit can learn the user's past input history and propose story settings that match the user's preferences. In the story setting input unit, for example, the generation AI analyzes the user's past input history and identifies the user's preferred themes and genres. For example, based on the story settings entered in the past, the unit proposes stories that include the user's preferred adventure and fantasy elements. The story setting input unit also learns the user's past input history and extracts specific character and plot patterns. For example, based on the user's preferred character personalities and story developments, the unit proposes new story settings. The generation AI also identifies the user's preferred story tempo and tone based on the user's past input history. For example, the unit proposes story settings that reflect the user's preferred serious tone or comical touch. This makes it possible to propose story settings that match the user's preferences.

[0031] The story setting input unit provides real-time feedback when the story setting is input, helping the user to create a more specific setting. For example, the generation AI in the story setting input unit provides real-time feedback to the user's input, increasing the specificity of the story setting. For example, when the user inputs "a story about going on an adventure," the generation AI suggests specific adventure locations and goals. In addition, when the user inputs the story setting, the generation AI suggests related ideas and plots in real time. For example, when the user inputs "a story about school life," the generation AI suggests specific episodes and character relationships. In addition, the story setting input unit complements the details of the story setting in real time based on the user's input. For example, when the user inputs "a story about the main character going on an adventure," the generation AI suggests details of the adventure's background and destination. This helps the user to create a more specific story setting.

[0032] The story setting input unit can accommodate voice input or gesture input for inputting story settings. For example, the story setting input unit accommodates voice input for inputting story settings, allowing the user to set the story simply by speaking. For example, if the user speaks, "A story about the main character going on an adventure," the generation AI analyzes the content and sets the story. The story setting input unit also uses gesture input to allow the user to set the story with hand movements or touch operations. For example, when the user makes a specific gesture on the screen, the generation AI suggests a story setting that corresponds to that gesture. The story setting input unit also combines voice input and gesture input to enable more intuitive story setting. For example, if the user uses hand movements to specify the position and movement of a character while speaking, the generation AI analyzes the content and sets the story. This allows for more intuitive operation by supporting voice input and gesture input.

[0033] The story setting input unit can automatically generate story settings for different genres and themes, providing new inspiration to the user. For example, the generation AI in the story setting input unit automatically generates story settings for different genres and themes and suggests them to the user. For example, the user can choose from genres such as fantasy, science fiction, and mystery. When the user inputs a specific theme, the generation AI automatically generates multiple story settings related to that theme. For example, if the theme is "adventure," the generation AI will suggest different adventure scenarios. The story setting input unit can also suggest story settings for new genres and themes based on the user's preferences and past input history. For example, the generation AI may suggest a new theme related to a genre that the user has previously liked. This allows the user to be provided with new inspiration by automatically generating story settings for different genres and themes.

[0034] The character setting input unit automatically generates character backstories and relationships, making it possible to create characters with more depth. In the character setting input unit, for example, the generation AI automatically generates a character's backstory and sets the character's background and past events in detail. For example, it proposes a backstory based on the protagonist's upbringing and past experiences. The character setting input unit also automatically generates relationships between characters and sets the interactions within the story. For example, it sets the relationships between the protagonist and his friends and enemies in detail. The character setting input unit also allows the generation AI to set the character's actions and personality in detail based on the character's backstory and relationships. For example, it sets how past experiences affect the character's personality and actions. In this way, by automatically generating character backstories and relationships, it is possible to create characters with more depth.

[0035] The character setting input unit can suggest customization options based on the user's preferences. For example, the generation AI in the character setting input unit suggests customization options for the character's appearance and personality based on the user's preferences. For example, it provides options based on the user's preferred hairstyle, clothing, and personality traits. The character setting input unit also learns the user's past input history and suggests customization options for character settings that match the user's preferences. For example, it suggests settings for a new character based on the characteristics of characters created in the past. The character setting input unit also analyzes the user's preferences in real time using the generation AI, and dynamically provides customization options based on the results. For example, it suggests options that match the user's preferences each time the user inputs something. This makes it possible to suggest customization options based on the user's preferences.

[0036] The character setting input unit can also allow character settings to be input from images or sketches. In the character setting input unit, for example, the generation AI automatically generates a character's appearance based on images or sketches uploaded by the user. For example, the character setting input unit analyzes a character sketch drawn by the user and sets the character's appearance in detail. The character setting input unit also uses image recognition technology to extract character features from the uploaded image, and the generation AI sets the character based on those features. For example, it extracts hairstyle and clothing features from the image. The character setting input unit also automatically generates a character's appearance and personality based on a sketch drawn by the user. For example, it analyzes the facial expression and pose in the sketch and sets the character's personality. This allows character settings to be input from images or sketches, making it possible to generate characters based on visual information.

