system

Abstract

The system according to this embodiment aims to automate the line drawing and coloring processes in the manga production process. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a coloring unit. The reception unit receives a rough sketch as input. The analysis unit analyzes the rough sketch input by the reception unit. The generation unit generates a line drawing based on the rough sketch analyzed by the analysis unit. The coloring unit colors the line drawing generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, operations that require time such as line drawing and coloring in the comic production process are not automated, and there is room for efficiency improvement.

[0005] The system according to the embodiment aims to automate operations such as line drawing and coloring in the comic production process.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a coloring unit. The reception unit receives a rough sketch as input. The analysis unit analyzes the rough sketch input by the reception unit. The generation unit generates a line drawing based on the rough sketch analyzed by the analysis unit. The coloring unit colors the line drawing generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the line drawing and coloring processes in the manga production process. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The manga production automation system according to an embodiment of the present invention is a system that automates the manga production process, particularly time-consuming tasks such as line drawing and coloring. This system includes a reception unit for inputting rough sketches drawn by artists. It includes an analysis unit for analyzing the rough sketches input by the reception unit. It includes a generation unit for generating line drawings based on the rough sketches analyzed by the analysis unit. It includes a coloring unit for coloring the line drawings generated by the generation unit. The coloring unit can select colors that correspond to various styles and genres by utilizing open-source training data. Furthermore, it includes a distribution unit and a feedback unit to deepen the connection between artists and readers. As a result, artists can significantly shorten the production period and publish more works. In addition, artists can freely express their values ​​and open up new possibilities. For example, when artists challenge themselves with new styles or genres, they can use this system to efficiently produce works. In this way, the manga production automation system improves the work efficiency of artists and allows them to concentrate on their creative work.

[0029] The manga production automation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a coloring unit. The reception unit receives rough sketches drawn by artists. Rough sketches include, but are not limited to, hand-drawn sketches and digital sketches. The reception unit can, for example, scan hand-drawn sketches and save them as digital data. The reception unit can also directly read sketches submitted in digital format. Furthermore, the reception unit can read printed sketches using OCR technology. For example, the reception unit scans hand-drawn sketches with a high-resolution scanner and converts them into text information using OCR technology. Digital sketches can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit uses AI to analyze the rough sketches input by the reception unit. The analysis is performed based on, for example, image analysis algorithms and feature extraction methods, but is not limited to these examples. For example, the analysis unit extracts the outlines of the sketch using image analysis algorithms. The analysis unit can also identify important parts of the sketch using feature extraction methods. The generation unit uses AI to generate line art based on the rough sketch analyzed by the analysis unit. The line art is generated based on, for example, the extraction of outlines or the line thickness and style, but is not limited to such examples. For example, the generation unit extracts outlines and adjusts the line thickness and style to generate line art. The generation unit can also use AI to generate line art that highlights important parts of the sketch. The coloring unit uses AI to color the line art generated by the generation unit. Coloring is performed based on, for example, color selection criteria or coloring algorithms, but is not limited to such examples. For example, the coloring unit utilizes open-source training data to select colors that correspond to various styles and genres. The coloring unit can also use AI to select colors that highlight important parts of the line art. As a result, the manga production automation system according to this embodiment improves the work efficiency of artists by automating the process from input of rough sketches to analysis, line art generation, and coloring.Some or all of the above-described processes in the reception, analysis, generation, and coloring units may be performed using AI or not. For example, the reception unit can input image data obtained by scanning a hand-drawn sketch into the generation AI and have the generation AI generate text data from the image data. The analysis unit can input the writer's stroke order data into the generation AI and have the generation AI extract the writer's characteristics. The generation unit can input the writer's stroke order data into the generation AI and have the generation AI extract the writer's characteristics. The coloring unit can input image data of students taken with a camera into the generation AI and have the generation AI estimate the students' emotions. As a result, the manga production automation system according to this embodiment improves the artist's work efficiency and allows them to concentrate on their creative work.

[0030] The reception department inputs rough sketches drawn by artists. These rough sketches include, but are not limited to, hand-drawn sketches and digital sketches. The reception department can, for example, scan hand-drawn sketches and save them as digital data. It can also directly read sketches submitted in digital format. Furthermore, the reception department can read printed sketches using OCR technology. For example, the reception department can scan hand-drawn sketches with a high-resolution scanner and convert them into text information using OCR technology. Digital sketches can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The reception department centrally manages this data, making it easily accessible to subsequent analysis and generation departments. For example, the reception department saves scanned image data to cloud storage, allowing the analysis department to access it in real time. The reception department also performs data format conversion and preprocessing to enable the analysis department to process the data efficiently. This allows the reception department to efficiently incorporate rough sketches in various formats provided by artists and to smoothly advance the overall system processing. Furthermore, the reception desk can provide feedback to artists through the user interface, offering guidelines on sketch quality and format. For example, it can display recommended settings for scanning resolution and file format, helping artists submit sketches in the optimal format. This allows the reception desk to improve artists' workflow efficiency and optimize overall system performance.

[0031] The analysis unit uses AI to analyze rough sketches input by the reception unit. The analysis is performed based on, but is not limited to, image analysis algorithms and feature extraction methods. For example, the analysis unit can extract the outline of the sketch using an image analysis algorithm. It can also identify important parts of the sketch using feature extraction methods. Specifically, the AI ​​utilizes deep learning technology to analyze each part of the sketch in detail. For example, it can use a convolutional neural network (CNN) to detect edges and textures of the sketch with high accuracy. Furthermore, the analysis unit classifies different elements such as characters, backgrounds, and objects based on the content of the sketch and performs appropriate processing on each element. For example, it can identify the position of a character's face and hands and perform analysis to emphasize these parts. The analysis unit also learns the sketch style and the artist's characteristics, allowing it to perform analysis tailored to each artist's style. This enables the analysis unit to provide analysis results that accurately reflect the artist's intentions, providing a foundation for subsequent generation and coloring units to produce high-quality output. Additionally, the analysis unit updates analysis results in real time, allowing for quick responses even if the artist modifies the sketch. This allows the analysis unit to streamline the sketch analysis process and improve the overall processing speed and accuracy of the system.

[0032] The generation unit uses AI to generate line art based on rough sketches analyzed by the analysis unit. Line art is generated based on, for example, the extraction of outlines or the adjustment of line thickness and style, but is not limited to these examples. For instance, the generation unit can extract outlines and adjust line thickness and style to generate line art. The generation unit can also use AI to generate line art that emphasizes important parts of the sketch. Specifically, the generation unit uses generation AI to extract the sketch's outlines with high accuracy and adjust line thickness and style to match the artist's intentions. For example, the generation unit can input instructions such as "emphasize the character's outline and thin the background lines" as a prompt to the generation AI, which then automatically generates appropriate line art. Furthermore, the generation unit can dynamically change line thickness and style to emphasize important parts of the sketch. For example, it can visually emphasize the character's face and hands by drawing them with thicker lines and the background with thinner lines. In addition, the generation unit can learn from past artwork data to generate line art that reflects the individual artist's style. This allows the generation unit to automatically produce high-quality line drawings that accurately reflect the artist's intentions, reducing the artist's workload. Furthermore, the generation unit can generate line drawings in real time and respond quickly even if the artist modifies the sketch. This streamlines the line drawing generation process and improves the overall processing speed and accuracy of the system.

[0033] The coloring unit uses AI to color the line art generated by the generation unit. Coloring is performed based on, for example, color selection criteria and coloring algorithms, but is not limited to such examples. For example, the coloring unit utilizes open-source training data to select colors that correspond to various styles and genres. The coloring unit can also use AI to select colors that highlight important parts of the line art. Specifically, the coloring unit uses generation AI to automatically assign appropriate colors to each part of the line art. For example, instructions such as "make the character's hair a light brown and the background sky blue" can be input as prompts to the generation AI, and the AI ​​will automatically select appropriate colors. The coloring unit can also dynamically adjust the brightness and saturation of colors to highlight important parts of the line art. For example, it can visually emphasize the character's face and hands by coloring them with lighter colors and the background with more subdued colors. Furthermore, the coloring unit can learn from past artwork data to select colors that match the artist's style and reflect the characteristics of each individual artist. This allows the coloring unit to automatically perform high-quality coloring that accurately reflects the artist's intentions, reducing the artist's workload. Furthermore, the coloring unit performs coloring in real time and can quickly respond to any revisions made by the artist to the line art. As a result, the coloring unit streamlines the coloring process and improves the overall processing speed and accuracy of the system.

