System

Abstract

A system in accordance with an embodiment is directed to generating and sharing cartoons in real time based on requests of online community members.SOLUTION: A system according to an embodiment includes a request reception unit, a generation unit, an image generation unit, and a sharing unit. The request reception unit receives a request of an online community member. The generation unit generates a story or lines on the basis of the request received by the request reception unit. The image generation unit generates an image of a comic panel or a character on the basis of the story or lines generated by the generation unit. The sharing unit shares the cartoon generated by the image generation unit with other members.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the drawback of making it difficult to instantly generate and share manga based on requests from online community members.

[0005] The system of the embodiment aims to instantly generate and share comics based on requests from online community members. [Means for solving the problem]

[0006] The system according to the embodiment includes a request receiving unit, a generation unit, an image generation unit, and a sharing unit. The request receiving unit receives requests from online community members. The generation unit generates a story and dialogue based on the requests received by the request receiving unit. The image generation unit generates images of manga frames and characters based on the story and dialogue generated by the generation unit. The sharing unit shares the manga generated by the image generation unit with other members. [Effects of the Invention]

[0007] An embodiment of the system allows for the instant creation and sharing of comics based on requests from online community members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The virtual comic publisher system according to an embodiment of the present invention is a system that uses generation AI and image generation AI to create comics in various styles. This system allows online community members to generate and enjoy comics based on their preferences and requests. This allows online community members to generate and enjoy comics that suit their tastes. Furthermore, by sharing the comics with other members, the entire community can enjoy them.

[0029] A virtual comic publishing system according to an embodiment includes a request receiving unit, a generation unit, an image generation unit, and a sharing unit. The request receiving unit receives requests from online community members. For example, members can input their favorite situations, desired lines, characters they want to see, and elements they dislike. The generation unit generates stories and lines based on the requests received by the request receiving unit. For example, a generation AI generates a story and creates lines based on the member's request. The image generation unit generates images of comic panels and characters based on the story and lines generated by the generation unit. For example, the image generation AI generates character images based on the generated story and lines. The sharing unit shares the comic generated by the image generation unit with other members. For example, the generated comic can be shared on social media or a dedicated platform, allowing members to exchange opinions with other members. This allows the virtual comic publishing system according to an embodiment to generate and share comics based on requests from online community members.

[0030] The request accepting unit can analyze the user's past request history, predict the user's preferences, and make suggestions. For example, when a user inputs a request, the request accepting unit has a generation AI that analyzes the past request history and makes suggestions based on the user's preferences. For example, a user who has requested many adventure stories in the past can be suggested a new adventure scene. Furthermore, when the request accepting unit accepts a request, the generation AI predicts preferences based on the user's past request history and automatically completes the request content. For example, it can suggest specific characters or situations. Furthermore, when a user inputs a request, the generation AI analyzes the user's past request history and suggests request content that matches the user's preferences. For example, it can make new suggestions based on lines or scenes requested in the past. This allows suggestions to be made based on the user's preferences.

[0031] The request receiving unit can provide feedback on the request content in real time and support the realization of the request content. For example, when a user inputs a request, the generation AI in the request receiving unit provides feedback in real time to specify the request content. For example, the generation AI suggests specific scenes and lines for the input situation. The request receiving unit also provides feedback on the request content in real time and offers advice to help the user specify the request. For example, the generation AI suggests details about the character and the setting of the situation. The request receiving unit also provides feedback on the request content in real time when a user inputs a request and supports the realization of the request content. For example, the generation AI suggests specific scenes and lines based on the request content. This can support the realization of the request content.

[0032] The request accepting unit supports voice input and gesture input, thereby providing a more intuitive method of making a request. For example, the request accepting unit supports voice input when a user inputs a request, allowing the user to make a request more intuitively. For example, what the user speaks is converted into text in real time and accepted as a request. The request accepting unit also supports gesture input when accepting a request, allowing the user to make a request with hand movements or facial expressions. For example, a character's movements are specified with specific gestures. The request accepting unit also supports voice input and gesture input when a user inputs a request, allowing the user to make a request more intuitively. For example, a situation is explained by voice and a character's movements are specified with gestures. This provides a more intuitive method of making a request.