[0037] The character setting input unit can automatically generate character settings with different cultural and historical backgrounds, providing the user with a variety of options. For example, the character setting input unit allows the generation AI to automatically generate character settings with different cultural and historical backgrounds and suggest them to the user. For example, settings such as ancient Egypt, medieval Europe, and a future world are provided. Furthermore, when the user selects a specific cultural or historical background, the character setting input unit allows the generation AI to automatically generate a character setting based on that background. For example, it suggests clothing and accessories related to the selected culture or historical period. Furthermore, the character setting input unit allows the generation AI to suggest character settings with new cultural or historical backgrounds based on the user's preferences and past input history. For example, it suggests new character settings related to cultures and historical periods that the user liked in the past. This allows the user to be provided with a variety of options by automatically generating character settings with different cultural and historical backgrounds.

[0038] The expression style selection unit can learn the user's past selection history and suggest an expression style that matches the user's preferences. For example, the generation AI in the expression style selection unit analyzes the user's past selection history and identifies the user's preferred expression style. For example, based on the styles selected in the past, it suggests a serious tone or a comical touch that the user prefers. The expression style selection unit also learns the user's past selection history and extracts specific art styles and techniques. For example, it suggests a new expression style based on the user's preferred color usage or line thickness. The expression style selection unit also identifies the user's preferred visual style based on the user's past selection history. For example, it suggests an expression style that reflects the user's preferred anime style or realistic depiction. This makes it possible to suggest an expression style that matches the user's preferences.

[0039] The expression style selection unit can provide a real-time preview when an expression style is selected, allowing the user to check the results of applying the selected style. For example, the generation AI of the expression style selection unit previews the user's selected expression style in real time and displays the application results. For example, the user can instantly check how the selected style will be reflected in the manga. Furthermore, when the user selects an expression style, the generation AI provides a real-time preview and visually displays the results of applying the style. For example, it displays how the selected style will affect the character and background. Furthermore, the expression style selection unit generates a real-time preview based on the user's selected expression style, allowing the user to check the results of applying the style. For example, it displays how the selected style will affect the entire scene. This allows the user to check the results of applying the selected style in real time.

[0040] The expression style selection unit can enable a user to select an expression style based on a style extracted from an image or photo uploaded by the user. For example, the expression style selection unit uses a generation AI to extract an expression style based on an image or photo uploaded by the user and applies that style to the manga. For example, the expression style selection unit proposes a visual style based on the style of an artwork uploaded by the user. The expression style selection unit also uses image recognition technology to extract expression style features from the uploaded image, and the generation AI sets the expression style based on those features. For example, the expression style selection unit extracts color usage and line thickness from the image. The expression style selection unit also uses a generation AI to automatically generate an expression style based on a photo uploaded by the user and applies that style to the manga. For example, the expression style selection unit proposes a visual style that reflects the atmosphere and tone of the photo. This allows a user to select an expression style based on a style extracted from an image or photo uploaded by the user.

[0041] The expression style selection unit can automatically generate different art styles and techniques to provide new inspiration to the user. For example, the generation AI automatically generates different art styles and techniques and suggests them to the user. For example, it offers styles such as oil painting, watercolor painting, and digital art. In addition, when the user selects a specific art style or technique, the generation AI automatically generates visuals based on that style. For example, it suggests characters and backgrounds that match the selected style. In addition, the generation AI suggests new art styles and techniques based on the user's preferences and past selection history. For example, it suggests new techniques related to styles that the user has previously preferred. This allows the automatic generation of different art styles and techniques to provide new inspiration to the user.

[0042] The manga generation unit learns from the user's past works and can suggest manga layouts and dialogue that match the user's preferences. For example, the generation AI of the manga generation unit analyzes the user's past works and identifies the layout and dialogue patterns that the user prefers. For example, it suggests the user's preferred panel layout and dialogue style based on the past works. The manga generation unit also learns from the user's past works and extracts the placement of specific characters and scenes. For example, it suggests a new manga layout based on the user's preferred character placement and scene development. The manga generation unit also identifies the user's preferred story tempo and tone based on the user's past works. For example, it suggests layouts and dialogue that reflect the user's preferred serious tone or comical touch. This makes it possible to suggest manga layouts and dialogue that match the user's preferences.

[0043] The manga generation unit can also make the automatic generation of manga compatible with different media formats. For example, the manga generation unit allows the generation AI to make the automatic generation of manga compatible with different media formats, allowing users to generate works as animations or videos. For example, it provides a function to animate manga scenes. The manga generation unit also allows the generation AI to automatically generate animations and videos based on manga generated by users. For example, it creates animations that display manga frames in succession. The manga generation unit also allows the generation AI to convert the automatic generation of manga into different media formats, allowing users to select various means of expression. For example, it provides a function to save manga scenes as video clips. This makes it possible to make the automatic generation of manga compatible with different media formats, thereby providing users with various means of expression.