[0034] The coloring unit can utilize open-source training data to select colors that correspond to various styles and genres. For example, the coloring unit can use open-source training data to select anime-style colors. It can also use open-source training data to select realistic-style colors. Furthermore, it can use open-source training data to select abstract-style colors. This makes it possible to color in a variety of styles and genres by utilizing open-source training data. Open-source training data includes, but is not limited to, the names and acquisition methods of datasets. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can perform coloring using an AI model that selects anime-style colors using open-source training data.

[0035] The distribution section provides functions to deepen the connection between artists and readers. For example, the distribution section can provide a function for artists to stream their creative process in real time. It can also provide a function for artists to record their creative process and stream it later. Furthermore, the distribution section can provide a function for artists to edit their creative process and stream highlights. This can deepen the connection between artists and readers. Specific methods and criteria for deepening the connection include, but are not limited to, the type and frequency of interaction. Some or all of the above processes in the distribution section may be performed using AI or not. For example, the distribution section can use an AI model for artists to stream their creative process in real time.

[0036] The distribution unit can provide a function for artists to stream their creative process in real time. For example, the distribution unit can provide a platform for artists to stream their creative process in real time. The distribution unit can also provide technology to minimize latency when artists stream their creative process in real time. Furthermore, the distribution unit can provide technology to optimize image and sound quality when artists stream their creative process in real time. This improves interaction with readers as artists stream their creative process in real time. Specific methods and technologies for real-time streaming include, but are not limited to, the distribution platform used and latency. Some or all of the above processing in the distribution unit may or may not be performed using AI. For example, the distribution unit can perform streaming using an AI model that provides a platform for artists to stream their creative process in real time.

[0037] The feedback section provides functionality for receiving feedback from readers. For example, it may provide a function for readers to post comments. It may also provide a function for readers to rate works. Furthermore, it may provide a function for readers to answer surveys. This allows artists to use reader feedback to improve their work. Specific methods and criteria for feedback include, but are not limited to, comments, ratings, and surveys. Some or all of the above processing in the feedback section may be performed using AI or not. For example, the feedback section can receive feedback using an AI model in which readers post comments.

[0038] The feedback unit can provide artists with feedback from readers. For example, the feedback unit can provide a function to notify artists of comments from readers. It can also provide a function to notify artists of ratings from readers. Furthermore, the feedback unit can provide a function to notify artists of the results of reader surveys. By providing artists with feedback from readers, the quality of their work can be improved. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can provide feedback using an AI model that notifies artists of comments from readers.

[0039] The reception department can analyze an artist's past rough sketch submission history and select the optimal submission method. For example, the reception department can analyze the frequency of rough sketches submitted by the artist in the past and propose an optimal submission schedule. The reception department can also evaluate the quality of rough sketches submitted by the artist in the past and adjust the submission timing. Furthermore, the reception department can select the most efficient submission method from the artist's past submission history. In this way, by analyzing past submission history, the reception department can select the optimal submission method and improve work efficiency. The optimal submission method includes, but is not limited to, submission time and submission format. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the artist's past submission history data into a generating AI and have the generating AI select the optimal submission method.

[0040] The reception desk can filter rough sketches upon receipt based on the artist's current projects and areas of interest. For example, the reception desk may only accept rough sketches related to the artist's current projects. It can also prioritize the acceptance of highly relevant rough sketches based on the artist's areas of interest. Furthermore, the reception desk can filter appropriate rough sketches according to the artist's project progress. This allows for the priority acceptance of highly relevant rough sketches by filtering based on current projects and areas of interest. Specific criteria and methods for filtering include, but are not limited to, definitions of project types and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's current project data into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize accepting rough sketches that are highly relevant based on the artist's geographical location information. For example, if the artist is in a specific region, the reception desk will prioritize accepting rough sketches related to that region. Furthermore, if the artist is traveling, the reception desk can prioritize accepting rough sketches related to their travel destination. Additionally, if the artist is at home, the reception desk can prioritize accepting rough sketches related to their home area. This improves work efficiency by prioritizing the acceptance of highly relevant rough sketches based on geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's geographical location data into a generating AI and have the generating AI select highly relevant rough sketches.

[0042] The reception desk can analyze the artist's social media activity when receiving rough sketches and accept relevant sketches. For example, the reception desk may prioritize accepting rough sketches related to works the artist has shared on social media. It can also accept rough sketches based on themes the artist has shown interest in on social media. Furthermore, the reception desk may prioritize accepting rough sketches requested by the artist's followers. This allows for the priority acceptance of highly relevant rough sketches by analyzing social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, follower count, and engagement rate. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's social media activity data into a generating AI and have the generating AI select relevant rough sketches.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the rough sketches during the analysis. For example, the analysis unit can perform a detailed analysis on rough sketches of high importance. Conversely, the analysis unit can also perform a concise analysis on rough sketches of low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the rough sketches. Specific evaluation criteria and methods for importance include, but are not limited to, project priority and client requirements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input rough sketch importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the rough sketch during analysis. For example, the analysis unit can apply a character-specific analysis algorithm to a character rough sketch. It can also apply a background-specific analysis algorithm to a background rough sketch. Furthermore, it can apply an action scene-specific analysis algorithm to an action scene rough sketch. This improves analysis accuracy by applying different analysis algorithms depending on the category of the rough sketch. Specific definitions and classification methods for categories include, but are not limited to, genre, theme, and style. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input rough sketch category data into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the submission date of the rough sketches. For example, the analysis unit may prioritize the analysis of rough sketches submitted earlier. It can also postpone the analysis of rough sketches submitted later. Furthermore, the analysis unit can adjust the priority of the analysis according to the submission date. This enables efficient analysis by determining the priority of the analysis based on the submission date. Specific evaluation criteria and methods for the submission date include, but are not limited to, the submission date and time. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input rough sketch submission date data into a generating AI and have the generating AI determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the rough sketches during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant rough sketches. It can also postpone the analysis of less relevant rough sketches. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on relevance. Specific evaluation criteria and methods for relevance include, but are not limited to, the degree of theme agreement or similarity of content. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of the rough sketches into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail in the line drawings based on the importance of the rough sketches during generation. For example, the generation unit can generate detailed line drawings for rough sketches of high importance. It can also generate simpler line drawings for rough sketches of low importance. Furthermore, the generation unit can adjust the level of detail in the line drawings according to their importance. This allows for efficient generation by adjusting the level of detail in the line drawings based on the importance of the rough sketches. Specific criteria and methods for adjusting the level of detail in the line drawings include, but are not limited to, the fineness of the drawing or the level of detail. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance data of the rough sketches into a generation AI and have the generation AI perform the adjustment of the level of detail in the line drawings.

[0048] The generation unit can apply different generation algorithms depending on the category of the rough sketch during generation. For example, the generation unit can apply a character-specific generation algorithm to character rough sketches. It can also apply a background-specific generation algorithm to background rough sketches. Furthermore, it can apply an action scene-specific generation algorithm to action scene rough sketches. By applying different generation algorithms depending on the category of the rough sketch, the generation accuracy is improved. Specific types and implementation methods of generation algorithms include, but are not limited to, deep learning algorithms and rule-based generation. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input rough sketch category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0049] The generation unit can determine the priority of line drawings based on the submission dates of the rough sketches during the generation process. For example, the generation unit can prioritize generating rough sketches that were submitted earlier. It can also postpone generating rough sketches that were submitted later. Furthermore, the generation unit can adjust the priority of line drawings according to the submission dates. This enables efficient generation by determining the priority of line drawings based on the submission dates. Specific criteria and methods for determining priority include, but are not limited to, submission dates and client requests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input rough sketch submission date data into a generation AI and have the generation AI determine the priority of line drawings.

[0050] The generation unit can adjust the order of line drawings based on the relevance of the rough sketches during generation. For example, the generation unit can prioritize generating rough sketches with high relevance. It can also postpone generating rough sketches with low relevance. Furthermore, the generation unit can adjust the order of line drawings according to their relevance. This allows for efficient generation by adjusting the order of line drawings based on relevance. Specific criteria and methods for adjusting the order include, but are not limited to, sorting by relevance or importance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance data of the rough sketches into a generation AI and have the generation AI perform the adjustment of the line drawing order.