[0033] The request accepting unit can provide a function for sharing the request content with other users and creating a request collaboratively. For example, when a user inputs a request, the request accepting unit provides a function for sharing the request content with other users and creating a request collaboratively. For example, multiple users request the same situation or character. The request accepting unit also adds a function for sharing the request content with other users and creating a request collaboratively. For example, a user publishes the request content, and other users make comments or suggestions. The request accepting unit also provides a function for sharing the request content with other users and creating a request collaboratively when a user inputs a request. For example, the request content can be edited in real time and created collaboratively. This allows the request content to be shared with other users and created collaboratively.

[0034] The generation unit can propose multiple story developments based on the request content, allowing the user to select one. For example, the generation AI can propose multiple story developments based on the request content, allowing the user to select one. For example, the generation unit can present multiple different developments of adventure scenes, allowing the user to select one. The generation unit can also propose multiple story developments based on the request content, allowing the user to select one. For example, the generation unit can present different variations of character actions and lines. The generation unit can also propose multiple story developments based on the request content, allowing the user to select one. For example, the generation AI can present multiple different developments of situations, allowing the user to select one. In this way, multiple story developments can be proposed, allowing the user to select one.

[0035] The image generation unit can automatically apply different art styles depending on the request content and allow the user to select from them. For example, the image generation unit can automatically apply different art styles depending on the request content using an image generation AI and allow the user to select from them. For example, the image generation unit can present a realistic style or a cartoon style. The image generation unit can also automatically apply different art styles depending on the request content and allow the user to select from them. For example, the image generation unit can present a monochrome or color style. The image generation unit can also automatically apply different art styles depending on the request content using an image generation AI and allow the user to select from them. For example, the image generation unit can present a simple style or a detailed style. This allows different art styles to be automatically applied and allow the user to select from them.

[0036] The generation unit and the image generation unit generate a 3D model based on the request content, allowing the user to experience the manga in a VR space. For example, the generation unit and the image generation unit generate a 3D model based on the request content using a generation AI and an image generation AI, allowing the user to experience the manga in a VR space. For example, they generate characters and scenes as 3D models. Furthermore, the generation unit and the image generation unit generate a 3D model based on the request content using a generation AI and an image generation AI, allowing the user to experience the manga in a VR space. For example, they reproduce a situation as a 3D model. Furthermore, the generation unit and the image generation unit generate a 3D model based on the request content using a generation AI and an image generation AI, allowing the user to experience the manga in a VR space. For example, they represent the development of a story as a 3D model. This allows the user to experience the manga in a VR space.

[0037] The generation unit and the image generation unit can automatically translate the generated manga into multiple languages ​​to accommodate international users. The generation unit and the image generation unit, for example, automatically translate the generated manga into multiple languages ​​to accommodate international users. For example, they translate into English, French, Chinese, etc. The generation unit and the image generation unit also use an automatic translation function to translate the generated manga into multiple languages ​​to accommodate international users. For example, they generate text corresponding to each language. The generation unit and the image generation unit also automatically translate the generated manga into multiple languages ​​to accommodate international users. For example, they apply the translated text to manga panels. This allows the generated manga to be automatically translated into multiple languages ​​to accommodate international users.

[0038] The sharing unit can use the generation AI to automatically extract highlight scenes and encourage sharing on social media. For example, in the share function, the generation AI automatically extracts highlight scenes and encourages sharing on social media. For example, it extracts moving scenes or climax scenes. The sharing unit can also use the generation AI to automatically extract highlight scenes and encourage sharing on social media through the share function. For example, it can extract scenes that have generated the most reactions from users. The sharing unit can also use the generation AI to automatically extract highlight scenes and encourage sharing on social media. For example, it can extract important turning points in the story. This allows highlight scenes to be automatically extracted and encourages sharing on social media.

