system

Abstract

The system according to this embodiment aims to enable people who do not have the skills to draw manga to bring their ideas to life. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit inputs the genre. The reception unit inputs the story development. The reception unit inputs the characters. The reception unit inputs the art style. The generation unit generates a manga based on the information received by each reception unit. The provision unit provides the manga generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for people without the skill of drawing comics to shape their own ideas.

[0005] The system according to the embodiment aims to enable people without the skill of drawing comics to shape their own ideas.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs a genre, inputs a story development, inputs a character, and inputs an art style. The generation unit generates a comic based on the information received by each reception unit. The provision unit provides the comic generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows even people without manga drawing skills to bring their ideas to life. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The manga generation system according to an embodiment of the present invention is a system that allows even people without manga drawing skills to easily create manga. This manga generation system allows users to input their preferred genre, desired story development, characters, and art style into the generation AI's questions, and the generation AI automatically creates manga based on the input. Users can also input their own stories or experiences to create manga. For example, if a user inputs "a fantasy adventure story where a hero defeats a dragon," the generation AI will draw characters and backgrounds and develop the story based on that input to generate a manga. This system allows even those without manga drawing skills to bring their ideas to life. For example, the user inputs their preferred genre, desired story development, characters, and art style. In this process, the user inputs information in specific categories. For example, they might input "fantasy," "adventure story," "hero," "dragon," and "anime style." This information is input to the generation AI. Next, the generation AI analyzes the input information and automatically creates manga. The generation AI determines the style of characters and backgrounds based on the genre and constructs scenes based on the story development. For example, based on information such as "a fantasy adventure story where a hero defeats a dragon," the system draws the characters of the hero and the dragon and develops the adventure scenes. The generated comic is then provided to the user. The user can view the comic that brings their ideas to life and make corrections or additional instructions as needed. For example, they can modify parts of the generated comic or add new scenes. This system allows even those without comic-drawing skills to bring their ideas to life. Users can create comics intuitively without complex operations. For example, it has a simple structure that can be easily used by children and the elderly. In short, the comic generation system allows even those without comic-drawing skills to bring their ideas to life.

[0029] The manga generation system according to this embodiment comprises a genre input input section, a story development input input section, a character input input section, an art style input input section, a generation section, and a provision section. The genre input input section is a section for the user to input their preferred genre. For example, the user can input genres such as "fantasy," "action," and "romance." The genre input input section can, for example, provide a dropdown menu or checkboxes for the user to select a genre. The story development input input section is a section for the user to input their desired story development. For example, the user can input story developments such as "adventure story," "romance story," and "mystery." The story development input input section can, for example, provide radio buttons or text boxes for the user to select a story development. The character input input section is a section for the user to input their desired character. For example, the user can input characters such as "hero," "dragon," and "wizard." The character input input section can, for example, provide a list box or icons for the user to select a character. The art style input input section is a section for the user to input their desired art style. For example, the user can input art styles such as "anime style," "realistic," and "cartoon." The input section for art style input can, for example, provide sample images and a preview function for users to select an art style. The generation section is the part that generates the manga based on the information received by the input section, using a generation AI. For example, the generation AI determines the style of characters and backgrounds based on the genre and constructs scenes based on the story development. For example, the generation AI, based on the information that it is a "fantasy adventure story in which a hero defeats a dragon," draws the characters of the hero and the dragon and develops the adventure scenes. The delivery section is the part that provides the manga generated by the generation section to the user. For example, the delivery section can provide a web application or mobile application to display the generated manga to the user.The provisioning unit can, for example, provide a link or button for the user to download the generated manga. This allows the manga generation system according to the embodiment to bring even people without manga drawing skills to life by turning their ideas into reality.

[0030] The generation unit can determine the style of characters and backgrounds based on genre using a generation AI. For example, the generation unit can use a generation AI to determine the style of characters and backgrounds based on genre. For instance, the generation AI can draw hero and dragon characters and create backgrounds of magical forests and castles based on the "fantasy" genre. The generation unit can also use a generation AI to draw superhero and villain characters and create backgrounds of cityscapes and battle scenes based on the "action" genre. Furthermore, the generation unit can use a generation AI to draw lovers and create backgrounds of parks and cafes based on the "romance" genre. This allows the generation unit to automatically determine the style of characters and backgrounds based on genre. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can use an AI model for determining the style of characters and backgrounds based on genre to determine the style of characters and backgrounds.

[0031] The generation unit can construct scenes based on the story development using a generation AI. For example, the generation unit can construct scenes based on the story development using a generation AI. For example, the generation AI can construct a scene where a hero fights a dragon based on the story development of an "adventure story". The generation unit can also construct a scene where a couple goes on a date based on the story development of a "romance story". Furthermore, the generation unit can also construct a scene where a detective solves a case based on the story development of a "mystery". In this way, the generation unit can automatically construct scenes based on the story development. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can construct scenes using an AI model for constructing scenes based on the story development.

[0032] The service provider can provide the generated manga to the user. For example, the service provider can provide a web application or mobile application to display the generated manga to the user. For example, the service provider can provide a link or button for the user to download the generated manga. The service provider can also provide an option to print the generated manga. For example, the service provider can save the generated manga in PDF format so that the user can print it. Furthermore, the service provider can provide a function to share the generated manga on social media. For example, the service provider can provide a button to post the generated manga to social media such as Facebook® or X (formerly Twitter®). In this way, the service provider can provide the generated manga to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model for providing the generated manga to the user to determine how to provide the manga.

[0033] The service provider can modify or add to the manga generated based on user instructions. For example, the service provider can provide an interface for users to modify parts of the generated manga. For example, the service provider can provide tools for users to change character expressions or poses. The service provider can also provide options for users to add new scenes. For example, the service provider can provide templates or guidelines for users to add new scenes. Furthermore, the service provider can provide functions for users to change the colors and fonts of the generated manga. For example, the service provider can provide palettes or font lists for users to customize the color scheme and font style of the manga. This allows the service provider to modify or add to the manga generated based on user instructions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model to determine how to modify or add to the manga generated based on user instructions.

[0034] The generation unit can generate comics based on stories or experiences entered by the user. For example, if a user enters "childhood memories," the generation unit can create a comic based on that content, drawing characters and backgrounds and developing a story. The generation unit can also generate a comic based on a user's "travel experience," depicting scenery and events from the travel destination. Furthermore, if a user enters "work episodes," the generation unit can generate a comic based on those episodes, depicting workplace scenes and interactions with colleagues. In this way, the generation unit can generate comics based on stories and experiences entered by the user. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate comics using an AI model for generating comics based on stories and experiences entered by the user.

[0035] The genre input section can analyze the user's past genre selection history and suggest the most suitable genre. For example, the genre input section might use an algorithm to analyze the user's past genre selection history. For instance, it could collect data on genres the user has previously selected and analyze their trends. It could also predict genres the user will select at specific times of day and suggest the most suitable genre for those times. For example, if the user tends to select relaxing genres at night, it would prioritize suggesting relaxing genres for nighttime. Furthermore, the genre input section could suggest new genres based on patterns in the user's past genre selections. For example, if the user frequently selects action genres, it would suggest related adventure genres. This allows the genre input section to suggest the most suitable genre based on the user's past selection history. Some or all of the above processing in the genre input section may be performed using AI, or not. For example, the genre input section could suggest genres using an AI model to analyze the user's past genre selection history and suggest the most suitable genre.

[0036] The genre input interface can filter genres based on the user's current interests and trends. For example, it might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant genres based on the user's recently searched keywords and browsing history. It could also prioritize currently trending or popular genres. For example, it could suggest the most suitable genre based on current trends and popular themes. Furthermore, it could analyze topics in communities and forums the user participates in and suggest relevant genres. For example, it could suggest relevant genres based on topics in online communities the user participates in. This allows the genre input interface to suggest the most suitable genre based on the user's current interests and trends. Some or all of the above processing in the genre input interface may be performed using AI, or not. For example, the genre input interface could suggest genres using an AI model to filter genres based on the user's current interests and trends.