[0044] The manga generation unit can automatically generate manga of different genres and themes to provide new inspiration to users. For example, the generation AI of the manga generation unit automatically generates manga of different genres and themes and suggests them to users. For example, the user can choose from genres such as fantasy, science fiction, and mystery. In addition, when a user inputs a specific theme, the generation AI automatically generates multiple manga related to that theme. For example, if the theme is "adventure," different adventure scenarios will be suggested. In addition, the manga generation unit can suggest manga of new genres and themes based on the user's preferences and past input history. For example, it can suggest new themes related to genres that the user has previously liked. This allows the automatic generation of manga of different genres and themes to provide new inspiration to users.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The story setting input unit can automatically add related historical background and cultural elements based on the story setting entered by the user. For example, if the user enters "adventures of a medieval knight," the unit will suggest a story setting that incorporates the historical background and cultural elements of that era. The story setting input unit can also automatically generate a story setting based on actual events and legends related to the theme selected by the user. For example, if the theme is "adventures in ancient Egypt," the unit will suggest a story that incorporates actual historical events and legends. The story setting input unit can also automatically generate a story setting that incorporates geographical elements related to the theme selected by the user. For example, if the theme is "exploration in the Amazon," the unit will suggest a story that reflects the geographical features and ecosystem of the Amazon. This allows the user to provide detailed background information related to the theme selected by the user, creating a more in-depth story setting.

[0047] The character setting input unit can automatically add character growth and changes based on the character settings entered by the user. For example, if the user enters "a brave boy," the unit will suggest a character setting that reflects the boy's growth process and experiences. The character setting input unit can also automatically generate a setting that depicts the character's internal conflicts and growth based on the character's personality and background selected by the user. For example, if the theme is "an introverted girl," the unit will suggest a setting that depicts how the girl becomes confident. Furthermore, the character setting input unit can automatically generate a setting that reflects the interactions and influences between characters based on the character relationships selected by the user. For example, if the theme is "the adventures of best friends," the unit will suggest a setting that depicts the bonds and conflicts between best friends. This allows for the creation of a character setting with depth that incorporates the character's growth and changes.

[0048] The expression style selection unit can suggest different art techniques and materials based on the expression style selected by the user. For example, if the user selects "watercolor style," watercolor painting techniques and materials to use are suggested. The expression style selection unit can also introduce art history and works by famous artists related to the style selected by the user. For example, if the user selects "oil painting style," the unit can introduce the history of oil painting and famous oil paintings. Furthermore, the expression style selection unit can provide tutorials and guides for trying out different art tools and techniques based on the style selected by the user. For example, if the user selects "digital art style," the unit can provide guides for learning digital art tools and techniques. This allows the user to learn art techniques and materials related to the expression style selected and gain a deeper understanding.

[0049] The manga generation unit can suggest different viewpoints and camera angles for scenes and characters in a manga generated by a user. For example, if a user generates a "scene in which the protagonist confronts an enemy," the unit suggests drawing that scene from different viewpoints and camera angles. The manga generation unit can also suggest different lighting and shadow effects based on the scene selected by the user. For example, if a "night scene" is selected, the unit suggests lighting effects that reflect moonlight or street lamps. Furthermore, the manga generation unit can also suggest different backgrounds and environmental settings based on the scene generated by the user. For example, if a "forest scene" is selected, the unit suggests backgrounds with different seasons and weather conditions. This allows a greater variety of viewpoints and effects to be incorporated into the scenes and characters in a manga generated by a user.

[0050] The story setting input unit can suggest related music and sound effects based on the story setting input by the user. For example, if the user inputs "a story about going on an adventure," the unit will suggest music and sound effects that enhance the atmosphere of the adventure. The story setting input unit can also automatically generate music and sound effects related to a theme selected by the user. For example, if the theme is "horror," the unit will suggest music and sound effects that enhance the sense of fear. Furthermore, the story setting input unit can also suggest music and sound effects appropriate for different scenes based on the story setting input by the user. For example, the unit will suggest intense music for a "battle scene" and moving music for an "emotional scene." This allows the unit to suggest music and sound effects related to the story setting input by the user, creating a more realistic story.

[0051] The character setting input unit can suggest related costumes and accessories based on the character set by the user. For example, if the user sets a "medieval knight," it will suggest costumes and accessories suitable for a medieval knight. The character setting input unit can also suggest costumes and accessories suitable for different situations based on the personality and background of the character selected by the user. For example, it will suggest costumes suitable for adventures for a "character going on an adventure," and costumes suitable for a party for a "character joining a party." Furthermore, the character setting input unit can also suggest outfits for characters based on the relationship between the characters set by the user. For example, it will suggest matching accessories for "characters who are best friends." This allows the user to create a more attractive character by suggesting costumes and accessories related to the character set by the user.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The story setting input unit accepts the story setting entered by the user. For example, the user may enter a prompt such as "A story in which the protagonist goes on an adventure." The story setting input unit accepts input that the generation AI uses to generate the framework of the story. Step 2: The character setting input unit analyzes the story setting received by the story setting input unit. For example, the generation AI analyzes a prompt such as "The main character is a brave boy and his friend is a magical girl" and generates the character's appearance and personality. Step 3: The expression style selection unit generates the character settings analyzed by the character setting input unit. For example, the user can select a style such as "serious tone" or "comical touch." The generation AI applies a manga visual style based on this selection. Step 4: The manga generation unit automatically generates a manga based on the expression style selected by the expression style selection unit. For example, the generation AI creates a storyboard and arranges the layout and dialogue for each scene. This allows the automatic generation of a manga based on the story setting, character setting, and expression style entered by the user.