[0039] The share unit can provide a widget that allows a user to embed the generated comic in their own blog or website. The share unit, for example, provides a widget that allows a user to embed the generated comic in their own blog or website. For example, an embed code can be easily generated. The share unit also provides a widget that allows a user to embed the generated comic in their own blog or website. For example, a drag-and-drop embedding function can be provided. The share unit also provides a widget that allows a user to embed the generated comic in their own blog or website. For example, customization options for embedding can be provided. This allows the generated comic to be embedded in a blog or website.

[0040] The share unit can provide a function that allows users to add their own comments or thoughts to specific scenes in a manga. For example, in the share function, the share unit provides a function that allows users to add their own comments or thoughts to specific scenes in a manga. For example, adding comments for each scene. The share unit also adds a function that allows users to add their own comments or thoughts to specific scenes in a manga when sharing. For example, adding thoughts to scenes that moved them. The share unit also provides a function that allows users to add their own comments or thoughts to specific scenes in a manga. For example, sharing thoughts for each scene. This allows users to add their own comments or thoughts to specific scenes in a manga.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The request receiving unit can automatically suggest related past works and reference materials based on the content of the user's request. For example, if the user requests a specific situation, the request receiving unit will suggest past works that depicted a similar situation. Furthermore, when the user inputs a request, the request receiving unit automatically displays related reference materials to help the user flesh out the request. For example, it can provide materials related to the design or background setting of a specific character. Furthermore, when the user inputs a request, the request receiving unit automatically suggests related past works and reference materials to help the user flesh out the request. For example, it can suggest scenes or lines that were popular in the past. This allows the user's request to be more specific.

[0043] The request receiving unit can automatically suggest related trends and topics based on the content of a user's request. For example, if a user requests a specific situation, elements related to current trends and topics are suggested. Furthermore, when a user inputs a request, the request receiving unit automatically displays related trends and topics to help the user specify the content of the request. For example, it can suggest currently popular characters and situations. Furthermore, when a user inputs a request, the request receiving unit automatically suggests related trends and topics to help the user specify the content of the request. For example, it can suggest scenes and lines influenced by recent movies and dramas. This allows the content of the user's request to be more specific.

[0044] The request accepting unit can provide relevant technical advice based on the content of a user's request. For example, if a user requests a specific situation, the request accepting unit provides technical advice for realizing that situation. Furthermore, when a user inputs a request, the request accepting unit automatically displays relevant technical advice to support the realization of the request content. For example, technical advice for realizing the movements and facial expressions of a specific character is provided. Furthermore, when a user inputs a request, the request accepting unit automatically suggests relevant technical advice to support the realization of the request content. For example, technical advice is provided regarding the background and lighting settings of a specific scene. This allows the content of the user's request to be made more specific.

[0045] The generation unit can propose multiple endings that the user can choose from based on the request content. For example, different endings such as a happy ending and a bitter ending are presented. The generation unit also allows the generation AI to propose multiple endings based on the request content and allow the user to choose from them. For example, it presents endings with different character fates. The generation unit also allows the generation AI to propose multiple endings based on the request content and allow the user to choose from them. For example, it presents endings with different situations. This makes it possible to propose multiple endings that the user can choose from.

[0046] The image generation unit can automatically apply different color palettes depending on the request content and allow the user to select from them. For example, it can present a color palette of warm colors or cool colors. The image generation unit can also automatically apply different color palettes using the image generation AI based on the request content and allow the user to select from them. For example, it can present vivid colors or pastel colors. The image generation unit can also automatically apply different color palettes depending on the request content and allow the user to select from them. For example, it can present a monochrome or sepia color palette. This allows different color palettes to be automatically applied and allow the user to select from them.

