System

Abstract

An object of the system according to the embodiment is to efficiently convert a conventional comic into a Webtoon format.SOLUTION: A system according to an embodiment includes a frame extraction unit, a layout generation unit, an extension unit, a colorization unit, and a motion addition unit. The frame extraction unit extracts a frame of a comic for which permission is obtained from an author. The layout generation unit generates an arrangement or layout of the frames extracted by the frame extraction unit in a form suitable for vertical reading or horizontal reading. The extension unit extends the edge of the frame based on the layout generated by the layout generation unit. The colorization part colorizes the frame added by the extension part. The motion imparting unit imparts a motion to the frame colorized by the colorization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, converting traditional comics into the Webtoon format was problematic in that it was costly and time-consuming.

[0005] The system according to the embodiment aims to efficiently convert traditional comics into Webtoon format. [Means for solving the problem]

[0006] The system according to the embodiment includes a frame extraction unit, a layout generation unit, a bleed unit, a colorization unit, and a motion addition unit. The frame extraction unit extracts frames from comics with permission from the author. The layout generation unit generates an arrangement or layout of each frame extracted by the frame extraction unit in a form suitable for vertical or horizontal reading. The bleed unit bleeds the edges of the frames based on the layout generated by the layout generation unit. The colorization unit colors the frames bled by the bleed unit. The motion addition unit adds motion to the frames colored by the colorization unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently convert traditional comics into Webtoon format. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The content conversion system according to an embodiment of the present invention is a system that converts between traditional comics and Webtoon format content using generative AI, thereby reducing the cost of creating and providing content and increasing the recognition of Webtoon authors.

[0029] A content conversion system according to an embodiment includes a frame extraction unit, a layout generation unit, a bleed unit, a colorization unit, and a motion application unit. The frame extraction unit extracts frames from a comic book with permission from the author. For example, the frame extraction unit extracts frames from image data of a digital comic book. The frame extraction unit can also scan a paper comic book and convert it into digital data to extract frames. The layout generation unit generates a layout or arrangement of each frame from the extracted frames suitable for vertical or horizontal reading. For example, when converting a webtoon to a vertically readable webtoon, the layout generation unit adjusts the order and size of each frame and generates a vertically arranged layout. When converting a webtoon to a horizontally readable comic, the layout generation unit can also optimize the frame arrangement. The bleed unit bleeds the edges of frames based on the generated layout. For example, the bleed unit automatically bleeds if the edges of frames are cut off to maintain frame consistency. The colorization unit colorizes the bled frames. For example, the colorization unit colorizes a black-and-white comic and automatically generates the color of each frame. The motion addition unit adds motion to the colorized frames. For example, the motion addition unit animates the movements of characters and changes in the background, providing readers with a more immersive experience. As a result, the content conversion system according to the embodiment can reduce the cost of creating and providing content and provide opportunities for many readers to consume vertically read comics.

[0030] The layout generation unit not only arranges the panels but also optimizes the story flow to help readers understand the story. For example, the layout generation unit uses generative AI to not only optimize the arrangement of each panel, but also analyze the entire story flow to help readers understand the story smoothly. For example, the order of panels can be adjusted to emphasize important scenes. This allows readers to understand the story smoothly.

[0031] The layout generation unit can fine-tune the facial expressions or poses of characters when converting frames to achieve a more natural flow. For example, the layout generation unit uses generation AI to fine-tune the facial expressions or poses of characters when converting frames to make the flow of the story more natural. For example, the facial expressions can be changed to emphasize the emotional expression of a character. This makes the flow of the story more natural.

[0032] In addition to converting between comics and webtoons, the generation AI can also convert them into light novels or animations. For example, the generation AI converts the content of comics or webtoons into light novel format. For example, it converts the content of panels into text and expresses the story in written form. The generation AI also converts the content of comics or webtoons into animation. For example, it animates the movements of characters and changes in background to provide readers with a more immersive experience. This allows for the provision of diverse content by converting into light novels and animations.