[0037] The genre input system can prioritize suggesting genres that are highly relevant to the user based on their geographical location. For example, the genre input system might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to determine the user's current location. Furthermore, the genre input system prioritizes suggesting genres that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, it might prioritize suggesting genres popular in that region. If the user is traveling, it could also suggest genres related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize suggesting genres related to that event. This allows the genre input system to suggest the most suitable genre based on the user's geographical location. Some or all of the above processing in the genre input system may be performed using AI, or not. For example, the genre input system could suggest genres using an AI model that considers the user's geographical location.

[0038] The genre input section can analyze the user's social media activity and suggest relevant genres when a genre is entered. For example, the genre input section might use an algorithm to analyze the user's social media activity. For instance, it could analyze the content of posts from accounts the user follows on social media and suggest relevant genres. It could also suggest relevant genres based on posts the user has "liked" or shared on social media. Furthermore, it could analyze the topics of social media groups and communities the user participates in and suggest relevant genres. For example, it could suggest relevant genres based on the topics of online communities the user participates in. This allows the genre input section to suggest the most appropriate genre based on the user's social media activity. Some or all of the above processing in the genre input section may be performed using AI, or not. For example, the genre input section could suggest genres using an AI model to analyze the user's social media activity and suggest relevant genres.

[0039] The input system for story development can analyze the user's past story development selection history and suggest the optimal story development. For example, the input system can use an algorithm to analyze the user's past story development selection history. For example, it can collect data on story developments the user has selected in the past and analyze the trends. It can also predict which story developments the user will select at a particular time of day and suggest the most suitable story development for that time. For example, if the input system tends to select relaxing story developments at night, it will prioritize suggesting relaxing story developments for nighttime. Furthermore, the input system can suggest new story developments based on patterns of story developments the user has selected in the past. For example, if the input system frequently selects action story developments, it will suggest related adventure story developments. This allows the input system to suggest the optimal story development based on the user's past selection history. Some or all of the above processing in the input system for story development may be performed using AI, for example, or without AI. For example, the input section for story development can analyze the user's past story development selection history and use an AI model to suggest the optimal story development.

[0040] The input interface for story development can filter the input based on the user's current interests and trends. For example, the input interface might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant story developments based on the user's recently searched keywords and browsing history. It could also prioritize presenting story developments that are currently trending or popular. For example, it could suggest the most suitable story development based on current trends and popular themes. Furthermore, the input interface could analyze topics in communities and forums the user participates in and suggest relevant story developments. For example, it could suggest relevant story developments based on topics in online communities the user participates in. This allows the input interface to suggest the most suitable story development based on the user's current interests and trends. Some or all of the above processing in the input interface may be performed using AI, or not. For example, the input interface could suggest story developments using an AI model to filter story developments based on the user's current interests and trends.

[0041] The input system for story development can prioritize presenting highly relevant story developments based on the user's geographical location information when a story development is input. For example, the input system for story development might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to identify the user's current location. Furthermore, the input system prioritizes presenting highly relevant story developments based on the user's geographical location information. For example, if the user is in a specific region, it might prioritize presenting story developments popular in that region. If the user is traveling, it could also suggest story developments related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize presenting story developments related to that event. This allows the input system for story development to propose the most suitable story development based on the user's geographical location information. Some or all of the above processing in the input system for story development may be performed using AI, or not. For example, the input system for story development could propose story developments using an AI model that considers the user's geographical location information.

[0042] The input unit for story development can analyze the user's social media activity and suggest relevant story developments when a story development is input. For example, the input unit can use an algorithm to analyze the user's social media activity. For instance, it can analyze the content of posts from accounts the user follows on social media and suggest relevant story developments. It can also suggest relevant story developments based on posts the user has "liked" or shared on social media. Furthermore, it can analyze the topics of social media groups and communities the user participates in and suggest relevant story developments. For example, it can suggest relevant story developments based on the topics of online communities the user participates in. This allows the input unit to suggest the most suitable story development based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can suggest story developments using an AI model to analyze the user's social media activity and suggest relevant story developments.

[0043] The character input system can analyze the user's past character selection history and suggest the most suitable character. For example, the system might use an algorithm to analyze the user's past character selection history. For instance, it could collect data on characters the user has previously selected and analyze their trends. It could also predict which characters the user will select at a given time and suggest the most suitable character for that time. For example, if the user tends to select relaxing characters at night, the system would prioritize suggesting relaxing characters for nighttime. Furthermore, the system could suggest new characters based on patterns in the user's past character selections. For example, if the user frequently selects action characters, the system would suggest relevant adventure characters. This allows the system to suggest the most suitable character based on the user's past selection history. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the system could use an AI model to analyze the user's past character selection history and suggest the most suitable character.

[0044] The character input system can filter characters based on the user's current interests and trends. For example, it might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant characters based on the user's recently searched keywords and browsing history. It could also prioritize characters that are currently trending or popular. For example, it could suggest the most suitable character based on current fashions and popular themes. Furthermore, it could analyze topics in communities and forums the user participates in and suggest relevant characters. For example, it could suggest relevant characters based on topics in online communities the user participates in. This allows the character input system to suggest the most suitable character based on the user's current interests and trends. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the character input system could suggest characters using an AI model to filter characters based on the user's current interests and trends.

[0045] The character input system can prioritize presenting characters that are highly relevant to the user's geographical location when the user inputs a character. For example, the character input system might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to determine the user's current location. Furthermore, the system prioritizes presenting characters that are highly relevant to the user's geographical location. For example, if the user is in a specific region, it might prioritize presenting characters popular in that region. If the user is traveling, it could also suggest characters related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize presenting characters related to that event. This allows the character input system to suggest the most suitable character based on the user's geographical location. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the character input system could suggest characters using an AI model that considers the user's geographical location.

[0046] The character input system can analyze the user's social media activity and suggest relevant characters when a character is entered. For example, the system might use an algorithm to analyze the user's social media activity. For instance, it could analyze posts from accounts the user follows on social media and suggest relevant characters. It could also suggest relevant characters based on posts the user has liked or shared on social media. Furthermore, it could analyze topics in social media groups and communities the user participates in and suggest relevant characters. For example, it could suggest relevant characters based on topics in online communities the user participates in. This allows the system to suggest the most suitable character based on the user's social media activity. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the system could use an AI model to analyze the user's social media activity and suggest relevant characters.

[0047] The input field for art style can analyze the user's past art style selection history and suggest the most suitable art style. For example, the input field for art style can use an algorithm to analyze the user's past art style selection history. For example, the input field for art style can collect data on art styles the user has selected in the past and analyze the trends. The input field for art style can also predict the art style the user will select at a particular time of day and suggest the most suitable art style for that time. For example, if the input field for art style tends to select relaxing art styles at night, it will prioritize suggesting relaxing art styles for nighttime. Furthermore, the input field for art style can suggest new art styles based on patterns of art styles the user has selected in the past. For example, if the input field for art style frequently selects action art styles, it will suggest related adventure art styles. In this way, the input field for art style can suggest the most suitable art style based on the user's past selection history. Some or all of the above processing in the input field for art style may be performed using AI, for example, or not. For example, the input section for art style can analyze the user's past art style selection history and use an AI model to suggest the most suitable art style.

[0048] The input interface for art styles can filter inputs based on the user's current interests and trends. For example, it might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant art styles based on the user's recently searched keywords and browsing history. It could also prioritize currently trending or popular art styles. For example, it could suggest the most suitable art style based on current fashions and popular themes. Furthermore, it could analyze topics in communities and forums the user participates in and suggest relevant art styles. For example, it could suggest relevant art styles based on topics in online communities the user participates in. This allows the input interface to suggest the most suitable art style based on the user's current interests and trends. Some or all of the above processing in the input interface may be performed using AI, or not. For example, the input interface could suggest art styles using an AI model to filter styles based on the user's current interests and trends.

[0049] The input system for art styles can prioritize presenting highly relevant art styles based on the user's geographical location information when an art style is entered. For example, the input system for art styles might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to determine the user's current location. Furthermore, the input system prioritizes presenting highly relevant art styles based on the user's geographical location. For example, if the user is in a specific region, it might prioritize presenting art styles popular in that region. If the user is traveling, it could also suggest art styles related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize presenting art styles related to that event. This allows the input system for art styles to suggest the most suitable art style based on the user's geographical location information. Some or all of the above processing in the input system for art styles may be performed using AI, or not. For example, the input system for art styles could suggest art styles using an AI model that considers the user's geographical location information.