[0047] The generation unit can propose multiple character designs that the user can choose from based on the request content. For example, it can present character designs with different clothing and hairstyles. The generation unit also allows the generation AI to propose multiple character designs based on the request content and allow the user to choose from them. For example, it can present character designs with different facial expressions and poses. The generation unit also allows the generation AI to propose multiple character designs based on the request content and allow the user to choose from them. For example, it can present character designs of different ages and genders. This makes it possible to propose multiple character designs that the user can choose from.

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

[0049] Step 1: The request reception section accepts requests from online community members. For example, members can enter their favorite situations, lines they want to hear, characters they want to see, and elements they dislike. Step 2: The generator generates a story and dialogue based on the request received by the request receiver. For example, the generator AI generates a story and creates dialogue based on the member's request. Step 3: The image generation unit generates images of manga panels and characters based on the story and dialogue generated by the generation unit. For example, the image generation AI generates character images based on the generated story and dialogue. Step 4: The sharing unit shares the manga generated by the image generation unit with other members. For example, the generated manga can be shared on social media or a dedicated platform, allowing other members to exchange their thoughts on it.

[0050] (Example 2) The virtual comic publisher system according to an embodiment of the present invention is a system that uses generation AI and image generation AI to create comics in various styles. This system allows online community members to generate and enjoy comics based on their preferences and requests. This allows online community members to generate and enjoy comics that suit their tastes. Furthermore, by sharing the comics with other members, the entire community can enjoy them.

[0051] A virtual comic publishing system according to an embodiment includes a request receiving unit, a generation unit, an image generation unit, and a sharing unit. The request receiving unit receives requests from online community members. For example, members can input their favorite situations, desired lines, characters they want to see, and elements they dislike. The generation unit generates stories and lines based on the requests received by the request receiving unit. For example, a generation AI generates a story and creates lines based on the member's request. The image generation unit generates images of comic panels and characters based on the story and lines generated by the generation unit. For example, the image generation AI generates character images based on the generated story and lines. The sharing unit shares the comic generated by the image generation unit with other members. For example, the generated comic can be shared on social media or a dedicated platform, allowing members to exchange opinions with other members. This allows the virtual comic publishing system according to an embodiment to generate and share comics based on requests from online community members.

[0052] The request accepting unit can analyze the user's past request history, predict the user's preferences, and make suggestions. For example, when a user inputs a request, the request accepting unit has a generation AI that analyzes the past request history and makes suggestions based on the user's preferences. For example, a user who has requested many adventure stories in the past can be suggested a new adventure scene. Furthermore, when the request accepting unit accepts a request, the generation AI predicts preferences based on the user's past request history and automatically completes the request content. For example, it can suggest specific characters or situations. Furthermore, when a user inputs a request, the generation AI analyzes the user's past request history and suggests request content that matches the user's preferences. For example, it can make new suggestions based on lines or scenes requested in the past. This allows suggestions to be made based on the user's preferences.

[0053] The request receiving unit can provide feedback on the request content in real time and support the realization of the request content. For example, when a user inputs a request, the generation AI in the request receiving unit provides feedback in real time to specify the request content. For example, the generation AI suggests specific scenes and lines for the input situation. The request receiving unit also provides feedback on the request content in real time and offers advice to help the user specify the request. For example, the generation AI suggests details about the character and the setting of the situation. The request receiving unit also provides feedback on the request content in real time when a user inputs a request and supports the realization of the request content. For example, the generation AI suggests specific scenes and lines based on the request content. This can support the realization of the request content.

[0054] The request receiving unit can use the emotion estimation function to analyze the emotion of the user when inputting a request and suggest request content that elicits positive emotions. For example, when the user inputs a request, the request receiving unit can use the emotion estimation function to analyze the user's emotion and suggest request content that elicits positive emotions. For example, when the user is having fun, the request receiving unit can suggest fun situations. The request receiving unit can also use the emotion estimation function to analyze the user's emotion when inputting a request and suggest request content that elicits positive emotions. For example, when the user is relaxing, the request receiving unit can suggest relaxing scenes. The request receiving unit can also use the emotion estimation function to analyze the user's emotion when inputting a request and suggest request content that elicits positive emotions. For example, when the user is excited, the request receiving unit can suggest exciting scenes. In this way, request content that elicits positive emotions can be suggested.