[0033] When converting between comics and webtoons, the generation AI can convert the layout to accommodate different languages ​​or cultural spheres, making it suitable for international markets. For example, the generation AI can translate the content of comics or webtoons into different languages ​​and generate a layout appropriate for that language. For example, it can create a layout suited to cultural spheres where reading is from right to left. This allows for layout conversion to accommodate different languages ​​and cultural spheres, making it suitable for international markets.

[0034] Generative AI can automatically generate details of a character's clothing or background, achieving a more realistic representation. Generative AI can automatically generate details of a character's clothing or background, for example. For example, it can realistically depict the patterns on a character's clothing or the details of the background. This allows the automatic generation of details of a character's clothing and background, achieving a more realistic representation.

[0035] The generation AI can automatically adjust the color tone according to the season or time of day. For example, the generation AI automatically adjusts the color tone according to the season or time of day when colorizing. For example, warmer colors are used for evening scenes. This automatically adjusts the color tone according to the season or time of day.

[0036] In addition to bleed and coloring, the generative AI can also convert the content of comics and webtoons into 3D models, achieving a three-dimensional effect. For example, in addition to bleed and coloring, the generative AI can convert the content of comics and webtoons into 3D models, achieving a three-dimensional effect for characters and backgrounds. This allows the generative AI to not only bleed and color but also convert the content into 3D models, achieving a three-dimensional effect.

[0037] Generative AI can convert into different art styles when bleed-in or coloring, providing a variety of visual expressions. For example, generative AI converts the content of comics or webtoons into different art styles. For example, it can automatically generate visual expressions in the style of watercolors or oil paintings. This allows for conversion into different art styles and provides a variety of visual expressions.

[0038] Generative AI can automatically generate not only character movements but also background movements and effects. For example, it can automatically generate not only character movements but also background movements and effects. For example, it can express the swaying of trees in a scene where the wind is blowing. This allows it to automatically generate not only character movements but also background movements and effects.

[0039] Generative AI can automatically generate audio or sound effects when motions are applied, providing a more immersive experience. Generative AI can automatically generate audio or sound effects when motions are applied. For example, footsteps can be added in a scene where a character is running. This allows audio and sound effects to be automatically generated when motions are applied, providing a more immersive experience.

[0040] In addition to adding motion, generative AI can also introduce interactive elements. For example, in addition to adding motion, generative AI can also introduce interactive elements where the story changes depending on the reader's choices. For example, choosing a character's actions can change the development of the story. This allows for the introduction of interactive elements in addition to adding motion.

[0041] Generative AI generates motions that are compatible with different devices, allowing for a more diverse experience. Generative AI generates motions that are compatible with different devices, for example, generating character movements from a 360-degree perspective for VR devices. This allows for motion generation that is compatible with different devices, providing a more diverse experience.

[0042] Generative AI can not only automatically generate content, but also automate editing or proofreading. Generative AI can, for example, not only automatically generate content, but also automate editing and proofreading. For example, it can automatically correct typos in text. This not only automatically generates content, but also automates editing and proofreading.

[0043] Generative AI can monitor the content generation process in real time and achieve an efficient workflow. For example, generative AI can build a system that monitors the content generation process in real time. For example, it can display progress and errors in real time. This allows the content generation process to be monitored in real time and achieve an efficient workflow.

[0044] Generative AI can automate marketing and promotion in addition to reducing the cost of creating and providing content. For example, generative AI can automate marketing and promotion in addition to reducing the cost of creating and providing content. For example, it can automatically generate posts on social media. This not only reduces the cost of creating and providing content, but also automates marketing and promotion.

[0045] Generative AI can generate content that is compatible with different platforms, enabling it to be provided through a variety of channels. Generative AI can generate content that is compatible with different platforms, for example, automatically generating short videos and images for social media. This allows it to generate content that is compatible with different platforms, enabling it to be provided through a variety of channels.