[0054] (Example 2) A manga creation system according to an embodiment of the present invention is a system that automatically creates manga based on a story setting, character setting, and expression style input by a user. This allows even users without technical skills to create and express their own manga.

[0055] A manga generation system according to an embodiment includes a story setting input unit, a character setting input unit, an expression style selection unit, and a manga generation unit. The story setting input unit accepts a story setting input by a user. For example, the user may input a prompt such as "A story about a protagonist going on an adventure." The story setting input unit accepts input for the generation AI to generate a story framework. The character setting input unit analyzes the story setting accepted by the story setting input unit. For example, the generation AI may analyze a prompt such as "The protagonist is a brave boy, and his friend is a magical girl" and generate the character's appearance and personality. The expression style selection unit generates the character setting analyzed by the character setting input unit. For example, the user can select a style such as "serious tone" or "comical touch." The generation AI applies a manga visual style based on this selection. The manga generation unit automatically generates a manga based on the expression style selected by the expression style selection unit. For example, the generation AI creates a storyboard and arranges the layout and dialogue for each scene. This allows manga to be automatically generated based on the story setting, character settings, and expression style entered by the user.

[0056] The story setting input unit can learn the user's past input history and propose story settings that match the user's preferences. In the story setting input unit, for example, the generation AI analyzes the user's past input history and identifies the user's preferred themes and genres. For example, based on the story settings entered in the past, the unit proposes stories that include the user's preferred adventure and fantasy elements. The story setting input unit also learns the user's past input history and extracts specific character and plot patterns. For example, based on the user's preferred character personalities and story developments, the unit proposes new story settings. The generation AI also identifies the user's preferred story tempo and tone based on the user's past input history. For example, the unit proposes story settings that reflect the user's preferred serious tone or comical touch. This makes it possible to propose story settings that match the user's preferences.

[0057] The story setting input unit provides real-time feedback when the story setting is input, helping the user to create a more specific setting. For example, the generation AI in the story setting input unit provides real-time feedback to the user's input, increasing the specificity of the story setting. For example, when the user inputs "a story about going on an adventure," the generation AI suggests specific adventure locations and goals. In addition, when the user inputs the story setting, the generation AI suggests related ideas and plots in real time. For example, when the user inputs "a story about school life," the generation AI suggests specific episodes and character relationships. In addition, the story setting input unit complements the details of the story setting in real time based on the user's input. For example, when the user inputs "a story about the main character going on an adventure," the generation AI suggests details of the adventure's background and destination. This helps the user to create a more specific story setting.

[0058] The story setting input unit can use the emotion estimation function to suggest a story setting based on the user's emotional state. For example, the story setting input unit uses the emotion estimation function to analyze the user's emotional state and suggest a story setting that is in line with that emotion. For example, if the user is in a positive emotional state, the story setting input unit suggests a fun adventure or a romantic story. The story setting input unit also analyzes the user's emotional state in real time and adjusts the story setting based on the results. For example, if the user is feeling stressed, the story setting input unit suggests a soothing story that will help the user relax. The story setting input unit also uses the emotion estimation function to automatically generate a story setting based on the user's emotions. For example, if the user is feeling sad, the story setting input unit suggests a moving story or a story that includes encouraging elements. This makes it possible to suggest a story setting based on the user's emotional state.

[0059] The story setting input unit can accommodate voice input or gesture input for inputting story settings. For example, the story setting input unit accommodates voice input for inputting story settings, allowing the user to set the story simply by speaking. For example, if the user speaks, "A story about the main character going on an adventure," the generation AI analyzes the content and sets the story. The story setting input unit also uses gesture input to allow the user to set the story with hand movements or touch operations. For example, when the user makes a specific gesture on the screen, the generation AI suggests a story setting that corresponds to that gesture. The story setting input unit also combines voice input and gesture input to enable more intuitive story setting. For example, if the user uses hand movements to specify the position and movement of a character while speaking, the generation AI analyzes the content and sets the story. This allows for more intuitive operation by supporting voice input and gesture input.

[0060] The story setting input unit can automatically generate story settings for different genres and themes, providing new inspiration to the user. For example, the generation AI in the story setting input unit automatically generates story settings for different genres and themes and suggests them to the user. For example, the user can choose from genres such as fantasy, science fiction, and mystery. When the user inputs a specific theme, the generation AI automatically generates multiple story settings related to that theme. For example, if the theme is "adventure," the generation AI will suggest different adventure scenarios. The story setting input unit can also suggest story settings for new genres and themes based on the user's preferences and past input history. For example, the generation AI may suggest a new theme related to a genre that the user has previously liked. This allows the user to be provided with new inspiration by automatically generating story settings for different genres and themes.