[0050] The input section for art style input can analyze the user's social media activity and suggest relevant art styles when an art style is input. For example, the input section can use an algorithm to analyze the user's social media activity. For instance, it can analyze the content of posts from accounts the user follows on social media and suggest relevant art styles. It can also suggest relevant art styles based on posts the user has "liked" or shared on social media. Furthermore, it can analyze the topics of social media groups and communities the user participates in and suggest relevant art styles. For example, it can suggest relevant art styles based on the topics of online communities the user participates in. This allows the input section to suggest the most suitable art style based on the user's social media activity. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can suggest art styles using an AI model to analyze the user's social media activity and suggest relevant art styles.

[0051] The generation unit can select an appropriate generation method by referring to the user's past input data during generation. For example, the generation unit may use an algorithm to refer to the user's past input data. For example, the generation unit may collect data that the user has entered in the past and analyze its trends. The generation unit may also select the optimal generation method based on the style and settings that the user has selected in the past. For example, the generation unit may analyze the trends of the style that the user has selected in the past and select a generation method based on that. Furthermore, the generation unit may also select the optimal generation method based on the user's evaluation of manga that have been generated in the past. For example, the generation unit may select a generation method based on the style and settings of manga that the user has given high ratings to. In this way, the generation unit can select the optimal generation method based on the user's past input data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit may select a generation method using an AI model for selecting an appropriate generation method by referring to the user's past input data.

[0052] The generation unit can customize the generated content based on the user's current interests and trends during the generation process. For example, the generation unit uses algorithms to identify the user's current interests and trends. For instance, it can generate comics with relevant content based on keywords the user has recently searched for and their browsing history. The generation unit can also generate comics incorporating current trends and popular themes. For example, it can generate comics with content optimized for the user based on current trends and popular themes. Furthermore, the generation unit can analyze topics in communities and forums the user participates in and generate comics with relevant content. For example, it can generate comics with relevant content based on topics in online communities the user participates in. This allows the generation unit to customize the generated content to be optimal based on the user's current interests and trends. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can customize the generated content using an AI model for customizing the generated content based on the user's current interests and trends.

[0053] The generation unit can select an appropriate generation method based on the user's geographical location information during generation. For example, the generation unit uses a location information service to obtain the user's geographical location information. For example, the generation unit obtains GPS data and identifies the user's current location. The generation unit also selects an appropriate generation method based on the user's geographical location information. For example, if the user is in a specific region, the unit can generate a manga with content that incorporates the culture and customs of that region. If the user is traveling, the unit can also generate a manga with content that incorporates the scenery and landmarks of the travel destination. Furthermore, if the user is participating in a specific event, the unit can generate a manga with content related to that event. This allows the generation unit to select the optimal generation method based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can select a generation method using an AI model for selecting an appropriate generation method based on the user's geographical location information.

[0054] The generation unit can analyze the user's social media activity during generation to customize the generated content. For example, the generation unit uses an algorithm to analyze the user's social media activity. For instance, the generation unit generates comics with relevant content based on the posts of accounts the user follows on social media. The generation unit can also generate comics with relevant content based on posts the user "likes" or shares on social media. Furthermore, the generation unit can analyze the topics of social media groups and communities the user participates in and generate comics with relevant content. For example, the generation unit generates comics with relevant content based on the topics of online communities the user participates in. This allows the generation unit to customize the optimal generated content based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can customize the generated content using an AI model for analyzing the user's social media activity to customize the generated content.

[0055] The service provider can select an appropriate display method by referring to the user's past browsing history at the time of delivery. For example, the service provider may use an algorithm to refer to the user's past browsing history. For example, the service provider may collect data on what the user has viewed in the past and analyze its trends. The service provider can also select the optimal display method based on the trends of display methods the user has previously selected. For example, the service provider may analyze the trends of display methods the user has previously selected and select a display method based on that. Furthermore, the service provider may predict the display method the user will select at a specific time of day and select the optimal display method for that time of day. For example, if the service provider has a tendency to select a display method that allows the user to relax at night, it will prioritize providing a display method that allows the user to relax at night. In this way, the service provider can select the optimal display method based on the user's past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may select a display method using an AI model for selecting an appropriate display method by referring to the user's past browsing history.

[0056] The service provider can customize the displayed content based on the user's current interests and trends at the time of delivery. For example, the service provider may use algorithms to identify the user's current interests and trends. For example, the service provider may display relevant content based on keywords the user has recently searched for or their browsing history. The service provider can also incorporate current trends and trending themes into its display. For example, the service provider may display content that is most relevant to the user based on current trends and popular themes. Furthermore, the service provider may analyze topics in communities and forums the user participates in and display relevant content. For example, the service provider may display relevant content based on topics in online communities the user participates in. This allows the service provider to provide optimal displayed content based on the user's current interests and trends. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider may customize the displayed content using an AI model for customizing the displayed content based on the user's current interests and trends.

[0057] The service provider can select an appropriate display method based on the user's geographical location information at the time of service provision. For example, the service provider uses a location information service to obtain the user's geographical location information. For example, the service provider obtains GPS data and identifies the user's current location. The service provider also selects an appropriate display method based on the user's geographical location information. For example, if the user is in a specific region, the service provider can provide a display method that incorporates the culture and customs of that region. If the user is traveling, the service provider can also provide a display method that incorporates the scenery and landmarks of the travel destination. Furthermore, if the user is participating in a specific event, the service provider can display content related to that event. This allows the service provider to select the optimal display method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model for selecting an appropriate display method based on the user's geographical location information.

[0058] The service provider can analyze the user's social media activity and customize the displayed content at the time of delivery. For example, the service provider uses algorithms to analyze the user's social media activity. For example, the service provider can display relevant content based on the content of posts from accounts the user follows on social media. The service provider can also display relevant content based on the content of posts the user has "liked" or shared on social media. Furthermore, the service provider can analyze the topics of social media groups and communities the user participates in and display relevant content. For example, the service provider can display relevant content based on the topics of online communities the user participates in. This allows the service provider to provide optimal displayed content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can customize the displayed content using an AI model for analyzing the user's social media activity and customizing the displayed content.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The generation unit can automatically adjust the comic's page layout based on user input. For example, it can generate a layout that uses large panels in action scenes and many small panels in dialogue scenes. It can also adjust the size and position of characters according to their importance as specified by the user. Furthermore, it can change the page's color scheme and background as the story progresses. This allows the generation unit to generate the optimal page layout based on user input.

[0061] The service provider can select a display format optimized for the user's device when delivering the generated manga to the user. For example, it can provide a vertical scrolling format for smartphones, a horizontal scrolling format for tablets, and a page-turning format for PCs. It can also automatically adjust the manga's resolution and font size according to the user's device screen size. Furthermore, it can provide a download option so that users can view the manga offline. This allows the service provider to deliver the manga in a display format optimized for the user's device.

[0062] The generation unit can generate interactive comics based on stories and experiences input by the user. For example, the generation unit can generate interactive comics where the story branches based on the user's choices. It can also allow the user to select character actions and dialogue. Furthermore, it can allow the user to change character expressions and poses in specific scenes. This enables the generation unit to create interactive comics in which the user actively participates in the story.

[0063] The service provider can select an appropriate display method when providing generated manga to users by referring to the user's past browsing history. For example, the service provider can collect data on what users have viewed in the past and analyze its trends. Furthermore, the service provider can select the optimal display method based on the trends of display methods previously selected by the user. In addition, the service provider can predict the display method a user will choose during a specific time period and select the most suitable display method for that time. This allows the service provider to select the optimal display method based on the user's past browsing history.

[0064] The generation unit can select an appropriate generation method by referring to the user's past input data during generation. For example, the generation unit can collect data that the user has entered in the past and analyze its trends. It can also select the optimal generation method based on the styles and settings the user has previously selected. Furthermore, it can select the optimal generation method based on the user's ratings of previously generated manga. In this way, the generation unit can select the optimal generation method based on the user's past input data.