[0046] The generative AI can automatically generate promotional content for not only the works of Webtoon authors, but also the authors themselves. For example, the generative AI can automatically generate promotional content for not only the works of authors, but also the authors themselves. For example, it can create interview videos and introductory articles for authors. This automatically generates promotional content for not only the works of Webtoon authors, but also the authors themselves.

[0047] Generative AI can collect reader feedback on a Webtoon author's work in real time and reflect it in the author's activities. Generative AI can, for example, build a system that collects reader feedback on an author's work in real time. For example, it can automatically tally comments and ratings. This allows reader feedback on an author's work to be collected in real time and reflected in the author's activities.

[0048] Generative AI can help Webtoon authors not only increase their recognition but also support their expansion into different media. For example, generative AI can help Webtoon authors expand their works into different media. For example, it can automatically generate scripts for movies and dramas. This not only helps Webtoon authors increase their recognition but also supports their expansion into different media.

[0049] Generative AI can conduct promotions that cater to different languages ​​or cultural spheres, thereby increasing international recognition. For example, generative AI can automatically generate promotional content translated into different languages. For example, it can create advertisements that are multilingual, such as in English and Chinese. This allows promotions that cater to different languages ​​and cultural spheres, thereby increasing international recognition.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The content conversion system can also be equipped with a voice synthesis unit. The voice synthesis unit automatically generates audio corresponding to each panel of a comic or webtoon. For example, it can convert character lines into audio so that readers can enjoy the content with audio. It can also automatically generate background sounds and sound effects, providing a more immersive experience. This allows for a new content experience that utilizes not only the visual sense but also the auditory sense.

[0052] The content conversion system may further include an interactive section, which provides the reader with choices that affect the progression of the story. For example, the reader may be able to change the course of the story by choosing a character's actions in a particular scene. The system may also be able to provide different endings depending on the reader's choices. This allows the reader to become more immersed in the story and enjoy a personalized experience.

[0053] The content conversion system may further include a character generation unit. The character generation unit allows readers to create their own avatars and have them appear in the story. For example, readers can upload a photo of their own face and a character is generated based on that. Readers can also customize the character's clothing and hairstyle. This allows readers to experience themselves as part of the story.

[0054] The content conversion system can further include an educational content generator. The educational content generator converts comic and webtoon content into educational content. For example, it can explain historical events in comic form. It can also generate content that explains scientific concepts in an easy-to-understand way. This allows the system to provide educational value in addition to entertainment.

[0055] The content conversion system may further include a different art style conversion unit. The different art style conversion unit converts comic or webtoon content into different art styles. For example, it can automatically generate visual expressions that look like hand-drawn or digital art. It can also imitate the style of a specific artist. This allows readers to have a diverse visual experience.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The frame extraction unit extracts frames from comics with permission from the copyright holder. For example, frames can be extracted from digital comic image data, or a paper comic can be scanned and converted into digital data, from which frames can be extracted. Step 2: The layout generator generates a layout or arrangement of each extracted frame suitable for vertical or horizontal reading. For example, when converting to a vertically readable webtoon, the order and size of each frame are adjusted to generate a vertically arranged layout. When converting to a horizontally readable comic, the frame arrangement is optimized. Step 3: The bleed section bleeds the edges of the frames based on the generated layout. For example, if the frame edges are cut off, it automatically bleeds them to maintain the consistency of the frames. Step 4: The colorizer colorizes the added frames. For example, it colorizes a black and white comic and automatically generates colors for each frame. Step 5: The motion generator adds motion to the colored panels, for example, animating character movements and background changes to provide a more immersive experience for the reader.

[0058] (Example 2) The content conversion system according to an embodiment of the present invention is a system that converts between traditional comics and Webtoon format content using generative AI, thereby reducing the cost of creating and providing content and increasing the recognition of Webtoon authors.