[0055] The request accepting unit supports voice input and gesture input, thereby providing a more intuitive method of making a request. For example, the request accepting unit supports voice input when a user inputs a request, allowing the user to make a request more intuitively. For example, what the user speaks is converted into text in real time and accepted as a request. The request accepting unit also supports gesture input when accepting a request, allowing the user to make a request with hand movements or facial expressions. For example, a character's movements are specified with specific gestures. The request accepting unit also supports voice input and gesture input when a user inputs a request, allowing the user to make a request more intuitively. For example, a situation is explained by voice and a character's movements are specified with gestures. This provides a more intuitive method of making a request.

[0056] The request accepting unit can provide a function for sharing the request content with other users and creating a request collaboratively. For example, when a user inputs a request, the request accepting unit provides a function for sharing the request content with other users and creating a request collaboratively. For example, multiple users request the same situation or character. The request accepting unit also adds a function for sharing the request content with other users and creating a request collaboratively. For example, a user publishes the request content, and other users make comments or suggestions. The request accepting unit also provides a function for sharing the request content with other users and creating a request collaboratively when a user inputs a request. For example, the request content can be edited in real time and created collaboratively. This allows the request content to be shared with other users and created collaboratively.

[0057] The generation unit can propose multiple story developments based on the request content, allowing the user to select one. For example, the generation AI can propose multiple story developments based on the request content, allowing the user to select one. For example, the generation unit can present multiple different developments of adventure scenes, allowing the user to select one. The generation unit can also propose multiple story developments based on the request content, allowing the user to select one. For example, the generation unit can present different variations of character actions and lines. The generation unit can also propose multiple story developments based on the request content, allowing the user to select one. For example, the generation AI can present multiple different developments of situations, allowing the user to select one. In this way, multiple story developments can be proposed, allowing the user to select one.

[0058] The image generation unit can automatically apply different art styles depending on the request content and allow the user to select from them. For example, the image generation unit can automatically apply different art styles depending on the request content using an image generation AI and allow the user to select from them. For example, the image generation unit can present a realistic style or a cartoon style. The image generation unit can also automatically apply different art styles depending on the request content and allow the user to select from them. For example, the image generation unit can present a monochrome or color style. The image generation unit can also automatically apply different art styles depending on the request content using an image generation AI and allow the user to select from them. For example, the image generation unit can present a simple style or a detailed style. This allows different art styles to be automatically applied and allow the user to select from them.

[0059] The generation unit can analyze the user's emotional response to the generated story or image using the emotion estimation function and reflect it in the next generation. The generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the generated story or image and reflect it in the next generation. For example, elements with a high number of positive emotional responses are incorporated into the next generation. The generation unit also analyzes the user's emotional response to the generated story or image and reflects it in the next generation. For example, elements with a high emotion score are reflected in the next generation. The generation unit also uses the emotion estimation function to analyze the user's emotional response to the generated story or image and reflect it in the next generation. For example, the generation content is adjusted based on the user's emotional response. This makes it possible to reflect the user's emotional response in the next generation.

[0060] The generation unit and the image generation unit generate a 3D model based on the request content, allowing the user to experience the manga in a VR space. For example, the generation unit and the image generation unit generate a 3D model based on the request content using a generation AI and an image generation AI, allowing the user to experience the manga in a VR space. For example, they generate characters and scenes as 3D models. Furthermore, the generation unit and the image generation unit generate a 3D model based on the request content using a generation AI and an image generation AI, allowing the user to experience the manga in a VR space. For example, they reproduce a situation as a 3D model. Furthermore, the generation unit and the image generation unit generate a 3D model based on the request content using a generation AI and an image generation AI, allowing the user to experience the manga in a VR space. For example, they represent the development of a story as a 3D model. This allows the user to experience the manga in a VR space.