[0061] The story setting input unit can use the emotion estimation function to collect other users' emotional reactions to the story settings input by the user and display the most popular settings in a ranking format. The story setting input unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the story settings input by the user. For example, it analyzes the emotional scores of other users when they read the story settings. The story setting input unit also builds a system that displays popular story settings in a ranking format based on the emotional reaction data of other users. For example, it displays story settings with high emotional scores at the top. The story setting input unit also uses the emotion estimation function to collect other users' emotional reactions in real time and dynamically update the ranking of story settings based on the results. For example, it updates the ranking every time a new emotional reaction is added. In this way, it is possible to collect other users' emotional reactions and display the most popular settings in a ranking format.

[0062] The character setting input unit automatically generates character backstories and relationships, making it possible to create characters with more depth. In the character setting input unit, for example, the generation AI automatically generates a character's backstory and sets the character's background and past events in detail. For example, it proposes a backstory based on the protagonist's upbringing and past experiences. The character setting input unit also automatically generates relationships between characters and sets the interactions within the story. For example, it sets the relationships between the protagonist and his friends and enemies in detail. The character setting input unit also allows the generation AI to set the character's actions and personality in detail based on the character's backstory and relationships. For example, it sets how past experiences affect the character's personality and actions. In this way, by automatically generating character backstories and relationships, it is possible to create characters with more depth.

[0063] The character setting input unit can suggest customization options based on the user's preferences. For example, the generation AI in the character setting input unit suggests customization options for the character's appearance and personality based on the user's preferences. For example, it provides options based on the user's preferred hairstyle, clothing, and personality traits. The character setting input unit also learns the user's past input history and suggests customization options for character settings that match the user's preferences. For example, it suggests settings for a new character based on the characteristics of characters created in the past. The character setting input unit also analyzes the user's preferences in real time using the generation AI, and dynamically provides customization options based on the results. For example, it suggests options that match the user's preferences each time the user inputs something. This makes it possible to suggest customization options based on the user's preferences.

[0064] The character setting input unit can also allow character settings to be input from images or sketches. In the character setting input unit, for example, the generation AI automatically generates a character's appearance based on images or sketches uploaded by the user. For example, the character setting input unit analyzes a character sketch drawn by the user and sets the character's appearance in detail. The character setting input unit also uses image recognition technology to extract character features from the uploaded image, and the generation AI sets the character based on those features. For example, it extracts hairstyle and clothing features from the image. The character setting input unit also automatically generates a character's appearance and personality based on a sketch drawn by the user. For example, it analyzes the facial expression and pose in the sketch and sets the character's personality. This allows character settings to be input from images or sketches, making it possible to generate characters based on visual information.

[0065] The character setting input unit can automatically generate character settings with different cultural and historical backgrounds, providing the user with a variety of options. For example, the character setting input unit allows the generation AI to automatically generate character settings with different cultural and historical backgrounds and suggest them to the user. For example, settings such as ancient Egypt, medieval Europe, and a future world are provided. Furthermore, when the user selects a specific cultural or historical background, the character setting input unit allows the generation AI to automatically generate a character setting based on that background. For example, it suggests clothing and accessories related to the selected culture or historical period. Furthermore, the character setting input unit allows the generation AI to suggest character settings with new cultural or historical backgrounds based on the user's preferences and past input history. For example, it suggests new character settings related to cultures and historical periods that the user liked in the past. This allows the user to be provided with a variety of options by automatically generating character settings with different cultural and historical backgrounds.

[0066] The character setting input unit uses the emotion estimation function to collect other users' emotional reactions to a character set by the user and can display popular character settings in a ranking format. The character setting input unit, for example, uses the emotion estimation function to collect other users' emotional reactions to a character set by the user. For example, it analyzes the emotional scores of other users when they view the character. The character setting input unit also builds a system that displays popular character settings in a ranking format based on the emotional reaction data of other users. For example, character settings with high emotional scores are displayed at the top. The character setting input unit also uses the emotion estimation function to collect other users' emotional reactions in real time and dynamically update the ranking of character settings based on the results. For example, the ranking is updated every time a new emotional reaction is added. In this way, it is possible to collect other users' emotional reactions and display popular character settings in a ranking format.

[0067] The expression style selection unit can learn the user's past selection history and suggest an expression style that matches the user's preferences. For example, the generation AI in the expression style selection unit analyzes the user's past selection history and identifies the user's preferred expression style. For example, based on the styles selected in the past, it suggests a serious tone or a comical touch that the user prefers. The expression style selection unit also learns the user's past selection history and extracts specific art styles and techniques. For example, it suggests a new expression style based on the user's preferred color usage or line thickness. The expression style selection unit also identifies the user's preferred visual style based on the user's past selection history. For example, it suggests an expression style that reflects the user's preferred anime style or realistic depiction. This makes it possible to suggest an expression style that matches the user's preferences.