[0065] The generation unit can customize the generated content based on the user's current interests and trends during the generation process. For example, the generation unit can generate comics with relevant content based on keywords the user has recently searched for and their browsing history. It can also generate comics incorporating current trends and popular themes. Furthermore, the generation unit can analyze topics in communities and forums the user participates in and generate comics with relevant content. This allows the generation unit to customize the optimal generated content based on the user's current interests and trends.

[0066] The service provider can select an appropriate display method based on the user's geographical location information at the time of delivery. For example, the service provider can acquire GPS data to determine the user's current location. Furthermore, the service provider can select an appropriate display method based on the user's geographical location information. For instance, if the user is in a specific region, the service provider can provide a display method that incorporates the culture and customs of that region. If the user is traveling, the service provider can also provide a display method that incorporates the scenery and landmarks of their travel destination. This allows the service provider to select the optimal display method based on the user's geographical location information.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The genre input section is where the user enters their preferred genre. For example, the user can enter genres such as "Fantasy," "Action," or "Romance." The genre input section can provide, for example, a dropdown menu or checkboxes for the user to select a genre. Step 2: The input section for story development is where the user enters their desired story development. For example, the user can enter a story development such as "adventure story," "romance story," or "mystery." The input section for story development can provide, for example, radio buttons or text boxes for the user to select a story development. Step 3: The character input area is where the user enters the character they want. For example, the user can enter characters such as "Hero," "Dragon," or "Wizard." The character input area can provide, for example, a list box or icons for the user to select a character. Step 4: The style input section is where the user enters their desired style. For example, the user can enter styles such as "anime style," "realistic," or "cartoon." The style input section can also provide sample images or a preview function for the user to select a style. Step 5: The generation unit is the part that generates the manga based on the information received by the reception unit, using a generation AI. For example, the generation unit's generation AI determines the style of characters and backgrounds based on the genre and constructs scenes based on the story development. For example, based on the information that it is a "fantasy adventure story in which a hero defeats a dragon," the generation unit's generation AI draws the characters of the hero and the dragon and develops the adventure scenes. Step 6: The provisioning section is the part that provides the manga generated by the generation section to the user. The provisioning section can, for example, provide a web application or mobile application to display the generated manga to the user. The provisioning section can, for example, provide a link or button to allow the user to download the generated manga.

[0069] (Example of form 2) The manga generation system according to an embodiment of the present invention is a system that allows even people without manga drawing skills to easily create manga. This manga generation system allows users to input their preferred genre, desired story development, characters, and art style into the generation AI's questions, and the generation AI automatically creates manga based on the input. Users can also input their own stories or experiences to create manga. For example, if a user inputs "a fantasy adventure story where a hero defeats a dragon," the generation AI will draw characters and backgrounds and develop the story based on that input to generate a manga. This system allows even those without manga drawing skills to bring their ideas to life. For example, the user inputs their preferred genre, desired story development, characters, and art style. In this process, the user inputs information in specific categories. For example, they might input "fantasy," "adventure story," "hero," "dragon," and "anime style." This information is input to the generation AI. Next, the generation AI analyzes the input information and automatically creates manga. The generation AI determines the style of characters and backgrounds based on the genre and constructs scenes based on the story development. For example, based on information such as "a fantasy adventure story where a hero defeats a dragon," the system draws the characters of the hero and the dragon and develops the adventure scenes. The generated comic is then provided to the user. The user can view the comic that brings their ideas to life and make corrections or additional instructions as needed. For example, they can modify parts of the generated comic or add new scenes. This system allows even those without comic-drawing skills to bring their ideas to life. Users can create comics intuitively without complex operations. For example, it has a simple structure that can be easily used by children and the elderly. In short, the comic generation system allows even those without comic-drawing skills to bring their ideas to life.

[0070] The manga generation system according to this embodiment comprises a genre input input section, a story development input input section, a character input input section, an art style input input section, a generation section, and a provision section. The genre input input section is a section for the user to input their preferred genre. For example, the user can input genres such as "fantasy," "action," and "romance." The genre input input section can, for example, provide a dropdown menu or checkboxes for the user to select a genre. The story development input input section is a section for the user to input their desired story development. For example, the user can input story developments such as "adventure story," "romance story," and "mystery." The story development input input section can, for example, provide radio buttons or text boxes for the user to select a story development. The character input input section is a section for the user to input their desired character. For example, the user can input characters such as "hero," "dragon," and "wizard." The character input input section can, for example, provide a list box or icons for the user to select a character. The art style input input section is a section for the user to input their desired art style. For example, the user can input art styles such as "anime style," "realistic," and "cartoon." The input section for art style input can, for example, provide sample images and a preview function for users to select an art style. The generation section is the part that generates the manga based on the information received by the input section, using a generation AI. For example, the generation AI determines the style of characters and backgrounds based on the genre and constructs scenes based on the story development. For example, the generation AI, based on the information that it is a "fantasy adventure story in which a hero defeats a dragon," draws the characters of the hero and the dragon and develops the adventure scenes. The delivery section is the part that provides the manga generated by the generation section to the user. For example, the delivery section can provide a web application or mobile application to display the generated manga to the user.The provisioning unit can, for example, provide a link or button for the user to download the generated manga. This allows the manga generation system according to the embodiment to bring even people without manga drawing skills to life by turning their ideas into reality.

[0071] The generation unit can determine the style of characters and backgrounds based on genre using a generation AI. For example, the generation unit can use a generation AI to determine the style of characters and backgrounds based on genre. For instance, the generation AI can draw hero and dragon characters and create backgrounds of magical forests and castles based on the "fantasy" genre. The generation unit can also use a generation AI to draw superhero and villain characters and create backgrounds of cityscapes and battle scenes based on the "action" genre. Furthermore, the generation unit can use a generation AI to draw lovers and create backgrounds of parks and cafes based on the "romance" genre. This allows the generation unit to automatically determine the style of characters and backgrounds based on genre. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can use an AI model for determining the style of characters and backgrounds based on genre to determine the style of characters and backgrounds.

[0072] The generation unit can construct scenes based on the story development using a generation AI. For example, the generation unit can construct scenes based on the story development using a generation AI. For example, the generation AI can construct a scene where a hero fights a dragon based on the story development of an "adventure story". The generation unit can also construct a scene where a couple goes on a date based on the story development of a "romance story". Furthermore, the generation unit can also construct a scene where a detective solves a case based on the story development of a "mystery". In this way, the generation unit can automatically construct scenes based on the story development. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can construct scenes using an AI model for constructing scenes based on the story development.

[0073] The service provider can provide the generated manga to the user. For example, the service provider can provide a web application or mobile application to display the generated manga to the user. For example, the service provider can provide a link or button for the user to download the generated manga. The service provider can also provide an option to print the generated manga. For example, the service provider can save the generated manga in PDF format so that the user can print it. Furthermore, the service provider can provide a function to share the generated manga on social media. For example, the service provider can provide a button to post the generated manga to social media such as Facebook or X (formerly Twitter). In this way, the service provider can provide the generated manga to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model for providing the generated manga to the user to determine how to provide the manga.

[0074] The service provider can modify or add to the manga generated based on user instructions. For example, the service provider can provide an interface for users to modify parts of the generated manga. For example, the service provider can provide tools for users to change character expressions or poses. The service provider can also provide options for users to add new scenes. For example, the service provider can provide templates or guidelines for users to add new scenes. Furthermore, the service provider can provide functions for users to change the colors and fonts of the generated manga. For example, the service provider can provide palettes or font lists for users to customize the color scheme and font style of the manga. This allows the service provider to modify or add to the manga generated based on user instructions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use an AI model to determine how to modify or add to the manga generated based on user instructions.

[0075] The generation unit can generate comics based on stories or experiences entered by the user. For example, if a user enters "childhood memories," the generation unit can create a comic based on that content, drawing characters and backgrounds and developing a story. The generation unit can also generate a comic based on a user's "travel experience," depicting scenery and events from the travel destination. Furthermore, if a user enters "work episodes," the generation unit can generate a comic based on those episodes, depicting workplace scenes and interactions with colleagues. In this way, the generation unit can generate comics based on stories and experiences entered by the user. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate comics using an AI model for generating comics based on stories and experiences entered by the user.