[0051] The coloring unit can adjust the level of detail in coloring based on the importance of the rough sketch. For example, the coloring unit will perform detailed coloring for rough sketches of high importance, and simpler coloring for rough sketches of low importance. Furthermore, the coloring unit can adjust the level of detail in coloring according to importance. This allows for efficient coloring by adjusting the level of detail in coloring based on the importance of the rough sketch. Specific criteria and methods for adjusting the level of detail in coloring include, but are not limited to, the number of colors and the level of detail. Some or all of the above processing in the coloring unit may be performed using AI, or not. For example, the coloring unit can input rough sketch importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in coloring.

[0052] The coloring unit can apply different coloring algorithms depending on the category of the rough sketch during the coloring process. For example, the coloring unit can apply a character-specific coloring algorithm to a character rough sketch. It can also apply a background-specific coloring algorithm to a background rough sketch. Furthermore, it can apply an action scene-specific coloring algorithm to an action scene rough sketch. This improves coloring accuracy by applying different coloring algorithms depending on the category of the rough sketch. Specific types and implementation methods of coloring algorithms include, but are not limited to, deep learning algorithms and rule-based coloring. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input rough sketch category data into a generating AI and have the generating AI apply the coloring algorithm.

[0053] The coloring unit can determine the order of coloring based on the submission dates of the rough sketches. For example, the coloring unit may prioritize coloring rough sketches that were submitted earlier. It can also postpone coloring rough sketches that were submitted later. Furthermore, the coloring unit can adjust the order of coloring according to the submission dates. This allows for efficient coloring by determining the order of coloring based on the submission dates. Specific criteria and methods for adjusting the order include, but are not limited to, submission dates and client requests. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input rough sketch submission date data into a generating AI and have the generating AI determine the order of coloring.

[0054] The coloring unit can adjust the order of coloring based on the relevance of the rough sketches during the coloring process. For example, the coloring unit can prioritize coloring rough sketches that are highly relevant. It can also postpone coloring rough sketches that are less relevant. Furthermore, the coloring unit can adjust the order of coloring according to relevance. This allows for efficient coloring by adjusting the order of coloring based on relevance. Specific criteria and methods for adjusting the order include, but are not limited to, sorting by relevance or importance. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input the relevance data of the rough sketches into a generating AI and have the generating AI perform the adjustment of the coloring order.

[0055] The distribution department can select the optimal distribution method by referring to the artist's past distribution history at the time of distribution. For example, the distribution department can analyze the content of past distributions by the artist and propose the optimal distribution method. The distribution department can also analyze viewer reactions from the artist's past distribution history and select the optimal distribution method. Furthermore, the distribution department can select the most effective distribution method based on the artist's past distribution history. In this way, by referring to past distribution history, the optimal distribution method can be selected and viewer satisfaction can be improved. Specific criteria and methods for the optimal distribution method include, but are not limited to, distribution time and content. Some or all of the above processing in the distribution department may be performed using AI or not. For example, the distribution department can input the artist's past distribution history data into a generating AI and have the generating AI perform the selection of the optimal distribution method.

[0056] The distribution unit can customize the content of broadcasts based on the artist's current projects and areas of interest. For example, the distribution unit can broadcast content related to the artist's current projects. It can also broadcast highly relevant content based on the artist's areas of interest. Furthermore, the distribution unit can customize the content appropriately according to the progress of the artist's projects. This makes it easier to attract viewers' attention by customizing the content based on current projects and areas of interest. Specific criteria and methods for customization include, but are not limited to, defining project types and areas of interest. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the artist's current project data into a generating AI and have the generating AI perform the customization of the broadcast content.

[0057] The distribution unit can select the optimal distribution method based on the artist's geographical location information at the time of distribution. For example, if the artist is in a specific region, the distribution unit will distribute content related to that region. Furthermore, if the artist is traveling, the distribution unit can distribute content related to their travel destination. Additionally, if the artist is at home, the distribution unit can distribute content related to their home area. This makes it easier to attract viewers' attention by selecting the optimal distribution method based on geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the artist's geographical location data into a generating AI and have the generating AI select the optimal distribution method.

[0058] The distribution department can analyze the artist's social media activity and adjust the content of the broadcast at the time of distribution. For example, the distribution department can distribute content related to the artist's work shared on social media. It can also distribute content based on themes the artist has shown interest in on social media. Furthermore, the distribution department can prioritize the distribution of content requested by the artist's followers. This allows the distribution department to provide content that is more likely to attract the audience's interest by analyzing social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, follower count, and engagement rate. Some or all of the above processing in the distribution department may be performed using AI or not. For example, the distribution department can input the artist's social media activity data into a generating AI and have the generating AI adjust the content of the broadcast.

[0059] The feedback unit can select the optimal feedback method by referring to the reader's past feedback history when providing feedback. For example, the feedback unit can analyze the content of feedback the reader has given in the past and propose the optimal feedback method. Furthermore, the feedback unit can analyze the reader's reactions from their past feedback history and select the optimal feedback method. In addition, the feedback unit can select the most effective feedback method based on the reader's past feedback history. This allows the feedback unit to select the optimal feedback method by referring to past feedback history, thereby improving reader satisfaction. Specific criteria and methods for the optimal feedback method include, but are not limited to, the format and timing of the feedback. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's past feedback history data into a generating AI and have the generating AI select the optimal feedback method.

[0060] The feedback unit can customize the feedback content based on the reader's current areas of interest. For example, the feedback unit can provide feedback related to the topic the reader is currently interested in. It can also provide highly relevant feedback based on the reader's areas of interest. Furthermore, the feedback unit can customize the feedback content to be appropriate according to the reader's areas of interest. This improves reader satisfaction by customizing the feedback content based on current areas of interest. Specific criteria and methods for customization include, but are not limited to, the definition of areas of interest and the content of the feedback. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's current areas of interest data into a generating AI and have the generating AI perform the customization of the feedback content.

[0061] The feedback unit can select the most appropriate feedback method based on the reader's geographical location information when providing feedback. For example, if the reader is in a specific region, the feedback unit can provide feedback relevant to that region. Furthermore, if the reader is traveling, the feedback unit can provide feedback relevant to their travel destination. Additionally, if the reader is at home, the feedback unit can provide feedback relevant to their surroundings. This improves reader satisfaction by selecting the most appropriate feedback method based on geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. Some or all of the processing described above in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's geographical location data into a generating AI and have the generating AI select the most appropriate feedback method.

[0062] The feedback unit can analyze the reader's social media activity and adjust the feedback content accordingly. For example, the feedback unit can provide feedback related to works the reader has shared on social media. It can also provide feedback based on themes the reader has shown interest in on social media. Furthermore, the feedback unit can prioritize providing feedback requested by the reader's followers. This allows the feedback unit to provide content that is more likely to attract the reader's attention by analyzing their social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, follower count, and engagement rate. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's social media activity data into a generating AI and have the generating AI adjust the feedback content.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception department can analyze the artist's past work data and customize the rough sketch submission process based on the artist's style and preferences. For example, it can analyze the style of the artist's past works and prioritize accepting rough sketches of a similar style. It can also prioritize accepting rough sketches of specific genres or themes based on the artist's preferences. Furthermore, it can suggest the optimal timing for submission based on the artist's past work data. This enables the submission of rough sketches in accordance with the artist's style and preferences, improving work efficiency. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the artist's past work data into a generating AI and have the generating AI perform the customization of the submission method.

[0065] The analysis unit can adjust the analysis speed when analyzing a rough sketch, taking into account the artist's drawing speed. For example, if the artist draws quickly, the analysis unit can perform a rapid analysis and provide immediate feedback. Conversely, if the artist draws slowly, the analysis unit can perform a detailed analysis and provide deeper feedback. Furthermore, the analysis priority can be adjusted according to the artist's drawing speed. This enables analysis that is tailored to the artist's drawing speed, improving work efficiency. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the artist's drawing speed data into a generating AI and have the generating AI adjust the analysis speed.

[0066] The generation unit can analyze the artist's past work data and customize the line art generation method based on the artist's style. For example, it can analyze the style of works the artist has drawn in the past and generate line art in a similar style. It can also generate line art in a specific genre or theme based on the artist's preferences. Furthermore, it can suggest the optimal line art generation method based on the artist's past work data. This makes it possible to generate line art that matches the artist's style and preferences, improving work efficiency. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the artist's past work data into a generation AI and have the generation AI perform the customization of the line art generation method.