[0068] The expression style selection unit can provide a real-time preview when an expression style is selected, allowing the user to check the results of applying the selected style. For example, the generation AI of the expression style selection unit previews the user's selected expression style in real time and displays the application results. For example, the user can instantly check how the selected style will be reflected in the manga. Furthermore, when the user selects an expression style, the generation AI provides a real-time preview and visually displays the results of applying the style. For example, it displays how the selected style will affect the character and background. Furthermore, the expression style selection unit generates a real-time preview based on the user's selected expression style, allowing the user to check the results of applying the style. For example, it displays how the selected style will affect the entire scene. This allows the user to check the results of applying the selected style in real time.

[0069] The expression style selection unit can use the emotion estimation function to suggest an expression style based on the user's emotional state. For example, the expression style selection unit uses the emotion estimation function to analyze the user's emotional state and suggest an expression style that is in line with that emotion. For example, if the user is in a positive emotional state, a bright and cheerful visual style is suggested. The expression style selection unit also analyzes the user's emotional state in real time and adjusts the expression style based on the results. For example, if the user is feeling stressed, a calm visual style that helps the user relax is suggested. The expression style selection unit also uses the emotion estimation function to automatically generate an expression style based on the user's emotion. For example, if the user is feeling sad, an emotional visual style is suggested. In this way, an expression style can be suggested based on the user's emotional state.

[0070] The expression style selection unit can enable a user to select an expression style based on a style extracted from an image or photo uploaded by the user. For example, the expression style selection unit uses a generation AI to extract an expression style based on an image or photo uploaded by the user and applies that style to the manga. For example, the expression style selection unit proposes a visual style based on the style of an artwork uploaded by the user. The expression style selection unit also uses image recognition technology to extract expression style features from the uploaded image, and the generation AI sets the expression style based on those features. For example, the expression style selection unit extracts color usage and line thickness from the image. The expression style selection unit also uses a generation AI to automatically generate an expression style based on a photo uploaded by the user and applies that style to the manga. For example, the expression style selection unit proposes a visual style that reflects the atmosphere and tone of the photo. This allows a user to select an expression style based on a style extracted from an image or photo uploaded by the user.

[0071] The expression style selection unit can automatically generate different art styles and techniques to provide new inspiration to the user. For example, the generation AI automatically generates different art styles and techniques and suggests them to the user. For example, it offers styles such as oil painting, watercolor painting, and digital art. In addition, when the user selects a specific art style or technique, the generation AI automatically generates visuals based on that style. For example, it suggests characters and backgrounds that match the selected style. In addition, the generation AI suggests new art styles and techniques based on the user's preferences and past selection history. For example, it suggests new techniques related to styles that the user has previously preferred. This allows the automatic generation of different art styles and techniques to provide new inspiration to the user.

[0072] The expression style selection unit can use the emotion estimation function to collect other users' emotional reactions to the expression style selected by the user and display popular styles in a ranking format. The expression style selection unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the expression style selected by the user. For example, it analyzes the emotional scores of other users when they view the style. The expression style selection unit also builds a system that displays popular expression styles in a ranking format based on the emotional reaction data of other users. For example, styles with high emotional scores are displayed at the top. The expression style selection unit also uses the emotion estimation function to collect other users' emotional reactions in real time and dynamically update the ranking of expression styles based on the results. For example, the ranking is updated every time a new emotional reaction is added. In this way, it is possible to collect other users' emotional reactions and display popular styles in a ranking format.

[0073] The manga generation unit learns from the user's past works and can suggest manga layouts and dialogue that match the user's preferences. For example, the generation AI of the manga generation unit analyzes the user's past works and identifies the layout and dialogue patterns that the user prefers. For example, it suggests the user's preferred panel layout and dialogue style based on the past works. The manga generation unit also learns from the user's past works and extracts the placement of specific characters and scenes. For example, it suggests a new manga layout based on the user's preferred character placement and scene development. The manga generation unit also identifies the user's preferred story tempo and tone based on the user's past works. For example, it suggests layouts and dialogue that reflect the user's preferred serious tone or comical touch. This makes it possible to suggest manga layouts and dialogue that match the user's preferences.

[0074] The manga generation unit can use the emotion estimation function to suggest manga scenes and lines based on the user's emotional state. For example, the manga generation unit uses the emotion estimation function to analyze the user's emotional state and suggest manga scenes and lines that are in line with that emotion. For example, if the user is in a positive emotional state, the manga generation unit suggests bright and cheerful scenes and lines. The manga generation unit also analyzes the user's emotional state in real time and adjusts the manga scenes and lines based on the results. For example, if the user is feeling stressed, the manga generation unit suggests calm scenes and lines that will help the user relax. The manga generation unit also uses the emotion estimation function to automatically generate manga scenes and lines based on the user's emotions. For example, if the user is feeling sad, the manga generation unit suggests moving scenes and encouraging lines. This makes it possible to suggest manga scenes and lines based on the user's emotional state.

[0075] The manga generation unit can also make the automatic generation of manga compatible with different media formats. For example, the manga generation unit allows the generation AI to make the automatic generation of manga compatible with different media formats, allowing users to generate works as animations or videos. For example, it provides a function to animate manga scenes. The manga generation unit also allows the generation AI to automatically generate animations and videos based on manga generated by users. For example, it creates animations that display manga frames in succession. The manga generation unit also allows the generation AI to convert the automatic generation of manga into different media formats, allowing users to select various means of expression. For example, it provides a function to save manga scenes as video clips. This makes it possible to make the automatic generation of manga compatible with different media formats, thereby providing users with various means of expression.