[0076] A genre input system can estimate the user's emotions and present genre options based on those estimated emotions. For example, the genre input system might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the user's past genre selection trends to estimate emotions. Furthermore, the genre input system presents genre options based on the estimated emotions. For example, if the user is stressed, it might prioritize relaxing genres (e.g., healing, comedy). If the user is excited, it might prioritize stimulating genres such as action or adventure. If the user is sad, it might prioritize emotionally moving genres (e.g., drama, human story). This allows the genre input system to present the most suitable genre options based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the genre input reception unit may be performed using AI, for example, or not using AI. For example, the genre input reception unit may use an AI model to estimate the user's emotions and present genre options based on the estimated emotions to present genre options.

[0077] The genre input section can analyze the user's past genre selection history and suggest the most suitable genre. For example, the genre input section might use an algorithm to analyze the user's past genre selection history. For instance, it could collect data on genres the user has previously selected and analyze their trends. It could also predict genres the user will select at specific times of day and suggest the most suitable genre for those times. For example, if the user tends to select relaxing genres at night, it would prioritize suggesting relaxing genres for nighttime. Furthermore, the genre input section could suggest new genres based on patterns in the user's past genre selections. For example, if the user frequently selects action genres, it would suggest related adventure genres. This allows the genre input section to suggest the most suitable genre based on the user's past selection history. Some or all of the above processing in the genre input section may be performed using AI, or not. For example, the genre input section could suggest genres using an AI model to analyze the user's past genre selection history and suggest the most suitable genre.

[0078] The genre input interface can filter genres based on the user's current interests and trends. For example, it might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant genres based on the user's recently searched keywords and browsing history. It could also prioritize currently trending or popular genres. For example, it could suggest the most suitable genre based on current trends and popular themes. Furthermore, it could analyze topics in communities and forums the user participates in and suggest relevant genres. For example, it could suggest relevant genres based on topics in online communities the user participates in. This allows the genre input interface to suggest the most suitable genre based on the user's current interests and trends. Some or all of the above processing in the genre input interface may be performed using AI, or not. For example, the genre input interface could suggest genres using an AI model to filter genres based on the user's current interests and trends.

[0079] The genre input section can estimate the user's emotions and determine genre priorities based on those estimated emotions. For example, the genre input section might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the trends of genres the user has previously selected to estimate emotions. Furthermore, the genre input section determines genre priorities based on the estimated emotions. For example, if the user is relaxed, it might prioritize displaying relaxing genres. If the user is excited, it might prioritize displaying stimulating genres such as action or adventure. Furthermore, if the user is sad, it might prioritize displaying emotional genres. This allows the genre input section to determine genre priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the genre input reception section may be performed using AI, for example, or without AI. For example, the genre input reception section may determine the genre priority using an AI model that estimates the user's emotions and determines the genre priority based on the estimated emotions.

[0080] The genre input system can prioritize suggesting genres that are highly relevant to the user based on their geographical location. For example, the genre input system might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to determine the user's current location. Furthermore, the genre input system prioritizes suggesting genres that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, it might prioritize suggesting genres popular in that region. If the user is traveling, it could also suggest genres related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize suggesting genres related to that event. This allows the genre input system to suggest the most suitable genre based on the user's geographical location. Some or all of the above processing in the genre input system may be performed using AI, or not. For example, the genre input system could suggest genres using an AI model that considers the user's geographical location.

[0081] The genre input section can analyze the user's social media activity and suggest relevant genres when a genre is entered. For example, the genre input section might use an algorithm to analyze the user's social media activity. For instance, it could analyze the content of posts from accounts the user follows on social media and suggest relevant genres. It could also suggest relevant genres based on posts the user has "liked" or shared on social media. Furthermore, it could analyze the topics of social media groups and communities the user participates in and suggest relevant genres. For example, it could suggest relevant genres based on the topics of online communities the user participates in. This allows the genre input section to suggest the most appropriate genre based on the user's social media activity. Some or all of the above processing in the genre input section may be performed using AI, or not. For example, the genre input section could suggest genres using an AI model to analyze the user's social media activity and suggest relevant genres.

[0082] The input system for story development can estimate the user's emotions and present story development options based on those emotions. For example, the input system might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the user's past choices to estimate emotions. Furthermore, the input system presents story development options based on the estimated emotions. For example, if the user is relaxed, it might prioritize calm story developments. If the user is excited, it might prioritize tense story developments. If the user is sad, it might prioritize emotionally moving story developments. This allows the input system to present the optimal story development options based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text-generating AI (e.g., LLM) or multimodal-generating AI. Some or all of the above processing in the input unit for story development may be performed using AI, or not using AI. For example, the input unit for story development may use an AI model to estimate the user's emotions and present story development options based on those estimated emotions.

[0083] The input system for story development can analyze the user's past story development selection history and suggest the optimal story development. For example, the input system can use an algorithm to analyze the user's past story development selection history. For example, it can collect data on story developments the user has selected in the past and analyze the trends. It can also predict which story developments the user will select at a particular time of day and suggest the most suitable story development for that time. For example, if the input system tends to select relaxing story developments at night, it will prioritize suggesting relaxing story developments for nighttime. Furthermore, the input system can suggest new story developments based on patterns of story developments the user has selected in the past. For example, if the input system frequently selects action story developments, it will suggest related adventure story developments. This allows the input system to suggest the optimal story development based on the user's past selection history. Some or all of the above processing in the input system for story development may be performed using AI, for example, or without AI. For example, the input section for story development can analyze the user's past story development selection history and use an AI model to suggest the optimal story development.

[0084] The input interface for story development can filter the input based on the user's current interests and trends. For example, the input interface might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant story developments based on the user's recently searched keywords and browsing history. It could also prioritize presenting story developments that are currently trending or popular. For example, it could suggest the most suitable story development based on current trends and popular themes. Furthermore, the input interface could analyze topics in communities and forums the user participates in and suggest relevant story developments. For example, it could suggest relevant story developments based on topics in online communities the user participates in. This allows the input interface to suggest the most suitable story development based on the user's current interests and trends. Some or all of the above processing in the input interface may be performed using AI, or not. For example, the input interface could suggest story developments using an AI model to filter story developments based on the user's current interests and trends.

[0085] The input system for story development can estimate the user's emotions and determine the priority of story developments based on those estimated emotions. For example, the input system might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the trends of story developments the user has previously selected to estimate emotions. Furthermore, the input system determines the priority of story developments based on the estimated emotions. For example, if the user is relaxed, it might prioritize displaying calm story developments. If the user is excited, it might prioritize displaying tense story developments. Furthermore, if the user is sad, it might prioritize displaying emotionally moving story developments. This allows the input system to determine the priority of story developments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit for story development may be performed using AI, or not using AI. For example, the input unit for story development can determine the priority of story development using an AI model that estimates the user's emotions and determines the priority of story development based on the estimated emotions.

[0086] The input system for story development can prioritize presenting highly relevant story developments based on the user's geographical location information when a story development is input. For example, the input system for story development might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to identify the user's current location. Furthermore, the input system prioritizes presenting highly relevant story developments based on the user's geographical location information. For example, if the user is in a specific region, it might prioritize presenting story developments popular in that region. If the user is traveling, it could also suggest story developments related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize presenting story developments related to that event. This allows the input system for story development to propose the most suitable story development based on the user's geographical location information. Some or all of the above processing in the input system for story development may be performed using AI, or not. For example, the input system for story development could propose story developments using an AI model that considers the user's geographical location information.

[0087] The input unit for story development can analyze the user's social media activity and suggest relevant story developments when a story development is input. For example, the input unit can use an algorithm to analyze the user's social media activity. For instance, it can analyze the content of posts from accounts the user follows on social media and suggest relevant story developments. It can also suggest relevant story developments based on posts the user has "liked" or shared on social media. Furthermore, it can analyze the topics of social media groups and communities the user participates in and suggest relevant story developments. For example, it can suggest relevant story developments based on the topics of online communities the user participates in. This allows the input unit to suggest the most suitable story development based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can suggest story developments using an AI model to analyze the user's social media activity and suggest relevant story developments.