[0067] The coloring unit can analyze the artist's past work data and customize the coloring method based on the artist's style and preferences. For example, it can analyze the color palette the artist has used in the past and use similar colors for coloring. It can also select colors suitable for a specific genre or theme based on the artist's preferences. Furthermore, it can suggest the optimal coloring method based on the artist's past work data. This enables coloring that matches the artist's style and preferences, improving work efficiency. Some or all of the above processes in the coloring unit may be performed using AI or not. For example, the coloring unit can input the artist's past work data into a generating AI and have the generating AI perform the customization of the coloring method.

[0068] The distribution department can analyze an artist's past distribution data and customize the content based on viewer reactions. For example, it can analyze content that received a good response from viewers in the past and prioritize similar content for distribution. It can also select content suitable for specific genres or themes based on viewer preferences. Furthermore, it can suggest the optimal distribution method based on the artist's past distribution data. This enables distribution content that responds to viewer reactions, improving viewer satisfaction. Some or all of the above processes in the distribution department may be performed using AI or not. For example, the distribution department can input an artist's past distribution data into a generating AI and have the generating AI perform the customization of the distribution content.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk inputs rough sketches drawn by the artist. These rough sketches can be hand-drawn or digital. Hand-drawn sketches are scanned and saved as digital data. Sketches submitted in digital format can be read directly. Printed sketches can also be read using OCR technology. Step 2: The analysis unit uses AI to analyze the rough sketch input by the reception unit. The analysis is performed based on image analysis algorithms and feature extraction methods. For example, it extracts the outline of the sketch and identifies important parts. Step 3: The generation unit uses AI to generate line drawings based on the rough sketches analyzed by the analysis unit. The line drawings are generated based on the extraction of outlines and the adjustment of line thickness and style. For example, outlines are extracted, and the line thickness and style are adjusted to generate the line drawings. Step 4: The coloring section uses AI to color the line drawings generated by the generation section. Coloring is performed based on color selection criteria and coloring algorithms. For example, open-source training data is used to select colors that correspond to various styles and genres.

[0071] (Example of form 2) The manga production automation system according to an embodiment of the present invention is a system that automates the manga production process, particularly time-consuming tasks such as line drawing and coloring. This system includes a reception unit for inputting rough sketches drawn by artists. It includes an analysis unit for analyzing the rough sketches input by the reception unit. It includes a generation unit for generating line drawings based on the rough sketches analyzed by the analysis unit. It includes a coloring unit for coloring the line drawings generated by the generation unit. The coloring unit can select colors that correspond to various styles and genres by utilizing open-source training data. Furthermore, it includes a distribution unit and a feedback unit to deepen the connection between artists and readers. As a result, artists can significantly shorten the production period and publish more works. In addition, artists can freely express their values ​​and open up new possibilities. For example, when artists challenge themselves with new styles or genres, they can use this system to efficiently produce works. In this way, the manga production automation system improves the work efficiency of artists and allows them to concentrate on their creative work.

[0072] The manga production automation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a coloring unit. The reception unit receives rough sketches drawn by artists. Rough sketches include, but are not limited to, hand-drawn sketches and digital sketches. The reception unit can, for example, scan hand-drawn sketches and save them as digital data. The reception unit can also directly read sketches submitted in digital format. Furthermore, the reception unit can read printed sketches using OCR technology. For example, the reception unit scans hand-drawn sketches with a high-resolution scanner and converts them into text information using OCR technology. Digital sketches can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit uses AI to analyze the rough sketches input by the reception unit. The analysis is performed based on, for example, image analysis algorithms and feature extraction methods, but is not limited to these examples. For example, the analysis unit extracts the outlines of the sketch using image analysis algorithms. The analysis unit can also identify important parts of the sketch using feature extraction methods. The generation unit uses AI to generate line art based on the rough sketch analyzed by the analysis unit. The line art is generated based on, for example, the extraction of outlines or the line thickness and style, but is not limited to such examples. For example, the generation unit extracts outlines and adjusts the line thickness and style to generate line art. The generation unit can also use AI to generate line art that highlights important parts of the sketch. The coloring unit uses AI to color the line art generated by the generation unit. Coloring is performed based on, for example, color selection criteria or coloring algorithms, but is not limited to such examples. For example, the coloring unit utilizes open-source training data to select colors that correspond to various styles and genres. The coloring unit can also use AI to select colors that highlight important parts of the line art. As a result, the manga production automation system according to this embodiment improves the work efficiency of artists by automating the process from input of rough sketches to analysis, line art generation, and coloring.Some or all of the above-described processes in the reception, analysis, generation, and coloring units may be performed using AI or not. For example, the reception unit can input image data obtained by scanning a hand-drawn sketch into the generation AI and have the generation AI generate text data from the image data. The analysis unit can input the writer's stroke order data into the generation AI and have the generation AI extract the writer's characteristics. The generation unit can input the writer's stroke order data into the generation AI and have the generation AI extract the writer's characteristics. The coloring unit can input image data of students taken with a camera into the generation AI and have the generation AI estimate the students' emotions. As a result, the manga production automation system according to this embodiment improves the artist's work efficiency and allows them to concentrate on their creative work.

[0073] The reception department inputs rough sketches drawn by artists. These rough sketches include, but are not limited to, hand-drawn sketches and digital sketches. The reception department can, for example, scan hand-drawn sketches and save them as digital data. It can also directly read sketches submitted in digital format. Furthermore, the reception department can read printed sketches using OCR technology. For example, the reception department can scan hand-drawn sketches with a high-resolution scanner and convert them into text information using OCR technology. Digital sketches can be directly read if submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The reception department centrally manages this data, making it easily accessible to subsequent analysis and generation departments. For example, the reception department saves scanned image data to cloud storage, allowing the analysis department to access it in real time. The reception department also performs data format conversion and preprocessing to enable the analysis department to process the data efficiently. This allows the reception department to efficiently incorporate rough sketches in various formats provided by artists and to smoothly advance the overall system processing. Furthermore, the reception desk can provide feedback to artists through the user interface, offering guidelines on sketch quality and format. For example, it can display recommended settings for scanning resolution and file format, helping artists submit sketches in the optimal format. This allows the reception desk to improve artists' workflow efficiency and optimize overall system performance.

[0074] The analysis unit uses AI to analyze rough sketches input by the reception unit. The analysis is performed based on, but is not limited to, image analysis algorithms and feature extraction methods. For example, the analysis unit can extract the outline of the sketch using an image analysis algorithm. It can also identify important parts of the sketch using feature extraction methods. Specifically, the AI ​​utilizes deep learning technology to analyze each part of the sketch in detail. For example, it can use a convolutional neural network (CNN) to detect edges and textures of the sketch with high accuracy. Furthermore, the analysis unit classifies different elements such as characters, backgrounds, and objects based on the content of the sketch and performs appropriate processing on each element. For example, it can identify the position of a character's face and hands and perform analysis to emphasize these parts. The analysis unit also learns the sketch style and the artist's characteristics, allowing it to perform analysis tailored to each artist's style. This enables the analysis unit to provide analysis results that accurately reflect the artist's intentions, providing a foundation for subsequent generation and coloring units to produce high-quality output. Additionally, the analysis unit updates analysis results in real time, allowing for quick responses even if the artist modifies the sketch. This allows the analysis unit to streamline the sketch analysis process and improve the overall processing speed and accuracy of the system.

[0075] The generation unit uses AI to generate line art based on rough sketches analyzed by the analysis unit. Line art is generated based on, for example, the extraction of outlines or the adjustment of line thickness and style, but is not limited to these examples. For instance, the generation unit can extract outlines and adjust line thickness and style to generate line art. The generation unit can also use AI to generate line art that emphasizes important parts of the sketch. Specifically, the generation unit uses generation AI to extract the sketch's outlines with high accuracy and adjust line thickness and style to match the artist's intentions. For example, the generation unit can input instructions such as "emphasize the character's outline and thin the background lines" as a prompt to the generation AI, which then automatically generates appropriate line art. Furthermore, the generation unit can dynamically change line thickness and style to emphasize important parts of the sketch. For example, it can visually emphasize the character's face and hands by drawing them with thicker lines and the background with thinner lines. In addition, the generation unit can learn from past artwork data to generate line art that reflects the individual artist's style. This allows the generation unit to automatically produce high-quality line drawings that accurately reflect the artist's intentions, reducing the artist's workload. Furthermore, the generation unit can generate line drawings in real time and respond quickly even if the artist modifies the sketch. This streamlines the line drawing generation process and improves the overall processing speed and accuracy of the system.