[0076] The manga generation unit can automatically generate manga of different genres and themes to provide new inspiration to users. For example, the generation AI of the manga generation unit automatically generates manga of different genres and themes and suggests them to users. For example, the user can choose from genres such as fantasy, science fiction, and mystery. In addition, when a user inputs a specific theme, the generation AI automatically generates multiple manga related to that theme. For example, if the theme is "adventure," different adventure scenarios will be suggested. In addition, the manga generation unit can suggest manga of new genres and themes based on the user's preferences and past input history. For example, it can suggest new themes related to genres that the user has previously liked. This allows the automatic generation of manga of different genres and themes to provide new inspiration to users.

[0077] The manga generation unit uses the emotion estimation function to collect other users' emotional reactions to the manga generated by the user, and can display popular works in a ranking format. The manga generation unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the manga generated by the user. For example, it analyzes the emotional scores of other users when they read the manga. The manga generation unit also builds a system that displays popular manga in a ranking format based on the emotional reaction data of other users. For example, it displays manga with high emotional scores at the top. The manga generation unit also uses the emotion estimation function to collect other users' emotional reactions in real time, and dynamically updates the manga rankings based on the results. For example, it updates the rankings every time a new emotional reaction is added. This allows the emotional reactions of other users to be collected and popular works to be displayed in a ranking format.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The story setting input unit can automatically add related historical background and cultural elements based on the story setting entered by the user. For example, if the user enters "adventures of a medieval knight," the unit will suggest a story setting that incorporates the historical background and cultural elements of that era. The story setting input unit can also automatically generate a story setting based on actual events and legends related to the theme selected by the user. For example, if the theme is "adventures in ancient Egypt," the unit will suggest a story that incorporates actual historical events and legends. The story setting input unit can also automatically generate a story setting that incorporates geographical elements related to the theme selected by the user. For example, if the theme is "exploration in the Amazon," the unit will suggest a story that reflects the geographical features and ecosystem of the Amazon. This allows the user to provide detailed background information related to the theme selected by the user, creating a more in-depth story setting.

[0080] The character setting input unit can automatically add character growth and changes based on the character settings entered by the user. For example, if the user enters "a brave boy," the unit will suggest a character setting that reflects the boy's growth process and experiences. The character setting input unit can also automatically generate a setting that depicts the character's internal conflicts and growth based on the character's personality and background selected by the user. For example, if the theme is "an introverted girl," the unit will suggest a setting that depicts how the girl becomes confident. Furthermore, the character setting input unit can automatically generate a setting that reflects the interactions and influences between characters based on the character relationships selected by the user. For example, if the theme is "the adventures of best friends," the unit will suggest a setting that depicts the bonds and conflicts between best friends. This allows for the creation of a character setting with depth that incorporates the character's growth and changes.

[0081] The expression style selection unit can suggest different art techniques and materials based on the expression style selected by the user. For example, if the user selects "watercolor style," watercolor painting techniques and materials to use are suggested. The expression style selection unit can also introduce art history and works by famous artists related to the style selected by the user. For example, if the user selects "oil painting style," the unit can introduce the history of oil painting and famous oil paintings. Furthermore, the expression style selection unit can provide tutorials and guides for trying out different art tools and techniques based on the style selected by the user. For example, if the user selects "digital art style," the unit can provide guides for learning digital art tools and techniques. This allows the user to learn art techniques and materials related to the expression style selected and gain a deeper understanding.

[0082] The manga generation unit can suggest different viewpoints and camera angles for scenes and characters in a manga generated by a user. For example, if a user generates a "scene in which the protagonist confronts an enemy," the unit suggests drawing that scene from different viewpoints and camera angles. The manga generation unit can also suggest different lighting and shadow effects based on the scene selected by the user. For example, if a "night scene" is selected, the unit suggests lighting effects that reflect moonlight or street lamps. Furthermore, the manga generation unit can also suggest different backgrounds and environmental settings based on the scene generated by the user. For example, if a "forest scene" is selected, the unit suggests backgrounds with different seasons and weather conditions. This allows a greater variety of viewpoints and effects to be incorporated into the scenes and characters in a manga generated by a user.

[0083] The story setting input unit can use the emotion estimation function to suggest a story ending based on the user's emotional state. For example, if the user is in a positive emotional state, it can suggest a happy ending. The story setting input unit can also analyze the user's emotional state in real time and adjust the story development based on the results. For example, if the user is feeling stressed, it can suggest a story development that will help the user relax. Furthermore, the story setting input unit can use the emotion estimation function to automatically generate a story theme or message based on the user's emotions. For example, if the user is feeling sad, it can suggest a story with an inspiring theme or an encouraging message. This makes it possible to suggest a story ending or development based on the user's emotional state.