[0088] The character input system can estimate the user's emotions and present character options based on those emotions. For example, the system might use an emotion estimation algorithm to determine the user's emotions. This could involve analyzing the user's facial expressions and voice to estimate their feelings. Alternatively, it could estimate emotions based on the user's input and past selection history. For instance, it could analyze the user's past character selections to estimate their emotions. Furthermore, the system presents character options based on the estimated emotions. For example, if the user is relaxed, it might prioritize characters with calm personalities. If the user is excited, it could prioritize active and energetic characters. If the user is sad, it could prioritize characters with emotionally resonant backgrounds. This allows the system to present the most suitable character options based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above processing in the character input reception unit may be performed using AI, for example, or not using AI. For example, the character input reception unit may use an AI model to estimate the user's emotions and present character options based on the estimated emotions to present character options.

[0089] The character input system can analyze the user's past character selection history and suggest the most suitable character. For example, the system might use an algorithm to analyze the user's past character selection history. For instance, it could collect data on characters the user has previously selected and analyze their trends. It could also predict which characters the user will select at a given time and suggest the most suitable character for that time. For example, if the user tends to select relaxing characters at night, the system would prioritize suggesting relaxing characters for nighttime. Furthermore, the system could suggest new characters based on patterns in the user's past character selections. For example, if the user frequently selects action characters, the system would suggest relevant adventure characters. This allows the system to suggest the most suitable character based on the user's past selection history. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the system could use an AI model to analyze the user's past character selection history and suggest the most suitable character.

[0090] The character input system can filter characters based on the user's current interests and trends. For example, it might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant characters based on the user's recently searched keywords and browsing history. It could also prioritize characters that are currently trending or popular. For example, it could suggest the most suitable character based on current fashions and popular themes. Furthermore, it could analyze topics in communities and forums the user participates in and suggest relevant characters. For example, it could suggest relevant characters based on topics in online communities the user participates in. This allows the character input system to suggest the most suitable character based on the user's current interests and trends. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the character input system could suggest characters using an AI model to filter characters based on the user's current interests and trends.

[0091] The character input system can estimate the user's emotions and determine character priorities based on those emotions. For example, the system might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the tendencies of characters the user has previously selected to estimate emotions. Furthermore, the system determines character priorities based on the estimated emotions. For instance, if the user is relaxed, it might prioritize displaying characters with calm personalities. If the user is excited, it might prioritize displaying active and energetic characters. If the user is sad, it might prioritize displaying characters with emotionally moving backgrounds. This allows the system to determine character priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the character input reception unit may be performed using AI, for example, or without AI. For example, the character input reception unit can determine the priority of characters using an AI model that estimates the user's emotions and determines the priority of characters based on the estimated emotions.

[0092] The character input system can prioritize presenting characters that are highly relevant to the user's geographical location when the user inputs a character. For example, the character input system might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to determine the user's current location. Furthermore, the system prioritizes presenting characters that are highly relevant to the user's geographical location. For example, if the user is in a specific region, it might prioritize presenting characters popular in that region. If the user is traveling, it could also suggest characters related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize presenting characters related to that event. This allows the character input system to suggest the most suitable character based on the user's geographical location. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the character input system could suggest characters using an AI model that considers the user's geographical location.

[0093] The character input system can analyze the user's social media activity and suggest relevant characters when a character is entered. For example, the system might use an algorithm to analyze the user's social media activity. For instance, it could analyze posts from accounts the user follows on social media and suggest relevant characters. It could also suggest relevant characters based on posts the user has liked or shared on social media. Furthermore, it could analyze topics in social media groups and communities the user participates in and suggest relevant characters. For example, it could suggest relevant characters based on topics in online communities the user participates in. This allows the system to suggest the most suitable character based on the user's social media activity. Some or all of the above processing in the character input system may be performed using AI, or not. For example, the system could use an AI model to analyze the user's social media activity and suggest relevant characters.

[0094] The input system for art style input can estimate the user's emotions and present art style options based on those emotions. For example, the input system might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate their emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the user's past art style choices to estimate their emotions. Furthermore, the input system presents art style options based on the estimated emotions. For example, if the user is relaxed, it might prioritize art styles with a soft touch. If the user is excited, it might prioritize art styles with strong lines and vibrant colors. If the user is sad, it might prioritize art styles with calm colors. This allows the input system to present the most suitable art style options based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input section for drawing style may be performed using AI, or not using AI. For example, the input section for drawing style may use an AI model to estimate the user's emotions and present drawing style options based on those emotions to present drawing style options.

[0095] The input field for art style can analyze the user's past art style selection history and suggest the most suitable art style. For example, the input field for art style can use an algorithm to analyze the user's past art style selection history. For example, the input field for art style can collect data on art styles the user has selected in the past and analyze the trends. The input field for art style can also predict the art style the user will select at a particular time of day and suggest the most suitable art style for that time. For example, if the input field for art style tends to select relaxing art styles at night, it will prioritize suggesting relaxing art styles for nighttime. Furthermore, the input field for art style can suggest new art styles based on patterns of art styles the user has selected in the past. For example, if the input field for art style frequently selects action art styles, it will suggest related adventure art styles. In this way, the input field for art style can suggest the most suitable art style based on the user's past selection history. Some or all of the above processing in the input field for art style may be performed using AI, for example, or not. For example, the input section for art style can analyze the user's past art style selection history and use an AI model to suggest the most suitable art style.

[0096] The input interface for art styles can filter inputs based on the user's current interests and trends. For example, it might use an algorithm to identify the user's current interests and trends. For instance, it could suggest relevant art styles based on the user's recently searched keywords and browsing history. It could also prioritize currently trending or popular art styles. For example, it could suggest the most suitable art style based on current fashions and popular themes. Furthermore, it could analyze topics in communities and forums the user participates in and suggest relevant art styles. For example, it could suggest relevant art styles based on topics in online communities the user participates in. This allows the input interface to suggest the most suitable art style based on the user's current interests and trends. Some or all of the above processing in the input interface may be performed using AI, or not. For example, the input interface could suggest art styles using an AI model to filter styles based on the user's current interests and trends.

[0097] The input section for art style input can estimate the user's emotions and prioritize art styles based on those emotions. For example, the input section might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the trends of art styles the user has previously selected to estimate emotions. Furthermore, the input section prioritizes art styles based on the estimated emotions. For example, if the user is relaxed, it might prioritize art styles with a soft touch. If the user is excited, it might prioritize art styles with strong lines and vibrant colors. If the user is sad, it might prioritize art styles with calm colors. This allows the input section to prioritize art styles based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input section for drawing style may be performed using AI, or not using AI. For example, the input section for drawing style may determine the priority of drawing styles using an AI model that estimates the user's emotions and determines the priority of drawing styles based on the estimated emotions.

[0098] The input system for art styles can prioritize presenting highly relevant art styles based on the user's geographical location information when an art style is entered. For example, the input system for art styles might use a location information service to obtain the user's geographical location. For instance, it might acquire GPS data to determine the user's current location. Furthermore, the input system prioritizes presenting highly relevant art styles based on the user's geographical location. For example, if the user is in a specific region, it might prioritize presenting art styles popular in that region. If the user is traveling, it could also suggest art styles related to the culture and customs of their travel destination. Additionally, if the user is participating in a specific event, it could prioritize presenting art styles related to that event. This allows the input system for art styles to suggest the most suitable art style based on the user's geographical location information. Some or all of the above processing in the input system for art styles may be performed using AI, or not. For example, the input system for art styles could suggest art styles using an AI model that considers the user's geographical location information.

[0099] The input section for art style input can analyze the user's social media activity and suggest relevant art styles when an art style is input. For example, the input section can use an algorithm to analyze the user's social media activity. For instance, it can analyze the content of posts from accounts the user follows on social media and suggest relevant art styles. It can also suggest relevant art styles based on posts the user has "liked" or shared on social media. Furthermore, it can analyze the topics of social media groups and communities the user participates in and suggest relevant art styles. For example, it can suggest relevant art styles based on the topics of online communities the user participates in. This allows the input section to suggest the most suitable art style based on the user's social media activity. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can suggest art styles using an AI model to analyze the user's social media activity and suggest relevant art styles.