[0076] The coloring unit uses AI to color the line art generated by the generation unit. Coloring is performed based on, for example, color selection criteria and coloring algorithms, but is not limited to such examples. For example, the coloring unit utilizes open-source training data to select colors that correspond to various styles and genres. The coloring unit can also use AI to select colors that highlight important parts of the line art. Specifically, the coloring unit uses generation AI to automatically assign appropriate colors to each part of the line art. For example, instructions such as "make the character's hair a light brown and the background sky blue" can be input as prompts to the generation AI, and the AI ​​will automatically select appropriate colors. The coloring unit can also dynamically adjust the brightness and saturation of colors to highlight important parts of the line art. For example, it can visually emphasize the character's face and hands by coloring them with lighter colors and the background with more subdued colors. Furthermore, the coloring unit can learn from past artwork data to select colors that match the artist's style and reflect the characteristics of each individual artist. This allows the coloring unit to automatically perform high-quality coloring that accurately reflects the artist's intentions, reducing the artist's workload. Furthermore, the coloring unit performs coloring in real time and can quickly respond to any revisions made by the artist to the line art. As a result, the coloring unit streamlines the coloring process and improves the overall processing speed and accuracy of the system.

[0077] The coloring unit can utilize open-source training data to select colors that correspond to various styles and genres. For example, the coloring unit can use open-source training data to select anime-style colors. It can also use open-source training data to select realistic-style colors. Furthermore, it can use open-source training data to select abstract-style colors. This makes it possible to color in a variety of styles and genres by utilizing open-source training data. Open-source training data includes, but is not limited to, the names and acquisition methods of datasets. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can perform coloring using an AI model that selects anime-style colors using open-source training data.

[0078] The distribution section provides functions to deepen the connection between artists and readers. For example, the distribution section can provide a function for artists to stream their creative process in real time. It can also provide a function for artists to record their creative process and stream it later. Furthermore, the distribution section can provide a function for artists to edit their creative process and stream highlights. This can deepen the connection between artists and readers. Specific methods and criteria for deepening the connection include, but are not limited to, the type and frequency of interaction. Some or all of the above processes in the distribution section may be performed using AI or not. For example, the distribution section can use an AI model for artists to stream their creative process in real time.

[0079] The distribution unit can provide a function for artists to stream their creative process in real time. For example, the distribution unit can provide a platform for artists to stream their creative process in real time. The distribution unit can also provide technology to minimize latency when artists stream their creative process in real time. Furthermore, the distribution unit can provide technology to optimize image and sound quality when artists stream their creative process in real time. This improves interaction with readers as artists stream their creative process in real time. Specific methods and technologies for real-time streaming include, but are not limited to, the distribution platform used and latency. Some or all of the above processing in the distribution unit may or may not be performed using AI. For example, the distribution unit can perform streaming using an AI model that provides a platform for artists to stream their creative process in real time.

[0080] The feedback section provides functionality for receiving feedback from readers. For example, it may provide a function for readers to post comments. It may also provide a function for readers to rate works. Furthermore, it may provide a function for readers to answer surveys. This allows artists to use reader feedback to improve their work. Specific methods and criteria for feedback include, but are not limited to, comments, ratings, and surveys. Some or all of the above processing in the feedback section may be performed using AI or not. For example, the feedback section can receive feedback using an AI model in which readers post comments.

[0081] The feedback unit can provide artists with feedback from readers. For example, the feedback unit can provide a function to notify artists of comments from readers. It can also provide a function to notify artists of ratings from readers. Furthermore, the feedback unit can provide a function to notify artists of the results of reader surveys. By providing artists with feedback from readers, the quality of their work can be improved. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can provide feedback using an AI model that notifies artists of comments from readers.

[0082] The reception desk can estimate the artist's emotions and adjust the timing of rough sketch submission based on the estimated emotions. For example, if the artist is stressed, the reception desk can delay submission to allow time for relaxation. Alternatively, if the artist is focused, the reception desk can immediately accept the rough sketch to avoid interrupting the workflow. Furthermore, if the artist is tired, the reception desk can encourage a break and accept the rough sketch at an appropriate time. This improves work efficiency by adjusting the timing of rough sketch submission according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0083] The reception department can analyze an artist's past rough sketch submission history and select the optimal submission method. For example, the reception department can analyze the frequency of rough sketches submitted by the artist in the past and propose an optimal submission schedule. The reception department can also evaluate the quality of rough sketches submitted by the artist in the past and adjust the submission timing. Furthermore, the reception department can select the most efficient submission method from the artist's past submission history. In this way, by analyzing past submission history, the reception department can select the optimal submission method and improve work efficiency. The optimal submission method includes, but is not limited to, submission time and submission format. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the artist's past submission history data into a generating AI and have the generating AI select the optimal submission method.

[0084] The reception desk can filter rough sketches upon receipt based on the artist's current projects and areas of interest. For example, the reception desk may only accept rough sketches related to the artist's current projects. It can also prioritize the acceptance of highly relevant rough sketches based on the artist's areas of interest. Furthermore, the reception desk can filter appropriate rough sketches according to the artist's project progress. This allows for the priority acceptance of highly relevant rough sketches by filtering based on current projects and areas of interest. Specific criteria and methods for filtering include, but are not limited to, definitions of project types and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's current project data into a generating AI and have the generating AI perform the filtering.

[0085] The reception desk can estimate the artist's emotions and determine the priority of rough sketches to accept based on the estimated emotions. For example, if the artist is relaxed, the reception desk will prioritize accepting high-priority rough sketches. If the artist is stressed, the reception desk may also prioritize accepting simpler rough sketches. Furthermore, if the artist is focused, the reception desk may also prioritize accepting more complex rough sketches. This improves work efficiency by prioritizing rough sketches according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0086] The reception desk can prioritize accepting rough sketches that are highly relevant based on the artist's geographical location information. For example, if the artist is in a specific region, the reception desk will prioritize accepting rough sketches related to that region. Furthermore, if the artist is traveling, the reception desk can prioritize accepting rough sketches related to their travel destination. Additionally, if the artist is at home, the reception desk can prioritize accepting rough sketches related to their home area. This improves work efficiency by prioritizing the acceptance of highly relevant rough sketches based on geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's geographical location data into a generating AI and have the generating AI select highly relevant rough sketches.

[0087] The reception desk can analyze the artist's social media activity when receiving rough sketches and accept relevant sketches. For example, the reception desk may prioritize accepting rough sketches related to works the artist has shared on social media. It can also accept rough sketches based on themes the artist has shown interest in on social media. Furthermore, the reception desk may prioritize accepting rough sketches requested by the artist's followers. This allows for the priority acceptance of highly relevant rough sketches by analyzing social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, follower count, and engagement rate. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's social media activity data into a generating AI and have the generating AI select relevant rough sketches.

[0088] The analysis unit can estimate the artist's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the artist is relaxed, the analysis unit can provide detailed analysis results. If the artist is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the artist is focused, the analysis unit can provide in-depth analysis results. This allows for a deeper understanding of the analysis results by adjusting the presentation of the analysis according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the rough sketches during the analysis. For example, the analysis unit can perform a detailed analysis on rough sketches of high importance. Conversely, the analysis unit can also perform a concise analysis on rough sketches of low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the rough sketches. Specific evaluation criteria and methods for importance include, but are not limited to, project priority and client requirements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input rough sketch importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the rough sketch during analysis. For example, the analysis unit can apply a character-specific analysis algorithm to a character rough sketch. It can also apply a background-specific analysis algorithm to a background rough sketch. Furthermore, it can apply an action scene-specific analysis algorithm to an action scene rough sketch. This improves analysis accuracy by applying different analysis algorithms depending on the category of the rough sketch. Specific definitions and classification methods for categories include, but are not limited to, genre, theme, and style. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input rough sketch category data into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0091] The analysis unit can estimate the artist's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the artist is relaxed, the analysis unit can perform a detailed analysis. If the artist is stressed, the analysis unit can perform a concise analysis. Furthermore, if the artist is focused, the analysis unit can perform a deep analysis. By adjusting the length of the analysis according to the artist's emotions, the understanding of the analysis results is deepened. The estimation of the artist's emotions is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0092] The analysis unit can determine the priority of the analysis based on the submission date of the rough sketches. For example, the analysis unit may prioritize the analysis of rough sketches submitted earlier. It can also postpone the analysis of rough sketches submitted later. Furthermore, the analysis unit can adjust the priority of the analysis according to the submission date. This enables efficient analysis by determining the priority of the analysis based on the submission date. Specific evaluation criteria and methods for the submission date include, but are not limited to, the submission date and time. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input rough sketch submission date data into a generating AI and have the generating AI determine the priority of the analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the rough sketches during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant rough sketches. It can also postpone the analysis of less relevant rough sketches. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on relevance. Specific evaluation criteria and methods for relevance include, but are not limited to, the degree of theme agreement or similarity of content. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of the rough sketches into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0094] The generation unit can estimate the artist's emotions and adjust the line drawing generation method based on the estimated emotions. For example, if the artist is relaxed, the generation unit can generate a detailed line drawing. If the artist is stressed, the generation unit can also generate a simple line drawing. Furthermore, if the artist is focused, the generation unit can generate a deep line drawing. This improves work efficiency by adjusting the line drawing generation method according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the artist's facial expression data into the generation AI and have the generation AI perform the estimation of the artist's emotions.