[0059] A content conversion system according to an embodiment includes a frame extraction unit, a layout generation unit, a bleed unit, a colorization unit, and a motion application unit. The frame extraction unit extracts frames from a comic book with permission from the author. For example, the frame extraction unit extracts frames from image data of a digital comic book. The frame extraction unit can also scan a paper comic book and convert it into digital data to extract frames. The layout generation unit generates a layout or arrangement of each frame from the extracted frames suitable for vertical or horizontal reading. For example, when converting a webtoon to a vertically readable webtoon, the layout generation unit adjusts the order and size of each frame and generates a vertically arranged layout. When converting a webtoon to a horizontally readable comic, the layout generation unit can also optimize the frame arrangement. The bleed unit bleeds the edges of frames based on the generated layout. For example, the bleed unit automatically bleeds if the edges of frames are cut off to maintain frame consistency. The colorization unit colorizes the bled frames. For example, the colorization unit colorizes a black-and-white comic and automatically generates the color of each frame. The motion addition unit adds motion to the colorized frames. For example, the motion addition unit animates the movements of characters and changes in the background, providing readers with a more immersive experience. As a result, the content conversion system according to the embodiment can reduce the cost of creating and providing content and provide opportunities for many readers to consume vertically read comics.

[0060] The layout generation unit not only arranges the panels but also optimizes the story flow to help readers understand the story. For example, the layout generation unit uses generative AI to not only optimize the arrangement of each panel, but also analyze the entire story flow to help readers understand the story smoothly. For example, the order of panels can be adjusted to emphasize important scenes. This allows readers to understand the story smoothly.

[0061] The layout generation unit can fine-tune the facial expressions or poses of characters when converting frames to achieve a more natural flow. For example, the layout generation unit uses generation AI to fine-tune the facial expressions or poses of characters when converting frames to make the flow of the story more natural. For example, the facial expressions can be changed to emphasize the emotional expression of a character. This makes the flow of the story more natural.

[0062] The layout generation unit uses the emotion estimation function to arrange panels in accordance with the reader's emotions, thereby maximizing the emotional impact. The layout generation unit, for example, uses the emotion estimation function to arrange panels based on the reader's emotions. For example, in moving scenes, the panels are made larger to draw out the reader's emotions. In this way, the panels are arranged based on the reader's emotions, maximizing the emotional impact.

[0063] In addition to converting between comics and webtoons, the generation AI can also convert them into light novels or animations. For example, the generation AI converts the content of comics or webtoons into light novel format. For example, it converts the content of panels into text and expresses the story in written form. The generation AI also converts the content of comics or webtoons into animation. For example, it animates the movements of characters and changes in background to provide readers with a more immersive experience. This allows for the provision of diverse content by converting into light novels and animations.

[0064] When converting between comics and webtoons, the generation AI can convert the layout to accommodate different languages ​​or cultural spheres, making it suitable for international markets. For example, the generation AI can translate the content of comics or webtoons into different languages ​​and generate a layout appropriate for that language. For example, it can create a layout suited to cultural spheres where reading is from right to left. This allows for layout conversion to accommodate different languages ​​and cultural spheres, making it suitable for international markets.

[0065] When converting comics to webtoons or vice versa, the generative AI can use its emotion estimation function to make layout suggestions based on the reader's emotions, improving reader satisfaction. For example, the generative AI can use its emotion estimation function to make layout suggestions based on the reader's emotions. For example, in moving scenes, it can make the panels larger to draw out the reader's emotions. This makes layout suggestions based on the reader's emotions, improving reader satisfaction.

[0066] Generative AI can automatically generate details of a character's clothing or background, achieving a more realistic representation. Generative AI can automatically generate details of a character's clothing or background, for example. For example, it can realistically depict the patterns on a character's clothing or the details of the background. This allows the automatic generation of details of a character's clothing and background, achieving a more realistic representation.

[0067] The generation AI can automatically adjust the color tone according to the season or time of day. For example, the generation AI automatically adjusts the color tone according to the season or time of day when colorizing. For example, warmer colors are used for evening scenes. This automatically adjusts the color tone according to the season or time of day.

[0068] The generative AI uses its emotion estimation function to select color tones that match the reader's emotions and draw out emotional empathy. The generative AI, for example, uses its emotion estimation function to select color tones based on the reader's emotions. For example, warm colors are used in moving scenes to draw out the reader's emotions. This allows the generative AI to select color tones that match the reader's emotions and draw out emotional empathy.