[0100] The generation unit can estimate the user's emotions and adjust the style of the generated comic based on the estimated emotions. For example, the generation unit uses an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, the generation unit can estimate emotions based on the user's input and past selection history. For example, it might analyze the user's past style selections to estimate emotions. Furthermore, the generation unit adjusts the style of the generated comic based on the estimated emotions. For example, if the user is relaxed, it might generate a comic with a soft touch. If the user is excited, it might generate a comic with strong lines and vibrant colors. If the user is sad, it might generate a comic with calm colors. This allows the generation unit to generate the optimal comic style based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may use an AI model to estimate the user's emotions and adjust the style of the manga to be generated based on those estimated emotions.

[0101] The generation unit can select an appropriate generation method by referring to the user's past input data during generation. For example, the generation unit may use an algorithm to refer to the user's past input data. For example, the generation unit may collect data that the user has entered in the past and analyze its trends. The generation unit may also select the optimal generation method based on the style and settings that the user has selected in the past. For example, the generation unit may analyze the trends of the style that the user has selected in the past and select a generation method based on that. Furthermore, the generation unit may also select the optimal generation method based on the user's evaluation of manga that have been generated in the past. For example, the generation unit may select a generation method based on the style and settings of manga that the user has given high ratings to. In this way, the generation unit can select the optimal generation method based on the user's past input data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit may select a generation method using an AI model for selecting an appropriate generation method by referring to the user's past input data.

[0102] The generation unit can customize the generated content based on the user's current interests and trends during the generation process. For example, the generation unit uses algorithms to identify the user's current interests and trends. For instance, it can generate comics with relevant content based on keywords the user has recently searched for and their browsing history. The generation unit can also generate comics incorporating current trends and popular themes. For example, it can generate comics with content optimized for the user based on current trends and popular themes. Furthermore, the generation unit can analyze topics in communities and forums the user participates in and generate comics with relevant content. For example, it can generate comics with relevant content based on topics in online communities the user participates in. This allows the generation unit to customize the generated content to be optimal based on the user's current interests and trends. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can customize the generated content using an AI model for customizing the generated content based on the user's current interests and trends.

[0103] The generation unit can estimate the user's emotions and determine the priority of the comics to generate based on the estimated emotions. For example, the generation unit uses an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, the generation unit can estimate emotions based on the user's input and past selection history. For example, it might analyze the user's past style choices to estimate emotions. Furthermore, the generation unit determines the priority of the comics to generate based on the estimated emotions. For example, if the user is relaxed, it might prioritize generating comics in a soft, gentle style. If the user is excited, it might prioritize generating comics in a style with strong lines and vibrant colors. Furthermore, if the user is sad, it might prioritize generating comics in a style with calming colors. This allows the generation unit to determine the priority of the comics to generate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can determine the priority of manga by using an AI model to estimate the user's emotions and determine the priority of manga to generate based on the estimated emotions.

[0104] The generation unit can select an appropriate generation method based on the user's geographical location information during generation. For example, the generation unit uses a location information service to obtain the user's geographical location information. For example, the generation unit obtains GPS data and identifies the user's current location. The generation unit also selects an appropriate generation method based on the user's geographical location information. For example, if the user is in a specific region, the unit can generate a manga with content that incorporates the culture and customs of that region. If the user is traveling, the unit can also generate a manga with content that incorporates the scenery and landmarks of the travel destination. Furthermore, if the user is participating in a specific event, the unit can generate a manga with content related to that event. This allows the generation unit to select the optimal generation method based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can select a generation method using an AI model for selecting an appropriate generation method based on the user's geographical location information.

[0105] The generation unit can analyze the user's social media activity during generation to customize the generated content. For example, the generation unit uses an algorithm to analyze the user's social media activity. For instance, the generation unit generates comics with relevant content based on the posts of accounts the user follows on social media. The generation unit can also generate comics with relevant content based on posts the user "likes" or shares on social media. Furthermore, the generation unit can analyze the topics of social media groups and communities the user participates in and generate comics with relevant content. For example, the generation unit generates comics with relevant content based on the topics of online communities the user participates in. This allows the generation unit to customize the optimal generated content based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can customize the generated content using an AI model for analyzing the user's social media activity to customize the generated content.

[0106] The service provider can estimate the user's emotions and adjust the display method of the manga based on the estimated emotions. For example, the service provider uses an emotion estimation algorithm to estimate the user's emotions. For example, the service provider analyzes the user's facial expressions and voice to estimate emotions. The service provider can also estimate emotions based on the user's input and past selection history. For example, the service provider analyzes the trends of display methods previously selected by the user to estimate emotions. Furthermore, the service provider adjusts the display method of the manga based on the estimated emotions. For example, if the user is relaxed, a display method with soft colors may be provided. If the user is excited, a display method with vivid colors may be provided. Furthermore, if the user is sad, a display method with calm colors may be provided. In this way, the service provider can provide the optimal display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may use an AI model to estimate the user's emotions and adjust the display method of the manga provided based on the estimated emotions.

[0107] The service provider can select an appropriate display method by referring to the user's past browsing history at the time of delivery. For example, the service provider may use an algorithm to refer to the user's past browsing history. For example, the service provider may collect data on what the user has viewed in the past and analyze its trends. The service provider can also select the optimal display method based on the trends of display methods the user has previously selected. For example, the service provider may analyze the trends of display methods the user has previously selected and select a display method based on that. Furthermore, the service provider may predict the display method the user will select at a specific time of day and select the optimal display method for that time of day. For example, if the service provider has a tendency to select a display method that allows the user to relax at night, it will prioritize providing a display method that allows the user to relax at night. In this way, the service provider can select the optimal display method based on the user's past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may select a display method using an AI model for selecting an appropriate display method by referring to the user's past browsing history.

[0108] The service provider can customize the displayed content based on the user's current interests and trends at the time of delivery. For example, the service provider may use algorithms to identify the user's current interests and trends. For example, the service provider may display relevant content based on keywords the user has recently searched for or their browsing history. The service provider can also incorporate current trends and trending themes into its display. For example, the service provider may display content that is most relevant to the user based on current trends and popular themes. Furthermore, the service provider may analyze topics in communities and forums the user participates in and display relevant content. For example, the service provider may display relevant content based on topics in online communities the user participates in. This allows the service provider to provide optimal displayed content based on the user's current interests and trends. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider may customize the displayed content using an AI model for customizing the displayed content based on the user's current interests and trends.

[0109] The service provider can estimate the user's emotions and determine the priority of the comics offered based on those estimated emotions. For example, the service provider might use an emotion estimation algorithm to estimate the user's emotions. For instance, it might analyze the user's facial expressions and voice to estimate emotions. Alternatively, it could estimate emotions based on the user's input and past selection history. For example, it might analyze the user's past display method preferences to estimate emotions. Furthermore, the service provider determines the priority of the comics offered based on the estimated emotions. For example, if the user is relaxed, it might prioritize comics displayed with soft colors. If the user is excited, it might prioritize comics displayed with vibrant colors. If the user is sad, it might prioritize comics displayed with calm colors. This allows the service provider to determine the priority of the comics offered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may determine the priority of manga by using an AI model to estimate the user's emotions and determine the priority of manga to be offered based on the estimated emotions.

[0110] The service provider can select an appropriate display method based on the user's geographical location information at the time of service provision. For example, the service provider uses a location information service to obtain the user's geographical location information. For example, the service provider obtains GPS data and identifies the user's current location. The service provider also selects an appropriate display method based on the user's geographical location information. For example, if the user is in a specific region, the service provider can provide a display method that incorporates the culture and customs of that region. If the user is traveling, the service provider can also provide a display method that incorporates the scenery and landmarks of the travel destination. Furthermore, if the user is participating in a specific event, the service provider can display content related to that event. This allows the service provider to select the optimal display method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model for selecting an appropriate display method based on the user's geographical location information.