[0095] The generation unit can adjust the level of detail in the line drawings based on the importance of the rough sketches during generation. For example, the generation unit can generate detailed line drawings for rough sketches of high importance. It can also generate simpler line drawings for rough sketches of low importance. Furthermore, the generation unit can adjust the level of detail in the line drawings according to their importance. This allows for efficient generation by adjusting the level of detail in the line drawings based on the importance of the rough sketches. Specific criteria and methods for adjusting the level of detail in the line drawings include, but are not limited to, the fineness of the drawing or the level of detail. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance data of the rough sketches into a generation AI and have the generation AI perform the adjustment of the level of detail in the line drawings.

[0096] The generation unit can apply different generation algorithms depending on the category of the rough sketch during generation. For example, the generation unit can apply a character-specific generation algorithm to character rough sketches. It can also apply a background-specific generation algorithm to background rough sketches. Furthermore, it can apply an action scene-specific generation algorithm to action scene rough sketches. By applying different generation algorithms depending on the category of the rough sketch, the generation accuracy is improved. Specific types and implementation methods of generation algorithms include, but are not limited to, deep learning algorithms and rule-based generation. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input rough sketch category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0097] The generation unit can estimate the artist's emotions and adjust the length of the line drawing based on the estimated emotions. For example, if the artist is relaxed, the generation unit can generate a detailed line drawing. If the artist is stressed, the generation unit can also generate a concise line drawing. Furthermore, if the artist is focused, the generation unit can generate a deep line drawing. This improves work efficiency by adjusting the length of the line drawing according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the artist's facial expression data into the generation AI and have the generation AI perform the estimation of the artist's emotions.

[0098] The generation unit can determine the priority of line drawings based on the submission dates of the rough sketches during the generation process. For example, the generation unit can prioritize generating rough sketches that were submitted earlier. It can also postpone generating rough sketches that were submitted later. Furthermore, the generation unit can adjust the priority of line drawings according to the submission dates. This enables efficient generation by determining the priority of line drawings based on the submission dates. Specific criteria and methods for determining priority include, but are not limited to, submission dates and client requests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input rough sketch submission date data into a generation AI and have the generation AI determine the priority of line drawings.

[0099] The generation unit can adjust the order of line drawings based on the relevance of the rough sketches during generation. For example, the generation unit can prioritize generating rough sketches with high relevance. It can also postpone generating rough sketches with low relevance. Furthermore, the generation unit can adjust the order of line drawings according to their relevance. This allows for efficient generation by adjusting the order of line drawings based on relevance. Specific criteria and methods for adjusting the order include, but are not limited to, sorting by relevance or importance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance data of the rough sketches into a generation AI and have the generation AI perform the adjustment of the line drawing order.

[0100] The coloring unit can estimate the artist's emotions and adjust the coloring method based on the estimated emotions. For example, if the artist is relaxed, the coloring unit can perform detailed coloring. If the artist is stressed, the coloring unit can perform simple coloring. Furthermore, if the artist is focused, the coloring unit can perform deep coloring. This improves work efficiency by adjusting the coloring method according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0101] The coloring unit can adjust the level of detail in coloring based on the importance of the rough sketch. For example, the coloring unit will perform detailed coloring for rough sketches of high importance, and simpler coloring for rough sketches of low importance. Furthermore, the coloring unit can adjust the level of detail in coloring according to importance. This allows for efficient coloring by adjusting the level of detail in coloring based on the importance of the rough sketch. Specific criteria and methods for adjusting the level of detail in coloring include, but are not limited to, the number of colors and the level of detail. Some or all of the above processing in the coloring unit may be performed using AI, or not. For example, the coloring unit can input rough sketch importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in coloring.

[0102] The coloring unit can apply different coloring algorithms depending on the category of the rough sketch during the coloring process. For example, the coloring unit can apply a character-specific coloring algorithm to a character rough sketch. It can also apply a background-specific coloring algorithm to a background rough sketch. Furthermore, it can apply an action scene-specific coloring algorithm to an action scene rough sketch. This improves coloring accuracy by applying different coloring algorithms depending on the category of the rough sketch. Specific types and implementation methods of coloring algorithms include, but are not limited to, deep learning algorithms and rule-based coloring. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input rough sketch category data into a generating AI and have the generating AI apply the coloring algorithm.

[0103] The coloring unit can estimate the artist's emotions and determine coloring priorities based on the estimated emotions. For example, if the artist is relaxed, the coloring unit will prioritize coloring high-importance rough sketches. If the artist is stressed, the coloring unit can also prioritize coloring simpler rough sketches. Furthermore, if the artist is focused, the coloring unit can prioritize coloring more complex rough sketches. This improves work efficiency by prioritizing coloring according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0104] The coloring unit can determine the order of coloring based on the submission dates of the rough sketches. For example, the coloring unit may prioritize coloring rough sketches that were submitted earlier. It can also postpone coloring rough sketches that were submitted later. Furthermore, the coloring unit can adjust the order of coloring according to the submission dates. This allows for efficient coloring by determining the order of coloring based on the submission dates. Specific criteria and methods for adjusting the order include, but are not limited to, submission dates and client requests. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input rough sketch submission date data into a generating AI and have the generating AI determine the order of coloring.

[0105] The coloring unit can adjust the order of coloring based on the relevance of the rough sketches during the coloring process. For example, the coloring unit can prioritize coloring rough sketches that are highly relevant. It can also postpone coloring rough sketches that are less relevant. Furthermore, the coloring unit can adjust the order of coloring according to relevance. This allows for efficient coloring by adjusting the order of coloring based on relevance. Specific criteria and methods for adjusting the order include, but are not limited to, sorting by relevance or importance. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input the relevance data of the rough sketches into a generating AI and have the generating AI perform the adjustment of the coloring order.

[0106] The distribution unit can estimate the artist's emotions and adjust the distribution method based on the estimated emotions. For example, if the artist is relaxed, the distribution unit can provide detailed content. If the artist is stressed, the distribution unit can provide concise content. Furthermore, if the artist is focused, the distribution unit can provide in-depth content. This improves interaction with viewers by adjusting the distribution method according to the artist's emotions. Estimation of the artist's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0107] The distribution department can select the optimal distribution method by referring to the artist's past distribution history at the time of distribution. For example, the distribution department can analyze the content of past distributions by the artist and propose the optimal distribution method. The distribution department can also analyze viewer reactions from the artist's past distribution history and select the optimal distribution method. Furthermore, the distribution department can select the most effective distribution method based on the artist's past distribution history. In this way, by referring to past distribution history, the optimal distribution method can be selected and viewer satisfaction can be improved. Specific criteria and methods for the optimal distribution method include, but are not limited to, distribution time and content. Some or all of the above processing in the distribution department may be performed using AI or not. For example, the distribution department can input the artist's past distribution history data into a generating AI and have the generating AI perform the selection of the optimal distribution method.

[0108] The distribution unit can customize the content of broadcasts based on the artist's current projects and areas of interest. For example, the distribution unit can broadcast content related to the artist's current projects. It can also broadcast highly relevant content based on the artist's areas of interest. Furthermore, the distribution unit can customize the content appropriately according to the progress of the artist's projects. This makes it easier to attract viewers' attention by customizing the content based on current projects and areas of interest. Specific criteria and methods for customization include, but are not limited to, defining project types and areas of interest. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the artist's current project data into a generating AI and have the generating AI perform the customization of the broadcast content.