[0061] The generation unit and the image generation unit can automatically translate the generated manga into multiple languages ​​to accommodate international users. The generation unit and the image generation unit, for example, automatically translate the generated manga into multiple languages ​​to accommodate international users. For example, they translate into English, French, Chinese, etc. The generation unit and the image generation unit also use an automatic translation function to translate the generated manga into multiple languages ​​to accommodate international users. For example, they generate text corresponding to each language. The generation unit and the image generation unit also automatically translate the generated manga into multiple languages ​​to accommodate international users. For example, they apply the translated text to manga panels. This allows the generated manga to be automatically translated into multiple languages ​​to accommodate international users.

[0062] The generation unit and the image generation unit can collect other users' emotional reactions to the generated manga using the emotion estimation function, and identify stories and characters that are likely to be emotionally relatable. The generation unit and the image generation unit, for example, use the emotion estimation function to collect other users' emotional reactions to the generated manga, and identify stories and characters that are likely to be emotionally relatable. For example, they identify elements that have a high number of positive emotional reactions. The generation unit and the image generation unit also collect other users' emotional reactions to the generated manga, and identify stories and characters that are likely to be emotionally relatable. For example, they identify elements with a high emotion score. The generation unit and the image generation unit also use the emotion estimation function to collect other users' emotional reactions to the generated manga, and identify stories and characters that are likely to be emotionally relatable. For example, they identify elements that have a high degree of relatability based on the user's emotional reactions. This makes it possible to identify stories and characters that are likely to be emotionally relatable.

[0063] The sharing unit can use the generation AI to automatically extract highlight scenes and encourage sharing on social media. For example, in the share function, the generation AI automatically extracts highlight scenes and encourages sharing on social media. For example, it extracts moving scenes or climax scenes. The sharing unit can also use the generation AI to automatically extract highlight scenes and encourage sharing on social media through the share function. For example, it can extract scenes that have generated the most reactions from users. The sharing unit can also use the generation AI to automatically extract highlight scenes and encourage sharing on social media. For example, it can extract important turning points in the story. This allows highlight scenes to be automatically extracted and encourages sharing on social media.

[0064] The sharing unit can use the emotion estimation function to analyze other users' emotional reactions to the shared manga and maximize the effect of sharing. The sharing unit, for example, uses the emotion estimation function to analyze other users' emotional reactions to the shared manga and maximize the effect of sharing. For example, it shares scenes with many positive emotional reactions. The sharing unit also analyzes other users' emotional reactions to the shared manga and maximizes the effect of sharing. For example, it prioritizes sharing scenes with high emotional scores. The sharing unit also uses the emotion estimation function to analyze other users' emotional reactions to the shared manga and maximize the effect of sharing. For example, it selects scenes to share based on the users' emotional reactions. This maximizes the effect of sharing.

[0065] The share unit can provide a widget that allows a user to embed the generated comic in their own blog or website. The share unit, for example, provides a widget that allows a user to embed the generated comic in their own blog or website. For example, an embed code can be easily generated. The share unit also provides a widget that allows a user to embed the generated comic in their own blog or website. For example, a drag-and-drop embedding function can be provided. The share unit also provides a widget that allows a user to embed the generated comic in their own blog or website. For example, customization options for embedding can be provided. This allows the generated comic to be embedded in a blog or website.

[0066] The share unit can provide a function that allows users to add their own comments or thoughts to specific scenes in a manga. For example, in the share function, the share unit provides a function that allows users to add their own comments or thoughts to specific scenes in a manga. For example, adding comments for each scene. The share unit also adds a function that allows users to add their own comments or thoughts to specific scenes in a manga when sharing. For example, adding thoughts to scenes that moved them. The share unit also provides a function that allows users to add their own comments or thoughts to specific scenes in a manga. For example, sharing thoughts for each scene. This allows users to add their own comments or thoughts to specific scenes in a manga.