[0069] In addition to bleed and coloring, the generative AI can also convert the content of comics and webtoons into 3D models, achieving a three-dimensional effect. For example, in addition to bleed and coloring, the generative AI can convert the content of comics and webtoons into 3D models, achieving a three-dimensional effect for characters and backgrounds. This allows the generative AI to not only bleed and color but also convert the content into 3D models, achieving a three-dimensional effect.

[0070] Generative AI can convert into different art styles when bleed-in or coloring, providing a variety of visual expressions. For example, generative AI converts the content of comics or webtoons into different art styles. For example, it can automatically generate visual expressions in the style of watercolors or oil paintings. This allows for conversion into different art styles and provides a variety of visual expressions.

[0071] The generative AI can use its emotion estimation function to suggest an art style based on the reader's emotions, providing expressions that match the reader's preferences. For example, the generative AI can use its emotion estimation function to suggest an art style based on the reader's emotions. For example, a watercolor style with soft colors can be used for moving scenes. This allows the generative AI to suggest an art style based on the reader's emotions, providing expressions that match the reader's preferences.

[0072] Generative AI can automatically generate not only character movements but also background movements and effects. For example, it can automatically generate not only character movements but also background movements and effects. For example, it can express the swaying of trees in a scene where the wind is blowing. This allows it to automatically generate not only character movements but also background movements and effects.

[0073] Generative AI can automatically generate audio or sound effects when motions are applied, providing a more immersive experience. Generative AI can automatically generate audio or sound effects when motions are applied. For example, footsteps can be added in a scene where a character is running. This allows audio and sound effects to be automatically generated when motions are applied, providing a more immersive experience.

[0074] Generative AI can use its emotion estimation function to generate motions that match the reader's emotions and enhance the emotional impact. Generative AI can, for example, use its emotion estimation function to generate motions based on the reader's emotions. For example, in emotional scenes, it can add slow movements to draw out the reader's emotions. This allows it to generate motions that match the reader's emotions and enhance the emotional impact.

[0075] In addition to adding motion, generative AI can also introduce interactive elements. For example, in addition to adding motion, generative AI can also introduce interactive elements where the story changes depending on the reader's choices. For example, choosing a character's actions can change the development of the story. This allows for the introduction of interactive elements in addition to adding motion.

[0076] Generative AI generates motions that are compatible with different devices, allowing for a more diverse experience. Generative AI generates motions that are compatible with different devices, for example, generating character movements from a 360-degree perspective for VR devices. This allows for motion generation that is compatible with different devices, providing a more diverse experience.

[0077] Generative AI can use its emotion estimation function to suggest interactive elements based on the reader's emotions, thereby improving reader engagement. Generative AI can, for example, use its emotion estimation function to suggest interactive elements based on the reader's emotions. For example, in emotional scenes, it can increase the number of options to draw out the reader's emotions. This allows it to suggest interactive elements based on the reader's emotions and improve reader engagement.

[0078] Generative AI can not only automatically generate content, but also automate editing or proofreading. Generative AI can, for example, not only automatically generate content, but also automate editing and proofreading. For example, it can automatically correct typos in text. This not only automatically generates content, but also automates editing and proofreading.

[0079] Generative AI can monitor the content generation process in real time and achieve an efficient workflow. For example, generative AI can build a system that monitors the content generation process in real time. For example, it can display progress and errors in real time. This allows the content generation process to be monitored in real time and achieve an efficient workflow.

[0080] Generative AI can use emotion estimation functions to generate content based on the reader's emotions, thereby increasing reader satisfaction. Generative AI can, for example, use emotion estimation functions to generate content based on the reader's emotions. For example, in moving scenes, expressions that elicit emotions are added. This allows content to be generated based on the reader's emotions, thereby increasing reader satisfaction.