[0109] The distribution unit can estimate the artist's emotions and determine the priority of the distribution based on the estimated emotions. For example, if the artist is relaxed, the distribution unit will prioritize distributing high-importance content. If the artist is stressed, the distribution unit can also prioritize distributing simpler content. Furthermore, if the artist is focused, the distribution unit can prioritize distributing more complex content. This improves viewer satisfaction by prioritizing distribution according to the artist's emotions. Estimating the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the artist's facial expression data into a generative AI and have the generative AI perform the estimation of the artist's emotions.

[0110] The distribution unit can select the optimal distribution method based on the artist's geographical location information at the time of distribution. For example, if the artist is in a specific region, the distribution unit will distribute content related to that region. Furthermore, if the artist is traveling, the distribution unit can distribute content related to their travel destination. Additionally, if the artist is at home, the distribution unit can distribute content related to their home area. This makes it easier to attract viewers' attention by selecting the optimal distribution method based on geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the artist's geographical location data into a generating AI and have the generating AI select the optimal distribution method.

[0111] The distribution department can analyze the artist's social media activity and adjust the content of the broadcast at the time of distribution. For example, the distribution department can distribute content related to the artist's work shared on social media. It can also distribute content based on themes the artist has shown interest in on social media. Furthermore, the distribution department can prioritize the distribution of content requested by the artist's followers. This allows the distribution department to provide content that is more likely to attract the audience's interest by analyzing social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, follower count, and engagement rate. Some or all of the above processing in the distribution department may be performed using AI or not. For example, the distribution department can input the artist's social media activity data into a generating AI and have the generating AI adjust the content of the broadcast.

[0112] The feedback unit can estimate the artist's emotions and adjust the feedback method based on the estimated emotions. For example, if the artist is relaxed, the feedback unit can provide detailed feedback. If the artist is stressed, the feedback unit can provide concise feedback. Furthermore, if the artist is focused, the feedback unit can provide deep feedback. This improves the quality of feedback by adjusting the feedback method according to the artist's emotions. The estimation of the artist's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0113] The feedback unit can select the optimal feedback method by referring to the reader's past feedback history when providing feedback. For example, the feedback unit can analyze the content of feedback the reader has given in the past and propose the optimal feedback method. Furthermore, the feedback unit can analyze the reader's reactions from their past feedback history and select the optimal feedback method. In addition, the feedback unit can select the most effective feedback method based on the reader's past feedback history. This allows the feedback unit to select the optimal feedback method by referring to past feedback history, thereby improving reader satisfaction. Specific criteria and methods for the optimal feedback method include, but are not limited to, the format and timing of the feedback. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's past feedback history data into a generating AI and have the generating AI select the optimal feedback method.

[0114] The feedback unit can customize the feedback content based on the reader's current areas of interest. For example, the feedback unit can provide feedback related to the topic the reader is currently interested in. It can also provide highly relevant feedback based on the reader's areas of interest. Furthermore, the feedback unit can customize the feedback content to be appropriate according to the reader's areas of interest. This improves reader satisfaction by customizing the feedback content based on current areas of interest. Specific criteria and methods for customization include, but are not limited to, the definition of areas of interest and the content of the feedback. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's current areas of interest data into a generating AI and have the generating AI perform the customization of the feedback content.

[0115] The feedback unit can estimate the artist's emotions and prioritize feedback based on the estimated emotions. For example, if the artist is relaxed, the feedback unit will prioritize providing high-importance feedback. If the artist is stressed, the feedback unit may also prioritize providing simple feedback. Furthermore, if the artist is focused, the feedback unit may prioritize providing complex feedback. This improves the quality of feedback by prioritizing it according to the artist's emotions. Estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0116] The feedback unit can select the most appropriate feedback method based on the reader's geographical location information when providing feedback. For example, if the reader is in a specific region, the feedback unit can provide feedback relevant to that region. Furthermore, if the reader is traveling, the feedback unit can provide feedback relevant to their travel destination. Additionally, if the reader is at home, the feedback unit can provide feedback relevant to their surroundings. This improves reader satisfaction by selecting the most appropriate feedback method based on geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. Some or all of the processing described above in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's geographical location data into a generating AI and have the generating AI select the most appropriate feedback method.

[0117] The feedback unit can analyze the reader's social media activity and adjust the feedback content accordingly. For example, the feedback unit can provide feedback related to works the reader has shared on social media. It can also provide feedback based on themes the reader has shown interest in on social media. Furthermore, the feedback unit can prioritize providing feedback requested by the reader's followers. This allows the feedback unit to provide content that is more likely to attract the reader's attention by analyzing their social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, follower count, and engagement rate. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the reader's social media activity data into a generating AI and have the generating AI adjust the feedback content.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The reception department can analyze the artist's past work data and customize the rough sketch submission process based on the artist's style and preferences. For example, it can analyze the style of the artist's past works and prioritize accepting rough sketches of a similar style. It can also prioritize accepting rough sketches of specific genres or themes based on the artist's preferences. Furthermore, it can suggest the optimal timing for submission based on the artist's past work data. This enables the submission of rough sketches in accordance with the artist's style and preferences, improving work efficiency. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the artist's past work data into a generating AI and have the generating AI perform the customization of the submission method.

[0120] The analysis unit can adjust the analysis speed when analyzing a rough sketch, taking into account the artist's drawing speed. For example, if the artist draws quickly, the analysis unit can perform a rapid analysis and provide immediate feedback. Conversely, if the artist draws slowly, the analysis unit can perform a detailed analysis and provide deeper feedback. Furthermore, the analysis priority can be adjusted according to the artist's drawing speed. This enables analysis that is tailored to the artist's drawing speed, improving work efficiency. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the artist's drawing speed data into a generating AI and have the generating AI adjust the analysis speed.

[0121] The generation unit can analyze the artist's past work data and customize the line art generation method based on the artist's style. For example, it can analyze the style of works the artist has drawn in the past and generate line art in a similar style. It can also generate line art in a specific genre or theme based on the artist's preferences. Furthermore, it can suggest the optimal line art generation method based on the artist's past work data. This makes it possible to generate line art that matches the artist's style and preferences, improving work efficiency. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the artist's past work data into a generation AI and have the generation AI perform the customization of the line art generation method.

[0122] The coloring unit can analyze the artist's past work data and customize the coloring method based on the artist's style and preferences. For example, it can analyze the color palette the artist has used in the past and use similar colors for coloring. It can also select colors suitable for a specific genre or theme based on the artist's preferences. Furthermore, it can suggest the optimal coloring method based on the artist's past work data. This enables coloring that matches the artist's style and preferences, improving work efficiency. Some or all of the above processes in the coloring unit may be performed using AI or not. For example, the coloring unit can input the artist's past work data into a generating AI and have the generating AI perform the customization of the coloring method.

[0123] The distribution department can analyze an artist's past distribution data and customize the content based on viewer reactions. For example, it can analyze content that received a good response from viewers in the past and prioritize similar content for distribution. It can also select content suitable for specific genres or themes based on viewer preferences. Furthermore, it can suggest the optimal distribution method based on the artist's past distribution data. This enables distribution content that responds to viewer reactions, improving viewer satisfaction. Some or all of the above processes in the distribution department may be performed using AI or not. For example, the distribution department can input an artist's past distribution data into a generating AI and have the generating AI perform the customization of the distribution content.

[0124] The reception desk can estimate the artist's emotions and adjust the method of accepting rough sketches based on the estimated emotions. For example, if the artist is relaxed, it can accept detailed rough sketches. If the artist is stressed, it can accept concise rough sketches. Furthermore, if the artist is focused, it can accept complex rough sketches. This allows for the acceptance of rough sketches in accordance with the artist's emotions, improving work efficiency. The estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0125] The analysis unit can estimate the artist's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the artist is relaxed, it can prioritize the analysis of high-importance rough sketches. If the artist is stressed, it can prioritize the analysis of simpler rough sketches. Furthermore, if the artist is focused, it can prioritize the analysis of more complex rough sketches. This allows for prioritizing the analysis according to the artist's emotions, improving work efficiency. The estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0126] The generation unit can estimate the artist's emotions and adjust the line drawing generation speed based on the estimated emotions. For example, if the artist is relaxed, the line drawing can be generated quickly. If the artist is stressed, the line drawing can be generated slowly. Furthermore, if the artist is focused, a detailed line drawing can be generated quickly. This allows for line drawing generation speeds that correspond to the artist's emotions, improving work efficiency. The estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the artist's facial expression data into the generation AI and have the generation AI perform the estimation of the artist's emotions.