[0084] The character setting input unit can use the emotion estimation function to suggest actions and lines based on the emotional state of the character set by the user. For example, if the user sets an "angry character," the unit suggests actions and lines that express the character's anger. The character setting input unit can also analyze the user's emotional state in real time and adjust the character's actions and lines based on the results. For example, if the user is feeling sad, the unit suggests actions and lines that the character will comfort. Furthermore, the character setting input unit can use the emotion estimation function to automatically generate settings that depict the character's internal conflicts and growth based on the user's emotions. For example, if the user is feeling anxious, the unit suggests a character's growth process that reflects that emotion. This makes it possible to suggest character actions and lines based on the user's emotional state.

[0085] The expression style selection unit can use the emotion estimation function to suggest color usage and design elements based on the user's emotional state. For example, if the user is in a positive emotional state, it can suggest bright and vivid color usage and design elements. The expression style selection unit can also analyze the user's emotional state in real time and adjust color usage and design elements based on the results. For example, if the user is feeling stressed, it can suggest calm color usage and design elements that will help the user relax. Furthermore, the expression style selection unit can use the emotion estimation function to automatically generate a visual style based on the user's emotion. For example, if the user is feeling sad, it can suggest an emotional visual style. This makes it possible to suggest color usage and design elements based on the user's emotional state.

[0086] The cartoon generation unit can use the emotion estimation function to collect other users' emotional reactions to cartoon scenes generated by the user and display an emotional score for each scene. For example, it can analyze what emotions a particular scene evokes in other users and display the results. The cartoon generation unit can also build a system that displays the emotional scores for each scene in a ranking format based on the emotional reaction data of other users. For example, scenes with high emotional scores can be displayed at the top. Furthermore, the cartoon generation unit can use the emotion estimation function to collect other users' emotional reactions in real time and dynamically update the emotional score for each scene based on the results. For example, the ranking can be updated each time a new emotional reaction is added. This makes it possible to collect other users' emotional reactions and display an emotional score for each scene.

[0087] The story setting input unit can suggest related music and sound effects based on the story setting input by the user. For example, if the user inputs "a story about going on an adventure," the unit will suggest music and sound effects that enhance the atmosphere of the adventure. The story setting input unit can also automatically generate music and sound effects related to a theme selected by the user. For example, if the theme is "horror," the unit will suggest music and sound effects that enhance the sense of fear. Furthermore, the story setting input unit can also suggest music and sound effects appropriate for different scenes based on the story setting input by the user. For example, the unit will suggest intense music for a "battle scene" and moving music for an "emotional scene." This allows the unit to suggest music and sound effects related to the story setting input by the user, creating a more realistic story.

[0088] The character setting input unit can suggest related costumes and accessories based on the character set by the user. For example, if the user sets a "medieval knight," it will suggest costumes and accessories suitable for a medieval knight. The character setting input unit can also suggest costumes and accessories suitable for different situations based on the personality and background of the character selected by the user. For example, it will suggest costumes suitable for adventures for a "character going on an adventure," and costumes suitable for a party for a "character joining a party." Furthermore, the character setting input unit can also suggest outfits for characters based on the relationship between the characters set by the user. For example, it will suggest matching accessories for "characters who are best friends." This allows the user to create a more attractive character by suggesting costumes and accessories related to the character set by the user.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The story setting input unit accepts the story setting entered by the user. For example, the user may enter a prompt such as "A story in which the protagonist goes on an adventure." The story setting input unit accepts input that the generation AI uses to generate the framework of the story. Step 2: The character setting input unit analyzes the story setting received by the story setting input unit. For example, the generation AI analyzes a prompt such as "The main character is a brave boy and his friend is a magical girl" and generates the character's appearance and personality. Step 3: The expression style selection unit generates the character settings analyzed by the character setting input unit. For example, the user can select a style such as "serious tone" or "comical touch." The generation AI applies a manga visual style based on this selection. Step 4: The manga generation unit automatically generates a manga based on the expression style selected by the expression style selection unit. For example, the generation AI creates a storyboard and arranges the layout and dialogue for each scene. This allows the automatic generation of a manga based on the story setting, character setting, and expression style entered by the user.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a story setting input unit that accepts a story setting input by a user; a character setting input unit that analyzes the story setting received by the story setting input unit; an expression style selection unit that generates a character setting analyzed by the character setting input unit; a cartoon generation unit that automatically generates a cartoon based on the expression style selected by the expression style selection unit. A system characterized by:

2. The story setting input unit The system learns the user's past input history and proposes a story setting that matches the user's preferences.

2. The system of claim 1.

3. The story setting input unit Real-time feedback is provided when the story setting is input, helping the user to create more specific settings.

2. The system of claim 1.

4. The story setting input unit Suggest a story setting based on the user's emotional state 2. The system of claim 1.

5. The story setting input unit The story setting input can be made to correspond to voice input or gesture input.

2. The system of claim 1.

6. The story setting input unit Automatically generate story settings for different genres and themes to provide new inspiration to the user 2. The system of claim 1.

Images