[0067] The sharing unit uses the emotion estimation function to display other users' emotional reactions to the shared manga in real time, thereby promoting emotional empathy. The sharing unit, for example, uses the emotion estimation function to display other users' emotional reactions to the shared manga in real time, thereby promoting emotional empathy. For example, it displays positive emotional reactions in real time. The sharing unit also displays other users' emotional reactions to the shared manga in real time, thereby promoting emotional empathy. For example, it displays emotion scores in real time. The sharing unit also uses the emotion estimation function to display other users' emotional reactions to the shared manga in real time, thereby promoting emotional empathy. For example, it displays users' emotional reactions in real time. This makes it possible to promote emotional empathy.

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

[0069] The request receiving unit can automatically suggest related past works and reference materials based on the content of the user's request. For example, if the user requests a specific situation, the request receiving unit will suggest past works that depicted a similar situation. Furthermore, when the user inputs a request, the request receiving unit automatically displays related reference materials to help the user flesh out the request. For example, it can provide materials related to the design or background setting of a specific character. Furthermore, when the user inputs a request, the request receiving unit automatically suggests related past works and reference materials to help the user flesh out the request. For example, it can suggest scenes or lines that were popular in the past. This allows the user's request to be more specific.

[0070] The request receiving unit can automatically suggest related trends and topics based on the content of a user's request. For example, if a user requests a specific situation, elements related to current trends and topics are suggested. Furthermore, when a user inputs a request, the request receiving unit automatically displays related trends and topics to help the user specify the content of the request. For example, it can suggest currently popular characters and situations. Furthermore, when a user inputs a request, the request receiving unit automatically suggests related trends and topics to help the user specify the content of the request. For example, it can suggest scenes and lines influenced by recent movies and dramas. This allows the content of the user's request to be more specific.

[0071] The request accepting unit can provide relevant technical advice based on the content of a user's request. For example, if a user requests a specific situation, the request accepting unit provides technical advice for realizing that situation. Furthermore, when a user inputs a request, the request accepting unit automatically displays relevant technical advice to support the realization of the request content. For example, technical advice for realizing the movements and facial expressions of a specific character is provided. Furthermore, when a user inputs a request, the request accepting unit automatically suggests relevant technical advice to support the realization of the request content. For example, technical advice is provided regarding the background and lighting settings of a specific scene. This allows the content of the user's request to be made more specific.

[0072] The request receiving unit uses the emotion estimation function to analyze the emotion of the user when inputting a request, and can suggest request content that matches the user's emotion. For example, when the user is sad, an emotional scene is suggested. The request receiving unit also uses the emotion estimation function to analyze the emotion of the user when inputting a request, and suggests request content that matches the user's emotion. For example, when the user is excited, an action scene is suggested. The request receiving unit also uses the emotion estimation function to analyze the user's emotion when the user inputs a request, and suggests request content that matches the user's emotion. For example, when the user is relaxed, a relaxing scene is suggested. In this way, request content that matches the user's emotion can be suggested.

[0073] The request receiving unit can use the emotion estimation function to analyze the emotion of the user when inputting a request and provide feedback according to the user's emotion. For example, when the user is feeling anxious, feedback that gives a sense of security is provided. The request receiving unit can also use the emotion estimation function to analyze the emotion of the user when inputting a request and provide feedback according to the user's emotion. For example, when the user is happy, feedback that increases the user's joy is provided. The request receiving unit can also use the emotion estimation function to analyze the user's emotion when the user inputs a request and provide feedback according to the user's emotion. For example, when the user is tired, feedback that makes the user feel refreshed is provided. In this way, feedback according to the user's emotion can be provided.

[0074] The request receiving unit uses the emotion estimation function to analyze the emotion of the user when inputting a request, and can suggest modifications to the request content in accordance with the user's emotion. For example, when the user is angry, a scene to help the user calm down is suggested. The request receiving unit also uses the emotion estimation function to analyze the emotion of the user when inputting a request, and can suggest modifications to the request content in accordance with the user's emotion. For example, when the user is sad, a scene to cheer the user up is suggested. The request receiving unit also uses the emotion estimation function to analyze the user's emotion when the user inputs a request, and can suggest modifications to the request content in accordance with the user's emotion. For example, when the user is excited, a scene to help the user calm down is suggested. This makes it possible to suggest modifications to the request content in accordance with the user's emotion.