[0127] The coloring unit can estimate the artist's emotions and determine coloring priorities based on those estimates. For example, if the artist is relaxed, it can prioritize coloring high-importance rough sketches. If the artist is stressed, it can prioritize coloring simpler rough sketches. Furthermore, if the artist is focused, it can prioritize coloring more complex rough sketches. This allows for coloring prioritization according to the artist's emotions, improving work efficiency. The estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coloring unit may be performed using AI or not. For example, the coloring unit can input the artist's facial expression data into the generative AI and have the generative AI perform the estimation of the artist's emotions.

[0128] The streaming unit can estimate the artist's emotions and adjust the streaming timing based on the estimated emotions. For example, if the artist is relaxed, the streaming can start immediately. If the artist is stressed, the streaming can be delayed to give them time to relax. Furthermore, if the artist is focused, the streaming can start at the optimal time. This enables streaming timing that is in line with the artist's emotions, improving interaction with viewers. Estimation of the artist's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the streaming unit may be performed using AI or not. For example, the streaming unit can input the artist's facial expression data into a generative AI and have the generative AI perform the estimation of the artist's emotions.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception desk inputs rough sketches drawn by the artist. These rough sketches can be hand-drawn or digital. Hand-drawn sketches are scanned and saved as digital data. Sketches submitted in digital format can be read directly. Printed sketches can also be read using OCR technology. Step 2: The analysis unit uses AI to analyze the rough sketch input by the reception unit. The analysis is performed based on image analysis algorithms and feature extraction methods. For example, it extracts the outline of the sketch and identifies important parts. Step 3: The generation unit uses AI to generate line drawings based on the rough sketches analyzed by the analysis unit. The line drawings are generated based on the extraction of outlines and the adjustment of line thickness and style. For example, outlines are extracted, and the line thickness and style are adjusted to generate the line drawings. Step 4: The coloring section uses AI to color the line drawings generated by the generation section. Coloring is performed based on color selection criteria and coloring algorithms. For example, open-source training data is used to select colors that correspond to various styles and genres.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, coloring unit, distribution unit, and feedback unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives a rough sketch drawn by the artist. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input rough sketch. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a line drawing based on the analyzed rough sketch. The coloring unit is implemented by the specific processing unit 290 of the data processing unit 12, which colors the generated line drawing. The distribution unit is implemented by the output device 40 of the smart device 14, which provides functions to deepen the connection between the artist and the reader. The feedback unit is implemented by the reception device 38 of the smart device 14, which receives feedback from the reader. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, coloring unit, distribution unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives a rough sketch drawn by the artist. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input rough sketch. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a line drawing based on the analyzed rough sketch. The coloring unit is implemented by the specific processing unit 290 of the data processing unit 12 and colors the generated line drawing. The distribution unit is implemented by the speaker 240 of the smart glasses 214 and provides a function to deepen the connection between the artist and the reader. The feedback unit is implemented by the microphone 238 of the smart glasses 214 and receives feedback from the reader. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, coloring unit, distribution unit, and feedback unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input of a rough sketch drawn by the artist. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input rough sketch. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a line drawing based on the analyzed rough sketch. The coloring unit is implemented by the specific processing unit 290 of the data processing unit 12 and colors the generated line drawing. The distribution unit is implemented by the display 343 of the headset terminal 314 and provides functions to deepen the connection between the artist and the reader. The feedback unit is implemented by the microphone 238 of the headset terminal 314 and receives feedback from the reader. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0176] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, coloring unit, distribution unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives a rough sketch drawn by the artist. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input rough sketch. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a line drawing based on the analyzed rough sketch. The coloring unit is implemented by the specific processing unit 290 of the data processing unit 12 and colors the generated line drawing. The distribution unit is implemented by the speaker 240 of the robot 414 and provides a function to deepen the connection between the artist and the reader. The feedback unit is implemented by the microphone 238 of the robot 414 and receives feedback from the reader. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0184] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0186] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0187] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0188] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0193] 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.

[0194] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0195] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0196] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0197] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0201] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0202] (Note 1) A reception area for entering rough sketches, An analysis unit analyzes the rough sketch input by the reception unit, A generation unit that generates a line drawing based on a rough sketch analyzed by the analysis unit, The system includes a coloring unit that performs coloring on the line drawing generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned colored portion is By utilizing open-source training data, it selects colors that correspond to various styles and genres. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a distribution section to deepen the connection between artists and readers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned distribution unit, It provides a feature that allows artists to stream their creative process in real time. The system described in Appendix 3, characterized by the features described herein. (Note 5) It includes a feedback section for receiving feedback from readers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Providing readers with feedback to artists The system described in Appendix 5, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the artist's emotions and adjust the timing for accepting rough sketches based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the artist's past rough sketch submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving rough sketches, we filter them based on the artist's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the artist's emotions and prioritizes the rough sketches to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting rough sketches, priority will be given to those with a high degree of relevance based on the artist's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When accepting rough sketches, we analyze the artist's social media activity and accept rough sketches that are relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the artist's emotions and adjust the method of expression of the analysis based on the estimated emotions of the artist. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the level of detail of the analysis is adjusted based on the importance of the rough sketch. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the rough sketch. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the artist's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the rough sketches were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of the rough sketches. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the artist's emotions and adjusts the line art generation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the line art is adjusted based on the importance of the rough sketch. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the rough sketch. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the artist's emotions and adjusts the length of the line drawings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of line art is determined based on when the rough sketches were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of the line drawings is adjusted based on the relationships between the rough sketches. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned colored portion is It estimates the artist's emotions and adjusts the coloring method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned colored portion is When coloring, adjust the level of detail based on the importance of the rough sketch. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned colored portion is When coloring, different coloring algorithms are applied depending on the category of the rough sketch. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned colored portion is The system estimates the artist's emotions and determines the coloring priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned colored portion is When coloring, the order of coloring is determined based on when the rough sketches were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned colored portion is When coloring, adjust the coloring order based on the relationships in the rough sketch. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned distribution unit, We estimate the artist's emotions and adjust the distribution method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned distribution unit, When streaming, the optimal streaming method is selected by referring to the artist's past streaming history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned distribution unit, During the live stream, the content will be customized based on the artist's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned distribution unit, It estimates the artist's emotions and determines the distribution priority based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned distribution unit, During streaming, the optimal streaming method is selected based on the artist's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned distribution unit, During the live stream, we analyze the artist's social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned feedback unit is It estimates the artist's emotions and adjusts the feedback method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned feedback unit is When providing feedback, refer to the reader's past feedback history to select the most appropriate feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned feedback unit is When providing feedback, customize the feedback content based on the reader's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned feedback unit is It estimates the artist's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned feedback unit is When providing feedback, the most suitable feedback method will be selected based on the reader's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned feedback unit is When providing feedback, we analyze readers' social media activity and adjust the feedback accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area for entering rough sketches, An analysis unit analyzes the rough sketch input by the reception unit, A generation unit that generates a line drawing based on a rough sketch analyzed by the analysis unit, The system includes a coloring unit that performs coloring on the line drawing generated by the generation unit. A system characterized by the following features.

2. The colored portion is, By utilizing open-source training data, it selects colors that correspond to various styles and genres. The system according to feature 1.

3. It includes a distribution section to deepen the connection between artists and readers. The system according to feature 1.

4. The aforementioned distribution unit, It provides a feature that allows artists to stream their creative process in real time. The system according to claim 3.

5. It includes a feedback section for receiving feedback from readers. The system according to feature 1.

6. The aforementioned feedback unit is Providing readers with feedback to artists The system according to claim 5, characterized in that it is the same as described in claim 5.

7. The aforementioned reception unit is We estimate the artist's emotions and adjust the timing for accepting rough sketches based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the artist's past rough sketch submission history to select the most suitable submission method. The system according to feature 1.

9. The aforementioned reception unit is When receiving rough sketches, we filter them based on the artist's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the artist's emotions and prioritizes the rough sketches to accept based on those estimated emotions. The system according to feature 1.

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