[0081] Generative AI can automate marketing and promotion in addition to reducing the cost of creating and providing content. For example, generative AI can automate marketing and promotion in addition to reducing the cost of creating and providing content. For example, it can automatically generate posts on social media. This not only reduces the cost of creating and providing content, but also automates marketing and promotion.

[0082] Generative AI can generate content that is compatible with different platforms, enabling it to be provided through a variety of channels. Generative AI can generate content that is compatible with different platforms, for example, automatically generating short videos and images for social media. This allows it to generate content that is compatible with different platforms, enabling it to be provided through a variety of channels.

[0083] Generative AI can use its emotion estimation function to propose marketing strategies based on readers' emotions and achieve effective promotions. Generative AI can, for example, use its emotion estimation function to propose marketing strategies based on readers' emotions. For example, it can create advertisements that emphasize moving scenes. This allows it to propose marketing strategies based on readers' emotions and achieve effective promotions.

[0084] The generative AI can automatically generate promotional content for not only the works of Webtoon authors, but also the authors themselves. For example, the generative AI can automatically generate promotional content for not only the works of authors, but also the authors themselves. For example, it can create interview videos and introductory articles for authors. This automatically generates promotional content for not only the works of Webtoon authors, but also the authors themselves.

[0085] Generative AI can collect reader feedback on a Webtoon author's work in real time and reflect it in the author's activities. Generative AI can, for example, build a system that collects reader feedback on an author's work in real time. For example, it can automatically tally comments and ratings. This allows reader feedback on an author's work to be collected in real time and reflected in the author's activities.

[0086] Generative AI can use its emotion estimation function to propose promotional strategies based on readers' emotions, thereby improving the recognition of authors. Generative AI can, for example, use its emotion estimation function to propose promotional strategies based on readers' emotions. For example, it can create advertisements that highlight moving scenes. This allows it to propose promotional strategies based on readers' emotions, thereby improving the recognition of authors.

[0087] Generative AI can help Webtoon authors not only increase their recognition but also support their expansion into different media. For example, generative AI can help Webtoon authors expand their works into different media. For example, it can automatically generate scripts for movies and dramas. This not only helps Webtoon authors increase their recognition but also supports their expansion into different media.

[0088] Generative AI can conduct promotions that cater to different languages ​​or cultural spheres, thereby increasing international recognition. For example, generative AI can automatically generate promotional content translated into different languages. For example, it can create advertisements that are multilingual, such as in English and Chinese. This allows promotions that cater to different languages ​​and cultural spheres, thereby increasing international recognition.

[0089] Generative AI can use its emotion estimation function to suggest media developments based on the reader's emotions, maximizing the appeal of an author's work. Generative AI can, for example, use its emotion estimation function to suggest media developments based on the reader's emotions. For example, it can create a movie or drama script that emphasizes moving scenes. This allows it to suggest media developments based on the reader's emotions, maximizing the appeal of an author's work.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The content conversion system can also be equipped with a voice synthesis unit. The voice synthesis unit automatically generates audio corresponding to each panel of a comic or webtoon. For example, it can convert character lines into audio so that readers can enjoy the content with audio. It can also automatically generate background sounds and sound effects, providing a more immersive experience. This allows for a new content experience that utilizes not only the visual sense but also the auditory sense.

[0092] The content conversion system may further include an interactive section, which provides the reader with choices that affect the progression of the story. For example, the reader may be able to change the course of the story by choosing a character's actions in a particular scene. The system may also be able to provide different endings depending on the reader's choices. This allows the reader to become more immersed in the story and enjoy a personalized experience.

[0093] The content conversion system can also use an emotion estimation function to automatically generate changes in a character's facial expression based on the reader's emotions. For example, if the reader is moved, the character's facial expression can be made more emotional. If the reader is nervous, the character's facial expression can be changed to one that shows tension. This provides changes in the character's facial expression that match the reader's emotions, enhancing the realism of the story.

[0094] The content conversion system can also use its emotion estimation function to automatically generate background music based on the reader's emotions. For example, if the reader is moved, it can play moving music. If the reader is nervous, it can play tense music. This provides background music that matches the reader's emotions, further enhancing the atmosphere of the story.