[0111] The service provider can analyze the user's social media activity and customize the displayed content at the time of delivery. For example, the service provider uses algorithms to analyze the user's social media activity. For example, the service provider can display relevant content based on the content of posts from accounts the user follows on social media. The service provider can also display relevant content based on the content of posts the user has "liked" or shared on social media. Furthermore, the service provider can analyze the topics of social media groups and communities the user participates in and display relevant content. For example, the service provider can display relevant content based on the topics of online communities the user participates in. This allows the service provider to provide optimal displayed content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can customize the displayed content using an AI model for analyzing the user's social media activity and customizing the displayed content. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit for inputting genre, the reception unit for inputting story development, the reception unit for inputting characters, and the reception unit for inputting art style are implemented by the control unit 46A of the smart device 14. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the generation AI generates a manga based on the genre and story development. The provision unit is implemented, for example, by the output device 40 of the smart device 14, where the generated manga is displayed to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit for inputting genre, the reception unit for inputting story development, the reception unit for inputting characters, and the reception unit for inputting art style are implemented by the control unit 46A of the smart glasses 214. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the generation AI generates a manga based on the genre and story development. The provision unit is implemented, for example, by the speaker 240 and display of the smart glasses 214, where the generated manga is displayed to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit for inputting genre, the reception unit for inputting story development, the reception unit for inputting characters, and the reception unit for inputting art style are implemented by the control unit 46A of the headset terminal 314. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the generation AI generates a manga based on the genre and story development. The provision unit is implemented, for example, by the display 343 and speaker 240 of the headset terminal 314, where the generated manga is displayed to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit for inputting genre, the reception unit for inputting story development, the reception unit for inputting characters, and the reception unit for inputting art style are implemented by the control unit 46A of the robot 414. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the generation AI generates a manga based on the genre and story development. The provision unit is implemented, for example, by the speaker 240 and display device of the robot 414, where the generated manga is displayed to the user.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The generation unit can automatically adjust the comic's page layout based on user input. For example, it can generate a layout that uses large panels in action scenes and many small panels in dialogue scenes. It can also adjust the size and position of characters according to their importance as specified by the user. Furthermore, it can change the page's color scheme and background as the story progresses. This allows the generation unit to generate the optimal page layout based on user input.

[0114] The service provider can select a display format optimized for the user's device when delivering the generated manga to the user. For example, it can provide a vertical scrolling format for smartphones, a horizontal scrolling format for tablets, and a page-turning format for PCs. It can also automatically adjust the manga's resolution and font size according to the user's device screen size. Furthermore, it can provide a download option so that users can view the manga offline. This allows the service provider to deliver the manga in a display format optimized for the user's device.

[0115] The generation unit can generate interactive comics based on stories and experiences input by the user. For example, the generation unit can generate interactive comics where the story branches based on the user's choices. It can also allow the user to select character actions and dialogue. Furthermore, it can allow the user to change character expressions and poses in specific scenes. This enables the generation unit to create interactive comics in which the user actively participates in the story.

[0116] When providing a generated manga to a user, the service provider can estimate the user's emotions and adjust the manga's display method based on those emotions. For example, if the user is relaxed, the service provider can provide a display method with soft colors. If the user is excited, it can provide a display method with vibrant colors. Furthermore, if the user is sad, it can provide a display method with calm colors. In this way, the service provider can provide the optimal display method based on the user's emotions.

[0117] The service provider can select an appropriate display method when providing generated manga to users by referring to the user's past browsing history. For example, the service provider can collect data on what users have viewed in the past and analyze its trends. Furthermore, the service provider can select the optimal display method based on the trends of display methods previously selected by the user. In addition, the service provider can predict the display method a user will choose during a specific time period and select the most suitable display method for that time. This allows the service provider to select the optimal display method based on the user's past browsing history.

[0118] The generation unit can estimate the user's emotions and adjust the style of the generated comic based on those estimated emotions. For example, the generation unit can analyze the user's facial expressions and voice to estimate emotions. It can also estimate emotions based on the user's input and past selection history. Furthermore, the generation unit adjusts the style of the generated comic based on the estimated emotions. For example, if the user is relaxed, it can generate a comic with a soft touch style. If the user is excited, it can generate a comic with strong lines and vibrant colors. In this way, the generation unit can generate the optimal comic style based on the user's emotions.

[0119] The generation unit can select an appropriate generation method by referring to the user's past input data during generation. For example, the generation unit can collect data that the user has entered in the past and analyze its trends. It can also select the optimal generation method based on the styles and settings the user has previously selected. Furthermore, it can select the optimal generation method based on the user's ratings of previously generated manga. In this way, the generation unit can select the optimal generation method based on the user's past input data.

[0120] The generation unit can customize the generated content based on the user's current interests and trends during the generation process. For example, the generation unit can generate comics with relevant content based on keywords the user has recently searched for and their browsing history. It can also generate comics incorporating current trends and popular themes. Furthermore, the generation unit can analyze topics in communities and forums the user participates in and generate comics with relevant content. This allows the generation unit to customize the optimal generated content based on the user's current interests and trends.

[0121] The service provider can estimate the user's emotions and determine the priority of the manga offered based on those estimated emotions. For example, the service provider can analyze the user's facial expressions and voice to estimate their emotions. It can also estimate emotions based on the user's input and past selection history. Furthermore, the service provider determines the priority of the manga offered based on the estimated emotions. For example, if the user is relaxed, it may prioritize manga displayed with soft colors. Conversely, if the user is excited, it may prioritize manga displayed with vibrant colors. In this way, the service provider can determine the priority of the manga offered based on the user's emotions.

[0122] The service provider can select an appropriate display method based on the user's geographical location information at the time of delivery. For example, the service provider can acquire GPS data to determine the user's current location. Furthermore, the service provider can select an appropriate display method based on the user's geographical location information. For instance, if the user is in a specific region, the service provider can provide a display method that incorporates the culture and customs of that region. If the user is traveling, the service provider can also provide a display method that incorporates the scenery and landmarks of their travel destination. This allows the service provider to select the optimal display method based on the user's geographical location information.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The genre input section is where the user enters their preferred genre. For example, the user can enter genres such as "Fantasy," "Action," or "Romance." The genre input section can provide, for example, a dropdown menu or checkboxes for the user to select a genre. Step 2: The input section for story development is where the user enters their desired story development. For example, the user can enter a story development such as "adventure story," "romance story," or "mystery." The input section for story development can provide, for example, radio buttons or text boxes for the user to select a story development. Step 3: The character input area is where the user enters the character they want. For example, the user can enter characters such as "Hero," "Dragon," or "Wizard." The character input area can provide, for example, a list box or icons for the user to select a character. Step 4: The style input section is where the user enters their desired style. For example, the user can enter styles such as "anime style," "realistic," or "cartoon." The style input section can also provide sample images or a preview function for the user to select a style. Step 5: The generation unit is the part that generates the manga based on the information received by the reception unit, using a generation AI. For example, the generation unit's generation AI determines the style of characters and backgrounds based on the genre and constructs scenes based on the story development. For example, based on the information that it is a "fantasy adventure story in which a hero defeats a dragon," the generation unit's generation AI draws the characters of the hero and the dragon and develops the adventure scenes. Step 6: The provisioning section is the part that provides the manga generated by the generation section to the user. The provisioning section can, for example, provide a web application or mobile application to display the generated manga to the user. The provisioning section can, for example, provide a link or button to allow the user to download the generated manga.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you enter the genre, The reception desk where you input the story development, The reception area where you enter your character, The reception area where you input your art style, A generation unit that generates manga based on the information received by each of the aforementioned reception units, A system comprising: a supply unit that provides the manga generated by the generation unit.

2. The generating unit is The AI ​​generates character and background styles based on genre. The system according to feature 1.

3. The generating unit is The AI ​​generates scenes based on the story's progression. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated manga to the user. The system according to feature 1.

5. The aforementioned supply unit is, Modify or add to the comics generated based on user instructions. The system according to feature 1.

6. The generating unit is Generate comics based on stories or experiences entered by users. The system according to feature 1.

7. The reception desk for inputting the aforementioned genre is: It estimates the user's emotions and presents genre options based on those estimated emotions. The system according to feature 1.

8. The reception desk for inputting the aforementioned genre is: It analyzes the user's past genre selection history and suggests appropriate genres. The system according to feature 1.

Images