[0075] The request receiving unit can use the emotion estimation function to analyze the emotion of the user when inputting a request and set a priority order for the request content according to the user's emotion. For example, when the user is very excited, request content that matches the user's emotion is processed with priority. The request receiving unit can also use the emotion estimation function to analyze the emotion of the user when inputting a request and set a priority order for the request content according to the user's emotion. For example, when the user is relaxed, request content that matches the user's emotion is processed with priority. The request receiving unit can also use the emotion estimation function to analyze the user's emotion when the user inputs a request and set a priority order for the request content according to the user's emotion. For example, when the user is sad, request content that matches the user's emotion is processed with priority. This makes it possible to set a priority order for the request content according to the user's emotion.

[0076] The generation unit can propose multiple endings that the user can choose from based on the request content. For example, different endings such as a happy ending and a bitter ending are presented. The generation unit also allows the generation AI to propose multiple endings based on the request content and allow the user to choose from them. For example, it presents endings with different character fates. The generation unit also allows the generation AI to propose multiple endings based on the request content and allow the user to choose from them. For example, it presents endings with different situations. This makes it possible to propose multiple endings that the user can choose from.

[0077] The image generation unit can automatically apply different color palettes depending on the request content and allow the user to select from them. For example, it can present a color palette of warm colors or cool colors. The image generation unit can also automatically apply different color palettes using the image generation AI based on the request content and allow the user to select from them. For example, it can present vivid colors or pastel colors. The image generation unit can also automatically apply different color palettes depending on the request content and allow the user to select from them. For example, it can present a monochrome or sepia color palette. This allows different color palettes to be automatically applied and allow the user to select from them.

[0078] The generation unit can propose multiple character designs that the user can choose from based on the request content. For example, it can present character designs with different clothing and hairstyles. The generation unit also allows the generation AI to propose multiple character designs based on the request content and allow the user to choose from them. For example, it can present character designs with different facial expressions and poses. The generation unit also allows the generation AI to propose multiple character designs based on the request content and allow the user to choose from them. For example, it can present character designs of different ages and genders. This makes it possible to propose multiple character designs that the user can choose from.

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

[0080] Step 1: The request reception section accepts requests from online community members. For example, members can enter their favorite situations, lines they want to hear, characters they want to see, and elements they dislike. Step 2: The generator generates a story and dialogue based on the request received by the request receiver. For example, the generator AI generates a story and creates dialogue based on the member's request. Step 3: The image generation unit generates images of manga panels and characters based on the story and dialogue generated by the generation unit. For example, the image generation AI generates character images based on the generated story and dialogue. Step 4: The sharing unit shares the manga generated by the image generation unit with other members. For example, the generated manga can be shared on social media or a dedicated platform, allowing other members to exchange their thoughts on it.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a request receiving unit that receives requests from online community members; a generation unit that generates a story and lines based on the request received by the request receiving unit; an image generation unit that generates images of comic frames and characters based on the story and dialogue generated by the generation unit; a sharing unit that shares the manga generated by the image generating unit with other members. A system characterized by:

2. The request receiving unit Analyzes the user's past request history, predicts the user's preferences, and makes suggestions 2. The system of claim 1.

3. The request receiving unit Providing real-time feedback on requests and helping to materialize said requests 2. The system of claim 1.

4. The request receiving unit Analyzes user emotions when typing and suggests request content that elicits positive emotions 2. The system of claim 1.

5. The request receiving unit Supports voice and gesture input, providing a more intuitive way to make requests 2. The system of claim 1.

6. The request receiving unit Provide the ability to share request content with other users and create said requests collaboratively 2. The system of claim 1.

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