[0095] The content conversion system may further include a character generation unit. The character generation unit allows readers to create their own avatars and have them appear in the story. For example, readers can upload a photo of their own face and a character is generated based on that. Readers can also customize the character's clothing and hairstyle. This allows readers to experience themselves as part of the story.

[0096] The content conversion system can also use an emotion estimation function to automatically generate a storyline based on the reader's emotions. For example, if the reader is moved, an emotional scene can be added. If the reader is excited, an action scene can be added. This allows the system to provide a storyline that matches the reader's emotions and increase reader satisfaction.

[0097] The content conversion system can further include an educational content generator. The educational content generator converts comic and webtoon content into educational content. For example, it can explain historical events in comic form. It can also generate content that explains scientific concepts in an easy-to-understand way. This allows the system to provide educational value in addition to entertainment.

[0098] The content conversion system can also use its emotion estimation function to automatically generate character lines based on the reader's emotions. For example, if the reader is moved, it can add moving lines. If the reader is laughing, it can add humorous lines. This provides character lines that match the reader's emotions, enhancing the appeal of the story.

[0099] The content conversion system may further include a different art style conversion unit. The different art style conversion unit converts comic or webtoon content into different art styles. For example, it can automatically generate visual expressions that look like hand-drawn or digital art. It can also imitate the style of a specific artist. This allows readers to have a diverse visual experience.

[0100] The content conversion system can further use its emotion estimation function to automatically generate an ending based on the reader's emotions. For example, if the reader is moved, it will provide an emotional ending. If the reader is excited, it will provide an action-packed ending. This allows the system to provide an ending that matches the reader's emotions and increase satisfaction with the story.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The frame extraction unit extracts frames from comics with permission from the copyright holder. For example, frames can be extracted from digital comic image data, or a paper comic can be scanned and converted into digital data, from which frames can be extracted. Step 2: The layout generator generates a layout or arrangement of each extracted frame suitable for vertical or horizontal reading. For example, when converting to a vertically readable webtoon, the order and size of each frame are adjusted to generate a vertically arranged layout. When converting to a horizontally readable comic, the frame arrangement is optimized. Step 3: The bleed section bleeds the edges of the frames based on the generated layout. For example, if the frame edges are cut off, it automatically bleeds them to maintain the consistency of the frames. Step 4: The colorizer colorizes the added frames. For example, it colorizes a black and white comic and automatically generates colors for each frame. Step 5: The motion generator adds motion to the colored panels, for example, animating character movements and background changes to provide a more immersive experience for the reader.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 frame extraction unit that extracts frames of comics that have been authorized by the copyright holder; a layout generation unit that generates an arrangement or layout of each frame extracted by the frame extraction unit in a form suitable for vertical or horizontal reading; a bleed unit that bleeds edges of frames based on the layout generated by the layout generation unit; a colorization unit that colors the frames that have been bled by the bleed unit; a motion imparting unit that imparts motion to the frames colored by the colorization unit. A system characterized by:

2. The layout generation unit Using emotion estimation functionality, panels are arranged to match the reader's emotions, maximizing emotional impact. The system of claim 1 .

3. The generated AI is In addition to converting the comics and webtoons to each other, it also converts them to light novels or animations. The system of claim 1 .

4. The generated AI is Automatically generate details for character clothing or backgrounds to achieve more realistic expressions The system of claim 1 .

5. The generated AI is Automatically generate not only character movements but also background movements and effects The system of claim 1 .

6. The generated AI is Using emotion estimation function, content is generated based on the reader's emotions, increasing reader satisfaction. The system of claim 1 .

7. The generated AI is Using emotion estimation function, we propose promotion strategies based on readers' emotions and improve the recognition of authors. The system of claim 1 .

8. The generated AI is Using emotion estimation functions, we propose media developments based on readers' emotions, maximizing the appeal of authors' works. The system of claim 1 .

Images